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BMC BioinformaticsBMC Bioinformatics1471-2105BioMed Central London 1471-2105-5-1961559601910.1186/1471-2105-5-196Research ArticleA functional hierarchical organization of the protein sequence space Kaplan Noam [email protected] Moriah [email protected] Menachem [email protected] Michal [email protected] Department of Biological Chemistry, Institute of Life Sciences, The Hebrew University of Jerusalem, Israel2 School of Computer Science and Engineering, The Hebrew University of Jerusalem, Israel2004 14 12 2004 5 196 196 10 9 2004 14 12 2004 Copyright © 2004 Kaplan 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
It is a major challenge of computational biology to provide a comprehensive functional classification of all known proteins. Most existing methods seek recurrent patterns in known proteins based on manually-validated alignments of known protein families. Such methods can achieve high sensitivity, but are limited by the necessary manual labor. This makes our current view of the protein world incomplete and biased. This paper concerns ProtoNet, a automatic unsupervised global clustering system that generates a hierarchical tree of over 1,000,000 proteins, based solely on sequence similarity.
Results
In this paper we show that ProtoNet correctly captures functional and structural aspects of the protein world. Furthermore, a novel feature is an automatic procedure that reduces the tree to 12% its original size. This procedure utilizes only parameters intrinsic to the clustering process. Despite the substantial reduction in size, the system's predictive power concerning biological functions is hardly affected. We then carry out an automatic comparison with existing functional protein annotations. Consequently, 78% of the clusters in the compressed tree (5,300 clusters) get assigned a biological function with a high confidence. The clustering and compression processes are unsupervised, and robust.
Conclusions
We present an automatically generated unbiased method that provides a hierarchical classification of all currently known proteins.
==== Body
Background
The explosive growth in the number of sequenced proteins has created a glut of proteins that are sequenced but whose structure and function are as yet unknown. A common way to tackle this problem is to use database searches to find proteins similar to a newly discovered protein, thus inferring protein function. This method is generalized by protein clustering or classification where databases of proteins are organized into groups or families in a manner that attempts to capture protein similarity. Such classification into families is a critical component in structural and functional genomics [1-4]. The number of protein families comprising the entire protein-space has been conjectured to range between 6,000-30,000, excluding rare and peculiar single proteins [5-8]. Various expert-based databases provide a good description of certain selected families but are limited in scope to thoroughly studied proteins (i.e. [9,10]). Other methods for classification strongly rely on 3D-structural information as in the case of SCOP [11], CATH [12], FSSP [13] and others.
Classifying the entire protein space into families serves not only as a method for large-scale protein annotations but also to support functional and structural genomic initiatives [14]. Some prominent examples for protein classification efforts are ProtoMap [15], Picasso [16], SYSTERS [17], iProClass [18] and ProtoNet [19]. These systems are based on a variety of algorithmic paradigms, each yielding a distinct hierarchical classification of proteins into families.
Amongst the clustering methods listed above only ProtoNet attempts to generate a global hierarchical arrangement of the entire protein space via agglomerative hierarchical clustering. The sequence similarity between every pair of protein sequences is taken as the BLAST [20] E-value between a given pair of proteins. Next, the proteins are clustered using a given merging strategy. The strategy used is Unweighted Pair Group with Arithmetic Mean (UPGMA), whereby in each iteration, the two most similar clusters (in terms of their average pairwise distance for every protein pair spanning the two clusters) are merged. ProtoNet (version 4.0) [21] provides a classification hierarchy of over 1,000,000 proteins including the SwissProt and TrEMBL protein databases [22]. Most proteins included in the SwissProt database are manually validated and furthermore, the degree of biological knowledge associated with them is much higher in comparison to the proteins archived in TrEMBL. Thus, this work concerns only the 114,033 proteins in the SwissProt database (version 40.28). An extended version that includes over one million protein sequences is available in the form of an interactive website at . For the SwissProt-based tree, there are 227,436 clusters (including the proteins as singletons). The classification provided by ProtoNet provides the full range of cluster granularity: from single proteins to huge protein clusters that carry no biological relevance (the root clusters). We test the biological validity of ProtoNet, by its examination from different perspectives, using external-defined protein keyword annotations. Four different annotation sources are used (InterPro [23], GO [24], SCOP [11] and ENZYME [25]) in order to be able to validate different biological aspects. First, we demonstrate that it is possible to match the majority of such external-defined protein families to specific clusters within the ProtoNet clustering. Second, we show that the hierarchy of the ProtoNet tree represents a valid functional hierarchy and correlates well with the GO hierarchical structure.
As mentioned, ProtoNet contains 227,436 clusters, which is obviously much more than the upper estimate of 30,000 protein families [8,26]. Therefore, we seek to cleverly discard those clusters that have less biological relevance. Compression of the protein space offers many advantages. It can yield a smaller set of biologically meaningful clusters, which will allow for a more manageable handling of the entire protein space. Furthermore, if this compression's correspondence to external, independent annotation sources can be validated, then this compression can be used to replace the original hierarchical structure, without sacrificing much information originally present in the whole system.
In this paper we describe methods for the unsupervised compression of the ProtoNet tree, by using intrinsic tree-based parameters of the clusters that correlate well with biological validity. By preserving the unsupervised nature of the ProtoNet data, we prevent biasing towards previously discovered findings and better allow for future generalizations, in addition to maintaining the automation of the process.
Finally, automatic functional annotation to proteins is of great importance. In ProtoNet, an automatic method for assignment of biological annotation to the protein clusters is used, yielding high-confidence functional assignments for a large majority of the proteins' clusters.
Results and discussion
Correspondence of clusters to external biological sources
In order to measure the correspondence between a given cluster and a specific annotation, and allow for supervised validation of the ProtoNet clusters, we define the notion of a correspondence score (CS). The CS for a certain cluster and a given keyword measures the correlation between the cluster and the keyword, using the well-known intersect-union ratio.
Let C be a cluster in the ProtoNet tree, and K be a keyword (from a specific source) that annotates (some of) the proteins in the system; Let c be the set of annotated proteins in cluster C; Let k be the set of proteins in the system annotated by keyword K; We define:
The cluster receiving the maximal score for keyword K is considered the cluster that best represents K within the ProtoNet tree (K's best cluster). The score for a given cluster on keyword K ranges from 0 (no correspondence) to 1 (the cluster contains exactly all of the proteins with keyword K, i.e. maximally corresponds to the keyword).
In order to assess the clustering's biological validity, the mean best CS on all annotations was examined for each of the following sources: InterPro, SCOP (Family, SuperFamily, and Fold levels), GO (Molecular Function) and ENZYME (subclass, sub-subclass and entry). The results (Table 1) show a high level of correspondence between the ProtoNet clusters to the various keyword sets of each of the external sources.
It can be argued that a good fit between a set of keywords and the ProtoNet set of protein clusters could happen by chance. In order to assess the statistical significance of these results, the mappings of the keywords to the proteins were randomized, creating a new group of random keyword sets that have the same size distribution but do not represent any biological features. For each random keyword set, the mean best CS was calculated. This randomized test showed a normal distribution, allowing the calculation of an approximate p-value for the mean best CS obtained by ProtoNet for the external sources. The results showed an extremely high level of statistical significance for all sources (all had P-values smaller than 10-100). Note that even for the SCOP fold level, which is associated with proteins that may be extremely remote in sequence, ProtoNet's relative success is extremely high (for details on ProtoNet's performance vis-à-vis structural entities, see [27]).
To avoid trivial correspondences between a keyword and a cluster, such as the assignment of a keyword that annotates only one protein to its singleton cluster, we tested our success only with keywords that annotated at least two proteins (for SCOP and ENZYME keywords). For InterPro and GO, we selected a threshold of 20 proteins per keyword, as the majority (85% in InterPro; 98% in GO) of the annotations is included above this threshold, thus allowing the test to focus on the more significant keywords.
Correspondence of ProtoNet hierarchy to external biological sources
In order to validate the hierarchical structure of ProtoNet, we compare it with the hierarchical structure of GO as described in Figure 1. To do this, we select, for each GO term, the best matching cluster in ProtoNet according to the CS. The subset of all terms that have highly matching clusters (best CS>0.5) was selected. In graph-theoretic terminology, this set of terms can be represented as vertices in a graph. We consider two possible sets of directed edges between the vertices: those defined by GO as the parent-child relationship of the clusters' respective terms, and those of the ProtoNet hierarchy. Thus we wish to compare these two sets of graph edges. We use a very conservative test, counting the number of edges that are common to both graphs.
A total of 1577 GO terms were selected as described, with 1798 edges between them according to the GO hierarchy. 771 out of 1291 (60%) edges that were produced by the ProtoNet hierarchy appear in the GO hierarchy. This number is highly significant considering the fact that there exist over 1,200,000 possible edges between the 1577 vertices in the graph (considering it as a DAG). It should be noted that there are some terms in GO that are connected to many other vertices. These vertices may bias the results of this test. In order to confirm that ProtoNet performs well without these vertices as well, the vertices were removed manually and the test was repeated, with similarly significant results (33% of the edges were correct).
Compression by using an intrinsic parameter
In order to allow unsupervised automatic compression of the ProtoNet tree, we searched for an intrinsic parameter of the clustering process that would specify clusters of biological validity. By applying such a parameter one could dispose of clusters that do not pass a certain threshold value, remaining with clusters of high biological validity. Once we remain with a subset of biologically valid clusters, the new hierarchy amongst them can be reconstructed according to the original tree hierarchy.
The agglomerative hierarchical clustering scheme defines a set of terms that are intrinsically associated with the process. In such a scheme, each cluster is created from smaller clusters, which are captured as its descendants in the clustering tree. The ProtoLevel (PL) ranges from 0-100 and is used as a standard quantitative measure of the relative height of a cluster in the merging tree. The PL of a cluster is defined as the arithmetic average of the BLAST E-score of the pairs of its proteins. The PL of the leaves of the tree is defined as 0, whereas the PL of a root equals 100. The larger the PL, the later the merging that created the cluster took place. Therefore, the PL scale is considered as an internal monotonic timer of merging, during the clustering process. As mentioned above, a cluster is said to be created when the merging of its two children clusters forms it. The PL at this point is said to be the birthtime of this cluster. The deathtime of a cluster is defined as the PL at its termination, i.e. the point at which it merges into its parent cluster (or 100 if it has no parent). The lifetime (LT) of a cluster is defined as:
LT = deathtime - birthtime
Therefore, the LT of a cluster reflects its remoteness from the clusters in its "vicinity" in protein sequence space.
We examined the LT distribution of the set of InterPro best clusters in comparison with the LT distribution of all clusters in ProtoNet (Figure 2). The results suggest that the best clusters have a substantially higher LT than other ProtoNet clusters. This poses the LT as a possible candidate that could allow a biologically-valid tree compression by disposing of all clusters with LT below a certain threshold value.
In order to search for a reasonable LT threshold value (that would eliminate a large number of clusters while maintaining biological validity), several threshold values were examined (Figure 3). The results show that by using a LT threshold for cluster elimination, in addition to removing the singleton clusters, 87.8% of the clusters may be eliminated with only a minimal reduction in performance (i.e., a reduction of 2.7% in mean best CS), leaving only 27,823 clusters. Furthermore, we compare the LT threshold scheme with a random elimination of similar amounts of clusters. The LT threshold convincingly outperforms the random elimination.
The mean best CS was examined for all four external sources (Table 2). The results show that the mean CS of ProtoNet were only slightly reduced, while the random mean CS are significantly reduced due to the much smaller amount of clusters.
Automatic functional annotation of clusters
The following scheme was used to annotate the protein clusters: For each cluster C and keyword K we define the following score:
Where TP is the amount of true positives (proteins in C that have the keyword K), FN is the amount of false negatives (proteins not in C that have the keyword K) and FP is the amount of false positives (proteins in C that do not have the keyword K).
For each cluster, we search against all keywords of GO and InterPro for the keyword with the highest AS. If the AS of the cluster is greater than 0.25, the cluster is assigned that keyword as its annotation. The logic behind the score and the threshold is as follows: the score is determined by two parameters, the specificity and the sensitivity; let us consider the two worst-case limit cases. In the first case, specificity>0.5 and sensitivity = 1: a majority of the proteins of the cluster share the keyword, and there exist no other known proteins that have the keyword. In the second case, specificity = 1 and sensitivity>0.25: all proteins of the cluster share the keyword and they constitute more than 1/4 of the total proteins known to have this keyword. In both cases, the keyword can be assigned to the protein cluster with a high degree of confidence. All other clusters fall in between these cases.
Using this method, all 6,879 clusters that contain 20 or more proteins and that remain after the compression were tested. 5,355 (77.8%) clusters passed the high confidence threshold and were therefore given an annotation. Figure 4 shows the plot of the highest AS score for each of the clusters and the threshold function. Naturally, by relaxing the threshold it would be possible to obtain a higher level of annotation.
Cation channels: a biological example
Figure 5 shows one of the trees that appear in ProtoNet after compression. The root cluster contains 249 proteins and is annotated as "Cation Channel". There appears to be a correct division between potassium channels to non-potassium channels. Furthermore there is an apparent inner division of the potassium channels into two-pore channels and voltage dependent channels, and of the non-potassium cation channels into sodium channels and TRP channels. Notably, an unannotated cluster of 2 proteins is categorized as potassium channel, but does not appear to be voltage-dependent or two-pore. Closer inspection shows that this cluster contains the 2 orthologs of the LctB bacterial protein. Experimental results suggest that LctB is a new type of non-voltage-mediated potassium channel [28]. This corresponds well to the fact that ProtoNet did not assign an annotation to this cluster and separated it from the other potassium channels.
Conclusions
The challenge of protein classification by using sequence similarity has been addressed extensively by several different methods. In order to assign function to proteins, advanced methods (such as Hidden Markov Models implemented in Pfam) have been used to learn sequence-based patterns on "seeds", manually validated alignments of known protein families. The widely-used BLAST algorithm is considered to be a reliable tool for sequence alignment, but has been shown to lack sensitivity for weak similarities that may be detected by signature detection methods. We show here that by using an unsupervised bottom-up clustering method based on BLAST E-values, we have been able to construct a global hierarchy of the SwissProt proteins that can be validated by external biological sources, merely by undertaking a global view of the protein space.
The four different external sources that were used for validation reflect different aspects of the protein space: InterPro annotation is predictive and is based on various signature detection methods; GO annotation assignments are both based on prediction and from known research, while the GO hierarchy was constructed completely manually; SCOP is a semi-manual classification of structures that is not necessarily reflected in sequence; the ENZYME database supplies Enzyme Commissions, which constitute a hierarchy that is based on the enzymes' chemical reactions. ProtoNet successfully constructs clusters that correspond highly to all four of the sources. Even high levels of SCOP (such as the Fold classification), which are considered to have no detectable sequence similarity, are partially matched (also see discussion in [27]). Notably, the correspondence of ProtoNet to InterPro is generally higher than the correspondence to the other sources. This is not surprising, considering the fact that InterPro is based on prediction from sequence. However, it is worthwhile to note that the InterPro families may be reconstructed almost perfectly without using the various sensitive detection methods that InterPro uses, and more importantly without using the manually constructed seeds.
After validating the biological relevance of the ProtoNet clusters by using external sources, we examined the hierarchy of ProtoNet. The test showed that the hierarchy presented by ProtoNet significantly corresponds to the manually-constructed biological hierarchy of GO. It is important to note that the method used by ProtoNet is not expected to fully recapture the GO hierarchy due to the fact that ProtoNet is structured as a collection of trees while GO is structured as a DAG. In this sense, the approach of ProtoNet may be limited in the portrayal of evolutionary complexity (as in cases of multiple domains). However by using a domain-based clustering approach, allowing multiple entities of each protein in the hierarchy, a substantial improvement in the CS quality measure may be achieved (unpublished results).
An intrinsic parameter that reflects the stability of clusters during the clustering process was used in order to compress the cluster sets, leaving 16.4% of the clusters; removing the singletons clusters as well leaves 12.2% of the clusters. As mentioned above (see Methods), the entire ProtoNet scaffold contains 227,436 clusters that are represented by 630 roots; following this condensation, there are only 27,823 clusters that are represented by 2,236 roots. We show that although a massive portion of the clusters is discarded, very little performance is lost by this condensation process. It is obvious that prior to the condensation process, ProtoNet holds within it both clusters that correctly represent biological groups and clusters that are irrelevant artifacts of the clustering process (e.g. the large root clusters that are constituted of tens of thousands of proteins). Therefore, by allowing a major reduction without significant loss of biological coherence ProtoNet seems to present a more correct view of the protein world.
An automatic unsupervised method for the classification of proteins has some important advantages over supervised methods (such as signatures based on manually validated seeds): First, an unsupervised method is unbiased in automatic assignment of function to proteins, a major goal in bioinformatics. Also, it allows high-throughput analysis of whole genomes and enhances understanding of global biological systems without the need for the manual annotation of every protein. Using an automatic annotation method, we are able to successfully annotate 77.8% of the major protein clusters (of size 20 or more) that remain after the compression of the ProtoNet tree. The annotation uses a relatively conservative threshold and therefore yields high-confidence annotations. This further suggests that the clusters remaining after the condensation process are relevant biological clusters and not mere artifacts.
One aspect that we have rigorously examined is the robustness of the ProtoNet tree: given a different set of proteins to cluster or a different clustering method, would the resulting tree be significantly different, or are its properties maintained? Interestingly, changing the underlying protein databases (ranging in size from 30,000 to over 1,000,000 proteins), the substitution matrices used for the preliminary BLAST, or the merging strategy [19] produced very similar trees (unpublished results), suggesting that the performance of ProtoNet is not due to a specific computational method but perhaps to the robust properties of the protein sequence space.
Methods
ProtoNet version 2.4 which was used for the analyses described in this paper is based on classification of the SwissProt database (version 40.28) that contains 114,033 proteins. The entire ProtoNet scaffold contains 227,436 clusters that are contained in 630 trees. Most trees (611) are singletons and only one contains most (>99%) of the proteins. For more details on the construction of the ProtoNet hierarchy see [19]. ProtoNet version 4.0 [21] which is available online contains a wider classification of over 1,000,000 proteins (a union of the SwissProt and TrEMBL databases).
Several external sources were used as a biological reference for validation of the ProtoNet tree: InterPro [23] is an extensive family and signature archive that integrates several different databases: PRINTS, Pfam, PROSITE, ProDom, Smart, TIGRFAMs, and recently also PIR SuperFamily and SUPERFAMILY. Each of these databases relies on a different detection method. Many of these signatures and family keywords are considered to be undetectable by a routine BLAST search. InterPro (version 5.2) contains 5,551 signatures. Gene Ontology (GO) [24] is a collaborative project of creating a hierarchy of biological terms. GO is represented as a directed acyclic graph (DAG), which is divided into three parts: Molecular Function, Cellular Localization and Cellular Process. In this study only the Molecular Function aspects of GO were used. GO's Molecular Function subdivision (July 2002) contains 5,947 biological terms. SCOP [11] is a hierarchical representation of protein structures. SCOP uses a tree-like hierarchy of 4 levels: Class, Fold, SuperFamily and Family. SCOP (version 1.57) contains 2,927 structures terms. The ENZYME database (as part of Swissprot data) indicates the EC number of a protein [25]. EC (Enzyme Commission) numbers are a classification scheme for enzymes, based on the chemical reactions they catalyze. The EC number includes 4 fields (for example, 1.2.3.4 represents the enzyme class, subclass, sub-subclass and entry number, respectively). ENZYME (updated July 2002) contains 3,958 enzyme classifications.
We have used EBI mappings of InterPro and GO to SwissProt proteins.
List of abbreviations
Annotation Score (AS), Correspondence Score (CS), Directed Acyclic Graph (DAG), Enzyme Commission (EC), Gene Ontology (GO), Lifetime (LT).
Authors' contributions
ML conceived of the compression of the ProtoNet tree. NK, MF and MF participated in implementation of the condensation and in the design and implementation of the various tests. All authors participated in the analysis of the results. All authors read and approved the final manuscript.
Acknowledgements
We would like to thank Nati Linial for instructive ideas, suggestions and endless discussions. We would like to thank the current and previous generations of the ProtoNet team for their effort over the last years. Special thanks are for Avishay Vaaknin who coined the term Lifetime, which served us throughout this work and to Hagit Mor-Ulanovsky for investigating the quality of hundreds of clusters. This study is based on the entire team that developed and maintains the ProtoNet Web site. Special thanks are to Uri Inbar, Hillel Fleischer and Alex Savenok for their long lasting effort and support. This work is partially supported by the SCCB – The Sudarsky Center for Computational Biology provided fellowships for N.K, M.F and M.F and by the NoE of BioSapiens (EC Framework VI).
Figures and Tables
Figure 1 Scheme of the ProtoNet hierarchy test. (A) The ProtoNet binary tree. Vertices in the graph are protein clusters. Crossed-out vertices are eliminated because they do not match any GO term. (B) Tree hierarchy of remaining ProtoNet clusters. Remaining nodes correspond to GO terms. (C) The corresponding GO DAG hierarchy between the vertices. (D) Intersection of graphs in B and C shows the amount of hierarchical correspondence between GO and ProtoNet. In this example, there are 6 edges common to GO and ProtoNet. 2 of the ProtoNet edges do not appear in GO and 4 GO edges do not appear in ProtoNet.
Figure 2 Lifetime (LT) distributions of the set of InterPro best clusters (black bars) in comparison to the LT distribution of all clusters in ProtoNet (gray bars).
Figure 3 Effect of compression on mean best CS (Correspondence Score) at various threshold values. (A) Mean best CS shown here is calculated for the InterPro keywords. Mean best CS decreases from right to left, as more clusters are eliminated due to compression. Filled circles represent the mean best CS for compression according to LT thresholds. Open circles represent the mean best CS of a random compression to the same extent as explained in the text. Standard deviation of the random mean best CS is too small to be seen on the graph.
Figure 4 AS (Annotation Score) plot for all clusters of 20 or more proteins, after compression. Each dot represents a cluster and is plotted according to the sensitivity and specificity of its highest-scoring AS (as defined in the text). The curve represents the high-confidence annotation threshold which was used. Dots in the upper right represent clusters that passed the threshold and were therefore annotated with high-confidence.
Figure 5 Graph of the ProtoNet tree of cation channels. Squares represent clusters, arrows represent tree hierarchy between clusters. Names are the annotations that were assigned to the clusters as described in text. Parantheses show the number of proteins in the cluster. Note one cluster containing two proteins that was not assigned an annotation.
Table 1 Correspondence of external biological keywords and ProtoNet clusters.
External Source ProtoNet Mean Best CSa Random Mean Best CS (std dev) # KW
InterPro 0.835 0.026 (0.9*10-4) 2034
GO molecular function 0.588 0.024 (0.8*10-4) 1220
SCOP family 0.720 0.299 (0.8*10-4) 742
SCOP superfamily 0.654 0.260 (0.9*10-4) 558
SCOP fold 0.598 0.230 (1*10-4) 408
ENZYME entry 0.848 0.179 (0.7*10-4) 1432
ENZYME sub-subclass 0.517 0.053 (2*10-4) 161
ENZYME subclass 0.412 0.025 (4*10-4) 56
aCS – correspondence score, see text for definition.
Table 2 Correspondence of external biological keywords and ProtoNet clusters after and before compression.
External Source ProtoNet Mean CS (before compression)a Random Mean CS (before compression)
InterPro all 0.808 (0.835) 0.025 (0.026)
GO molecular function 0.558 (0.588) 0.020 (0.024)
SCOP family 0.702 (0.720) 0.124 (0.299)
SCOP superfamily 0.635 (0.654) 0.116 (0.260)
SCOP fold 0.580 (0.598) 0.107 (0.230)
ENZYME entry 0.643 (0.848) 0.090 (0.179)
ENZYME sub-subclass 0.471 (0.517) 0.036 (0.053)
ENZYME subclass 0.371 (0.412) 0.018 (0.025)
aCS – correspondence score, see text for definition.
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| 15596019 | PMC544566 | CC BY | 2021-01-04 16:02:45 | no | BMC Bioinformatics. 2004 Dec 14; 5:196 | utf-8 | BMC Bioinformatics | 2,004 | 10.1186/1471-2105-5-196 | oa_comm |
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BMC NeurosciBMC Neuroscience1471-2202BioMed Central London 1471-2202-5-581560691110.1186/1471-2202-5-58Research ArticleCovariates of corticotropin-releasing hormone (CRH) concentrations in cerebrospinal fluid (CSF) from healthy humans Baraniuk James N [email protected] Hilda [email protected] Gail [email protected] Daniel J [email protected] Division of Rheumatology, Immunology and Allergy, Room GL-002, Lower Level Gorman Building, Georgetown University, 3800 Reservoir Road, NW, Washington, DC 20007-21972 Center for the Advancement of Clinical Research, The University of Michigan, Ann Arbor, MI2004 17 12 2004 5 58 58 9 3 2004 17 12 2004 Copyright © 2004 Baraniuk 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
Define covariates of cerebrospinal corticotropin-releasing hormone (CRH) levels in normal humans. CRHCSF was measured in 9 normal subjects as part of an intensive study of physiological responses stressors in chronic pain and fatigue states. CRHCSF was first correlated with demographic, vital sign, HPA axis, validated questionnaire domains, baseline and maximal responses to pain, exercise and other stressors. Significant factors were used for linear regression modeling.
Results
Highly significant correlations were found despite the small number of subjects. Three models were defined: (a) CRHCSF with blood glucose and sodium (explained variance = 0.979, adjusted R2 = 0.958, p = 0.02 by 2-tailed testing); (b) CRHCSF with resting respiratory and heart rates (R2 = 0.963, adjusted R2 = 0.939, p = 0.007); and (c) CRHCSF with SF-36 Vitality and Multidimensional Fatigue Inventory Physical Fatigue domains (R2 = 0.859, adjusted R2 = 0.789, p = 0.02).
Conclusions
Low CRHCSF was predicted by lower glucose, respiratory and heart rates, and higher sodium and psychometric constructs of well being. Responses at peak exercise and to other acute stressors were not correlated. CRHCSF may have reflected an overall, or chronic, set-point for physiological responses, but did not predict the reserves available to respond to immediate stressors.
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Background
Corticotropin – releasing hormone (CRH) plays a major role in regulating the hypothalamic – pituitary – adrenal (HPA) axis, acute responses to stressors, and other neurological functions [1]. The cerebrospinal concentrations of CRH (CRHCSF) and neuropeptide Y (NPY), another important neuropeptide involved in pain, autonomic and stress responses [2,3], were measured in 9 normal humans to better understand these functions. Subjects were studied as part of a large scale investigation of subjective and objective (functional magnetic resonance imaging) responses to pain testing, exercise, and other stressors [4]. Subjects completed questionnaires that assessed pain, anxiety, depression, coping skills, and other psychometric variables. Normal control, fibromyalgia, [5], chronic fatigue syndrome [6], and veterans of the first Persian Gulf War [7] were included. This pilot investigation focuses on the statistical strategy used to analyze the control population (n = 9), and to develop methods for effective evaluation of patient populations. Three multiple linear regression models [8] were defined that predicted CRHCSF based on (a) metabolic, (b) autonomic, and (c) psychometric measures.
Results
Demographics
The mean age for the group was 35.2 yr (95% C.I.: 30.5 to 39.9) years. There were 2 females, 5 African-Americans, 3 Caucasians, and 1 Caucasian Hispanic (TABLE 1). These subjects were a very normal and healthy group based upon their histories and the strict exclusion criteria.
NPYCSF
Mean NPYCSF was 121.9 (87.8 to 156.0) pg/ml. NPY did not correlate with any variable.
Variables correlated with CRHCSF
Significant covariates of CRHCSF could be grouped into: metabolic, autonomic function, and perceptional and cognitive functions (TABLE 3). Serum glucose was positively, and sodium negatively, correlated with CRHCSF (FIGURE 1). Explained variances (R2) were 0.97 and 0.66, respectively. Glucose and sodium were also negatively correlated (R2 = 0.85). Resting norepinephrine levels at 2 time points and heart rate at 4 time points were colinear and positively correlated to CRHCSF.
CRHCSF was negatively correlated with the Holter monitor-derived measure of log heart rate summed for the daytime, and the threshold temperature causing an initial, mild sensation of burning pain (Stressor I). The latter indicated that subjects with higher CRHCSF perceived the burning pain of the cutaneous forearm hyperthermic stimulation at a lower threshold temperature than their peers. This may indicate an increased sensitivity to nociceptive stimuli. However, there was no correlation with deep pressure – induced pain. Negative correlations were also found with the SF-36 Vitality and SES Manage Symptoms domains. High scores were normal for these questionnaires, with lower scores indicating dysfunction.
Effects in males
All parts of the study were completed by at least 5 males. Analysis of the male subgroup gave some information about the role of gender. The pattern of significant covariables was different from the total group (TABLE 4). Respiratory rate was the only vital or physiological sign related to CRHCSF. Perceptions of vulnerability, physical functioning and self-efficacy were more highly related.
Statistical models
Sets of the significant metabolic, autonomic, and perceptual variables were grouped and analyzed by 3 linear regression models in order to detect significant relationships between independent variables and CRHCSF. The 3 models had high significance levels (TABLE 5). A metabolic model related CRHCSF to glucose and sodium. An autonomic model linked CRHCSF to resting respiratory and heart rates. The optimum perceptual model predicted CRHCSF based on SF-36 Vitality and MFI Physical Fatigue. The latter 2 domains were not the same as those in TABLES 3 and 4 because many of these variables were co-linear or surrogates of one another. This was reinforced by the similarity of R2 and p values where these domains were substituted for Vitality and Physical Fatigue. These results were remarkable because it is very unusual for models with such small numbers of observations (n ≤ 9) to be so significant (p < 0.02). The explained variances were very high (R2 > 0.85) suggesting that the factors may be causally connected.
CRHCSF did not correlate with variables associated with maximum exercise, heat – and pressure – (dolorimetry) induced pain, or other rapid onset stressors (TABLE 1).
Discussion
Despite the small numbers of normal subjects in this pilot investigation, the data, the explained variances for relationships, and final 3 models predictive of CRHCSF were highly significant. The models were of importance, since they reflected the specific neurological functions of this neurohormone. CRH and NPY were co-expressed in the hypothalamus [2], but NPYCSF did not correlate with CRHCSF or any other variable.
CRH and the hypothalamic-pituitary-adrenal axis maintain numerous systemic functions. Our metabolic model showed a tightly correlated relationship between 2 pm serum glucose, sodium and CRHCSF. Relatively higher CRHCSF levels were associated with elevated serum glucose levels. When glucose is elevated, it is pumped into cells along with sodium ions [9]. In our model, this may have been reflected by the reduced serum sodium concentrations (FIGURE 1). These measurements were taken at different times, suggesting that the CRHCSF set a long-term operating range for this system. The variables were interrelated. CRHCSF in the low normal range was inferred from a low glucose and relatively high sodium. Other reports also suggest a role of CRH and energy balance [10].
Neuroendocrine responses such as these rely solely on CRH type 1 (CRH1) receptors and the HPA axis [11]. The other CRH receptor gene, CRH2, has 3 splice variants (α, β and γ) but only CRH2α is expressed in the brain. CRH1 and CRH2α receptors have nonoverlapping distributions, but mediate many similar defensive behaviors suggesting that they act in parallel neural circuits. Different stressors may act by separate circuits and have distinct feedback and control systems [11-13]. CRH1 receptors in the central nucleus of the amygdala may participate in conditioned fear responses [12]. These CRH neurons may project to the hippocampus and CRH1 receptors in the locus ceruleus to induce defensive behaviors and autonomic reflexes [13,14]. Dorsal raphe nucleus neurons may release CRH that acts on inhibitory CRH2α receptors in the lateral septum [15]. Neurons from the lateral septum tonically inhibit periaqueductal grey regions that induce similar defensive behaviors. These nuclei are probably additional sources of CRH in the CSF.
Resting respiratory and pre-exercise heart rates and CRHCSF were positively correlated. The statistical model indicated that a low CRHCSF was predicted by low respiratory and heart rates. Respiratory and cardiac functions are rigorously controlled by brainstem and other nuclei that integrate incoming signals of plasma O2, CO2 and H+ concentrations, activity needs, anxiety and other stressors. Efferent cardiovascular and other autonomic reflexes are modulated by CRH in man [16]. These central nervous system effects may be due to, or highly correlated with, CRHCSF. This is supported by studies in mice that genetically overexpress CRH. They develop chronic stress – like autonomic and physiological alterations [17]. Stressors acting via conditioned fear responses may involve CRH1 receptors in the central nucleus of the amygdala. Some of these CRH neurons project to locus ceruleus neurons [17] that activate autonomic reflexes and defensive behaviors such as "freezing" (immobility) in rodents [14]. An example of this valuable defense would be the freezing of prey in the presence of a predator. Immobility would allow the prey's camouflage to blend into the surroundings without generating motion – induced visual cues for the predator. In humans, excessive, aberrant or dysregulated manifestions of defense behaviors such as freezing may contribute to the immobility, inertia, or even catatonia that contribute to the clinical picture of depression [1].
Extrapolation of these concepts suggests that elevated CRH may be related to anxiety, depression, or other disorders associated with chronic stress responses. If so, then lower, but normal, CRHCSF should be present in persons lacking these stressor states. This was supported by the negative correlations of CRHCSF with scores for the SF-36 Vitality and Change in Health, Self Efficacy Scale Manage Symptoms, MIQ Vulnerability, and MFI Physical Functioning domains. Each scale has an idealized "normal" end of the range of scores. Deviation towards either higher (MFI) or lower (SF-36) ends of the scales provides an estimate of dysfunction. For each of these domains, the lower CRHCSF were associated with more normal scores, while higher CRHCSF was associated with scores that were beginning to shift away from the normal pole of each scale. These trends were further supported by the statistical model where the optimum covariates of CRHCSF were SF-36 Vitality and MFI Physical Fatigue domains. The model predicted that CRHCSF would be in the low normal range when Vitality and Physical Fatigue domain scores were high (normal). Taken together, these results confirm the consistent physical status, mental coping skills, and general health of these subjects.
Studies of intraventricular CRH injection in primates support our findings [33]. The CRH diffused to brain regions that led to 3 types of behavioral changes. Externally oriented behaviors such as locomotion and environmental exploration were significantly decreased. Anxiety – related self – clasping was increased. Depression – like behaviors of avoidance of social contact, huddling, slouching, and wall facing were seen only in social settings. There were high interindividual differences in responses, but a key element was the social context in this study. This social context is lacking in rodent studies where animals are typically studied in isolation.
Conclusion
This small but intensively studied group of normal humans demonstrated surprisingly robust relationships between CRHCSF and metabolic, autonomic, and psychometric measures. These are novel findings in humans, but are consistent with data on CRH in acute and chronic stress models, and proposed CRH neural circuits. It will now be of great interest to contrast these statistical models identified for normal subjects with the other chronic pain and fatigue patient subsets to determine if CRHCSF correlates with different sets of variables.
Methods
Subjects
Nine normal, healthy subjects (2 females) gave informed consent for this paid, Institutional Review Board – approved protocol. They had a comprehensive screening evaluation to exclude: severe physical impairment, morbid obesity, autoimmune/inflammatory diseases, cardiopulmonary disorders, uncontrolled endocrine or allergic disorders, malignancy, severe psychiatric illnesses (e.g., schizophrenia, substance abuse), factors known to affect the HPA axis or autonomic function (cigarette smoking, daily intake of caffeine exceeding the equivalent of 2 cups of coffee), or medication use. Subjects had a history and physical examination, were administered the Structured Clinical Interview for the Diagnostic and Statistical Manual of Mental Disorders, fourth edition (DSM-IV) (SCID II) [19,20], Composite International Diagnostic Interview (CIDI) [21,22] and Center for Epidemiologic Studies Depression scale (CESD) [23] to detect psychiatric co-morbidities, and completed the self – report Short Form 36 (SF-36) [24,25], Self Efficacy Scale (SES) [26], Meaning of Illness Questionnaire (MIQ) [27], and Multidimensional Fatigue Inventory (MFI) [28].
Study protocol
Subjects were admitted to the G-CRC on the evening of Day 1. An 18-gauge catheter was inserted antecubitally and infused with normal saline at 50 ml/hr. A Holter monitor was attached to monitor autonomic regulation of cardiac rhythm [29-31]. Upon awakening, or at 6:30 am, subjects stayed in bed until they had "pre-awakening" blood tests drawn. Breakfast was served between 7:00 and 8:00 a.m. and baseline Day 2 blood samples drawn at 8:30 a.m. Vital signs, questionnaire responses, and other blood samples were drawn from the catheter to assess HPA axis and stress system responses before, during and after Day 2 stressors. Pain responses were tested by pressure applied to each subject's thumbnail and heat to the forearm applied in random staircase testing paradigms (Stressor I) [32,33]. Stressor II was a series of cognitive challenges including the Benton Visual Retention Test of visual-spatial memory; Digit Span recitation; and Pig Latin, a test of verbal working memory [34]. Lunch was served, followed by a 2 to 2.5 hours rest break to allow post-prandial catecholamine levels to reequilibrate. At 2 pm, serum glucose, electrolytes, plasma catecholamines and other analytes were measured. Subjects then squeezed an isometric hand grip dynamometer to test autonomic function and muscular fatigue [35] (Jamar, Sammons Preston, Bollingbrook, IL) (Stressor III). After 30 min of rest, they had a sub-maximal exercise test on an electronically-braked cycle ergometer (Sensormedics, Yorba Linda, CA). The test was graded in 3 min stages and ended when the subjects' heart rates reached 85% of their age-predicted maximum (Stressor IV). Lumbar punctures were performed 30 min later (approximately 4 pm) (Stressor V). Subjects sat while sterile technique was used to prepare the skin over the L4–L5 lumbar region, infiltrate the subcutaneous and deep tissues with 2% lidocaine, and insert a 22G spinal catheter. CSF was collected as 3 to 4 aliquots of about 2 ml each. Catheters were withdrawn and subjects allowed to rest in their preferred position for 30 min.
Lumbar punctures and CRH and NPY radioimmunoassays (RIA)
Tubes of CSF were immediately placed on ice and then centrifuged at 4°C. The supernatants were rapidly frozen at -80°C. Tube 2 or 3 was removed from the freezer and thawed at 4°C. Peptides were extracted by precipitating high molecular weight proteins by adding an equal volume of 100% ethanol, 0.1 M acetic acid, 0.2% sodium bisulfite [36,37]. The supernatant was dried (SpeedVac, Thermo Savant, Holbrook, NY), resuspended in phosphate buffered saline with 1% bovine serum albumin (RIA buffer; Peninsula Laboratories, Inc., San Carlos, CA.). Samples and standard amounts of each peptide were aliquoted and peptide specific rabbit antibodies added. After overnight incubation at 4°C, I125-CRH or -NPY was added, tubes gently vortexed, and again incubated overnight. Goat anti-rabbit antibodies were added, tubes incubated, and immune complexes precipitated by centrifugation. Radioactivity was counted for the standards, and the concentrations for each sample interpolated from the standard curves. Spiking CSF with fixed amounts of I125-peptides and performing the RIA shifted the curves to the left by the anticipated concentrations. Standard curves were reproducible to within 10%.
Plasma catecholamine levels were measured by HPLC (Mayo Clinic Laboratories, Rochester, MN).
Statistical methods
All the data from the provocation studies, questionnaire domains, blood work, and neuropeptide analysis were entered into a SAS spreadsheet (SAS, Carey, NC) using sequential hand or scanner entry followed by data checking routines.
Means with 95% confidence intervals were reported.
Simple correlations between our outcome measure (CRHCSF) and other objective and subjective patient variables were conducted as an exploratory tool. Given the small sample size (n = 9), both parametric and non-parametric correlations were used to evaluate the robustness of the correlation coefficients. Partial and intra-class correlations examined the structure of the relationships between CRHCSF and the independent variables. Original data were always checked to make sure that correlations were not spurious, due to outliers, colinearity, or sets of virtually identical scores.
Our data included a very wide range of covariates which spanned the physical and psychological attributes of the patients. Scatter plots were used to determine the response slopes with CRHCSF. Independent variables with flat slopes or high scatter that did not generate correlations with CRHCSF, that were highly correlated with each other (collinear), or that had < 5 recorded values were excluded from further analysis.
The remaining suitable independent variables fell into 3 categories: metabolic, autonomic and psychometric function. Three 3 separate linear regression models [8] were constructed to predict CRHCSF, the dependent variable. Separate models were required because of the low degrees of freedom available for the analyses, and to ensure that the explained variance (R2) was not inflated due to overloading the model with potentially collinear variables. Variance inflation factors and tolerance values were incorporated to optimize the selection of the most independent combinations of variables that also maximized the R2 and p values. The linear regression procedure took into account the multiple comparisons for each model.
Abbreviations
CESD, Center for Epidemiologic Studies Depression scale; CFS, Chronic Fatigue Syndrome; CIDI, Composite International Diagnostic Interview; CRH, corticotropin-releasing hormone; CSF, cerebrospinal fluid; MFI, Multidimensional Fatigue Inventory; MIQ, Meaning of Illness Questionnaire; NPY, neuropeptide Y; R2, explained variance; SCID II, Structured Clinical Interview for the Diagnostic and Statistical Manual of Mental Disorders, fourth edition (DSM-IV); SF-36, Short Form 36; SES, Self Efficacy Scale
Authors' contributions
JNB directed the RIA studies, correlation of clinical variables, and wrote the manuscript. HM confirmed the accuracy of the study results by double data entry methods, vetted the SAS database, performed the statistical analysis, and co-wrote the manuscript. GW performed the CRH and NPY assays and maintained the repository of these samples. DJC was the Principal Investigator for the overall study and helped review the draft and final manuscript.
Acknowledgements
Supported by Department of Defense Award DAMD 170020018, Public Health Service Award RO1 AI42403, and 1 M01-RR13297-01A1 from the General Clinical Research Center Program of the National Center for Research Resources, National Institutes of Health.
Figures and Tables
Figure 1 Relationships between CRHCSF, plasma glucose, and serum sodium. Data from single individuals are shown with identical symbols (e.g. solid black squares on left). Open circles indicate sodium without corresponding glucose values. The linear regression lines for CRHCSF with sodium (top line) and glucose (bottom line) gave explained variances (r2) of 0.66 and 0.97, respectively.
Table 1 Physiological data for each individual stratified by CRHCSF
Age (yr) CRHCSF (pg/ml) Sex HRa HRV log p (day)b Heat Scorec Tender points (#) Pressure threshold (kg/cm2) Epid Norepie
48 32.4 M 76 7.0039 37.7 0 8.32 0 330
31 31.5 F 76 7.128 37.8 0 2.90 0
29 29.7 M 73 7.2561 0 13
35 24 M 68 7.3232 40.5 1 10.11 14 430
31 23.8 M 89 7.2077 0 9.78 20 202
43 19.8 M 48 7.6277 43.9 1 8.67 16 223
25 19.4 M 77 41.4 0 4.76 10 100
36 16 F 76 7.5555 48.8 0 11.71 0 199
39 13.5 M 59 7.3973 43.6 4 7.48 13 182
HRa, heart rate; HRV log p(day)b, log10 of summed daytime heart rate intervals by Holter monitor; Heat Scorec, unpleasantness rating given for the first sensation of heat; Epid and Norepie, plasma concentrations of epinephrine and norepinephrine immediately prior to bicycle exercise
Table 2 Psychometric data for each individual stratified by CRHCSF
Age (yr) CRHCSF (pg/ml) Sex SF36 CHa SF36 Vitality CESD SES manage symptoms
48 32.4 M 25 35 6 5.2
31 31.5 F 50 45 10 7.5
29 29.7 M
35 24 M 50 85 0 8
31 23.8 M 50 80 1 10
43 19.8 M 100 85 0 10
25 19.4 M 75 65 14 10
36 16 F 100 100 0 10
39 13.5 M 85 1 10
SF36 CHa, Change in Health
Table 3 Significant covariates of CFHCSF in normal subjects.
Co-Variables with CRHCSF rho P N
Glucose @ 2 pm 0.987 0.0018 8
Sodium, mmol/L -0.812 0.0079 9
Norepinephrine, baseline, pre-pain testing 0.790 0.035 7
Norepinephrine 15 min after bike exercise 0.918 0.0035 7
Heart rate, resting, pre-hand grip 0.796 0.018 8
Heart rate, hand grip, 1 min recovery 0.818 0.013 8
Heart rate, hand grip, 2 min recovery 0.747 0.033 8
Heart rate, hand grip, 3 min recovery 0.756 0.049 8
Heart rate, resting, pre-bike exercise 0.825 0.043 6
Heart rate, log, summed for the day -0.810 0.015 8
Pain threshold for hyperthermia -0.844 0.017 8
SF-36 Vitality -0.838 0.0092 8
Self-Efficacy Scale – Manage Symptoms -0.851 0.0073 8
Table 4 Significant covariates of CFHCSF and relevant statistics in males.
Co-Variables with CRHCSF Rho P N
Respiratory Rate 0.953 0.0009 7
MIQ Vulnerability -0.911 0.031 5
MFI Physical Functioning -0.909 0.032 5
SF-36 Change in Health -0.887 0.044 5
Self-efficacy – manage symptoms -0.853 0.030 6
Table 5 Linear regression models of CRHCSF
Components of 3 Linear Regression Models R2 (adjusted R2) * P ** DoF ***
CRHCSF, Glucose @ 2 pm, serum Sodium (mmol/L) 0.979 (0.958) 0.02 4
CRHCSF, Respiratory Rate, Resting Heart Rate 0.963 (0.939) 0.007 7
CRHCSF, SF-36 Vitality, MFI Physical Fatigue 0.859 (0.789) 0.02 6
* R2 = expected variance; ** P = 2 tailed probability for the model; *** Degrees of freedom
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| 15606911 | PMC544567 | CC BY | 2021-01-04 16:03:46 | no | BMC Neurosci. 2004 Dec 17; 5:58 | utf-8 | BMC Neurosci | 2,004 | 10.1186/1471-2202-5-58 | oa_comm |
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Int J Health GeogrInternational Journal of Health Geographics1476-072XBioMed Central London 1476-072X-3-291558827910.1186/1476-072X-3-29ResearchSmall area mapping of prostate cancer incidence in New York State (USA) using fully Bayesian hierarchical modelling Johnson Glen D [email protected] New York State Cancer Registry, New York State Department of Health, Albany, NY, USA2 Department of Environmental Health and Toxicology, School of Public Health, University at Albany, Albany, NY, USA2004 8 12 2004 3 29 29 1 9 2004 8 12 2004 Copyright © 2004 Johnson; licensee BioMed Central Ltd.2004Johnson; 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
As part of a long-term initiative to improve cancer surveillance in New York State, small area maps of relative risk, expressed as standardized incidence ratios (SIRs), were produced for the most common cancers. This includes prostate cancer, the focus of this paper, since it is the most common non-dermatologic malignancy diagnosed among men and the second leading cause of cancer deaths for men in the United States.
ZIP codes were chosen as mapping units for several reasons, including the need to balance between protecting personal privacy and public demand for fine geographic resolution. Since the population size varies greatly among such small mapping units, hierarchical Bayes spatial modelling was applied in this paper to produce a map of smoothed SIRs. It is further demonstrated how other characteristics of the large sample from the stationary posterior distribution of SIRs can be mapped to investigate various aspects of the statewide spatial pattern of prostate cancer incidence.
Results
Thematic mapping of the median and 95 percentile range of SIRs provided, respectively, a map of spatially smoothed values and the uncertainty associated with these smoothed values. Maps were also produced to identify ZIP codes expressing a 95% probability, in the Bayesian paradigm, of being less than or greater than the null value of 1.
Conclusion
The model behaved as expected since areas that were statistically elevated coincided with areas identified by the spatial scan statistic, plus the relative uncertainty increased as a ZIP code's population decreased, with an exaggerated effect for low population ZIP codes on the edge of the state border.
The overall smoothed pattern, along with identified high and low areas, may reflect difference across the state with respect to socio-demographics and risk factors; however, this is confounded by potential differences in screening and diagnostic follow-up. Nevertheless, the Bayes modelling approach is shown to provide not only smoothed results, but also considerable other information from a large empirical distribution of outcomes associated with each mapping unit.
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Background
Geographic surveillance of chronic disease is central to understanding spatial or spatial-temporal patterns that may help to identify discrepancies in disease burden among different regions or communities. As part of ongoing efforts in New York State to understand spatial patterns of cancer and to help implement cancer prevention and control programs, small area maps of cancer relative risk, expressed as standardized incidence ratios (SIRs), have been produced and shared with the public [1] for the most common anatomical cancer sites.
Prostate cancer, the focus of this paper, was included because it is the most common non-dermatologic malignancy diagnosed among men and the second leading cause of cancer deaths for men in the United States (US) [2]. Although mortality from this disease in the US has statistically significantly decreased at a rate of 2.6% per year from 1990 to 2000 [3], unexplained geographic discrepancies in mortality rates do exist [4]. Furthermore, several treatment options appear to be associated with excellent long-term disease-specific survival for otherwise healthy men with localized disease [5].
Results for prostate cancer (all stages combined) are reproduced in Figure 1, where ZIP code-level standardized incidence ratios (SIRs) are presented along with results from analyzing these data with the spatial scan statistic [6]. The circles in Figure 1 represent statistically elevated regions based on Poisson likelihood ratios comparing rates inside the circle to those outside the circle. Details of how the scan statistic results were reduced to the circles presented in Figure 1 are found in Boscoe et al [7].
Figure 1 All stage prostate cancer Incidence by ZIP Code in New York State, 1994–1998. ZIP code-level ratios of observed incidence to age- and race-adjusted expected incidence, along with significant spatial scan statistic circles that are non-overlapping within specified ranges of standardized incidence ratios, based on reference [1]. Select cities and regions overlaid for reference.
It is well recognized that the stability of population-based statistics like the SIRs in Figure 1 can vary greatly among small geographic areas due to varying population size. Different methods of smoothing have been developed to address this issue, where all are based on the phenomenon that observations close together in space are more likely to share similar properties than those that are far apart [8]. While this positive spatial autocorrelation may be problematic for statistical methods that require independent observations, it can also be embraced to help smooth noisy maps by borrowing strength from neighbors for those mapping units with small populations. Of the different approaches to spatial smoothing, only a few appear to have gained acceptance in spatial epidemiology. Non-parametric approaches include spatial filtering [9,10] and the head-banging algorithm [11], both of which are basically variations on a moving window kernel-type smoother.
The parametric approach of generalized linear modelling [12] treats the observed response, y, as a random variable that has arisen from a probability distribution with expectation θ. This expectation is modeled, via an appropriate link g(·), as a linear function g(θ) = α + x'β + ε, for a common value α, explanatory covariates x'β and a random effect ε that captures unexplained variation. If the random effect is associated with exchangeable spatial heterogeneity, estimates are smoothed towards a global mean, whereas if the random effect is associated with local spatial autocorrelation, estimates are smoothed towards a local neighborhood mean, which is typically more meaningful in geographic epidemiology. There are different approaches to modelling local spatial dependence, and section 6.3 of Cressie [13] presents several arguments in favor of the conditional autoregressive (CAR) model originally conceived by Besag [14].
Estimation of model parameters can proceed by maximum likelihood [13]; however, the hierarchical nature of generalized linear models lends itself well to Bayesian analysis whereby linear terms in the model are assigned prior distributions that, in turn, have "hyperprior" parameters. Earlier applications employed empirical Bayes methods [15], where hyperparameters are estimated directly from the data. This approach is limited because it assigns a point estimate to the hyperparameter without allowing for variability that may be associated with it, and this variability can be large [16,17].
Fully Bayesian modelling assigns hyperprior distributions to these hyperparameters, so that every parameter of the hierarchical model is allowed to vary over a prior distribution and no single point estimate is used to represent an unknown parameter value. Furthermore, the fully Bayesian approach allows the convolution model that incorporates both a heterogeneous and spatially structured random effect [18], thus allowing the most flexibility in model development. Several reviews consistently support the fully Bayesian approach over empirical Bayes modelling [16,17,19].
Since there are no closed form analytical solutions for parameter estimates of a fully Bayesian model, nor likelihood profiles to maximize, Markov Chain Monte Carlo (MCMC) methods are used to generate large samples from the posterior distributions of all stochastic nodes of the hierarchical model, given the likelihood describing the original data distribution and all appropriate prior and hyperprior distributions of the likelihood parameters [16]. Estimation and inference in the fully Bayesian paradigm are based upon these large sample approximations of the posterior distributions.
In what follows, the fully Bayesian approach is applied to simulating large samples from the posterior distribution of prostate cancer relative risk in each of 1412 ZIP codes in New York State. Various aspects of these distributions are then mapped to reveal information on the geographic patterns of prostate cancer.
Results
The model defined by equations 1–5 was applied to simulate a sample of 1000 independent observations from the stationary posterior distribution of standardized incidence ratios for each ZIP code. Summary statistics and graphical analysis of these empirical distributions indicated that they arose from generally symmetric posterior distributions. Since the sample mean, median and mode were very similar for each ZIP code, the median was chosen to represent central tendency and is mapped in Figure 2. This "smoothed" map of SIRs provides a picture of spatial pattern inherent in the raw data mapped in Figure 1. Uncertainty associated with these estimates of relative risk is mapped in Figure 3 as the 95 percentile range (97.5th – 2.5th percentile) of the 1000 values sampled from the posterior distribution of SIRs for each ZIP code.
Figure 2 Bayesian smoothed prostate cancer incidence. Median of the posterior distribution of ZIP code-level standardized incidence ratios. Thematic categories based on natural breaks method, with slight adjustment.
Figure 3 Uncertainty of Bayesian Smoothed prostate cancer incidence. ZIP code-level 95th percentile range of posterior distribution of standardized incidence ratios. Thematic categories based on Natural Breaks Method.
The posterior distributions can also be used to identify ZIP codes where a specified mass of the distribution of relative risk is greater or less than the null value. For example, Figure 4 shows ZIP codes where 95% of the simulated SIRs exceed the value of one. In the Bayesian paradigm, those ZIP codes highlighted in Figure 4 have a 95% probability of higher than expected risk. Likewise, Figure 5 highlights ZIP codes expressing a 95% probability of lower than expected risk.
Figure 4 ZIP codes with a 95% probability of relative risk exceeding 1. The lower 5th percentile of the posterior distribution of standardized incidence ratios exceeds or equals 1.
Figure 5 ZIP codes with a 95% probability of relative risk being less than 1. The upper 95th percentile of the posterior distribution of standardized incidence ratios is less than or equal to 1.
Discussion
Methodology
The Poisson model applied in this paper is a particular application of hierarchical Bayes spatial generalized linear models for the exponential family of likelihoods [20]. Particular models are specified by the likelihood that is assumed to give rise to the observations, the structure of the prior, and the hyperprior distributions of variance components, which are typically vague to allow learning from the data. An aspect of these models that will influence outcomes is the neighborhood weights, as in Equation (4); however, defining these weights remains an open area of research.
Most applications to date use the first order binary weighting scheme where wij = 1 if a mapping unit j shares a common border with unit i, and wij = 0 otherwise. This weighting scheme actually has its roots in image analysis, for which this type of modelling was developed [14], and it makes sense when the spatial units are equal size and shape pixels and the response variable has a constant variance for each pixel. However, this is not the case when smoothing disease maps where the mapping units are of irregular size and shape, and stability of the response variable estimates varies with changing population size. This problem is especially relevant for mapping units like ZIP codes.
A proper approach may be to define weights as a decay function of geographic distance between population-weighted centroids of the mapping units. This function may be obtained by fitting a model correlogram to residuals that are obtained from a model that does not include a random effect to account for spatial autocorrelation. Cressie and Chan [21] used the empirical variogram of the response variable to determine the range of spatial autocorrelation. For neighborhood distances within this range, weights were defined as a function of Euclidean distance. Meanwhile, Griffith [22] provides some "rules of thumb" for defining geographic weights, but they are very general. Ferrandiz, et al [23] used a weight of ninj/dij for neighboring mapping units separated by geographic distance dij and of population sizes ni and nj. These authors applied such a weight to prostate cancer mortality mapping; however, this gravity-type weighting may be better suited for infectious disease, not chronic disease.
If a decay function is fit from the data, the varying stability of disease rates among the mapping units presents a challenge. Since the Bayesian model is designed to adjust for varying stability, perhaps a hierarchical model like the one applied in this paper can be extended so that the weights in Equation (4) are defined as a decay function whose unknown parameters can be assigned "hyperprior" distributions.
Application to prostate cancer mapping
Maps like in Figure 1 present a compromise between the need to protect personal privacy and public demand for fine geographic resolution. Such small mapping units are necessary for discerning among communities that can vary drastically across a region with respect to possible risk factors and both population density and demographics. However, this comes with the cost of unstable risk estimates for many mapping units that have small populations. Smoothing is therefore applied to help visualize spatial pattern that is inherent in the data of Figure 1. It is demonstrated how hierarchical Bayes spatial modelling has the appealing feature of providing a whole distribution of possible outcomes that can be used for not only smoothing, but also to explore other aspects of spatial pattern.
Viewing Figures 1 through 3 indicate that the Bayesian model is behaving as expected since the smoothed estimates are increasingly dependent on the prior model as uncertainty increases due to decreasing population, whereas for ZIP codes with large populations, like in New York City, the smoothed estimates are similar to the raw SIRs.
We also see that many edge ZIP codes in less populated areas tend to have greater uncertainty relative to their non-edge neighbors because there are fewer neighbors to borrow strength from. For the heterogeneous Poisson model applied in this paper, Lawson et al [24] suggest treating edge mapping units as a guard and not as part of the actual study area. However, presenting the width of Bayesian posterior distributions provides a way to retain the edge units while also showing the relative uncertainty associated with their smoothed values.
The smoothed pattern in Figure 2 is highlighted for areas of high and low incidence in Figures 4 and 5, respectively. Geographic patterns seen in these maps are potentially influenced by many factors, including differences between regions of the state in terms of racial, ethnic and socio-demographic composition. Yet it is well recognized that interpreting any possible relationships with risk factors is confounded by differences in screening and diagnostic practices across the state. Prostate-specific antigen (PSA) testing remains high among US males over 40 [25] and there is evidence of a steady increase in testing rates in New York State during the years corresponding to the data analyzed in this paper [26]. Along these lines, we note that a relatively large proportion of the four most populated counties of New York City reveal a high probability of less than expected incidence (see Figure 5 inset). This may be partially explained by the large immigrant population in these four counties, as indicated by much higher proportions of people who are foreign-born and/or do not speak English at home [27], which may translate to lower screening rates.
Although the patterns seen in Figures 2, 4 and 5 may be partially explained by geographic variations in PSA testing and diagnostic follow up, such variation is not actually known, therefore we cannot adjust for this potential confounder. In the neighboring state of Connecticut, geographic variation of invasive prostate cancer incidence was large and revealed some consistency before and after the introduction of PSA testing, while the pattern was completely different and variation was much smaller during the years of PSA introduction [28]. These authors suggest that such a space-time pattern reflects the impact of introducing PSA testing, although this cannot be confirmed in the absence of data on geographic differences in PSA use.
Some areas appear elevated that are in popular vacation spots, such as the eastern forks of Long Island and the "north country" of New York State including the Thousand Islands area along the Saint Lawrence River (near Watertown in Figure 1) and the Adirondack region. This may possibly be due to a seasonal residence effect whereby vacation areas tend to have artificially inflated chronic disease rates [29]. This occurs when seasonal residents provide a health care provider with a local address of a vacation home, while their primary residence is where they are counted by the decennial US census. Consequently, if their residence at time of diagnoses is in the vacation area, this record inflates the SIR numerator for that area, while they are counted in the denominator for the area of their primary residence as captured by the census. This effect is enhanced since the population spending extended periods in vacation areas tends to be over age 55, which is the age cohort at highest risk of chronic disease. Boscoe and Mclaughlin [29] have presented evidence of increased overall cancer rates in areas with seasonally resident populations in New York State, especially the Thousand Islands area. This uncertainty is reflected in Figure 3 where the width of the posterior distribution of SIRs for these areas is relatively large.
While the smoothed results in Figure 2 present an advantage over mapping raw data, a limitation of smoothing is that the pattern we decipher is subject to confounding by spatially varying population sizes [30]. In other words, smoothed maps like in Figure 2 reveal high and low relative risk in areas with larger populations, while areas with small populations tend to be smoothed towards the null value. While this means that areas with small populations that actually have abnormally high or low disease rates may be obscured, it is still well recognized that many extreme values associated with small populations may simply reflect random noise.
Other methods like the spatial scan statistic can be used in conjunction with Bayes smoothing to strengthen overall spatial analysis. Statistically significant scan statistic circles like those in Figure 1 can vary in size, potentially encompassing many ZIP codes, so are not restricted to only pre-defined neighborhoods like the conditional autoregressive model used by the Bayes smoothing algorithm. In this regard, the spatial scan statistic is similar to smoothing by spatial filtering with variable-radius circles [10]. General regions of statistically elevated relative risk may be identified by the scan statistic, with supporting evidence from Bayesian posterior distributions to help identify the mapping units that contribute strongly to a scan statistic circle. Indeed, each spatial scan statistic circle reported in Figure 1 contains at least one elevated ZIP code identified in Figure 4 by the Bayesian model.
There is ample flexibility for exploratory analysis by varying the display parameters for results from these two methods. For example, the results in Figure 4 can be either more generalized or further focused by identifying ZIP codes where, say, 90% or 99%, respectively, of the posterior probability mass exceeds the null value of one. At the same time, we can display the set of non-overlapping scan statistic circles that correspond to lower levels of relative risk than are shown in Figure 1, thus capturing more geographic area. In fact, when this is done for scan statistic circles corresponding to 15–49% relative risk (not shown), all of the elevated ZIP codes in Figure 4 are spatially associated with significant scan statistic circles.
There is extensive literature on hierarchical Bayes spatial modelling for disease mapping; however, most papers are theoretical in nature and use illustrative examples, often with the same data sets. One exception was recently published by Short et al [31], who applied Bayes modelling to produce maps of cancer control variables. Specifically, they smoothed maps of different outcomes (mortality, incidence, staging and screening) for each of breast, colorectal and lung cancer in Minnesota (USA) counties. Cancer control maps were created for each cancer site by obtaining a weighted sum of each smoothed outcome, and an overall cancer control map was obtained by a weighted sum of the individual cancer control map values. These results can help guide resource allocation for state cancer prevention and control efforts.
While there are open areas for improvement in the methodology of hierarchical Bayes spatial modelling, it is a valuable tool for geo-spatial assessment of disease patterns that can help identify differences among communities. This may in turn indicate patterns of health care access, screening and diagnostic follow up and possibly indicate etiologic clues about causal relationships.
Methods
Data
Observed and expected values of prostate cancer incidence used to calculate SIRs for 1412 New York State ZIP codes [1] were obtained for the years 1994 to 1998 from the New York State Cancer Registry (NYSCR). The expected values used in the SIR denominator are based on indirect standardization using the age-by-race distribution in each ZIP code and the statewide age- and race-specific incidence rates as a reference. Age and race distributions correspond to the year 1997, as estimated by the Claritas Corporation™ based on prior census values. ZIP code boundaries were delineated by the GDT Corporation™ in 1999.
ZIP Code delivery areas are prone to change over time [32], particularly in rapidly growing parts of the country. According to the NYSCR [personal communication], a review of all of the issues of the Postal Bulletin, where these changes are documented, from 1990 to the present revealed that New York has had stable ZIP Code delivery areas. Approximately 50 small, rural post offices were closed, 3 new post offices were added, and none were realigned. ZIP codes were combined in instances where service delivery area changed between 1990 and 1999 or for confidentiality reasons where necessary [33].
Modelling
Letting the geographic domain (New York State) be subdivided into i = 1, ..., n distinct mapping units (n = 1412 ZIP codes for our application), the number of cases within each unit, Yi, conditional on location i, is defined as a Poisson random variable with expectation Eiθi, where Ei equals the age- and race-adjusted expected number of prostate cancer cases, and θi equals the area-specific relative risk. Given an observed response yi, note that the maximum likelihood estimate of relative risk is = yi / Ei, the standardized incidence ratio (SIR).
The relative risk parameter θi is assigned a log-normal prior distribution, log(θi) ~ N(μi, ), where the expectation and variance are defined by a linear function of a common value (intercept), α, and two independent random effects, a heterogeneous component, ui, that does not depend on geographic location (exchangeable) and an autocorrelated component, vi, that reflects local spatial structure by incorporating the influence of neighboring geographic units. Prior distributions are then assigned to these linear terms and consequent hyperprior distributions are assigned to the variance terms, thus creating a 4-level hierarchical model as follows.
Level 1, define the likelihood: Yi ~ Poisson(Eiθi) (1)
Level 2, link to a linear function: log(θi) = α + ui + vi (2)
Level 3, assign prior distributions: α ~ N(0,0.0001), noting that 0.0001 is the precision, thus defining a vague prior,
for a neighborhood of geographic units δi with respect to unit i and wij is a weight defining the relationship between geographic unit i and its neighbor j. The weight is defined simply as wij = 1 if ZIP codes i and j are adjacent (share a common border) and wij = 0 otherwise.
Level 4, assign hyperprior distributions to precision terms:
τu = 1 / ~ Gamma(a,b) and τv = 1 / ~ Gamma(c,d) (5)
for shape parameters a and c, and inverse scale parameters b and d.
This is the convolution model originally proposed by Besag, York and Mollie [18], where the random effect associated with spatial autocorrelation, vi, is defined according to the conditional auto-regressive model (CAR) [14]. Note that the distribution of vi is conditional on geographic location, whereby its expectation equals a local neighborhood average. The Bayesian model puts increasing emphasis on this term as the underlying population at location i decreases.
Although covariates can be incorporated into the log-linear expression at the second level of the model, our interest is with estimating and mapping the relative risk, θi = exp(α + ui + vi).
Choosing Gamma Hyperpriors
While it is established that a vague prior is acceptable for the linear term α in Equation (2) (i.e. Ghosh et al [20]), the model should be evaluated for sensitivity to choice of the Gamma hyperprior distributions of the precision terms, as in Equation (5). Two very different hyperprior specifications that appear in the literature for this convolution model were experimented with. Hyperparameters were specified for one model as Γ(1,1), which yields a probability of 99% that the precision lays between 0.01 and 4.6, and for the other model as Γ(0.5,0.0005), which yields a probability of 99% that the precision lies between 0.16 and 6635, with most of the probability concentrated towards 0. Note that these parameter choices also satisfy sufficient conditions for ensuring a proper joint posterior distribution of all the stochastic nodes [20]. For the statewide collection of New York ZIP code log(SIR)s to be smoothed in this paper, the sample precision equals 6.25 (variance = 0.16) and the precision of first order neighborhood means equals 20 (variance = 0.05). Therefore, it may be desired to retain the model with hyperprior specification of Γ(0.5,0.0005) to at least capture the sample-based precision estimates, while also defining a vague hyperprior that allows more learning from the data.
Final smoothed results from each model are compared in Figure 6 where we see, in agreement with Bernardinelli et al [34], that it essentially makes no difference which hyperprior is used. Therefore, the Γ(0.5,0.0005) hyperprior specification was chosen. It is not the intention of this paper to perform a rigorous sensitivity analysis with respect to hyperprior specification; however, the two models assessed in Figure 6 represent very different distributions and therefore indicate that the fully Bayes hierarchical model is quite robust with respect to hyperprior specification when smoothed relative risks are the objective.
Figure 6 Comparison of smoothed standardized incidence ratios for two different specifications of the hyperprior distributions. Each point represents a ZIP code with two medians of the posterior distribution of standardized incidence ratios obtained from different models.
Running the Gibbs Sampler
WINBUGS 1.4 [35] was used for running three independent Markov Chains. Initial values of all stochastic nodes of the model were chosen to provide dispersed initial values without being excessively overdispersed. For the common intercept, α, and heterogeneous random effect, ui, zero (0) was used to initiate one chain, plus/minus four standard deviations of the statewide log(SIR) were used to initiate the other respective chains. Zero is the statewide average log(SIR) that provides a point estimate for α, plus it is the expected value of ui. For the random effect associated with local spatial clustering, vi, the initial values were based on the average plus/minus four standard deviations of the log of first order neighborhood average SIRs. For the precision terms τu and τv, the inverse of the sample variances of the log(SIR) and the log of the first order neighborhood average SIRs were used respectively for one chain, plus lower and higher values were chosen for the other two chains in order to be well dispersed from the middle value, but not wildly so with respect to what is reasonably expected based on the observed spatial variability.
Convergence of relative risk for the three independent chains was confirmed by graphing their traces and observing random mixing of all chains, which revealed white noise variation around a common value, with no trend. This was supported by observing Brooks-Gelman-Rubin diagnostics that clearly satisfied convergence criteria [36]. After a burn-in of 10,000 iterations, which was far more than actually necessary, the following 1000 iterations were sampled from each of the three chains by choosing every third iteration to help avoid possible autocorrelation within a chain. This large sample approximation of the stationary posterior distribution for each ZIP code relative risk was then summarized in WINBUGS and brought into a Geographic Information System [37] for mapping.
Model Selection
Variations of the model defined above were compared by evaluating the mean deviance of 1000 iterations chosen from the three independent Markov Chains after burn-in. This was done by obtaining the mean of -2(log likelihood) for each iteration, as provided by the deviance node in WINBUGS. The mean deviance was then calculated as D(y, μ) = 2 [l(y, y) - l(y, μ)], where l(y, y) is the mean maximum achievable log likelihood, obtained for a saturated model where a parameter is assigned to each datum, and l(y, μ) is the mean log likelihood obtained for the model in question. This takes the conventional assessment of deviance for generalized linear models [12] and applies it to the many outcomes of Monte Carlo simulation, as per Spiegelhalter et al [38].
Incorporating a random effect associated with local spatial structure (CAR term) provides much stronger prior information than the exchangeable random effect alone (Table 1), which assumes purely heterogeneous variation across the state. This agrees with findings by Spiegelhalter et al [38], who developed the Deviance Information Criterion as a penalized version of deviance and applied it to Scottish lip cancer data. The convolution model was therefore chosen, which incorporates both random effects.
Table 1 Deviance analysis. See text for explanation.
Model Mean (-2 LL) Mean Deviance
Saturated 8082.0
Convolution 8194.0 112.0
CAR only 8191.0 109.0
Exchangeable only 10800.0 2718.0
Acknowledgements
The author sincerely appreciates the encouragement and dialogue with colleagues throughout this project; namely, from the New York State Department of Health, Colleen McLaughlin and Maria Schymura of the New York State Cancer Registry, and Edward Fitzgerald, Syni-An Hwang, Thomas Talbot and Francis Boscoe of the Bureau of Environmental and Occupational Epidemiology, along with Thomas Richards of the US Centers for Disease Control.
Contract/grant sponsor: This publication was made possible through a Cooperative Agreement between the Centers for Disease Control and Prevention (CDC) and the Association of Schools of Public Health (ASPH), award number S1355-20/22. Its contents are the responsibility of the author and do not necessarily reflect the official views of the CDC or ASPH.
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| 15588279 | PMC544568 | CC BY | 2021-01-04 16:39:02 | no | Int J Health Geogr. 2004 Dec 8; 3:29 | utf-8 | Int J Health Geogr | 2,004 | 10.1186/1476-072X-3-29 | oa_comm |
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BMC Plant BiolBMC Plant Biology1471-2229BioMed Central London 1471-2229-4-211562500910.1186/1471-2229-4-21Research ArticleAn evaluation of the effects of exogenous ethephon, an ethylene releasing compound, on photosynthesis of mustard (Brassica juncea) cultivars that differ in photosynthetic capacity Khan NA [email protected] Department of Botany, Aligarh Muslim University, Aligarh 202 002, India2004 30 12 2004 4 21 21 30 8 2004 30 12 2004 Copyright © 2004 Khan; 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 stimulatory effect of CO2 on ethylene evolution in plants is known, but the extent to which ethylene controls photosynthesis is not clear. Studies on the effects of ethylene on CO2 metabolism have shown conflicting results. Increase or inhibition of photosynthesis by ethylene has been reported. To understand the physiological processes responsible for ethylene-mediated changes in photosynthesis, stomatal and mesophyll effects on photosynthesis and ethylene biosynthesis in response to ethephon treatment in mustard (Brassica juncea) cultivars differing in photosynthetic capacity were studied.
Results
The effects of ethephon on photosynthetic rate (PN), stomatal conductance (gS), carbonic anhydrase (CA) activity, 1-aminocyclopropane carboxylic acid synthase (ACS) activity and ethylene evolution were similar in both the cultivars. Increasing ethephon concentration up to 1.5 mM increased PN, gS and CA maximally, whereas 3.0 mM ethephon proved inhibitory. ACS activity and ethylene evolution increased with increasing concentrations of ethephon. The corresponding changes in gs and CA activity suggest that the changes in photosynthesis in response to ethephon were triggered by altered stomatal and mesophyll processes. Stomatal conductance changed in parallel with changes in mesophyll photosynthetic properties. In both the cultivars ACS activity and ethylene increased up to 3.0 mM ethephon, but 1.5 mM ethephon caused maximum effects on photosynthetic parameters.
Conclusion
These results suggest that ethephon affects foliar gas exchange responses. The changes in photosynthesis in response to ethephon were due to stomatal and mesophyll effects. The changes in gS were a response maintaining stable intercellular CO2 concentration (Ci) under the given treatment in both the cultivars. Also, the high photosynthetic capacity cultivar, Varuna responded less to ethephon than the low photosynthetic capacity cultivar, RH30. The photosynthetic capacity of RH30 increased with the increase in ethylene evolution due to 1.5 mM ethephon application.
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Background
Photosynthesis is controlled by several intrinsic and extrinsic factors. Of these, plant hormones have received considerable attention in the past in photosynthetic responses of plants. Ethylene is a phytohormone that influences every aspect of plant growth and development [1]. It is synthesized by the activity of 1-aminocyclopropane carboxylic acid synthase (ACS). The response of plants to ethylene depends on the sensitivity of plants to the gas. Conflicting results on the effects of ethylene-releasing compounds on net photosynthetic rate (PN) have been reported. It has been shown to increase PN [2-7] or decrease it [8,9], but no definite reason has been assigned for this. It has been shown that the increase in PN with ethylene-releasing compounds was due to the increase in chlorophyll per unit leaf area [10] or by greater light interception [11]. In my earlier report it has been shown that alteration in photosynthesis was due to the changes in ACS activity [12]. The goal of this work was to compare stomatal and mesophyll effects on PN in response to ethephon treatment. For that, PN, stomatal conductance (gS) and carbonic anhydrase (CA) activity were recorded. To find a possible relationship of ethylene-mediated changes in foliar gas exchange parameters, activity of ACS and ethylene evolution were also determined. The work was carried out in two cultivars of mustard previously shown to have different photosynthetic capacity [12].
Results
The effects of ethephon on PN, gS and CA were found significant in both the cultivars (Figures 1, 2). Ethephon at 1.5 mM increased the characteristics maximally, increasing PN by 31.8 and 41.8%, gS by 15.0 and 17.1% and CA by 84.6 and 71.4% in Varuna and RH30, respectively. Higher concentration of ethephon (3.0 mM) decreased the characteristics in both the cultivars. The ratio of intercellular to ambient CO2 concentration (Ci/Ca) was constant.
Ethephon application significantly affected ACS activity and ethylene evolution, and were greatest with 3.0 mM ethephon (Table 1). Low photosynthetic capacity cultivar, RH30 was more responsive to ethephon than the high photosynthetic capacity cultivar, Varuna. Application of 1.5 mM ethephon increased ethylene by 52.6% in Varuna and 75.0% in RH30. Increase in ethylene with 1.5 mM ethephon was associated with the increase in PN, gS and CA. Ethylene evolution with 3.0 mM ethephon proved inhibitory for photosynthetic parameters.
Discussion
Maximum rates of photosynthesis were found with 1.5 mM ethephon. The increase in PN due to ethephon has been reported [4-6]. Increased gs and CA values in both the cultivars showed stomatal and mesophyll effects on photosynthesis. Mesophyll effects are characterized as a product of CO2 binding capacity and the electron transport capacity. The carboxylation capacity determines the mesophyll effects [13,14]. Increase in CA activity at the site of CO2 fixation exhibited the enhanced carboxylation reaction [15-17]. The changes in stomatal conductance due to ethephon were to maintain stable intercellular CO2 concentration (Ci) under the given treatment. Thus, stomatal and mesophyll processes contributed to the increase in PN in response to ethephon. The ethephon-induced effects on photosynthetic parameters were mediated by ethylene evolved due to ethephon treatment. Taylor and Gunderson [18] showed a relationship between ethylene-enhanced gS and ethylene-enhanced PN. Higher concentration of ethephon (3.0 mM) decreased the PN and gS. Such condition of inhibition of PN by ethylene-releasing compound has been observed by Kays and Pallas [8] and Rajala and Peltonen-Sainio [9]. In all these studies ethylene has been attributed to the changes in PN due to its effect on gS. Mattoo and White [19] reported that ethylene affected CO2 assimilation and the plant responded depending on the tissue concentration. On the similar lines, Dhawan et al. [20], Kao and Yang [21] and Grodzinski et al. [22] reasoned that decrease in CO2 regulated PN and was related to ethylene evolution. In the present study, low photosynthetic cultivar, RH30 responded more to ethephon than the high photosynthetic cultivar, Varuna. In control plants, lesser ethylene evolution in RH30 than Varuna was responsible for lesser PN. As the ethylene evolution increased with ethephon application, the capacity of RH30 for PN also increased resulting in higher per cent increase in PN than the Varuna. An increase of 75% ethylene in RH30 due to 1.5 mM ethephon increased PN by 41.8%, whereas 52.6% increase in ethylene in Varuna due to the same treatment increased PN by 31.8%. Earlier strong positive correlation between ACS activity and PN has been shown [12].
It therefore, appears possible that the threshold value for ethylene with 1.5 mM ethephon was comparable to that which elicits the ethylene-mediated hormonal responses, which differ with the cultivars inherent capacity of physiological processes. It is that there is some requirement of ethylene for optimum response. Low and high concentration represent the two ends of an optimum curve, promoting at low concentration and inhibiting at high.
Conclusions
This study shows that ethephon affects PN in both high and low photosynthetic capacity cultivars, Varuna and RH30. In both the cultivars, changes in PN were due to stomatal and mesophyll effects. Ethephon-induced PN was attributed to ethylene evolution. The high photosynthetic capacity cultivar, Varuna responded less to ethephon than the low photosynthetic capacity cultivar, RH30. The low PN of RH30 was due to low level of ethylene. The low photosynthetic capacity of RH30 could be enhanced to give higher PN through increase in ethylene evolution. However, for both the cultivars there is a range of physiologically active concentration of ethylene beyond which it exerts inhibitory effects.
Methods
Two cultivars of mustard (Brassica juncea L. Czern & Coss.), namely Varuna (high photosynthetic capacity) and RH30 (low photosynthetic capacity) were grown from seeds in 10 m2 field plots in complete randomized design with five replications. At seedling establishment a plant population of 12 plants m-2 was maintained and recommended plant cultivation procedures were adopted. A uniform recommended soil application of 18 g N, 3 g P and 3 g K m-2 was given at the time of sowing so as the nutrients were non-limiting.
At 30 d after sowing, 0, 0.75, 1.5 and 3.0 mM ethephon (2-chloroethyl phosphonic acid) was sprayed with a hand sprayer. Ethephon is a direct ethylene source when applied to plants and elicits response identical to those induced by ethylene gas [23,24]. Since ethephon on hydrolysis releases ethylene and phosphorus, therefore equivalent amount of phosphorus present in 3.0 mM ethephon was given to all treatments including control to nullify the effects of phosphorus.
At 45 d after sowing (15 d after ethephon treatment) PN, gS, Ci, CA activity, ACS activity and ethylene evolution were determined.
Measurement of photosynthetic parameters
PN, gS and Ci were measured using infrared gas analyzer (LiCOR 6200, Lincoln, NE) on fully expanded upper most leaves at saturating light intensity on four plants from each replicate. The atmospheric conditions during the experiment between 1100–1200 h were: photosynthetic active radiation about 1050 μmol m-2 s-1, relative humidity 64% and temperature 23°C, atmospheric CO2 concentration 360 μmol mol-1.
Measurement of carbonic anhydrase activity
The leaves used for photosynthesis measurement were selected for CA activity determination. CA was measured by the method of Dwivedi and Randhava [25]. Leaves were cut into small pieces in 10 mL of 0.2 M cystein at 4°C. The solution adhering to the leaf surface was removed and immediately transferred to a tube having 4 mL phosphate buffer (pH 6.8). A 4 mL of 0.2 M sodium bicarbonate in 0.002 M sodium hydroxide and 0.2 mL of 0.002% bromothymol blue was added to the tube. The tubes were kept at 4°C for 20 min after shaking. Liberated CO2 during the catalytic action of enzyme on sodium bicarbonate was estimated by titrating the reaction mixture against 0.05 N hydrochloric acid.
Measurement of ACS activity and ethylene evolution
Activity of ACS was measured adopting the methods of Avni et al. [26] and Woeste et al. [27]. Leaf tissue was grind in 100 mM N-2 hydroxyethylenepiperazine N-2 ethanesulfonic acid buffer (pH 8.0) containing 4 mM dithiothreitol, 2.5 mM pyridoxal phosphate and 25% polyvinylpolypyrrolidone. The preparation was homogenized and centrifuged at 12000 g for 15 min. One mL of the supernatant was placed in a 30 mL tube and 0.1 mL of 5 mM S-adenosyl methionine (AdoMet) was added. This was incubated for 1 h at 22°C. The 1-aminocyclopropane carboxylic acid formed was determined by its conversion to ethylene by the addition of 0.1 mL of 20 mM HgCl2 followed by 0.1 mL of 1:1 mixture of saturated NaOH/NaCl and incubated on ice for 10 min, and ethylene evolution was measured on a gas chromatograph. For control set AdoMet was not added. For ethylene evolution 5 mL of gas phase was removed with a syringe and ethylene was measured on a gas chromatograph (GC 5700, Nucon, New Delhi) equipped with 1.8 m Porapack N (80/100 mesh) column, a flame ionization detector and an integrator. Nitrogen was used as carrier gas. The flow rates of nitrogen, hydrogen and oxygen were 0.5, 0.5 and 5 mL s-1, respectively. The oven temperature was 100°C and detector was at 150°C. Ethylene identification was based on the retention time and quantified comparing with the peaks from standard ethylene concentrations.
Data analysis
Data were analyzed statistically and standard error of the mean value was calculated. Analysis of variance was performed to identify the significant differences among treatments at P < 0.05 [28].
Abbrevations
Adomet – S-adenosyl methionine; ACS – 1-aminocyclopropane carboxylic acid synthase; CA – carbonic anhydrase; Ci – intercellular CO2 concentration; gS – stomatal conductance; PN – net photosynthetic rate
Acknowledgements
The author gratefully acknowledges financial assistance by the University Grants Commission, New Delhi for the work and to the two anonymous reviewers for constructive criticism on the earlier version of the manuscript.
Figures and Tables
Figure 1 Effects of ethephon on photosynthesis and stomatal conductance in mustard Effect of different concentrations of ethephon (2-chloroethyl phosphonic acid) applied at 30 d after sowing on net photosynthetic rate (PN) (A) and stomatal conductance (gS) (B) in high photosynthetic capacity cultivar Varuna and low photosynthetic capacity cultivar RH30 of mustard (Brassica juncea) at 15 d after the treatment. Each data point represent treatment mean ± SE. Values at each data point within the cultivar sharing the same letter are not significantly different at P < 0.05.
Figure 2 Effects of ethephon on intercellular CO2 concentration and carbonic anhydrase activity in mustard Effect of different concentrations of ethephon (2-chloroethyl phosphonic acid) applied at 30 d after sowing on intercellular CO2 concentration (Ci) (A) and carbonic anhydrase (CA) activity (B) in high photosynthetic capacity cultivar Varuna and low photosynthetic capacity cultivar RH30 of mustard (Brassica juncea) at 15 d after the treatment. Each data point represent treatment mean ± SE. Values at each data point within the cultivar sharing the same letter are not significantly different at P < 0.05.
Table 1 Effects of different concentrations of ethephon (2-chloroethyl phosphonic acid) applied at 30 d after sowing on the activity of 1-aminocyclopropane carboxylic acid synthase (ACS; (ng ACC kg-1 leaf (FM) s-1) and ethylene evolution (ng kg-1 leaf (FM) s-1) in two cultivars of mustard (Brassica juncea) at 15 d after the treatment. Values ± SE. Data followed by the same letter within a column are significantly not different.
Ethephon Treatments (mM) High photosynthetic capacity cultivar (Varuna) Low photosynthetic capacity cultivar (RH 30)
ACS Ethylene ACS Ethylene
0 62.2 ± 5.4c 3.8 ± 0.2c 40.8 ± 3.6c 2.4 ± 0.2c
0.75 64.6 ± 5.8c 4.4 ± 0.3bc 44.4 ± 4.0c 3.2 ± 0.2bc
1.5 68.2 ± 5.8b 5.8 ± 0.3b 52.8 ± 4.6b 4.2 ± 0.3b
3.0 73.5 ± 6.0a 7.0 ± 0.5a 62.3 ± 5.6a 6.0 ± 0.4a
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| 15625009 | PMC544569 | CC BY | 2021-01-04 16:03:51 | no | BMC Plant Biol. 2004 Dec 30; 4:21 | utf-8 | BMC Plant Biol | 2,004 | 10.1186/1471-2229-4-21 | oa_comm |
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BMC Cell BiolBMC Cell Biology1471-2121BioMed Central London 1471-2121-5-491561757410.1186/1471-2121-5-49Research ArticleRegulation of cell cycle by the anaphase spindle midzone Murata-Hori Maki [email protected] Greenfield [email protected] Yu-li [email protected] Department of Physiology, University of Massachusetts Medical School, 377 Plantation St., Worcester, Massachusetts, 01605, USA2 Mammalian Cell Biology Group, Temasek Life Sciences Laboratory, 1 Research Link, National University of Singapore, 117604, Singapore3 Department of Cell Biology, University of Massachusetts Medical School, 377 Plantation St., Worcester, Massachusetts, 01605, USA2004 23 12 2004 5 49 49 20 11 2004 23 12 2004 Copyright © 2004 Murata-Hori et al; licensee BioMed Central Ltd.2004Murata-Hori 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 number of proteins accumulate in the spindle midzone and midbody of dividing animal cells. Besides proteins essential for cytokinesis, there are also components essential for interphase functions, suggesting that the spindle midzone and/or midbody may play a role in regulating the following cell cycle.
Results
We microsurgically severed NRK epithelial cells during anaphase or telophase, such that the spindle midzone/midbody was associated with only one of the daughter cells. Time-lapse recording of cells severed during early anaphase indicated that the cell with midzone underwent cytokinesis-like cortical contractions and progressed normally through the interphase, whereas the cell without midzone showed no cortical contraction and an arrest or substantial delay in the progression of interphase. Similar microsurgery during telophase showed a normal progression of interphase for both daughter cells with or without the midbody. Microsurgery of anaphase cells treated with cytochalasin D or nocodazole indicated that interphase progression was independent of cortical ingression but dependent on microtubules.
Conclusions
We conclude that the mitotic spindle is involved in not only the separation of chromosomes but also the regulation of cell cycle. The process may involve activation of components in the spindle midzone that are required for the cell cycle, and/or degradation of components that are required for cytokinesis but may interfere with the cell cycle.
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Background
Microtubules undergo striking reorganization during anaphase and telophase. During anaphase, antiparallel, interdigitating microtubules and many associated proteins become organized into discrete bundles in the spindle midzone [1], the region between separated chromosomes. As the cell enters cytokinesis, these midzone microtubule bundles merge into a single compact, electron-dense structure called midbody. It is generally recognized that, at least for cultured animal cells, midzone microtubules play a major role in cytokinesis. For example, cleavage furrows, both normal and ectopic, are associated with similar microtubule bundles [2-4], while regions physically blocked from midzone microtubules by micromanipulation are unable to undergo cytokinesis [5]. Moreover, continuous interactions of midzone microtubules with the cell cortex are required for sustaining the cytokinesis of cultured animal cells [6].
Recent progress suggested that, in addition to cytokinesis, the spindle midzone might be involved in additional functions. For example, midzone and midbody microtubules are associated with many regulatory proteins apparently unrelated to cortical contraction, such as the DNA replication initiator Orc6 [7], the inhibitor of apoptosis survivin [8], and the tyrosine kinase binder Nir2 [9]. In addition, mother centrioles were found to migrate to the midbody during telophase before returning to their interphase position, possibly activating some centrosomal components for cell cycle progression [10]. Furthermore, treatment of dividing cells with dihydrocytochalasin B, an inhibitor of cytokinesis, caused not only the inhibition of cytokinesis but also G1 arrest after mitosis [11], raising the possibility that ploidy, cortical contraction, and/or activation/deactivation of proteins during cytokinesis, may play a role in the regulation of the following cell cycle.
To address this possibility, we severed cells at anaphase or telophase by microsurgery, to bypass the normal mechanism of cytokinesis. The functional role of the spindle midzone or midbody in the following cell cycle was then tested by manipulating the position of the microsurgery or by applying pharmacological agents. Using extended time-lapse microscopy, we found that anaphase midzone microtubules play an important role in the progression of the subsequent interphase. However, there was no evidence of the involvement of telophase midbody in cell cycle progression.
Results and discussion
To investigate if anaphase spindle midzone affects interphase progression in daughter cells, we cut NRK52E cells at early anaphase, as soon as sister chromosomes have separated completely, with a fine glass fiber to form daughter cells with or without spindle midzone structures. Staining of severed daughter cells with antibodies against aurora B (Figure 1A), which is known to relocate from centromeres to the spindle midzone during early anaphase [12,13], confirmed that the spindle midzone was removed from one of the daughter cells. However, both daughter cells showed typical interphase microtubules organization (Figure 1B), and normal cellular and nuclear morphology (Figure 2, d), indicating that cells recovered fully from the microsurgery and exited mitosis successfully.
Figure 1 Microsurgery of dividing NRK cells. The site of severing is indicated by dotted lines (A, a; B, a). Immunofluorescence indicates that aurora B, a protein associated with midzone microtubules, is partitioned predominantly to the cell with midzone microtubules (A, d). Microtubules, staining of another cells cut in a similar way, showed an indistinguishable network in both daughter cells after recovering for one hour (B, d). Bar, 20 μm.
Figure 2 Inhibition of the interphase progression following surgical removal of the spindle midzone structures. An NRK cell was cut at anaphase to form daughter cells with (top) and without (bottom) the spindle midzone structures (cutting site indicated by the dotted line, a). Subsequent time-lapse recording indicates that both daughter cells formed nuclear envelop (arrows, d). However, only the daughter cell with spindle midzone (top) showed cytokinesis-like contractions (c, arrows). The daughter cell with spindle midzone entered the subsequent mitosis 11 hours after microsurgery (e, arrow), whereas the daughter cell without spindle midzone entered mitosis 22 h after microsurgery (f, arrow). Time elapsed since cutting is shown in hours:minutes. Bar, 20 μm.
A total of eight manipulated cells were followed by extended time-lapse imaging through the following interphase. All the daughter cell with spindle midzone showed cytokinesis-like cortical contraction activities, while the daughter cell without spindle midzone showed no cortical contraction (Figure 2, see Additional file 1). The daughter cell with spindle midzone subsequently progressed with normal timing through the following interphase, entering mitosis at a time similar to that of adjacent control cells (Figure 2, Table 1). However, in 5 out of 8 cases, the cell without midzone showed a duration of interphase more than twice that of the sister cell with spindle midzone (Table 1). In the example shown in Figure 2, interphase lasted for 22 h, as compared to the normal 11 h for the sister cell with the spindle midzone. In two cases, cell cycle appeared to be arrested in interphase since no mitosis was observed over a period of 34 – 46 h.
Table 1 Duration of interphase for daughter cells with and without the spindle midzone or midbody#
midbody/spindle midzone
+ -
spindle midzone (8) 721 ± 66 1444 ± 236**
spindle midzone (5)* 671 ± 28 1724 ± 301**
midbody (9) 610 ± 92 678± 113
control*** (66) 611 ± 16
#Average time in minutes ± SEM
*Average of 5 experiments that showed at least a doubling in the length of interphase for the daughter without the midzone.
**In 3 cases, the daughter cell without spindle midzone failed to enter mitosis during the period of time-lapse recording. For these cells, the length of interphase was assumed to equal the length of observation. Therefore, this average value represents an under-estimate.
***Two unmanipulated cells near the manipulated cell were measured in each experiment as controls.
The inhibition of interphase progression was affected by the position of severing. When cells were severed near the equator to divide midzone structures between the two daughter cells, both cells showed cytokinesis-like cortical contractions and no effect on the progression of interphase. This observation was repeated with 4 cells. To test the possibility that the duration of interphase was affected by the size of the daughter cell, we took pairs of normally divided daughter cells and cut away a lateral region near the pole from one of the daughter cells. In all 5 cases, both daughter cells entered the subsequent mitosis with a timing similar to that of neighboring unmanipulated cells (Figure 3). Together, these results indicate that the progression of interphase was affected directly or indirectly by the presence of the anaphase spindle midzone.
Figure 3 Normal interphase progression following manipulation of the size of a daughter cell. A lateral region near the pole was cut away from one of the daughter cells at the end of cytokinesis (cutting site indicated by the dotted line, a). Subsequent time lapse imaging revealed a normal interphase duration in both daughter cells (b, c). Time elapsed since cutting is shown in hours:minutes. Bar, 20 μm.
To test if cortical contraction is the primary determining factor of the rate of interphase progression, we treated cells with cytochalasin D at early anaphase for 10 min then severed them at various positions in the spindle midzone, at a time when midbody started to form and ingression started to appear in control cells. Cytochalasin D was removed 20 min later to ensure that there was no lingering cytokinesis activity upon removal of the drug [[14] see Methods]. In all 5 cases, both daughter cells entered the subsequent mitosis with a timing similar to that of neighboring cells, despite the complete inhibition of cortical contraction (Table 2). In addition, unmanipulated, binucleated cells, which failed cytokinesis spontaneously, also entered subsequent mitosis with a similar timing. Thus, the present observation is distinct from the "tetraploidy checkpoint", which was identified with dihydrocytochalasin B-treated REF52 cells [11], but remained controversial with regard to its universal existence [15].
Table 2 Duration of interpahse for cells severed in the presence of cytoskeletal inhibitors#
Severed cells## control###, ##
cytochalasin D 599 ± 49 (5) 587 ± 24 (10)
nocodazole 892 ± 71 (7) 621 ± 30 (14)
#Average time in minutes ± SEM
##
Statistics was calculated by grouping the daughter cells. Therefore, the number of daughter cells analyzed was twice the number of experiments as indicated in the parentheses.
###Two unmanipulated, dividing cells near the manipulated cell were followed in each experiment as controls.
We then asked if midzone microtubules, or proteins associated with the anaphase spindle midzone, are involved in interphase progression. Treatment of cells with nocodazole for 6–13 min at early anaphase before severing, with or without additional incubation with the drug, caused a significant delay (~4.5 h; p = 0.002) in the progression of interphase for both daughter cells, as compared to neighboring cells (Table 2). Aurora B in these cells showed a complete dispersal from the midzone (Figure 4) [12], with a similar amount distributed in the two severed cells. These observations suggest the midzone microtubules may provide a scaffold for the activation/deactivation of some components to allow normal progression of the cell cycle. We suspect that the weaker effect compared to that caused by severing may be due to the known resistance of some midzone microtubules to nocodazole [6]. Alternatively, a brief association of the components with the midzone microtubules before and during nocodazole treatment may be sufficient for partial activation/deactivation.
Figure 4 Presence of aurora B in both daughter cells following microsurgery of nocodazole-treated cells. An NRK cell at early anaphase was treated with 10 μM nocodazole for ~10 min before microsurgical cut between segregated chromosomes (cutting site indicated by the dotted line, a). Then the daughter cells were released from nocodazole by washing twice with fresh medium. Immunofluorescence of aurora B showed that both daughter cells contained dot-like structures of aurora B along the cell cortex (c, d, arrows). Bar, 20 μm.
We also tested if telophase midbody is required for interphase progression. Cells were severed at mid- or late cytokinesis to form daughter cells with and without the midbody (Figure 5, see Additional file 2). Using cells expressing aurora B-GFP, which is known to associate with the midbody [12,13,16], we confirmed that the midbody was completely segregated from one of the daughter cells (Figure 5B). All nine manipulated pairs showed a normal progression of interphase indistinguishable from that of unmanipulated control cells, irrespective of the presence of the midbody (Table 1). We conclude that the presence or absence of telophase midbody no longer affects the progression of subsequent interphase, despite the similarity of molecular components to those in the earlier spindle midzone. Thus, most likely it is transient catalytic reactions in the anaphase midzone that are crucial for the progression of cell cycle.
Figure 5 Normal interphase progression following removal of the midbody. A cell was cut at the end of cytokinesis to form daughter cells with or without the midbody (A, b, arrow). Subsequent long-term time-lapse imaging indicated a normal phase morphology for both cells (A). The cell with midbody entered mitosis 10 h 40 min after cutting, whereas the cell without midbody entered mitosis ~1 h afterwards (e, f, large arrows). Fluorescence imaging of aurora-B-GFP, a midbody component, confirmed that the midbody is completely segregated into one of the daughter cells (B; cutting indicated by dotted lines) Bar, 20 μm.
Some of major mitotic regulators are degraded by anaphase promoting complex/cyclosome (APC) during anaphase [17]. Degradation of the polo-like kinase (Plk1) and aurora A by APC occurred while they were localized along midzone microtubules [17], raising the possibility that interphase progression may require the degradation of some mitotic/cytokinetic proteins, which may then cause activation of downstream components crucial for cell cycle. In addition, some molecules associated with midzone microtubules may be directly involved in cell cycle events such as DNA synthesis, as suggested by the chromosomal passenger protein-like dynamics of a DNA replication initiating factor, Orc6 during cell division [7].
Conclusions
Our results suggest that anaphase midzone not only play a role in the stimulation of cytokinesis in cultured cells, but also provide a scaffold for the activation/deactivation of factors essential for the progression of subsequent cell cycle.
Methods
Cell culture, microscopy, and image processing
Normal Rat Kidney epithelial cells (NRK-52E; American Type Culture Collection, Rockville, MD) were cultured in Kaighn's modified F12 (F12K) medium supplemented with 10% FBS (JRH Bioscience, Lenexa, KS), 50 U/ml penicillin, and 50 μg/ml streptomycin, on glass chamber dishes as previously described [18]. The cells were maintained at 37°C in a stage incubator built on top of a Zeiss Axiovert S100TV or an Axiovert 35 inverted microscope (Carl Zeiss, Thornwood, NY), and viewed with 10X, NA 0.25 Achrostigmat, 40X, NA 0.75 Plan-Neofluor or 100X, NA 1.30 Plan-Neofluor lens. All images were acquired with a cooled charge-coupled device camera (ST133 controller and CCD57 chip; Roper Scientific, Trenton, NJ) and processed with custom software for background subtraction.
Microsurgery and drug treatment
Glass needles for microsurgery were prepared with a David-Kopf Model 700 vertical puller. The tip of the needle was melted and elongated into a fine fiber with a Narishige microforge (Model MF900). Microsurgery of the cells was achieved by carefully lowering a fiber onto the target cell followed by slow dragging with a micromanipulator (Leica, Deerfield, IL).
Cytochalasin D (Sigma, St. Louis, MO) were stored at -20°C as 2.5 mM stock in DMSO, and diluted into warm medium before application to cells. We found that treatment of early anaphase cells with cytochalasin D for 30 min completely inhibited cytokinesis even upon removal of the drug, similar to what was reported with dihydrocytochalasin B [14]. Thus, early anaphase cells were treated with cytochalasin D at a final concentration of 2 μM for 10 min before microsurgery. The cells were then incubated for additional 20 min and washed at least twice with fresh medium. Nocodazole (Sigma, St. Louis, MO) was stored at -20°C as 10 mM stocks in DMSO and was diluted with warmed medium before use. Early anaphase cells were incubated with nocodazole for 6–13 min and then cut into two daughter cells with microsurgery. Immediately after microsurgery or following 50 min incubation, the daughter cells were released from nocodazole by incubating with two changes of fresh medium for at least 5 min each. The significance of these results was assessed using analysis of variance (ANOVA) and t-test in Microsoft Excel.
Transfection and immunofluorescence
Aurora B-GFP was constructed and transfected into NRK cells as described previously [12]. Immunofluorescence of tubulin and reconstruction of microtubules images were carried out as described previously [19]. For immunofluorescence, cells were rinsed with warm cytoskeleton buffer and fixed with 4% paraformaldehyde (EM Science, Gibbstown, NJ) in warm cytoskeleton buffer for 10 min [6]. They were then rinsed thoroughly in the cytoskeleton buffer and permeablized by incubation with 0.5% Triton X-100 in cytoskeleton buffer for 5 min. Fixed cells were rinsed with the cytoskeleton buffer, blocked for 10 min with 1% BSA (Boehringer Mannheim, Indianapolis, IN) in PBS, then incubated with anti-AIM-1 monoclonal antibodies (BD Biosciences, San Jose, CA) at a dilution of 1:200 in PBS with 1% BSA for 45 min at 37°C. After washing with PBS/BSA thoroughly, cells were incubated with Alexa 546-conjugated goat anti-mouse antibodies (Molecular Probes, Eugene, OR) at a dilution of 1:100 for 30 min at 37°C.
Authors' contributions
MMH carried out all the experimental work and drafted the manuscript. GS and YLW participated in its design and coordination of the research and edited the manuscript. All authors read and approved the final manuscript.
Supplementary Material
Additional File 1
Interphase progression of the daughter cells with and without spindle midzone. An NRK cell was severed at early anaphase to form daughter cells with and without spindle midzone. Subsequently, time-lapse imaging was performed to investigate if anaphase spindle midzone was involved in interphase progression. The daughter cell with spindle midzone showed a cytokinsis-like cortical contraction and entered into the subsequent mitosis 11 h 23 min after microsurgery, while the daughter cell without spindle midzone showed no cortical contraction and entered into mitosis 23 h 8 min after microsurgery.
Click here for file
Additional File 2
Interphase progression of the daughter cells with and without midbody. An NRK cell was severed at telophase to form daughter cells with and without midbody. Subsequently, time-lapse imaging was performed to investigate if midbody was involved in interphase progression. The daughter cell with midbody entered into the subsequent mitosis 7 h 35 min after microsurgery, while the daughter cell without midbody entered into mitosis 9 h 25 min after microsurgery.
Click here for file
Acknowledgments
This study was supported by NIH grant GM-32476 to YLW, GM-30758 to GS, and intramural funds from the Temasek Life Sciences Laboratory to MMH.
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| 15617574 | PMC544570 | CC BY | 2021-01-04 16:31:37 | no | BMC Cell Biol. 2004 Dec 23; 5:49 | utf-8 | BMC Cell Biol | 2,004 | 10.1186/1471-2121-5-49 | oa_comm |
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Mol CancerMolecular Cancer1476-4598BioMed Central London 1476-4598-3-351557919710.1186/1476-4598-3-35ResearchCan gene expression profiling predict survival for patients with squamous cell carcinoma of the lung? Sun Zhifu [email protected] Ping [email protected] Marie-Christine [email protected] Farhad [email protected] Chiaki [email protected] Julian [email protected] George [email protected] Department of Health Sciences Research, Mayo Clinic, Rochester, Minnesota 55905, USA2 Division of Anatomic Pathology, Mayo Clinic, Rochester, Minnesota 55905, USA3 Laboratory of Bioinformatics and Molecular Biology, Mayo Clinic, Rochester, Minnesota 55905, USA4 Division of Medical Oncology, Mayo Clinic, Rochester, Minnesota 55905, USA2004 3 12 2004 3 35 35 7 7 2004 3 12 2004 Copyright © 2004 Sun et al; licensee BioMed Central Ltd.2004Sun 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
Lung cancer remains to be the leading cause of cancer death worldwide. Patients with similar lung cancer may experience quite different clinical outcomes. Reliable molecular prognostic markers are needed to characterize the disparity. In order to identify the genes responsible for the aggressiveness of squamous cell carcinoma of the lung, we applied DNA microarray technology to a case control study. Fifteen patients with surgically treated stage I squamous cell lung cancer were selected. Ten were one-to-one matched on tumour size and grade, age, gender, and smoking status; five died of lung cancer recurrence within 24 months (high-aggressive group), and five survived more than 54 months after surgery (low-aggressive group). Five additional tissues were included as test samples. Unsupervised and supervised approaches were used to explore the relationship among samples and identify differentially expressed genes. We also evaluated the gene markers' accuracy in segregating samples to their respective group. Functional gene networks for the significant genes were retrieved, and their association with survival was tested.
Results
Unsupervised clustering did not group tumours based on survival experience. At p < 0.05, 294 and 246 differentially expressed genes for matched and unmatched analysis respectively were identified between the low and high aggressive groups. Linear discriminant analysis was performed on all samples using the 27 top unique genes, and the results showed an overall accuracy rate of 80%. Tests on the association of 24 gene networks with study outcome showed that 7 were highly correlated with the survival time of the lung cancer patients.
Conclusion
The overall gene expression pattern between the high and low aggressive squamous cell carcinomas of the lung did not differ significantly with the control of confounding factors. A small subset of genes or genes in specific pathways may be responsible for the aggressive nature of a tumour and could potentially serve as panels of prognostic markers for stage I squamous cell lung cancer.
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Background
Lung cancer remains to be the leading cause of cancer death in many European and North American countries [1,2]. It accounts for 13% of all cancer diagnoses but is responsible for nearly 30% cancer deaths in the United States [2]. Substantial effort has been made to identify prognostic factors that can be used for better patient management and improved survival. As of 2001, as many as 169 prognostic factors were identified in Non-Small Cell Lung Cancer (NSCLC) [3]. However, only very few such as TNM stage or patient performance status are consistent predictors, but they still can not predict individuals' prognosis accurately within a stage. Indeed, why do some patients with stage I lung cancer progress very quickly while others survive for a long time cancer free? This puzzle naturally has prompted researchers to contemplate whether the aggressive nature of NSCLC is genetically predetermined and whether the difference in gene expression could be identified as a more reliable clinical outcome predictor.
Searching for molecular prognostic markers is traditionally carried out by analyzing one or several gene expression products at a time, which can only touch a very small fraction of expressed genes in the genome. Fortunately, recently developed high-throughput technologies such as DNA microarray provide promising and efficient screening tools for this purpose. It has been used in lung cancer research to identify the subclasses associated with tumour differentiation and patient survival [4,5], to predict patient survival or potential metastasis of a tumour based on gene expression profiles [6-8], and to compare two predefined classes such as tumour vs. normal or smokers vs. non-smokers to reveal differentially expressed genes [9-13]. However, some of these findings are simply a reiteration of diagnoses that can be easily made by standard pathologic evaluation, and their added clinical values are limited. In addition, two major issues exist in most of those studies to search for prognostic markers: (1) Case selection criteria were not clearly defined. Different tumour type, grade, stage, treatment, and smoking history were often mixed together, making it difficult to assess whether gene expression profiling discriminated patient survival independent of other known predictors. Although tumour type and grade of differentiation are not consistently documented as prognostic factors, they are very important in determining a sample's class membership in gene expression profiling [4-6]. (2) A clustering approach has been used as a major analytical tool to characterize cancer phenotypes including histological type, metastatic potential or patient survival. However, clustering is more appropriate to visualize gene expression patterns, and its results are heavily affected by the distance matrix and linkage method selected [14]. The existing evidence supports the notion that a clustering algorithm mainly groups samples based on histology, a variable not yet proven as an independent factor in NSCLC prognosis. This reemphasizes a central question of whether a clustering approach can discern the aggressive nature of a tumour with the same histological type.
In order to answer the question why do some patients with stage I squamous cell carcinoma progress rapidly after curative resection while others survive a long time without disease recurrence, we designed a case control study matching on important prognostic factors so that only the tumour genetic factor was assumed to be a major determinant in patients' prognosis. We explored whether the widely-used hierarchical clustering was applicable in our study and whether the differentially expressed genes or functionally related groups of genes had any predictive value in an independent group of similar patients.
Results
Clinical Characteristics of Selected Patients
The clinical characteristics of the 15 stage I squamous cell carcinoma patients in our study is provided in the Additional file 1. Since the first ten samples were matched and used for the initial marker selection, the two groups (sample# 1–5 vs. 6–10) were well balanced in terms of age, gender, tumour size, smoking history, and treatment. The characteristics of the additional five test samples were very similar to the group of low aggressive samples.
Unsupervised Clustering
When a subset of 2810 filtered genes was used to conduct hierarchical clustering for all 15 samples, two main clusters were formed (Figure 1). However, the clusters did not distinguish the two groups by survival outcome: high-aggressive and low-aggressive tumours were almost evenly distributed within each cluster. Three of the high-aggressive tumours were present in the left cluster and two in the right. For the ten low-aggressive tumours, five were in the left cluster and five were in the right.
Figure 1 Hierarchical clustering for 15 samples. 2810 probe sets filtered by: standard deviation/mean across all samples > 0.06; and the expression level on the log2 scale ≥ 4.00 in ≥ 60% of the samples. H: indicates high aggressive tumors.
Class Comparison and Top Candidate Gene Selection
To identify a panel of genes that are differentially expressed between the high and low aggressive groups as potential prognostic biomarkers, we applied matched (pair of 1–6, 2–7, 3–8, 4–9, 5–10) and unmatched (group 1–5 vs. 6–10) t statistics to the ten well-matched samples. At p < 0.05, 294 and 246 genes were significant in matched and unmatched comparison, respectively, with 126 selected by both. The majority of significant genes were within a two-fold mean difference between the two comparison groups with p values ranging from 0.05 to 0.01 (Figure 2, 3).
Figure 2 Distribution of significant genes from matched analysis. 294 significant genes (P < 0.05) selected by matched analysis are plotted by fold difference (x-axis) vs. p value using t-test (y-axis) A y-axis greater than 1.3 is equivalent to a p value less than 0.05, and greater than 2 is equivalent to a p value less than 0.01. A positive or negative value at the x-axis indicates genes are up or down regulated in the high-aggressive group compared to the low aggressive group.
Figure 3 Distribution of significant genes from unmatched analysis. 246 significant genes (p < 0.05) selected by unmatched analysis are plotted by fold difference (x-axis) vs. p value using t-test (y-axis) A y-axis greater than 1.3 is equivalent to a p value less than 0.05, and greater than 2 is equivalent to a p value less than 0.01. A positive or negative value at the x-axis indicates genes are up or down regulated in the high-aggressive group compared to the low aggressive group.
From the list generated by matched analysis, we used 1–10, 15, 20, 30, 40, 50, 60, 70, 80, 90, 100, 150, 200, 250, and 294 genes each time and evaluated their discriminating capability for training samples by leave-one-out algorithm as illustrated in Figure 4. As few as 10 genes were found sufficient to achieve 100% accuracy. The same procedure was performed for the gene list generated by unmatched analysis and similar results were obtained. The first 20 genes from each procedure, which had the highest signal-to-noise ratio and therefore accurately distinguished the training samples with contrasting outcome, were selected and combined. Table 1 lists the 27 unique genes between the two procedures. (There were 2 selected probe sets for each gene ATP1B1 and IGFBP3, and they were counted once.)
Figure 4 Leave-one-out prediction on training samples. The x-axis represents different numbers of significant genes from matched analysis that was used to predict a membership of a sample by the leave-one-out algorithm. The y-axis shows the correct prediction rate for the 10 training samples.
Table 1 The top 27 unique genes with highest signal-to-noise ratios
Gene symbol Gene name Unmatched Matched
Up-regulated genes ATP1B1 ATPase, Na+/K+ transporting, beta 1 polypeptide
TP53 tumor protein p53 (Li-Fraumeni syndrome)
CYP26A1 cytochrome P450, family 26, subfamily A, polypeptide 1
SYCP2 synaptonemal complex protein 2
IGFBP3 insulin-like growth factor binding protein 3
CPOX coproporphyrinogen oxidase (coproporphyria, harderoporphyria)
MAGEA1 melanoma antigen, family A, 1 (directs expression of antigen MZ2-E)
H1F0 H1 histone family, member 0
MAGEA12 melanoma antigen, family A, 12
* Homo sapiens clone 23705 mRNA sequence
* Homo sapiens cDNA: FLJ21672 fis, clone COL09025.
FLJ20477 Homo sapiens cDNA FLJ39734 fis, clone SMINT2016146.
Down-regulated genes P2RY5 purinergic receptor P2Y, G-protein coupled, 5
DKFZp586G0123 hypothetical protein DKFZp586G0123
EPB41L3 erythrocyte membrane protein band 4.1-like 3
DKFZP586A0522 DKFZP586A0522 protein
CSAD cysteine sulfinic acid decarboxylase
BRIP1 BRCA1-interacting protein 1
MYC v-myc myelocytomatosis viral oncogene homolog (avian)
PDCD4 programmed cell death 4 (neoplastic transformation inhibitor)
TAF6L TAF6-like RNA polymerase II, p300/CBP-associated factor (PCAF)-associated factor, 65 kDa
ABCA12 ATP-binding cassette, sub-family A (ABC1), member 12
ZNF198 zinc finger protein 198
NOTCH2 Notch homolog 2 (Drosophila)
TncRNA Human clone 137308 mRNA, partial cds.
CLK1 CDC-like kinase 1
POGZ pogo transposable element with ZNF domain
Significant in that particular analysis (matched or unmatched)
* No gene symbol for these genes
Linear Discrimination Analysis
We applied linear discrimination analysis to the 27 genes selected from the previous step to assess the accuracy of class membership prediction for both the training and the test samples (Table 2). The overall error rate was 20% (3/15). Interestingly, the linear discrimination score of incorrectly classified samples was among the lowest (absolute value), suggesting a borderline expression pattern between the high and low aggressive tumours.
Table 2 LDA classification using 27 top genes
Sample LD score Class Prediction Probability Correct?
48521 -0.52 1 1 0.75 Yes
48536 -0.94 1 1 0.87 Yes
41923 -0.26 1 1 0.63 Yes
48549 -0.52 1 1 0.74 Yes
44680 -2.9 1 1 1 Yes
42613 1.0 2 2 0.89 Yes
76981 0.52 2 2 0.75 Yes
44661 2.08 2 2 0.99 Yes
86043 -0.19 2 1 0.59 No
86011 1.71 2 2 0.97 Yes
42616 0.12 ? 2 0.56 No
48556 0.05 ? 2 0.52 No
41932 -0.88 ? 1 0.86 Yes
42081 -0.52 ? 1 0.75 Yes
44656 -0.08 ? 1 0.54 Yes
LD score: Linear discrimination function calculated value for a given sample; Class: a sample's membership to the low aggressive group (1) or high aggressive group (2), "?" is a test sample whose membership is not known for the procedure and needs to be predicted. Prediction: predicted sample membership. Probability: probability of a sample belonging to a given class based on the classifiers. "Correct?": Whether the prediction is correct compared to the true class of a sample.
Gene Network Analysis
The test statistics for all genes (22215) using R-package "global test" was not significant, indicating that the overall pattern was similar between high-aggressive and low aggressive groups in our study sample. Using the 126 overlapped genes between matched and unmatched comparisons, we found 24 gene networks from Ingenuity Pathways Knowledge Base. We performed an association test on each network and found that seven were strongly associated with survival (Table 3). We then used the genes in each of the seven networks to predict all 15 samples separately and detected an error rate ranging from 0 to 47%, with RAB6A network genes predicting all samples correctly.
Table 3 Gene networks associated with survival
Network genes† Score‡ Association test p Prediction error
APLP2, ARL6IP, CASP3, CCNG1, CSF1, DNMT1, EPHA2, ERCC3, ERCC5, F2, F5, FGF2, FUBP1, GPI, HAS2, HMOX2, IGFBP3, LOC283120, LOC91768, MDM4, MYC, P53AIP1, PCNA, PEG3, RARB, RPL21, RPS6, RRM2B, TAGLN2, THBD, TMSB4X, TP53, TP53I3, TP73, WT1 17 0.01* 7/15
AMSH, AR, ATF2, BAG1, BCL2L1, CREBBP, CYBA, ENO1, EP300, FOXG1B, HOXA9, HOXC8, HSF2, MADH1, MADH2, MADH3, MADH4, NCF1, NCF2, RBM14, RNF14, RTN1, RUNX2, SIAH1, SP3, TOB1, TP53, UBE2E3, UBE2I, ZNF8 9 0.02* 3/15
SAC, TEC 2 0.01* 4/15
RIPK1, TRIAD3 2 0.03* 4/15
MIR, TMEM4 2 0.01* 3/15
NSF, PIK3CG, RAB6A, RAB6KIFL 2 0.01* 0/15
ATP12A, ATP1A1, ATP1A2, ATP1A3, ATP1B1, FXYD7 1 0.004* 3/15
† Genes in bold face are focus genes (among 126 genes submitted to the Ingenuity knowledge base). ‡ Score indicates the probability that a collection of focus genes could be found in a given network by chance. It is the negative logarithm of the possibility. A score of 2 indicates that chance is only 1%. * Indicates a strong association between the expressions of genes in a network and survival.
Discussion
To address the critical clinical question of whether the aggressive nature of squamous cell carcinoma of the lung is genetically pre-programmed, we conducted a matched experiment using DNA microarray. The purpose of the design is to control for known confounding factors so that the true association between gene expression and patient survival can be determined. Our results have shown that microarray technology provides both opportunities and challenges in the identification of potential molecular prognostic markers.
In our study, unsupervised clustering did not accurately separate patients based on their clinical outcome behavior. This is in contrast to some investigators [4,5] who, using a similar approach, have identified subclasses of tumours showing differing gene expression profiles correlated with varied clinical outcomes. Using the 19.2 K cDNA microarray chip, Wigle et al [7] successfully partitioned 39 mixed histological types and stages of non-small cell lung cancer into two distinct clusters, those with early recurrence and those without recurrence regardless of tumour types. There are several possible explanations for discrepant results between the studies. First, the formation of clusters is heavily affected by the number of genes used for clustering, the gene selection method, and the clustering algorithm. Highly varied genes generally dominate the clustering process and thus explain why highly different groups such as among subtypes of non-small cell lung cancer (squamous cell carcinoma vs. adenocarcinoma), primary vs. metastatic cancer, or cancer vs. normal tissue, can be reliably differentiated using this technique. However, for the same primary tumour where the clinical outcome is the only noticeable difference, as in our study, this approach might not be as useful. Second, it is not clear in Bhattacharjee et al and Garber et al studies [4,5] whether the gene expression profile was influenced by other prognostic factors such as stage, or whether it was truly a specific and an independent prognostic factor. Finally, differences in tumour series, microarray chip platforms, or data pre-processing could affect results across studies, even within a study [15].
In searching for genes responsible for tumour behavior and patient survival, a case control comparison between two different clinical outcomes (long survival vs. short survival or disease free vs. quick recurrence) or a survival cohort using Cox's proportional hazards model to find gene-outcome association are among the most common options [6,7,9,13]. However, a careful design and implementation for this type of study needs to be taken into consideration since a case-control or survival cohort design is prone to selection bias, i.e., patients enrolled in comparison groups are different other than the factors under study, which makes them incomparable [16]. Without taking any strategy such as randomization, matching, or stratification to deal with the potential biases, the study results should be reviewed with skepticism [16]. Specifically for microarray study of lung cancer outcome, there are many tumour, host, and environmental related factors that are associated with patient prognosis. The imbalance of these factors between the two comparison groups such as the extent of disease (stage), the presence of other diseases, and treatment makes it difficult to establish the true association. Although results have not been consistent in reporting tumour histology of NSCLC as an independent prognostic factor, available evidence has indicated that it could be important in gene profiling as major histological types could be easily separated by the clustering approach [4-6]. If we do not take histology into consideration in case selection and comparison, a distorted result might occur. In contrast, we focused on one subtype of NSCLC within the same stage and matched on all potential confounding factors of survival. The results showed little overall difference in the gene expression profile between the two outcome groups. Less than 300 probe sets were significant at p < 0.05 from over 22,000 probe sets and 20–30 of them were greater than 1.5 fold change between the two groups, which were hardly separable from random noise.
It is a big challenge for microarray analysis to identify reliable genomic prognostic marker panels that can be generalized to independent samples. In our study, the markers based on signal-to-noise ratio did not perform very well on the independent samples although the small sample size could be partly responsible. The result may suggest: (1) survival of patients with squamous cell carcinoma could be the result of genetic and non-genetic factors acting together. Gene expression difference is only a partial explanation. (2) Gene expression among tumours is very heterogeneous, even for the same histological type. By examining our series of tumours, we noticed that even though cell type and grade were matched, there were still some other variables hard to control for, such as cancer cell growth patterns or the constitutions of cancer stroma. Different amounts of lymphocytes or fibroblasts may contribute to the heterogeneous gene expressions. (3) The aggressive nature of a tumour may be determined by a small portion of cells that acquire metastatic capacity through somatic mutation [17], and it is hard to capture these cells since microarray analysis can only examine a very limited portion of a tumour. (4) The genes responsible for tumour aggressiveness may be part of one or multiple pathways. The genes within specific pathways may not be the most differentially expressed and may be often overwhelmed by background noises across tumours; however, as a functional group, they could potentially determine the behavior of a tumour.
We evaluated the pathway hypothesis by finding related genes in specific gene networks using our candidate genes and tested the correlation between the genes and prognosis. Using this strategy, we identified seven gene networks strongly associated with squamous cell carcinoma survival. Although the functional explanation of an entire gene network to survival is yet to be determined, the association of some individual genes such as p53, c-myc, and PDCD4 (programmed cell death 4) with lung cancer survival has been well-documented in the literature [18-22]. Rab6A and related genes, the network accurately separated tumours with 100% accuracy in our study, are involved in intracellular transport. Whether they are functionally relevant to cancer aggressiveness or just surrogate markers of the true underlying mechanism needs to be further clarified.
Although using carefully-matched samples could potentially unveil a true association, the subjects eligible for inclusion are dramatically reduced, often leading to a relatively small sample size and insufficient power to detect a minor difference or overcome randomness [15]. Facing the reality of low reproducibility using microarray technology, it is important that an experiment starts with a good design to minimize various biases [15]. If results from a well-controlled study are promising, a larger scaled follow-up investigation will be warranted.
Conclusions
We found that the overall gene expression pattern between the high and low aggressive squamous cell carcinomas of the lung was similar after controlling for confounding factors. However, our results suggest a difference between high and low aggressive cancers may be due to a small number of functionally related genes; these are so-called pathway genes that are often overlooked by commonly used analytical approaches. Whether pathway genes work collectively as more reliable prognostic markers or not needs to be further investigated by more studies with a large number of samples.
Methods
Study Design and Sample Selection
Cases were defined as the patients who survived less than 24 months after surgery (high-aggressive group) and controls were those who survived more than 54 months after surgery (low-aggressive group). The patient population, from which the cases and controls were drawn, was comprised of patients diagnosed with lung cancer from 1997 to 2001 who underwent curative resection at Mayo Clinic, Minnesota, USA. These patients were prospectively enrolled and had been actively followed since their initial surgery [23]. We restricted this study to stage I squamous cell carcinoma to gain more homogeneity in morphology and to be focused on a common type of lung cancer. Each case was matched to a control by tumour size and grade, age, gender, and smoking status so that the potential confounding factors could be minimized. For each potential patient, we carefully reviewed their medical records and follow-up data to confirm their clinical outcome and the cause of death if the patients were deceased. From a pool of 304 patients with stage I squamous cell carcinoma, five well-matched pairs were finally selected (See Additional file 1) and used for most of the analyses. Five additional patients who survived more than 52 months were included as a test group (See Additional file 1).
All enrolled patients and use of their tissue samples in the study were approved by our Institutional Review Board. The resected tumour and adjacent lung tissues were fast frozen in -80°C within 30 minutes after the tissues were surgically removed.
RNA Extraction and Microarray Hybridization
All tissue specimens were reviewed by a pulmonary pathologist (MCA) to confirm their diagnosis and ensure that the tissue was appropriate for the experiments. Specifically, the percentage of total tumour, tumour necrosis, amount of inflammation associated with tumour, and cellularity of stroma were evaluated. In the frozen tissue blocks containing cancer, the non-neoplastic tissue was manually cut away from the block to assure at least 80% of the cancer component. Thirty mm3 of each tissue were sectioned at 20 or 35 μm, collected in a buffer RLT (Qiagen, Valencia, CA) supplemented with β-mercaptoethanol, and homogenized using PT 1200C (Kinematica AG, Luzern, Switzerland) rotor/stator. The total RNA was isolated using the RNeasy kit (Qiagen, Valencia, CA) following the manufacturer's specifications. Microarray experiments were performed at the Mayo Clinic Microarray Core Facility. The quality and quantity of RNA samples were controlled by spectrophotometry and the Agilent 2100 Bioanalyzer. Hybridization washes and scanning were performed following the manufacturer's protocols (Affymetrix, Santa Clara, CA). The HG-U133A chip from Affymetrix was used and contains 22,283 probe sets, which we conveniently refer to as genes in this paper.
Data Processing and Analysis
The Affymetrix Microarray Analysis Suite version 5 (MAS5) was used to process the scanned chip images. This software generates a cell intensity file for each chip, which contains a single intensity value for each probe cell (cel file). Dchip 1.3 [24,25]() was used to calculate the Model Based Expression Index (MBEI). All chips were normalized against an array with a median overall intensity using the invariant set method, and their images were visually inspected for potential problems prior to any data processing and analysis. The MBEI was calculated using the Perfect Match (PM) only model with outlier detection and correction. The calculated expressions were log2 transformed. Control probe sets were excluded in the down-stream analyses.
As a first step, we employed hierarchical clustering to evaluate the similarity and disparity in overall expression patterns among all 15 samples using a subset of 2810 genes, which were filtered by the following criteria: the standard deviation/mean across all samples >0.06 and the expression level on the log2 scale ≥ 4 in at least 60% of the samples. The distance matrix applied in the clustering was one minus the Pearson correlation coefficient (1-r), which measures the closeness between genes or samples, and the linkage method was centroid, which uses the centers of newly formed clusters (genes or samples) to calculate the distance between clusters [26,27].
In order to detect differentially expressed genes between the high and low aggressive groups, we conducted both matched and unmatched t statistics for the ten matched samples at the criteria of p < 0.05 and at least one present call in each comparison group. Next, we applied a feature selection process to isolate a subset of genes selected from the previous step that had a high discriminate power in separating the two distinct groups of tumours. In each step, one sample was withheld as a test sample, and a signal-to-noise ratio as described by Ramaswamy and colleagues [28] was calculated for each gene using the remaining nine samples in the two groups. Based on the number of genes (features) specified, the procedure chose the top genes with highest signal-to-noise ratios and created a linear model to predict the membership of the withheld sample using a weighted-voting algorithm [28]. This process was repeated 10 times (10 samples), and an error prediction rate was obtained for the specified number of genes. By trying out different numbers of genes, a zero error rate for training samples could be achieved. The minimum number of genes obtaining the zero error rate was chosen as the best candidates.
Linear Discrimination Analysis [29] was applied using the subset of genes selected from the previous step to assess whether the genes can discriminate the high from the low aggressive nature of the training and test samples. This method utilizes all input genes (independent variables) to create a discriminant function that maximizes the ratio of between-group variance and within-group variance so that different classes (dependent variable, either low or high aggressive group in our study) can be better separated. Implicitly, each gene is assigned a weight in the function depending on how a gene separates in the two groups and how this gene correlates with other genes. After computation, each sample was given a discriminant score, predicted class membership, and probability for the assigned class. The prediction rate was calculated to evaluate the performance of the classifiers.
Because of stringent matching criteria, we did not expect dramatic difference between the two comparison groups, as reported by other investigators who did not match comparison groups closely. We hypothesized that genes in certain pathways might play a role in squamous cell carcinoma prognosis. We submitted the significant genes selected by both matched and unmatched analysis to the Ingenuity Pathways Analysis application ()and generated gene interaction networks. A test statistic on the association of gene members in a network with a clinical outcome was carried out by using the R package "global test" [30]. If a small p value (<0.05), particularly permutated when sample size is small, is obtained, there is a strong indication that the group of genes, no matter whether they are up or down regulated in the network, is associated with clinical outcome, i.e., long or short survival in our study.
Authors' contributions
ZS carried out the data analysis and participated in drafting the manuscript. PY designed the study, oversaw the analysis and interpretation, and participated in writing the manuscript. MC analyzed the tissue samples and contributed to the development of the manuscript. FK prepared the tissue samples for microarray assay. CE contributed to the development of the manuscript. JM contributed to the development of the manuscript. GM contributed to analysis of the data.
Supplementary Material
Additional File 1
Clinical characteristics of 15 cases of stage I Squamous Cell Carcinoma of the Lung.
Click here for file
Acknowledgements
This work was supported by research grants from the U.S. National Cancer Institute (CA80127 and CA84354 – Yang) and Mayo Foundation Funds.
We thank Susan Ernst for her technical assistance with the manuscript.
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| 15579197 | PMC544571 | CC BY | 2021-01-04 16:36:34 | no | Mol Cancer. 2004 Dec 3; 3:35 | utf-8 | Mol Cancer | 2,004 | 10.1186/1476-4598-3-35 | oa_comm |
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BMC Public HealthBMC Public Health1471-2458BioMed Central London 1471-2458-4-621560146310.1186/1471-2458-4-62Research ArticleColorectal cancer screening among African American church members: A qualitative and quantitative study of patient-provider communication Katz Mira L [email protected] Aimee S [email protected] Michael P [email protected] Marlyn A [email protected] Ethel [email protected] Veronica [email protected] Marci K [email protected] Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA2 School of Public Health and The Ohio State University Comprehensive Cancer Center, Columbus, Ohio, USA3 Department of Internal Medicine, School of Medicine, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA4 Department of Nutrition, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA2004 15 12 2004 4 62 62 15 10 2003 15 12 2004 Copyright © 2004 Katz et al; licensee BioMed Central Ltd.2004Katz 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 healthcare provider's recommendation to undergo screening has been shown to be one of the strongest predictors of completing a colorectal cancer (CRC) screening test. We sought to determine the relationship between the general quality of self-rated patient-provider communication and the completion of CRC screening.
Methods
A formative study using qualitative data from focus groups and quantitative data from a cross-sectional survey of church members about the quality of their communication with their healthcare provider, their CRC risk knowledge, and whether they had completed CRC screening tests. Focus group participants were a convenience sample of African American church members. Participants for the survey were recruited by telephone from membership lists of 12 African American churches located in rural counties of North Carolina to participate in the WATCH (Wellness for African Americans Through Churches) Project.
Results
Focus Groups. Six focus groups (n = 45) were conducted prior to the baseline survey. Discussions focused on CRC knowledge, and perceived barriers/motivators to CRC screening. A theme that emerged during each groups' discussion about CRC screening was the quality of the participants' communication with their health care provider. Survey. Among the 397 participants over age 50, 31% reported CRC screening within the recommended guidelines. Participants who self-rated their communication as good were more likely to have been screened (36%) within the recommended guidelines than were participants with poor communication (17%) (OR = 2.8, 95% CI 1.2, 6.4; p = 0.013). Participants who had adequate CRC knowledge completed CRC screening at a higher rate than those with inadequate knowledge (p = 0.011). The percentage of participants with CRC screening in the recommended guidelines, stratified by communication and knowledge group were: 42% for good communication/adequate knowledge; 27% for good communication/inadequate knowledge; 29% for poor communication/adequate knowledge; and 5% for poor communication/inadequate knowledge.
Conclusions
Participants who rated their patient-provider communication as good were more likely to have completed CRC screening tests than those reporting poor communication. Among participants reporting good communication, knowledge about colorectal cancer was also associated with test completion. Interventions to improve patient-provider communication may be important to increase low rates of CRC screening test completion among African Americans.
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Background
Colorectal cancer (CRC) screening among average risk adults 50 years and older can decrease the incidence and mortality rates for CRC [1-7]. National policy-making expert organizations recognize and support this evidence by recommending a variety of CRC screening testing strategies [8-13]. However, the 2001 Behavioral Risk Factor Surveillance System (BRFSS) reported only 23.5% of adults age ≥ 50 had fecal occult blood test (FOBT) within the last year, 38.7% had sigmoidoscopy or colonoscopy within the past 5 years, and only 53.1% of adults had been screened with either test within the recommended time periods. Data specifically from North Carolina are similar: 30% have had FOBT within 1 year, 31% have had sigmoidoscopy within 5 years, and 45% have had either test within the recommended time periods [14].
A healthcare provider's recommendation to undergo screening has been shown to be one of the strongest predictors of completing a CRC screening test [15-17], and has also been shown to be strongly correlated with initial and repeat mammography [18,19], and the completion of Pap smears[20]. Such recommendations may be more likely to occur when patients and providers communicate well, but previous research has not directly explored the relationship between patients' perceptions about the quality of patient-provider communication and the use of CRC screening. We sought to examine the association between perceived communication and CRC s screening in a sample of African-American church members in rural North Carolina.
Methods
Data for this study were obtained as part of a larger study, the WATCH (Wellness for African Americans Through Churches) Project . The WATCH Project was a church-based colorectal cancer prevention study designed to increase fruit and vegetable consumption, reduce fat intake, increase moderate physical activity, and increase CRC screening among church members. In this study of patient-provider communication and CRC screening, we used data from two different sources: 1) focus groups of African American men and women, and 2) surveys of church members. The Institutional Review Board from the University of North Carolina at Chapel Hill approved this study.
Focus groups
As part of the formative data collection in early 1998, we conducted six focus groups of African American adults. There were three focus groups of African American adult men and three of African American adult women. The focus groups comprised of a convenience sample drawn from members of African American churches located in central North Carolina. The focus groups were conducted in the churches, lasted approximately 60 minutes, were tape recorded, transcribed, and reviewed for accuracy. A $15 incentive and refreshments were provided for the participants. The focus groups were led by sex-matched African American moderators and explored issues associated with colon cancer, CRC screening (barriers and motivators), nutrition, and physical activity. The information from the focus groups was used to develop the questionnaires and to help guide the intervention strategies used in the culturally appropriate colon cancer prevention program.
Survey
The second source of participants for the survey study was the baseline intervention sample for the larger WATCH project. The telephone survey collected information focused on general health status, nutrition, physical activity, patient-provider communication, CRC risk knowledge and screening behaviors. The baseline survey was completed, on average, in approximately 40 minutes and was administered by trained interviewers prior to the intervention. The baseline telephone surveys were conducted between October 1998 and October 1999.
In this study, a history of CRC screening was defined as the self-report of undergoing a fecal occult blood test (FOBT), flexible sigmoidoscopy, or colonoscopy within the recommended time period. Participants were asked whether they had each of the CRC screening tests, and if they responded yes, the participants were asked when they had their last test. The items in the survey included a brief explanation of each screening test. Items were described as follows: FOBT, "which is stool slides"; sigmoidoscopy, "which is a tube inserted in the rectum to look at the colon and the bowel"; and colonoscopy, "which is a tube inserted in the rectum to look at the entire intestine, usually given in a hospital or specialist's office." Responses included, "less than 1 year"; "1–2 years"; "3–5 years"; or "more than 5 years."
CRC screening was considered to be within the recommended time period based on an algorithm using ACS guidelines considering which test and how recent the test was performed (e.g. a person who reported having FOBT within the past year was considered within the recommended time period). Participants were considered to have been screened within the recommended time period if they had a FOBT within the preceding year, and sigmoidscopy within the preceding 5 years. Although current CRC screening guidelines recommend colonoscopy every 10 years for average-risk adults, we examined colonoscopy use in the past 5 years because of the limitations of the survey instrument. Self-report of CRC screening behavior has been demonstrated to be a reliable endpoint for intervention trials [22].
Statistical analyses
Analyses of data from this study included factor analysis, analysis of variance, and logistic regression and were conducted using SPSS, version 10.1. Logistic regression analyses were performed to evaluate whether the level of perceived patient-provider communication was significantly related to CRC screening behavior in this population. Sociodemographic variables were identified as potential covariates if there was plausible theoretical or empirical evidence that the variable might be associated with the communication variable or with CRC screening. Variables that were significantly associated with communication level (p < 0.05) were retained and tested as covariates in the logistic models. Only the sex of the participant, receiving healthcare at a doctor's office versus a clinic/emergency room, and knowledge of CRC risk were significantly associated with communication and only these three covariates were entered into the initial logistic model. Variables in the model were evaluated by the Wald test and interpreted using odds ratios and confidence intervals. The overall fit of each model was evaluated using the Hosmer-Lemeshow Goodness of Fit test and by examining classification tables [23].
Results
Focus groups
Several major themes associated with CRC emerged from the personal experiences expressed about the medical community during the focus groups. One of the themes discussed by the participants in each focus group was patient-provider communication. Selected comments by focus group members about CRC and patient-provider communication are listed in Table 1. This important theme that emerged during the focus group was addressed in the baseline survey by adding items to the questionnaire that specifically addressed perceived communication with health care providers (Table 2).
Table 1 Focus groups (>50 years old): selected comments about patient-provider communication, colon cancer knowledge, and screening
"My doctor never even told me that I needed a digital or colonoscopy, or the technical term.
The only concern is said was prostate. Nobody said anything about colon.''
[Male]
"I'm sixty years old and he's never told me to take one of these. I need to change doctors.''
[Female]
"Well you know, I think of men when I think of colorectal cancer.''
[Female]
"I've heard from 48 on up is a prostate exam more than anything else. I haven't heard anything about the colon. I really hadn't."
[Male]
Table 2 Patient-provider communication scale*. Five Items**
A) I receive enough understandable information from my doctor/healthcare provider to make good decisions about my health.
B) I feel rushed during visits.
C) My doctor/healthcare provider involves me in decisions about my health care treatment.
D) I feel uncomfortable asking my doctor for tests or information if he/she doesn't mention it.
E) My doctor/healthcare provider understands my health needs.
*These items were included in the baseline survey because of the importance of the patient-provider communication theme that emerged from the focus group participants.
**Responses: Always, Almost always, Sometimes, Rarely, Never
Items A, C, and E loaded on the same factor (α = 0.74) and these three items were used in the final measure.
Survey
Originally, 2480 names were obtained from the 12 church rosters located in five rural counties in North Carolina. Many members were ineligible (not 18 years old, deceased, no longer living in the state, medically incapable, phone number no longer working) or we were unable to contact them by telephone, and 239 members who declined to participate in the WATCH Project. There were 850 church members who participated in the WATCH Project and completed the baseline survey. The adjusted response rate was 66% using a calculation method, suggested by the Council of American Survey Organizations (CASRO) [21], that accounted for individuals whose eligibility and response status were unknown because program staff were never able to contact them.
The participants in this study were the 397 church members who participated in the WATCH Project and were 50 years and older. The characteristics of the 397 participants are shown in Table 3. Participants were mostly female (74%) and African American (98%). The mean age was 63 years (SD = 9.7). About half of the sample was currently married, 25% were widowed, and 14% were divorced. Thirty-seven percent had less than a high school education, 30% had a high school diploma or GED, 16% had some college or trade/beauty school, and 18% had a college degree or post-college education. Household income was answered by only 52% of the participants, and of the responders, 51% reported an income of less than $20,000.
Table 3 Communication and the characteristics of the participants ≥ 50 years old*
Communication*
Total** n (%) Good n (%) Fair n (%) Poor n (%) F-test p-value
Sex
Male 103(25.9) 71(23.8) 14(28.0) 18 (42.9) F(2, 387) = 3.503
Female 293(73.8) 227(76.2) 36(72.0) 24 (57.1) p = .031
Education
Less than HS 145(36.5) 103(34.6) 21(41.2) 18 (42.9) F(2, 388) = 0.409
HS/ GED 117(29.5) 91 (30.5) 14(27.5) 9 (21.4) p = .665
Trade School/ College 135(34.0) 104(34.9) 16(31.4) 15 (35.7)
Income
<$20,000 184(51.0) 132(48.4) 26(57.8) 23 (59.0) F(2, 354) = 1.134
$20,000–$49,999 128(35.5) 102(37.4) 12(26.7) 13 (33.3) p = .323
≥ $50,000 49 (13.6) 39 (14.3) 7 (15.6) 3 (7.7)
Marital Status
Married 213(53.8) 164(55.2) 22(43.1) 24 (57.1) F(2, 387) = 1.350 p=.261
Divorced/ Widowed/ Separated 162(40.9) 116(39.1) 26 (51.0) 18 (42.9)
Never married 21 (5.3) 17 (5.7) 3 (5.9) -----
Healthcare Facility***
Doctor's office 325(82.7) 250(85.0) 40(78.4) 29 (69.0) F(2, 384) = 3.605
Clinic/ER/Health Dept. 68 (17.3) 44 (15.0) 11(21.6) 13 (31.0) p = .028
Insurance****
Medicaid/Medicare 176(44.3) 137(46.0) 19 (37.3) 19 (45.2) F(2, 388) = 0.669 p = .513
No health insurance 21 (5.3) 14 (4.7) 4 (7.8) 3 (7.1) F(2, 388) = 0.566 p = .568
Employer/self-paid 219(55.2) 167(56.0) 29 (56.9) 18 (42.9) F(2, 388) = 1.344 p = .262
*Communication categories: the mean score for each category was calculated from the individual scores (continuous values)
** Numbers may not reflect the total n = 397 because of participants' refusals or missing data
***This variable is based on the questionnaire item: Where do you usually go when you need health care?
****Insurance variables were dichotomized for each category. Totals may exceed 100%, some respondents marked multiple categories (e.g., they had Medicare in addition to self-paid insurance).
Factor analysis of the five communication items was performed from the baseline survey responses and two factors were identified; one with three items and the other with two items. The second factor was dropped because it had only two items and did not add reliability to the scale. The three communication items about shared decision-making and patient satisfaction demonstrated good reliability (Cronbach's alpha = 0.74) and were summed to calculate a communication score. The communication score was used to categorize the participants into three groups: good, fair, and poor communication with providers. Participants were categorized as having "good" communication if they perceived receiving enough information from their provider, being involved in medical decisions, and thinking that their provider understood their health needs almost all the time or always. Participants who rated all three items 'sometimes', 'rarely', or 'never' scored "poor" on the communication scale, and individuals who rated the items with a mix of the above listed responses were assigned to the "fair" group.
In terms of quality of communication, 75% (298/397) responded positively to all 3 questions and were considered have "good" communication; 10% (42/397) responded positively to none of the 3 questions and were considered to have "poor" communication; and 13% (50/397) had fair results. Participants in the good communication group were more likely to be female (p = 0.031), and were more likely to receive their healthcare at a doctor's office versus a clinic/emergency room/health department (p = 0.028). None of the other sociodemographic factors listed in Table 3 appeared to vary significantly among communication groups. Participants categorized in the good communication group were more likely to report having been screened for CRC in the recommended time period compared to those in the poor communication group (35.9% vs. 16.7%; OR = 2.8, CI 1.2, 6.4, p = 0.013).
Only 45% (175/389) of the participants reported that their providers had recommended CRC screening, and just 31% (120/389) of all participants reported being screened within the recommended time interval. Of the individuals who reported being screened, 65% (78/120) stated that their doctor had recommended CRC screening, compared with 36% (97/269) of those who did not report screening.
Knowledge of CRC was assessed using seven items (Table 4) with a mean correct response of 3.8. If the participants answered at least four out of the seven items correctly, they were categorized as having adequate knowledge about colorectal cancer. The participants were considered to have inadequate CRC knowledge if they answered incorrectly or 'don't know' to ≥ 4 of the 7 items.
Table 4 Knowledge of colorectal cancer risk factors among 397 African American participants (≥50 years old) in the WATCH Project
Seven Items Correct Answer Percent*
1. A low fat and high fiber diet helps decrease colorectal cancer risk. True 70.8%
2. The risk of colorectal cancer is higher in men than women. False 13.6%
3. Physical activity decreases the risk for colorectal cancer. True 42.6%
4. Colorectal cancer risk increases after age 50. True 69.3%
5. A family history of colorectal cancer does not increase your risk. False 49.1%
6. Finding cancer early will not increase the chances of surviving it. False 65.7%
7. You only need to have a colorectal cancer screening test if you are having symptoms. False 67.5%
*The percentage of participants who responded with the correct answer to each CRC knowledge item (n = 397)
Knowledge about CRC was considered adequate (knowledge score > = 4) for 57% (228/397) and inadequate for 43% (197/397). Participants with adequate CRC knowledge were more likely to have completed a CRC screening test within the recommended time period compared to those with inadequate CRC knowledge (21% vs. 10%). Adequate knowledge was associated with a higher level of education (p < 0.001), a higher level of income (p < 0.001), having health insurance (p < 0.001), and having Medicare/ Medicaid as one's health insurance (p < 0.001).
Multivariate analyses
Results of the logistic regression analyses are shown in Table 5. Results were similar when using communication and CRC risk knowledge as continuous exposure variables, and when using a history of CRC screening anytime in the past as the outcome variable (instead of recent screening). For ease of interpretation, we chose to present the categorical analyses and use recent screening as the outcome of interest. After adjustment for the sex of the participant and source of healthcare, quality of communication remained significantly associated with completion of a CRC test.
Table 5 Factors associated with receiving CRC screening among 397 African American participants in the WATCH Project
Variable OR (95% CI) p-value
Sex .65 (0.39, 1.07) 0.093
Source of healthcare (M.D. office vs. Clinic/ER) 1.07 (0.58, 1.95) 0.838
CRC Knowledge (Adequate vs. Inadequate) 1.82 (1.14, 2.89) 0.011
Patient-provider communication (Good vs. Poor/Fair) 1.95 (1.29, 2.94) 0.002
CRC screening within recommended guidelines by perceived communication and knowledge is listed in Table 6. The poor and fair communication groups were combined because of the small numbers within each category. Adequate knowledge is statistically significant for the good communication group but not for the fair/poor communication group. A test for interaction of communication and knowledge was performed for CRC within recommended guidelines and demonstrated no significant interaction.
Table 6 CRC screening results by communication and knowledge
CRC screening in recommended time (%)
Poor and Fair communication
Inadequate knowledge 15.0
(n = 40) p = 0.654
Adequate knowledge 18.5
(n = 54)
Good communication
Inadequate knowledge 27.4
(n = 124) p = 0.012
Adequate knowledge 41.6
(n = 173)
Discussion
Our study found that participants had higher rates of CRC screening when their self-rated communication with their healthcare provider was classified as perceived as more positive. In addition, we found that participants who self-rated their communication as good and who had adequate CRC knowledge completed recent CRC screening at higher rates than those with good communication and inadequate knowledge. In addition, screening rates are higher with both good communication and adequate CRC knowledge than when only one factor is present. These findings suggest that both good patient-provider communication and CRC knowledge are important for CRC screening. Because of the cross-sectional nature of our study, we cannot determine causality, and it is possible that CRC knowledge improves by going through the screening process.
Improving CRC screening rates in the African American population may require strategies that address both improving physician-patient communication skills and increasing CRC knowledge. Good patient-provider communication is fundamental to a patient's perceived quality and satisfaction with their healthcare. Better communication, including the use of shared decision making, is associated with trust between patients and providers [24]. However, trusting the medical community remains a key concern for many African Americans. This is due, in part, to a long history of justifiable fear and mistrust of the medical and research communities stemming from the historical Tuskegee incident and other discriminatory practices in health care [25,26]. Our findings suggest that African Americans in this study generally had positive perceptions about communicating with health providers. Prospective data about communication and trust are needed to determine whether these perceptions predict greater screening compliance.
The association between the lack of physician recommendations for cancer screening tests and low patient utilization of those tests has been documented in previous investigations [18,19,27-30]. In a recent study of rural primary care practices, discussion about CRC screening occurred in only 14% of eligible patients [31]. Additionally, previous research has documented that African American patients receive less physician recommendations for cancer screening tests and utilize cancer screening tests at lower rates than other ethnic groups [27,32-34]. In another recent study of 150 African Americans (aged 50–79), 39% reported never having a recommendation for FOBT, 60% never had a flexible sigmoidoscopy recommended, and 57% never had a colonoscopy recommended [35]. In our study, only 45% of participants stated that CRC screening had been previously recommended by their healthcare provider.
The results of our study and other previously published work [24,35-39] suggest that CRC screening rates may improve by: 1) a focus on methods to improve patient-provider communication skills both for the patient and for providers; 2) addressing physician attitudes and behaviors toward recommending tests, and 3) providing patient CRC education that takes into account patients' literacy skills and preferred style of receiving information. Providing CRC risk knowledge and training to improve communication skills may be accomplished with various interventions [40]. Decision aids, systematically developed tools to provide information, increase knowledge, and encourage shared decision making, may also empower individuals to become more involved with their healthcare. Decision aids for CRC screening may be very useful because there is evidence that patients vary in their preference for how to be screened [36,37].
Limitations
Although the results from our cross-sectional study have implications for developing colorectal cancer prevention programs, it must be recognized that prospective longitudinal studies are needed that specifically address the effect of patient-provider communication on CRC screening. Because of its cross-sectional design, we cannot infer causal relationships. In addition, since all participants were recruited from 12 churches, there may be some clustering of exposures and outcomes by physician that may affect the results; we did not measure this effect. Patient-provider communication was measured by using only three self-reported communication items, which may not fully capture the full extent of the patient-provider relationship. In addition, we do not know whether our participants had racially discordant or concordant physicians, and we did not have information regarding the physician's beliefs or practices about CRC screening or the physicians' assessment of the quality of communication with the patients. The outcome measure of having undergone CRC screening was self-reported, and these results were not validated. Finally, because of the relatively small number of participants and the use of convenience sampling, results from this study may not be generalizable.
Conclusions
The findings from this cross-sectional study suggest that not only do patients need to be informed about their CRC risk and the importance of screening tests but that having good communication with their healthcare provider may also important. The burden of having good communication in the patient-provider relationship is the responsibility of both individuals. Both the patient and the provider should strive to improve their communication skills so that patients who want to participate in the decision about how to be screened for colorectal cancer may do so effectively.
Because culturally distinct factors may contribute to poor communication and mistrust for African-Americans, specific new strategies need to be developed [38]. Beliefs, attitudes, and concerns about cancer, prevention behaviors, and participation in medical decisions may not be the same for all races. If new prevention strategies for the African American population are developed, then there may be a chance at reducing the disparities associated with colon cancer and race.
Competing interests
The author(s) declare that they have no competing interests.
Authors' contributions
M.L.K. participated in the conception of the study, the analyses, the interpretation of the results, writing the first draft and revising the manuscript. A.S.J. participated in the conception of the study, performed the statistical analyses, participated in the interpretation of the results, and the editing of the manuscript. M.P.P. participated in the interpretation of the results and the writing of the manuscript. M.H., E.J., and V.O. assisted with the qualitative analysis and surveys. M.K.C. participated in the conception of the study, the statistical analyses, interpretation of the results, and the writing 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 research was supported by grants from the American Cancer Society
(ACS RPG-97-141-01-PBP) and the NC Nutrition Network (00073-00).
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| 15601463 | PMC544572 | CC BY | 2021-01-04 16:28:47 | no | BMC Public Health. 2004 Dec 15; 4:62 | utf-8 | BMC Public Health | 2,004 | 10.1186/1471-2458-4-62 | oa_comm |
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BMC PediatrBMC Pediatrics1471-2431BioMed Central London 1471-2431-4-261561055510.1186/1471-2431-4-26Research ArticleThe Angiotensin Converting Enzyme Insertion/Deletion polymorphism is not associated with an increased risk of death or bronchopulmonary dysplasia in ventilated very low birth weight infants Yanamandra Krishna [email protected] John [email protected] R John [email protected] Department of Pediatrics Louisiana State University Health Sciences Center 1501 Kings Highway Shreveport, Louisiana, 71130-3932, USA2 Department of Pediatrics and Child Health University of Manitoba WR116 735 Notre Dame Avenue Winnipeg, Manitoba, R3E 0L8, Canada2004 20 12 2004 4 26 26 20 6 2004 20 12 2004 Copyright © 2004 Yanamandra et al; licensee BioMed Central Ltd.2004Yanamandra 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 ACE gene contains a polymorphism consisting of either the presence (insertion, I) or absence (deletion, D) of a 287 bp alu repeat in intron 16. The D allele is associated with increased ACE activity in both tissue and plasma. The DD genotype is associated with risk of developing ARDS and mortality. The frequency of the D allele is higher in patients with pulmonary fibrosis, sarcoidosis and berylliosis. The role of this polymorphism has not been studied in the development of BPD in the premature newborn.
Methods
ACE I/D genotype was determined in 245 (194 African-American, 47 Caucasian and 4 Hispanic) mechanically ventilated infants weighing less than 1250 grams at birth and compared to outcome (death and/or development of BPD).
Results
The incidence of the D allele in the study population was 0.58. Eighty-eight (35.9%) infants were homozygous DD, 107 (43.7%) were heterozygous ID and 50 (20.4%) were homozygous II. There were no significant differences between genotype groups with respect to ethnic origin, birth weight, gestation, or gender. There was no effect of the ACE I/D polymorphism on mortality or development of BPD (O2 on 28 days or 36 weeks PCA). Secondary outcomes (intraventricular hemorrhage and periventricular leukomalacia) similarly were not influenced by the ACE ID polymorphism.
Conclusions
The ACE I/D polymorphism does not significantly influence the development of BPD in ventilated infants less than 1250 grams.
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Background
Prematurely born infants who require mechanical ventilation (MV) frequently develop chronic lung disease known as bronchopulmonary dysplasia (BPD). Oxidant injury, mechanical disruption of the lung, inflammation and subsequent failure of lung development are considered the major mechanisms in the pathogenesis of BPD. The development of BPD is characterized by an initial acute inflammatory component followed by variable degrees of lung fibrosis and failure of alveolar septation, both of which ultimately impair the development of the immature lung [1-5]. The inflammatory component, which consists interstitial and alveolar edema, hyaline membrane formation, epithelial cell necrosis and influx of activated neutrophils, is similar (albeit not identical) to that seen in other forms of acute lung injury (ALl).
There is increasing evidence to support the role for the activation of the renin-angiotensin system (RAS) system during ALI. In ARDS there is an increase in bronchoalveolar lavage and a concomitant decrease in circulating angiotensin converting enzyme (ACE) activity[6,7]. This increase in local ACE activity may influence the course of acute lung injury by its effects on vascular permeability, epithelial cell survival and fibroblast activity [8-12]. Angiotensin-II (AT-II) concentrations are increased in patients with ARDS, consistent with activation of the RAS with ALI[13]. Inhibition of AT-II with type 1 angiotensin receptor antagonists delayed the onset of ARDS and inhibited neutrophil influx in to the lung in experimental models[14]. The role of RAS activation in the lungs of premature infants with respiratory distress syndrome and its evolution into BPD has not been determined.
There is significant variation in circulating ACE activity among individuals, which may be genetically determined. The human ACE gene is located on chromosome 17 (17q23) and contains a polymorphism consisting of either the presence (insertion, I) or absence (deletion, D) of a 287 bp alu repeat in intron 16[15]. The deletion is associated with increased ACE activity in both tissue and in the circulation and accounts for about 47% of the intra-individual variation in plasma ACE activity in Caucasians[15,16]. A role for genetic variation in ACE activity in both acute and chronic lung disease has recently been suggested [17-21]. Higher intrinsic ACE activity (DD genotype) is associated with an increased risk of developing ARDS and other lung diseases [17-21]. The potential role of this polymorphism to influence risk of developing chronic lung disease has not been studied in the premature newborn.
Because of the relationship between inflammation and activation of the RAS and the association with lung fibrosis and adverse outcome in ARDS, the ACE l/D polymorphism may modify risk for the development of chronic lung disease or death in mechanically ventilated VLBW infants. The purpose of this study was to determine if there is a relationship between the ACE I/D polymorphism and respiratory outcomes of death or the development of BPD, in mechanically ventilated very low birth weight (VLBW) infants.
Methods
Genomic DNA used for this case controlled study was extracted from archival tracheal aspirate (TA) pellets (223 patients) or blood (22 patients) collected prospectively as part of an ongoing study of genetic factors in the development of complications of prematurity. The TAs that were used as a source of genomic DNA were originally collected as part of long term longitudinal studies examining cytokine concentrations and the development of CLD[22,23]. Infants were included in this study if they fulfilled the following inclusion criteria: birth weight less than 1250 grams, mechanical ventilation (MV) during the first week of life, complete clinical data on pulmonary outcome and a genomic DNA sample that could be used for genotyping. Infants were excluded if complete data on pulmonary outcome was not available or suitable DNA was not available. The study was approved by the Institutional Review Board for Human Research at Louisiana State University Health Sciences Center in Shreveport.
Cultures for genital mycoplasmas were performed on samples collected in the first few days of life. Clinical and outcome data were abstracted from the clinical record and included information on respiratory outcome, survival and development of complications of prematurity.
Laboratory methods
DNA isolation
Isolation of total DNA from blood or TA pellets was performed using the QIAmp DNA Mini kits™ (Qiagen Incorporated, Chatsworth, CA). Briefly, TA pellets were suspended in 200 μl of sterile phosphate buffered saline by vigorous vortexing, then digested in proteinase K and applied to silica gel spin columns. Columns were washed in the manufacturer's supplied buffers and the total DNA was eluted in 200 μl elution buffer. Blood (200 μl) was extracted similarly to the TA pellets.
ACE I/D genotyping
ACE I/D polymorphism was performed by microplate PCR method as described previously[24]. (Primers used in the assay: 5'-CTG GAG ACC ACT CCC ATC CTT TCT-3' AND 5'-GAT GTG GCC ATC ACA TTC GTC AGA T-3'. In 10 microliter PCR volume, the following components were added with the final concentration of MgCl2 1.5 mM, KCl 50 mM, 5%DMSO, Triton X-100 0.1%, 200 micromolar each of dNTPs, 10 pmol each primer, 1–2 U of Taq polymerase, and 1 microliter of DNA. DNA was denatured at 95°C for 5–10 min, followed by 30 cycles of denaturation at 94° for 60 sec, annealing at 67 for 60 sec, and extension at 72° for 2 minutes, with a final extension at 72° for 7 min. PCR products were separated on 2% agarose gel containing 0.5-microgram/ml ethidium bromide. After gel electrophoresis the bands were visualized by UV-transillumination. The PCR product is a 190 bp fragment in the absence of the insertion (D genotype) and a 490 bp fragment in the presence of the insertion (I genotype).
Data analysis
Several definitions of BPD have been used through the years. Initially BPD was defined as oxygen dependency at 28 days of age[25,26]. More recently, the use of oxygen dependency at 36 weeks postconceptional age (PCA) has been proposed as a more suitable definition of BPD.[27] Both definitions of BPD predict long term respiratory abnormalities. Data analysis consisted of comparing the frequencies of the ACE I/D genotypes in infants with and without the outcome of interest (supplemental oxygen administration at 28 days or 36 weeks PCA, death or BPD /death before 36 weeks PCA) by Chi Square. All statistical analysis was performed using the SPSS for Windows version 12.0 (SPSS Inc., Chicago, IL). The Student t-test was used to assess normally distributed variables. The Wilcox Rank Sum test was used for analysis of factors that were not normally distributed. A probability value of less than 0.05 was considered statistically significant. The data are presented as mean ± standard error of the mean (SEM).
Results
Two hundred and forty five (245) patients had complete clinical information on respiratory outcome and genomic DNA available for genotyping. Mean gestational age and birth weight of the study population was 26.4 ± 0.1 weeks and 869 ± 12 grams respectively. One hundred and ninety-four (79%) infants were African-American, 47 (19 %) were Caucasian and 4 (2%) were Hispanic. Male: female ratio was 147:98. All patients required MV at birth and 228 (93%) infants were treated with exogenous surfactant therapy (Survanta®, Ross Products Division, Abbott Laboratories, Columbus, OH). Tracheal aspirate cultures obtained during the first few days of life grew Ureaplasma urealyticum (Uu) on at least one occasion from 74 of 217 infants cultured (34%) and Mycoplasma hominis (Mh) from 29 (13%). One hundred and fifty one (67%) infants were oxygen dependent at 28 days and 55 (25%) were oxygen dependent at 36 weeks PCA. There were 39 (16%) patients who died (from all causes) during their initial hospitalization (24 before 28 days of age and 15 after 28 days).
All 243 infants were genotyped for the ACE I/D polymorphism. The frequency of the D allele in the study population was 0.58. The frequency of the D allele was similar between African-American (0.57) and Caucasian infants (0.59) (p = 0.664). Fifty (20.4%) infants were homozygous II, 107 (43.7 %) were heterozygous ID and 88 (35.9%) were homozygous DD.
Baseline clinical characteristics of birth weight, gestational age, race, gender, TA isolation of Mh, and the need for surfactant replacement were not different between genotype groups (Table 1). Isolation of Ureaplasma urealyticum from the trachea was more frequent in Caucasian infants who had the ACE II genotype (II 55%, ID 9%, DD 22%; p = 0.046). Uu isolation frequencies were similar between genotype groups in African-American infants. Baseline clinical characteristics for Caucasian and African-American infants separately can be found in the online supplement.
Table 1 Baseline Clinical Characteristics
ACE Genotype
II (n = 50) ID (n = 107) DD (n = 88) P value
Birth Weight 874 ± 29 876 ± 18 858 ± 20 0.793
Gestation 26.4 ± 0.3 26.5 ± 0.2 26.2 ± 0.2 0.386
Race (Caucasian) 11 (22) 17(16) 19 (22) 0.664
Gender (Males) 28 (56) 59 (55) 59 (67) 0.166
Uu isolated from TAa 18/46 (39) 29/88 (33) 27/83 (33) 0.719
Mh isolated from TAa 4/46 (9) 14/88 (16) 11/83 (13) 0.507
Surfactant Replacement 48 (96) 99 (93) 81 (92) 0.651
Data are presented as Mean ± Standard error of Mean. Numbers in parenthesis represent percentages.
aNot all infants had TA cultures performed for Uu and Mh
Uu Ureaplasma urealyticum
Mh Mycoplasma hominis
TA Tracheal Aspirate
Clinical characteristics of surviving infants who were or were not oxygen dependent at days are shown in Table 2. Infants who were oxygen dependent at 28 days were less mature, of lower birthweight, more likely to have TA isolation of Uu or Mh, and more likely to have received surfactant replacement therapy than those weaned from oxygen by 28 days of age. Ethnic groups and gender were not different between outcome groups. Infants who died or who were oxygen dependent at 36 weeks PCA (BPD) were similarly less mature, of lower birthweight, and were more likely to have received surfactant replacement therapy than surviving infants without BPD (Table 3). Isolation of either Uu or Mh from the TA had no influence on this outcome.
Table 2 Comparison of Infants Oxygen Dependent at 28 days
No Oxygen at 28 days (n = 75) Oxygen at 28 days (n = 151) P value
Gestation (weeks) 27.4 ± 0.1 26.0 ± 0.1 <0.001
Birth Weight (grams) 996 ± 18 826 ± 14 <0.001
Race (Caucasian) 20 (27) 26 (17) 0.103
Gender (Males) 39 (52) 95 (63) 0.102
Surfactant therapy 75 (87) 144 (95) 0.020
Ureaplasma isolateda 16/67 (24) 55/134 (41) 0.016
Mycoplasma isolateda 4/67 (6) 23/134 (17) 0.028
IVHb 8/74 (11) 55/151 (36) <0.001
IVH = Grade 3b 4/74 (5) 35/151 (23) <0.001
PVLb 2/74 (3) 15/151 (10) 0.054
Data are presented as Mean ± Standard error of Mean. Numbers in parenthesis represent percentages.
aNot all infants had TA cultures performed for Uu and Mh
bNot all infants had cranial US evaluations
Uu Ureaplasma urealyticum Mh Mycoplasma hominis IVH Intraventricular Hemorrhage PVL Periventricular leukomalacia TA Tracheal Aspirate
Table 3 Clinical Characteristics of Infants who Died or Developed BPD
Survival without BPD (n = 162) Death or BPD (n = 83) P value
Gestation (weeks) 26.6 ± 0.1 25.8 ± 0.2 <0.001
Birth Weight (grams) 925 ± 14 760 ± 17 <0.001
Race (Caucasian) 33 (20) 14 (17) 0.737
Gender (Males) 96 (60) 50 (60) 0.926
Surfactant therapy 147 (91) 81 (98) 0.046
Ureaplasma isolateda 49/144 (34) 25/73 (34) 0.974
Mycoplasma isolateda 20/144 (14) 9/64 (12) 0.750
IVHb 31/161 (19) 41/80 (52) <0.001
IVH = Grade 3b 15/161 (9) 31/80 (39) <0.001
PVLb 8/161 (5) 10/80 (13) 0.033
Data are presented as Mean ± Standard error of Mean. Numbers in parenthesis represent percentages.
aNot all infants had TA cultures performed for Uu and Mh
bNot all infants had cranial US evaluations
Uu Ureaplasma urealyticum Mh Mycoplasma hominis IVH Intraventricular Hemorrhage PVL Periventricular leukomalacia TA Tracheal Aspirate
Because the ACE I/D polymorphism may have different functional effects on plasma and tissue ACE activities in different ethnic groups, we analyzed the effects of ACE on the incidence of BPD separately for Caucasian and African-American infants. (The effects of the ACE I/D polymorphism on the incidence of BPD and other outcomes for the combined group can be found in the online data supplement). Table 4 shows the effects of ACE genotype on outcomes in ventilated Caucasian infants less than 1250 grams. There was no significant effect of ACE genotype on mortality, oxygen dependency at either 28 days or 36 weeks PCA or the combined outcome of death or BPD. The incidence of periventricular leukomalacia (PVL) was significantly higher in Caucasian infants with the II genotype. The incidence and severity of intraventricular hemorrhage (IVH) was not affected by ACE genotype.
Table 4 Effect of ACE genotype on Outcomes in Caucasian Infants
ACE Genotype
II (n = 11) ID (n = 17) DD (n = 19) P value
Oxygen at 28 days 6/11 (55) 10/15 (67) 9/18 (47) 0.620
Oxygen at 36 weeks PCA 3/10 (30) 4/15 (27) 3/18 (17) 0.674
Death <28 days 0 (0) 1 (16) 1 (5) 0.724
Death or Oxygen at 36 weeks 4 (36) 6 (35) 4 (21) 0.558
Death ≥ 28 days 3 (27) 1 (6) 0 (0) 0.039
IVHa 2/10 (20) 6/17 (35) 1/19 (5) 0.076
IVH ≥ Grade 3a 2/10 (20) 3/17 (18) 1/19 (5) 0.415
PVLa 3/10 (30) 0/17 (0) 1/19 (5) 0.022
Numbers in parenthesis represent percentages.
aNot all infants had cranial US evaluations
PCA Postconceptional age IVH Intraventricular Hemorrhage PVL Periventricular leukomalacia
The effects of ACE genotype on outcome in ventilated African-American infants less than 1250 grams are shown in Table 5. There was no significant effect of ACE genotype on mortality, oxygen dependency at either 28 days or 36 weeks PCA or the combined outcome of death or BPD. The incidence of PVL was not affected by ACE genotype in African-American infants. Similar to that observed in Caucasian infants, there was no apparent effect of the ACE I/D polymorphism on the incidence and severity of IVH in African American infants.
Table 5 Effect of ACE genotype on Outcomes in African-American Infants
ACE Genotype
II (n = 39) ID (n = 88) DD (n = 66) P value
Oxygen at 28 days 24/36 (67) 57/82 (70) 43/59 (73) 0.805
Oxygen at 36 weeks PCA 7/34 (21) 22/79 (28) 16/58 (28) 0.698
Death <28 days 3 (8) 6 (7) 8 (12) 0.517
Death or Oxygen at 36 weeks 12 (31) 31 (35) 25 (37) 0.792
Death ≥ 28 days 3 (8) 7 (9) 4 (7) 0.925
IVHa 14/38 (37) 23/86 (27) 25/67 (37) 0.311
IVH ≥ Grade 3a 7/38 (18) 15/86 (17) 17/67 (25) 0.438
PVLa 1/38 (3) 5/86 (6) 8 (12) 0.145
Numbers in parenthesis represent percentages.
aNot all infants had cranial US evaluations
PCA Postconceptional age IVH Intraventricular Hemorrhage PVL Periventricular leukomalacia
Since birth weight and gestation are the primary determinants of adverse outcome, analysis was repeated in both infants greater than 750 grams and infants ≤ 750 grams. There was no significant effect of ACE genotype when this subgroup analysis was performed (data not shown).
Discussion
The RAS is activated during lung injury and plays a role in several pathological processes. In addition to the pulmonary endothelium, respiratory epithelium also possesses significant ACE activity. Fas-induced alveolar epithelial cell apoptosis is dependent on local AT-II production and interaction with it receptor.[10,11] Further, AT-II is mitogenic for lung fibroblasts and aberrant AT-II production has been linked with some forms of pulmonary fibrosis[12,18,28-30]. Inhibition of AT-II with type 1 angiotensin receptor antagonists delayed the onset of ARDS and inhibited neutrophil influx into the lung in experimental models[14]. In adults, there is an increase in bronchoalveolar lavage ACE activity and AT-II during ALI, however the contribution of activation of the RAS to neonatal lung injury has received little study[6,13].
The frequency of the D allele in our study population was not different than reported in our local population or for other groups[16,24,31]. The ACE D allele is common with a frequency approximately 50–60% in Caucasians and 60–65% in African-Americans. This suggests that the D allele is neither a risk factor nor a protective factor for either premature birth or the need for mechanical ventilation. We did not study early cardiovascular adaptation or record measures of early illness severity, as did Harding et al[32] In that study the DD genotype was associated with a worse cardiovascular adaptation. In our study, the infants are much more immature, of lower gestational age and almost universally had hyaline membrane disease.
In our study population, the ACE ID polymorphism was not associated with an altered risk for the development of BPD, oxygen dependency at 28 days, death (early, late or total mortality) or composite outcomes (BPD or death) suggesting that factors other than genetic variation at this locus contribute to these outcomes. This is in contrast to that seen in adults with ARDS or other lung disorders such as pulmonary fibrosis, sarcoidosis and berylliosis where the D allele is associated with disease [18-21].
Indeed genetic factors may not have a large influence in a disease (BPD) where maturity at birth has such an overwhelming influence. Our previous study in this population failed to find any association of the tumor necrosis factor-α-308 G/A, MCP-1-2518 A/G or transforming growth factor-β1 +915 G/C SNPs on the development of BPD[33] Additionally we have found that the IL-10-1082 G/A SNP has no effect on the incidence of BPD (Yanamandra et al, Pediatric Pulmonology, in press).
The finding of increased incidence of PVL in Caucasian infants with the II genotype may be due more to the greatly increased incidence of Ureaplasma urealyticum colonization in these infants. Chorioamnionitis, which is highly correlated with isolation of Uu, is a risk factor for PVL.[34] Because of the retrospective nature of this study we were not able to systematically review placental pathology.
Our study has several limitations. The ACE I/D polymorphism has only been demonstrated to be functional in Caucasians.[16] Data linking this polymorphism and ACE activity in other ethnic groups are either lacking or suggests the polymorphism is non functional (African-Americans)[31]. If this were true, then one would expect to find no effect of the ACE I/D polymorphism on outcomes in African-American infants. The numbers of Caucasian infants in this study are few and an effect of the ACE I/D polymorphism on the incidence of chronic lung disease or other outcomes may not be detected. Thus it may be relevant to examine the contribution of the ACE I/D polymorphism to chronic lung disease in a larger cohort of Caucasian infants.
The observations of this retrospective case controlled study are also limited somewhat by selection bias. Because only infants who were mechanically ventilated were included, the true impact of this polymorphism on the complications of prematurity may be underestimated. Since this was a retrospective study using stored material no attempt was made to correlate phenotype with genotype (ie TA ACE measurements).
Lung injury and the subsequent maladaptive repair process that leads to the development of BPD is complex with a great many factors that interplay to determine outcome. It is very likely that other genetic play a role in determining the risk of poor outcome. Polymorphisms in cytokines, their receptors, bacterial pattern recognition molecules, surfactant proteins, and heme oxygenase-1 are known to alter the course of other pulmonary diseases and should be examined as to their potential role in the development of BPD in the premature infant.
Conclusions
The ACE I/D polymorphism does not significantly influence the development of BPD in ventilated infants less than 1250 grams.
Competing interests
The author(s) declare that they have no competing interests.
Authors' contributions
RJB conceived and organized the study, prepared the manuscript and performed the statistical analyses. KY performed the genotyping, and assisted with manuscript preparation. JL oversaw collection tracheal aspirates, assisted with recruitment of subjects into the original studies, and assisted with editing 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:
Supplementary Material
Additional File 1
Contains Tables showing baseline clinical characteristics of African American and Caucasian infants separately by Genotype group
Click here for file
Acknowledgments
The authors gratefully acknowledge the expert technical assistance of Dawn Napper in performance of this investigation.
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| 15610555 | PMC544573 | CC BY | 2021-01-04 16:31:00 | no | BMC Pediatr. 2004 Dec 20; 4:26 | utf-8 | BMC Pediatr | 2,004 | 10.1186/1471-2431-4-26 | oa_comm |
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BMC Health Serv ResBMC Health Services Research1472-6963BioMed Central London 1472-6963-4-391562034510.1186/1472-6963-4-39Research ArticleThe effectiveness of metal on metal hip resurfacing: a systematic review of the available evidence published before 2002 Wyness Laura [email protected] Luke [email protected] Kirsty [email protected] Adrian [email protected] Miriam [email protected] Department of Public Health, University of Aberdeen, Aberdeen, UK2 Health Services Research Unit, University of Aberdeen, Aberdeen, UK3 Health Economics Research Unit, University of Aberdeen, Aberdeen, UK2004 27 12 2004 4 39 39 30 1 2004 27 12 2004 Copyright © 2004 Wyness et al; licensee BioMed Central Ltd.This is an Open Access article distributed under the terms of the Creative Commons Attribution License (), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Background
Conventional total hip replacement (THR) may be felt to carry too high a risk of failure over a patient's lifetime, especially in young people. There is increasing interest in metal on metal hip resurfacing arthroplasty (MoM) as this offers a bone-conserving option for treating those patients who are not considered eligible for THR. We aim to evaluate the effectiveness of MoM for treatment of hip disease, and compare it with alternative treatments for hip disease offered within the UK.
Methods
A systematic review was carried out to identify the relevant literature on MoM published before 2002. As watchful waiting and total hip replacement are alternative methods commonly used to alleviate the symptoms of degenerative joint disease of the hip, we compared MoM with these.
Results
The data on the effectiveness of MoM are scarce, as it is a relatively new technique and at present only short-term results are available.
Conclusion
It is not possible to make any firm conclusions about the effectiveness of MoM based on these early results. While the short-term results are promising, it is unclear if such results would be replicated in more rigorous studies, and what the long-term performance might be. Further research is needed which ideally should involve long-term randomised comparisons of MoM with alternative approaches to the clinical management of hip disease.
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Background
The treatment of younger people with disease of the hip joint presents a difficult clinical problem. Conventional total hip replacement (THR) may be felt to carry too high a risk of failure over a patient's lifetime. Overall, long-term results of THR in younger patients with a variety of underlying conditions indicate that 25–30% may require revision by 15 years [1], compared with less than five percent at ten years for older patients, and less than ten percent at ten or more years for all patients [2]. Specific subgroups of young active patients, such as those with osteoarthritis, may experience a revision rate of 50% [3]. In 1999/00 in the NHS in England 18% (8,389) of THRs were performed on people aged between 15 and 59, 46% (21,440) in people aged 60 to 74, and 36% (27,965) in people aged 75 and over [4]. Data on the number of revisions performed was not so readily available. A previous report suggested that out of approximately 2700 THRs per year, 2100 (78%) are primary THRs and 600 (22%) are revisions [5]. More recent data on revisions of THRs as a percentage of the total number of THR procedures suggest that in 1998/99 over ten percent of all THRs were carried out as revisions [6]. Due to concerns about the risks of revision, people who are expected to outlive a primary THR are often managed with non-surgical interventions, such medication to alleviate pain and to delay or prevent the need for surgery; collectively these interventions have been referred to as 'watchful waiting' (WW). People are typically referred for surgery only when their symptoms (e.g. pain, loss of physical function) become unmanageable by non-surgical means. Figures for the number of people who have their symptoms managed by pain control and other non-surgical interventions (such as the use of transcutaneous electrical nerve therapy and strengthening exercises) within England and Wales are difficult to determine. Evidence from a population survey suggest that 15.2 people per 1000 aged 35 to 85 years had hip disease severe enough for surgery. This equates to approximately 760,000 people within England and Wales [7].
Metal on metal hip resurfacing arthroplasty (MoM) offers a bone-conserving option for treating those patients who are not considered eligible for THR. MoM may also represent a more attractive alternative to other procedures such as osteotomy, bone fusion and arthroscopy, which have previously been used or been advocated as means of delaying or preventing the need for a THR. MoM involves the removal and replacement of the surface of the femoral head with a hollow metal hemisphere, which fits into a metal acetabular cup. This technique conserves femoral bone (although it is not conservative on the acetabular side), maintains normal femoral loading and stresses, and may not therefore compromise future total hip replacements. Data on the use of MoM within the NHS in England and Wales could not be obtained in this review. Never the less, because of increasing interest in MoM, we conducted a systematic review of the evidence of effectiveness aiming to compare it with THR and watchful waiting.
Methods
Search strategy
Initial searches failed to identify any randomised or comparative observational studies comparing MoM with any of the chosen alternatives. A structured search was conducted to identify evidence relating to the clinical effectiveness and cost-effectiveness of MoM for treatment of hip disease. The search strategy comprised of: (1) A free text search to identify any potentially relevant papers evaluating MoM (free text search terms were used because of the anticipated scarcity of published literature); and (2) A search for RCTs and systematic reviews of RCTs for THR using a modified version of the search strategy used for a recent review [8]. The search strategies used are presented in the appendix. Appendix [see Additional file 1] The following databases were searched to identify relevant published literature: Cochrane database of systematic reviews (CDSR), Database of abstracts of reviews of effectiveness (DARE), Cochrane Controlled Trials Register, MEDLINE and PREMEDLINE, EMBASE, HealthSTAR, CINAHL, NHS Economic Evaluation Database (EED), and Allied or Alternative Medicine (AMED). Relevant audit databases and the World-Wide Web were also searched. Unpublished data sources were sought by contacting experts in this field and industries with an interest in this area of orthopaedics. Studies from 1990 to 2001 were searched for.
Inclusion and exclusion criteria
All identified abstracts were assessed for subject relevance independently by two reviewers. Full papers were then obtained and formally assessed for inclusion. It was agreed at the outset of the review that the search strategy would not be limited by language. It was agreed that non-English studies would be identified, but due to time and resource limitations would not be translated and assessed for their relevance to the review. No restrictions on the type of patient were imposed. Comprehensive systematic reviews of THR was carried out in Health Technology Assessment in 1998. These reviews were updated by the National Institute of Clinical Excellence (NICE) in 2000. Therefore, in this review a search for systematic reviews and RCTs published subsequent to the completion of the systematic reviews was carried out. Table 1 describes the inclusion and exclusion criteria applied for each of the treatments considered here.
Data abstraction and quality assessment
Two reviewers independently abstracted data and quality assessed the included studies. Where a difference in opinion occurred, an arbiter was consulted. A data abstraction form was developed to record details of trial methods, participants, interventions, patient's characteristics and pre-specified outcomes (See Table 2). The quality assessment form was based on a checklist developed by Morris, 1988 [2] to assess the quality of studies appearing in orthopaedic research journals.
Results
The initial search identified 352 potentially relevant MoM studies, 699 potentially relevant THR studies and 177 potentially relevant watchful studies. After reviewing titles and abstracts and applying the inclusion and exclusion criteria, data were abstracted from four published MoM studies [9-12], four published THR studies [2,8,13,14] and one watchful waiting study [15-17]. Four unpublished studies were also included [18-21]. These were obtained from companies that manufacture alternative MoM devices and also through personal communication with the Robert Jones and Agnes Hunt Orthopaedic and District Hospital.))No comparative studies were found.
Quality of studies
The majority of studies rated poorly in terms of description of study sample, control of bias, and statistical and analytical considerations. Most studies rated favourably in terms of clarity of the study question and definition of outcome, although less favourably with respect to the description of the intervention. The duration and completeness of follow-up was of variable quality, in terms of the interval between surgery and follow-up being clearly stated and the consideration of patients lost to follow-up. Of the three systematic reviews included, two were of high quality [2,13], although there were some limitations on the comprehensiveness of the literature searches. The other systematic review was of lower quality with poor reporting of the methodology [8]. A summary of the quality assessment of the remaining ten included studies is presented in Table 3.
Relative effectiveness of metal on metal hip resurfacing arthroplasty
Metal on metal hip resurfacing arthroplasty included studies
The MoM studies included in the review were four published studies, three unpublished reports from the manufactures of MoM prostheses, and one unpublished report. (Refer to table 4) The length of follow-up was less than five years for all the studies and ranging from 8.3 months [10] to 48 months [20]. The majority of the studies were small, (4424 [20] to four patients [11]). There was wide variation of patients' pre-operative diagnoses.
Metal on metal hip resurfacing arthroplasty study outcomes
Only one study reported details on the duration of the operation [11]. The mean operation time was reported as 247 minutes (range 180 to 370 minutes). McMinn et al, 1996 [10], reported that all patients were mobilised on the first post-operative day and at 12 days post-operation all patients had partial weight bearing of 25 kg on the surgically treated leg, with this weight being increased after 12 weeks. Patients in one study [12] spent a median of 21 days in hospital. All except one of the MoM studies reported the revision rates to THR. They ranged from 0% to 14.3%. Two groups of patients in the McMinn et al, 1996 [17] study were reported to have no revision to THR. Details on patients who were pain free were reported in one published study [17]. In this study 91% (60/66 patients) were pain free after a mean follow-up of 50.2 months (range 44 to 54 months). One of the manufacturers of MoM prostheses reported 71.1% (69/97 patients) to be pain free after a mean follow-up of 16.9 months [18].
The studies reported few complications. In one study [11] 10.5% (2/19 patients) were reported to have complications, one a femoral nerve palsy and one a haematoma. McMinn et al, 1996 [10] reported out of 235 patients, three patients had infections and one patient had sciatic nerve palsy. The only complication reported by Wagner et al, 1996 [12] (a study of 35 patients), was one patient with a femoral neck fracture, which was due to a traffic accident. The Oswestry Outcome Centre [20] reported the majority of revision surgery was due to fractures (56%), followed by loosening (19%), infection (11%), avascular necrosis (11%) and dislocation (3%). One manufacturer reported 6.4% (7/110 patients) to have complications [18]. Another manufacturer reported 3% (3/100 patients) to have complications [19]. The most common type of complication in these two studies was loosening.
Alternative treatments to MoM
Only one watchful waiting study was included in this review. (Refer to table 5) The results of the study were reported in two papers, one with results up to three years [16] and the other up to eight years [17]. All the patients included in the study suffered from osteoarthritis of the hip. The study reported that the THR surgery performed increased from 9 patients (32%) at 3 years, to 14 patients (48%) at eight years. The number of patients using walking aids also increased from 8 patients (29%) at three years, to 12 patients (41%) at eight years. Patients' level of pain showed a slight increase from three to eight years.
Three systematic reviews provided the majority of information on THR for this review [2,8,13]. One of these reviews [2] included 11 RCTs (mean sample 168 patients), 18 comparative observational studies including two very large studies based on Scandinavian registry data [22], and 159 observational studies. The second systematic review [13] included 17 RCTs, 61 comparative studies and 145 observational studies. The third review [8] included the two systematic reviews mentioned above in addition to four RCTs, ten prospective comparative observational studies and Swedish Registry data [22]. One additional recent RCT [14] not included in the earlier systematic reviews was found from the search in this review. (Refer to table 6) The review by Fitzpatrick et al 1998 [2], reported an adjusted revision rate per 100 person years at risk of 0.37(+/- 0.02). Faulkner et al, 1998 [13] reported that cemented designs show good survival at ten to 15 years. The review by NICE, 2001 [8] reported that a number of prostheses achieved a revision rate of 10% or less after ten or more years follow-up. The study by Sharp et al, 2000 [14] reported a revision rate of 27.5% at a mean follow-up of 5.2 years. It was also reported in this study that two out of 91 patients (2.2%) had a dislocation within one year post-operation. No evidence on the extent or nature of complications was reported in any of the systematic reviews.
Discussion
Despite extensive searching for relevant studies, the evidence base for making comparisons between MoM and any of the comparators is limited. Initial searches had already shown a lack of comparative studies and therefore the focus of the literature search was on identifying less methodologically robust studies such as data from case series. Although such searches are problematic due to lack of specific indexing terms, an extensive search strategy was devised to identify as many eligible studies as possible.
The early data pertaining to MoM suggests that MoM has the potential to be an effective technique for the management of hip disease. However, due to the lack of any controlled studies, it is difficult to know how much more or less effective it is compared to any comparators. The data available with which to make comparisons is uncontrolled and the studies identified have, in many cases, considered patient populations that are dissimilar in many ways. Identified studies also did not always use comparable outcomes and had different lengths of follow-up.
The lack of long-term data on MoM makes it difficult to compare with the other comparators. In particular the failure rates for some types of THR prosthesis increase significantly after ten years [2], and it is possible the same could occur with MoM. It is also unclear whether the success rates reported for THR could be replicated in younger or more active populations. Comparisons between MoM and THR studies are difficult as the MoM studies included younger patients and had shorter follow-up than the THR studies. The evidence from the systematic reviews of different methods of THR reported that several prostheses had revision rates of ten percent or less at ten years or more [2,13]. Revision rates reported in the MoM studies ranged from 0% to 14% for up to 5 years follow-up. The only other outcome that could be compared is the percentage of patients who were pain-free at follow-up. This was reported to be 90.9% at 50.2 months follow-up in one group of patients in one MoM study [10]. The systematic review conducted by Fitzpatrick and colleagues in 1998 report a mean of 84.1% (range 46–100%) of patients pain-free at a follow-up of 11 years [2].
In the MoM and WW studies, most of the patients had a preoperative diagnosis of osteoarthritis and were all of a similar younger age. The watchful waiting study reported 32% of patients requiring surgery at 3 years and 48% by eight years follow-up [15-17]. In the MoM studies revision rates ranged from 0% to 14.3%, after a follow-up of less than five years. During the 8-year follow-up period, people managed with WW had a slight increase in their pain levels, whereas the MoM patients hip scores all improved. 91% (60/66) of MoM patients were pain free after a mean follow-up of 50.2 months in one study [10], and 71% (69/97) after a mean follow-up of 16.9 months in the only other study that reported this outcome [18]. The very limited evidence available suggests that MoM is more effective in terms of better quality of life (measured by pain scores for WW and hip scores for MoM) than WW over a follow-up of approximately three years.
As the relative effectiveness of MoM is unclear the cost-effectiveness of MoM is also uncertain. It is likely the MoM procedure would cost approximately £5,500 whereas a THR would cost about £4,200 and the annual cost of WW (including the cost of NSAID (Non steroidal anti-inflammatory drugs) therapy, physiotherapy and treatment of side effects of medications) would be about £640 [23]. Whether MoM proves to be cost-effective against these alternatives depends upon the rates of revision to THR of MoM and WW, and the rates of revision of THR. The operation rates reported from the one WW study [15-17] and the revision rates of MoM suggest that MoM may provide better outcome at lower cost over a ten-year period. Such information remains at best tentative due to the small number of people to whom the watchful waiting data relate, the short follow-up of the MoM studies, and the uncontrolled nature of the comparison.
Conclusions
The use of MoM in the UK is still relatively rare. However, there has been increasing interest from younger people with hip disease who are not currently considered eligible for THR and amongst surgeons who strive for better ways to treat the patients whom they see. However, only very limited evidence are currently available on MoM and although the procedure does appear promising the lack of robust comparisons with the other treatment options and of long term data make it virtually impossible to draw robust conclusions about its relative effectiveness. Given the early promise shown by MoM there is a real need for more rigorous research. Such research would be challenging, not least because of ethical considerations, but should attempt some form of prospective, preferably randomised, comparison of MoM with a policy of delayed selective surgery. These studies should preferably be large-scale, long-term, and use standard outcome measures, both pre- and post-operatively.
Competing interests
The author(s) declare that they have no competing interests.
Authors' contributions
LW carried out the critical appraisal of the included studies and assisted in the writing up. LV coordinated the project and assisted in the writing up. KM developed the methodology for the literature search and assisted in the writing up. AG participated in the design and coordination of the study. MB assisted in the critical appraisal of the included studies. 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
Search strategies. The search strategies used to search electronic databases to identify studies relevant to this review.
Click here for file
Acknowledgements
This review was commissioned by the HTA Programme on behalf of the National Institute for Clinical Excellence (NICE). The Health Services Research Unit and the Health Economics Research Unit are core funded by the Scottish Executive Health Department. The views expressed are those of the authors and not necessarily those of the funding bodies.
Figures and Tables
Table 1 Inclusion and exclusion criteria
Treatment Inclusion/Exclusion Criteria
Metal on metal hip resurfacing A minimum of two years follow-up was applied; Studies not reporting the specified outcomes (ref. table 2) such as laboratory only studies were excluded.
Watchful waiting Observational data of people receiving WW with a follow-up of greater than five years were included
Total hip replacement RCTs or systematic reviews of RCTs with a minimum of five years follow-up of different methods of THR and systematic reviews of such trials
Table 2 Outcomes sought from all included studies
Timescale Outcomes
Short term Duration of operation
Serious complications (e.g. nerve palsy, haematoma dislocation, infection, re-operation within 6 months
Time in hospital
Time to return to "normal activities" prior to operation
Long term Revision rate
Time to revision surgery
Functional result
Percentage of patients pain free
Quality of life (any recognised generic or condition specific measure e.g. SF-36)
Mortality
Table 3 The number of studies identified for different treatments
Number of studies
Assessment Item Yes No Unable to judge Not applicable
Clarity of study question and definition of outcome
Is the purpose of the study clearly stated? 8 1 1 -
Is the definition of prosthesis failure clear? 7 2 - 1
Is there a clear definition of primary outcome(s)? 7 3 - -
Are standardised outcome measures used? 9 - 1 -
Are the outcome measures used appropriate for the purpose of the study? 9 1 - -
Description of prosthesis and method of fixation
Is the prosthesis design adequately described? 7 2 - 1
Is the method of fixation adequately described? 7 2 - 1
Description of study sample
Is the method of selection of the sample adequately described? 2 7 1 -
Are the study exclusion and inclusion criteria stated? 2 7 1 -
Is the baseline sample clearly described in terms of basic characteristics (age, sex etc)? 6 3 1 -
Is the study sample sufficiently homogenous in terms of disease/diagnosis? 5 1 4 -
Is the study sample sufficiently homogenous in terms of co-morbidity? 2 - 8 -
Control of bias in study design
Is the method of randomisation adequate? - - 1 9
Is the method of masking the patient to the intervention allocated stated? - 2 1 7
Were outcome assessors blind to intervention allocation? - 1 3 6
Are baseline values for groups compared? - 1 1 8
Has the study adequately controlled for confounding factors? 1 7 2 -
Duration and completeness of follow-up
Are intervals between surgery and follow-up assessment clearly stated? 8 2 - -
Are reasons for loss of patients at follow-up stated? 2 2 2 4
Are those lost to follow-up compared to the rest of the sample? 1 2 3 4
Is there an appropriate length of follow-up? 8 2 - -
Is the length of follow-up at least 5 years? 3 7 - -
Statistical and analytical considerations
Has the study sample size been justified? - 9 1 -
Are the data clearly presented? 8 2 - -
Was the data analyst masked to interventions? - 5 3 2
Has type of statistical test and actual probability value been stated? 2 1 1 6
Are statistical tests appropriate to study? 2 - 2 6
Is the sample on which failures are assessed adequate? - 2 7 1
Are conclusions justified by evidence? 5 1 3 1
Table 4 Metal-on-metal studies included in review.
Study (Study design) Funding source Prostheses Mean duration of follow up (range) Mean patient age (range) Revision Rate (unless otherwise stated) Hip Score Pre-operation/Post-operation
Amstutz (2000) [9] Specialist orthopedic hospital, USA. (Observational) Not reported Cemented, modified McMinn acetabular (7 Hips)
Conserve Plus (29 Hips) 22 months (NR) 40 years (NR) NR NR
McMinn (1996) [10] 3 Birmingham Hospitals, UK. (Observational) Not reported Uncemented, Uncoated (70 Hips) 50.2 months (44–54)a 48.7 years (NR) 8/66 patients = 12.1%
60/66 = 90.9% patients pain free pain 3.1b/5.3
mobility 3.1b/5.3
walking 3.1b/5.3
Uncemented, Hydroxyapetite coating (6 Hips) 40.2 months (38–42)a 0/6 patients = 0% pain 3.0b/5.5
mobility 3.1b/6.0
walking 2.7b/5.7
Cemented acetabular (43 Hips) 33.2 months (23–38)a 4/39 patients = 10.3% pain 2.9b/5.4
mobility 3.0b/5.4
walking 3.2b/5.4
Cemented acetabular, Hydroxyapetite coating (116 Hips) 8.3 months (1–19)a 0/109 patients = 0% pain 3.0b/5.7
mobility 3.2b/5.7
walking 3.2b/5.7
Schmalzried (1996) [11] Specialist orthopedic hospital, USA. (Observational) Not reported Cementless Wagner (4 Hips)
Cemented McMinn (17 Hips) 16 months (10–25) 42 years (22–64)a 1/19 patients = 5.3% pain 4c/9
walking 6c/9
function 6c/9
activity 4cd/7
Wagner (1996) [12] Specialist orthopedic hospital, Germany. (Observational) Not reported 2 Ti pins on cupshell (12 Hips)
Press fit version (23 Hips) 20 months (6–54) 36 years (15–64)a 5/35 patients = 14.3% 32 (5–51)e
d/94 (72–100)ed
Corin Group Ltd, 2001 [18] 4 UK Hospitals, 1 UK clinic, 3 surgeons (Industry submission) Corin Group Ltd Cormet 2000 21.36 monthsf 50.8 years (26–69)f Revision Rate CIC
69/97 = 71.1%f
Patients pain free NR
Midland Medical Technologies Ltd, 2001 [19] Hospitals in Birmingham Southampton Liverpool and Belgium (Industry submission) Midland Medical Technologies Ltd Birmingham Hip Resurfacing (1761 Patients) NR 49.2 years (15–86) 8/1382 hips = 0.6% NR
Wright Cremascoli Ortho Ltd, 2001 [21] (Industry submission) Wright Cremascoli Conserve Plus (100 Hips) NR (24–51.6) NR 3/100 hips = 3% NR
Oswestry Outcome Centre Database [20] Oswestry Outcome Centre (Unpublished observational data) Oswestry Outcome Centre McMinn (1378 Hips)
All consultants (4424 Hips) 0–4 years
0–4 years 53.1 (NR)
49.2 (NR) 7/1378 = 0.5%
34/4424 = 0.77% 66.2e/98.1e
61.3e/95.9e
a Median (range)
b Charnley hip score
c UCLA hip score
d Results of groups reported together
e Harris hip score
f Data from 97 patients (110 hips);
CIC – Data marked as "Commercial in confidence" in the industry report
NR – Not Reported
Table 5 Watchful waiting studies
Study (Study Design) Funding source Patient Characteristics Patients pain level at baseline Patients pain level at follow-up Use of walking aids Notes
Dieppe (1997) [16] Single orthopedic unit (UK) Follow up: Mean (range) 37.6 months (31–41) (Observational study Arthritis and Rheumatism Council N = 84 patients
Mean age (SD): 50 (12.1) None = 7%
Mild = 48%
Moderate = 31%
Severe = 10% None = 4%
Mild = 50%
Moderate = 32%
Severe = 14% 9 patients (32 %) at baseline
8 patients (29%) at 3 year follow up All patients had symptomatic limb joint osteoarthritis. Surgery performed in 9 patients (32%)
Dieppe (2000) [17] Single orthopedic unit (UK) Follow up: Mean (range) NR (36–96) (Observational study) Arthritis and Rheumatism Council N = 29 patients
Mean age (SD): 50 (12.1) None = 7%
Mild = 48%
Moderate = 31%
Severe = 10% None = 3%
Mild = 34%
Moderate = 48%
Severe = 14% 12 patients (41%) at 8 year follow up All patients had symptomatic limb joint osteoarthritis. Surgery performed in 14 patients (48%)
Note: The studies above involve the same population, but have different length of follow up.
NR: Not Reported.
Table 6 Total hip replacement RCTs of 5 years or more duration not included in the systematic reviews [2,8,13].
Study (Study Design) Prostheses Patient Characteristics Mean duration of follow up (range) Revision Ratebc Hip score post-opd
Sharp (2000) [14] 2 Hospitals (UK)a (Observational study) No funding received C-Fit uncemented with hydroxyapatite porous coating of components N = 91 in total for both groups
Mean age: <66 years 5.2 years (1 month–8 yrs) 25/91 = 27.5% score/patients
12–20/35
21–30/13
31–40/12
41–50/1
50–60/2
a: 1 center randomised, 1 center not randomised
b: Revision rate at latest follow-up point & crude survival rate based on all patients
c: Results reported by patient number on entry to trial i.e. intention to treat
d: Oxford Hip Score
NR: Not Reported
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McMinn D Treacy R Lin K Pynsent P Metal on metal surface replacement of the hip Clinical Orthopaedics and Related Research 1996 329S 89S 98S
Schmalzried TP Fowble VA Ure KJ Amstutz HC Metal on metal surface replacement of the hip Clinical Orthopaedics and Related Research 1996 329S 106S 114S
Wagner M Wagner H Preliminary results of uncemented metal on metal stemmed and resurfacing hip arthroplasty Clinical Orthopaedics and Related Research 1996 329S 78S 88S
Faulkner A Kennedy LG Baxter K Donovan J Wilkinson M Bevan G Effectiveness of hip prostheses in primary total hip replacement: a critical review of evidence and an economical model Health Technology Assessment 1998 2
Sharp RJ O'Leary ST Falworth M Cole A Jones J Marshall RW Analysis of the results of the C-Fit uncemented total hip arthroplasty in young patients with hydroxyapatite or porous coating of components Journal of Arthroplasty 2000 15 627 634 10960002 10.1054/arth.2000.4350
Cushnaghan J Dieppe P Study of 500 patients with limb joint osteoarthritis. Analysis by age, sex, and distribution of symptomatic joint sites Annals of the Rheumatic Diseases 1991 50 8 1994877
Dieppe P Cushnaghan J Shepstone L The Bristol 'OA500 study': progression of osteoarthritis (OA) over 3 years and the relationship between clinical and radiographic changes at the knee joint Osteoarthritis and Cartilage 1997 5 87 97 9135820
Dieppe P Cushnaghan J Tucker M Browning S Shepstone L The Bristol 'OA500 study': progression and impact of the disease after 8 years Osteoarthritis and Cartilage 2000 8 63 10772234 10.1053/joca.1999.0272
Corin Group Ltd Vale L, Wyness L, McCormack K, McKenzie L, Brazzelli M, Stearns SC Industry Submission 2001 Systematic review of the effectiveness and cost-effectiveness of metal on metal hip resurfacing arthroplasty for treatment of hip disease. Health Technology Assessment 2002 6 15
Midland Medical Technologies Ltd Vale L, Wyness L, McCormack K, McKenzie L, Brazzelli M, Stearns SC Industry Submission 2001 Systematic review of the effectiveness and cost-effectiveness of metal on metal hip resurfacing arthroplasty for treatment of hip disease. Health Technology Assessment 2002 6 15
Oswestry Outcome Centre Vale L, Wyness L, McCormack K, McKenzie L, Brazzelli M, Stearns SC Oswestry Outcome Centre, The Robert Jones and Agnes Hunt Orthopaedic and District Hospital NHS Trust Systematic review of the effectiveness and cost-effectiveness of metal on metal hip resurfacing arthroplasty for treatment of hip disease. Health Technology Assessment 2002 6 15
Wright Cremascoli Ortho Ltd Vale L, Wyness L, McCormack K, McKenzie L, Brazzelli M, Stearns SC Industry Submission 2001 Systematic review of the effectiveness and cost-effectiveness of metal on metal hip resurfacing arthroplasty for treatment of hip disease. Health Technology Assessment 2002 6 15
Swedish Hip Registry Prognosis of Total Hip Replacement 2000
Vale L Wyness L McCormack K McKenzie L Brazzelli M Stearns SC Systematic review of the effectiveness and cost-effectiveness of metal on metal hip resurfacing arthroplasty for treatment of hip disease Health Technology Assessment 2002 6 15
| 15620345 | PMC544574 | CC BY | 2021-01-04 16:03:28 | no | BMC Health Serv Res. 2004 Dec 27; 4:39 | utf-8 | BMC Health Serv Res | 2,004 | 10.1186/1472-6963-4-39 | oa_comm |
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BMC BiolBMC Biology1741-7007BioMed Central London 1741-7007-2-261561324010.1186/1741-7007-2-26Research ArticleBrief inactivation of c-Myc is not sufficient for sustained regression of c-Myc-induced tumours of pancreatic islets and skin epidermis Pelengaris Stella [email protected] Sylvie [email protected] Linda [email protected] Vasiliki [email protected] Sevasti [email protected] Michael [email protected] Biomedical Research Institute, Department of Biological Sciences, University of Warwick, Gibbet Hill Road, Coventry CV4 7AL, UK2004 21 12 2004 2 26 26 10 12 2004 21 12 2004 Copyright © 2004 Pelengaris 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
Tumour regression observed in many conditional mouse models following oncogene inactivation provides the impetus to develop, and a platform to preclinically evaluate, novel therapeutics to inactivate specific oncogenes. Inactivating single oncogenes, such as c-Myc, can reverse even advanced tumours. Intriguingly, transient c-Myc inactivation proved sufficient for sustained osteosarcoma regression; the resulting osteocyte differentiation potentially explaining loss of c-Myc's oncogenic properties. But would this apply to other tumours?
Results
We show that brief inactivation of c-Myc does not sustain tumour regression in two distinct tissue types; tumour cells in pancreatic islets and skin epidermis continue to avoid apoptosis after c-Myc reactivation, by virtue of Bcl-xL over-expression or a favourable microenvironment, respectively. Moreover, tumours progress despite reacquiring a differentiated phenotype and partial loss of vasculature during c-Myc inactivation. Interestingly, reactivating c-Myc in β-cell tumours appears to result not only in further growth of the tumour, but also re-expansion of the accompanying angiogenesis and more pronounced β-cell invasion (adenocarcinoma).
Conclusions
Given that transient c-Myc inactivation could under some circumstances produce sustained tumour regression, the possible application of this potentially less toxic strategy in treating other tumours has been suggested. We show that brief inactivation of c-Myc fails to sustain tumour regression in two distinct models of tumourigenesis: pancreatic islets and skin epidermis. These findings challenge the potential for cancer therapies aimed at transient oncogene inactivation, at least under those circumstances where tumour cell differentiation and alteration of epigenetic context fail to reinstate apoptosis. Together, these results suggest that treatment schedules will need to be informed by knowledge of the molecular basis and environmental context of any given cancer.
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Background
Various mouse models of tumourigenesis have been established using conditional systems to either induce or knockout particular genes (oncogenes and tumour suppressors, respectively) in a tissue-specific and time-dependent manner. The ability to switch expression of a given oncogene 'on' or 'off' in vivo has provided insight into the mechanisms by which certain oncogenes can initiate tumourigenesis either alone or in combination with other genetic lesions, and importantly, whether inactivation of the initiating oncogene is sufficient to cause tumour regression (reviewed in: [1,2]). Given the importance of oncogene activation in human cancers, specific targeting of oncogenic pathways provides a potentially effective therapeutic strategy. For example, targeting of the HER2/Neu receptor tyrosine kinase (which is overexpressed in up to 30% of primary human breast cancers) with the neutralizing antibody Trastuzumab has been used successfully in clinical trials, in combination with other agents, to slow disease progression (refs in [3]). Similarly, patients with chronic myelogenous leukaemia (CML) have been effectively treated with the ABL kinase inhibitor, Imatinib (Gleevec), inducing clinical remission whilst in the CML phase [4,5].
Several studies using conditional mouse models of various cancers have unexpectedly shown that inactivation of the initiating oncogene is sufficient for reversal not only of the primary tumour but also of invasive and metastatic lesions, many of which contain multiple genetic and epigenetic alterations [6-18].
The tumour regression observed in many of these models following sustained oncogene inactivation provides a powerful platform on which to build a deeper understanding of fundamental tumour biology and with which to preclinically evaluate novel therapeutics to target specific genes. A recent study has shown that brief inactivation (10 days) of c-Myc was sufficient for the sustained regression of c-Myc induced invasive osteogenic sarcomas in transgenic mice [19]; subsequent re-activation of c-Myc led to extensive apoptosis rather than restoration of the neoplastic phenotype. Possible explanations for this outcome include changes in epigenetic context that may have occurred within the cell type, that is, presumably between the immature cell in which c-Myc was originally activated and the more differentiated cell resulting from subsequent (brief) inactivation of c-Myc. In this tumour model, although c-Myc expression is initiated in immature osteoblasts during embryogenesis, subsequent inactivation of c-Myc in osteogenic sarcoma cells induces differentiation into mature osteocytes. Therefore, re-activation of c-Myc now takes place in a different cellular context and induces apoptosis rather than neoplastic progression. However, irrespective of the actual underlying mechanisms, these intriguing findings suggest the novel possibility of employing transient inactivation of c-Myc as a therapeutic strategy in certain cancers, thus limiting potential toxic effects resulting from prolonged therapeutic inactivation [1,2]. In fact, there is widespread interest in determining the optimal timing of existing therapies, including trials of pulsatile or 'metronomic' chemotherapy regimens in various cancers. Self-evidently, therefore, it was essential to determine if this phenomenon was unique to this mouse model or if sustained regression of tumours originating in different tissues and under differing circumstances could also be induced by transient c-Myc inactivation.
Previously, we have shown that sustained c-Myc inactivation in locally invasive pancreatic islet tumours (induced by c-Myc activation in β-cells on a background of Bcl-xL overexpression) induced β-cell growth arrest and re-differentiation into mature β-cells, accompanied by the collapse of tumour vasculature and tumour cell mass resulting from apoptosis, despite the constitutive expression of Bcl-xL in the tumour cells [9]. Similarly, in pre-malignant skin epidermal tumours (papillomatosis) induced following activation of c-Myc alone, skin lesions completely regressed within 4 weeks after sustained inactivation of c-Myc [8]. However, given the continual outward migration and shedding of growth-arrested and re-differentiated keratinocytes from the skin surface, it was not established whether this action alone was responsible for the removal of skin tumour cells or if apoptosis also played a part.
These conditional models, in which c-Myc-induced tumours of pancreatic islets and skin epidermis can be initiated at any given time in the adult animal, were chosen for the studies presented here and offer several advantages (reviewed in [20]). First, tumourigenesis after c-Myc activation is initiated and proceeds by different routes, with inherent pro-apoptotic activity avoided by an additional genetic alteration (expression of the anti-apoptotic protein, BclxL) as is the case in the islet tumourigenesis model, or by the presence of survival cues in the microenvironment, as seen in c-Myc-induced skin tumours. Second, we already know that sustained c-Myc inactivation leads to regression in both cases.
Here we show the consequences of inactivating c-Myc transiently, for a period of 4 to 9 days, in these distinct tumour types in vivo. In contrast to the osteogenic sarcoma model, re-activating c-Myc in islet tumours does not lead to accelerated β-cell apoptosis, but rather restores the oncogenic properties of c-Myc, rapidly re-initiating β-cell proliferation, loss of differentiation, loss of E-cadherin, local invasion and angiogenesis. This occurs despite the re-differentiation of previously c-Myc-activated tumour cells to a more mature phenotype and the loss of some of the newly acquired vasculature, occurring during the period of c-Myc inactivation. Moreover, as no new β-cells arise during the period of c-Myc inactivation, replication is probably restored in those same cells that have previously experienced c-Myc activation. Similarly, in epidermis, reactivating c-Myc in suprabasal keratinocytes does not result in apoptosis, which remains confined to the shedding areas of parakeratosis at the skin surface, but restores the papillomatous phenotype, inducing cell proliferation and dysplasia. These results are in line with a very recent study in a different system, published while this manuscript was under consideration. Shachaf and colleagues demonstrated that invasive c-Myc-induced hepatocellular carcinomas regress when c-Myc expression is turned off but, interestingly, some tumour cells remain 'dormant' even for prolonged periods and contribute to cancer progression if c-Myc expression is subsequently reinitiated [21].
Taken together, these findings suggest that a cautious approach is required in considering cancer therapies aimed at transient oncogene inactivation. First, a more comprehensive understanding of the genetic basis and environmental context of any individual tumour would be required in order to predict the likely success of such a treatment schedule. Second, at least under those circumstances where tumour cell differentiation and alteration of epigenetic context would not be predicted to reinstate apoptosis and no alternative mechanism exists for tumour cell removal, sustained inactivation of the offending oncogene would seem the desired therapeutic goal.
Results
Brief inactivation of c-Myc in islet tumours does not sustain tumour regression
Islet tumours were induced in nine mice by activation of c-MycERTAM (c-Myc) in β-cells of adult pIns-c-MycERTAM mice cross-bred with RIP7-Bcl-xL mice [22] by daily intraperitoneal (IP) administration of 4-OHT for 14 days as previously described [9]. Three mice from different litters were sacrificed for histological examination of the pancreas. c-Myc was then inactivated by 4-OHT withdrawal (see Methods) for 9 days in the remaining six littermates (two from each litter), after which time one mouse from each litter was sacrificed for collection of pancreata and analysis. In the remaining three mice, reactivation of c-Myc by daily IP injection of 4-OHT was carried out for 5 days before sacrifice and analysis. For each analysis, sections (5–10μm) were cut throughout the length of the pancreas and every tenth section was selected for histological and immunohistochemical examination.
In the absence of 4-OHT, pIns-c-MycERTAM/RIP-Bcl-xL double transgenic mice exhibited normal islet morphology (Figure 1) with no measurable β-cell proliferation or apoptosis (Figure 2). As expected, activation of c-Myc for 14 days triggered the progression of angiogenic islet tumours (Figure 1) accompanied by β-cell proliferation, loss of differentiation (as demonstrated by down-regulation of insulin) and down-regulation of the intercellular adhesion molecule E-cadherin (Figure 2). Following inactivation of c-Myc for 9 days, histological analyses of pancreata showed signs of vasculature collapse particularly in larger islets (Figure 1-extravasated erythrocytes), accompanied by cessation of β-cell proliferation, re-differentiation (up-regulation of insulin) and reestablishment of cell-cell contacts as E-cadherin expression was restored (Figure 2). Vascular endothelial cell apoptosis was detected in larger islets by co-immunostaining of vascular basal lamina with anti-laminin antibodies together with TUNEL (Figure 2), with only a small number of apoptotic β-cells detected at this time-point (average of 1 cell per islet section) following co-immunostaining of β-cells with anti-insulin antibodies together with TUNEL (Figure 2).
Reactivation of c-Myc for 5 days in partially regressed islet tumours (9 days of inactive c-MycERTAM) led to restoration of the oncogenic properties of c-Myc (Figure 1 and 2): β-cell proliferation, loss of differentiation (down-regulation of insulin), and loss of cell-cell contacts (down-regulation of E-cadherin). In contrast to the mouse model of osteogenic sarcoma [19], where reactivation of c-Myc in growth-arrested, re-differentiated osteocytes induced their rapid demise through apoptosis, reactivation of c-Myc in islet tumours did not lead to an increase in the number of cells undergoing apoptosis compared to the original islet tumours formed after 14 days of c-Myc activation (Figure 2). Importantly, in contrast to partially regressed islet tumours (9 days of c-Myc inactivation), where most apoptotic cells were found in the vasculature (Figure 2), the majority of apoptotic cells in both the original 14-day islet tumours and reactivated tumours were not present within the vasculature but rather found predominantly adjacent to blood vessels (averages of 3–4 cells per islet section c-Myc 'on' versus 3–5 cells per islet c-Myc 'on-off-on', both representing less than 0.1% of islet cells). A small number of these TUNEL-positive cells could be identified as β-cells, as deduced following co-staining with insulin (Figure 2) or Nkx6.1 (data not shown). Of the remaining apoptotic cells, some could be identified as leukocytes by co-staining with CD45 (data not shown). The identity of the remainder is unclear, but could include some β-cells with complete loss of normal differentiation markers. Despite the presence of apoptotic cells during c-Myc activation, levels are clearly insufficient to prevent islet tumour progression as c-Myc-induced β-cell proliferation far exceeds apoptosis and tumours rapidly and inexorably expand over time.
In order to establish whether islet tumour progression was indeed being maintained following reactivation of c-Myc we examined a longer period of c-Myc reactivation. In this case, mice were treated daily with IP 4-OHT for 14 days (following an initial 2 week period of c-Myc activation and 9 day transient period of c-Myc inactivation). Sections were cut throughout the pancreas and selected sections at every 100 μm were stained with H&E for histological analysis as well as with immunohistochemical markers for proliferation (Ki-67), differentiation (insulin), apoptosis (TUNEL) and loss of cell-cell contact (E-cadherin). Importantly, pancreatic islets following 14 days of c-Myc reactivation showed no signs of tumour regression but rather progression of islet tumourigenesis, with the vast majority of islets showing more pronounced invasion (adenocarcinoma) (Figure 3).
Brief inactivation of c-Myc in papillomatous lesions of the skin does not sustain tumour regression
Development of papillomatosis (a pre-malignant lesion of skin epidermis resembling actinic keratosis in humans) was induced in nine mice following activation of c-MycERTAM (c-Myc) in suprabasal keratinocytes of adult Involucrin-c-MycERTAM transgenic skin by daily topical administration of 4-OHT for 14 days as previously described [8]. Three mice from different litters were sacrificed for histological examination of the skin. c-Myc was then inactivated by 4-OHT withdrawal (see Methods) for 5 days in the remaining six littermates (two from each litter), after which time one mouse from each litter was sacrificed for collection of skin and analysis. In the remaining three mice, reactivation of c-Myc by daily topical application of 4-OHT was carried out for 5 days before sacrifice and analysis. We chose to inactivate c-Myc for 5 days in order to minimize the loss of neoplastic keratinocytes through outward migration and shedding from the skin surface, prior to reactivation of c-Myc, in order to maximise potential detection of any keratinocyte apoptosis. For each analysis, sections (5–10μm) were cut throughout two 10 mm pieces of skin and every tenth section was selected for histological and immunohistochemical examination.
In the absence of 4-OHT, Involucrin-c-MycERTAM transgenic mice exhibited normal skin morphology (Figure 4) with no detectable suprabasal keratinocyte proliferation or apoptosis (Figure 5). As expected, activation of c-Myc for 14 days triggered the progression of papillomatosis with marked epidermal hyperplasia, dysplasia, angiogenesis, and formation of nucleated cornified layers (parakeratosis) (Figure 4). These "parakeratotic tiers" gave the appearance of dry, scabby lesions, which were eventually lost through lifting or flaking from the surface. The hyperplastic phenotype resulted from c-Myc induced proliferation of suprabasal keratinocytes, as detected using antibodies specific for the proliferation marker Ki-67 (Figure 5). As demonstrated in our earlier studies [8], c-Myc alone in the absence of any ectopic anti-apoptotic lesion is sufficient to induce pre-malignant neoplastic lesions in the skin. This contrasts with results in the pancreatic islet β-cells, where c-Myc activation alone induces widespread apoptosis [9]. Activation of c-Myc in the epidermis, however, is associated with essentially no detectable apoptosis when examined by co-staining with the specific suprabasal keratinocyte marker keratin 1 together with TUNEL (Figure 5). The only detectable TUNEL positive cells were those present at the surface of the skin about to be shed and nucleated cells within the parakeratotic tiers (Figure 5).
Subsequent inactivation of c-Myc for 5 days led to redifferentiation of suprabasal keratinocytes as evidenced by the appearance of granular cells and loss of dysplasia (Figure 4) concomitant with a marked reduction in the number of proliferating suprabasal keratinocytes (Figure 5). Importantly, there was no increase in the number of cells undergoing apoptosis, with TUNEL positive cells again confined to shedding keratinocytes (Figure 5), indicating that regression of these skin lesions occurs through loss of neoplastic keratinocytes by shedding. Although tumour vasculature was examined for endothelial apoptosis, there was no measurable increase in cell death at this particular time-point. It is likely, however, that a more extensive analysis would determine the particular stages at which vascular collapse occurs following c-Myc inactivation.
Reactivation of c-Myc in skin lesions for 5 days led to restoration of papillomatosis (Figure 4). In contrast to the osteogenic sarcoma model, reactivation of c-Myc in growth arrested, redifferentiated keratinocytes did not result in increased apoptosis, despite the absence of an anti-apoptotic lesion (Figure 5), but again resulted in increased levels of suprabasal proliferation extending beyond the basal compartment (Figure 5); but as in the c-Myc 'on', no replicating cells were present in the granular layer. However, we cannot confirm what proportion of replicating suprabasal keratinocytes, following c-Myc reactivation, had previously experienced c-Myc activation. This question arises as new 'c-Myc naïve' keratinocytes are likely to have entered the suprabasal compartment as a result of on-going basal layer replication during the transient period of c-Myc inactivation.
Discussion
The potential to inactivate or mitigate the action of oncogenes is increasingly being exploited in the design of new therapeutic agents to reverse tumourigenesis in cancer therapy [1,23-25]. Importantly, despite the seeming genetic complexity of many cancers, a compelling body of evidence suggests that inactivation of single oncogenes can be sufficient for tumour regression. In several transgenic mouse models of cancer, the generation of invasive/metastatic tumours in which more than one genetic event is involved can be reversed following inactivation of a single oncogene [7,9,12,16]. In addition, c-Myc-induced lymphomas shown to be genomically complex and unstable regressed following c-Myc inactivation [15]. Strikingly, extensive lung metastases, arising in mice bearing Neu-induced mammary tumours, rapidly and fully regressed following inactivation of Neu, despite tumour cells acquiring additional mutations [14]. These findings suggest that some metastatic lesions may remain responsive to therapeutic intervention originally targeted to the primary lesion, although some tumours have been shown to escape dependence upon the initiating oncogene [6,7,11,14,16,19].
Recent data from a mouse model of osteogenic sarcoma showed that even transient c-Myc inactivation can result in sustained tumour regression [19]. In this model, reactivation of c-Myc after a brief period of tumour regression led to extensive apoptosis of osteoblasts rather than restoration of the tumour phenotype. This increased sensitivity to apoptosis may be due to epigenetic changes that have occurred within the newly differentiated cells (when c-Myc was inactivated). In light of these findings, it was important to establish whether reactivating c-Myc in other tumour models, consisting of other cell types, would also lead to apoptosis. In other words, would such cells behave differently from the original differentiated cell (in which c-Myc was first activated) and become more sensitive to Myc-induced apoptosis as a result of epigenetic changes?
Here we show, in contrast to the osteogenic sarcoma model, that re-activating c-Myc in islet β-cell tumours restores the oncogenic properties of c-Myc, rapidly re-initiating β-cell proliferation, loss of differentiation, loss of E-cadherin, local invasion and angiogenesis. This occurs despite the re-differentiation of previously c-Myc-activated tumour cells to a more mature phenotype and the loss of some of the newly acquired vasculature, occurring during the period of c-Myc inactivation. Similarly, in epidermis, reactivating c-Myc in suprabasal keratinocytes does not result in apoptosis, which remains confined to the shedding areas of parakeratosis at the skin surface, but restores the papillomatous phenotype, inducing cell proliferation and dysplasia.
Self-evidently, to restore vulnerability to the pro-apoptotic activity of c-Myc would necessitate the tumour cells losing their resistance to apoptosis. In this case the results of Jain et al. [19] may be explained by the origin of osteosarcomas in their model in bone progenitor cells, which we assume were able to avoid c-Myc-induced apoptosis. Subsequently, despite the retention of some features of immaturity, these cells progress to a differentiated phenotype upon transient c-Myc inactivation, which renders them susceptible to apoptosis when c-Myc is reactivated. The assumption we must make here is that whatever uncharacterised additional mutations these cells may have acquired along their journey to malignancy, these did not include mutations able to confer resistance to apoptosis in the more mature osteocyte. One may speculate about the underlying mechanisms, one possibility being the lack of a selective evolutionary advantage to acquisition of an anti-apoptotic lesion, given that some of these cells must already have been capable of avoiding apoptosis – perhaps due to their primitive developmental stage.
In our pIns-MycERTAM mice, where mature β-cells are normally highly sensitive to the pro-apoptotic activity of c-Myc [9], invasive β-cell tumours only originate once apoptosis is prevented, in this case by constitutive over-expression of the protein BclxL. Without such apoptosis suppression the majority of β-cells undergo apoptosis upon c-Myc activation, rendering mice diabetic within a few days. This may be one difference between our own results and those of Jain et al. [19], namely that in our system an acquired resistance to apoptosis is required from the outset, but once this is in place and stays in place, tumours develop and progress whatever the differentiation status of these cells. In contrast, in the system studied by Jain et al. [19], cells can lose their resistance to apoptosis whilst c-Myc is inactivated. This notion is supported by the rapid continuation of replication and tumour growth in our mice after a transient period of c-Myc inactivation despite this being sufficient to restore a differentiated phenotype in β-cells. In contrast to the osteogenic sarcoma model [19], it is less likely that epigenetic changes have occurred: c-Myc is originally activated in mature islet β-cells of the adult pancreas, and following inactivation of c-Myc in islet tumours there is a restoration to differentiated adult β-cells. Importantly, despite the beginning of vascular collapse during c-Myc inactivation, reactivating c-Myc appears to result not only in further growth of the tumour, but also re-expansion of the accompanying angiogenesis and more pronounced islet invasion (adenocarcinoma). The persistence of β-cell resistance to the pro-apoptotic activity of c-Myc is probably conferred by the continued presence of the anti-apoptotic protein BclxL.
Although the general consensus view is that human cancers largely arise from stem cells or precursor cells, it is equally plausible that some cancers might also originate from more mature cells. Intriguingly, recent studies in pancreatic islets of the adult mouse suggest that putative stem cells do not proliferate and produce new β-cells in adult animals, but rather β-cell turnover is maintained by proliferation of mature β-cells [26]. It is therefore quite plausible that islet tumours might also originate from these more mature cells, which as the major source of replicating cells in the adult would also be those most likely to acquire cancer-causing mutations. Another illuminating study from Bachoo et al. [27] shows that dysregulation of specific genetic pathways, rather than cell-of-origin, dictates the emergence and phenotype of high-grade gliomas.
In order to investigate whether the absence of an anti-apoptotic mutation would result in a shift from resistance to apoptosis towards vulnerability following transient inactivation of c-Myc, we examined a different mouse tumour model. In suprabasal keratinocytes, c-Myc activation induces relentless replication leading to hyperplasia, angiogenesis and a premalignant phenotype resembling actinic keratosis. In this system, differentiated suprabasal keratinocytes presumably avoid apoptosis due to the permissive environment of the epidermis, as they readily undergo apoptosis when removed from this environment [8]. It was, however, possible that the dramatic increase in cell numbers following c-Myc-induced epidermal hyperplasia might overwhelm the survival signals within this tissue, which although sufficient to prevent any discernible apoptosis during sustained c-Myc activation might no longer be able to prevent it after a transient inactivation (with any accompanying restoration of normal differentiation). In fact, we see no obvious change in the behaviour of keratinocytes from before to after a period of c-Myc inactivation. Apoptosis remains confined to the area of parakeratosis, which accompanies c-Myc-induced hyperplasia and papillomatosis. At these time-points we do not see any prominent endothelial cell apoptosis, so the exact point at which the angiogenesis collapses is not known.
Interestingly, a recent publication suggests that following a transient period of c-Myc inactivation, at least some previously c-Myc activated suprabasal keratinocytes may differentiate to an extent sufficient to render them unable to re-enter the cell cycle after c-Myc reactivation [28]. This is supported by our original work suggesting that more differentiated suprabasal keratinocytes in the granular compartment are generally refractory to c-Myc-induced replication [8]. In this case, restoration of papillomatous lesions in Involucrin-c-MycERTAM mice might instead result largely from the replication of 'c-Myc naïve' keratinocytes newly generated from the basal layer during the period of c-Myc inactivation [28]. Whatever the underlying explanation, it is difficult to be sure that all previously c-Myc activated cells have undergone irreversible growth arrest and therefore make no contribution to restoration of the tumour phenotype. This issue may only be resolved fully by the development of a suitable labelling technique, which could indelibly mark all c-Myc activated keratinocytes, but only up to the point at which c-Myc is inactivated and not when c-Myc is reactivated.
Fortunately, with the islet model, there is no such element of doubt. In this case no new β-cells are formed during the period of c-Myc inactivation, with replication essentially absent within the pancreas during this period. The replication of β-cells after c-Myc reactivation must, therefore, be taking place in those same cells that have re-differentiated whilst c-Myc was deactivated. Therefore, one can confidently state that transient c-Myc inactivation in tumours originating in pIns-c-MycERTAM/RIP7-Bcl-xL double transgenic mice will not lead to either apoptosis or irreversible growth arrest in tumour cells. In a recently published study, Shachaf and colleagues demonstrate in a Tet-regulatable conditional mouse model that invasive c-Myc-induced hepatocellular carcinomas regress when ectopic c-Myc expression is turned off. Importantly, by employing a bioluminescence technique to label hepatocellular cancer cells, it was shown that some erstwhile tumour cells re-differentiate but avoid apoptosis and remain 'dormant' even for prolonged periods after c-Myc transgene expression is turned off. These labelled cells can then once more contribute to cancer progression if c-Myc transgene expression is subsequently restored [21].
Extrapolating from these various results one may assume that where avoidance of c-Myc induced apoptosis is a product of cellular immaturity (as may be the case in some stem cell populations), then as long as c-Myc inactivation induces differentiation, and, presumably, no anti-apoptotic mutation has been acquired, a transient period may suffice for sustained tumour regression. However, in many cases where an anti-apoptotic lesion is also present (loss of p53/p19ARF; upregulation of antiapoptotic Bcl2 family members etc), or potentially the microenvironment continues to prevent apoptosis, sustained inactivation would be essential for tumour regression. Moreover, it can also be stated that partial reversal of angiogenesis, at least in the islet tumours, will not have any lasting impact on tumour progression if angiogenesis automatically continues apace of further growth of the tumour. Finally, it seems likely that reacquisition of a differentiated phenotype does not preclude previously c-Myc activated cancer cells from re-exhibiting cancer behaviour once c-Myc is reactivated – removal of these cells by apoptosis or other means would seem necessary to remove the threat of cancer recrudescence.
Identifying these key differences in behaviour between different cell types/developmental stages is not an academic exercise, but can give vital information about the mechanisms and context whereby oncogene activity may be determined, which in addition to the biological interest might also provide new knowledge of direct relevance to human cancer.
Given the fact that deregulation of c-Myc expression is one of the most frequently described abnormalities in human cancers and has been observed in β-cell derived tumours and in human skin epidermal tumours [29-32], our observations may have important ramifications for human cancers. It seems likely that therapies directed at oncogene targets will need to be individually tailored to fit the individual tumour types. Thus, detailed knowledge of the molecular 'road map' to cancer for any individual tumour would be needed before determining the optimal treatment targets and therapeutic schedule. In some cases, where describing the molecular basis of the tumour suggests no inherent resistance to apoptosis, transient c-Myc inactivation may prove an effective part of the therapeutic strategy, whereas identifying the presence of lesions known to suppress c-Myc apoptosis would direct therapy at maintaining sustained c-Myc inactivation. Moreover, such detailed molecular information on the cancer cells would have to be interpreted in the context of the relevant microenvironments within which these cells exist. However, although we are still some distance from realising these goals of molecular fingerprinting and individualised therapy for cancer, the continually expanding literature surrounding successful tumour regression with various strategies aimed at oncogene inactivation and the knowledge gained strongly suggest that the journey is worth undertaking.
Conclusions
In several transgenic mouse models of cancer, the generation of invasive/metastatic tumours in which more than one genetic event was involved can be reversed following inactivation of a single oncogene. These findings suggest that some metastatic lesions may remain responsive to therapeutic intervention originally targeted to the primary lesion. Recent data from a mouse model of osteogenic sarcoma showed that even transient c-Myc inactivation can result in sustained tumour regression [19].
Here we show the consequences of inactivating c-Myc transiently in two distinct tumour types in vivo. In contrast to the osteogenic sarcoma model, re-activating c-Myc in islet β-cell tumours does not lead to accelerated β-cell apoptosis, but rather restores the oncogenic properties of c-Myc, rapidly re-initiating β-cell proliferation, loss of differentiation, loss of E-cadherin, local invasion and angiogenesis. This occurs despite the re-differentiation of previously c-Myc-activated tumour cells to a more mature phenotype and the loss of some of the newly acquired vasculature, occurring during the period of c-Myc inactivation. Similarly, in epidermis, reactivating c-Myc in suprabasal keratinocytes does not result in apoptosis, which remains confined to the shedding areas of parakeratosis at the skin surface, but restores the papillomatous phenotype, inducing cell proliferation and dysplasia.
The differences between the conditional tumour models used by ourselves and Jain et al. [19], rather than detracting from the conclusions drawn as is frequently the case, serve to highlight the importance of identifying different cellular contexts in which transient inactivation of oncogenes may provide a valid therapeutic approach. These results are significant in that they suggest that epigenetic changes resulting in increased sensitivity to apoptotic stimuli will be determining the effects of altering Myc levels. Although it remains to be seen whether transient inactivation of other oncogenes can result in sustainable tumour regression, these studies begin to define the requirements necessary for transient c-Myc inactivation to be effective as a cancer therapy. Thus, we would challenge the potential for cancer therapies aimed at transient oncogene inactivation, at least under those circumstances where tumour cell differentiation and alteration of epigenetic context fail to reinstate apoptosis and no alternative mechanism exists for tumour cell removal. One would also have to be cautious about therapies that instead of removing cancer cells might rely largely on promoting re-differentiation – such 're-differentiated' cancer cells could probably all too readily reacquire their cancer potential.
Together, these results suggest that treatment schedules will need to be informed by knowledge of the molecular basis and environmental context of any given cancer.
Methods
Transgenic mice
pIns-c-MycERTAM and Involucrin-c-MycERTAM mice were generated by cloning a full-length human c-myc cDNA fused to the hormone-binding domain of a modified estrogen receptor (c-MycERTAM) downstream of the rat insulin promoter and the human involucrin promoter, respectively, as previously described [8,9]. DNA constructs were injected into male pronuclei of day 1-fertilized (CBA × C57BL/6)F1 embryos and injected embryos were transferred into day 1-plugged pseudopregnant foster mice and the litters screened for presence of the transgene by Southern blotting. Heterozygous founder mice were backcrossed appropriately to establish transgenic lines.
Heterozygous RIP7-Bcl-xL mice were obtained from Dr Doug Hanahan [22]. Litters from all transgenic mice and appropriate F1 crosses were routinely genotyped by PCR analysis on genomic DNA (1 to 5 μl) isolated from ear biopsies. DNA was extracted by incubating each ear disc in "Hotshot" reagent (25 mM NaOH, 0.2 mM disodium EDTA; pH12) for 10 minutes at 95°C. Following this, 75 μl of neutralizing agent (40 mM Tris-HCl, pH5) was added and the sample cooled to 4°C overnight. Primers used for the detection of c-MycERTAM cDNA: (forward) 5' CCA AAG GTT GGC AGC CCT CAT GTC 3'; (reverse) 5' AGG GTC AAG TTG GAC AGT GTC AGA GTC 3'. PCR program: 94°C 2 min 1 cycle, [94°C 1 min, 57°C 1 min, 72°C 2 min] 30 cycles, 72°C 10 min 1 cycle. PCR product size: 413 bp. Primers used for the detection of RIP7-Bcl-xL cDNA: (forward) 5' AGC ACT TTC TGC AGA CCT AGC AC 3'; (reverse) 5' CAG CTC CCG GTT GCT CTG AGA C 3'. PCR program: [94°C 1 min, 60°C 30 s, 72°C 2 min] 30 cycles, 72°C 3 min 1 cycle.
Transgenic mice were housed under barrier conditions with a 12 hour light/dark cycle and access to food and water ad libitum.
Activation and inactivation of c-MycERTAM protein
Expression of the chimeric protein, c-MycERTAM, was targeted to pancreatic β-cells using a rat insulin promoter, or to suprabasal keratinocytes using the human involucrin promoter. As shown in our previous publications [8,9], the transgenically expressed c-MycERTAM protein remains inactive due to association of the cells' own hsp90 with the ERTAM. Upon administration of 4-hydroxytamoxifen (4-OHT), hsp90 is displaced allowing association of c-Myc's partner, Max, to form transcriptionally active heterodimers [33].
To activate c-MycERTAM protein in pancreatic β-cells of adult transgenic mice, 1 mg of 4-OHT (Sigma) sonicated in peanut oil (1 mg/0.2 ml) was administered daily by IP injection. To activate c-MycERTAM protein in skin epidermis of adult transgenic mice, 1 mg of 4-OHT (Sigma) dissolved in ethanol (1 mg/0.2 ml) was administered daily by topical application to a shaved area of dorsal skin.
Inactivation of c-MycERTAM protein was achieved following withdrawal of 4OHT. As c-MycERTAM RNA and protein levels remain unchanged in the presence or absence of 4OHT, Northern and Western blot analysis will not confirm whether the protein is inactive. Thus, to confirm inactivity of the c-MycERTAM protein in pancreatic β-cells, we show reversal of several markers of Myc activation by day 4 of 4OHT withdrawal -growth arrest, re-differentiation, re-establishment of cell-cell contact – by immunohistochemistry (see ref [9] and Results section). In addition, we have gene array data confirming the rapid normalisation of Myc-regulated gene expression, of tamoxifen withdrawal (eg. insulin, pdx-1, Isl-1, cyclin D; data not shown).
Similarly, inactivation of c-MycERTAM protein in skin epidermis was confirmed using immunohistochemistry for markers of re-differentiation (K1 and K14) and growth arrest (see Results section). The tight regulation of the c-MycERTAM protein in skin epidermis was also previously shown in [8] using in situ hybridisation for detection of ODC RNA, a known c-Myc target gene; by day 5 following withdrawal of 4OHT, ODC RNA is no longer detected.
Histological and immunohistochemical analysis of pancreatic tissue
Pancreata or skin were excised from mice and 5–10 mm pieces of tissue were fixed overnight in neutral-buffered formalin, embedded in paraffin wax and sectioned (5–10 μm). Frozen sections were prepared from tissue embedded in OCT and frozen in foil on a bath of dry ice and ethanol. Prior to staining, frozen sections were air-dried and fixed in 4% paraformaldehyde for 15 minutes. Alternatively, for frozen sections, tissue was fixed in 4% paraformaldehyde for 2 hours followed by incubation in 30% sucrose overnight at 4°C. For pancreata analysis, sections (5–10μm) were cut throughout the entire pancreas and every tenth section was selected for histological and immunohistochemical examination. For skin, sections (5–10μm) were cut through two 10 mm pieces of tissue and every tenth section was selected for analysis.
Primary antibodies were as follows: rabbit polyclonals Ki-67 (Novacastra) and Nkx6.1 (Ole Madsen, NovoNordisk); rabbit anti-mouse laminin (Sigma); guinea-pig anti-porcine insulin (Dako); rat anti-mouse E-cadherin, (Zymed); rabbit anti-mouse keratin 1 (BabCo); rat anti-mouse CD45 (AbCam). E-cadherin and laminin antibodies were found to label reliably only frozen tissue sections. Other antibodies were effective when used on both paraffin-embedded and frozen sections, although Ki-67 and Nkx6.1 required epitope retrieval by microwaving paraffin-embedded sections at 700 W for 2 × 10 minutes in 0.01 M citrate buffer, pH6.0 (Vector) followed by immersion in cold water.
Antibodies were diluted in incubation buffer: PBS/0.5% Triton X-100 containing 1:25 dilution of serum from the same species as the secondary antibody. Primary antibodies for insulin and Ki-67 were applied together to sections for 1 hour. Sections were then incubated in Texas Red-conjugated goat anti-guinea pig Ig secondary antibody together with FITC-conjugated goat anti-rabbit secondary antibody (Vector). After washing, sections were mounted in Vectashield mounting medium (Vector).
To detect cells undergoing apoptosis, costaining with TUNEL/insulin, TUNEL/laminin and TUNEL/K1, immunofluorescent staining was performed by applying insulin, laminin or K1 antibodies to sections for 1 hour at room temperature followed by Texas Red-conjugated goat anti-guinea pig (for insulin antibodies) and goat anti-rabbit (for laminin and K1 antibodies) Ig secondary antibody (Vector). TUNEL staining was subsequently performed using ApopTag Fluorescein Direct kit (Chemicon) for frozen tissue sections and ApopTag Fluorescein Indirect kit (Chemicon) for paraffin-embedded tissue sections.
Authors' contributions
SP participated in the design of the study, administered 4OHT to relevant mice, collected tissue, carried out immunohistochemical staining, coordinated and analysed data, drafted the manuscript, and provided part of the funds. SA carried out genotyping, assisted with the administering of 4OHT to mice, collected tissues, cut sections and assisted with capturing of images. LC assisted with genotyping, cutting of sections, and immunohistochemical staining. VI assisted with genotyping and immunohistochemical staining. SZ assisted with genotyping and immunohistochemical staining. MK participated in the design of the study, assisted with the coordination and analyses of data, helped draft the manuscript and provided funding.
Acknowledgements
We would like to thank all animal unit staff at the University of Warwick especially Sam Dixon and Ian Bagley. We thank John Gregory for help with paraffin-embedded tissues, and Sam Robson for help with genotyping and immunohistochemistry.
S.P. thanks the Association for International Cancer Research (AICR) for funding.
V.I. and S.A. are supported by grants from the BBSRC.
S.Z. is supported by the Leverhulme Trust.
We would also like to thank Coventry General Charities and the Samuel Scott of Yews Trust for their support.
Figures and Tables
Figure 1 Histological comparison of pancreatic islets from pIns-c-MycERTAM/Bcl-xL transgenic mice following activation, inactivation and reactivation of c-MycERTAM (c-Myc). Paraffin-embedded fixed sections of pancreata from c-MycERTAM/Bcl-xL mice were examined histologically by H&E staining. In the absence of 4-OHT (inactive c-Myc) islets exhibited normal morphology (Normal). Activation of c-Myc for 14 days following daily IP injection with 4-OHT induced the formation of hyperplastic, angiogenic islets (c-Myc on). Islets following subsequent inactivation of c-Myc for 9 days by withdrawing 4-OHT show evidence of vasculature collapse (c-Myc on-off). Reactivation of c-Myc for 5 days in partially regressed islets restored the neoplastic phenotype (c-Myc on-off-on). Scale bar: 100 μm
Figure 2 Immunohistochemical comparison of pancreatic islets from pIns-c-MycERTAM/Bcl-xL transgenic mice following activation, inactivation and reactivation of c-MycERTAM (c-Myc). Frozen or paraffin-embedded fixed sections of pancreata from c-MycERTAM/Bcl-xL mice were examined immunohistochemically for the detection of: β-cell proliferation using anti-Ki-67 antibodies; apoptosis of β-cells or vascular endothelial cells (anti-insulin (red) or anti-laminin (red) antibodies together with TUNEL (green). Apoptotic nuclei are highlighted with arrows; loss of cell-cell contact (anti-E-cadherin antibodies). In the absence of 4-OHT (inactive c-Myc) less than 0.1% of β-cells are proliferating or undergoing apoptosis (Normal). Islets from c-MycERTAM/Bcl-xL mice following activation of c-Myc for 14 days showed a substantial degree of β-cell proliferation concomitant with down-regulation of insulin immuno-staining (loss of differentiation) and loss of cell-cell contact (c-Myc on). Some apoptotic β-cells were detected but no apoptotic cells were present in the vascular endothelium. Inactivation of c-Myc for 9 days led to cessation of β-cell proliferation, up-regulation of insulin and E-cadherin with some vascular endothelial cell apoptosis (c-Myc on-off). Reactivation of c-Myc for 5 days induced β-cell proliferation, down-regulation of insulin and E-cadherin (c-Myc on-off-on). Importantly, the same degree of apoptosis as that seen in "c-Myc on" tumours was evident. General nuclear stain: Dapi. Scale bar: 100 μm
Figure 3 Pancreatic islets from two pIns-c-MycERTAM/Bcl-xL transgenic mice following activation, inactivation, and then reactivation of c-MycERTAM (c-Myc). Paraffin-embedded fixed sections of pancreata from c-MycERTAM/Bcl-xL mice were examined histologically by H&E staining. Activation of c-Myc for 14 days by daily IP injection with 4-OHT was followed by subsequent inactivation of c-Myc for 9 days by withdrawing 4-OHT, and then reactivation of c-Myc for 14 days results in further progression of the neoplastic phenotype -c-Myc on-off-on (2 weeks). Extending the period of c-Myc reactivation results in further expansion of the tumour mass, marked dysplasia and invasion.
Figure 4 Histological comparison of skin epidermis from Involucrin-c-MycERTAM transgenic mice following activation, inactivation, and reactivation of c-MycERTAM (c-Myc). Paraffin-embedded fixed sections of skin from Involucrin-c-MycERTAM mice were examined histologically by H&E staining. In the absence of 4-OHT (inactive c-Myc) the epidermis shows normal histology (Normal). Activation of c-Myc in skin treated topically with 4-OHT daily for 14 days induced hyperplasia, dysplasia and angiogenesis within papillomatous lesions (c-Myc on). Subsequent inactivation of c-Myc for 5 days following 4-OHT withdrawal led to redifferentiation of suprabasal keratinocytes as evidenced by the appearance of the granular cells and loss of dysplasia (c-Myc on-off). Reactivation of c-Myc for 5 days restored the papillomatous phenotype (c-Myc on-off-on). Scale bar: 100 μm
Figure 5 Immunohistochemical comparison of skin epidermis from Involucrin-c-MycERTAM transgenic mice following activation, inactivation, and reactivation of c-MycERTAM (c-Myc). Frozen or paraffin-embedded fixed sections of skin from Involucrin-c-MycERTAM mice were examined immunohistochemically for proliferation using anti-Ki-67 antibodies, and apoptosis of suprabasal keratinocytes using anti-keratin 1 antibodies (red) together with TUNEL (green). In the absence of 4-OHT (inactive c-Myc) there were no proliferating or apoptotic keratinocytes within the suprabasal layer (Normal). Activation of c-Myc in skin treated topically with 4-OHT daily for 14 days induced proliferation of a substantial proportion of suprabasal keratinocytes (c-Myc on). The only detectable apoptotic cells were confined to shedding keratinocytes present at the surface and in nucleated cornified layers (parakeratosis). Inactivation of c-Myc for 5 days led to a reduction in the proportion proliferating suprabasal keratinocytes and no increase in the presence of apoptotic cells (c-Myc on-off). Reactivation of c-Myc for 5 days restored the tumour phenotype by inducing proliferation of suprabasal keratinocytes in the absence of any further apoptosis, TUNEL-positive cells again confined to shedding keratinocytes present at the surface and in nucleated cornified layers (c-Myc on-off-on). General nuclear stain: Dapi. Scale bar: 100 μm
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| 15613240 | PMC544575 | CC BY | 2021-01-04 16:02:56 | no | BMC Biol. 2004 Dec 21; 2:26 | utf-8 | BMC Biol | 2,004 | 10.1186/1741-7007-2-26 | oa_comm |
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BMC Evol BiolBMC Evolutionary Biology1471-2148BioMed Central London 1471-2148-4-511556657710.1186/1471-2148-4-51Research ArticleStructure and evolution of protein interaction networks: a statistical model for link dynamics and gene duplications Berg Johannes [email protected]ässig Michael [email protected] Andreas [email protected] Institut für Theoretische Physik, Universität zu Köln, Zülpicherstr. 77, 50937 Köln, Germany2 University of New Mexico, Department of Biology, 167A Castetter Hall, Albuquerque, NM 817131-1091, USA2004 27 11 2004 4 51 51 17 5 2004 27 11 2004 Copyright © 2004 Berg et al; licensee BioMed Central Ltd.2004Berg 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 structure of molecular networks derives from dynamical processes on evolutionary time scales. For protein interaction networks, global statistical features of their structure can now be inferred consistently from several large-throughput datasets. Understanding the underlying evolutionary dynamics is crucial for discerning random parts of the network from biologically important properties shaped by natural selection.
Results
We present a detailed statistical analysis of the protein interactions in Saccharomyces cerevisiae based on several large-throughput datasets. Protein pairs resulting from gene duplications are used as tracers into the evolutionary past of the network. From this analysis, we infer rate estimates for two key evolutionary processes shaping the network: (i) gene duplications and (ii) gain and loss of interactions through mutations in existing proteins, which are referred to as link dynamics. Importantly, the link dynamics is asymmetric, i.e., the evolutionary steps are mutations in just one of the binding parters. The link turnover is shown to be much faster than gene duplications. Both processes are assembled into an empirically grounded, quantitative model for the evolution of protein interaction networks.
Conclusions
According to this model, the link dynamics is the dominant evolutionary force shaping the statistical structure of the network, while the slower gene duplication dynamics mainly affects its size. Specifically, the model predicts (i) a broad distribution of the connectivities (i.e., the number of binding partners of a protein) and (ii) correlations between the connectivities of interacting proteins, a specific consequence of the asymmetry of the link dynamics. Both features have been observed in the protein interaction network of S. cerevisiae.
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Background
Molecular interaction networks are ubiquitous in biological systems. Examples include transcription control [1], signal transduction, and metabolic pathways [2]. These networks have become a focus of recent research, because of their important roles in metabolism, gene expression, and information processing. Data on such networks are rapidly accumulating, massively aided by high-throughput experiments. Some of these networks are suffciently complex that their characterization requires statistical analysis, an area of considerable recent interest [3-5]. One key issue in this area is the distinction between structures reflecting biological function and those arising by chance. To address this issue requires an understanding of the biological processes that shape the network on evolutionary time scales. More precisely, one has to identify the statistical observables containing specific information about the evolutionary dynamics that shape a network.
In this paper we focus on protein interaction networks, whose nodes correspond to proteins, and whose links correspond to physical interactions between two proteins. Several complementary experimental techniques have been used to analyze pairwise protein and domain interactions, as well as protein complexes, in genome-scale assays [6-13]. Common to these approaches is a high rate of individual false negative and false positive interactions [14,15]. Different protein interaction data sets thus differ in many ways, but they also reveal similar aggregate (or global) network features, such as the fraction of nodes with a given connectivity. This implies that only large-scale statistical features of protein interaction networks can currently be reliably identified by high-throughput approaches. We here present an empirically grounded model that explains empirically observed statistical features of such networks.
The currently best characterized protein interaction network is that of the baker's yeast Saccharomyces cerevisiae. On evolutionary time scales, this network changes through two processes, illustrated by figure 1. These are (i) modifications of interactions between existing proteins and (ii) the introduction of new nodes and links through gene duplications. Duplications of a single gene result in a pair of nodes with initially identical binding partners. Segmental and global duplications of the genome lead to the simultaneous duplication of many genes. On the other hand, processes affecting the interactions between existing proteins are referred to as link dynamics. Link dynamics results primarily from point mutations leading to modifications of the interface between interacting proteins [16]. Both kinds of processes, link dynamics and gene duplications, can be inferred from a statistical analysis of the network data, and their rates can be estimated consistently with independent information.
Figure 1 The elementary processes of protein network evolution. The progression of time is symbolized by arrows. (a) Link attachment and (b) link detachment occur through nucleotide substitutions in the gene encoding an existing protein. These processes affect the connectivities of the protein whose coding sequence undergoes mutation (shown in black) and of one of its binding partners (shown in gray). Empirical data shows that attachment occurs preferentially towards partners of high connectivity, cf. fig. 3. (c) Gene duplication usually produces a pair of nodes (shown in black) with initially identical binding partners (shown in gray). Empirical data suggests duplications occur at a much lower rate than link dynamics and that redundant links are lost subsequently (often in an asymmetric fashion), which affects the connectivities of the duplicate pair and of all its binding partners [22,25,38].
Of course, proteome function in vivo is influenced by further factors, notably gene regulation, which determines the concentrations of the proteins interacting in a living cell. The very definition of a bound state depends on the concentrations of the binding partners: A pair of proteins which binds at high concentrations may no longer form a bound state at lower concentrations. Here we concentrate on protein interactions at constant concentrations as they can be inferred from high-throughput datasets.
Previous work by others [17-19] shows how structural features of the network can in principle be explained through mathematical models of network evolution based on gene duplications alone. (For similar duplication-based models of regulatory and metabolic networks, see [20,21].) However, the overall rate of link dynamics has been estimated from empirical data in [22] and is at least an order of magnitude higher than the growth rate of the network due to gene duplications. It must therefore be included in any consistent evolutionary model.
In this paper, we present a model of network evolution that is based on observed rates of link and duplication dynamics. At these rates, the model predicts that important structural features of the network are shaped solely by the link dynamics. Hence, the evolutionary scenario of our model is quite different from the duplication-based models [17-19]. The statistical network structure predicted by the model is in accordance with empirical observations, see the discussion below.
This paper has two parts. In the first part, we estimate the rates of link attachment and detachment from empirical data. Specifically, we do not just estimate average rates of link dynamics for the whole network, because this has been done previously [22], but we show how the dependence of link attachment and detachment rates depends on the connectivities of both nodes (proteins) involved. (The connectivity of a protein is defined as the number of its interaction partners). We find evidence that the basic rate of link attachment is asymmetric. That is, this rate increases with the connectivity of only one of two the nodes involved. This reflects an asymmetry in the underlying biological process: a new protein-protein interaction is typically formed through a mutation in only one of two proteins.
In the second part of the paper, we assemble the estimated rates of link dynamics into a model of network evolution. Unlike for most other cases studied so far [3,4], the dynamics of these networks cannot be written as a closed equation dependent on the connectivity distribution, i.e. the fraction of nodes with a given number of neighbors. Instead, the analysis of networks under asymmetric link dynamics involves the link connectivity distribution, defined as the fraction of links connecting a pair of nodes with given connectivities.
The model has only one free parameter, the average connectivity of nodes in the network. Its stationary solution correctly predicts statistical properties observed in the data. Central properties of this solution are connectivity correlations between neighboring vertices, in accordance with recent observations in high-throughput protein interaction data [23]. These correlations are a consequence of the asymmetric link attachment process.
Results and discussion
Estimates of evolutionary rates
Two kinds of processes contribute to the evolutionary dynamics of protein interaction networks. The first consists of point mutations in a gene affecting the interactions of the encoded protein. As a result, the corresponding node may gain new links or loses some of the existing links to other nodes, as illustrated in fig. 1(a) and 1(b), respectively. We refer to these attachment and detachment processes, which leave the number of nodes fixed, as link dynamics. The second kind of process consists of gene duplications followed by either silencing of one of the duplicated genes or by functional divergence of the duplicates [24-26]. In terms of the protein interaction network, a gene duplication corresponds to the addition of a node with links identical to the original node, followed by the divergence of some of the now redundant links between the two duplicate nodes; see fig. 1(c).
Individual yeast genes have been estimated to undergo duplication at a rate of the order of 10-2 per gene and per million years [27]. Some 90% of single gene duplicates become silenced shortly after the duplication, leading to an effective rate g of duplications one order of magnitude lower, i.e., ~ 10-3 per million years [22,25,27,28]. Only a fraction of the yeast proteome is part of the protein interaction network, and gene duplicates involving proteins that are not part of the network do not contribute to its growth. Hence, g ~ 10-3 per million years should be considered an upper bound for the growth rate of the protein interaction network by gene duplications. A crude lower bound for the link attachment rate is a ~ 10-1 new interaction partners per node and million years. For instance, [22] estimated the rate at which new interactions were formed as no less than 294.5 new interactions per million years and approximately 1000 proteins. (These estimates are based on the formation of physical interactions between products of duplicate genes, and the approximately known age of the duplicates [22]. Importantly, most of these new interactions form between old duplicates, duplicates that are no longer under the relaxed selection pressure that is characteristic of young duplicates.) The above estimate gives a number of new interaction partners per protein per million years of a = 2 × 294.5/1000 = 0.589, five times greater than the lower bound of 0.1. To maintain an average network connectivity at the empirically observed value κ ≈ 2.5 interaction partners per protein [25,29], the link detachment rate d has to be close to a, thus d ~ a ~ 10-1 per million years. This rate of link attachment and detachment is much larger than the duplication rate of g ~ 10-3 per protein and million years. Hence, the link dynamics is decoupled from the much slower duplication dynamics. On intermediate evolutionary time-scales, the network reaches a stationary state of the link dynamics, while its number of nodes does not change significantly. This stationary state determines the structural statistics of the network, in particular the distribution of connectivities. On long time-scales, however, the network may grow through duplications. We emphasize that all these evolutionary rates are order-of-magnitude estimates, and that such estimates are suffcient for our model and the conclusions we derive from it.
One basic but important empirical observation about link dynamics is the fast loss of connectivity correlations of proteins encoded by duplicate genes. Fig. 2(a) shows this loss, as estimated from empirical data. Specifically, the figure shows the average relative connectivity difference |k - k'|/(k + k') of duplicate protein pairs as a function of the time since duplication, parameterized by the fraction Ks of synonymous (silent) nucleotide substitutions per silent site. (As an order of magnitude estimate, a value of Ks = 0.1 corresponds to a duplication age of 10 million years [25,27].) In the shortest time interval after duplication, the connectivities are still measurably similar. Soon thereafter, however, the relative connectivity difference becomes statistically indistinguishable from that of a randomly chosen pair of nodes, indicated by the horizontal line in fig. 2(a). Hence, diversification after duplication is a rapid process, with a time constant of the order of several 10 million years, consistent with the fast rate of link dynamics discussed above. An additional empirical observation underscores the minor importance of gene duplication in shaping the observed network structure. In models of network evolution based on gene duplication [17-19], a protein acquires new links through duplications of its neighbors (see, for example, the grey nodes in fig. 1(c)), at a rate proportional to its connectivity. This mechanism would generate an abundance of high-connectivity nodes. In addition, it would also generate a high fraction of pairs of neighbors that are products of a gene duplication. This is also true for intermediate models, incorporating both gene duplications and link dynamics, provided the duplication rate is comparable to the rate of link dynamics, or exceeds it. However this prediction of models based on gene duplications is not supported by the data. Fig. 2(b) shows the fraction of duplicate protein pairs among the k(k - 1)/2 neighbor pairs of a node of connectivity k. This fraction is small and it does not increase significantly with k. The data in this figure are also consistent with the earlier observation that the majority of duplicate pairs have few or no interaction partners in common [25].
Figure 2 (a) Duplicate protein pairs lose their connectivity correlations over time. The average relative connectivity difference |k - k'|/(k + k') of duplicate pairs with connectivities k, k' > 0 is plotted against the time since duplication, parameterized by the synonymous (silent) nucleotide divergence Ks. The horizontal line indicates the value expected for two randomly chosen nodes. The average number of duplicate pairs per bin was 16 (from low values of Ks to high ones the number of duplicate pairs per bin were 12, 5, 3, 6, 6, 8, 13, 27, 44 respectively). (b) Duplications do not strongly influence network structure. The histogram shows the fraction of duplicate pairs among the k(k - 1)/2 neighbor pairs of a node of connectivity k plotted versus k. A high number of duplicate pairs would be expected if duplications were a significant mechanism of link gain, see text. The mean and the standard error of this fraction were determined using proteins which are products of duplicate genes with sequence similarity Ka < 1. The number of vertices used per column ranges from 374 for k = 2 to 8 for k = 12.
We note that in our discussion of node dynamics we have not separately considered the effects of ancient genome duplications [39,40]. The conclusion that gene duplications do not shape the statistical features of the protein interaction network applies both to single gene duplications and to genome duplications. Indeed, the analysis of duplicates presented in figure 2 includes both pairs of genes resulting from single duplications and those stemming from genome duplications. Furthermore, the evolutionary dynamics of individual duplicated genes is similar for the products of single genome and whole genome duplications. For example, individual gene duplicates are lost with approximately the same probability in single duplications and in whole genome duplications. For this reason we do not, at this stage, include genome duplications separately in our model.
Dependency of attachment rates on connectivities
The total rates a and d at which links are attached and detached in a protein interaction network allow no inference of how these processes shape the statistical properties of the network. To make such an inference, one must also know how the link dynamics depends on the connectivities of the nodes involved. The simplest possibility is that link attachment rates a and detachment rates d are functions of a node's connectivity k. The rates ak and dk at which links are attached or detached from a node of connectivity k have been estimated previously using interactions between products of duplicate genes [22]. They increase approximately linearly with k.
In representing attachment and detachment rates (a, d) as functions of connectivity k (ak, dk), one assumes implicitly that that the mechanism of link attachment and detachment is identical (symmetric) for the two nodes involved in a changed link. Previous analyses of protein network evolution [22] as well as models of network evolution [30] were based on such a symmetric process. However, the biological mechanism underlying link dynamics is intrinsically asymmetric. When a new link is formed, typically only one node undergoes a mutation, whereas the other node remains unchanged. This asymmetry means that the rate of link dynamics will generically depend in one way on the connectivity of the node undergoing mutation, and in another way on that of the unchanged node. As a result the rates ak and dk of link attachment and detachment are insuffcient to describe the dynamics of the network, since these rates will be different depending on whether the node is undergoing a mutation or not. This observation motivates the following estimate of the dependency of the link dynamics rate on node connectivities.
We define ak,k' as the probability per unit time that a given non-interacting pair of proteins with respective connectivities k and k' will acquire a link, multiplied by the number of proteins N. Analogously, we define the detachment rate dk,k' as the probability per unit time that a given interacting pair of proteins with respective connectivities k and k' will lose their link. The scaling convention of both rates is chosen such that the average connectivity of the network remains constant as the number of nodes N increases: the number of nodes pairs (where a link may be added) is proportional to N2, whereas the total number of links (which may be deleted) is proportional to N. We refer to the special case where the rates factorize, i.e. ak,k' ~ akak', as symmetric attachment (and analogously for the detachment rates dk,k'). The specific form of these rates assumes that link dynamics is a local process, so the probability for the formation or destruction of a link depends on the connectivities of only the two proteins involved in this process.
We now explain how one can estimate the dependency of ak,k' on its arguments, k and k'. As described earlier [22], one can use the observed number of physical interactions among duplicate gene products (cross-interactions) to estimate attachment rates. Briefly, such cross-interactions may arise in two ways. First, a protein that forms homodimers (a self-interacting protein) may undergo duplication, leading to two identical self-interacting proteins which also interact with each other. If both self-interactions are subsequently lost independently, yet the interaction between the nodes is retained, a cross-interaction is formed. This scenario does probably not account for the majority of cross-interactions, because it is inconsistent with data suggesting that self-interactions do not get lost overly frequently after duplication [22]. The second avenue of forming interactions between duplicate gene products involves a non-homodimerizing protein that undergoes duplication. Subsequently, an interaction between the duplicate proteins may form. If this mechanism is dominant, as we argue, one may use the number of cross-interactions to obtain order-of-magnitude estimates of the attachment rate [22]. From the number of interactions that each of the two involved proteins has with other proteins, one can estimate how the attachment rate depends on k and k'. The main caveat of this approach is that the connectivity of the duplicates may have changed since the time the link between them was formed.
The result of this procedure is shown in fig. 3. The sample size of 38 cross-interactions is extremely limited, but suffcient to demonstrate an increase of the attachment rate along the diagonal k = k', and no systematic change along other directions. A different representation of the same data in fig. 3b) also shows an increase of the attachment rate consistent with k + k'.
Figure 3 Link attachment occurs preferentially towards proteins of high connectivity. (a) The color-coded plot shows the fraction of duplicate pairs with connectivities (k, k') that have gained a mutual interaction (cross-interaction) since duplication, as a function of k and k'. Points where all duplicate pairs have cross-interactions are shown in white, points where none carry a cross-interactions are shown black. Points (particularly at high connectivities) where no data is available are also shown in black. The number of duplicate pairs with given connectivities ranges from 2 to 39. Points in the k, k'-plane where only a single pair of duplicates exists are excluded. (b) For this histogram the data from a) are binned for low, medium, and high k + k' and the average for each bin is shown against k + k'. The number of k, k' values contributing to each bin are 10, 14, and 11, from left to right. Error bars give the standard error. (c) Assuming the functional form fk + fk' for the probability of a cross-interaction between nodes with connectivities k and k' (asymmetric attachment), the most likely values of fk may be deduced from the data (see text). The maximum-likelihood result shows an approximately linear increase of fk with k. The alternative scenario, symmetric attachment, yields a smaller maximum likelihood. Only duplicate pairs with Ka ≤ 0.4 were used in this analysis in order to avoid overcounting of cross-interactions of duplicates of even older duplicates.
An attachment process where one node with connectivity k is chosen with a probability , and a second one is chosen with probability , gives an attachment rate . The attachment rate akk' ~ k + k' which we observe empirically is thus explained by an asymmetric attachment process where one node is chosen uniformly at random ( = constant), and the other node is chosen with a probability proportional to its connectivity ( ~ k). Note that the rate ak,k' itself is symmetric under interchange of the labels k and k', since either of the two nodes may take on the role of being preferentially chosen. However, the rate ak,k' does not factorize, exactly as required for an asymmetric attachment process.
We now present an additional, complementary approach, based on maximum likelihood analysis, which validates the functional form of ak,k'. The probability that out of nkk' pairs of duplicates with given connectivities k and k', mkk' pairs interact is , where gkk' gives the probability for a cross-interaction. are the binomial coefficients. The probability p for observing for each pair k ≤ k' mkk' interactions in nkk' pairs of duplicates is then given by . Symmetric and asymmetric attachment differ in how the probability of a cross-interaction gkk' depends on k and k'. In the symmetric case, gkk' = gkgk'. In the asymmetric case where one node is chosen uniformly, the other with a probability fk, we have gkk' = fk + fk'. Using simulated annealing [31] we have calculated the (maximal) likelihoods p that the connectivity correlation pattern shown in fig. 3a resulted from either an asymmetric process, or a symmetric process, respectively, by maximizing p with respect to fk and gk. We find that the maximal likelihood for asymmetric attachment exceeds that for symmetric attachment by a factor pasym/psym ~ 4. The data thus favor an asymmetric attachment process, consistently with the biological interpretation given above. In addition, in the maximum likelihood analysis of the asymmetric model, fk shows an approximately linear increase with k (see figure 3c). Although this result is by no means conclusive, the data shows there is no reason to a priori consider only symmetric processes.
Thus far, we have only discussed the link attachment rate. For the detachment of links, we analogously assume that links are lost due to mutations at one of two linked nodes, and that the rate of this process does not depend on the properties of the other node that is unaffected by a mutation. The simplest mechanism reflecting these assumptions is one where a protein loses on average d links per unit time. A protein is chosen in an equiprobable manner from all nodes for removal of one of its links. The link to be removed is chosen at random from all its links. (An alternative detachment process consists in the loss of a certain fraction of links and leads to very similar results.) The resulting detachment rate is dk,k' ~ (1/k) + (1/k'), where the inverse terms stem from nodes (rather than links) being chosen uniformly.
Dynamical model of network evolution
The rates of the link dynamics discussed above, together with a slow growth of the network due to duplications, define a simple model for the evolution of protein interaction networks. Unlike previous models of the evolution of protein interaction networks [17-19] which emphasize the role of gene duplications, our model is based on the asymmetric link dynamics deduced from empirical data in the preceding section. By analytical solution or by numerical simulation one may investigate the networks generated by our model and compare their statistical properties to those of the empirical data on protein-interaction networks. This will be done in the present section. Before analyzing this model in the limit of large networks, we discuss the specific values of model parameters we used, and present the results of numerical simulations of a finite network.
We chose the initial network size such that after a suffcient waiting time, when a stationary state has been reached, the size of the simulated network matches that of the protein interaction data set we used (see methods). Duplication of nodes is modeled simply by adding new nodes with connectivity zero to the network at a rate of g = 10-3 per node per million years, as motivated above. Using this simplistic growth mechanism is appropriate since, as shown above, the link dynamics will quickly alter the initial connectivity of a new node, as well as connectivity correlations with its neighbors. We begin with a total number of 4600 nodes, uniformly linked at random (giving a Poissonian connectivity distribution) such that the average connectivity of nodes with non-zero connectivity is κ = 2.5, the average connectivity found in the data set we used. After a waiting time of 25 million years there are 4696 nodes in total, of which 1872 nodes have non-zero connectivity. This is the size of the pooled protein interaction data set we used. The waiting time of 25 million years is of the same order of magnitude as the time scale on which connectivity correlations of duplicate nodes decay in figure 2a) of a few 10 million years.
New links are added at a rate of a = 0.59 new interactions per node per million years, using the asymmetric preferential linking rule we motivated above. Specifically, to form a new link we chose one node uniformly and a second node preferentially (i.e., with a probability proportional to its connectivity k) and link the two nodes. We removed links at a rate that keeps the average connectivity constant.
Specifically, at each time-step a link is deleted by choosing a node uniformly for link deletion if the average network connectivity exceeds κ = 2.5. The link to be deleted is chosen equiprobably from the links of this node. The connectivity distribution of a network whose evolution was simulated in this manner is shown in figure 4a) (open circles, °). This distribution is robust with respect to changes in the ratio of duplication to link dynamics g/a over at least an order of magnitude (results not shown).
Figure 4 (a) The asymmetric link dynamics produces a broad connectivity distribution. The model prediction of the connectivity distribution of nodes with non-zero connectivity agrees well with yeast protein interaction data (filled diamonds). The solution of the rate equation (4) is shown as a solid line, the result of a computer simulation emulating the link dynamics encapsulated in (4) for a network of finite size is shown as circles (°). Nodes with the highest k (lower right) occur only once in the network. (b) High-connectivity vertices are preferentially connected to low-connectivity vertices, as also observed empirically. The figure shows the relative likelihood of the link distribution and the 'null distribution' of an uncorrelated random network, see text.
We now turn to the consequences of this evolutionary dynamics for the statistical properties of the network. Since the link dynamics places and removes a link with a rate depending only on the connectivities of the nodes at either end, the evolutionary dynamics of the network can be represented in terms of the link connectivity distribution qk,k'. This distribution is defined as the fraction of network links that connect vertices of connectivities k and k',
where cij = 1 if node i is linked to j and 0 otherwise. For convenience, a factor κ has been included in the normalization, i.e., ∑k,k' qk,k' = κ. The link connectivity distribution qk,k' captures correlations between the connectivities of neighboring vertices [23,32-34]. It is related to the single-vertex connectivity distribution by
for k > 0 and p0 = 1 - ∑k > 0 pk. The rates ak,k' and dk,k' are related to the total rates a and d of link detachment per unit time by the normalization
For a network of infinite size, link and growth dynamics result in a deterministic differential equation for the evolution of the link connectivity distribution qk,k'
The terms Jk,k' arise from links that are not added or removed but that change their values (k,k'),
These are the links joining a mutated protein or its binding partner with third vertices, shown as open circles in fig. 1. The parameter g accounts for a uniform increase of the number of nodes caused by gene duplications.
In writing eq. (4), we have assumed that next-nearest neighbor connectivity correlations vanish. This assumption is self-consistent since the stationary solution has indeed only nearest-neighbor correlations. Truncating all correlations and writing down an evolution equation for the connectivity distribution pk turns out to be inconsistent since asymmetric link dynamics generates non-trivial connectivity correlations. This distinguishes the present model from simpler models of network growth, which can be self-consistently formulated at the level of the distribution pk.
We solved equation eq. (4), which describes the evolution of the connectivity correlations numerically for its steady state. For initial conditions we use a Poissonian connectivity distribution where the average connectivity of connected nodes is 2.5, and connectivity correlations which factorize qk,k' ~ kk'pkpk'. We followed the time evolution of qk,k' defined by eq. (4) until a steady state was reached using the parameters a and g given above and choosing d such that the average connectivity of connected nodes remains at a constant κ = 2.5. This procedure leads to a stationary link connectivity distribution and a resulting connectivity distribution independent of initial conditions. Because the evolution equation is a rate-equation that applies to a network of infinite size, the parameters determining the stationary state are the ratio between growth and attachment rate, the functional form of the attachment and detachment rates, and the average connectivity. The stationary state turns out to be asymptotically independent of the duplication rate for small duplication rates. In fact, if we solve eq. (4) numerically for any ratio g/a < 10-1, the results are statistically indistinguishable from that for g = 0, implying great robustness against errors in the rate estimates discussed above.
The statistical properties of our model in its stationary state may now be compared with the corresponding quantities in the protein-interaction network. The connectivity distribution agrees well with the empirical data as shown in fig. 4(a) along with the results of numerical simulations. The distribution is broad but not scale free. (From the empirical data with connectivities distributed over little more than a single decade the scale-free property of protein networks – meaning that connectivities are distributed according to a power law – can not be confidently ascertained. Furthermore the empirical data shown in fig. 4 distinctly deviates from a power-law.) This also holds for uniform detachment, where dkk' = constant, and it is a crucial difference to models with symmetric attachment, where preferential attachment leads to scale-free networks, both at constant network size [30], and in growing networks [3,35].
For the connectivity correlations, we find that vertices of high k are more frequently linked to vertices of low k' than in an uncorrelated random network with the same connectivity distribution . Fig. 4(b) shows the relative likelihood is the link connectivity distribution of the network with no connectivity correlations. Correlations with this property have recently been reported for the protein interaction network in yeast [23], but a quantitative comparison with the prediction of our model will have to await a greater amount of reliable protein interaction data. We note that connectivity correlations are a specific property of networks shaped by asymmetric dynamics, and are absent in the case of symmetric dynamics, as discussed in the appendix. In other words, the empirically observed non-trivial connectivity correlations require asymmetric link dynamics. This is an a posteriori reason for considering asymmetric link dynamics.
A further consequence of asymmetric attachment is that our model does not obey detailed balance (as is the case of symmetric link dynamics, where attachment and detachment rates do factorize, see [30]). Asymmetric attachment or detachment rules violate the condition, necessary for detailed balance, that the product of transition probabilities along a circular trajectory in the space of networks is independent of the direction of this tour. This may be demonstrated easily by considering, e.g. four nodes labeled 1 – 4 to be connected linearly and disconnected again. Starting and ending with a single link between nodes (1, 2), say, the product of the rates of adding a link between (2, 3), then (3, 4) before removing the links between (2, 3) and then (3, 4) is , that for the same tour in reverse is , which are generally equal only if the rates facorize in their arguments.
Conclusions
We have presented a stochastic evolution model for protein networks, which is based on fast link dynamics due to mutations of the coding sequence of existing proteins and a slow growth dynamics through gene duplications. The crucial ingredient of the link dynamics is an asymmetric preferential attachment rule, which is supported by empirical data. The asymmetry has a simple biological interpretation, namely that mutations in one gene may lead to a new interaction of its product with that of another, unchanged, gene. Such a mechanism, where the two nodes involved in the generation of a new link play different roles, is probably the norm, rather than the exception, in biological networks. This holds particularly for regulatory networks, where a new interaction between two genes is formed by changes in the regulatory region of only one of them.
Asymmetric link dynamics leads to a network model, where the aggregate variables necessary to describe network structure are the connectivity correlations qk,k', which give the fraction of links with connectivities k and k'. In our case, the model successfully reproduces the connectivity distribution found in empirically available protein interaction data. The asymmetry of the link dynamics also leads to connectivity correlations between interacting proteins, which have been observed empirically [23]. A model with symmetric link dynamics, on the other hand, produces no such correlations. Higher order correlations of this kind [33] are of particular interest for future work as they may be a quantitative signature of natural selection on the level of the network as a whole.
Methods
Data processing
The protein interaction data in this paper was pooled from three sources. The first of these sources is a large-scale high-throughput experiment using the yeast two-hybrid assay [13] (data available from [41]). It comprises 899 pairwise interactions among 985 proteins. The second source is also a high-throughput two-hybrid experiment, from which we used a "core" set of 747 interactions between 780 proteins, interactions that had been confirmed through replicated experiments [9,42]. The third source is the public MIPS database [36,43] of May 2001. From this database, we included only pairwise interactions that were not produced by the two-hybrid assay, but instead by other techniques such as cross-linking or co-purification of two proteins. This resulted in 899 interactions between 680 proteins After pooling the three data-sets and eliminating redundant interactions, we were left with a network of 2463 interactions and 1893 proteins.
While enormously valuable in their own right, analyses of protein complexes do not identify pairwise protein interactions, and were thus not suitable for our analysis [7,8]. We also excluded interaction data derived from experiments identifying domain-specific rather than whole-protein interactions [10-12]. For all three data sets taken separately, the connectivity distributions are statistically indistinguishable [22]. Moreover, the observations on link addition we use here [22], as well as the patterns in Fig. 2 hold qualitatively for each data set individually.
Data on yeast gene duplicates, generated as described in [27], was kindly provided by John Conery (University of Oregon, Department of Computer Science). Briefly, gapped BLAST [37] was used for pairwise amino acid sequence comparisons of all yeast open reading frames as obtained from GenBank. All protein pairs with a BLAST alignment score greater than 10-2were retained for further analysis. Then, the following conservative approach was taken to retain only unambiguously aligned sequences: Using the protein alignment generated by BLAST as a guide, a sequence pair was scanned to the right of each alignment gap. The part of the sequence from the end of the gap to the first "anchor" pair of matched amino acids was discarded. The remaining sequence (apart from the anchor pair of amino acids) was retained if a second pair of matching amino acids was found within less than six amino acids from the first. This procedure was then repeated to the left of each alignment gap (see [27] for more detailed description and justification). The retained portion of each amino acid sequence alignment was then used jointly with DNA sequence information to generate nucleotide sequence alignments of genes. For each gene pair in this data set, the fraction Ks of synonymous (silent) substitutions per silent site, as well as the fraction Ka of replacement substitutions per replacement site were estimated using the method of Li [28].
Asymmetric link dynamics and connectivity correlations
The existence of non-trivial correlations may be attributed directly to the asymmetry of the link dynamics. Symmetric link dynamics, which is a standard mechanism in models of networks at constant size [30], leads to networks with uncorrelated connectivities: Generalizing the approach of [30] to include connectivity-dependent detachment, one obtains for symmetric link dynamics with rates ak and dk an equilibrium distribution giving the probability of finding the network in the state given by adjacency matrix cij of . This results in a connectivity distribution and trivial connectivity correlations , which factorize in the connectivities. This results ina constant . A model with symmetric link dynamics can thus produce any empirically observed connectivity distribution, but no networks with statistically significant connectivity correlations.
Authors' contributions
ML and AW contributed equally to this work. All authors read and approved the final manuscript.
Acknowledgements
Many thanks to S. Maslov for discussions. JB acknowledges financial support through DFG grant LA 1337/1-1, AW through NIH grant GM063882-01 and the Santa Fe Institute.
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| 15566577 | PMC544576 | CC BY | 2021-01-04 16:29:01 | no | BMC Evol Biol. 2004 Nov 27; 4:51 | utf-8 | BMC Evol Biol | 2,004 | 10.1186/1471-2148-4-51 | oa_comm |
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BMC MicrobiolBMC Microbiology1471-2180BioMed Central London 1471-2180-4-491561932910.1186/1471-2180-4-49Methodology ArticleExtended-Spectrum β-lactamase (ESBL) producing Enterobacter aerogenes phenotypically misidentified as Klebsiella pneumoniae or K. terrigena Claeys Geert [email protected] Baere Thierry [email protected] Georges [email protected] Patricia [email protected] Gerda [email protected] An [email protected] Mario [email protected] Department of Microbiology, Ghent University Hospital, Ghent, Belgium2 Unit of Medical Microbiology, Université Catholique Louvain, Brussels, Belgium3 Department of Microbiology, Jan Yperman Hospital, Ieper, Belgium4 Department of Microbiology, Streeklaboratorium Zeeuws-Vlaanderen, Terneuzen, The Netherlands2004 24 12 2004 4 49 49 3 9 2004 24 12 2004 Copyright © 2004 Claeys 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
Enterobacter aerogenes and Klebsiella pneumoniae are common isolates in clinical microbiology and important as producers of extended spectrum β-lactamases (ESBL). The discrimination between both species, which is routinely based on biochemical characteristics, is generally accepted to be straightforward. Here we report that genotypically unrelated strains of E. aerogenes can be misidentified as K. pneumoniae by routine laboratories using standard biochemical identification and using identification automates.
Results
Ten clinical isolates, identified as K. pneumoniae or K. terrigena with the routinely used biochemical tests and with API-20E, were identified as E. aerogenes by tDNA-PCR – an identification that was confirmed by 16S rRNA gene sequencing for five of these isolates. Misidentification also occurred when using the automated identification systems Vitek 2 and Phoenix, and was due to delayed positivity for ornithine decarboxylase and motility. Subculture and prolonged incubation resulted in positive results for ornithine decarboxylase and for motility. It could be shown by RAPD-analysis that the E. aerogenes strains belonged to different genotypes.
Conclusions
Clinical E. aerogenes isolates can be easily misidentified as Klebsiella due to delayed positivity for ornithine decarboxylase and motility. The phenomenon may be widespread, since it was shown to occur among genotypically unrelated strains from different hospitals and different isolation dates. A useful clue for correct identification is the presence of an inducible β-lactamase, which is highly unusual for K. pneumoniae. In several instances, the use of genotypic techniques like tDNA-PCR may circumvent problems of phenotypic identification.
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Background
Enterobacteriaceae with β-lactam resistance due to the production of Extended-Spectrum β-Lactamases (ESBL) were discovered in the eighties and since that time became epidemic and endemic in hospitals worldwide [1]. Since two decades, 20 to 40 ESBL-producing strains are isolated monthly in our hospital. Amongst the clinical isolates from our hospital, two new TEM-β-lactamase genes were described [2]. In Belgium, as well as in other countries, a shift occurred from Klebsiella pneumoniae isolates as the predominant ESBL-producers [3] to predominance of Enterobacter aerogenes. It is also known that most of the E. aerogenes isolates in the Belgian hospitals belong to one of two predominant Belgian clones (BEI and BEII) [4], a situation which is comparable to that in other countries [5]. During the last years however, we found that several isolates that were identified as K. pneumoniae or K. terrigena by conventional biochemical testing were in fact E. aerogenes as could be shown by the use of genotypic methods, i.e. tDNA-PCR, validated by 16S rRNA gene sequencing, and by extensive phenotypic testing (including subculture and prolonged incubation).
Results
For ten clinical isolates, i.e. seven ESBL-producing clinical isolates collected during 2001 and three more recently collected isolates (Table 1), the API20E codes 5205773 (isolates GA1, GA2, GA3, MN2, MN3 and VGM), 5205753 (isolates DHJ1 and DHJ3) or 5204673 (isolates RA and DBH) were obtained. The first code resulted in a weak identification as either E. aerogenes, K. pneumoniae or Raoultella (Klebsiella) planticola, the second code did not yield any identification, and the third code resulted in a weak but acceptable identification as K. terrigena. The isolates with the codes 5205773 and 5205753 were identified as K. pneumoniae by additional biochemical testing due to negative reactions for motility (tested in semi-solid agar) and ornithine decarboxylase. However, these isolates all possessed an inducible cefalosporinase, as detected on the antibiogram using a disk approximation test, a finding which strongly contradicts an identification as K. pneumoniae or Klebsiella sp.
In fact, the first hint that these strains, phenotypically identified as K. pneumoniae, were actually E. aerogenes, came from tDNA-PCR based identification. Using this method, all isolates were identified as E. aerogenes, and this by comparison of the obtained fingerprint – composed of amplified intergenic tRNA spacers of 101, 106, 111, 115, 121, 189, 190, 198 or 289, and 391 bp in length – with a library containing fingerprints of more than 3000 strains belonging to hundreds of species, available at .
Confirmation of this genotypic identification was obtained by 16S rRNA gene sequencing for five isolates (Table 1). Analysis yielded a similarity of between 99.8% and 100% to E. aerogenes Genbank entries.
The presence of a genuine K. pneumoniae isolate in patient DHJ (Table 1) further complicated the identification.
This observation lead us to carry out additional phenotypic testing. Using the hanging drop method for testing motility, a few motile cells were observed, and upon retesting in semi-solid medium, weak migration could be observed. Like most biochemical tests in the routine laboratory, ornithine decarboxylase is read after overnight or 24 hours of incubation, but when the incubation period was prolonged to up to 2–5 days, all isolates tested positive.
Because at present automated systems are frequently used for routine identification, a selection of six strains, containing four isolates of the study and two controls, i.e. phenotypically correctly identified E. aerogenes (LBV268) and K. pneumoniae (BG) isolates, were tested in two different systems, i.e. Vitek 2 (bioMérieux, Marcy l'Etoile, France) and Phoenix (BD Biosciences, Sparks, Md.). Both automated systems yielded the same results as the API20E, i.e. that the E. aerogenes isolates, aberrant due to a slow reaction for motility and ornithine decarboxylase, were misidentified as K. pneumoniae (Table 1). This misidentification by the automated systems is not unexpected, since they are based on biochemical testing only and a reading time of 24 hours or less. The control strains were correctly identified.
Disk diffusion antibiotic susceptibility testing, carried out according the NCCLS guidelines, revealed basically the same resistotype for all isolates, characterized by resistance to ceftazidime and susceptibility to ceftriaxone. Additional resistance to aztreonam was observed for some isolates, reflecting the most dominant resistance patterns for the E. aerogenes isolates in our hospital. All isolates were also found to carry high-level resistance to cefoxitin, which is highly unusual for Klebsiella spp. Furthermore, the disk-approximation test with an amoxycillin-clavulanic acid disk close to β-lactam disks on Mueller-Hinton II agar, showed a combined pattern of synergy (broadening of the inhibition zone in the direction of clavulanic acid) and antagonism (flattening of the inhibition zone), which is suggestive for a combination of an ESBL and an inducible β-lactamase. Again, inducible β-lactamases are very rare in Klebsiella spp. but typical for Enterobacter spp. It should be noticed that this phenomenon will not be detected by automated MIC-determination systems like Vitek 2 and Phoenix. Using PCR and sequencing as described previously [2], the presence of TEM-5 could be shown in the isolates of patients GA and MN, and SHV-4 in the isolate of patient DHJ.
The genotypic relationship of the phenotypically aberrant isolates was investigated with AP-PCR. Isolates GA1, GA2, GA3, MN2, MN3 and DBH were corresponding to Belgian clone BEI (Figure 1, pattern A), isolates VGM, DHJ1 and DHJ3 were closely related to Belgian clone BEII (Figure 1, pattern B, differing from pattern C, characteristic of clone BEII, by a single extra band) while isolate RA was not related to any of the others (Figure 1, pattern D). This genetic diversity among the phenotypically aberrant strains makes it probable that strains with this kind of aberrant phenotype are not restricted to a single clone within E. aerogenes.
Discussion
K. pneumoniae and E. aerogenes are taxonomically closely related species [6] which share many characteristics. Our sequencing results (unpublished) and those of others [6] confirm that the genus Enterobacter is polyphyletic and that E. aerogenes should be placed within the genus Klebsiella. However, differentiation between E. aerogenes and K. pneumoniae is usually straightforward when based on testing for ornithine decarboxylase and motility, both positive for E. aerogenes. This is also reflected in the name "Klebsiella mobilis" [7], which is known as a valid synonym for E. aerogenes. Apparently, in some E. aerogenes isolates, the expression of these characteristics can be weak and/or delayed, and these are therefore scored negative when reading is done after the incubation periods that are routinely applied (overnight – 24 hours).
However, the combination of an inducible β-lactamase and/or high-level cefoxitin resistance, which are rare in Klebsiella spp., and an identification as Klebsiella sp. should warrant further investigation.
The phenomenon of E. aerogenes misidentified as K. pneumoniae or K. terrigena due to delayed or negative ornithine decarboxylase and motility was reported previously, and was also discovered because of unexpected imipenem resistance of the so-called K. pneumoniae isolates [8]. Also in this case, subculture and prolonged incubation restored the positivity for these characteristics.
The problem of misidentification of E. aerogenes as K. pneumoniae (or even K. terrigena) is probably not uncommon and probably also geographically widespread. This can be deduced from the following considerations: i) this phenomenon of misidentification of E. aerogenes was already reported in 1993 [8], ii) the phenomenon occurred in genotypically different organisms, iii) the isolates were found over an extended period of time – also recently, and finally iv) we received similar strains from other Belgian hospitals (unpublished data). It should be noted that misidentification also occurred when using the newer and automated systems like Vitek2 and Phoenix.
On the other hand, the problem seems to be largely unknown. In a recent study, Hansen and colleagues [9] carried out an interlaboratory comparison of the efficacy of 18 biochemical tests for the identification of 242 strains of different Klebsiella species and of Enterobacter aerogenes, but do not mention the problem of possible delayed activity, possibly also because the study started from validated strains of each species.
Conclusions
Identification in a routine clinical microbiology laboratory of the most commonly encountered Enterobacteriaceae is usually considered to be fast and straightforward, but apparently identification problems may occur due to diminished or delayed expression of some characteristics, even for well-established species like E. aerogenes and K. pneumoniae. Here we showed that E. aerogenes isolates exist for which ornithine decarboxylase and motility are negative or delayed positive, and that as such these isolates can be misidentified as K. pneumoniae. This phenomenon may be quite frequent and geographically widespread. Genotypic identification techniques like tDNA-PCR, which moreover are cheaper than phenotypic testing for many bacterial species, can be semi-automatized, are faster and mostly have a higher discriminatory power, which is also reflected in this study.
Methods
tDNA-PCR
tDNA-PCR was carried out using the outwardly directed tRNA-gene consensus primers T5A (5'AGTCCGGTGCTCTAACCAACTGAG) and T3B (5'AGGTCGCGGGTTCGAATCC), thus amplifying the intergenic tRNA-spacers, as described previously [10,11]. Electropherograms were normalized using GeneScan Analysis software, version 2.1 (Applied Biosystems). Transformation of GeneScan tables (ABI310, McIntosh) to tables on IBM, separation into separate digital fingerprints, and comparison of the digital tDNA-PCR fingerprints with a library of tDNA-PCR-fingerprints obtained from a large collection of reference strains, was done using in house software described previously [10].
16S rRNA gene sequencing
For five of the phenotypically aberrant isolates, the complete 16S rRNA sequence was determined by amplification of the 16S rRNA-gene with the primers 5'-AGTTTGATCCTGGCTCAG and 5'-TACCTTGTTACGACTTCGTCCCA [12], and sequencing was performed as described previously [12]. Comparison of the obtained 16S rDNA-sequence with all known sequences in Genbank was carried out using the BLAST software (National Center for Biotechnology Information ).
RAPD-analysis for strain typing
The genotypic relationship of the isolates was investigated using arbitrarily primed PCR with RAPD Ready-to-Go beads (Amersham Pharmacia, Uppsala, Sweden) and the ERIC II primer 5'-AAGTAAGTGACTGGGGTGAGCG [13]. Analysis of the fingerprints was obtained by visual interpretation on ethidium bromide stained electrophoresis gels.
Authors' contributions
GC, GV and AM were responsible for sample collection and initial biochemical identification. PVD and GW carried out automated biochemical identification. GW in addition carried out extended biochemical characterization. TDB and MV carried out the molecular analysis. GC, GV, TDB and MV drafted the manuscript. All authors read and approved the final manuscript.
Acknowledgements
We thank Leen Van Simaey, Catharine De Ganck, Inge Bocquaert and Rita De Beul for excellent technical assistance.
Figures and Tables
Figure 1 RAPD analysis of Enterobacter aerogenes and Klebsiella pneumoniae strains used in this study Lane M: DNA molecular weight marker (100 base pair ladder). The E. aerogenes RAPD-types are indicated as A, B, C and D and the K. pneumoniae types are indicated as K1 an K2. RAPD-type A corresponds to clone BEI, type B to clone BEII, type C to BEII-related clone and type D presents a strain unrelated to clones BEI and BEII. Negative image of ethidium bromide stained agarose electrophoresis
Table 1 Clinical data and phenotypic and genotypic identification results of the Klebsiella pneumoniae and Enterobacter aerogenes isolates used in this study
Strain Sample Motilityb Ornithine decarboxylaseb API Code API Identificationc Vitek2d Phoenixd tDNA-PCRe Clonef
Group 1
LBV268 + + 5305773 E. aerogenes E. aerogenes E. aerogenes BEI
MN1 Aspirate + + 5305773 E. aerogenes NT E. aerogenes BEII
BEI 166 + + 5305773 E. aerogenes NT E. aerogenes BEI
BEII 169 + + 5305773 E. aerogenes NT E. aerogenes BEII
Group 2
BG Blood culture - - 5005763 K. pneumoniae/K. terrigena K. pneumoniae K. pneumoniae K1
DHJ2 Aspirate - - 5215773 K. pneumoniae K. pneumoniae K. pneumoniae K2
Group 3
GA1 Wound (-) (-) 5205773 Weak NT E. aerogenes BEI
GA2 Urine (-) (-) 5205773 Weak NT E. aerogenes BEI
GA3 Urine (-) (-) 5205773 Weak K. pneumoniae E. aerogenes* BEI
MN2 Ascites (-) (-) 5205773 Weak K. pneumoniae E. aerogenes BEI
MN3 Urine (-) (-) 5205773 Weak NT E. aerogenes* BEI
VGM Sputum (-) (-) 5205773 Weak K. pneumoniae E. aerogenes BEII-related
DHJ1 Urine (-) (-) 5205753 No identification NT E. aerogenes* BEII-related
DHJ3 Blood culture (-) (-) 5205753 No identification NT E. aerogenes BEII-related
RA Throat isolate (-) (-) 5204673 K. terrigena NT E. aerogenes* Non-related
DBH Urine (-) (-) 5204673 K. terrigena NT E. aerogenes* BEI
a: Data for genuine E. aerogenes isolates are presented first (group 1). Clinical isolate LBV268 was used as control for analysis on automated phenotypical identification systems. The isolates BEI 166 and BEII 169 were shown previously [4] to belong to the two major E. aerogenes clones (BEI and BEII) in Belgium. Group 2 presents data for genuine K. pneumoniae. Clinical isolate BG was used as control for analysis on automated phenotypical identification systems. The third group presents the data for the phenotypically aberrant E. aerogenes isolates.
b: +, positive; -, negative; (-), negative after standard incubation time, only positive after subculturing and/or retesting with prolonged incubation periods.
c: Weak: Weak identification with possibilities: E. aerogenes, K. pneumoniae or R. planticola.
d: NT: not tested.
e: * indicates that identification was confirmed by 16S rDNA sequence analysis.
f: Clonal relationships were determined using RAPD-analysis.
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| 15619329 | PMC544577 | CC BY | 2021-01-04 16:03:38 | no | BMC Microbiol. 2004 Dec 24; 4:49 | utf-8 | BMC Microbiol | 2,004 | 10.1186/1471-2180-4-49 | oa_comm |
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J NanobiotechnologyJournal of Nanobiotechnology1477-3155BioMed Central London 1477-3155-2-121558828010.1186/1477-3155-2-12ReviewNanoparticles – known and unknown health risks Hoet Peter HM [email protected]üske-Hohlfeld Irene [email protected] Oleg V [email protected] Katholieke Universiteit Leuven, Pneumologie, Longtoxicologie, Campus GHB, Herestraat 49, Leuven B-3000, Belgium2 GSF-Forschungszentrum für Umwelt und Gesundheit, GmbH Ingolstädter Landstraß1, D-85764 Neuherberg, Germany3 Sir William Dunn School of Pathology, University of Oxford, South Parks Road, Oxford OX1 3RE, UK2004 8 12 2004 2 12 12 28 9 2004 8 12 2004 Copyright © 2004 Hoet et al; licensee BioMed Central Ltd.2004Hoet 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.
Manmade nanoparticles range from the well-established multi-ton production of carbon black and fumed silica for applications in plastic fillers and car tyres to microgram quantities of fluorescent quantum dots used as markers in biological imaging. As nano-sciences are experiencing massive investment worldwide, there will be a further rise in consumer products relying on nanotechnology. While benefits of nanotechnology are widely publicised, the discussion of the potential effects of their widespread use in the consumer and industrial products are just beginning to emerge. This review provides comprehensive analysis of data available on health effects of nanomaterials.
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1. Introduction
Scientists world-wide are continuing to discover unique properties of everyday materials at the sub micrometer scale [1,2]. This size domain is better known as nano- (a billionth) meter domain. These novel properties of common materials observable only at the nano-scale dimensions have already found their first commercial applications [3]. For example, nanomaterials are present in some sunscreens, toothpastes, sanitary ware coatings and even food products. Manmade nanoparticles ranges from the well-established multi-ton production of carbon black and fumed silica for applications in plastic fillers and car tyres to microgram quantities of fluorescent quantum dots used as markers in biological imaging. As nano-sciences are experiencing massive investment worldwide [4,5], there will be a further rise in consumer products relying on nanotechnology [6].
While benefits of nanotechnology are widely publicised, the discussion of the potential effects of their widespread use in the consumer and industrial products are just beginning to emerge [7,8]. Both pioneers of nanotechnology [9] and its opponents [10] are finding it extremely hard to argue their case as there is limited information available to support one side or the other. It has been shown that nanomaterials can enter the human body through several ports. Accidental or involuntary contact during production or use is most likely to happen via the lungs from where a rapid translocation through the blood stream is possible to other vital organs [11]. On the cellular level an ability to act as a gene vector has been demonstrated for nanoparticles [12]. Carbon black nanoparticles have been implicated in interfering with cell signalling [13]. There is work that demonstrates uses of DNA for the size separation of carbon nanotubes [14]. The DNA strand just wraps around it if the tube diameter is right. While excellent for the separation purposes it raises some concerns over the consequences of carbon nanotubes entering the human body.
In this review we summarise the known facts about nanomaterial hazards, discuss the potential entry points of nanoparticles into the human body, explore their likely pathways inside the body and analyse published experimental results on the bioactivity of nanomaterials.
2. General background
Human skin, intestinal tract and lungs are always in direct contact with the environment. Whereas skin acts as a barrier, lungs and intestinal tract also allow transport (passive and/or active) of various substances like water, nutrients or oxygen. Because of that fact they are likely to be a first port of entry for nanomaterials journey into the human body. Our knowledge in this field mainly comes from drug delivery (pharmaceutical research) and toxicology (xenobiotics) studies. Human skin functions as a strict barrier and no essential elements are taken up through the skin (except radiation necessary to build up vitamin D). The lungs exchange oxygen and carbon dioxide with the environment, and some water escapes with warm exhaled air. The intestinal tract is in close contact with all the materials taken up orally; there all nutrients (except gasses) are exchanged between the body and the environment.
The histology of the environmental contact sides of these three organs is significantly different. The skin of an adult human is roughly 1.5 m2 in area, and is at most places covered with a relatively thick first barrier (10 micron) which is build of strongly keratinised dead cells (Fig 1). This first barrier is difficult to pass for ionic compounds as well as water soluble molecules.
Figure 1 schematic representation of human skin; Stratum corneum is the top of the five layers making epidermis, it is composed of keratinised dead cells glued by lipids. It is shed off and replaced every two weeks. Depending on the part of the body its thickness varies from 0.05 mm to 1.5 mm.
The lung consists of two different parts, airways (transporting the air in and out the lungs) and alveoli (gas exchange areas). Human lungs contain about 2300 km of airways and 300 million alveoli (gas exchange areas) (Fig 2). The surface area of the lungs is 140 m2 in adults, as big as a tennis court. The airways are a relatively robust barrier, an active epithelium protected with a viscous layer of mucus. In the gas exchange area, the barrier between the alveolar wall and the capillaries is very thin. The air in the lumen of the alveoli is just 0.5 micron away from the blood flow. The large surface area of the alveoli and the intense air-blood contact in this region makes the alveoli less well protected against environmental damage when compared with airways.
Figure 2 Cross-section of alveoli; Schematic cross-section of alveoli showing a very thin (500 nm) separation between blood and air. An SEM image of the alveoli is shown in the inset.
The intestinal tract is a more complex barrier – exchange side, it is the most important portal for macromolecules to enter the body. From the stomach, only small molecules can diffuse through the epithelium. The epithelium of the small and large intestines is in close contact with ingested material so that nutrients can be utilized. A mixture of disaccharides, peptides, fatty acids, and monoglycerides generated by digestion in small intestine are further transformed and taken in the villi (Fig 3). Villi, in turn, are covered with micro-villi, which bring overall surface available to nutrients to 200 square meters.
Figure 3 Villi in small intestine; A surface structure of villi covered with micro-villi is dramatically multiplies the area of gastero-intestine tract to 200 m2. Inset shows an SEM image of villi.
3. Lung
3.1 Inhalation and pulmonary clearing of insoluble solids
The pathogenic effects of inhaled solid material depend primarily on achieving a sufficient lung burden [15]. The lung burden is determined by the rates of deposition and clearance. Logically, for any dust or fibre, a steady-state dose level will be achieved when the rates come into balance. This is only true when the solid material does not interfere with the clearance mechanisms. In respect to the burden the chemical and physical properties of the material itself are important insofar as they influence deposition and clearance rates. Spherical solid material can be inhaled when its aerodynamic diameter is less than 10 micron. The smaller the particulates the deeper they can travel into the lung, particles smaller than 2.5 micron will even reach the alveoli. Ultrafine particles (nanoparticles with an aerodynamic diameter of less than 100 nm) are deposited mainly in the alveolar region. Fibres are defined as solid materials with a length to diameter ratio of at least 3:1. Their penetration into the lung depends on the aerodynamic properties. Fibres with a small diameter will penetrate deeper into the lungs, while very long fibres (>>20 micron) are predominantly stuck in the higher airways [16-21].
The mucociliary escalator dominates the clearance from the upper airways; clearance from the deep lung (alveoli) is predominantly by macrophage phagocytosis. The mucociliary escalator is an efficient transport system pushing the mucus, which covers the airways, together with the trapped solid materials towards the mouth. The phagocytosis of particles and fibres results in activation of macrophages and induces the release of chemokines, cytokines, reactive oxygen species, and other mediators; this can result in sustained inflammation and eventually fibrotic changes. The phagocytosis efficiency can be affected by the (physical-chemical) characteristics of the solid material (see below); moreover, fibres too long to be phagocytized (fibres longer than the diameter of the alveolar macrophage) will only be cleared very slowly.
Laboratory exposure studies have shown that if the inhaled concentrations are low, such that the deposition rate of the inhaled particles is less than the mechanical alveolar macrophage-mediated clearance rate in the lung, then the retention half time is about 70 days (steady-state lung burden during continuous exposure). If the deposition rate of the inhaled particles exceeds this clearance rate, the retention half time is significantly increased, reflecting an impaired or prolonged alveolar macrophage-mediated clearance function with continued accumulation of lung burden (overload). Inhaled fibres, which are persistent in the alveoli, can interact with the pulmonary epithelial cells or even penetrate the alveolar wall and enter the lung tissue. These fibres are often described as being in the "interstitial" where they may lie between or within the cells making up the alveolar walls. Bio-persistent solid materials, certainly those particles containing mutagenic substances or asbestos fibres or silica, which remain for years in the lungs, increase the risk of developing cancer.
3.2 Deposition and clearing of solid nanomaterials
It has been reported recently that nanotubes show a sign of toxicity [22], confirmed in two independent publications by Warheit et al [23] and Lam et al [24], which demonstrated the pulmonary effects of single walled cabon nanotubes in vivo after intratracheal instillation, in both rats and mice. Both groups reported granuloma formation, and some interstitial inflammation. The research group of Warheit et al [23] concluded that these findings (multifocal granulomas) may not have physiological relevance, and may be related to the instillation of a bolus of agglomerated nanotubes. But for the authors of [24] their results indicate that if carbon nanotubes reach the lungs, they are much more toxic than carbon black and can be more toxic than quartz. These studies have to be read with some caution because a study by the National Institute for Occupational Safety and Health (NIOSH) showed that none or only a small fraction of the nanotubes present in the air can be inhaled [25].
Clearance from the lung depends not only on the total mass of particles inhaled but also on the particle size and, by implication, on particle surface, as shown in the following studies. A sub-chronic 3 months inhalation exposure of rats to ultrafine (~20 nm) and fine (~200 nm) titanium dioxide (TiO2) particles demonstrated that the ultrafine particles cleared significantly slower, showed more translocation to interstitial sites and to regional lymph nodes when compared to the fine TiO2 particles [26]. By comparing carbon black particles of similar size and composition but with significant specific surface area difference (300 versus 37 m2/g), it was found that the biological effects (inflammation, genotoxicity, and histology) were dependent on specific surface area and not particle mass. Similar findings were reported in earlier studies on tumorigenic effects of inhaled particles. It was shown that tumour incidence correlated better with specific surface area than with particle mass [27,28].
Comparing the health effects of chronically inhaled TiO2 particles with distinctly different sizes, it is remarkable that the low exposure (10 mg/m3) study [29] resulted in a greater lung tumour incidence than the high exposure (250 mg/m3) study [30]. The inhaled particles in both studies consisted of aggregated primary particles, with an aerodynamic diameter that was probably not very different. The primary particle size of the low dose study was 20 nm, while it was approximately 300 nm in the latter study.
In summary, most nano-sized spherical solid materials will easily enter the lungs and reach the alveoli. These particles can be cleared from the lungs, as long as the clearance mechanisms are not affected by the particles themselves or any other cause. Nano-sized particles are more likely to hamper the clearance resulting in a higher burden, possibly amplifying any possible chronic effects caused by these particles. It is also important to note that specific particle surface area is probably a better indication for maximum tolerated exposure level than total mass. Inhaled nano-fibres (diameter smaller than 100 nm) also can enter the alveoli and their clearing would, in addition, depend on the length of the specific fibre. Recent publications on the pulmonary effects of carbon nanotubes confirm the intuitive fear that nano-sized fibre can induce a rather general non-specific pulmonary response.
3.3 Particle surface and biocompatibility
Reports on the surface properties of nanoparticles, both physical and chemical, stress that nanoparticles differ from bulk materials. Their properties depend heavily on the particle size. Therefore, nanoparticles are not merely small crystals but an intermediate state of matter placed between bulk and molecular material. Independently of the particle size, two parameters play dominant role. The charges carried by the particle in contact with the cell membranes and the chemical reactivity of the particle [31].
3.3.1 Surface charges
Polycationic macromolecules show a strong interaction with cell membranes in vitro. A good example can be found in the Acramin F textile paint system. Three poly-cationic paint components exhibited considerable cytotoxicity (LD50 generally below 100 mg/ml for an incubation of 20–24 hours) in diverse cell cultures, such as primary cultures of rat and human type II pneumocytes, and alveolar macrophages and human erythrocytes. The authors argued that the multiple positive charges play an important role in the toxic mechanism [32,33]. Biocompatibility studies [34] revealed that the cytotoxicity of polycationic materials such as DEAE-dextran and poly-L-lysine (PLL) [35,36], dendrimers [37] and polyethylenimine (PEI) [38] increases with the increase in their molecular weight. However, these findings apply only to polymers having same chemical structure, but not for different types of polycations. Consequently, to explain the toxicity of polymers with different structures further parameters have to be taken into account.
Dekie et al [39] concluded that the presence of a primary amine group on poly L-glutamic acid derivatives has a significant toxic effect on red blood cells causing them to agglutinate. Not only the type of amino function but also the charge density resulting from the number and special arrangement of the cationic residues is an important factor for cytotoxicity. Ryser [40] suggested that a three-point attachment is necessary for eliciting a biological response on cell membranes, and argued that the activity of a polymer will decrease when the space between reactive amine groups is increased. The arrangement of cationic charges depends on the three-dimensional structure and flexibility of the macromolecules and determines the accessibility of their charges to the cell surface. For example, branched molecules were found to be more efficient in neutralising the cell surface charge than polymers with linear or globular structure, as rigid molecules have more difficulties to attach to the membranes than flexible molecules [41]. Therefore, high cationic charge densities and highly flexible polymers should cause higher cytotoxic effects than those with low cationic charge densities. Globular polycationic macromolecules (cationised Human Serum Albumine (cHSA), ethylenediamine-core poly(amidoamine) dendrimers (PAMAM) were found to be polymers with a good biocompatibility (low cytotoxicity), whereas polymers with a more linear or branched and flexible structure (e.g. polydiallyldimethylammonium chloride (DADMAC), PLL, PEI) showed higher cell damaging effects.
3.3.2 The surfactant interaction and surface chemistry
Geiser et al [42] studied the influence of the particle surface chemistry on its interaction with the lung's surface-lining layer. They found that, regardless of the nature of their surfaces, particles will be submersed into the lining layer after their deposition in small airways and alveoli. This displacement is promoted by the surfactant film itself, whose surface tension falls temporarily to relatively low values [42,43]. On the other hand, reactive groups on a particle surface will certainly modify the biological effects. For silica, it has been shown that surface modification of quartz affects its cytotoxicity, inflammogenicity and fibrogenicity. These differences are mainly due to particle surface characteristics [44]. Specific cytotoxicity of silica is strongly correlated with the appearance of surface radicals and reactive oxygen species (ROS), which is considered to be the key event in the development of fibrosis and lung cancer by this compound [45].
Although the type of particle does not seem to play an important role in whether it is embedded in the surfactant lining of the alveoli, the embedding process itself is crucial. Particle-cell interaction is possible only after the immersion of the particulates in the lining fluid and research is needed to study this phenomenon in detail in relation to inhaled nanoparticles. Logically, as described in the report for silica [45], the reactive groups on nanoparticles influence their interaction with the lung (or more general with biological material). In some instances it might be possible to predict the reactivity of the nano-surface. However, considering the scarcity of data, it would be sensible to verify these predictions by some laboratory testing.
3.4 Systemic translocation of inhaled particles
The impact of inhaled particles on other organs has only recently been recognised. Most research has concentrated on the possible consequences of particle related malfunction of the cardio-vascular system, such as arrhythmia, coagulation [46] etc. However, recent data support the concept that the autonomic nervous system may also be a target for the adverse effects of inhaled particulates [47,48,11]. Two complementary hypotheses explain the cardiovascular malfunctions after inhalation of ultra-fine particles. The first hypothesis explains the observed effects by the occurrence of strong (and persistent) pulmonary inflammatory reactions in the lungs, leading to the release of mediators (see above), which may influence the heart, coagulation, or other cardiovascular endpoints. The second hypothesis is that the particles translocate from the lungs into the systemic circulation and thus, directly or indirectly, influence haemostasis or cardiovascular integrity.
In the evaluation of the health effects of inhaled nanoparticles the translocation to the systemic circulation is an important issue. Conhaim and co-workers [49] found that the lung epithelial barrier was best fitted by a three-pore-sized model, including a small number (2%) of large-sized pores (400-nm pore radius), an intermediate number (30%) of medium-sized pores (40-nm pore radius), and a very large number (68%) of small-sized pores (1.3-nm pore radius). The exact anatomical location of this structure, however, remains to be established (see the review by Hermans and Bernard [50]). Until recently, the possible passage of xenobiotic particles has not been attracting much attention, although, the concept is now gaining acceptance in pharmacology for the administration of macromolecular drugs by inhalation [51]. Nemmar et al [11] studied the particle-translocation of inhaled ultrafine technetium (99mTc) labelled carbon particles to the blood. These particles, which are very similar to the ultrafine fraction of actual pollutant particles, diffused rapidly – within 5 minutes – into the systemic circulation (Fig 4). The authors concluded that phagocytosis by macrophages and/or endocytosis by epithelial and endothelial cells are responsible for particle-translocation to the blood but other roots must also exist.
Figure 4 Translocation of inhaled ultrafine particles. Time-activity curve over liver and bladder expressed as percent of initial lung radioactivity. Insert, Whole body gamma camera image of 1 representative volunteer recorded at 60 minutes. The radioactivity over the organs is expressed as counts per minute (CPM) per pixel within each region of interest (ROI). The values recorded over the stomach were not included because this radioactivity may also come partly from swallowing of particles deposited in the mouth. Reproduced with permission from Nemmar et al, "Passage of inhaled particles into the blood circulation in humans", Circulation 2002;105(4):411-41.
The literature on the translocation of very small particles from the lungs into the blood circulation is limited and often conflicting. A recent study has reported deposition and clearance over 2 h of an ultrafine (60 nm) 99mTc labelled aerosol in human volunteers. No significant radioactivity was found in the liver (1–2 % of the inhaled radioactivity) but, unfortunately, no radioactivity measurements with blood were reported [52]. In agreement with findings of Nemmar et al [11], Kawakami et al. [53] have reported the presence of radioactivity in blood immediately after inhalation of 99mTc-technegas in human volunteers. It is also known [54] that aerosolised insulin gives a rapid therapeutic effect although the pathways for this translocation are still unclear. In addition to human studies, in experimental animal studies, we [11] and others [55,16,57] have reported extra-pulmonary translocation of ultrafine particles after intra-tracheal instillation or inhalation. However, the amount of ultrafine particles that translocate into blood and extra-pulmonary organs differed among these studies. It has also been shown that, following intranasal delivery, polystyrene microparticles (1.1 micron) can translocate to tissues in the systemic compartment [58]. A recent study [59] has provided, for the first time, morphological data showing that inhaled polystyrene particles are transported into the pulmonary capillary space, presumably by trans-cytosis. Another alley of translocation from the lungs towards other organs has been undertaken by Oberdörster et al [19]. In inhalation experiments with rats, using 13C-labelled particles, they found that nano-sized particles (25 nm) were present in several organs 24 hours after exposure. The most extraordinary finding was the discovery of particles in the central nervous system (CNS). The authors examined this phenomenon further and found that particles, after being taken up by the nerve cells, can be transported via nerves (in this experiment via the olfactory nerves) at a speed of 2.5 mm per hour [56].
Passage of solid material from the pulmonary epithelium to the circulation seems to be restricted to nanoparticles. The issue of particle translocation still need to be clarified: both the trans-epithelial transport in the alveoli and the transport via nerve cells. Thus, the role of factors governing particle translocation such as the way of exposure, dose, size, surface chemistry and time course should be investigated. For instance, it would be also very important to know how and to what extent lung inflammation modulates the extra-pulmonary translocation of particles.
3.5 Fibre bio-persistence
Long non-phagocytizable fibres (in humans longer than 20 micron) will not be effectively cleared from the respiratory tract. The main determinants of fibre bio-persistence are species specific physiological clearance and fibre specific bio-durability (physical-chemical processes). In the alveoli the rate at which fibres are physically cleared depends on the ability of alveolar macrophages to phagocytose them. Macrophages containing fibres longer than their own diameter may not be mobile and be unable to clear the fibres from the lung. The bio-durability of a fibre depends on dissolution and leaching as well as mechanical breaking and splitting. Long fibres in the lung can disintegrate, leading to shorter fibres that can be removed by the macrophages. Bio-persistent types of asbestos, where breakage occurs longitudinally, result in more fibres of the same length but smaller diameter. Amorphous fibres break perpendicular to their long axis [60,61], resulting in fibres that can be engulfed by the macrophages.
It is self-evident that the slower the fibres are cleared (high bio-persistence), the higher is the tissue burden and the longer the fibres reside in a tissue the higher is the probability of an adverse response. A milestone was set by Stanton et al [62,63] who undertook a series of experiments with 17 samples of carefully sized fibrous glass. They found that for mesothelioma induction in rats, the peak activity was in the fibres greater than 8 micron in length and less than 1.3 micron in diameter. These findings are known as the "Stanton hypothesis". However these results do not strictly indicate that all fibres longer than the lower threshold are equally active or that shorter fibres are not, although fibres less than 5 micron in length did not appear to contribute to lung cancer risk in exposed rats [64]. Risk appears to increase with length, with fibres more than 40 micron in length imposing the highest risk. For the recent review see Schins [65].
The bio-durability of fibres with a diameter < 100 nm will probably not differ from larger inhalable fibres. Therefore, great caution must be taken in case of the contact with nano-fibres, Bio-durability tests must be performed before releasing any products containing them. Carbon nanotubes, which are of high technical interest, are one of the materials which need to be tested in depth concerning bio-persistence and cancer risk. The first toxicological studies indicated that carbon nanotubes can be a risk for human health [22-24], while exposure assessment did indicate that these materials are probably not inhaled [25].
4. Intestinal tract
Already in 1926 it was recognised by Kumagai [66] that particles could translocate from the lumen of the intestinal tract via aggregations of intestinal lymphatic tissue (Peyer's patches (PP)), containing M-cells (specialised phagocytic enterocytes). Particulate uptake happens not only via the M-cells in the PP and the isolated follicles of the gut-associated lymphoid tissue, but also via the normal intestinal enterocytes. There have been a number of excellent reviews on the subject of intestinal uptake of particles [51,66]. Uptake of inert particles has been shown to occur trans-cellularly through normal enterocytes and PP via M-cells, and to a lesser extent across para-cellular pathways [67]. Initially it was assumed that the PP did not discriminate strongly in the type and size of the absorb particles. Later it has been shown that modified characteristics, such as particle size [68] the surface charge of particles [69], attachment of ligands [70,71] or coating with surfactants [72], offers possibilities of site-specific targeting to different regions of the gastro intestine tract (GIT), including the PP [73].
The kinetics of particle translocation in the intestine depends on diffusion and accessibility through mucus, initial contact with enterocyte or M-cell, cellular trafficking, and post-translocation events. Charged particles, such as carboxylated polystyrene nanoparticles [69] or those composed of positively charged polymers exhibit poor oral bioavailability through electrostatic repulsion and mucus entrapment. Szentkuti [74] determined the rate of particle diffusion across the mucus layer to the enterocyte surface with respect to both size and surface charge of the particles. In brief, Szentkuti [74] observed that cationic nanometer-sized latex particles became entrapped in the negatively charged mucus, whereas repulsive carboxylated fluorescent latex nanoparticles were able to diffuse across this layer. The smaller the particle diameter the faster they could permutate the mucus to reach the colonic enterocytes; 14 nm diameter permeated within 2 min, 415 nm particles took 30 min, while 1000-nm particles were unable to translocate this barrier. Within, the time of the experiment (30 min) none of the particles was endocytosed by the enterocytes despite the fact that the latex nanoparticles preferentially bound the cell surface more strongly than the mucus. After a longer time window (oral gavage for several days) a sparse accumulation of charged particulates in the lamina propria (connective tissue under the epithelia) was found compared to uncharged latex nanoparticles in the same size range [69].
Particulates, once in the sub-mucosal tissue, are able to enter both lymphatic and capillaries. Particles entering the lymphatic are probably important in the induction of secretory immune responses while those which enter the capillaries become systemic and can reach different organs. In one study [75], the body distribution after translocation of polystyrene particles was examined in some detail. Polystyrene spheres (ranging from 50 nm to 3 micron) were fed by gavage to female Sprague Dawley rats daily for 10 days at a dose of 1.25 mg/kg. As much as 34 % and 26% of the 50 and 100 nm particles were absorbed respectively. Those larger than 300 nm were absent from blood. No particles were detected in heart or lung tissue.
4.1 Intestinal Translocation and Disease
Crohn's disease is characterised by transmural inflammation of the gastrointestinal tract. It is of unknown aetiology, but it is suggested that a combination of genetic predisposition and environmental factors play a role. Particles (0.1–1.0 micron) are associated with the disease and indicated as potent adjuvants in model antigen-mediated immune responses. In a double-blind randomised study, it has been shown that a particle low diet (low in calcium and exogenous microparticles) alleviates the symptoms of Crohn's disease [76]. Although there is a clear association between particle exposure and uptake and Crohn's disease, little is known of the exact role of the phagocytosing cells in the intestinal epithelium. It has been suggested that the disruption of the epithelial barrier function by apoptosis of enterocytes is a possible trigger mechanism for mucosal inflammation. The patho-physiological role of M cells is unclear; e.g., it has been found that in Crohn's disease M cells are lost from the epithelium. Other studies found that material uptake (endocytose) capacity of M cells is induced under various immunological conditions, e.g. a greater uptake of particles (0.1 micron, 1 micron and 10 micron diameter) has been demonstrated in the inflamed colonic mucosa of rats compared to non-ulcerated tissue [77,78] and inflamed oesophagus [79].
Diseases other than of gut origin also have marked effects on the ability of GIT to translocate particles. The absorption of 2-micron polystyrene particles from the PP of rats with experimentally induced diabetes is increased up to 100-fold (10% of the administered dose) compared to normal rats [80]. However, the diabetic rat displayed a 30% decrease in the systemic distribution of the particles. One possible explanation for this discrepancy is the increased density of the basal lamina underlying the GI mucosa of diabetic rats that may impede particle translocation into deeper villous regions. This uncoupling between enhanced intestinal absorption and reduced systemic dissemination has also been observed in dexamethasone treated rats [81].
From the literature cited above it is clear that engineered nanoparticles can be taken up via the intestinal tract. In general the intestinal uptake of particles is better understood and studied in more detail than pulmonary and skin uptake. Because of this advantage it is maybe possible to predict the behaviour of some particles in the intestines but precaution should be taken. For those nanoparticles designed to stabilise food or to deliver drug via intestinal uptake other, more demanding, rules exist and should be followed before marketing these compounds.
5. Skin
Skin is an important barrier, protecting against insult from the environment. The skin is structured in three layers: the epidermis, the dermis and the subcutaneous layer. The outer layer of the epidermis, the stratum corneum (SC), covers the entire outside of the body and only contains dead cells, which are strongly keratinized. For most chemicals the SC is the rate-limiting barrier to percutaneous absorption (penetration). The skin of most mammalian species is, on most parts of the body, covered with hair. At the sites, where hair follicles grow, the barrier capacity of the skin differs slightly from the "normal" stratified squamous epidermis. Most studies concerning penetration of materials into the skin have focussed on whether or not drugs penetrate through the skin using different formulations containing chemicals and/or particulate materials as a vehicle. The main types of particulate materials commonly used are: liposomes; solid poorly soluble materials such as TiO2 and polymer particulates and submicron emulsion particles such as solid lipid nanoparticles. The penetration of these particulate carriers has not been studied in detail.
TiO2 particles are often used in sunscreens to absorb UV light and therefore to protect skin against sunburn or genetic damage. It has been reported by Lademann et al in [82] that micrometer-sized particles of TiO2 get through the human stratum corneum and even into some hair follicles – including their deeper parts. However, the authors did not interpret this observation as penetration into living layers of the skin, since this part of the follicular channel (the acroinfundibulum) is covered with a horny layer barrier too [82]. A different interpretation has been suggested in a recent review by Kreilgaard [83], who argued that "very small titanium dioxide particles (e. g. 5–20 nm) penetrate into the skin and can interact with the immune system". Tinkle et al [84] demonstrated that 0.5- and 1.0 micron particles, in conjunction with motion, penetrate the stratum corneum of human skin and reach the epidermis and, occasionally, the dermis. The authors hypothesised that the lipid layers within the cells of the stratum corneum form a pathway by which the particles can move [85] into the skin and be phagocytized by the Langerhans cells. In this study the penetration of particles is limited to particle diameter of 1 micron or less. Nevertheless, other studies reported penetration through the skin using particles with diameters of 3–8 micron [86,87,82] but only limited penetration was found often clustered at the hair follicle (see above).
Penetration of non-metallic solid materials such as biodegradable poly(D,L-lactic-co-glycolic acid (PLGA) microparticles, 1 to 10 micron with a mean diameter of 4.61 ± 0.8 micron was studied after application on to porcine skin. The number of microparticles in the skin decreased with the depth (measured from the airside towards the subcutaneous layer). At 120 micron depth (where viable dermis present) a relatively high number of particles was found, at 400 micron (dermis) some micro-particles were still seen. At a depth of 500 micron no microparticles were found [88]. In the skin of individuals, who had an impaired lymphatic drainage of the lower legs, soil microparticles, frequently 0.4–0.5 micron but as larger particles of 25 micron diameter, were found in the in the dermis of the foot in a patient with endemic elephantiasis. The particles are seen to be in the phagosomes of macrophages or in the cytoplasm of other cells. The failure to conduct lymph to the node produces a permanent deposit of silica in the dermal tissues (a parallel is drawn with similar deposits in the lung in pneumoconiosis). This indicates that soil particles penetrate through (damaged) skin, most probably in every individual, and normally are removed via the lymphatic system [89,90]. Liposomes penetrate the skin in a size dependent manner. Micro-sized, and even submicron sized, liposomes do not easily penetrate into the viable epidermis, while liposomes with an average diameter of 272 nm can reach into the viable epidermis and some are found in the dermis. Smaller sized liposomes of 116 and 71 nm were found in higher concentration in the dermis.
Emzaloid™ particles, a type of submicron emulsion particle such as liposomes and nonionic surfactant vesicles (niosomes), with a diameter of 50 nm to 1 micron, were detected in the epidermis in association with the cell membranes after application to human skin [91]. The authors suggested that single molecules, which make up the particles, may penetrate the intercellular spaces and, at certain regions in the stratum corneum, are able to accumulate and reform into micro spheres. In a subsequent experiment, it was shown that the used formulation allowed penetration of the spheres into melanoma cells, even to the nucleus [92].
A recent review by Hostynek [93] stated that the uptake of metals through the skin is complex, because of both exogenous factors (e.g. dose, vehicle, protein reactivity, valence) and endogenous factors (e.g. age of skin, anatomical site, homeostatic control). Attempts to define rules governing skin penetration to give predictive quantitative structure-diffusion relationships for metallic elements for risk assessment purposes have been unsuccessful, and penetration of the skin still needs to be determined separately for each metal species, either by in vitro or in vivo assays.
Only limited literature on nanoparticles penetrating the skin is available, but some conclusions can already be drawn. Firstly, penetration of the skin barrier is size dependent, nano-sized particles are more likely to enter more deeply into the skin than larger ones. Secondly, different types of particles are found in the deeper layers of the skin and at present it is impossible to predict the behaviour of a particle in the skin. And finally, materials, which can dissolve or leach from a particle (e.g. metals), or break into smaller parts (e.g. Emzaloid™ particles), can possibly penetrate the skin. We did not find any direct indication that particles, that had penetrated the skin, also entered the systemic circulation. The observation that particles in the skin can be phagositized by macrophages, Langerhans cells or other cells is a possible road towards skin sensitisation. Tinkle et al [84] have shown that topical application of beryllium, to C3H mice, generated beryllium-specific sensitisation. These data are consistent with the development of a hapten-specific, cell-mediated immune response.
5.1 Mechanical irritation of skin
Glass fibres and Rockwool fibres are widely distributed man-made mineral fibres because of their multiple applications, mainly as insulation materials, which have become important for replacing asbestos fibres. In contact with the skin, these fibres can induce dermatitis through the mechanical irritation. Why these fibres are such strong irritant has not been examined in detail. In occlusion irritant patch tests in humans it was found that Rockwool fibres with a diameter of 4.20 ± 1.96 micron were more irritating than those with a mean diameter of 3.20 ± 1.50 micron. The fact that "small" fibres can cause strong skin irritation has been known for a long time, e.g. itching powder. It is also commonly accepted that some types of man made fibres can easily induce non-allergic dermatitis. Although this is common knowledge, it is not clear what makes these fibres irritants. In search for reports on skin irritation caused by fibres with a diameter of < 100 nm no information could be found, indicating that more research is needed.
6. Body distribution and systemic effects of particulates
The body distribution of particles is strongly dependent on their surface characteristics. For example, coating poly(methyl methacrylate) nanoparticles with different types and concentrations of surfactants significantly changes their body distribution [116]. Coating these nanoparticles with ≥ 0.1 % poloxamine 908 reduces their liver concentration significantly (from 75 to 13 % of total amount of particles administrated) 30 min after intravenous injection. Another surfactant, polysorbate 80, was effective above 0.5%. A different report [94] shows that modification of the nanoparticle surface with a cationic compound, didodecyldimethylammonium bromide (DMAB), facilitates the arterial uptake 7–10-fold. The authors noted that the DMAB surface modified nanoparticles had a zeta potential of +22.1 +/- 3.2 mV (mean +/- sem, n = 5) which is significant different from the original nanoparticles which had a zeta potential of -27.8 +/- 0.5 mV (mean +/- sem, n = 5). The mechanism for the altered biological behaviour is rather unclear, but surface modifications have potential applications for intra-arterial drug delivery.
Oral uptake (gavage) of polystyrene spheres of different sizes (50 nm to 3 micron) in female Sprague Dawley rats (for 10 days at a dose of 1.25 mg/kg/day) resulted in systemic distribution of the nanoparticles. About 7% (50 nm) and 4% (100 nm), was found in the liver, spleen, blood and bone marrow. Particles larger than 100 nm did not reach the bone marrow and those larger than 300 nm were absent from blood. No particles were detected in heart or lung tissue [75].
Irrespective of the uptake route, the body distribution of particles, is most dependent on the surface characteristics and the size of the particles. It is an important issue in drug-design in order to help to deliver medication to the right target. In unintentional uptake of nanoparticles these characteristics can strongly influence the accumulation of a specific type of particle in the particular body site.
6.1 Nanoparticles, thrombosis and lung inflammation
Epidemiological studies have reported a close association between particulate air pollution and cardiovascular adverse effects such as myocardial infarction [95]. The latter results from rupture of an atherosclerotic plaque in the coronary artery, followed by rapid thrombus growth caused by exposure of highly reactive subendothelial structures to circulating blood, thus leading to additional or complete obstruction of the blood vessel. Nemmar et al [96] studied the possible effects of particles on haemostasis, focusing on thrombus formation as a relevant endpoint. Polystyrene particles of 60 nm diameter (surface modifications: neutral, negative or positive charged) have a direct effect on haemostasis by the intravenous injection. Positively charged amine-particles led to a marked increase in prothrombotic tendency, resulting from platelet activation. A similar effect could be obtained after the intratracheal administration of these positively charged polystyrene particles, which also caused lung inflammation [97]. It is important to indicate that the pulmonary instillation of larger (400 nm) positive particles caused a definite pulmonary inflammation (of similar intensity to 60 nm particles), but they did not lead to a peripheral thrombosis within the first hour of exposure. This lack of effect of the larger particles on thrombosis, despite their marked effect on pulmonary inflammation, suggests that pulmonary inflammation by itself was insufficient to influence peripheral thrombosis. Consequently, the effect found with the smaller, ultrafine particles is most probably due, at least in part, to their systemic translocation from the lung into the blood.
Pollutant particles such as diesel exhaust particles (DEP), may cause a marked pulmonary inflammation within an hour after their deposition in the lungs. Moreover, intratracheal instillation of DEP promotes femoral venous and arterial thrombosis in a dose-dependent manner, already starting at a dose of 5 μg per hamster (appr. 50 μg/kg). Subsequent experiments showed that prothrombotic effects persisted at 6 h and 24 h after instillation (50 μg/animal) and confirmed that peripheral thrombosis and pulmonary inflammation are not always associated [97]. Solid inhaled particles are a risk for those who suffer from cardiovascular disease. Experimental data indicate that many inhaled particles can affect cardiovascular parameters, via pulmonary inflammation. Nano-sized particles, after passage in the circulation, can also play a direct role in e.g. thrombogenisis.
Epidemiologic studies have provided valuable information on the adverse health effects of particulate air pollution in the community, indicating that nanoparticles act as an important environmental risk factor for cardiopulmonary mortality. Particle-induced pulmonary and systemic inflammation, accelerated atherosclerosis, and altered cardiac autonomic function may be part of the patho-physiological pathways, linking particulate air pollution with cardiovascular mortality. Also, it has been shown that particles deposited in the alveoli lead to activation of cytokine production by alveolar macrophages and epithelial cells and to recruitment of inflammatory cells. An increase in plasma viscosity, fibrinogen and C-reactive protein has been observed in samples of randomly selected healthy adults in association with particulate air pollution [95,98,99].
6.2 Nanoparticles and cellular uptake
A number of reports on cellular uptake of micro- and nano- sized particles has been published. Reports on particle uptake by endothelial cells [100,101], pulmonary epithelium [102,79,103,59], intestinal epithelium [51,79] alveolar macrophages [104-107,57], other macrophages [89,108,76,109], nerve cells [110] and other cells[111] are available. This is an expected phenomenon for phagocytic cells (macrophages) and cells that function as a barrier and/or transport for (large) compounds. Except for macrophages, the health effects of cellular uptake of nanoparticles have not been studied in depth.
6.3 Nanoparticles and the blood-brain barrier
One of the promising alleys of nanotechnology is organ- or cell- specific drug delivery mediated by nanoparticles [112-114]. It is expected that transport of nanoparticles across the blood-brain barrier (BBB) is possible by either passive diffusion or by carrier-mediated endocytosis. Coating of particles with polysorbates (e.g. polysorbate-80) results in anchoring of apolipoprotein E (apo E) or other blood components. Surface modified particles seem to mimic LDL particles and can interact with the LDL receptor leading to uptake by endothelial cells. Hereafter, the drug (which was loaded in the particle) may be released in these cells and diffuse into the brain interior or the particles may be trans-cytosed.
Also, other processes such as tight junction modulation or P-glycoprotein (Pgp) inhibition also may occur [115]. Oberdörster et al 2002 reported the translocation of inhaled nanoparticles via the olfactory nerves [56]. Drug delivery systems crossing the BBB are certainly welcome, but this also implicates that unintended passage through the BBB is possible; therefore good safety evaluations are needed.
6.4. Nanoparticles and oxidative stress
It has been shown that nanoparticles, that enter the liver, can induce oxidative stress locally. A single (one day; 20 and 100 mg/kg) and repeated (14 days) intravenous administration of poly-isobutyl cyanoacrylate (PIBCA, a biodegradable particle) or polystyrene (PS, not biodegradable) nanoparticles induced a depletion of reduced glutathione (GSH) and oxidised glutathione (GSSG) levels in the liver, as well as inhibition of superoxide dismutase (SOD) activity and a slight increase in catalase activity. The nanoparticles did not distribute in the hepatocytes, implicating that the oxidative species most probably were produced by activated hepatic macrophages, after nanoparticle phagocytosis.
Uptake of polymeric nanoparticles by Kupffer cells in the liver induces modifications in hepatocyte antioxidant systems, probably due to the production of radical oxygen species [108]. We have discussed above that nano-sized particles in the lung can induce, via the pulmonary inflammatory response as well as via spontaneously surface related reactions, oxidative stress. Besides pulmonary studies, not many have studied particle-induced oxidative stress in tissues. However, the authors [108] reported that the depletion in glutathione was not sufficient enough to initiate significant hepatocytic damage (no lipid peroxidation). It needs to be stressed that long-term studies are needed to prove the safe use of these nanoparticles because chronic depletion of the anti-oxidant defence can lead to severe health problems.
7. Differences in conditions between the lung and intestinal tract
Although the contact with nanomaterials in the lungs and intestinal tract shows many similarities important differences between inhalation and ingestion of nanomaterials exist from the toxicological point of view. In the intestinal tract a complex mix of compounds – such as secreted enzymes, ingested food, bacteria of the gut flora, etc – is present, which can interact with the ingested nanomaterial. Non-specific interaction often reduces the toxicity of the ingested material. It has been described that in vitro particles are less cytotoxic when dosed in a medium with high protein content. In the lungs, mucus or surfactant is present, in which antioxidants are present, but these can be easily neutralised when a high number of oxidative compounds is inhaled.
The transit through the intestinal tract is a relatively fast process, the continuous decay and renewal of the epithelium makes sure that nanomaterials will not remain long in the intestinal tract. The presence of solid material in the lumen of the intestines will not automatically induce an inflammatory response. Inhaled materials < 10 micron and > 5 micron will not enter the alveolar spaces of the lungs, and therefore these will be cleared easily in healthy persons via the muco-ciliary escalator. Particles that are smaller than 5 micron will deposit in the alveolar space via Brownian movement. In the alveoli, water insoluble materials can only be removed via phagocytosis by macrophages or other cells, or via transportation through the epithelium to the interstitium or systemic circulation. These processes are often accompanied by the onset of (persistent) inflammation. The particles themselves can – depending on the physical-chemical characteristics of the material – remain for a long period in the alveoli.
In the intestinal tract, the ingested materials are stressed from acidic (stomach) to basic conditions. The shift in pH markedly changes the solubility and the ionic state of the material via changing the surface characteristics. In the lungs, the milieu of the lumen is more constant.
8. Conclusions
Particles in the nano-size range can certainly enter the human body via the lungs and the intestines; penetration via the skin is less evident. It is possible that some particles can penetrate deep into the dermis. The chances of penetration depend on the size and surface properties of the particles and also on the point of contact in the lung, intestines or skin. After the penetration, the distribution of the particles in the body is a strong function of the surface characteristics of the particles. A critical size might exist beyond which the movement of the nanoparticles in parts of the body is restricted. The pharmaco-kinetic behaviour of different types of nanoparticles requires detailed investigation and a database of health risks associated with different nanoparticles (e.g. target organs, tissue or cells) should be created. The presence of the contaminates, such as metal catalysts present in nanotubes, and their role in the observed health effects should be considered along with the health effect of the nanomaterials.
The increased risk of cardiopulmonary diseases requires specific measures to be taken for every newly produced nanoparticle. There is no universal "nanoparticle" to fit all the cases, each nanomaterial should be treated individually when health risks are expected. The tests currently used to test the safety of materials should be applicable to identify hazardous nanoparticles. Proven otherwise, it would be a challenge for industry, legislators and risk assessors to construct a set of high throughput and low cost tests for nanoparticles without reducing the efficiency and reliability of the risk assessment. Nanoparticles designed for drug delivery or as food components need special attention.
Acknowledgements
This work was supported by NANOSAFE (Risk Assessment in Production and Use of Nanoparticles with Development of Preventive Measures and Practice Codes) project funded by the European Community under the "Competitive and Sustainable Growth" Programme, Contract G1MA-CT-2002-00020. Full report can be found at
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BMC GastroenterolBMC Gastroenterology1471-230XBioMed Central London 1471-230X-4-331562500510.1186/1471-230X-4-33Research ArticlePhospholipids reduce gastric cancer cell adhesion to extracellular matrix in vitro Jansen Marc [email protected] Karl-Heinz [email protected] Britta [email protected] Jens [email protected] Petra Lynen [email protected] S [email protected] Volker [email protected] Department of Surgery, University Clinic, Pauwelsstr. 30, 52057 Aachen, Germany2 Interdisciplinary Centre of Clinical Research (IZKF) Biomat; University Clinic, Pauwelsstr. 30, 52057 Aachen, Germany3 Institute of Pathology, University Clinic, Pauwelsstr. 30, 52057 Aachen, Germany2004 29 12 2004 4 33 33 1 7 2004 29 12 2004 Copyright © 2004 Jansen et al; licensee BioMed Central Ltd.2004Jansen 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
Nidation of floating tumour cells initiates peritoneal carcinosis and limits prognosis of gastro-intestinal tumours. Adhesion of tumour cells to extracellular matrix components is a pivotal step in developing peritoneal dissemination of intraabdominal malignancies. Since phospholipids efficaciously prevented peritoneal adhesion formation in numerous animal studies we investigated their capacity to reduce adhesions of gastric cancer cells to extracellular matrix components (ECM).
Methods
Human gastric cancer cells (NUGC-4, Japanese Cancer Research Resources Bank, Tokyo, Japan) were used in this study. Microtiter plates were coated with collagen IV (coll), laminin (ln) and fibronectin (fn). Non-specific protein binding of the coated wells was blocked by adding 1% (w/v) BSA (4°C, 12 h) and rinsing the wells with Hepes buffer. 50.000 tumour cells in 100 μl medium were seeded into each well. Beside the controls, phospholipids were added in concentrations of 0.05, 0.1, 0.5, 0.75 and 1.0/100 μl medium. After an incubation interval of 30 min, attached cells were fixed and stained with 0.1% (w/v) crystal violet. The dye was resuspended with 50 μl of 0.2% (v/v) Triton X-100 per well and colour yields were then measured by an ELISA reader at 590 nm. Optical density (OD) showed a linear relationship to the amount of cells and was corrected for dying of BSA/polystyrene without cells.
Results
The attachment of gastric cancer cells to collagen IV, laminin, and fibronectin could be significantly reduced up to 53% by phospholipid concentrations of 0.5 mg/100 μl and higher.
Conclusion
These results, within the scope of additional experimental studies on mice and rats which showed a significant reduction of peritoneal carcinosis, demonstrated the capacity of phospholipids in controlling abdominal nidation of tumour cells to ECM components. Lipid emulsions may be a beneficial adjunct in surgery of gastrointestinal malignancies.
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Background
In the treatment of gastro-intestinal cancer the detection of free, isolated tumour cells in the peritoneal cavity serve as a prognostic marker for postoperative survival [1-4]. Since surgery frequently proofs insufficient for tumour control, numerous additional treatments have been evaluated. A pivotal step in developing peritoneal dissemination seems to be adhesion of tumour cells to mesothelial cells or extracellular matrix components [5-7]. Experimental studies suggest that peritoneal metastases tend to occur in areas of injured peritoneum [8]. Cell-matrix interactions are promoted by transmembrane receptors with integrins as a major family. Many attempts were made to inhibit tumour cell attachment by antibodies against adhesion molecules [9], dextran sulphate [10], or sodium hyaluronate [11] with different results concerning tumour adhesion.
Phospholipids, polar phosphoric acid di-esters, are natural constituents of the abdominal fluid. The substance is able to form a lubricant layer on the peritoneal surface [12]. Additionally, integrin function, particularly in control of cell motility is affected by exogenous addition of phospholipids (e.g. gangliosides) [13,14]. Intraperitoneal use of phospholipids (PL) led to a significant decrease of adhesion formation especially at sites of peritoneal injury [15,16]. The objective of the underlying in vitro study was focused on the influence of phospholipids on adhesion of gastric cancer cells to extracellular matrix components with broad reactivity to several integrins. Collagen IV (coll IV), and laminin (ln) are main components of the basement membrane and fibronectin (fn) plays an important role in wound healing [17,18].
Methods
Tumour cells
The human gastric cancer cell line NUGC-4 was purchased from the Japanese Cancer Research Resources Bank (Tokyo, Japan). The cells were maintained in monolayers in tissue culture flasks (75 cm2, Falcon, Becton Dickinson-Gambil, Heidelberg, Germany) in RPMI 1640 medium (GIBCO, Karlsruhe, Germany), supplemented with 10% foetal bovine serum (GIBCO), penicillin and streptomycin (GIBCO). Cell cultures were incubated at 37°C in a humidified atmosphere of 5% CO2 in air. Cells were passaged after treatment with 0.125% trypsin for 6 min. The cells were pelleted after centrifugation for 10 min at 200 g, suspended in 20 ml PBS, and pelleted. The cell pellet was resuspended in 30 ml complete medium and seeded with a splitting ratio of 1:3. Only cells from three passages were used for the experiments.
Extracellular matrix (ECM) components
Flat-bottom polystyrene microtiter plates (Becton Dickinson, Heidelberg, Germany) were coated for adhesion experiments. The purified ECM components were dissolved in PBS with the following concentrations: coll IV – 2,5 μg/ml (Biomol, Hamburg, Germany), fn – 10 μg/ml (Boehringer, Mannheim, Germany), ln – 50 μg/ml (Boehringer, Mannheim, Germany). We found these concentrations to be optimal in foregoing dilution series. They were added to the wells and incubated at 4°C for 24 hours (coll IV, fn), or at 37°C for 45 min in a humidified atmosphere of 5% CO2 in air (ln), respectively. Nonspecific protein binding of the coated wells was blocked by adding 1% (w/v) BSA (4°C, 12 h) and rinsing the wells with Hepes buffer.
Adhesion assay
For adhesion experiments gastric cancer cells were detached with collagenase I (15 min, 37°C, Worthington, Freehold, USA), washed once with RPMI 1640, centrifuged (200 g for 10 min), resuspended in RPMI 1640, and preincubated for 30 min in a humidified atmosphere of 5% CO2 in air (37°C). Fifty thousand tumour cells in 100 μl medium were seeded into each well. Evaluation of adherent cells was performed using crystal violet staining according to the method described by Aumeilley et al., and Tietze et al. [19,20]. After an incubation period of 30 min the supernatant with non-adherent cells was removed by two washes with warmed RPMI 1640. Attached cells were fixed with 30% (v/v) methanol/ethanol for 15 min at room temperature. Cells were stained with 0.1% (w/v) crystal violet (Sigma, Hamburg, Germany), extensively washed with distilled water, and dried at room temperature. The dye was resuspended with 50 μl of 0.2% (v/v) Triton X-100/well and colour yields were then measured using an ELISA reader at 590 nm (Titertek Multiscan Plus MKII, Flow Laboratories GmbH, Meckenheim, Germany). Optical density (OD) showed a linear relationship to the amount of cells between 1 × 103 and 5 × 104 cells per well, as determined by a dilution series.
Control dying of BSA/polystyrene without cells led to Optical Density (OD) values of 0.01–0.07. These values were subtracted from those obtained in the experiments.
Phospholipids
After complete preparation of the tumour cell suspension, the PL solution was added in the following concentrations: 0.05, 0.1, 0.5, 0.75, and 1 mg per 100 μl medium. The concentrations used were correlated to our in vivo experiments. The phospholipid solution consists of phosphatidylcholine 70% by weight, phosphatidylethanolamine 15% by weight, neutral lipids 8% by weight, sphingomyelin <3% by weight and lysophosphatidylcholine <3% by weight.
Statistical analysis
All experiments were performed three times in quadruplicate. The data are expressed as means +/- standard error of the mean (SEM). Student's t-test for unpaired data was used for statistical analysis. Differences were regarded as significant for p values < 0.05.
Results
The analysis of tumour cell adhesion to BSA 1% resulted in a mean extinction of 0.27 (SEM 0.01) at 590 nm. Coating with ln and fn led to a nearly twofold increase of tumour cell adhesion with mean values of 0.59 (0.03, ln) and 0.63 (0.03, fn). The cancer cells showed a most pronounced adhesion to coll IV with a mean extinction of 0.97 (0.02).
The tumour cell adhesion to ln registered after addition of PL was significantly reduced. The effect was concentration dependent compared to the controls. Even the minimum amount of PL 0.05 mg/100 μl led to a reduced extinction of 0.4 (0.01). Treatment with 0.1 or 0.5 mg/100 μl PL revealed extinction values of 0.32 (0.02) and 0.28 (0.02), respectively. The maximum effect could be demonstrated with 0.75 mg/100 μl PL with an extinction of 0.24 (0.02). The relative reduction of tumour cell adhesion compared to the control amounts to 59%. Treatment with 1 mg/100 μl PL showed no further decrease of tumour cell adhesion to ln. The mean extinction was 0.26 (0.01) (table 1).
Table 1 Influence of different phospholipid concentrations on adhesion of gastric cancer cells to laminin. Optical density (OD) measured in an ELISA reader at 590 nm
Extinction at 590 nm SEM p
Control 0.59 0.03
PL 0.05 mg/well 0.4 0.01 p < 0.05
PL 0.1 mg/well 0.32 0.02 p < 0.05
PL 0.5 mg/well 0.28 0.02 p < 0.05
PL 0.75 mg/well 0.24 0.02 p < 0.05
PL 1 mg/well 0.26 0.01 p < 0.05
The tumour cell adhesion on fn could not be reduced significantly with low concentrations of Pl. Addition of 0.05 mg/100 μl PL and 0.1 mg/100 μl resulted in a slight reduction of the extinction with mean values of 0.59 (0.02) and 0.59 (0.01). However, a significant reduction of tumour cell adhesion could be observed after treatment with 0.5 mg/100 μl PL, 0.42 (0.02); as well as with 0.75 mg/100 μl PL (0.39 (0.02)) and 1 mg/100 μl PL (0.38 (0.02)). We found a similar situation compared to ln with equal effects of 0.75 mg/100 μl and 1 mg/100 μl PL indicating that the maximum influence on adhesion is reached. The relative reduction of tumour cell adhesion compared to the control values amounts to 40% (table 2).
Table 2 Influence of different phospholipid concentrations on adhesion of gastric cancer cells to fibronectin. Optical density (OD) measured in an ELISA reader at 590 nm
Extinction at 590 nm SEM p
Control 0.63 0.03
PL 0.05 mg/well 0.59 0.02 n. s.
PL 0.1 mg/well 0.59 0.01 n. s.
PL 0.5 mg/well 0.42 0.02 p < 0.05
PL 0.75 mg/well 0.39 0.02 p < 0.05
PL 1 mg/well 0.38 0.02 p < 0.05
NUGC-4 gastric cancer cells prominently adhere to collagen IV compared to all other examined extracellular matrix components. The influence of PL on cell adhesion to coll IV was also concentration dependent. The reduction ranged from an extinction of 0.89 (0.01) after administration of 0.05 mg/100 μl PL to a maximum effect after treatment with 1 mg/100 μl PL with a value of 0.44 (0.02) (table 3). In comparison to the control value, this means a reduction of adherent tumour cells of 55%.
Table 3 Influence of different phospholipid concentrations on adhesion of gastric cancer cells to collagen IV. Optical density (OD) measured in an ELISA reader at 590 nm
Extinction at 590 nm SEM p
Control 0.97 0.02
PL 0.05 mg/well 0.9 0.01 p < 0.05
PL 0.1 mg/well 0.74 0.02 p < 0.05
PL 0.5 mg/well 0.61 0.02 p < 0.05
PL 0.75 mg/well 0.45 0.03 p < 0.05
PL 1 mg/well 0.44 0.02 p < 0.05
Discussion
Cell adhesion to the extracellular matrix plays a fundamental role in peritoneal carcinosis. The adhesion is mediated by transmembrane Integrins. Several proteins including fibronectin in the interstitial matrix, laminin and collagen IV in the basement membrane were identified as important ligands [21,22]. Many attempts were made to inhibit tumour cell adhesion by integrin antibodies or competitive inhibitors against specific peptide sequences [23-26]. Gui et al. showed that adhesion of different breast cancer cells to extracellular matrix components could be reduced by specific integrin antibodies [27]. However, different antibodies for different cell lines were necessary according to the expression of specific integrins on the cell surface. Haier et al. could find different adhesive capacities to collagen I in two subtypes of the HT-29 colon carcinoma cell line. The cells with a very limited capability to induce hepatic metastases showed a significant higher rate of adhesions compared to those inheriting a high potential for involvement of the liver [28]. The influence of three examined phosphotyrosine kinase inhibitors on integrin mediated tumour cell adhesion to collagen I was unspecific. Dennis et al. found different cell-surface receptors responsible for cell attachment to fibronectin and collagen as compared to laminin. They concluded with the hypothesis that specific glycolipids may be receptors for interaction with fibronectin [29].
In our experiments the reduced rate of cell attachment in the presence of phospholipids was independent from the extracellular matrix. A similar effect on intraperitoneal tumour growth was described by Jacobi et al. who could demonstrate that taurolidine/heparin and povidone iodine lead to a significant reduction of tumour cell growth in vitro as well as a reduction of tumour weight after intraperitoneal tumour injection [30]. Predominantly the result seems to be a attributed to the cytotoxic effect of the used substances and benefits to a lesser degree from adhesion prevention. Other substances used to prevent adhesion failed in the treatment of inhibiting tumour cell attachment. Sodium hyalunorate increased the metastatic potential of colo-rectal tumour cells, probably mediated by the CD44 receptor [31]. Dextran sulphate resulted in reduced tumour cell nidation at sites of injury to abdominal wall in mice [10,32]. However, several side effects were described in the use of dextrane for adhesion prevention. Main problems were oedema, pleura effusion, life-threatening coagulation disorders and severe allergic reactions [33,34]. Phospholipids, polar phosphoric acid di-esters, are natural constituents of the abdominal cavity fluid and cell membranes.
The hypothesis is that phospholipids form a lubricant layer on the peritoneum by binding with its negatively charged cholin branch chain to the positively charged peritoneal surface [12,14,35]. Phospholipids cover the entire peritoneal membrane by a thin fluid layer. By separating tumour cells from the peritoneal surface they proved to significantly reduce peritoneal carcinosis. Phospholipipds seem to reduce the expression of integrins and adhesion molecules on the cell surface to the effect that adhesions can be prevented reducing tumour cell attachment independent from their origin [15,16].
The in situ tumour cell – ECM interaction is influenced by adhesive and non-adhesive ECM components and can be understood as a three dimensional network [36]. Therefore the in vitro experiments with tumour cells as soluble agents added to ECM immobilized onto plastic surfaces cannot appropriately mimic the situation in situ. Recently we found that phospholipids significantly reduce the attachment area and the tumour volume of peritoneal carcinosis caused by the colonic cancer cell line DHD/K12/TRb in rats. These results were supported by a prolonged survival rate of the treated animals as compared to the control group. Additionally, we found a similar effect of phospholipids on the adhesion of the human rectal cancer cell line HRT-18 on the same ECM-components in vitro [37]. Consistent with results of other groups, the tumour cell attachment was found predominantly in areas of previously injured peritoneum [5,6,38,39].
We performed this study to ascertain the results of the foregoing animal experiments and to demonstrate the influence of phospholipids to three different ECM components, even though matrices of collagen IV, laminin and fibronectin alone may not be predictive of peritoneal membrane nidation.
Conclusion
These results, within the scope of additional experimental studies on mice and rats which showed a significant reduction of peritoneal carcinosis, demonstrated the capacity of phospholipids in controlling abdominal nidation of tumor cells to ECM components. Lipid emulsions may be a beneficial adjunct in surgery of gastrointestinal malignancies.
Competing interests
The work was financially supported by Fresenius Kabi, Bad Homburg, Germany.
The results are part of an international patent application.
Pre-publication history
The pre-publication history for this paper can be accessed here:
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| 15625005 | PMC544579 | CC BY | 2021-01-04 16:29:54 | no | BMC Gastroenterol. 2004 Dec 29; 4:33 | utf-8 | BMC Gastroenterol | 2,004 | 10.1186/1471-230X-4-33 | oa_comm |
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BMC CancerBMC Cancer1471-2407BioMed Central London 1471-2407-4-801554470210.1186/1471-2407-4-80Research ArticleNeuroblastoma and pre-B lymphoma cells share expression of key transcription factors but display tissue restricted target gene expression Lagergren Anna [email protected] Christina [email protected] Håkan [email protected] Mikael [email protected] Stem Cell Center, Lund University, S-221 85 Lund, Sweden2 Department of Laboratory Medicine, Division of Molecular Medicine, Lund University, University Hospital MAS, S-205 02 Malmö, Sweden2004 15 11 2004 4 80 80 16 7 2004 15 11 2004 Copyright © 2004 Lagergren 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
Transcription factors are frequently involved in the process of cellular transformation, and many malignancies are characterized by a distinct genetic event affecting a specific transcription factor. This probably reflects a tissue specific ability of transcription factors to contribute to the generation of cancer but very little is known about the precise mechanisms that governs these restricted effects.
Methods
To investigate this selectivity in target gene activation we compared the overall gene expression patterns by micro-array analysis and expression of target genes for the transcription factor EBF in lymphoma and neuroblastoma cells by RT-PCR. The presence of transcription factors in the different model cell lines was further investigated by EMSA analysis.
Results
In pre-B cells mb-1 and CD19 are regulate by EBF-1 in collaboration with Pax-5 and E-proteins. We here show that neuroblastoma cells express these three, for B cell development crucial transcription factors, but nevertheless fail to express detectable levels of their known target genes. Expression of mb-1 could, however, be induced in neuroblastoma cells after disruption of the chromatin structure by treatment with 5-azacytidine and Trichostatin A.
Conclusion
These data suggest that transcription factors are able to selectively activate target genes in different tissues and that chromatin structure plays a key role in the regulation of this activity.
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Background
The complex process of tumor development often involves changes in the transcription regulatory networks. In human cancer, genetic changes involving the transcription factor p53 gene is particularly common and the gene is found mutated in cancers originating from numerous cell types. This factor is, however, broadly expressed and directly involved in cell cycle regulation and apoptosis explaining the common involvement of the protein in tumor development. Many malignancies are characterized by specific chromosomal translocations that frequently affects the expression or structure of transcription factors with a more tissue specific expression pattern, often with an important function during development [1]. Examples of this can be found within the hematopoetic system where for instance translocations of Tal-1 is associated with T cell leukemia's [2] while modified BCL-6 [3] or c-myc [4] is associated with B cell non-Hodgkin's lymphomas. The close correlation with a specific tumor type and pathology to a specific transcription factor modification could well be explained by differential expression patterns and accessibility of the gene for translocation events. Another possibility could be that the action of the transcription factor is context dependent and therefore the ability of the modified protein to contribute to tumor development depends on the cell in which it arises.
To investigate mechanisms involved in lineage specific gene regulation and transcription factor target gene selection in tumor cells we have compared transcription factor expression in neuroblastoma and pre-B lymphoma cells. This revealed that both these highly divergent tumor types expressed the transcription factor EBF [5] that has been contributed a central role in B cell development [6]. The protein is a helix-loop-helix family member [7,8] essential for B-lymphopoesis in mice [6] where it has been shown to regulate a large number of pre-B cell restricted genes including the surrogate light chains [9], CD19 [10] and the signal transduction proteins Igα (mb-1) [7,11] and Igβ (B29) [12]. The EBF-1 protein is highly conserved between human and mouse [13] and it also appears as if the target gene spectra has been conserved between species even though the primary promoter sequences of these genes has diverged [13,14]. Pre-B cells express exclusively EBF-1 [15] while neuroblastoma cells express other family members including EBF-2 and -3 [5]. There are limited information about EBF target genes in neuroblastoma cells but binding sites for EBF proteins were identified in the promoters controlling the expression of the neuron restricted Chromogranin A (CGA) and SCG10 genes [5].
In the present study we were interested in the mechanisms of tissue specific target gene activation by comparing transcription factor function and gene expression patterns in neuroblastomas and pre-B cells. We here report that even though neuroblastoma cells express both Pax-5 and E-proteins, both suggested to be crucial co-activators for EBF target genes in pre-B cells, the cells do not express the pre-B cell restricted target genes mb-1 and CD19. The expression of the mb-1 gene could, however, be activated by treatment of the neuroblastoma cells with chromatin disrupting agents. This suggests that chromatin structure is a key component in the regulation of transcription factor function, by restricting the accessibility of target genes, possibly contributing to the apparent link between a transcription factor and specific malignancies.
Methods
Cell culture
HeLa, THP-1, KM3 and Nalm6 cells were grown in RPMI 1640 medium supplemented with 7.5% fetal calf serum (FCS), 10 mM HEPES, 2 mM pyruvate and 50 μg/ml gentamicin (complete RPMI media) (Life Technologies) at 37°C and 5% CO2. SH-SY5Y, IMR-32 and SK-N-BE(2)c neuroblastoma cells were cultured in Eagle's Minimum Essential Medium (MEM) with 10% FCS, 100 U/ml penicillin and 100 μg/ml streptomycin in an atmosphere of 5% CO2 at 37°C (Life Technologies). KCN-69n and LA-N-1 neuroblastoma cells were cultured in RPMI 1640 medium supplemented with FCS and antibiotics as above. The SK-N-BE(2)c cells were differentiated with 10 μM all-trans-retinoic acid (RA) in MEM containing 10% FCS for 0, 2, 8, 24, and 96 hours. 5-azacytidine (5-azaC) treatments were performed on SK-N-BE(2)c and SH-SY5Y cells at a concentration of 400 nM. After 64 h, fresh medium containing 50 nM Trichostatin A (TSA) was added. The cells were harvested in PBS after 24 h.
RT-PCR analysis
RNA was prepared from cells using Trizol (Life Technology) and cDNA was generated by annealing 1 μg of total RNA to 0,5 μg of random hexamers in 10 μl DEPC-treated water. Reverse transcriptase reactions were performed with 200 units of SuperScript Reverse Transcriptase (Life Technologies) in the manufacturers' buffer supplemented with 0,5 mM dNTP, 10 mM DTT and 20 units RNase inhibitor (Boeringer Mannheim, Bromma, Sweden) in a total volume of 20 μl, at 37°C for 1 hr. One-twentieth of the RT reactions were used in the PCR assays. PCR reactions were performed with 1 unit of Taq-polymerase (Life Technologies) in the manufacturers' buffer supplemented with 0.2 mM dNTP, in a total volume of 25 μl. GADPH was amplified by 25 cycles (94°C, 30 s, 55°C, 30 s and 72°C, 30 s) while 30 cycles were used to amplify CGA, SCG10, mb-1 and CD19 message (94°C, 30 s, 61°C, 30 s, 72°C 30 s). Primers were added to a final concentration of 1 mM. PCR products were blotted onto Hybond N+ nylon membranes (Amersham) using capillary blotting with 0.4 M NaOH. Membranes were pre-hybridized in 5X Denhardt's, 6XSSC, 0.1% SDS and 50 μg/ml Salmon Sperm DNA, at 57°C for 90 minutes and hybridized with γ[32P] labeled oligonucleotide for 12 hours at 57°C in the same solution. Membranes were washed at room temperature 2 times in 2XSSC supplemented with 0.1% SDS for 15 minutes.
Oligonucleotides used for RT-PCR were:
GADPH sense; 5'-CCACCCATGGCAAATTCCATGGCA;
GADPH antisense; 5'-TCTAGACGGCAGGTCAGGTCCACC;
CGA sense: 5'-GAAGATGAACTCTCAGAGGTTC
CGA antisense: 5'-GGATCTCCTTGTAGCCAAGGCTCG
CD19 sense: 5'-AGTCATTGCTGAGCCTAGAGCTG
CD19 antisense: 5'-CTCGGAGTCCTCCTCACTGTCAG
mb-1 sense; 5'-CCAGCATCATTGATGGTGAGCC
mb-1 antisense: 5'-GACATCTCCTATGTTGAGGCTGC
mb-1 hybridization; 5'-CCCGCACAATAGCAGCAACAACGCCAACGT
SCG10 sense; 5'-ATGCTGTCACTGATCTGCTCTTGC
SCG10 antisense; 5'-CAGGTTGAACTGTCTGGCTGAAG
EMSA
DNA probes were labeled with γ[32P] ATP by incubation with T4 polynucleotide kinase (Roche Molecular Biochemicals), annealed and purified on a 5% polyacrylamide Tris-borate-EDTA (TBE) gel. Nuclear extract [16], or in vitro transcribed-translated protein, was incubated with labeled probe (20,000 cpm, 3 fmol) for 30 min at room temperature in binding buffer (10 mM HEPES pH [7.9], 70 mM KCl, 1 mM Dithiothreitol, 1 mM EDTA, 2.5 mM MgCl2, 1 mM ZnCl2, 5% Glycerol) with 0.75 μg Poly(dI/dC) (Amersham Pharmacia). Antibodies (anti Pax-5 SC-1974, anti Pu.1 SC-352, anti actin SC-1616, ets1/2 SC-275 and anti myc SC-764 all from Santa Cruz Biotech and anti E2-2 from Pharmingen) were added 10 min before the addition of the DNA probe. The samples were separated on a 6% polyacrylamide TBE gel, which was dried and subjected to autoradiography.
Oligonucleotides used for EMSA were the following:
mb-1 sense 5'-AGCCACCTCTCAGGGGAATTGTGG;
mb-1 antisense 5'-CCACAATTCCCCTGAGAGGTGGCT;
CD19-BSAP sense 5'-GCAGACACCCATGGTTGAGTGCCCTCCAGG;
CD19-BSAP antisense 5'-CCTGGAGGGCACTCAACCATGGGTGTCTGC;
μE5 sense: 5'-GGCCAGAACACCTGCAGACG;
μE5 antisense: 5'-CGTCTGCAGGTGTTCTGGCC;
Oct binding site sense 5'-CATCTCAAGTGATTTGCATCGCATGAGACG;
Oct binding site antisense 5'-CGTCTCATGCGATGCAAATCACTTGAGATC;
Lambda B (Pu.1 site) sense: 5'-GAAAAAGAGAAATAAAAGGAAGTGAAACCA AG;
Lambda B antisense: 5'-CTTGGTTTCACTTCCTTTTATTTCTCTTTTTC;
CRE (ATF5 site) sense: TCA TGG TAA AAA TGA CGT CAT GGT AAT TA
CRE antisense: TAA TTA CCA TGA CGT CAT TTT TAC CAT GA
cDNA micro-array analysis
RNA from Nalm6, SK-N-BE(2)c, SH-SY5Y, KM3, THP-1 and HeLa cells was extracted using Trizol™ (Invitrogen, Carlsbad, California). A common RNA control obtained by mixing a variety of cell lines was used for all hybridizations. RNA was concentrated to 50 μg total RNA (25 μg sample RNA and 25 μg control RNA) to generate aminoallyl-modified cDNA. Sample cDNA was labeled with Cy3-dCTP and control RNA was labeled with Cy5-dCTP using CyScribe Post-Labeling Kit (Amersham Pharmacia Biosciences). A hybridization solution was made by combining labeled cDNA with 20 μl Cot-1 DNA (1 mg/ml), 3 μl Poly dA (4 mg/ml) and 1.5 μl yeast t-RNA (4 mg/ml), dry down by speed-vac and resuspended in 40 μl Pronto! Universal Hybridization Solution™ (Pronto!™ Universal Microarray Reagent System, Corning). The hybridization solution was added to a pre-hybridized microarray slide (DNA microarrays were obtained from the SWEGENE DNA Microarray Resource Center, Lund University). The arrays were hybridized at 42°C for 18 hrs, washed according to the manufacturers recommendations (Pronto!™ Universal Microarray Reagent System, Corning), dried by centrifugation and scanned on Agilent microarray scanner. Scans were analyzed using GenePix Pro versions 4.0.1.9 and 4.1.1.4. BioArray Software Environment (BASE) (Saal et al. Genome Biology 2002, 3(8):software0003.1–0003.6). The settings for the analysis presented were, Background Correlation: Mean FG – Mean BG, Spot filter: (Raw) SNR ch1 mean > = 2, (Raw) SNR ch2 mean > = 2, (Raw) Flags = 0, (Raw) Spot diameter > = 40, Normalization: Lowess, Reporter filter: in # of assays = 16, Analysis: Hierarchical clustering (reporter)
Results
Neuroblastoma and lymphoma cell lines display similarities in overall gene expression patterns but not of known EBF target genes
Knowing that neuroblastoma and pre-B cells share the expression of EBF [5,13,14], we wanted to investigate the potential similarities in overall gene expression patters in these two types of tumor cells. To this end we used cDNA micro-array analysis with material from two pre-B cell lines (Nalm6 and KM3), one EBF high and one EBF low expressing neuroblastoma cell line (SK-N-BE(2)c and SH-SY5Y) [5] (Data not shown, 13) one epithelial cell line (HeLa) and one myeloid cell line (THP-1). The level of specific mRNAs in the samples were compared to that obtained by a pooled reference RNA on cDNA gene chip (Appendix A). This allowed us to reliably investigate variations in expression of 2 162 genes in our cell lines and to group the different samples based on overall similarity in gene expression patterns (Figure 1). This suggested that the pre-B and neuroblastoma cell lines clustered to the same half of the expression tree even though THP-1 is a hematopoetic cell line. It also appeared as if while the two pre-B cell lines were rather similar the two neuroblastoma lines displayed larger discrepancies in overall expression patterns. It should be noted that the SK-N-BE(2)c cell line carries an amplified N-myc gene, which in the clinic is correlated to adverse outcome of the disease, while the SH-SY5Y cell line carry an N-myc gene in germ line configuration [18].
The finding that these two different types of tumors did display a degree of similarity in overall gene expression patterns opened the possibility that the previously identified EBF target genes would be expressed in both cell types. To investigate this possibility we extracted RNA from pre-B cell lines as well as neuroblastoma cells and investigated the expression of the pre-B cell EBF target genes CD19 [19] and mb-1 [13] as well as the potential neuroblastoma EBF target genes SCG10 and CGA [5] by RT-PCR (Figure 2). This indicated that while the neuroblastoma cells expressed SCG10 and CGA message they did not express either CD19 or mb-1 message. The opposite pattern was observed in the pre-B cell lines where CD19 and mb-1 but not SCG10 or CGA message could be detected. This show that even though neuroblastoma and pre-B lymphoma cells to some extent share gene expression patterns, they do not appear to share the expression of specific EBF target genes.
Neuroblastoma and pre-B lymphoma cells share the expression of the transcription factor Pax-5
Previous work has shown that both the mb-1 and CD19 genes are genetic targets for both EBF and the paired domain protein Pax-5 (BSAP) [13,19-21]. Thus, one possibility to explain lineage-restricted expression of these genes could be selective expression of Pax-5 in the Pre-B cells. To investigate this possibility we performed EMSA analysis with nuclear extracts from two human pre-B cell lines (Nalm6 and KM3) and two neuroblastoma cell lines (SH-SY5Y and SK-BE(2)c) (Figure 3A). As probes we used a consensus Oct binding site and the Pax-5 binding site from the human CD19 promoter [20]. These experiments revealed that not only the pre-B cell lines, but also the neuroblastoma cell lines expressed proteins able to interact with the CD19 Pax-5 binding site. No such binding activity was detected in extracts from epithelial cells (Data not shown). To verify that the binding activity was due to the presence of Pax-5 we performed a super-shift experiment using a Pax-5 specific antibody, a control antibody or no antibody in the binding reaction (Figure 4). While the control antibody did not affect formation of the complex the Pax-5 antibody resulted in a reduced DNA binding and also the appearance of a weak super-shifted band using either pre-B (Data not shown) or neuroblastoma nuclear extracts. These data confirmed that the DNA/protein complex observed in extracts from the neuroblastoma cells was composed of Pax-5 protein. Pax-5 protein could also be detected in the neuroblastoma cell lines IMR-32, KCN-69n and LA-N-1 (Figure 4). Since the binding activity of several transcriptions factors such as E-proteins and EBF appear to be modulated during the induced differentiation of neuroblastoma cells [5,22-24] we wanted to investigate if this was the case also for Pax-5. To this end, SK-BE(2)c cells were treated with retinoic acid and proteins were extracted at 2, 8, 24, 48, 72 and 96 hours after stimulation. The induction of differentiation was assayed by morphological change and dendrite outgrowth of the stimulated cells. We then analyzed the amount of Oct protein as well as of Pax-5 proteins at the different time points after stimulation by EMSA (Figure 4). This indicated that by using the octamer binding activity as a reference there were no major alterations in Pax-5 DNA binding in the course of SK-BE(2)c differentiation. Thus, several neuroblastoma cell lines express both EBF proteins and Pax-5, two genes known to induce expression of the mb-1 and CD19 genes in pre-B cells, but neuroblastoma cells nevertheless fail to express these two target genes.
Pre-B lymphoma and neuroblastoma cell lines display differential expression of E-, Ets and ATF proteins
In addition to provide information of overall relationships between different types of tumors, the micro-array analysis also yield preliminary information about differential gene expression patterns of individual genes (Appendix A). The data obtained in our experiments suggested differential expression of genes such as myb, CBFβ and HMG 1, 3 and 4 proteins with high expression restricted to the pre-B cells while Bcl-6 appeared to be over-expressed specifically in neuroblastoma cells (Appendix A). To further investigate other potential differences and similarities between pre-B and neuroblastoma cells with regard to expression of gene regulatory proteins we extracted expression data regarding additional genes encoding transcription factors from our data set (Figure 4). This analysis indicated that even though the expression of several factors appeared similar, there were large discrepancies in the mRNA expression of the E-protein E47 and the ATF5 protein. These genes were expressed to a high level in lymphoma cells but to a lower level in the neuroblastoma cells. To investigate this further we analyzed the expression of E-proteins by EMSA using a binding site from the mouse Immunoglobulin heavy chain intron enhancer as probe. This suggested that while one prominent complex was formed in the pre-B cell lines, the SK-N-BE(2)c cells did not contain any significant amounts of μE5 binding activity (Figure 5A). Several complexes were formed using nuclear extracts from SH-SY5Y neuroblastoma cells, but the migration in the gel was different from that of the complexes observed in pre-B cells. Investigating the μE5 binding activity in additional neuroblastoma cell lines suggested that nuclear extracts from IMR-32 cells gave rise to a rapidly migrating complex while extract from KCN-69n cells contained proteins forming two different complexes. This suggests that there might exist differences in E-protein expression in different neuroblastoma cell lines.
To further investigate the E-box binding activities in SH-SY5Y cells as compared to Nalm6 cells we performed super-shift assays using antibodies against E2-2, E47 or c-myc (Figure 5B). The major complexes formed by nuclear extracts from the pre-B cell lines could be super-shifted either by the addition of anti E2-2 or anti E47 antibody while the complex formed in SH-SY5Y extracts reacted only on the addition of anti E2-2 antisera. None of the complexes in any of the cell lines were affected by the addition of either the c-myc or the actin antibody. This indicates that even though E2-2 is a major component in the formation of μE5 binding activity in the pre-B cells, it appears to form complex together with E47. This was not seen in the SH-SY5Y cells suggesting that there exist a distinct difference in the composition of E-box binding complexes in the two cell types.
The micro-array analysis did also suggest a difference in the expression of activator of transcription 5 (ATF5/ATF7/ATFX) [25,26]. ATF5 is a transcription factor of the basic-leucinezipper family that has been suggested to modulate neurogenesis [27] making it potentially interesting in the biology of the neuroblastoma cells. This protein binds to c-AMP responsive element (CRE) [25,26] hence, we performed EMSA analysis using oligonucleotide with a CRE binding site and nuclear extracts from either pre-B cell lines or neuroblastoma cells (Figure 6A). Both the pre-B cells and the neuroblastoma cells generated two major complexes (C1, C2) migrating with apparently the same mobility as compared to the Oct proteins. The relative abundance of these two complexes did however differ since the pre-B cells contained larger relative amounts of the slowly migrating complex (C1). This suggests that there are differences in the expression of CRE binding proteins in pre-B and neuroblastoma cells lines investigated.
EBF has also been suggested to share pre-B cell restricted target genes with Ets proteins and one factor belonging to this family of proteins suggested to contribute to B lineage identity is the Ets protein Pu.1 [28,29]. In order to investigate the DNA binding activities in neuroblastoma and pre-B lymphoma we incubated a PU.1 binding site (λB) from the mouse Immunoglobulin λ enhancer with nuclear extracts from the two cell types (Figure 7). This showed a complex pattern of DNA binding activities but only the pre-B cells contained a complex reacting to the addition of anti Pu.1 anti-sera. Thus, we conclude, that even though neuroblastoma and lymphoma co-express several potent transcription factors there are also specific differences. Thus, even though both cell types express EBF, the protein would have to act in a different transcription factor context in the two cell types.
Chromatin structure participates directly in the regulation of tissue specific gene expression patterns
These experiments suggested that there indeed are specific differences in transcription factor expression between the two cell types, but also that they share expression of several key regulatory molecules. This indicates that additional components of gene regulation may be of crucial importance for target gene selection. One potential barrier to gene activation could be the chromatin structure, that is, the epigenetic setting of the cells directs the transcription factors to their tissue specific target genes. To investigate this possibility we analyzed the expression of the mb-1 gene in neuroblastoma cells after disruption of the chromatin structure by culturing the cells in the presence of the DNA de-methylating agent 5-azaC and the histone deacetylase inhibitor TSA. RT-PCR analysis suggested that while the untreated control cells did not express any detectable amount of mb-1 transcripts the 5-azaC/TSA treated cells expressed significant amounts of mb-1 transcripts (Figure 7). To obtain an approximation of the obtained expression levels we compared the amount of mb-1 message in the treated neuroblastoma cells to that of a serially diluted cDNA from Nalm6 pre-B cells. This suggested that while the expression obtained in SK-N-BE(2)c cells treated with 5-azaC/TSA was about one twentieth of that observed in a pre-B cell, the SH-SY5Y cells were induced to express levels comparable to that observed in the pre-B cells. In contrast we were unable to observe an induction of CD19 expression (Data not shown). Thus, we conclude that the lineage specific regulation of the mb-1 gene may be directly dependent on chromatin status, while expression of other pre-B cell specific EBF target genes, such as CD19, must be explained by other mechanisms.
Discussion
We here report that neuroblastoma and pre-B lymphoma cells share the expression of transcription factors believed to be essential for normal B cell development while the expression of their B lineage target genes remains tissue specific. The finding that neuroblastoma cells expressed Pax-5 was somewhat surprising even though this factor has been shown to be involved in midbrain development. Mice carrying a homologous disruption of the Pax-5 gene lack both B-lymphoid cells and a fully developed central nervous system [30,31]. In the B cell compartment it also appears as if Pax-5 is crucial to lock the cell in the B-lymphoid developmental pathway. That is, even though pro-B cells from Pax-5 deficient mice express a whole set of early B cell markers [30,31], they can, in contrast to normal pro-B cells, be differentiated into other hematopoetic lineages [32,33]. Thus, Pax-5 appears to be essential for lineage fidelity in addition to be crucial for the completion of the B lymphoid development pathway. The former has been attributed to an ability of Pax-5 to inhibit the expression of cytokine receptors like M-CSF [32] and also of Notch-1 [34]. Interestingly, Notch signaling plays a key role in fate selection of neuronal cells and we have previously shown that over expression of a constitutively active form of Notch-1 inhibits induced differentiation of neuroblastoma cells [23]. It is currently not clear whether a link between Pax-5 and Notch1 expression is at hand in neuronal cells, but it is noteworthy that neuroblastoma cells express relatively high levels of Pax-5. Elevated expression levels of Pax-5 has been detected in two other tumors of neuronal origin, medulloblastoma [35] and astrocytoma [36] and in vitro data [37] as well as observed translocations in human B lineage tumors [38-40] indicate that Pax-5 have oncogenic properties.
In contrast to the stable expression of Pax-5 there appeared to be large differences in E-protein expression in the neuroblastoma cell lines investigated. In neuronal tissues a number of tissue specific bHLH proteins have been defined. Importantly, these pro-neuronal bHLH proteins require heterodimerization with E-proteins for the formation of DNA binding complexes. In the developing sympathetic nervous system HASH-1 is transiently expressed and is of pivotal importance for the formation of the autonomic and olfactory nervous system, neuroendocrine cells of the lung and specific regions of the telencephalon (reviewed in [41]). We have previously shown that a majority of the neuroblastomas express HASH-1, supporting the notion that the tumor is of embryonal origin [22]. We also showed that E2-2 is the preferential binding partner of the pro-neuronal bHLH protein HASH-1 in SH-SY5Y neuroblastoma cells [42], and the EMSA results presented in this paper, showing that E2-2 is the main E-protein binding a μE5-E-box, corroborate this observation. Some findings indicate that E2-2 has specific and important functions in neuronal tissues [43,44]. The gene is expressed at substantial level in brain compared to most other tissues [44] and E2-2 has been shown to bind and regulate several neuronal/neuroendocrine promoters, such as brain-specific FGF-1.B [44]. Several lines of investigation has suggested that E-proteins are redundant in B cell development and that the apparent need of E2A protein in B cell development is due to lack of sufficient doses of functional E-proteins [43,45]. This notion is substantiated by the finding that E2A deficiency can be rescued by expression of another E-protein, Heb [46], and that all these proteins appear able to activate the pre-B cell restricted λ5 promoter in synergy with EBF [47]. Thus, based on these findings it is unlikely that the E-protein composition would have any dramatic impact on EBFs ability to activate target genes in neuroblastoma.
Our data also suggest that the chromatin structure of DNA is able to modulate the function of transcription factors in a dramatic manner. In the case of the mouse mb-1 promoter is has been shown that methylation of specific C residues in the promoter prevents the complex formation between Pax-5 and Ets proteins, thereby reducing functional activity of the promoter [48]. Both the Ets and Pax-5 binding sites are conserved between mouse and human and similar mechanisms acting on the human promoter may well account for the observations in this report [49]. It is also noteworthy that we obtained a much higher level of mb-1 transcription in the SH-SY5Y cells then in the SK-N-BE(2)c cells (Figure 7), a finding that could be expected due to that the SH-SY5Y cells expressed high levels of BSAP and E-proteins while SK-N-BE(2)c cells appeared to express somewhat lower levels of BSAP and no detectable amounts of μE5 binding E-proteins (Figure 3A and 5A). However, we were unable to observe any activation of CD19 gene even after treatment with demethylating agents suggesting that gene regulation is exerted at several different levels or that the human CD19 gene is highly dependent also of other factors, not present in the neuroblastoma cells. In this study we disrupt chromatin structure by treating the cells with a combination of a methylation inhibitor and a HDAC inhibitor. The processes of DNA methylation and histone acetylation are intimately connected. It has been known for a long time that histone deacetylase inhibitors, by reactivating gene expression, can inhibit growth and/or survival of cancer cells. Even though the precise mechanisms behind the effect of these drugs are largely unknown several of them are now evaluated in clinical trials. Furthermore, the observations of abnormal DNA methylation patterns in malignant cells are becoming increasingly interesting, as new information of the link between gene activation and methylation status increase.
One dilemma, however, resides in the issue if a gene is demethylated due to activation or activated due to demethylation. In our specific case the latter would be the most likely explanation. We cannot however exclude that our observations are due to the induction of other unknown proteins. The selective activation of mb-1 and the presence of all the known crucial transcription factors in the neuroblastoma cells would, however, be in line with de-methylation being the primary event. The shared expression of a whole set of transcription factors in such diverse malignancies as lymphoma and neuroblastoma may also be of importance for our view on the relationships between different tumor types. That is, even though these tumors are highly divergent and show large differences in the clinical outcome, there may be genetic links that can be used for drug targeting thus reducing the complexity of cancer treatment dramatically.
Conclusion
These data suggest that transcription factors are able to selectively activate target genes in different tissues and that chromatin structure plays a key role in the regulation of this activity.
Competing interests
The author(s) declare that they have no competing interests.
Authors' contributions
Anna Lagergren has performed the EMSAs, the micro-array experiments and the PCR experiments under the supervision of Mikael Sigvardsson while Christina Manetopoulus has performed the cell culture work as well as 5-aza-Cytidine treatments and RNA purifications under the supervision of Håkan Axelson. All contributors have been involved in the writing of the manuscript.
Pre-publication history
The pre-publication history for this paper can be accessed here:
Supplementary Material
Additional File 1
The data shows hierarchical clustering of the microarray data displaying differential gene expression patterns of individual genes in the different cell lines used in this study.
Click here for file
Acknowledgements
We are grateful to the Swegene micro-array facility for help with cDNA expression analysis.
This work was supported by grants from the Swedish Cancer Society, the Children's Cancer Foundation of Sweden, The Swedish Research Council, Kock's Foundation and The Crafoord Foundation.
Figures and Tables
Figure 1 Pre-B cells and Neuroblastoma cells display similarities in overall gene expression patterns. The figure shows a hierarchical tree based on cDNA micro-array analysis of the expression of 2162 genes in a set of cell lines as indicated. Each individual hybridization experiment is represented by the name of the cell line and the number of the experiment. THP-1 is a human myeloma cell line, HeLa a human epithelial cell line, Nalm6 and KM3 represents human pre-B lymphoma. SY represents the neuroblastoma cell line SH-SY5Y and SK the neuroblastoma cell line SK-N-BE(2)c.
Figure 2 EBF target genes are expressed in a tissue specific manner. The figure show ethidium bromide stained agarose gels with the products from RT-PCR analysis of Nalm6, KM3, SH-SY5Y and SK-N-BE(2)c cells. The panel shows the expression of actin, SCG 10, CGA, mb-1 and CD19 mRNA as indicated. Serial dilutions using 1, 1/5 and 1/25 of the template cDNA after 25 (actin) and 30 (SCG 10, CGA, mb-1 and CD19) cycles of PCR were used.
Figure 3 Neuroblastoma cells express constitutive levels of Pax-5 proteins. Panel (A) display EMSAs where Oct or BSAP (Pax-5) binding sites was incubated with nuclear extracts from Nalm6 (pre-B), KM3 (melanoma), SH-SY5Y (neuroblastoma) and SK-N-BE(2)c (neuroblastoma) cells.
Figure 4 Neuroblastoma cells express constitutive levels of Pax-5 proteins. Panel (B) shows EMSA analysis of Pax-5 (BSAP) super-shift using nuclear extracts from a set of neuroblastoma cell lines as indicated after addition of either no, anti-Pax-5 or anti actin antibody. Panel (C) shows auto radiogram with EMSAs using either a Pax-5 or an Oct binding site and nuclear extracts from SK-N-BE(2)c (neuroblastoma) cells stimulated to undergo differentiation with retinoic acid (RA) for the indicated times. The left panel displays a super-shift experiment using either actin or Pax-5 antisera.
Figure 5 Neuroblastoma cells and lymphoma cells display specific differences in transcription factor expression. The diagram shows the average expression levels of a set of transcription factors in the cell lines analyzed by cDNA micro-arrays. The relative expression levels are based on the comparison to a pooled human reference RNA.
Figure 6 Neuroblastoma cells express variable levels of E-protein activity. Panel (A) display auto radiograms with EMSAs of Oct and ?E-5 (E-protein) DNA binding activity in nuclear extracts from Nalm6 and KM3 pre-B lymphoma cells and SH-SY5Y, SK-N-BE(2)c, IMR-32, KCN-69n and LA-N-1 neuroblastoma cells.
Figure 7 Neuroblastoma cells express variable levels of E-protein activity. Panel (B) show a EMSA analysis where nuclear extracts from either SH-SY5Y neuroblastoma or Nalm6 pre-B lymphoma cells were pre-incubated with either no antibody, anti E2-2, anti E47, anti myc or anti actin antibody as indicated.
Figure 8 Neuroblastoma and pre-B lymphoma differs in expression of CRE and Pu.1 site binding proteins. Panel (A) display EMSA analysis of nuclear extracts from pre-B (KM3) or neuroblastoma cells (SH-SY5Y and SK-N-BE(2)c) incubated with a consensus cAMP responsive element (CRE). The two major complexes are denoted ATFC1 and ATFC2. Panel (B) show an EMSA analysis using nuclear extracts from either pre-B lymphoma cells (Nalm6) or the neuroblastoma cells SH-SY5Y and SK-N-BE(2)c, incubated with a labelled Pu.1 binding site from the mouse Immunoglobulin l enhancer (lB). Ets1/2 or PU.1 antibody was included in the reactions as indicated. The arrow indicates the complex super-shifted by the anti PU.1 antibody.
Figure 9 Disruption of the chromatin structure allows for the activation of mb-1 transcription in neuroblastoma cells. (A) The top part of the figure shows ethidium bromide stained agarose gels with actin expression after RT-PCR analysis of Nalm6, SH-SY5Y and SK-N-BE(2)c cells without or after treatment with 5-azaC and TSA. Serial dilutions using 1, 1/5 and 1/25 of the template cDNA after 25 cycles of PCR were used. Panel (B) display auto-radiograms of southern blot analysis of mb-1 RT-PCR products obtained from untreated or 5-azaC/TSA treated SH-SY5Y and SK-N-BE(2)c cells and a serial dilution of untreated Nalm6 cells after 30 cycles of PCR as indicated.
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| 15544702 | PMC544580 | CC BY | 2021-01-04 16:03:02 | no | BMC Cancer. 2004 Nov 15; 4:80 | utf-8 | BMC Cancer | 2,004 | 10.1186/1471-2407-4-80 | oa_comm |
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World J Surg OncolWorld Journal of Surgical Oncology1477-7819BioMed Central London 1477-7819-2-451559834810.1186/1477-7819-2-45ResearchMetallopanstimulin as a marker for head and neck cancer Stack Brendan C [email protected] Christopher S [email protected] Christopher M [email protected] Frank R [email protected] Val J [email protected] Paul D [email protected] Division of Otolaryngology-Head and Neck Surgery, Penn State College of Medicine, Hershey, PA, USA2 Departments of Surgery and Health Evaluation Sciences, Penn State College of Medicine, Hershey, PA, USA3 Department of Health Studies, Lehigh Valley Hospital, Allentown, PA, USA4 Department of Radiation Oncology, University of Utah Medical Center, SLC, UT, USA5 Department of Medicine, Division of Oncology, Duke University Medical Center, Durham, NC, USA6 Department of Radiology, Mayo Clinic, Rochester, MN, USA7 James A. Cochran VA Medical Center, St. Louis, MO, USA2004 14 12 2004 2 45 45 11 10 2004 14 12 2004 Copyright © 2004 Stack et al; licensee BioMed Central Ltd.2004Stack 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
Metallopanstimulin (MPS-1) is a ribosomal protein that is found in elevated amounts in the sera of patients with head and neck squamous cell carcinoma (HNSCC). We used a test, denoted MPS-H, which detects MPS-1 and MPS-1-like proteins, to determine the relationship between MPS-H serum levels and clinical status of patients with, or at risk for, HNSCC.
Patients and methods
A total of 125 patients were prospectively enrolled from a university head and neck oncology clinic. Participants included only newly diagnosed HNSCC patients. Two control groups, including 25 non-smokers and 64 smokers, were studied for comparison. A total of 821 serum samples collected over a twenty-four month period were analyzed by the MPS-H radioimmunoassay.
Results
HNSCC, non-smokers, and smokers had average MPS-H values of 41.5 ng/mL, 10.2 ng/mL, and 12.8 ng/mL, respectively (p = 0.0001).
Conclusion
We conclude that MPS-1 and MPS-1-like proteins are elevated in patients with HNSCC, and that MPS-H appears to be a promising marker of presence of disease and response to treatment in HNSCC patients.
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Background
Effective therapy for head and neck squamous cell carcinoma (HNSCC), which constitutes approximately 95% of head and neck malignancies, is dependent upon early diagnosis and intervention. Despite the obvious advantage to earlier diagnosis of head and neck malignancies, no strategy has proven to effectively detect these tumors at early stages. Most head and neck neoplasms are detected when the patient has become symptomatic from the effects of the primary disease or when lymphatic metastases are palpable. These tumors are infrequently found incidentally on physical exam, and in these cases are often discovered at an earlier stage. Stage of disease at time of diagnosis is the primary metric used for determination of therapy and prognostication of life expectancy [1]. As tumor stage advances, the morbidity of surgical resection worsens due to an increased loss of tissue volume and involvement of vital structures. Organ-sparing approaches to head and neck malignancies have been developed in an attempt to treat advanced stage lesions while avoiding conventional surgical morbidities. They have, however, not produced universally superior results to surgery both in terms of local-regional control and function.
Surveillance in the post-treatment head and neck cancer patients has traditionally centered on regular physical examination [2,3]. Office flexible fiberoptic exams of these patients have provided an excellent means of diagnosis for early mucosal recurrences, but are dependent upon the patient's compliance with regular follow-up and often cannot detect submucosal recurrence. Anatomic imaging is used as an adjunct to regular physical exam when recurrence is suspected, when findings are suggestive of cervical lymphatic involvement, or when a patient's symptoms are out of proportion or unexplained by physical exam findings. Imaging of anatomic structures is complicated by alterations in anatomy due to previous surgery or irradiation. Furthermore, despite many promising early reports, no tumor marker has yet been adopted for clinical use which shows high specificity or sensitivity for primary or recurrent HNSCC [4-8].
Metallopanstimulin-1 (MPS-1) was identified, cloned and characterized in the laboratory of Dr. Fernandez-Pol from a cDNA library constructed from a human mammary carcinoma cell line (MDA-468) that was stimulated by the growth factors TGF-β1 and EGF in the presence of cyclohexamide [9]. MPS-1, a multifunctional S27 ribosomal protein, is an 84 amino acid 9.5 kD ribosomal subunit, "zinc finger" protein that is present in all tissues and expressed in large quantities in a wide spectrum of proliferating tissues and oncogenic processes [10-16]. When MPS-1 is over-expressed, it is either secreted or passively released down a concentration gradient into the extra-cellular space.
Conventionally, ribosomal proteins are thought to be confined in their function to intracellular protein synthesis. Many recent reports have drawn attention to "extraribosomal functions" of ribosomal proteins [17-20]. Moreover, these extraribosomal functions have been observed in relation to oncogenesis in various models [21,22]. The zinc finger motif of MPS-1 and other ribosomal proteins may allow binding to nucleic acids which may result in interference with transcription and translation [10,17,18]. Practical applications of this include: 1) DNA repair, 2) gene suppression, 3) cell-cycle control, or 4) control of oncogenesis. Another related ribosomal protein, S27a, is ubiquitinilated and over expressed in human colon cancer. Like MPS-1, it is involved in cell-cycle control and DNA replication [23].
The physiology of MPS-1 expression and our initial experience with this protein in HNSCC has led us to conclude that MPS-1 and MPS-1-like proteins may be useful markers in the effort to screen for and analyze the extent of HNSCC [24]. The purpose of this study was to use an empirical MPS-H test, which measures both MPS-1 and MPS-1-like proteins, to 1) compare average MPS-H levels between HNSCC patients and normal controls and 2) to illustrate how the MPS-H test may be useful for surveillance and evaluating response to treatment for HNSCC.
Patients and methods
Patients
A total of 125 volunteers with newly diagnosed HNSCC were prospectively enrolled from a university head and neck oncology clinic. Serum collection consisted of a pre-treatment specimen followed by collections every six weeks during the first year and quarterly during the second year. At the time of specimen collection, presence of HNSCC was judged by all available data including: physical examination (that included an office endoscopy when indicated), biopsy, and radiology [computerized tomography (CT), magnetic resonance imaging (MRI), or positron emission tomography (PET)]. A clinical assessment was rendered based on all available data as "no evidence of disease (NED)", "alive with disease", or an indeterminate status. Serum MPS-H levels in these patients (N = 709) were compared to two control groups. The first control group was comprised of 25 normal, healthy, non-smoking volunteers. The second control group included 64 actively smoking volunteers who were screened for HNSCC and were found to be free of disease during the 1999 Yul Brynner Head and Neck Screening day in St. Louis, Missouri. A total of 821 serum samples collected over a twenty-four month period were analyzed by the MPS-H radioimmuno assay (follow-up days, mean: 217, median: 166.). All patients gave informed consent under an IRB-approved protocol.
MPS-H Serum Assay
Technical details for the preparation of reagents for MPS-H antigen determinations, RIA procedure, and patient sample preparation are published elsewhere [9,10]. Each serum sample was run in duplicate by a single technician who was blinded to specimen identity. The targets of this assay are the MPS-N-terminus of both MPS-1 and MPS-1-like proteins. These proteins are activated or released from the precursor or carrier proteins by heat-denaturing of the serum under controlled conditions. The resulting proteins are collectively designated as MPS-H. Immunoreactive substances detected by the MPS-H test do not reflect the true levels of authentic immunoreactive MPS-1/S27 ribosomal protein in the circulation under non-denatured conditions.
Statistical Analysis
Average MPS-H levels were compared between HNSCC patients and the two control groups and across American Joint Committee on Cancer (AJCC) stages and subsites using analysis of variance (ANOVA). Based on the results from the clinical assessment we computed the receiver operating characteristic (ROC) curve for MPS-H. The ROC curve plots the trade-off between sensitivity and specificity for a range of threshold values for defining a positive result. All ANOVA and contingency table analysis were performed using SAS (version 8.1, Cary, NC) statistical software. ROC curve analysis was performed using R software, an open source implementation of the S language (version 1.4.1, ). Statistical significance was defined as p < 0.05.
Results
Of the 125 HNSCC patients studied, 90 were male (Table 1). Table 2 presents the distribution of stage and site of the primary tumor. Most patients presented in stage III (24.0%) or IV (50.4%) with primary tumors of the oral cavity (26.4%) and larynx (37.6%).
Table 1 Demographic and treatment characteristics of cases
Gender Radiation/Chemo Surgery Total
Female N 7 28 35
% 5.6% 22.4%
Male N 19 71 90
% 15.2% 56.8%
Total N 26 99 125
% 20.8% 79.2% 100.0%
Table 2 Summary of primary tumor stage and location.
Primary Stage
Tumor Site I II III IV Total Percent
Hypopharynx 0 1 0 1 2 1.6%
Larynx 7 4 18 18 47 37.6%
Nasopharynx 0 0 0 4 4 3.2%
Neck 0 1 1 3 5 4.0%
Oral Cavity 5 8 4 16 33 26.4%
Oropharynx 0 0 2 18 20 16.0%
Parotid 0 0 2 0 2 1.6%
Skin 4 2 3 3 12 9.6%
Total 16 16 30 63 125
Percent 12.8% 12.8% 24.0% 50.4% 100.0%
MPS-H levels in this group were compared to a control group of healthy volunteers and to a control group who volunteered for screening for head and neck cancer (Figure 1). Mean MPS-H was 10.2 ng/mL for the healthy control group, and 12.8 ng/mL for the smoking control group. Mean MPS-H for the HNSCC group was 41.5 ng/mL, which was significantly higher than both control groups (p < 0.0001). Furthermore, Figure 2 illustrates that among HNSCC patients, those who were successfully treated and clinically free of disease had consistently lower MPS-H levels over time than patients living with active head and neck cancer.
Figure 1 Mean serum MPS-H level for patients with SCC (n = 125, all stages and sites within the head and neck), healthy control group (n = 51) and actively smoking volunteers who were screened for HNSCC (N = 64). Mean of SCC group is 41.5 ng/mL, mean of healthy control group is 10.2 ng/mL, and mean of screening controls is 12.8 ng/mL (p < 0.0001). Error bars denote 1 standard deviation.
Figure 2 Serial MPS-H levels in HNSCC patients treated and without clinical disease (No Disease) and patients with unresectable disease or receiving palliative therapy with persistent clinical disease (Alive with Disease). Error bars denote 1 standard deviation and vary widely in the AWD group due to its small size and patient attrition over time from death.
We next computed the receiver operating characteristic (ROC) curve for the MPS-H levels. Figure 3 shows that the area under the ROC curve (0.73; 95% CI: 0.71–0.75; p = 0.001), is significantly different from 0.5, which suggests that there is moderate diagnostic accuracy associated with MPS-H. Analysis of MPS-H levels as a function of AJCC stage or head and neck sub site were performed and were not significant. Larger numbers of earlier stage (I and II) tumors and greater numbers among the various sub sites might result in significance.
Figure 3 Receiver operating characteristic (ROC) curve for MPS-H test. Area under the curve is 0.73 (CI95: 0.69 – 0.76, p = 0.001)
We have observed several instances where elevated MPS-H levels in patients presenting with head and neck neoplasms dropped to normal levels following successful therapy. We have also noted examples of persistent elevations or increases in MPS-H levels in patients with failure to respond to therapy or with recurrence of tumor respectively. Several cases of patients presented in Figures 4, 5, 6 illustrate these phenomenons. Patient 1 (Figure 4) was a female with a T4N0M0 SCC of the floor of mouth with positive tumor margins on the cut edge of the mandible, having failed a recent limited surgery by another surgeon. Her presenting MPS-H value was borderline positive when she had little clinical disease [1 on the x axis] and rose immediately postoperatively. A transient rise after surgical ablation or induction chemotherapy is a documented phenomenon observed with numerous tumor markers (personal communication J.A. Fernandez-Pol). The elevation likely results from a large initial disruption of cells within the tumor resulting in a dumping of intracellular MPS-1 into the circulation. This usually returns to baseline in 4 to 6 weeks if the tumor is successfully treated. The patient was clinically NED in the immediate postoperative period and during radiation treatment. Approximately 6 months post operatively, the patient developed a neck mass and by the next visit skin metastasis were seen. On last follow-up, the patient had progression of skin and neck metastasis at which time she decided to pursue hospice care. She expired 6 weeks later.
Figure 4 Patient without evidence of disease from surgery through radiation therapy (draws 1–5). The patient suffered a recurrence of clinical disease following radiation therapy (draws 6–8), which progressed until death.
Figure 5 Patient followed for 24 months without evidence of recurrence on physical exam, endoscopy, or FDG-PET.
Figure 6 Patient with unresectable tumor. Note MPS-H level response to chemotherapy. Patient subsequently expired after refusing further therapy.
Patient 2 (Figure 5) is a male who was diagnosed with a T4N2cM0 SCC of the larynx, which required total laryngectomy, bilateral neck dissections, and postoperative radiation therapy. The patient joined this study 3 months following his surgery, while clinically NED, and has remained free of clinical recurrence for 24 months. The patient had three FDG-PET scans at six-month intervals following his surgery that were all negative for recurrent disease or metastasis. His persistently low MPS-H levels over time (range from 7.7–19.2 ng/mL), was suggestive of ongoing disease-free status.
Patient 3 (Figure 6) was a female who presented with a T4N0M0 SCC of the hypopharynx. She was severely malnourished with multiple medical problems and thought to be a poor surgical candidate. She decided to pursue induction chemotherapy (Carboplatin and Taxol) to be followed by radiation therapy. She underwent three rounds of chemotherapy at 21-day intervals and had a 50% decrease in her tumor size as judged by office endoscopy. She suffered severe GI problems during her therapy, opted not to continue on to radiation, and enrolled in hospice care. The patient did not present for additional follow-up after entering hospice and died three months later. This case illustrates the potential utility of MPS-H as a marker for tumor response to chemotherapy and/or irradiation.
Discussion
Histological examination of the tumor, surgical margins, and cervical nodes are the current means of determining extent of disease. When surgical extirpation is not undertaken, staging is performed based on a radiological assessment, biopsy, and physical examination. These methods are used either to determine adequacy of resection and the need for adjuvant therapy or to select an alternative primary (non-surgical) therapy, respectively. Limitations of the histologic method include microscopic disease that escapes diagnosis due to a small number of malignant cells, subtle histological changes not classified as cancer, previous treatment effect upon tissues, or pathologic sampling error. CT and physical examination both suffer from modest sensitivity and specificity in detecting many early head and neck neoplasms. As a result, local and regional treatment failures are not uncommon in both surgical and non-surgical treatments of head and neck cancer. This may be due to an underestimation of the tumor burden. This may also be explained by the current diagnostic emphasis upon analysis of structure (microscopic) or anatomic extent of disease, both of which are imperfect, rather than its biologic activity as might be measured by a tumor marker or a functional scanning technique such as FDG-PET.
The MPS-H test has been used in conjunction with conventional tumor-specific markers to improve sensitivity and specificity of tumor serodiagnosis [12]. Of all malignancies in which MPS-H has been studied to date, epithelial malignancies possess no alternative tumor markers in clinical use that have been effective for diagnosis or surveillance [4-8,23]. This observation in conjunction with the current dependence on anatomic evaluation for diagnosis of epithelial malignancies has led us to preliminary investigations of the utility of MPS-H serologic diagnosis for the detection of head and neck epithelial neoplasms.
Control groups of healthy volunteers and those with systemic, non-malignant diseases have been studied using the MPS-H test. Statistical analysis has revealed that those without malignancy have MPS-H serum levels less than 10 ng/mL, those with malignancy have levels greater than 20 ng/mL, and those with bony metastasis have levels in excess of 100 ng/mL [12]. Additionally, MPS-H levels have been documented to decline with successful treatment of malignant disease whereas non-responders to therapy persisted with high levels of MPS-H [12].
Serum samples from prostate carcinoma patients with high levels of MPS-H (>500 ng/mL) have demonstrated the authentic 9.4 kDa MPS-1 protein, at least one protein with sequence homology to the N-terminus of MPS-1, and a high molecular weight precipitation interfering protein.
More accurate detection of recurrent SCC of the head and neck by the MPS-H test may provide a new way to improve survival. Physical exam, conventional imaging, and biopsy are the current gold standard to determine recurrence. Currently, FDG-PET is the most promising way to assess early tumor recurrence of the head and neck but is quite expensive [25,26]. Its sensitivity and specificity have been reported to be approximately 90% [25,26]. No standard serum tumor marker is routinely used for head and neck cancer surveillance, which limits alternatives to conventional exams or frequent FDG-PET imaging [4-8,25].
In the present study we compared FGD-PET to MPS-H levels in head and neck cancer patients. FDG-PET interpretation and MPS-H level determination were performed independently and blinded from the results of the other test. FDG-PET positive scans were not all confirmed by biopsy in our study. A statistically significant correlation was noted between FDG-PET positive cases and high MPS-H serum levels in head and neck cancer patients. MPS-H and FDG-PET agreed in 103 out of 183 cases. In 12 cases MPS-H was elevated but no cancer was found by FDG-PET, suggesting that the patients may have had an early recurrence detectable by MPS-H but not yet by FDG-PET. The 68 FDG-PET positive cases that show low MPS-H levels suggest that some tumors were unable to produce high levels of MPS-H, perhaps due to previous chemotherapy, or that some results represent false positive PET scans. Further study with larger patient groups is ongoing to assess the optimal cutoff levels of MPS-H and the correlation between FDG-PET and MPS-H.
Shortcomings of this study include a limited time span, a large proportion of advanced stage cancers, limited controls (both size, smoking status, and age/gender match), and ability to define an absolute cut off value for normal vs. abnormal. We recognize that other factors such as age and other malignancies may effect MPS levels. Future studies will attempt to address and/or control for these issues.
Considering the data presented in this paper, which agrees with previous results with other tumor types, and the compelling need to expedite the early diagnosis of primary and recurrent epithelial malignancies of the head and neck, we are further evaluating the MPS-H tests as a tool for diagnosis in a larger group of HNSCC patients. Additionally, we are working to improve the diagnostic technology used to detect MPS-H [27-29]. Since there is a reasonable correlation between detection of MPS-H in the sera and FDG-PET positivity for SSC, these results raises the potential of the MPS-H test for becoming a test for HNSCC, followed by selective confirmatory FDG-PET imaging. These preliminary results will be verified in a larger population. The role for using MPS-H as a general screening tool among at-risk populations without the diagnosis of HNSCC is also currently being evaluated.
Competing interests
The authors declare that they have no competing interests.
Authors' contributions
BCS: Principal Investigator, Principal Editor
CSH: Data analysis and manuscript preparation
CL: Medical student research assistant for project
FD: Member of multidisciplinary head and neck oncology team, recruiter of subjects
VJL: Member of multidisciplinary head and neck oncology team, supplier of PET data, data analysis.
PDH: Laboratory technical support with MPS assay
Funding sources
This study was funded by the clinical salaries of BCS, FRD, and VJL. CML and CSH were students at the time of this study. MPS assays were performed at the Department of Veterans Affairs James A. Cochran Medical Center, Molecular Oncology Laboratory, St. Louis, MO. PDH was an employee of the US Government at the time of this research.
Acknowledgements
Presented at the 5th International Conference on Head and Neck Cancer, American Head and Neck Society, San Francisco, California, August 1, 2000.
The authors wish to thank J. Alberto Fernandez-Pol, M.D., Kate Hackett, B.S.N., Amy Hudgins, B.S.N., M.S.N., James H. Boyd, M.D., for their support of and contributions to this work.
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| 15598348 | PMC544581 | CC BY | 2021-01-04 16:38:38 | no | World J Surg Oncol. 2004 Dec 14; 2:45 | utf-8 | World J Surg Oncol | 2,004 | 10.1186/1477-7819-2-45 | oa_comm |
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Reprod Biol EndocrinolReproductive biology and endocrinology : RB&E1477-7827BioMed Central London 1477-7827-2-811558833010.1186/1477-7827-2-81ResearchPotential action of androstenedione on the proliferation and apoptosis of stromal endometrial cells Maliqueo Manuel A [email protected] Susana [email protected] Marisa [email protected] Ketty [email protected] Mabel [email protected] Cecilia [email protected] Margarita [email protected] Institute of Maternal and Child Research, School of Medicine, University of Chile, Santiago, Chile2 Laboratory of Endocrinology and Metabolism, Department of Internal Medicine, School of Medicine, University of Chile, Santiago, Chile2004 10 12 2004 2 81 81 19 10 2004 10 12 2004 Copyright © 2004 Maliqueo et al; licensee BioMed Central Ltd.2004Maliqueo 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
Hyperandrogenic conditions have been associated with a high prevalence of endometrial pathologies related to cell survival. However, the action of androgens on proliferation and apoptosis in endometrial cells is poorly understood. Therefore, the aim of the present study was to evaluate the effect of androstenedione on cell proliferation, cell death and expression of estrogen receptor (ER) isoforms and proteins related to apoptosis in endometrial cells using two in vitro experimental approaches.
Methods
The endometrial tissue was obtained from 20 eumenorrheic women [28.7 (25 – 35) years] during the early secretory phase. We analyzed cell proliferation (immunohistochemistry of Ki-67 and spectrophotometric assay); apoptosis (DNA fragmentation (TUNEL) and Annexin V-FITC binding); ER-alpha, ER-beta bcl-2 and bax mRNA abundance (RT-PCR) in explants and isolated endometrial epithelial (EEC) and stromal cells (ESC) incubated with androstenedione 1 micro mol/l (A4) or A4 plus hydroxyflutamide 10 micro mol/l (F) for 24 h.
Results
In explants, A4 induced an increase of cell proliferation and a decrease on apoptosis in the stromal compartment (p < 0.05). In isolated ESC, proliferation augmented with A4 (p < 0.05), whereas, no significant modifications in the expression of ER-alpha, ER-beta bcl-2 and bax nor in the apoptotic index were observed. In EEC, A4 increase the ER-beta mRNA abundance (p < 0.05) and a decrease of the bcl-2/bax ratio (p < 0.05), without an increase in the apoptotic index. Hydroxyflutamide reverted the effect of androstenedione on the parameters described.
Conclusions
These results indicate that androstenedione may modulate cell survival, expression of ER-beta and proteins related to apoptosis, suggesting a potential mechanism that associates the effect of hyperandrogenemia on the endometrial tissue.
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Background
Polycystic ovary syndrome (PCOS) is a complex endocrine-metabolic disorder, associated to hyperandrogenism, menstrual disturbances and in many cases to insulin resistance [1,2]. It has been observed that in some of PCOS women, the endometrium is thicker than that of normal cycling women [3] and a higher prevalence of endometrial hyperplasia and adenocarcinoma have also been described in these women [4-6]. The latter may indicate that the mechanisms that regulate the process of cell survival may be disrupted in the endometrium of PCOS women. Recently, we have shown that the expression of proteins involved in the regulation of apoptosis in PCOS endometria were altered [7]. Besides, we and other investigators demonstrated an elevated expression of the estrogen receptor (ER) and its co-activators in endometria of women bearing this syndrome [7-9]. Nevertheless, in those studies it was difficult to establish the exact contribution of androgens as a regulatory steroid of endometria of PCOS women, since multiple factors could be affecting their endometrial function, including hyperinsulinemia and the possible contribution of the unopposed effect of estrogens [10,11]. Therefore, in vitro experimental models such as tissue and cell culture may constitute interesting approaches to determine the potential role of androgens in the regulation of endometrial cell survival.
Early reports in isolated endometrial stromal cells (ESC) have shown that androgens can induce decidualization and inhibition of the expression of ER and progesterone receptors [12,13]. Moreover, in endometrial epithelial cells (EEC), androgens altered the expression of proteins related to uterine receptivity [8] and induced a decrease in the proliferation capacity of those cells [14].
Cell proliferation and apoptosis of the endometrium are importantly regulated by the expression of ER [15], which exists in two major subtypes, estrogen receptors alpha (ERα) and estrogen receptor beta (ERβ). The two isoforms of ER derive from separate genes, with different ligand binding affinities and the response of the tissue to estrogens will depend upon their relative concentrations [16].
On the other hand, in some tissues including the human endometrium, the control of apoptosis has been associated to proteins related to the Bcl-2 family, like Bcl-2 that promotes cell survival and Bax which is an inducer of apoptosis [17,18]. Evenmore, other proteins are involved in the machinery of cell death like caspases which are associated with the cleavage and thus, breakdown of cell structure [19]. In regard to this issue, we have demonstrated that the expression of bcl-2 and bax is increased in the stromal compartment of the endometrium of PCOS women, but we could not observe an increase in the apoptotic index in the endometria of these women [7].
Previous reports have shown that androstenedione is an important androgen detected in the endometrial tissue [20,21]. Therefore, based on the amount of androgens normally found in endometria and their potential importance in alterations of the endometrial cell survival in PCOS women, the objective of the present study was to evaluate the effect of androstenedione on cell proliferation, apoptosis and the expression of ER isoforms and proteins related to apoptosis using two in vitro experimental approaches.
Methods
Subjects
Endometrial tissue was obtained with pipelle suction curette from the corpus of the uteri of 20 women with regular menstrual cycles, aged 28.7 (25 – 35) years, at the time of bilateral tubal ligation at the San Borja-Arriarán Clinical Hospital, National Health Service, Santiago, Chile. The tissue was obtained during the early secretory phase since the cells obtained from this phase maintain a high degree of proliferation capacity [22]. None of these women had taken oral contraceptives or other medications for at least 6 months before starting the study. Women who had evidences of PCOS, endometriosis and/or endometrial hyperplasia were excluded. This investigation was approved by the Institutional Ethics Committee of the San Borja-Arriarán Clinical Hospital and an informed written consent was obtained from all subjects.
Culture System
Explants
Human endometrium was cut into slices (20 to 50 mg wet weight) and incubated in 1 mL of Hank's media supplemented with 2 mmol/L glutamax-I (BRL, Life Technology, Bethesda, MD, USA), insulin-transferrin-selenium (ITS) solution (BRL), 0.1% w/v bovine serum albumin (BSA; Sigma Chemical Co., St Louis, MO, USA), 26 mmol/L of NaHCO3, 25 mmol/L of HEPES aminoacids solution, 100 IU/ml of penicillin, and 5 mg/mL of streptomycin (Sigma). Incubation was performed during 6 h at 37°C in 5% CO2/air in humidified atmosphere in the absence or presence of androstenedione 10-7 to 10-5 M (Sigma) or androstenedione 10-6 M plus hydroxyflutamide 10-5 M (Sigma), the latter added 30 min before androstenedione. After incubation, one piece from both basal and treated tissue explants were frozen in liquid N2 and maintained at -70°C for RT-PCR protocols. Another piece was fixed in 4% buffered formaldehyde for 24 h, embedded in paraffin, and cut into 5 μm thick sections before in situ analysis of apoptosis and immunohistochemistry.
Cells
The cells were separated and purified according to previously described methods [23]. Briefly, the tissue was cut into small pieces and suspended in Dulbecco's modified Eagle medium (DMEM) (GibcoBRL), collagenase (370 IU/100 mg tissue) (Worthington, Biochemical Corp. Freehold, NJ, USA) and DNAse (14 KU/100 mg tissue) (Sigma) during 1 h at 37°C. Epithelial cells, predominantly from glands, were separated from ESC by decantation and the supernatant containing the ESC was filtered, centrifuged and the cell pellet washed in DMEM. Stromal cells were incubated in appropriate cell culture media (ESC media) (DMEM/MCDB-105 (3:1 v/v), 2% charcoal stripped fetal bovine serum (FBS) (GibcoBRL), insulin-transferrin-selenium (ITS) solution, 2 mmol/L glutamax-I (GibcoBRL), 0.25 μg/mL ascorbic acid (GibcoBRL), 0.25 μg/mL fungizone (GibcoBRL), 100 IU/mL penicillin and 5 mg/mL streptomycin at 37°C in 5% CO2/air in humidified atmosphere until confluence.
The glands cells were washed in DMEM and incubated for 1 h (30 min in each side of the culture flask), and then cultured in EEC culture media (DMEM/MCDB-105 (3:1, v/v), 10% charcoal stripped FBS, 2 mmol/L glutamax-I, 0.25 μg/mL ascorbic acid, 0.5 mg% insulin (Sigma), 1 μg% transferrin (Sigma), antibiotic and fungizone), similarly to ESC. In both cell cultures, the media were changed every 3 days. Upon reaching confluence, ESC and EEC were passaged by treatment with 0.5 g/L tripsin-0.2 g/L EDTA solution (GibcoBRL).
The purity of cell cultures was greater than 90% for ESC and EEC, evaluated by immunocytochemistry of vimentin and cytokeratin, respectively.
Epithelial and stromal cell culture
Cells, EEC and ESC, were grown in appropriate medium in 6-well or 96-well plates at 2.5 × 105 cells/well or 0.1 × 105 cells/well, respectively. When cells were subconfluent (48 – 72 h of culture), the media were changed to Hank's media and incubated for 24 h. Then, the cells were incubated in fresh Hank's media at 37°C in 5% CO2/air in humidified atmosphere in the absence or presence of androstenedione 10-6 M or androstenedione 10-6 M plus hydroxyflutamide 10-5 M. The latter was added 15 min before androstenedione. The culture was carried out for 24 h to evaluate the early effect of androstenedione on cell survival. The concentration of androstenedione used in the present investigation was established in dose-response experiments and are in agreement with those previously reported [14].
Immunohistochemical detection
Sections (4 to 6 μm) of human endometrial tissue obtained from cultured explants were deparaffinized in xylene and hydrated gradually through graded alcohols. Endogenous peroxidase activity was prevented by incubating the samples in 3% hydrogen peroxide for 5 min. The sections were incubated in 10 mM sodium citrate buffer (pH 6.0) at 95°C for 20 min. Nonspecific antibody binding was prevented with 2% PBS-BSA for 1 h. Primary antibody of Ki-67 (1:200; Novocastra Laboratories, Newcastle, UK) was applied to the samples and incubated overnight at 4°C; the antibody for caspase-3 (1: 100; R&D System, Inc., Minneapolis, MN) was incubated for 1 h at 37°C and the antibody for androgen receptor (AR) was incubated overnight at 4°C (1: 75; Santa Cruz, CA, USA). The second antibody used in both cases was a biotinylated rabbit antimouse immunoglobulin. The reaction was developed by the streptavidin-peroxidase system and 3,3'diaminobenzidine was used as the chromogen; counterstaining was carried out with hematoxylin. The slides were evaluated in a Nikon optical microscope (Nikon Inc., Melville, NY, USA). The immunohistochemical evaluation was determined as the percentage of positive stained cells. In all cases, at least 500 cells were evaluated by three independent observers.
In Situ 3'-End Labeling of DNA in Apoptotic cells (TUNEL)
Programmed cell death was detected using TdT-mediated dUTP nick end labeling as previously described (Promega, Madison, WI, USA) [18]. Briefly, paraffin sections were dewaxed with xylene and rehydrated for 3'-end labeling. Tissue sections were incubated with proteinase K (20 μg/ml) at room temperature for 10 min and washed with PBS for 5 min. Then, incubated for 1 h at 37°C with the nucleotide mix labelled with fluorescein and terminal deoxynucleotidyl transferase enzyme and counterstained with propidium iodide. The number of apoptotic cells was quantified by at least counting 1000 cells in a fluorescence microscope by three independent observers. The number of apoptotic cells was expressed as the percentage of positive cells.
Cell proliferation
CellTiter 96 AQueous Cell Proliferation Assay (Promega) was used to perform cell proliferation, following manufacturer's instructions. Briefly, EEC or ESC were plated in 96-well until sub-confluence; then, the cells were cultured with androstendione 10-6 M alone or androstenedione 10-6 M with hydroxyflutamide 10-5 M, as described above. Twenty μL of a mix of tetrazolium compound (3-(4,5-dimethylthiazol-2-yl)-5-(3-carboxymethoxyphenyl)-2-(4-sulfophenyl) 2H-tetrazolium; MTS) and an electron coupling reagent (phenazine methosulfate; PMS) were added to each well and incubated for 2 h at 37°C. The quantity of the soluble formazan product was measured at 450-nm absorbance in an ELISA reader (Sigma) and expressed as optical density units (OD).
Detection of apoptosis by Annexin V
Apoptosis was performed using Annexin V-FITC Apoptosis Detection Kit (Oncogene Reasearch Products, Boston, MA, USA). Briefly, after EEC and ESC achieved sub-confluence in 6-well plates, the cells were incubated with androstenedione 10-6 M alone or androstenedione 10-6 M with hydroxyflutamide 10-5 M, as described above. The cells were dissociated by 0.25 g/L trypsin-0.1 g/L EDTA treatment, gently re-suspended in cold binding buffer to approximately 1 × 106 cells, and incubated with Annexin V-FITC, as indicated by the manufacturer. The cells were counter-stained with propidium iodide, analyzed and counted by two independent observers in at least 1000 cells in each experimental condition using a fluorescent and optical microscope (Nikon, UFX-NX model. Nikon Inc., Melville, NY, USA). The results are expressed as percentage of apoptotic Annexin V-FITC positive cells respect to total cells counted.
RNA isolation and semiquantitative Reverse Transcription-Polymerase Chain Reaction
Total RNA was isolated using TRIzol Reagent (GibcoBRL) from endometrial tissue and ESC culture according to the manufacturer's instructions. Total RNA was then reverse transcribed, and cDNA was subjected to polymerase chain reaction (PCR) using specific primers for ERα and ERβ[24], bcl-2 and bax cDNA (NIDig 4558699) [7,18]. β-actin was used as an internal control.
Semiquantitative RT-PCRs were achieved in the exponential linear zone amplification for each gene studied. Two μg of total RNA were used for reverse transcription in a total volume of 20 μl using the Revertaid H Minus M-Mulv Reverse (Fermentas Hannover, MD, USA). The PCR condition for ERα and ERβ was 2 mM MgCl2, 0.15 mM of dNTP, 0.63 U of Taq DNA polymerase (Fermentas Hannover, MD, USA), and 0.4 μM of each primer; for bcl-2 and bax was 3 mM MgCl2, 0.25 mM of dNTP, 0.63 U of Taq DNA polymerase, and 0.4 μM of each primer. The PCR amplification was carried out in the Thermocycler, model PTC-100 (MJ Research Inc, Watertown, MA), as previously reported [23].
The PCR products were electrophoretically resolved on 1% agarose gel and stained with ethidium bromide. The bands were evaluated using an image analyser (Kodak Electrophoresis Documentation and Analysis System [EDAS 290] and Kodak 1D Image Analysis software, Rochester, NY, USA), and normalized relative to β-actin PCR product and expressed as arbitrary units (AU).
To confirm the specificity of RT-PCR products, the fragments were purified with CONCERT Rapid PCR Purification System (GibcoBRL) and sequenced using an ABI PRISM310 automated Sequencer (Perkin Elmer; Norwalk, CT, USA).
Statistical evaluation
The results are expressed as the percentage of changes obtained in the treated respect to the basal condition. Comparisons between groups were performed by ANOVA following Dunnett test. The significance level was set at 5%. Results are expressed as mean ± standard error of the mean (SEM).
Results
Explants culture
In all samples, the expression of the AR was present in the nucleus of EEC and ESC cells (data not shown). Moreover, in EEC showed a positive staining in the cytoplasm. The nucleolar antigen Ki-67 was detected in the nucleus of EEC and ESC. After six hours of incubation with androstenedione at different concentrations (10-7 M – 10-5 M), the percentage of positive cells increased significantly only in the presence of androstenedione 10-6 M and on the stromal compartment (basal: 9,9 ± 2.5%; treated: 21.2 ± 6.4%; p < 0.05) (Figures 1A and 1D). Therefore, the following experiments were performed at a concentration of androstenedione 10-6 M.
Figure 1 Effect of androstendione on endometrial cell proliferation and apoptosis of human endometria. Basal condition (left panel) and androstenedione-treated explants (right panel). The nucleolar antigen Ki-67, evaluated by immunohistochemistry, was detected in the nucleus of both cell compartments (A, D), indicative of cell proliferation. The nucleus of positive cells for TUNEL, determined by TdT-mediated dUTP nick end labeling, were stained in yellow and counterstained with propidium iodide (B, E), showing DNA fragmentation. The positive staining for caspase-3, determined by immunohistochemistry in paraffin wax sections of endometria, was found in the cell cytoplasm of both compartments (C, G). Negative controls (inserts) for inmunohistochemistry was performed with non-immune rabbit serum in the place of the respective primary antibody and for TUNEL, by the replacement of TdT enzyme. Arrowheads indicate positive staining of the respective proteins. Magnification in all panels, ×400.
On the other hand, TUNEL positive cells were lower in ESC after androstenedione treatment (basal: 21.2 ± 5.0%; treated:10.3 ± 3.7%; p < 0.05), (Figures 1B and 1E). The expression of caspase-3 did not change with the androstenedione treatment in both compartments (Figures 1C and 1F). The addition of hydroxyflutamide plus androstenedione did not modify the degree of proliferation or cell death in tissue explants.
Isolated endometrial cells
Basal values of cell proliferation were 0.39 ± 0.09 OD and 0.64 ± 0.16 OD for EEC and ESC, respectively, whereas, the percentage of annexin V positive cells was 16.3 ± 4.6% for EEC and 12.7 ± 4.3% for ESC. The addition of androstenedione significantly increased cell proliferation in ESC cultures (Table 1; p < 0.05), and this effect was reverted by the addition of hydroxyflutamide (0.63 ± 0.16 OD vs 0.70 ± 0.19 OD); no changes were observed in the EEC subpopulation (Table 1).
Table 1 Effect of androstenedione in cell proliferation and apoptotic index in endometrial epithelial cells (EEC) and endometrial stromal cells (ESC) in vitro.
Cell Proliferation (%) Apoptotic index (%)
EEC ESC EEC ESC
Basal 100 100 100 100
Androstenedione (10-6 M) 91.6 ± 6.9 135.4 ± 1.2* 165.0 ± 59.0 120.0 ± 11.0
Androstenedione (10-6 M) plus hydroxyflutamide (10-5 M) 99.1 ± 10.0 109.2 ± 4.2 111.2 ± 6.3 106.7 ± 7.4
*p < 0.05. Values are calculated as percentage of basal and are expressed as mean ± SEM.
The percentage of cells with positive signs of apoptosis was similar between the basal and the treated conditions, independently of the cell type analyzed. The addition of hydroxyflutamide plus androstenedione to both cell cultures did not modify the degree of cell death (Table 1).
Effect of androstenedione on the abundance of messenger RNA for bcl-2 and bax
A similar mRNA abundance for bcl-2 and bax was obtained in tissue explants without treatment (bcl-2; 0.96 ± 0.12 AU; bax: 0.99 ± 0.17 AU). Table 2 shows the effect of androstenedione on the mRNA abundance of bcl-2 and bax in endometrial explants. For bcl-2, the level of its mRNA decreased with androstenedione treatment (p < 0.05) and hydroxyflutamide inhibited this effect, whereas, the mRNA abundance of bax did not change with the treatment. Despite these results, a similar bcl-2/bax ratio was obtained (basal: 1.06 ± 0.21; treated: 0.99 ± 0.12).
Table 2 Effect of androstenedione in the mRNA abundance for bcl-2 and bax in endometrial tissue explants.
bcl-2 (%) bax (%)
Basal 100 100
Androstenedione (10-6 M) 73.6 ± 3.9* 90.0 ± 20.9
Androstenedione (10-6 M) plus hydroxyflutamide (10-5 M) 86.7 ± 14.5 113.3 ± 37.1
*p < 0.05. Values are calculated as percentage of basal and are expressed as mean ± SEM.
In isolated cells, the basal mRNA abundance for bcl-2 in EEC was 0.47 ± 0.07 AU and for bax 0.39 ± 0.25 AU and in ESC, basal expression of bcl-2 mRNA was 0.87 ± 0.16 AU and 1.09 ± 0.10 AU for bax. Androstenedione induced a decrease of 58% of bcl-2 mRNA expression (p < 0.05) and a 30% increase of bax mRNA (p < 0.05) in EEC (Figure 2); therefore, the ratio bcl-2/bax was significantly lower compared to the basal condition (p < 0.05). In ESC, no significant differences on mRNA expression for bcl-2 and bax were found between the basal and androgen-treated conditions. No important changes in mRNAs expression were observed when hydroxyflutamide was added to both cell culture systems.
Figure 2 Polymerase chain reaction (PCR) amplification from reverse-transcribed cDNA from endometrial epithelial cells (EEC) under the stimulation with androstenedione 10-6 M, using primers for bcl-2, bax and β-actin. Results represent six experiments performed in duplicate. Normalized yield for bcl-2 and bax PCR fragments relative to β-actin. PCR products from different experiments are shown as percentage respect to basal. The values are expressed as mean ± SEM. ap < 0.05 between basal vs androstenedione.
Effect of androstenedione on messenger RNA abundance of steroid receptors
In the endometrial explant cultures, the abundance of mRNA for ERα was similar between the basal condition and the tissue treated with androstenedione (0.44 ± 0.12 AU; 0.41 ± 0.07 AU, respectively). In contrast, androstenedione treatment induced a decrease in ERβ mRNA abundance (basal: 0.91 ± 0.11 AU ; treated: 0.75 ± 0.08 AU; p < 0.05). However, the ratio between the level of ER isoforms did not change. No significant modifications were observed when hydroxyflutamide was added to the cultures.
On the other hand, basal expression of ERα mRNA was 0.31 ± 0.05 AU and 0.39 ± 0.08 AU for ERβ in EEC; in ESC was 0.82 ± 0.23 AU for ERα and 0.86 ± 0.28 AU for ERβ.
Androstenedione tended to modify mRNA abundance of ERα in EEC and ESC, a 30% and 25% diminution was obtained, respectively (p = 0.07). In EEC, gene expression of ERβ increased 48% with androstenedione (0.39 ± 0.08 AU vs 0.56 ± 0.18, p < 0.05) (Figure 3), with no modification in ESC. Therefore, the ratio between the expression of ER isoforms decreased 70% in EEC. Hydroxyflutamide reverted the effect of androstenedione on gene expression of ERβ and on the ratio ERα/ERβ.
Figure 3 Polymerase chain reaction (PCR) amplification from reverse-transcribed cDNA from endometrial epithelial cells (EEC) under the stimulation with androstenedione 10-6 M, using primers for ERα, ERβ and β-actin. Results represent six experiments performed in duplicate. Normalized yield for ERα and ERβ PCR fragments relative to β-actin. PCR products from different experiments are shown as percentage respect to basal. The values are expressed as mean ± SEM. a P < 0.05 between basal vs androstenedione.
Discussion
The present investigation represents an interesting approach that associates the potential effect of androgens on endometrial cell survival. By means of two in vitro models, we could observe that androstenedione can modulate the proliferation and apoptosis of the stromal compartment and modify the mRNA abundance of proteins related to apoptosis and β-isoform of ER in EEC.
Thereby, in explant cultures, androstenedione stimulated cell proliferation in stroma. One possible explanation to this finding may be related to the fact that androgens can induce the expression of the receptor of epidermal growth factor in the stromal compartment, as reported previously [25]. In turn, the increase in the proliferation rate may occur through an indirect effect of the growth factor on stromal cells.
Also, in the tissue explants model we observed that androstenedione induced a diminution in the apoptosis degree; although, the abundance of bcl-2 gene decreased. This observation may be in contrast to the mechanism that regulates apoptosis in cells; however, the ratio bcl-2/bax did not change and, concomitantly, we did not observe important differences in the expression of caspase-3. This is a relevant point because according to previous reports the control of cell death is principally associated with an unbalance in the expression of proteins related to the bcl-2 family, mainly bcl-2 and bax [17,18]. These findings may suggest that alternative apoptotic pathways can be also operating in the endometrial cells.
On the other hand, it is well known that in endometrial tissue, estrogen actions mediated through their own receptors have been related to cell survival and progression of proliferation. Furthermore, during the menstrual cycle, it has been demonstrated the presence of the isoforms α and β of ER in endometrial tissue [26,27]. Moreover, it has been postulated that the expression of ERα may be associated to bcl-2 gene expression [28]. Studies in other reproductive tissues have also suggested that ERβ could be involved in the inhibition of cell proliferation [29,30]. Therefore, it acquires great relevance the relationship between α and β isoforms of ER, considering that both isoforms could have an antagonistic action ligand-dependent, in accordance to the relative expression of each isoform in different tissues [16,31]. In the tissue explant model used in the present study, in contrast to a previous report [8], we were unable to demonstrate an increase of ERα. Nevertheless, we observed a decrease of the β-isoform of ER under the effect of androstenedione, which could be associated to the increase of stromal cell proliferation detected in the explants. Numerous clinical and in vitro studies have suggested that the imbalanced of ERα/ERβ is a common feature and could be critical in the progression of estrogen-dependent tumors. It seems that ERβ is an important modulator of the mitogenic action estrogen and it confers protection against the ERα hyperproliferation [32]. Moreover, in prostate carcinoma cells the expression of ERβ has been associated with triggers of apoptotic pathway, similar to observed in models of ovarian cancer cells [33,34]. Therefore, the observations in our models open an important field in a possible relationship between androgens effects with control of ERβ-mediated cell proliferation
When isolated cells were evaluated, some differences were obtained in the parameters studied compared to the explant cultures, which highlight the importance of the relationship between the different cell compartments in the regulation of the cell survival [35].
In ESC, androstenedione stimulated cell proliferation with no changes in the apoptosis degree nor on the expression of the genes bcl-2 and bax. This observation suggests that ESC exhibit an independent capacity to respond to androstenedione, whose action may be mediated by the androgen receptor. The latter is based mainly on the effect of the competitive inhibitor of the androgen receptor, hydroxyflutamide, which reverts the effect of androstenedione in the two models.
In EEC, androstenedione induced a diminution in the ratio between the mRNA abundance of bcl-2 and bax without an evident increase in the apoptosis degree. Previous studies in other models, such as breast cancer cell lines, have demonstrated that androgens can induce a decrease on the expression of bcl-2 and also an atrophy of the mammary ephitelium [36]. The present observations suggest that the mechanisms of control of cell death in EEC are different from those of ESC, indicating that ESC may be responsible in providing molecular and physical interactions that can inhibit the early changes in the balance of apoptosis control genes in EEC.
In contrast to our results, a previous study showed that in EEC cultures, androstenedione produces a fall in cell proliferation [14]. In fact, we did not observe this phenomenon, most likely due to technical differences in which the treatment was performed for 24 h in the present study with the aim to evaluate the early expression of genes related to apoptosis.
Moreover, in EEC we observed that androstenedione up-regulated the ERβ expression and hence, a decrease in ER/ERβ ratio was obtained. The meaning of this effect on cell survival is difficult to evaluate in our experimental model, since no estradiol was added to the culture system. Furthermore, it is unlikely that androstenedione may produce estrogen by P450 aromatase activity, because normal endometrial cells present a very low expression of this enzyme, as previously demonstrated [37]. However, in EEC the decrease in ERα/ERβ may be related to an unbalance of the bcl-2/bax ratio although this hypothesis needs further studies.
Taking together our results, we can speculate that in the presence of androgens the regulatory mechanisms of cell survival in endometrial cells would be associated to the interaction capacity elicited by the different cellular components of the tissue. In fact, we postulate that androgens induce an increase of proliferation in stroma, probably related to growth factors and that these signals interact with epithelial cells, promoting an inhibition on the expression of genes related to cell death. In isolated cells, the mechanisms that allow these interactions between cell compartments are lost and the cells act according to their proper feature.
Our findings on the effect of androstendione on cell proliferation and apoptosis of ESC allows us to suggest a potential regulatory action of androgen in the physiology of the endometrium and its implications in the genesis of endometrial pathologies frequently found in women with hyperandrogenism. Even more, we have observed that endometrial androstenedione concentration in women with PCOS are three times higher than in normal women during the proliferative phase [unpublished data). Therefore, these in vitro models are important approaches to understand the potential role of androstenedione in the PCOS endometria. It is well established that women with PCOS exhibit a high prevalence of hyperplasia and endometrial cancer, which is associated with disturbances in the regulation of cell survival [4-6]. According to our results, androgens may be involved in these endometrial alterations. However, we cannot ruled out the possible action of hyperinsulinaemia, common feature observed in PCOS women, which has been associated to increase the potential for neoplastic change through theirs effects on sex hormone binding-globulin (SHBG), insulin-like growth factor-1 and estrogen concentrations [6,38].
Conclusions
In summary, our results indicate that androstenedione may modulate cell survival, the expression of β-ER isoforms and proteins related to apoptosis. These observations are closely related to the control of endometrial cell function and may help to understand the possible effect of the hyperandrogemia on endometrial tissue.
Authors' contributions
MM conceived and designed the study, carried out the experimental protocols, and drafted the manuscript. SQ, MC and KB performed the RT-PCR and scored of immunohistochemistry, MA carried out the score of immunohistochemistry studies. CJ reviewed and supported in the drafting the manuscript and MV conceived the study as principal investigator and participated in drafting the manuscript.
Acknowledgements
The authors are grateful to Fondo Nacional de Ciencia y Tecnología (FONDECYT) # 1010821 for financial support, to Dr. Fernando Gabler (Pathology Department, San Borja-Arriarán Clinical Hospital) for his role in histological procedures of endometria biopsies, Dr. Alberto Palomino for his contribution in recruitment and surgical procedures of subjects, and to the women who donated tissue.
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| 15588330 | PMC544582 | CC BY | 2021-01-04 16:36:42 | no | Reprod Biol Endocrinol. 2004 Dec 10; 2:81 | utf-8 | Reprod Biol Endocrinol | 2,004 | 10.1186/1477-7827-2-81 | oa_comm |
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BMC Public HealthBMC Public Health1471-2458BioMed Central London 1471-2458-4-671561933110.1186/1471-2458-4-67Study ProtocolTreatment of pregnancy-related pelvic girdle and/or low back pain after delivery design of a randomized clinical trial within a comprehensive prognostic cohort study [ISRCTN08477490] Bastiaenen Caroline HG [email protected] Bie Rob A [email protected] Pieter MJC [email protected] Johan WS [email protected] Janneke M [email protected] Aldegonda BA [email protected] Annie [email protected] den Brandt Piet A [email protected] Gerard GM [email protected] Department of Epidemiology, Maastricht University, P.O.Box 616,6200 MD Maastricht, The Netherlands2 Department of Obstetrics and Gynaecology, University Hospital Maastricht, Maastricht, The Netherlands3 Department of Medical, Clinical and Experimental Psychology, Maastricht University, The Netherlands4 Department of Physiotherapy, Hogeschool Zuyd, Heerlen, The Netherlands5 Midwifery practice, Meerssen, The Netherlands2004 24 12 2004 4 67 67 23 11 2004 24 12 2004 Copyright © 2004 Bastiaenen et al; licensee BioMed Central Ltd.2004Bastiaenen 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
Pregnancy-related pelvic girdle and/or low back pain is a controversial syndrome because insight in etiology and prognosis is lacking. The controversy relates to factors eliciting pain and some prognostic factors such as the interpretation of pain at the symphysis. Recent research about treatment strategies also reflects those various opinions, in fact suggesting there is professional uncertainty about the optimal approach. Currently, physiotherapists often prescribe a pain-contingent treatment regime of relative rest and avoiding several day-to-day activities. Additionally, treatment more often includes an exercise program to guide rectification of the muscle imbalance and alignment of the pelvic girdle. Effectiveness of those interventions is not proven and the majority of the studies are methodologically flawed. Investigators draw particular attention to biomedical factors but there is growing evidence that important prognostic issues such as biopsychosocial factors appear to be even more important as point of action in a treatment program.
Methods/design
This pragmatic randomized controlled trial is designed to evaluate the effectiveness of a tailor-made treatment program with respect to biopsychosocial factors in primary care. The effect of the experimental intervention and usual care are evaluated as they are applied in primary health care. The trial is embedded in a cohort study that is designed as a longitudinal, prospective study, which studies prevalence, etiology, severity and prognosis during pregnancy until one year after delivery. The present paper focuses on choices regarding recruitment procedures, in-/exclusion criteria and the development of a well-timed intervention.
Discussion
This section briefly discusses the actions taken to minimize bias in the design, the proper time-window for the experimental intervention and the contrast between the experimental intervention and usual care.
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Background
Since 1962[1], diagnosis, prognosis and treatment of pregnancy-related pelvic girdle and/or low back pain have inflicted debate and have led to considerable differences of opinions. Many articles appeared mainly in International journals and some etiological mechanisms were hypothesized. However, the subject remains controversial, mainly because insight in etiology and prognosis is lacking. Moreover, diagnostic investigation into the exact definition and classification of pregnancy-related pelvic girdle and/or low back pain shows various opinions between leading experts on this topic. The controversy relates to factors eliciting pain[2] and prognostic factors such as the interpretation of pain at the symphysis [3,4], the question whether pelvic girdle pain is a syndrome separate from low back pain [4,5] and the importance of questions about limitations in activities [6]. Also recent research about treatment strategies reflects those various opinions [7], in fact suggesting there is professional uncertainty about the optimal approach. Investigators draw particular attention to biomedical factors but there is growing evidence that important prognostic issues such as biopsychosocial factors appear to be even more important as basis in a treatment program[8,9]. Although the group of musculoskeletal disorders holds many different biomedical labels, the process of developing chronic disability has shown surprising similarities with regard to biopsychosocial factors [10]. For the moment, pregnancy-related pelvic girdle and/or low back pain is a subjective experience comprising pain and limitations in activities for which classification criteria are insufficient in guiding to a treatment approach (Bastiaenen et al. personal communication). Results of various therapeutic interventions have been published but excepting one recent study[11], their effectiveness remain unproven. Furthermore, the majority of the studies are methodologically flawed [7].
Currently, physicians and physiotherapists usually prescribe a pain contingent treatment regimen of relative (bed) rest and avoiding several day-to-day activities such as using the stairs, bending, twisting, lifting and cycling. Additionally, the usual treatment approach of a physiotherapist more often includes an exercise program to guide rectification of the muscle imbalance and alignment of the pelvic girdle [12]. Therapists rely on knowledge of pain duration and intensity during goal-setting for treatment, for a great deal.
Why publish a study protocol
There are several reasons for publishing a study protocol before obtaining research data. The main reason is to reflect on the study design independently of the results. Considerations and choices concerning methodology and treatment can be described more detailed. The present paper focuses on choices about recruitment procedures, in-/exclusion criteria and the development of a well-timed experimental intervention. We also present details about the enrollment of women with pregnancy-related pelvic girdle and/or low back pain in the controlled trial.
Methods/design
Study design and research question
The trial is embedded in a cohort study that is designed as a longitudinal, prospective study, which studies the prevalence, etiology, severity and prognosis of pregnancy-related pelvic girdle and/or low back pain until one year after delivery (Figure 1). The present study is designed as a pragmatic trial aimed to compare the effects of interventions carried out in primary health care.
Figure 1 Design of the study
We performed a randomized controlled trial in primary care to determine whether a tailor-made program with respect to biopsychosocial factors (intervention group) benefits women with pregnancy-related pelvic girdle and/or low back pain more in terms of effectiveness and costs than usual care would on a traditional pain contingent basis (control group).
Recruitment and informed consent
The medical ethics committee of the Maastricht University Hospital approved the intervention and cohort study. The study is performed in the Southeast of the Netherlands. Midwives and gynecologists recruited the women during early pregnancy (10–14 weeks). Participation of midwives and gynecologists in the recruitment of eligible pregnant women is of major importance for the success of the cohort and intervention study [13]. We have paid a lot of attention to difficulties in recruitment such as busy consultation hours and not feasible recruitment procedures. Therefore, we designed a recruitment protocol that is as simple as possible, not restrictive, and demanding a minimum of time from midwives and gynecologists. Standardized written information about the cohort and intervention study is available for every potentially eligible woman and to be handed out by the midwife or gynecologist. Several steps are taken to encourage participation of the midwives and gynecologists. We distributed newsletters about the developments in the cohort and intervention study every three months and visited the practices and meetings of midwives on a regular basis. Any questions regarding trial questions received prompt feedback. The flexibility of the trial procedure is also guaranteed by assessing potential candidates for the trial at home.
Women are included in the cohort if they are at least 18 years old, pregnant and well versed in Dutch language. Women are given written information explaining the aims and contents of the cohort and intervention study before they decide to participate. Concerning the intervention study they are told that to current knowledge the two investigating treatment options are considered to be equally effective. The moment of inclusion for the intervention study lies at about three weeks after delivery. An individual woman enters the intervention study after signing informed consent for both the cohort and intervention study during early pregnancy and meets the in- and exclusion criteria of the intervention study three weeks after delivery. Women are included when having pain in the pelvic girdle and/or low back with an onset during pregnancy or just after delivery (cohort data), are restricted in their normal daily activities because of pelvic girdle and/or low back pain and if there is a delay in recuperation (not yet in the condition to participate satisfactory in housekeeping and care of children because of the complaints under investigation). The severity of symptoms must be varying with physical activities and time during the day. Women diagnosed with a relevant specific pathology (such as nerve root pathology, rheumatoid disorders, carcinoma, obstetric complications) that affects pain and activities of daily life are excluded. Exclusion also occurs in case of family related or psychosocial problems or when a disablement procedure is not yet finished.
Final important aspects for in-/exclusion are the willingness of a woman to participate in the study or having a clear treatment preference[13]. We only included women who did not indicate such a preference and who were willing to take the 50% risk of receiving a referral to a participating physiotherapist (and treatment option) or the freedom of choosing a therapist by themselves (usual care). Including only women who are naïve (who never received treatment for their complaints during this pregnancy or earlier pregnancies) will result in an unacceptable reduction in the number of eligible patients. However, we excluded all the women who already received treatment after their current delivery.
A basic principle for selection of eligible women in this study is that inclusion criteria must have a meaningful influence in goal setting for treatment. We therefore focused on criteria such as a delay in recuperation and restrictions in normal daily activities caused by pregnancy-related pelvic girdle and/or low back pain. However, other studies in this field formulated inclusion/exclusion criteria based on certain diagnostic classification strategies. Although rationales of these strategies greatly differ, they all attach great importance to the outcomes of particular (albeit different) diagnostic tests. In the absence of a clear definition and reference standard to diagnose pregnancy-related pelvic girdle and/or low back pain, the outcomes of these procedures not only led to different selections of women having complaints, the prognostic and diagnostic importance of these subgroups also remain unclear (Bastiaenen et al. personal communication).
Exclusion of differential diagnoses is a major point of concern. For that reason we included a history taking and a physical examination protocol that focuses on differential diagnoses at first and then on the formulated inclusion criteria. The various specific physical examination tests to diagnose pregnancy-related pelvic girdle pain are left aside. For a better understanding of the complaints and tailoring treatment, application of these tests has no supplemental value (Bastiaenen et al. personal communication).
An experienced research-physiotherapist visited women at home, about three weeks after delivery. This visit is called for on the basis of a short self-administered questionnaire and/or initiated by midwives. A positive answer from a participating woman from the cohort and/or her midwife on the question: "Do you or does this woman need treatment?" took a central position in these questionnaires. In advance of a possible home visit, a short history taking by telephone took place about two weeks after delivery. History taking focuses on exclusion criteria such as: willing to participate in this part of the study, a diagnosis with relevant specific pathology, limitations in daily life caused by pregnancy-related pelvic girdle and/or low back pain and a delay in recuperation. During a home visit, a standardized history is taken and physical examination to exclude specific pathology is performed. Self-administered questionnaires are used to question the women about pain, limitations in activities, restrictions in participation, pain-related fear, pain catastrophizing, positive and negative affectivity, depression, expectancy of treatment result and quality of life. The questionnaires contain clear instructions for completion with no help or support from others. If a woman meets the selection criteria, she is informed about the aim and method of the intervention study and if she is willing to participate, the informed consent procedure is completed. The research-physiotherapist collecting the baseline data is trained in performing the measurements in a standardized way and is unaware of the women's treatment assignments.
Randomization and blinding
Randomization takes place after collecting the baseline data. In this study we used a block randomization (size of four). An independent research assistant (unaware of the baseline data) carried out the randomization procedure according to a random computer-generated list. When a woman is allocated to the intervention group, the participating physiotherapist in the environment of the woman is contacted and we ensured that treatment could start as soon as possible (within one week). Treatment is covered for all participants in the intervention group on a research-physician's referral. Women, allocated to the usual care group, are free to choose usual treatment by a (not participating) physiotherapist. Information about a possible guidance by a general practitioner and feasible treatments received after randomization is collected by means of questionnaires in the follow-up period.
Women are blinded to a certain extent to the allocated treatment because they are kept naïve of the exact content of both treatment options. Participating physiotherapists are not blinded to the treatment option but not involved in the baseline and effect measurements. Researchers dealing with the baseline and outcome data are unaware of the treatment assignments.
History and physical examination
During a home visit a standardized history is taken [8] and physical examination is performed. History taking focuses on on-going pain, its location, intensity and modalities, variation of symptoms with physical activities, radiation into the legs, back pain versus leg pain, neurological signs, deformity, obstetric complications, a case history of low back and pelvic girdle pain prior to this pregnancy and other differential diagnoses. The format of the answers is presented as a dichotomous "yes or no". Demographic characteristics and data about education, work, income, use of alcohol, smoking, medication, the onset of pain and functional status during pregnancy have already been gathered as part of the cohort study at 14 and 30 weeks gestation period and two weeks after delivery.
After history taking a short standardized clinical examination program is performed, which includes tests of nerve root radiation (exclusion)[8]. The research-physiotherapist fills out the Pain Behavior Scale, a standardized observation scale for quantifying pain behavior [14,15], after clinical examination.
Interventions
Usual care
Prior to the trial, detailed information is gathered about the contents of traditional treatment options. Part of the information is collected by means of group discussions with experienced physiotherapists and occupational therapists and interviews on an individual basis with affected women out of the cohort. An independent rehabilitation specialist, specialized in pain treatment chaired the meetings with the therapists. Some subjects for discussion were: differences in clinical spectrum seen by the therapists, contents of treatment programs during pregnancy and after delivery, common knowledge by the therapists about etiology, prognosis and prevalence of the syndrome, the optimal time-window for treatment in the course of complaints and the therapist-patient relationship. Items that provided important topics of conversation between the therapists were: the moment of taking up and finishing off treatment, the contents of education and advice given to the patient and the (lack of) compliance. The most striking characteristics of a traditional treatment were the character of the therapist-patient relation and the way of goal setting, focusing on disease management [16]. There was an explicit professional input and an accent on biomedical factors. A pain contingent regimen of avoiding and limiting several day-to-day activities was important. Compliance and adherence based on these goals played an important part. Therapists were often highly concerned about their patient's pain themselves.
However, interviews with affected women made clear that most of the women were irritated about this regimen in an increasing degree after starting the treatment sessions. The regimen was too strict and on a number of points not geared to the wishes and concerns of the women. These aspects caused a lack of compliance and an unremitting hesitation about a good prognosis and in particular about reassuming certain day-to-day activities after delivery. Therapists did not realize the nature of this problem although they did mention problems with compliance. Some women were not able to get a grip on their condition and left management of their pain and activities of daily life to the therapist. A larger part of the women was more or less uncertain about picking up their full range of activities again after delivery. Their beliefs and concerns about origin and prognosis of their complaints clearly bore the stamp of the introduced biomedical label. The relatively favorable prognosis after delivery was largely unknown to the women as well as to the physiotherapists.
Experimental therapy
Women, allocated to the intervention group, are referred to a participating physiotherapist in their own neighborhood. These physiotherapists received an educational course about the treatment protocol prior and during the study. All physiotherapists were already experienced and specialized in treating women with pregnancy-related pelvic girdle pain prior to the study. The contents of the experimental therapy are based on the latest literature, results of interviews with affected women (participating in the cohort study) and group discussions with experienced physical and occupational therapists.
A search procedure in literature resulted in various therapeutic interventions. However, effectiveness of those interventions remain unproven. An important common goal of these treatments is restoration of optimal biomechanics, although this is not based on established theoretical principles [7]. The search did not provide enough possibilities to design a treatment protocol. However, as mentioned above, results of interviews and group conversations showed interesting contradictions.
During development of the experimental intervention we focused on the following contradictions: patient-therapist relationship, education, and hesitation or avoiding of activities. Theoretical concepts of self-management [16,17] and fear-avoidance [18] were integrated in the treatment protocol. A treatment program that demands a much more active involvement of a participating woman was designed. Interventions with a self-management approach are considered to be able to build a bridge between patients' needs and caregivers' services to meet those needs. Self-management refers to the individual's ability to manage the symptoms, treatment, physical and psychosocial consequences and life style changes inherent to living with a chronic condition [17]. Self-management approaches are either group-based or individualized. We performed an individualized approach of 7–9 sessions of 30 minutes once a week. Standardized information is presented through a treatment protocol for the therapists and booklets for the patients [16,19]. Topics included back and pelvis anatomy, "red flags" indicating a serious medical condition, factors contributing to fluctuations in pain, appropriate pacing of exercises [12] and activity, handling pain flare-ups, cognitive restructuring, some graded exposure techniques [18,20,21], communication and social persuasion. Therapists had to employ problem-solving techniques that engaged women in identifying day-to-day problems or limitations related to pelvic girdle and/or low back pain, setting personal goals, brainstorming options for achieving these goals and developing personal action plans. In subsequent sessions, women reviewed their action plans and their progress towards goals and engaged in problem-solving skills around difficulties that arose in trying to implement their plans. Information about two opposing behavioral responses of pain-related fear (avoidance and confrontation) is given, and a hierarchy of individual fear-eliciting movements and activities is made. Therapists encouraged women in making action plans for specific activities that were avoided.
Complaint-related problem solving is a key skill. The role of the therapist is to encourage women to identify possible causes of a problem, find a number of potential solutions, select one, then try it and finally evaluate the results and possibly adjust the solution. The second important key skill is action planning or goal setting. Often a plan must have been generally unacceptable (such as "go skiing") for a therapist in the usual care. Nevertheless, the protocol of the experimental intervention embraced the point of view that a woman is her own best judge of what is possible. Another major point of action planning is that a woman could not only receive but also give feedback on her own accomplishments. Endorsement by the therapist is very important for a woman to accept her new role. This way of collaborating with a therapist on short-term action planning enabled women to master new skills and to make changes that are realistic and feasible for them.
Therapists also have a role in assisting women in understanding their symptoms. Knowledge of the course of the complaints during pregnancy and after delivery including pain flare-ups in the year after delivery, factors contributing to fluctuations in pain, evidence-based knowledge about etiology and the concept about pain-related fear are essential. Symptoms are explained as having many but not alarming causes, which offers the possibility to choose different actions by the concerning woman. Finally, therapists have a task in practicing social persuasion. A woman is more likely to change her behavior and have confidence in doing so if she perceives those around her, including the therapist to be supportive.
A relationship in which the physiotherapist and the woman make health care decisions together is the basic assumption of the intervention. Generally, a time contingent policy is followed in which women set the pace by means of action plans. The expertise of the physiotherapists of the condition in general and of the women about their own specific condition and lives are equally important [22].
Outcome measurements
Outcome measures (Table 1) chosen to explore the success of any intervention need to match the desired aims of that intervention. It is a process in which a standardized attempt is made to observe an often complex clinical picture. Primary domain for improvement of the treatment under investigation is limitations in activities. Other important domains are the severity of the main complaints, the woman's global feeling of recovery, pain and participation.
Table 1 Timing of measures
Baseline (about 3 weeks after delivery) 12 weeks after randomization 6 months (after delivery) 1 year (after delivery)
History taking X
Physical Examination: X
PBS X
GPE X X X
MC X X X X
MPQ(VAS) X X X X
RDQ X X X X
QBPDS X X X X
TSK X X X X
PCS X
BDI X
NEM X
PEM X
Expectancy treatment result: X
SF-36 X X X X
EuroQol X X X X
IPA X X X X
Cost-diary X X X
Satisfaction treatment : X X
Recurrence X X
Co-interventions X
Compliance X X
Subsequent pregnancy : X X
PBS = Pain Behavior Scale GPE = Global erceived Effect MC = Main Complaint MPQ = McGill Pain Questionnaire RDQ = Roland Disability Questionnaire QBPDS = Quebec Back Pain Questionnaire TSK = Tampa Scale For Kinesiophobia PCS = Pain Catastrophizing Scale BDI = Beck Depression Inventory NEM = Negative Emotionality Scale PEM = Positive Emotionality Scale SF-36 = Short-Form-36 IPA = Impact on Participation and Autonomy
Limitations in activities are measured with the Dutch translation of the Roland Disability Questionnaire (RDQ) [23] and the Quebec Back Pain Disability Scale (QBPDS) [24,25]. We added the phrase "because of my back and/or pelvic pain" in both questionnaires.
Subjective measurements like global feeling of recovery (global perceived effect, GPE) and severity of the main complaints (MC) reflecting a patient-specific approach are also selected. Global Perceived Effect (GPE) is measured by self-assessment on a 7-point scale (1 = completely recovered, 7 = worse than ever). The main complaints (MC) are selected by the woman in a standardized approach by selecting three activities, which are an essential and frequently performed part of her everyday life. However, the performance is difficult or impossible because of low back and/or pelvic girdle complaints at the moment of baseline measurement. Severity of a main complaint is rated on a visual analog scale (VAS). [26,27].
Pain is measured with two VAS-scales of the McGill Pain Questionnaire (MPQ-DLV) [28,29] to record the intensity of pain the last week and day.
The impact on participation and autonomy (IPA) is used to measure person-perceived restriction in participation and autonomy [30,31]. The used subscales are autonomy in self-care, mobility and leisure, social relationships and family role.
Other important prognostic factors that can influence treatment results are fear of movement, pain catastrophizing, depression, negative and positive affect, expectancy of treatment result and pain behavior.
Fear of movement is measured by the Dutch translation of the Tampa Scale for Kinesiophobia(TSK)[32,33]. We used the TSK and the both subscales "fear avoidance" and "harm"[34,35]
Pain catastrophizing is measured by the Pain Catastrophizing Scale (PCS)[36,37].
The Beck Depression Inventory (BDI) [38] measures depressive symptoms [39]. Analyses of the BDI in this study did not include items concerning weight loss, sleeping disturbance and work inhibition [40]
To measure the experience of negative affect we used the 14-item Negative Emotionality Scale (NEM) [41]. To measure positive affect we used the 11-item Positive Emotionality Scale (PEM) [41]. Both are subscales of the Multidimensional Personality Questionnaire.
Health status is evaluated by the Short-Form 36 (SF-36)[42,43] and the EuroQol [44]. We used the subscale "general health".
A cost-diary [45] is used to obtain data on physical activities, health care utilization, and days of sick leave. Women are instructed to record costs on a weekly basis until one year after delivery.
Expectancy of therapy result [46] is measured by means of a 100 mm visual analog scale (VAS). The woman is asked to what extent she believes that a treatment is beneficial to her.
The Pain Behavior Scale (PBS)[14,15] is an observation scale tapping 8 pain behaviors that the physiotherapist completes after physical examination. These are verbal complaints, vocal complaints, facial grimaces, standing posture, mobility, body language, use of visible supportive equipment and stationary movement.
Follow-up
Women are asked to complete follow-up questionnaires at 12 weeks after randomization, 6 months after delivery and one year after delivery. Women who did not return their follow-up questionnaires were contacted by mail or phone and were asked to continue participation.
Compliance, other interventions and confounding
The follow-up questionnaires ask all women how many treatment sessions they have followed in the previous period of time. Furthermore, information on contents, satisfaction and the aspects of the (experimental) treatment which benefited them most, is gathered. Co-interventions, medication, aids, additional medical consumption, recurrence of complaints, return to gainful employment and a possible subsequent pregnancy are also registered.
Physiotherapists who treat the participants of the intervention group also answered questions about the number and contents of the treatment sessions after conducting the last meeting.
Statistical analyses
Statistical analyses are carried out according to the "intention-to-treat" approach. The baseline status of the study groups is compared with respect to the distribution of all independent prognostic variables and the baseline values of the outcome variables. For the outcome measures recorded at baseline and at follow-up, we computed the difference between the baseline and the follow-up score for each woman. Differences between groups and 95%CI are calculated for each outcome measure according to the intention to treat approach. Primary analysis is done by means of analysis of an independent t-test (for continuous outcome variables) and chi-square test (for categorical outcome variables). In order to adjust for possible baseline differences a multiple linear regression analysis for continuous outcome measures is performed with the change scores as dependent variable, treatment option as independent variable and base line scores of the prognostic variables as co-variables. Missing data at the baseline-measurement are substituted by the "mean of series" imputation method. Longitudinal missing data are substituted with the "last value carried forward method". In all comparisons between the two treatment options a two-tailed p-value of 0.05 is considered to indicate statistical significance. Prognostic status at baseline for women with and without missing values for the outcome variables is compared for both groups. Analyses are done by using SPSS statistical software, version 12.0 (SPSS, Inc., Chicago, Illinois). Short term and long term effect analyses are performed separately.
Economic analyses
A cost-effectiveness analysis compares the costs and health effects of the experimental intervention to assess whether it is beneficial from an economic perspective. The costs of the intervention are calculated separately for the intervention group. For the whole study group all relevant health care costs, production loss and patient and family costs are measured by means of a cost-diary [45] and follow-up questionnaires collected 6 months and one year after delivery. Both direct health care costs (such as physician visits, the number of treatment sessions and medication), direct non-health care costs (such as transport to therapist) and indirect costs associated to the complaints (like sick leave, professional as well as voluntary aid and extra baby sitter) are registered until one year after delivery. Quality of life is measured using the EuroQol. [44]. For the validation of the healthcare costs, patient and family costs, an update of the Dutch manual for costing in economic evaluations is used. The primary outcome measure for the cost-effectiveness analysis is the difference in limitations in activities (RDQ)[23].
Details about enrollment in the study
During the study, 397 of the 7526 women (5%) signed only for the cohort study (n = 7526) and were therefore beforehand excluded for taking part in the intervention study. Throughout pregnancy, 73% of all women in the cohort reported pain in the lumbar/pelvic region leveling off to 35.9% three weeks after delivery (Figure 2). The "three weeks after delivery" prevalence rate of "wanted to be referred for treatment" was 4.8% at that moment and remained remarkably stable in the year after delivery.
Figure 2 Prevalence of pelvic girdle and/or low back pain during pregnancy and after delivery
Since November 2000 (Figure 3), 682 women reported that they need treatment during pregnancy (9% of the total cohort). 384 times midwives indicated that a woman need treatment at the time of 10 days after delivery (5% of the total cohort). On 197 occasions, both the woman and her midwife responded positive. The outcomes resulted in 869 possible eligible participants (11.5% of the total cohort). However, these data resulted in only 147 home visits, 99 visits indicated by a midwife (67 times in combination with the woman concerned) and 115 indicated by the woman (Figure 3). On basis of history taking by telephone, 722 women were excluded from participation. Ten women did not give informed consent for the intervention study, 3 women moved outside the area intervention was provided, 13 women were excluded because of specific pathology, 49 women did not want to be randomized (clear treatment preference deviating from the study protocol) and 12 women did not feel like participation on second thought. The majority, 635 women, were excluded because of a quick recovery.
Figure 3 Enrollment in the intervention study
After the home visits, 21 women were excluded. Indicated by a midwife; one because of family reasons, one because of specific pathology and two women because of quick recovery. Indicated by themselves, 17 women were excluded. Two women because of family and social problems, one woman because of specific pathology, one because of a clear treatment preference and 13 women because of quick recovery.
Eventually, 126 women were included in the intervention study. 93 times indicated by midwives, 56 times indicated both by the woman and her midwife and 89 times by themselves. Finally, only 24.2% of the women indicated by a midwife were included and 13% indicated by themselves. Indicated by both the woman and her midwife, 28.4% was included.
Discussion
This study is designed to evaluate the effectiveness of a tailor-made program with respect to biopsychosocial factors. A pragmatic design provides the opportunity to evaluate the value of the experimental intervention without depriving participating patients of the best current treatment option. Including only women who would take the 50% risk of depriving any treatment at all for their complaints during the first 12 weeks after delivery was not a realistic option. The effects of the experimental intervention and usual care are evaluated as they are applied in primary health care. It is not feasible to blind a woman to the applied treatment option, which increases the risk of information bias. We have tried to minimize this type of bias by assessing treatment preference before randomization and excluding women with a clear treatment preference. Details about the enrollment of the trial underscored this necessity (n = 50 excluded because of a clear treatment preference). The research-physiotherapist dealing with the baseline measurement was therefore unaware of treatment allocation.
Details about the enrollment out of the cohort into the trial also show that the start of the experimental intervention is well timed. Most women have complaints during pregnancy. However, a considerable drop in the number of women having persistent complaints in the first weeks after delivery is observed. Then again, numbers of women having one or more episodes of pain complaints remained stable in the year after delivery. We have seen similar trends of proportions of women with a request for treatment for their complaints during pregnancy (9%), just after delivery (4.8%) and in the year following delivery.
The aim of the experimental intervention is to increase the level of activities. Therefore the primary outcome measure is limitations in activities. The contrast between both interventions is an important issue in this study. Major features that underscore the contrast are the character of the patient-therapist relationship, pain-contingent versus time-contingent treatment, compliance to a regime of avoiding and limiting activities versus action planning and personal goal setting by the women themselves. Among therapists, the approach of the experimental intervention is not widespread at all. The participating therapists are explicitly asked to not give any information about the contents of the experimental treatment to therapists who do not participate in the experimental intervention. It is necessary to interest physiotherapists in the trial for an efficient performing of the experimental treatment option, which can be achieved by a relevant research question and in practice applicable results.
Competing interests
The author(s) declare that they have no competing interests
Authors' contributions
CHGB: first author, is involved in the design, data collection, statistical analyses, and the development of the experimental intervention.
RAdB: participated in the design, coordination, statistical analyses, have made substantial contributions to the development of the experimental intervention, is involved in revising the article for important intellectual content.
PMJCW: participated in the design, has made substantial contribution to the development of the experimental intervention and is involved in revising the article for important intellectual content
JWSV: has made substantial contributions to the development of the experimental intervention and is involved in revising the article for important intellectual content
JMB: participated in the design and statistical analyses, is involved in revising the article for important intellectual content
ABAK: participated in the design and collecting of the data
AH: participated in the design, recruitment and collecting of the data
PvdB and GGME; participated in the design, have made substantial contributions to conception, design and experimental intervention and revising the article critically for important intellectual content.
All authors read and approved the final manuscript.
Pre-publication history
The pre-publication history for this paper can be accessed here:
Acknowledgments
The funding for this study was provided by the Dutch Board of health insurance companies (Cvz)as a part of a research project titled " Peripartum pelvic pain during pregnancy and after delivery". We would like to express our gratitude to all the participating women, thank Foekje Stelma as the research-physician of this project and Conny de Zwart for the logistic assistance (including randomization).
We also like to thank Gerda Kraag for performing and analyzing the interviews, Jeroen de Jong and Peter Heuts for their contribution to the training of the therapists in the experimental intervention group and Peter Heuts also for chairing the group meetings. Also very important for the project were the participating policlinics gynecology and midwives. Leonie van Gemert and Jacqueline Heesters, both occupational therapists from Blixembosch rehabilitation center Eindhoven provided important information and advices for the booklets, especially the ergonomic hints for young mothers. Finally, we thank the entire group of therapists for their enthusiastic participation during the project.
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| 15619331 | PMC544583 | CC BY | 2021-01-04 16:28:48 | no | BMC Public Health. 2004 Dec 24; 4:67 | utf-8 | BMC Public Health | 2,004 | 10.1186/1471-2458-4-67 | oa_comm |
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Genet Vaccines TherGenetic Vaccines and Therapy1479-0556BioMed Central London 1479-0556-2-171557920210.1186/1479-0556-2-17ReviewDNA vaccines: designing strategies against parasitic infections Ivory Catherine [email protected] Kris [email protected] Institute of Parasitology of McGill University, Macdonald Campus, 21,111 Lakeshore Road, Ste. Anne de Bellevue, Quebec, Canada, H9X 3V92004 3 12 2004 2 17 17 5 10 2004 3 12 2004 Copyright © 2004 Ivory and Chadee; licensee BioMed Central Ltd.2004Ivory and Chadee; 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 complexity of parasitic infections requires novel approaches to vaccine design. The versatility of DNA vaccination provides new perspectives. This review discusses the use of prime-boost immunizations, genetic adjuvants, multivalent vaccines and codon optimization for optimal DNA vaccine design against parasites.
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Introduction
DNA vaccination was introduced in 1990 by a study that demonstrated the induction of protein expression upon direct intramuscular injection of plasmid DNA in myocytes [1]. DNA vaccines are new types of sub-unit vaccines allowing protein expression in mammalian cells after introduction of plasmid or recombinant viral vectors encoding the selected protective antigen. Protective immunity conferred by DNA vaccines has been shown in many animal models of various diseases including HIV, tuberculosis and cancer [2-4]. DNA vaccines induce strong humoral and cellular immunity and have the potential to increase immunogenicity through modifications of the vector or incorporation of adjuvant-like cytokine genes.
Successful vaccines should be able to induce strong immune responses which are long-lasting and in most cases providing protection against different strains of the same pathogen. Progress has been made towards development of DNA vaccines against viral and bacterial pathogens showing protection and lasting immunity [5]. Application of this new vaccination technology with regard to parasitic infection provides new hope for significant advances in anti-parasitic vaccine research. An important consideration in developing vaccines against parasites is the complexity of parasitic diseases. Parasite infections, unlike most viral and bacterial infections, tend to be chronic and associated with immunodepression or inappropriate immune responses [6]. Parasites have complex life cycles and host immunity to stage-specific antigens may not overlap with other later stages or vector-borne stages. Antigenic variation and other immune evasion mechanisms also complicate the development of vaccines against parasites. However, with recombinant DNA technology and the versatility of DNA vaccination, it is now possible to take rational parasite specific strategies to vaccine design and overcome the obstacles presented by parasitic diseases. Improving DNA vaccine efficacy against parasitic disease can be achieved by: prime-boost immunizations, genetic adjuvants, multivalent vaccines or codon optimization. This review describes the application of these strategies, using specific parasites as examples, to improve DNA vaccine efficacy (see Table 1[7-19]).
Table 1 Summary of DNA vaccine optimization in parasites
Optimization Method Parasite Specific Modifications and Improved Responses Reference
Genetic Adjuvant Malaria Co-immunization of merozoite surface protein-1 (MSP1) of P. yoelii with IL-12 in A/J mice elicited strong Th1 type responses characterized by high levels of IFN-γ. Parasite specific antibodies also protected against parasite infection. [7]
Construction of DNA plasmid encoding C-terminal region of MSP1 (P. falciparum) was tested with plamids expressing GM-CSF or recombinant GM-CSF protein in monkeys. Co-immunization with GM-CSF protein lead to higher Ab titers and higher response to boosting with MSP1. [8]
MuStDO5 is a multivalent vaccine composed of 5 plasmids encoding P. falciparum proteins and GM-CSF. When tested for safety in mice and rabbits via i.m/i.d. injections, the vaccine was determined safe and well tolerated without development of autoimmunity. [9]
Leishmania Vaccination with plasmids encoding L. amazonensis P4 nuclease, HSp70 or murine IL-12 was tested in the susceptible Balb/c mouse model. Co-immunization with P4 nuclease and IL-12 protected mice against parasite challenge as determined by 4 log reduction in parasite burden and increased levels of IFN-γ and TNF-α. [10]
Following p36/LACK prime-boost immunization with a combination of DNA vectors expressing IL-12 and IL-18 in mice, highest protection was observed compared to controls. [11]
Schistosoma Co-administration of DNA plasmids encoding IL-18 and S. mansoni glutathione S-transferase elicited 30 fold increase in antigen specific IFN-γ secreting cells, 28% reduction in egg laying and 23% reduction in worm burden in mice. [12]
Multivalent vaccine Malaria Prime boost regimen with vectors encoding functional domains of TRAP and CS antigens of P. cynomogli was more effective at reducing peak parasitemia in rhesus monkeys. [13]
A multistage P. knowlesi vaccine with plasmids encoding 2 pre-erythrocytic, 2 blood stage antigens and GM-CSF was administered to rhesus monkeys followed by a boost with a pox virus encoding all 4 antigens. Monkeys developped Abs against sporozoites, infected erythrocytes and CPS protein. [14]
Six pre-erythrocytic antigens linked together to produce a polyprotein in a DNA vaccine and either MVA or FP9 were tested in mice against P. falciparum. Greater responses were seen when a heterologous viral regimen was used, producing multispecific T cells. [15]
Leishmania L. major TSA and LmST11 antigens were expressed either as single genes or as digene construct and tested in the susceptible Balb/c model. Administration of the genes in either constructs lead to protection via polyspecific immune responses. [16]
Schistosoma Three doses of 4 plasmids encoding S. japonicum antigens, Sj62, Sj28, Sj23 and Sj14 3-3-, induced high levels of IFN-γ and partial protection from challenge infection when administered in mice. [17]
Entamoeba DNA plasmids encoding either Entamoeba histolytica cysteine protease 112 or adhesin 112 were co-administered to hamsters, leading to protection against liver abscess formation. No protection was observed with either plasmid alone. [18]
Codon optimization Malaria P. falciparum erythrocyte binding protein and MSP1 antigens were codon optimized for expression in mammals. 10 to 100 fold less optimized plasmid DNA was required to induce high Ab titers in mice. [19]
Prime-Boost Immunizations
Current sub-unit vaccines predominantly induce strong antibody responses and weak cellular immunity. DNA vaccines in animal models can induce both strong humoral and cellular mediated responses, but although safe in humans, DNA vaccines do not produce the same magnitude of cellular immunity [20]. In cases where the pathogen is intracellular, an antibody response is not sufficient for protection and cell-mediated immunity is required. This is the case with malaria, where the parasite infects hepatocytes and erythrocytes, and cytotoxic T cells play an important role in protection. Therefore, it is important to devise vaccination strategies that enhance T cell immunogenicity and confer a protective cellular immune response to intracellular pathogens. A novel approach to increase T cell responses to vaccination is the heterologous prime-boost immunization strategy [21]. This method consists of priming and boosting with different vectors encoding the same antigen. The principle of the strategy is to first prime some T cells to be antigen-specific and then boost to induce rapid T cell expansion upon repeated exposure to the specific antigen. DNA plasmids are good priming agents since they are internalized by antigen presenting cells and can induce antigen presentation via MHC class I or class II. DNA plasmid backbones are immunogenic due to the presence of stimulatory unmethylated CpG motifs that readily induce Th1 cytokine expression, leading to cellular mediated immunity. Recombinant viral vectors, which are non-replicating and safe, are excellent for boosting. Viral vectors induce high protein expression and presentation via MHC class I which leads to greater antigen specific T cell expansion [22]. Common boosting vectors in vaccine trials include modified Vaccinia virus Ankara (MVA), recombinant Vaccinia virus (rVv), attenuated adenoviruses, and attenuated pox viruses like fowl pox (FP9). These viruses are highly attenuated and non-replicating but still able to produce proteins. The MVA vector, for example, was developed by over 500 serial passages in chicken embryo fibroblasts and has acquired a replication defect in late stage virion assembly. This vector was used for smallpox vaccinations in 1970 and is known to be safe as well as highly immunogenic. Viral vectors induce strong production of proinflammatory cytokines, which generate greater levels of cell-mediated immunity. Overall the immunogenicity of viruses is greater than that of plasmid DNA, however when administered alone the immune response is generally targeted to vector components. For this reason heterologous vaccination, priming and boosting with different vectors, promotes antigen-specific responses rather than vector-specific responses. The resulting effect when using the heterologous prime-boost technique is the generation of memory T cells to the antigen by priming then amplification of these cells by boosting. This approach has been used extensively to create effective immunizations against malaria, and in a variety of parasites [23-32] (see Table 2).
Table 2 Prime-boost immunization trials against parasites
Parasite Antigen Priming agent Boosting agent Response Reference
Malaria Circumsporozoite protein of P. berghei Attenuated fowlpox virus or DNA MVA Potent CD8+ T cell responses were elicited in mice with FPV/MVA vaccination. Novel regimen was more protective against challenge than DNA-MVA immunizations. [7]
P. falciparum surface protein (Pfs25) DNA Recombinant protein Intramuscular injections in rhesus monkeys showed significant increase in transmission blocking antibodies. [8]
Circumsporozoite protein of P. yoelii DNA Pox virus Immunized neonatal mice showed 93% protection which was CD8+ T cell dependent. [9]
P. falciparum erythrocyte binding protein DNA Recombinant protein Higher antibody titers and the ability to reduce parasitemia without drug intervention in Aotus monkeys. [10]
Circumsporozoite protein of P. falciparum DNA RTS, S/ASOZA Malaria volunteers develop P. falciparum specific Abs and Th1 specific CD4+ and CD8+ T cells upon vaccination. [11]
Leishmania Leishmania infantum LACK DNA Recombinant vaccinia virus 60% protection, associated with cell mediated responses, was observed in dogs after challenge compared to controls. [12]
p36/LACK DNA Recombinant vaccinia virus Vaccination in mice resulted in 70% reduction in lesion size and 1000-fold reduction in parasite loads. [13]
L. infantum acidic ribosomal protein PO (LiPO) DNA Recombinant protein Boosting elicited stronger IgG2a titers but could not protect against challenge compared to DNA alone. [14]
Schistosome Cu/Zn cytosolic superoxide dismutase (SOD), signal peptide SOD and glutathione peroxidase (GP) DNA MVA DNA vaccines were tested against S. masoni challenge in mice. Boosting with MVA for the same genes had no increased effect expect for mutated GP antigen were boosting lead to 85 % protection. [15]
To further improve the efficacy of a Plasmodium yoelii DNA vaccine, mice were primed intramuscularly with DNA vaccine and granulocyte/macrophage colony stimulating factor (GM-CSF) plasmid and boosted with rVv encoding the same circumsporozoite protein (CSP) [33]. This combined strategy of genetic adjuvant and prime-boost immunization elicited improved responses and protection while also reducing the dose of initial DNA vaccine required. In chimpanzees, a DNA-prime and MVA-boost regimen encoding thrombosin-related adhesion protein (TRAP) with GM-CSF protein as adjuvant induced specific T cell and antibody response that was long lasting against P. falciparum [34]. Complete protection against P. berghei challenge characterized by strong CD8+ T cell responses was observed in mice after intradermal adenovirus-prime-MVA-boost encoding CSP [35]. These studies led to the assessment of prime-boost immunizations in humans in both naive volunteers and field trials in endemic areas. DNA-prime-MVA-boost vaccines encoding a polyepitope string fused to P. falciparum pre-erythrocytic TRAP antigen were administered via gene-gun to healthy volunteers with no adverse effects [36]. The polyepitope in the vaccine encodes a single polypeptide, which constitutes of a string of T and B cell epitopes from different sources, including tetanus toxin and BCG. In fact, this heterologous prime-boost immunization elicited interferon-γ (IFN-γ) secreting, antigen-specific T cells in humans, which were significantly higher than responses observed with either vector alone [37]. Furthermore, this study demonstrated partial protection, measured by delayed parasitemia, after challenge with a different strain of P. falciparum. Another group demonstrated that priming with DNA vaccine for P. falciparum CSP and boosting with a recombinant protein vaccine in adjuvant (RTS, S/AS02A) induced the production of significant antibody and T cell responses in healthy volunteers [38]. Phase I clinical trials in The Gambia in semi-immune adults have demonstrated that heterologous DNA-prime-MVA-boost regimen encoding P. falciparum TRAP antigen is safe, well tolerated and induces responses greater than those observed in naive volunteers [39]. Boosting with the MVA vaccine 12 months after the initial prime-boost immunization in this clinical trial was successful in re-expanding the T cell population and demonstrated the safe use of MVA to boost at different periods to maintain T cell immunity.
Genetic Adjuvants
Adjuvants are used to strengthen the immune response to a vaccine and have been critical in modern vaccine development. Genetic adjuvants are expression vectors encoding biologically active molecules such as cytokines, chemokines and co-stimulatory molecules. These adjuvants can be encoded on the same vector as the antigen or expressed on a separate vector and co-injected with the vaccine. This method provides adjuvant activity at the site of antigen production, with lasting effect from transfected cells. Cytokines are chosen as genetic adjuvants because they regulate cells involved in host defense and can be used to modulate immune responses. Co-delivery of cytokines in DNA vaccine formulation has been used extensively for a wide range of infectious and parasitic diseases (see Table 2) to enhance the T cell subset responses known to be protective. Vaccine development against schistosomiasis has been hindered by a lack of consensus on the type of immune response that would be protective. However, it is generally believed that the best strategy for an anti-pathology vaccine is immune deviation. Pathology in schistosomiasis is associated with egg-induced granuloma formation for which there is evidence for a role for Th2 cytokines. The strategy here is to use genetic adjuvants of the Th1 cytokine subset, like interleukin-12 (IL-12), to skew the immune response and provide protection [40]. Therefore immune deviation is attained with the use of selected genetic adjuvants.
Siddiqui et al. [41] generated DNA vaccines encoding Schistosoma mansoni large subunit of calpain (Sm-p80) and either mouse GM-CSF or IL-4 to determine their adjuvant effect in mice. GM-CSF may work as adjuvant through its activating effect on dendritic cells and macrophages. Intramuscular vaccination with Sm-p80 alone provided 39% protection and this protection was significantly increased to 44% with GM-CSF co-administration and 42% with IL-4. The addition of GM-CSF led to an increase in total IgG and IgG1 while Th1 type IgG2a antibody titers remained high in protected animals [42]. Since protection was associated with Th1 type antibodies, the Sm-p80 DNA vaccine was further enhanced with co-delivery of plasmids encoding mouse IL-2 or IL-12 [43]. Greater protection was observed with IL-2 and modest but significantly higher protection was provided by IL-12 co-delivery. Both IL-2 and IL-12 are key cytokines in Th1 cell differentiation. The co-delivery of these cytokines increased IgG2a antibody levels and decreased IgG1 levels, indicating that these genetic adjuvants were successful as Th1 enhancers. Other studies reported no enhancement of protection or immune responses when IL-12 was co-injected, but these differences may be attributed to the nature of the vaccine antigen [44].
Multivalent Vaccines
Another advantage of DNA vaccines is the possibility to integrate several antigens into the plasmid or to administer a mixture of plasmid vectors. The development of multivalent vaccines consisting of several antigens is a novel approach to create broad range protection against different parasite strains and parasite life cycle stages (see Table 2). Parasites are complex organisms with multiple life cycle stages and antigenic variation mechanisms to evade immune system recognition. Furthermore, not all individuals respond to the same antigens in natural infections. Multivalent vaccines have a greater amount of protective epitopes and could be effective in a greater proportion of the population. However, in multivalent vaccines, the optimal association or combination of antigens must be assessed to obtain synergistic effects.
Vaccination studies against leishmaniasis in mice have identified various parasite antigens with varying degrees of protection as protein vaccines. When combined into multivalent DNA vaccines these antigens have the ability to confer complete or enhanced protection. In fact, a DNA vaccine including a mixture of plasmids encoding three antigens, Leishmania major-activated C kinase (LACK), thio-specific antioxidant (TSA), and L. major stress-inducible protein (LmST11) was able to induce complete and long lasting protection after parasite challenge in mice compared to killed Leishmania parasites and rIL-12 [45]. This protection was characterized by reduced parasite load and the recruitment of CD8+ and CD4+ T cells to the site of infection. The same group tested the combination of these antigens and the route of administration to optimize the results of the previous study [46]. It was determined that a cocktail vaccine composed of all three antigens was more effective than LACK alone or LmSt11 and TSA combined. Furthermore, intradermal injection of the plasmid mixture was more effective than intramuscular or subcutaneous injections, reducing the dose of vaccine required five-fold. Another study also demonstrated that prime-boost co-injection of plasmids encoding two different L. major cysteine proteinase genes (Cpa/Cpb) was protective and characterized by IFN-γ production by spleen cells, while separate injections were not protective [47]. The cysteine proteinases are expressed at different levels during parasite development and are thought to be involved in modulation of the host response for parasite survival. In this study the cysteine proteinases, only when combined, had the capacity to induce long lasting immunity of the Th1 type. Comparative evaluations of potential protective antigens is necessary to determine optimal DNA vaccine design [48] as the nature of antigens can have important effects on vaccine efficacy.
Codon Optimization
Interspecies differences in codon usage are a major obstacle in DNA vaccine development. This is due to the fact that DNA vaccines use host cells for transcription and translation of proteins. Every species has a codon bias for which most genes are encoded and this use of selected codons is related to gene expression efficiency. Closely related species use similar codons. However, in cases where there is a great difference in codon usage between the pathogen and mammals, codon optimization may be required. This strategy involves the modification of codon usage for the genes encoded in a DNA vaccine to a suitable codon bias for increased expression in mammals. This method has proved effective in many systems [19,49,50], increasing protein expression in vitro and antigen specific responses in vaccinated animals. In our laboratory, we have developed a codon-optimized DNA vaccine encoding a portion of the Entamoeba histolytica Gal-lectin [51]. E. histolytica genes are rich in A:T codons, whereas mammalian codons are more G:C rich. Protein expression of the E. histolytica Gal-lectin protein using the wild type sequence was difficult and stable clones were difficult to obtain in mammalian cells. Codon optimization was performed to ultimately increase protein expression in gerbils, a model for experimental amoebiasis; therefore gerbil codon usage was used to re-write the Gal-lectin Hg1l gene. Transfection of Cos-7 cells with the optimized vaccine construct produced a protein which was immunoreactive with a Gal-lectin specific monoclonal antibody (3F4), demonstrating successful expression of this amoebic protein. Upon vaccination with this codon optimized DNA plasmid, mice developed antigen specific antibodies of the Th1 isotype and Gal-lectin specific cellular immune responses.
Conclusions
In this review, strategies for increased DNA vaccine efficacy against parasitic diseases to date, i.e. prime-boost immunizations, genetic adjuvants, multivalent vaccines and codon optimization, have been discussed. DNA vaccine technology provides the versatility required to separate protective components of immunity from counter-protective responses. As seen with genetic adjuvants, DNA vaccines can focus on the protective cytokines involved and include antigens that stimulate the production of specific cytokines. This allows designing vaccination strategies that are tailored to a particular infection or even a specific stage of infection. Parasitic diseases are complex, involving changes in immunological responses during the course of infection and changes in immunity to stage specific antigens. The advent of optimization strategies with DNA vaccines presents researchers with the tools to design effective vaccines with specific purposes. It is possible to enhance DNA vaccine efficacy, thus increasing immune responses and protection, through the use of these methodologies. However, it is important to note that these strategies need to be adjusted to the parasite system in order to provide the greatest benefit upon vaccination. For example, Sedegah et al. [52] reported reduced immunogenicity of multistage P. falciparum DNA vaccines when administered as a mixture of plasmids compared to single plasmid injections. Another study, however, demonstrated that a mixture of three plasmids encoding P. falciparum blood-stage antigens had no reduction in immunogenecity when co-injected [53]. Therefore many aspects of a DNA vaccine can contribute to its efficacy, and each must be evaluate to understand the interactions between vaccine components. In fact, it is clear that other factors are important in vaccine design, such as the nature of the antigen, the presence of immunostimulatory CpG motifs in the plasmid backbone, the vaccine delivery system or the site of injection [8,9,16,24,32,54].
The method of vaccine delivery is an important variable in vaccination design. DNA vaccination has been successful through a variety of injection routes, including intradermal, intramuscular, and intranasal. Although intramuscular injections are most common and give consistent responses, alternative routes of delivery may be desired depending on the disease model. Mucosal DNA vaccine immunizations, against intestinal parasites for example, are effective to generate mucosal immune responses at the site of infection. For leishmaniasis, where the disease manifests itself as cutaneous lesions, an intradermal injection targeting Langerhans' cells may be optimal [46]. The gene-gun is a unique method of DNA vaccine delivery, which has been used successfully against a variety of parasites. The gene-gun accelerates plasmid-coated gold particles to supersonic speed with helium gas and delivers them to the outer layers of the skin. In reality this vaccine delivery system is being tested alongside intramuscular injections in The Gambia field trials for malaria vaccines in humans. The Powderject® XR1 is a needle-free powder injection system that delivers fine gold particles coated with the vaccine vectors directly into epidermal cells, specifically dendritic cells. This vaccination method is advantageous since it eliminates the cold chain requirement and reduces the chances of needle-borne contamination. Moreover, the gene-gun method is safe and seems as immunogenic as intramuscular injections in these trials [36].
The greatest challenge in designing DNA vaccines against parasites is making the vaccine suitable for humans while providing strong, long lasting immune responses. Many studies in laboratory animals are successful but the results cannot be replicated in humans. The prime-boost strategy has shown the most success as a delivery technique in larger animals or humans. Field trials with prime-boost malaria vaccines are ongoing and will provide experts with insight with regards to the safety and the immune responses required for protection in humans. Meanwhile, other groups are reporting improved responses in mice or larger mammals with other vaccines, suggesting that this vaccination strategy may be applicable to many other parasitic diseases [3,29]. A variety of combinations of other enhancement strategies with prime-boost immunization have been explored, including the use of genetic adjuvants or multivalent plasmids [11,14,15]. Prime-boost immunizations against Leishmania parasites in mice have improved cellular immune responses when plasmids expressing IL-12 and IL-18 are co-injected [11]. A DNA prime-protein boost vaccine in monkeys encoding two P. cynomolgi antigens (CSP/TRAP) resulted in lower peak parasitemia and higher antibody and cellular responses than controls [13]. Taken together, the techniques described above will allow parasitologists to develop effective DNA vaccines that are designed to target a specific immune response during parasitic infection. The optimized approach provided by DNA vaccine technology will produce vaccines ready for clinical and practical applications, as well as providing a greater understanding of the underlying complexity of immunity in parasitic infections.
Competing interests
The author(s) declare that they have no competing interests.
Author's contribution
CI and KC produced the manuscript together. All authors read and approved the final manuscript.
Acknowledgements
Research at the Institute of Parasitology is partially funded by the Fonds pour la Formation du Québec. CI is the recipient of the McGill University Lynden Laird Lyster Memorial Fellowship.
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| 15579202 | PMC544584 | CC BY | 2021-01-04 16:39:08 | no | Genet Vaccines Ther. 2004 Dec 3; 2:17 | utf-8 | Genet Vaccines Ther | 2,004 | 10.1186/1479-0556-2-17 | oa_comm |
==== Front
J Transl MedJournal of Translational Medicine1479-5876BioMed Central London 1479-5876-2-421558829010.1186/1479-5876-2-42ResearchRetroviral transduction of peptide stimulated t cells can generate dual t cell receptor-expressing (bifunctional) t cells reactive with two defined antigens Langerman Alexander [email protected] Glenda G [email protected] Michael I [email protected] Surgical Oncology Laboratory, Department of Surgery, Section of General Surgery, University of Chicago, Chicago IL USA2 Pritzker School of Medicine, University of Chicago, Chicago IL USA2004 8 12 2004 2 42 42 28 9 2004 8 12 2004 Copyright © 2004 Langerman et al; licensee BioMed Central Ltd.2004Langerman 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
Tumors and viruses have developed many mechanisms to evade the immune system, including down-regulation of target antigens and MHC molecules. These immune escape mechanisms may be able to be circumvented by adoptively transferring T cells engineered to express two different T cell receptors, each specific for a different antigen or MHC restriction molecule.
Methods
PBMC from the blood of normal healthy donors were stimulated for three days with an antigenic peptide from cytomegalovirus (CMV) pp65. These CMV reactive cultures were transduced with a encoding the TIL 5 T cell receptor (TCR) that mediates recognition of the dominant epitope of the melanoma antigen MART-1. Following selection for transduced cells, the cultures were evaluated for recognition of CMV pp65 and MART-1 expressing targets.
Results
We were able to rapidly create bifunctional T cells capable of recognizing both CMV pp65 and MART-1 using a combination of HLA-A2 tetramer staining and intracellular staining for interferon-γ. These bifunctional T cells were sensitive to very low levels of antigen, recognize MART-1+ tumor cells, and maintained their bifunctionality for over 40 days in culture.
Conclusion
Bifunctional T cells can be engineered by transducing short term peptide stimulated T cell cultures. These bifunctional T cells may be more effective in treating patients with cancer or chronic virus infections because they would reduce the possibility of disease progression due to antigen and/or MHC loss variants.
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Background
It has long been established that tumors and viruses have multiple mechanisms for evading the immune system including the inhibition of T cell function through the release of inhibitory cytokines and factors [1,2], down-regulation of MHC molecules [2,3] and the spontaneous generation of antigen-loss variants [1-3]. In latter case, despite the loss of a single antigen on a tumor or virus-infected cell, there can remain functional HLA class I molecules and multiple antigens that can serve as targets for immune destruction. Therefore, immunotherapy strategies which target multiple antigens and/or multiple HLA class I molecules may be more effective than therapies targeting single antigens presented by a single HLA class I molecule.
We and others have shown that it is possible to use retroviral vectors encoding TCRs isolated from tumor- or virus-reactive T cell clones to engineer human T cells to recognize any antigen [4]. While every T cell that is transduced to express a second TCR expresses its own TCR capable of recognizing some antigen, it has only recently been shown that "bifunctional" T cells capable of recognizing two known antigens can be generated [5]. Using this technology, it may be possible to treat patients with T cells bearing two functional T cell receptors (TCRs) with each TCR being specific for a different tumor-associated antigen (TAA) or viral antigen restricted by one or more HLA molecule. These bifunctional T cells would retain effectiveness against single antigen-loss variants or HLA loss variants and may have improved efficacy over monospecific T cells for the treatment of tumors or viruses.
In the current study, we show that it is possible to rapidly generate T cell populations containing T cells reactive with two defined antigens, CMV pp65 and MART-1. These T cell cultures are highly avid for both antigens and retain their reactivity for at least six weeks. More importantly, this methodology could easily be adapted to closed culture systems making it more attractive for use in clinical trials.
Methods
Tumor Cell Lines
All melanoma and renal cell carcinoma cell lines used in this study were established from surgical specimens obtained from cancer patients undergoing immunotherapy at the Surgery Branch, National Cancer Institute. Melanoma cell lines 624 MEL (HLA-A2+, MART-1+), 624-28 MEL (HLA-A2-, MART-1+), 1300 MEL (HLA-A2+, MART-1+), and SK23 MEL (HLA-A2+, MART-1+) and renal cell carcinoma cell line UOK131 (HLA-A2+, MART-1-) were maintained complete medium (CM) which consisted of RPMI 1640 medium (Life Technologies, Gaithersburg, MD) supplemented with 10% heat inactivated fetal bovine serum (Life Technologies), and penicillin (100 U/ml)/streptomycin (100 μg/ml)/L-glutamine (2.92 mg/ml)(Life Technologies) as described [6]. The PG13 A7 retroviral producer cell line, the source of the TIL 5 TCR retrovirus used in this study, has been described elsewhere [7]. Retroviral production by PG13 A7 cells was carried out using an optimized protocol described by Lamers et al [8] in T175 flasks at 32°C in CM. T2 cells and COS A2 were maintained in CM as described [6].
T Cells
R6C12 is an HLA-A2 restricted, gp100:209–217 reactive CTL clone that was isolated from the peripheral blood of a melanoma patient vaccinated with the gp100:209–217 210M peptide at the Surgery Branch, National Cancer Institute. R6C12 cells were expanded using 10 ng/ml anti-CD3 mAb (Ortho Biotech, Raritan, NJ) and 300 IU/mL recombinant human IL-2 (rhIL-2) (Chiron, Berkeley, CA) in T cell medium (TCM) which consisted of RPMI 1640 supplemented with 10% pooled human AB serum (Valley Biochemical, Winchester VA), HEPES (Life Technologies), 2-mercaptoethanol (Life Technologies), penicillin (100 U/ml)/streptomycin (100 μg/ml)/L-glutamine (2.92 mg/ml)(Life Technologies) as described [9]. Peripheral blood mononuclear cells (PBMC) obtained from leukapheresis of healthy donors were used as a source of T cells for establishing CMV pp65 peptide stimulated T cell cultures and feeders for T cell expansion were purchased from BRT Laboratories (Baltimore, MD).
Peptides
All peptides used in this study were purchased from Synthetic Biomolecules (San Diego, CA). Peptides used were MART-1:27–35 (AAGIGILTV), Influenza M1:58–66 (GILGFVFTL), CMV pp65:495–503 (NLVPMVATV), or gp100:209–217 (ITDQVPFSV). Each peptide was maintained as a concentrated stock (2–5 mg/ml) in 100% DMSO (Sigma, St. Louis, MO) and diluted in the appropriate medium prior to immediate use.
CMV pp65 Expressing Targets
Given that it is technically difficult to obtain CMV infected targets for immunologic assays, COS A2 cells were engineered to express a mini-gene encoding the CMV pp65:495–503 peptide epitope (COS A2 CMV). A CMV minigene was constructed using complementary synthetic oligonucleotide primers (sense primer: 5'-GGCCCGCGCAGGCAGCATGAACCTGGTGCCCATGGTGGCTACGGTTTAGTGA-3', anti-sense primer: 5'-GGCCTCACTAAACCGTAGCCACCATGGGCACCAGGTTCATGCTGCCTGCGCG-3', Integrated DNA Technologies, Coralville, IA) that encoded the CMV pp65:495–503 peptide epitope with an ATG translation initiation codon, a Kozak consensus sequence [10] and Not I compatible "sticky ends" to facilitate insertion into the Not I site of the SAMEN CMV/SRα retrovirus. Equal molar amounts of each synthetic oligonucleotude were mixed and ligated into the SAMEN CMV/SRa retrovirus using a rapid ligation protocol and transformed into DH5α competent E. coli cells (Life Technologies) as described [11]. Recombinant clones were sequenced to insure proper orientation and retroviral supernatants were produced by cotransiently transfecting 293GP cells with plasmids encoding the retroviral backbone and the vesicular stomatitis virus envelope as described [11]. COS A2 CMV cells were generated by culturing COS A2 cells overnight with retroviral supernatants supplemented with 8 μg/ml polybrene (Sigma).
Peptide Stimulation and Transduction of PBMC
PBMC from healthy donors were stimulated in vitro with 5 μg/ml of CMV pp65:495–503 peptide in TCM containing 300 IU/mL IL-2 for 3 days. T cell cultures were then transduced using a modified Retronectin (TaKaRa, Otsu, Japan) protocol with the A7 retrovirus as follows: 24-well plates were coated with Retronectin then were preloaded with retrovirus according to the manufacturer's instructions. 2.6 × 106 T cells were added to each well in 1.3 ml (2 × 106 cells/ml) of A7 retroviral supernantant supplemented with 300 IU/ml rhIL-2 and the plates were centrifuged for 90 min at 1000 g. The next day the medium was replaced with fresh A7 retroviral supernatant and the centrifugation was repeated. The cells were rested for 24 hours and then transduced cells were selected in 1 mg/ml of G418 (Research Products International, Mt. Prospect, IL) for five days. Cultures were assayed for antigen reactivity, cyropreserved, and/or expanded for additional assays.
Transduction of T Cell Clones
T cell clone R6C12 was cultured at 2 × 106 cells/ml in TCM supplemented with 300 IU/ml rhIL-2, and 2 μg/ml anti-CD28 mAb (Becton, Dickenson, and Company, Franklin Lakes, NJ) in 24 well tissue culture plates pre-coated overnight with 10 μg of anti-CD3 mAb (Ortho Biotech, Bridgewater, NJ) for three days prior to transduction. Transduction of R6C12 was carried out as described above for CMV peptide stimulated T cell cultures except 2 μg/ml anti-CD28 mAb was added to the medium and 10 μg anti-CD3 mAb was bound to the culture plates in addition to Retronectin.
Antigen Recognition Assays
The antigen reactivity of each T cell culture (TIL 5 TCR transduced and untransduced) was assayed for MART-1:27–35 and CMV pp65:495–503 or gp100:209–217 reactivity in interferon-γ release assays. 5 × 104 T cells were cocultured in a 1:1 ratio overnight in 0.2 ml of CM in duplicate individual wells of a 96-well plate with a panel of stimulators that included T2 cells loaded with 5 μg/mL MART-1:27–35, Influenza M1:58–66, CMV pp65:495–503, or gp100:209–217 peptide and a panel of tumor cells. The amount of interferon-γ released was measured by ELISA as described [6].
Intracellular Cytokine Release Assay and Tetramer Staining
The existence of CMV pp65:495–503/MART-1:27–35 reactive bifunctional T cells was determined by first staining T cells for intracellular interferon-γ production following coculture with HLA-A2+ MART-1+ stimulator cells followed by fluorescence staining with HLA-A2/CMV pp65:495–503 tetramers. 1 × 105 T cells were cocultured in a 1:1 ratio peptide loaded T2 cells or tumor cells for five hours in CM supplemented with 10 μg/ml brefeldin-A. Cells were then collected and stained with PE-conjugated HLA-A2/CMV pp65:495503 tetramers (Beckman Coulter Immunomics, San Diego, CA), fixed in 1% paraformaldehyde (Sigma), permeabilized using 0.5% saponin (Sigma), and then stained with FITC-conjugated anti-interferon-γ (Biosource International, Camarillo, CA). Relative log fluorescence of 104 live cells was measured by flow cytometry using a FACS Scan flow cytometer (BD Biosciences, Mountain View, CA).
Results
Recognition of Peptides and Tumor Cells by TIL 5 TCR-transduced CMV peptide stimulated T cells
Bifunctional T cells reactive with CMV and MART-1 were engineered by first stimulating donor PBMC with CMV pp65:495–503 peptide for three days then transducing the T cell cultures with a retrovirus encoding a TCR specific for MART-1:27–35 presented by HLA-A2. After five days of selection in G418, the T cells were assayed for reactivity against the CMV pp65:495–503 and MART-1:27–35 antigens in interferon-γ release assays. Significant amounts of interferon-γ were released when the TIL 5 TCR-transduced CMV peptide stimulated T cells were cocultured with CMV pp65:495–503 or MART-1:27–35 peptide-loaded T2 cells, COS cells engineered to express HLA-A2 with a CMV pp65:495–503 mini-gene, or HLA-A2+ MART-1+ tumor cells (Figure 1). These cells did not release interferon-γ when stimulated with T2 cells loaded with Flu M1:58–66 peptide, COS A2 (MART-1- CMV-), 624-28 MEL (HLA-A2- MART-1+ CMV-), or RCC UOK131 (HLA-A2+ MART-1- CMV-) cells. Untransduced CMV peptide stimulated T cells only released interferon-γ when stimulated with CMV pp65:495–503 peptide loaded T2 or COS HLA-A2+ CMV+ cells. These cultures were extremely sensitive to antigen stimulation since significant amounts of interferon-γ were released when stimulated with T2 cells loaded with 5 × 10-4 μg/mL MART-1:27–35 peptide and <5 × 10-7 μg/mL CMV pp65:495–503 peptide (Figure 2). These bulk cultures also continued to be reactive to both antigens more than 40 days post transduction (Figure 3). These results indicate that three day peptide stimulated PBMC cultures can be activated in vitro for efficient retroviral transduction. Furthermore, the antigen reactivity of these T cells is consistent with bifunctional T cells capable of recognizing both CMV and MART-1.
Figure 1 Recognition of peptides and tumor cells by TIL 5 TCR-transduced CMV-stimulated T cells. TIL 5 TCR-transduced and untransduced CMV-stimulated bulk cultures were cocultured for 24 hours in a 1:1 ratio with T2 cells loaded with 5 μg/ml of peptide, COS A2 cells with or without a CMV minigene, or A2+, MART-1+ tumor cells (SK23 MEL, 624 MEL), A2-, MART-1+ tumor cells (624.28 MEL), or A2+, MART-1- tumor cells (UOK131 RCC). Supernatants were collected and the amount of interferon-γ released was measured using ELISA. Values are the average of triplicate wells.
Figure 2 Sensitivity of bifunctional cultures to low levels of recognized antigens. TIL 5 TCR-transduced cultures were cocultured for 24 hours in a 1:1 ratio with T2 cells loaded with decreasing concentrations of peptide. Supernatants were collected and the amount of interferon-γ released was measured using ELISA. Values are the average of triplicate wells. Asterisk (*) indicates value was greater than maximum point on standard curve.
Figure 3 Long term maintenance of bifunctionality in culture. TIL 5 TCR-transduced cultures were cocultured for 24 hours in a 1:1 ratio with T2 cells pulsed with 5 μg/ml peptide at varying time points beyond transduction. Supernatants were collected and the amount of interferon-γ released was measured using ELISA. Values are the average of triplicate wells. Asterisk (*) indicates value was greater than maximum point on standard curve.
Antigen Recognition by CMV-tetramer positive T cells
While the antigen reactivity of our T cell cultures was consistent with us having engineered bifunctional T cells, it was necessary to confirm that individual T cells possess the capability to recognize both CMV pp65 and MART-1. To confirm that we had engineered bifunctional cells, each T cell culture was stained with HLA-A2/CMV pp65:495–503 tetramers for anti-CMV reactivity and with intracellular anti-interferon-γ monoclonal antibodies following stimulation with HLA-A2+ MART-1+ cells for anti-MART-1 reactivity. It should be noted that the reciprocal experiment, staining with HLA-A2/MART-1:27–35 tetramers and intracellular anti-interferon-γ staining following CMV pp65:495–503 peptide stimulation could not be performed since TIL 5 TCR expressing cells do not bind HLA-A2/MART-1:27–35 tetramers (unpublished).
Cells that were double stained with tetramers and for intracellular anti-interferon-γ were considered to be reactive with both antigens and therefore bifunctional. As shown in Figure 4, 2.7% of the TIL 5 TCR-transduced T cells were double stained following stimulation with MART-1:27–35 loaded T2 cells compared to 0.06% of the untransduced T cells. When stimulated with CMV pp65:495–503 peptide loaded cells, 28.35% of the TIL 5 TCR-transduced T cells were double stained. These results are representative of multiple cultures which routinely have approximately 10% of the CMV reactive T cells also recognizing MART-1. These results confirm that bifunctional T cells can be obtained by transducing three day peptide stimulated PBMC cultures with retroviral vectors encoding TCR genes.
Figure 4 Peptide recognition by CMV tetramer+ T cells. TIL 5 TCR-transduced and untransduced CMV-stimulated bulk cultures were cocultured in the presence of 10 μg/ml brefeldin A for 5 hours in a 1:1 ratio with T2 cells pulsed with 5 μg/ml of peptide. Cells were then collected, stained with PE-conjugated HLA-A2 MHC CMV tetramer, fixed, permeabilized, and stained with FITC-conjugated anti-IFN-γ mAb. Samples were analysed using two-color flow cytometry. The percentage of dual positive staining cells (upper right quadrant) is as indicated.
Although the reactivity with MART-1:27–35 peptide loaded T2 cells shown in Figure 4 confirmed that we successfully engineered CMV pp65 peptide stimulated PBL-derived T cell cultures to contain bifunctional T cells, it was important to determine if these cultures could recognize the physiologic levels of antigen presented by tumor cells. When stimulated with HLA-A2+ MART-1+ tumor cells, 0.65% (1300 MEL) and 0.52% (SK23 MEL) of the T cells were HLA-A2/CMV pp65 tetramer positive and interferon-γ positive indicating that approximately 20% of the peptide reactive T cells were also tumor reactive (Figure 5). Tumor cells are poor antigen presenters relative to T2 cells because they often fail to express the accessory molecules required for efficient T cell recognition. Furthermore, only those T cells with sufficient TIL 5 TCR expression to yield high avidity T cells are capable of responding to the levels of processed antigen on the surface of tumor cells. This explains why a smaller fraction of bifunctional T cells are reactive with tumor cells compared to peptide-loaded T2 cells.
Figure 5 Tumor cell recognition by CMV tetramer+ T cells. TIL 5 TCR-transduced and untransduced CMV-stimulated bulk cultures were cocultured in the presence of 10 μg/ml brefeldin A for 5 hours in a 1:1 ratio with A2+, MART+ tumor cells (SK23 MEL, 1300 MEL) or A2+, MART-1- tumor cells (UOK131 RCC). Cells were then collected, stained with PE-conjugated HLA-A2 MHC CMV tetramer, fixed, permeabilized, and stained with FITC-conjugated anti-IFN-γ mAb. Samples were analyzed using two-color flow cytometry. The percentage of dual positive staining cells (upper right quadrant) is as indicated.
MART-1 Recognition by a TIL 5 TCR-transduced gp100-reactive T cell clone
To demonstrate that the creation of bifunctional T cells capable of recognizing two tumor antigens was possible using our transduction methods, T cell clone R6C12 cells were activated with anti-CD3 and anti-CD28 mAb then transduced to express the TIL 5 TCR. TIL 5 TCR-transduced R6C12 cells were cocultured with gp100:209–217, Influenza M1, or MART-1:27–35 peptide-loaded T2 cells. As shown in Figure 6, TIL 5 TCR transduced R6C12 cells released interferon-γ when stimulated with MART-1:27–35 or gp100:209–217 loaded T2 cells, but not T2 cells loaded with the irrelevant influenza M1 peptide. In contrast, untransduced R6C12 cells only released interferon-γ when stimulated with gp100:209–217 loaded T 2 cells.
Figure 6 Peptide recognition by TIL 5 TCR-transduced gp100-reactive T cell clone. TIL 5 TCR-transduced and untransduced R6C12 cells were cocultured for 24 hours in a 1:1 ratio with T2 cells loaded with 5 μg/ml of peptide. Supernatants were collected and the amount of interferon-γ released was measured using ELISA. Values are the average of duplicate wells.
Discussion
Here we report the successful engineering of T cells that are able to respond independently to two unrelated known antigens via both an endogenous and a retrovirally-transduced T cell receptor. These T cells were able to respond to low concentration of peptide, and were able to recognize antigen-positive tumor cells. By utilizing the initial antigen response as the activation for transduction, our 12-day protocol represents an efficient technique for generating bifunctional T cells from donor blood, and theoretically can be applied to any tumor or viral antigen in the context of one or more MHC restricting elements.
Many previous efforts at creating TCR transductants used non-specific activation of bulk or clonal populations [7,8,12] or, for the creation of bifunctional T cells, specific activation of semi-clonal populations with peptide-loaded autologous PBMC [5]. Non-specific T cell activation fails to expand T cell populations with known reactivity hence making it virtually impossible engineer T cells reactive with two know antigens. Engineering clonal or semi-clonal populations of T cells will create T cells reactive with two known antigens [5]. However, this process necessitates the establishment of antigen reactive T cell clones or long term T cell cultures prior to transduction. Although technically feasible, the creation of bifunctional T cells from T cell clones (this study) or long term T cell cultures [5] is time consuming and in our experience has a comparatively low yield of bifunctional cells. Furthermore, it is likely that the reactivity and therapeutic efficacy of T cells are diminished with extended culturing (13). Therefore, any method capable of rapidly producing bifunctional T cells will be better suited to clinical applications.
In contrast to using anti-CD3 mAb, in vitro stimulation with antigenic peptides will preferentially activate antigen reactive T cells to expand. These proliferating antigen reactive T cells can be transduced to express a second TCR. Based on our tetramer analysis, only 0.7% of the unstimulated donor PBL stained with the CMV tetramer (data not shown), compared to 44.6% of our peptide stimulated populations (data not shown). This profound expansion allowed for more efficient transduction, and 2.7% of the resulting culture was measurably bifunctional (figure 4). As retroviral transduction and in vitro selection for transduced T cells becomes more efficient, the frequency of bifunctional T cells in these cultures will increase to the point where it is feasible to treat patients.
The combination tumor/viral bifunctional cells we have generated here may have novel uses in immunotherapy, such as bypassing tumor- or viral-induced T cell unresponsiveness. Fossati and colleagues demonstrated that naïve bifunctional T cells "preactivated" via one TCR prior to adoptive transfer would then mediate cytotoxicity via the second TCR [14]. Animal and in vitro studies have shown that peripherally-induced tolerance can be reversed, resulting in regained T cell responsiveness [15,16]. It may be possible to reactivate tolerized T cells in vitro or in vivo by activating a second T cell receptor specific for a non-tolerized antigen [16,17]. In addition, viral antigens such as those associated with influenza, trigger alternate T cell activation pathways [18] and have been shown to elicit a strong T cell immune response [19]. Redirecting the vigorous anti-viral T cells which have not been exposed to the immunologic tolerance associated with most tumor-reactive T cells may be effective in eradicating tumor burden.
The substantial proliferation in response to strong immunogens such as viral antigens can also be used to improve the localization of T cells that also have anti-tumor activity. Using murine bifunctional T cells created by retroviral transfer of a chimeric immunoglobulin receptor specific for an ovarian cancer-associated tumor antigen to alloreactive T cells, Kershaw and colleagues were able to demonstrate in vivo expansion in response to alloimmunization and demonstrated anti-tumor activity [20]. It is possible that tumor/viral bifunctional cells would also behave in this way, and we are currently working on murine models with human/mouse chimeric TCRs to test this hypothesis.
In addition, some current immunotherapy protocols for the treatment of metastatic melanoma involve immunodepletion prior to adoptive cell transfer [21]. Such protocols are similar to solid organ and stem cell transplantation in that the patients are temporarily immunosuppressed and at risk for reactivation of latent viruses such as CMV and Epstein-Barr virus. Tumor/viral bifunctional T cells may be particularly useful in this setting, where the anti-viral activity may help treat reactivation, and the reactivation of the virus may further boost the anti-tumor activity of the T cells by inducing additional stimulation of the bifunctional cells.
Another consideration to bear in mind with the creation of bifunctional T cells is alternate pairing of the alpha and beta chains resulting in the combination of novel T cell receptors within a bifunctional cell. These T cells could have undesirable autoimmune properties. This could be circumvented by identifying T cells within a bifunctional population that have maximal expression of both the endogenous and introduced TCRs, indicating minimal cross-pairing of chains [6]. Screening for these T cells and selectively expanding them would reduce the risk of untoward autospecificity.
In our experiences, it has been difficult to transduce PBL-derived T cells from normal donors that are stimulated with antigenic peptides derived from self-antigens (data not shown). This is likely due to the low precursor frequency and/or the state of immunologic tolerance of T cells reactive with antigens such as gp100 or tyrosinase [20,22,23]. These limitations do not preclude generating T cells capable of recognizing two different tumor antigens, for we have demonstrated here that a T cell clone reactive with gp100:209–217 can be engineered to also recognize MART-1. However, transducing T cell clones is more time consuming since it is first necessary to isolate the T cells clones prior to transduction. There are two potential strategies for overcoming the limitations of transducing T cells with low precursor frequencies or that are immunologically tolerant. First, is transducing actively expanding tumor infiltrating lymphocyte cultures which contain tumor antigen-reactive T cells [24]. Second, patients vaccinated against tumor associated self antigens often have increased frequencies of antigen reactive T cells in their peripheral blood [23], and these T cells may lend themselves to activation and expansion in vitro to enable efficient retroviral transduction.
Conclusion
The approach for generating bifunctional T cells we describe in this study may be feasible for viral infections and malignancies and may represent a powerful approach for those patients that otherwise would fail immunotherapy due to the accumulation antigen- or MHC-loss variants.
Abbreviations
PBMC, peripheral blood mononuclear cells; TCR, T cell receptor; TAA, tumor-associated antigen; CMV, cytomegalovirus
Competing interests
The authors declare that they have no competing interests.
Authors' Contributions
AL designed the experiments, performed the transductions, carried out the cocultures and flow cytometry and prepared the manuscript. GC engineered the CMV minigene and the CMV-expressing COS cells, and edited the manuscript. MN conceived of the study, oversaw design and execution of the experiments, and finalized the manuscript.
Acknowledgements
The authors wish to acknowledge Jeffrey Roszkowski for technical assistance on this project. This work was supported by the Howard Hughes Medical Institute Medical Student Research Training Fellowship (AL) and by grants CA90873; CA100240; CA10228 from the National Institutes of Health (MIN).
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| 15588290 | PMC544585 | CC BY | 2021-01-04 16:39:24 | no | J Transl Med. 2004 Dec 8; 2:42 | utf-8 | J Transl Med | 2,004 | 10.1186/1479-5876-2-42 | oa_comm |
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BMC Public HealthBMC Public Health1471-2458BioMed Central London 1471-2458-4-651561558810.1186/1471-2458-4-65Research ArticleInduced abortion and effecting factors of ever married women in the Southeast Anatolian Project Region, Turkey: a cross sectional study Bozkurt Ali Ihsan [email protected]Özcirpici Birgul [email protected] Servet [email protected] Saime [email protected] Turgut [email protected] Gunay [email protected] Ali [email protected] Ersen [email protected] Hamit [email protected] Yilmaz [email protected] Feridun [email protected] Mucide [email protected] Department of Public Health, Faculty of Medicine, Pamukkale University, Denizli, Turkey2 Department of Public Health, Faculty of Medicine, Gaziantep University, Gaziantep, Turkey3 Medical Directorate of Gaziantep Province, Gaziantep, Turkey4 Department of Public Health, Faculty of Medicine, Dicle University, Diyarbakır, Turkey5 Department of Medical Biology, Faculty of Medicine, Harran University, Şanlıurfa, Turkey6 Department of Parasitology, Faculty of Medicine, Ege University, İzmir, Turkey2004 22 12 2004 4 65 65 15 10 2003 22 12 2004 Copyright © 2004 Bozkurt et al; licensee BioMed Central Ltd.2004Bozkurt 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
Nearly 10% of the population of Turkey lives in the Southeast Anatolian Project (SEAP) region. The population growth rate and the rate of unintended pregnancies are high and family planning services are insufficient in this region. Lifetime induced abortion rate is also high in this region.
Public health problems of the SEAP region were investigated in the "SEAP Public Health Project" in 2001 and 2002. As it is one of the most important health problems of the women living in this region; induced abortion was also investigated in this project.
Methods
An optimumsample size representing the rural and urban area of the region (n = 1150) was chosen by the State Institute of Statistics by a sampling method proportional to size. 1126 of the area's 1150 houses have been visited and data about induced abortions have been obtained by applying a questionnaire to 1491 ever married women who live in the region.
Results
It has been found that 9.0% of these women who had at least one pregnancy in their life had at least one induced abortion. The lifetime induced abortion per 100 pregnancies was found to be 2.45. The primary reason given for induced abortions was "wanting no more children" (64.6%). Lifetime induced abortions were 5.3 times greater with women using a family planning method than women not using family planning methods. Lifetime induced abortions were 4.1 times greater with unemployed women than working women. Most of the women have used private doctors in order to have an induced abortion.
Although 32.29% have not yet begun to use a contraceptive method after their last induced abortion, 43.75% of the women have since started to use an effective contraceptive method. 23.96% of them have begun to use an ineffective contraceptive method.
Conclusions
Induced abortion is still an important problem at the SEAP region. The results of the study remind us that unemployed women and women who have more than four children is our target group in the campaign against induced abortions. Most of the women use private doctors in order to have an induced abortion. Thus, priority must be given to educate private gynecologists with respect to induced abortion. After induced abortions, a qualified family planning consultant can be given to women and they can be secured to use a suitable contraceptive method.
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Background
Abortion is defined by World Health Organization (WHO) as a pregnancy that ends before 28th week of gestation. Abortions are divided into two groups as 1) induced abortion and 2) spontaneous abortion. The spontaneous abortion rate increases when the maternal and natal care is insufficient. Induced abortions occur at the desire of the couple and an increase in induced abortion rate is a good indicator of insufficient family planning services. The aim of the family planning services is the prevention of unwanted pregnancies. Inadequate access to contraceptive methods, method failure caused by misuse of the methods and non-use of effective methods are the reasons of unwanted pregnancies, which lead women to induced abortion [1].
Induced abortions have been used as a family planning method for many years and become an important problem in women's health especially in developing countries. It is one of the main causes of death of women of reproductive age [2]. Induced abortions have many health disadvantages especially when performed in unsafe conditions. In a study it has been found out that abortion may be a risk factor for subsequent depression for a period of 8 years after pregnancy occurs [3]. In another study the mortality rate for induced abortion was found to be 5.3% and this accounted for 21.1% of the total maternal deaths for this period [4]. As it is seen from these studies, induced abortions have many health disadvantages for women and thus induced abortions should not be used as a family planning method.
In Turkey, the Population Planning Law legalized the provision of safe abortion services within ten weeks in May 1983. As a result, the facility to terminate unwanted pregnancies in safe conditions has been provided [2]. But induced abortion rates are different in the different regions of Turkey. Lifetime induced abortion rate is 26.6% for the whole of Turkey. However, this rate differs from 17.8% to 30.9% for the different regions of Turkey [2].
Nearly 10% of the population of Turkey lives in the Southeast Anatolian Project (SEAP) region. The population growth rate and the rate of unintended pregnancies are high and family planning services are insufficient in this region.
Public health problems of the SEAP region were investigated in the "SEAP Public Health Project" in 2001 and 2002. Induced abortion was one of the health problems investigated in this project.
Methods
The Southeast Anatolian Project (SEAP) region has a population of approximately 6 million people and nearly 10% of the population of Turkey lives in this region. The population growth rate and the rate of unintended pregnancies are high and family planning services are insufficient in this region.
Public health problems of the SEAP region was investigated in the "SEAP Public Health Project" and this project was supported by the SEAP Regional Development Management of Prime Ministry Republic of Turkey and conducted by a consortium constituted by the Turkish Parasitology Association, Gaziantep University, Dicle University (in Diyarbakır province) and Harran University (in Şanlıurfa province). Induced abortion – an important problem for women – was investigated in this project in 2001 and 2002.
The population of the nine provinces in the region is 6,128,973. In order to investigate the public health problems of the region such as abortion, an optimumsample size which represents the rural and urban area of the region was determined as 6900 (d = 0.03, p = 0.04, α = 0.01). This number (6900) was divided to the average number of households (approximately 6 people live in each house in the SEAP region) and the number of houses in the sample was found to be 1150. An optimum sample size representing the rural and urban area of the region was chosen by the State Institute of Statistics by a sampling method proportional to size.
Questionnaires were prepared by the academic staff of public health departments of medical faculties of the two universities (Gaziantep and Dicle Universities). Three of the questionnaires were for individuals (the questionnaire of 5 year and older girls and women, the questionnaire of 5 year and older boys and men, and the questionnaire of 0–59 month's old children) and one of the questionnaires was about the house conditions. Before the study, the questionnaires were applied to houses that were not in the study sample as a pilot study and then checked.
A team for questionnaire application was constituted in every province and the teams were educated about the questionnaires. These teams visited all of the houses in the sample with a public health specialist (the head) and applied the questionnaires by face-to-face interview. Data about the people living in the house were obtained by the house questionnaire. Data about the demographic features of women, fertility, and features about abortion were obtained by the questionnaire from 5 year and older girls and women. Educated nurses (all of them were women) applied the questionnaire to women by face-to-face interview in a separate room.
1126 households of the area's 1150 houses participated to the survey. Households of the 24 houses were not found at home during the study. There were 1491 ever married (married, divorced or widow) women in the 1126 houses that participated in the survey.
The data were evaluated using the SPSS 5.0 and Excel programs. Chi-square, Student's t test and logistic regression analysis were used for the statistical analysis.
Results
There were 1491 ever married (married, divorced or widow) women in the 1126 houses that participated in the survey. 1266 (84.9%) of these women had at least one pregnancy in their life.
9.0% of the women who were ever married and who had at least one pregnancy in their life have had at least one induced abortion in the past. The rate of the women who have had two or more induced abortions was found to be 3.63%. The lifetime induced abortion rates of the women who were ever married and who had at least one pregnancy in their life according to some basic factors are shown in Table 1.
Table 1 The lifetime induced abortion rates of the women who were ever married and who had at least one pregnancy in their life according to some basic factors
Number of induced abortions
0 1 ≥2 Women who have made at least one induced abortion
n % n % n % n % Total
Type of residence Rural 462 93,15 21 4,23 13 2,62 34 6,85 496
Urban 690 89,61 47 6,10 33 4,29 80 10,39 770
Statistical result *p < 0.05
Age groups (year) 15–19 38 100,00 0 0,00 0 0,00 0 0,00 38
20–24 143 95,33 7 4,67 0 0,00 7 4,67 150
25–29 199 93,87 11 5,19 2 0,94 13 6,13 212
30–34 145 91,77 9 5,70 4 2,53 13 8,23 158
35–39 177 87,62 12 5,94 13 6,44 25 12,38 202
40–44 99 84,62 10 8,55 8 6,84 18 15,38 117
45–49 91 82,73 10 9,09 9 8,18 19 17,27 110
50+ 260 93,19 9 3,23 10 3,58 19 6,81 279
Statistical result *p < 0,01
Education Illiteracy 753 92,85 35 4,32 23 2,84 58 7,15 811
Literacy 85 89,47 4 4,21 6 6,32 10 10,53 95
Graduated a primary school 248 86,71 23 8,04 15 5,24 38 13,29 286
Graduated a secondary school 26 89,66 1 3,45 2 6,90 3 10,34 29
Graduated a high school or higher 40 88,89 5 11.11 0 0,00 5 11.11 45
Statistical result *p < 0.05
Employment Unemployed 567 86,17 54 8,21 37 5,62 91 13,83 658
Employed 585 96,22 14 2,30 9 1,48 23 3,78 608
Statistical result *p < 0.0001
Ethnicity Turkish 398 84,65 39 8,32 33 7,04 72 15,35 470
Kurdish 629 94,16 28 4,19 11 1,65 39 5,84 668
Arabic 93 100,00 0 0,00 0 0,00 0 0,00 93
Zaza 32 91,43 1 2,86 2 5,71 3 8,57 35
Statistical result *p < 0.0001
TOTAL 1152 91,00 68 5,37 46 3,63 114 9,00 1266
*One and two or more induced abortions have been evaluated together in the statistical analyses.
The percentage of women who have made at least one lifetime induced abortion was higher in women living in urban areas (10.39%) than women living in rural areas (6.85%, p < 0.05). The percentage of lifetime induced abortion was higher in 35–49 age group (especially in 45–49 age group) than the other age groups (p < 0.01). Lifetime induced abortion rate was 7.15% in illiterate women and 10.53% in literate women and was higher among women who graduated from a primary school or higher (%12.77, p < 0.05). The percentage of women who have had at least one induced abortion was found to be higher in unemployed women and Turkish women than the other groups (p < 0.0001, Table 1).
The lifetime induced abortion rates of the women who were ever married and who had at least one pregnancy in their life according to some fertility characteristics are shown in Table 2. The age of the women at her first birth, number of still birth and spontaneous abortion did not affect the rate of lifetime induced abortion. The number of living children of the women was related to the number of lifetime induced abortion. The induced abortion rate was significantly high in women having 4 or more children (p < 0.01). A similar relationship was found between induced abortion and total number of pregnancies. Lifetime induced abortion was 12.09% among women who had five and more pregnancies and this was higher than the other groups (p < 0.001).
Table 2 The lifetime induced abortion rates of the women who were ever married and who had at least one pregnancy in their life according to some fertility characteristics
Number of induced abortions
0 1 ≥2 Women who have made at least one induced abortion
n % n % n % n % Total
The age of women at her first birth 12–19 770 90,06 49 5,73 36 4,21 85 9,94 855
20–24 299 91,72 17 5,21 10 3,07 27 8,28 326
25–29 43 97,73 1 2,27 0 0,00 1 2,27 44
30+ 13 100,00 0 0,00 0 0,00 0 0,00 13
Statistical result *p > 0.05
Number of still births 0 1068 91,20 61 5,21 42 3,59 103 8,80 1171
1 64 86,49 6 8,11 4 5,41 10 13,51 74
2+ 19 95,00 1 5,00 0 0,00 1 5,00 20
Statistical result *p > 0.05
Number of spontaneous abortion 0 753 90,07 47 5,62 36 4,31 83 9,93 836
1 220 91,67 15 6,25 5 2,08 20 8,33 240
2+ 179 94,21 6 3,16 5 2,63 11 5,79 190
Statistical result *p > 0.05
Number of living children 0 32 100,00 0 0,00 0 0,00 0 0,00 32
1 118 100,00 0 0,00 0 0,00 0 0,00 118
2 147 90,18 10 6,13 6 3,68 16 9,82 163
3 173 92,02 11 5,85 4 2,13 15 7,98 188
4+ 682 89,15 47 6,14 36 4,70 83 10,85 765
Statistical result *p < 0.001
Number of total pregnancies
1 96 100,00 0 0,00 0 0,00 0 0,00 96
2 120 100,00 0 0,00 0 0,00 0 0,00 120
3 127 93,38 9 6,62 0 0,00 9 6,62 136
4 104 92,86 6 5,36 2 1,79 8 7,14 112
5+ 705 87,91 53 6,61 44 5,49 97 12,09 802
Statistical result *p < 0.001
TOTAL 1152 91,00 68 5,37 46 3,63 114 9,00 1266
*One and two or more induced abortions have been evaluated together in the statistical analyses.
Lifetime induced abortion was found to be significantly higher in women who had got pregnant with their last child without the desire of both of the couples, who wanted no more children and who were using a family planning method (13.31%, 11.95% and 15.21% respectively) (Table 3).
Table 3 The number of lifetime induced abortions of women who were ever married and who had at least one pregnancy in their life according to some factors related with family planning
Number of induced abortions
0 1 ≥2 Women who have made at least one induced abortion
n % n % n % n % Total**
Last pregnancy Wanted by both of the couples 619 92,39 34 5,07 17 2,54 51 7,61 670
Wanted by only one of the couples 139 91,45 7 4,61 6 3,95 13 8,55 152
Not wanted by both of the couples 306 86,69 25 7,08 22 6,23 47 13,31 353
Total 1064 90,55 66 5,62 45 3,83 111 9,45 1175
Statistical result *p < 0,05
The state of wanting another child Wants no more children 707 88,04 55 6,84 41 5,10 96 11,95 803
Wants immediately, wants in the future, undecided 351 95,90 11 3,00 4 1,09 15 4,09 366
Total 1058 90,50 66 5,64 45 3,84 111 9,49 1169
Statistical result *p < 0.0001
Are you using a family planning method No 596 95,97 17 2,74 8 1,29 25 4,03 621
Yes 485 84,79 49 8,56 38 6,64 87 15,21 572
Total 1081 90,61 66 5,53 46 3,86 112 9,39 1193
Statistical result *p < 0.0001
*One and two or more induced abortions have been evaluated together in the statistical analyses.
**The evaluations include the ones who have answered the questions.
The lifetime induced abortion rates have been evaluated considering all of the factors thought to be related with induced abortion and has been shown in Tables 1, 2, 3. When we evaluate the results of logistic regression analysis; the number of total pregnancies has been found to be the factor mostly affecting the lifetime induced abortion status (Table 4). Every one point increase of the total number of pregnancies increases the risk of making induced abortion by 1.17 times. The family planning method usage status of the women and the employment status of the women were the other two variables affecting the lifetime induced abortion status of the women. The risk of lifetime induced abortion was found to be 5.4 times greater with women using a family planning method than women not using family planning methods. The lifetime induced abortion risk was found to be 4.1 times greater with unemployed women than working women.
Table 4 The results of logistic regression
Independent Variables Induced Abortion
p Odds Ratio Confidence Interval (95%)
Number of total pregnancies 0,0000 1,17 1,10–1,24
Family planning method Not using 1 1
Using 0,0000 5,35 3,25–8,81
Employment Employed 1 1
Unemployed 0,0000 4,12 2,51–6,77
The rate of induced abortions per 100 lifetime pregnancies – one of the most common indicators of induced abortions – was found to be 2.45. This rate is 1.38 at the rural areas and it rises to 3.33 at the urban areas (p < 0.05) (Table 5).
Table 5 The induced abortion rate per 100 lifetime pregnancies among ever married women
The induced abortion rate per 100 lifetime pregnancies
Type of residence Rural 1,38 p < 0,05
Urban 3,33
Total 2,45
"Wanting no more children" is the primary reason given for lifetime induced abortion (64.58%). In 63.54% of the lifetime induced abortions both of the couples have decided to the induced abortion together. Most of the lifetime induced abortions take place at the private doctors' consultant room (46.88%) (Table 6).
Table 6 Some characteristics of the women's last induced abortion
Rural Urban Total
n % n % n %
The reason of the last induced abortion Wanting no more children 19 65,52 43 64,18 62 64,58
Short interval between the last two pregnancies 0 0,00 12 17,91 12 12,50
Mother's health 7 24,14 4 5,97 11 11,46
Children's health 3 10,34 3 4,48 6 6,25
The health of mother and children 0 0,00 3 4,48 3 3,13
Other 0 0,00 2 2,99 2 2,08
Who decided to the last induced abortion Both of the couples together 19 65,52 42 62,69 61 63,54
Women 2 6,90 18 26,87 20 20,83
Doctor 7 24,14 5 7,46 12 12,50
Men 1 3,45 2 2,99 3 3,13
Where did the last induced abortion take place Private doctor 15 51,72 30 44,78 45 46,88
Public hospital 9 31,03 17 25,37 26 27,08
Maternity hospital 3 10,34 4 5,97 7 7,29
Home 1 3,45 6 8,96 7 7,29
Private hospital/private polyclinic 1 3,45 5 7,46 6 6,25
Social Insurance Association 0 0,0 4 5,97 4 4,16
Mother and child health centers 0 0,0 1 1,49 1 1,04
Total 29 100,0 67 100,0 96* 100,0
*96 women have given answer to these questions.
After lifetime induced abortion, 32.29% of the women have not yet begun to use a family planning method. 43.75% of them have since started to use effective methods and 23.96% of them have begun to use ineffective methods. The usage of effective methods was higher in urban areas, while the usage of ineffective methods was higher in rural areas. Intra uterine devices (IUD) (52.38%) took the first and condom (26.19%) took the second place among the effective family planning methods. Withdrawal, with a rate of 87%, took the first sequence among the ineffective family planning methods (Table 7).
Table 7 Usage of family planning methods after lifetime induced abortion
Type of residence Women using none of the family planning methods Women using an effective family planning method Women using an ineffective family planning method Total
IUD Condom Oral contraceptives Sterilization of women Total effective methods Withdrawal Other ineffective family planning methods Total ineffective family planning methods
n % n n n n n % n n n % n
Rural 11 33,33 5 1 2 - 8 24,24 14 - 14 42,42 33
Urban 20 31,75 17 10 5 2 34 53,97 6 3 9 14,29 63
Total 31 32,29 22 11 7 2 42 43,75 20 3 23 23,96 96
In the study, lifetime induced abortions carried out by the women were also evaluated. The number of the women who have stated that "they have tried to make an induced abortion by themselves" in the past was 64. 24 of these women were from rural areas and 40 of them were from urban areas. The women who intended to carry out an induced abortion by themselves firstly preferred to use drugs (43.8%). Lifting heavy things (35.4%) took the second place. Women who live in rural areas preferred to lift heavy things (64.3%) while women in urban areas preferred to take drugs (50.0%).
Discussion
The percentage of having at least one induced abortion among ever married women who had at least one pregnancy in their life in the SEAP region was 9.0% (lifetime induced abortion rate). Approximately one out of ten ever married women has made at least one induced abortion in their life. Also, 2.45 induced abortion per 100 lifetime pregnancies occurred at the region. When we evaluated the results of the Turkish Demographic and Health Survey 1998; (TDHS 1998) (which is conducted to collect data on subjects such as fertility, infant and child mortality, family planning, and maternal and child health on a representative sample of Turkey through the interviews conducted with women of fertile age) the percentage of lifetime induced abortion among ever married women was reported as 18.2% and induced abortion per 100 pregnancies during the five-year period before the survey was 7.6 for the East Anatolian region (the East Anatolian and the Southeast Anatolian Regions were evaluated together as one region and the SEAP provinces take part in this region). The SEAP rates were lower than the TDHS 1998 [5]. In the TDHS 1998 the lifetime induced abortion rates of the East Anatolian Region were given. The Southeast Anatolian region provinces were evaluated in this region. This study was conducted in the Southeast Anatolian Region only. The general features and health conditions of the Southeast Anatolian Region are worse than the East Anatolian Region, explaining why the rate (9%) is lower than the TDHS 1998.
There is a decrease in the lifetime induced abortion rate in the course of time compared with the TDHS 1998. Also, there is a decrease in the lifetime induced abortion rate in the same region (in the East Anatolian provinces) when the data of the TDHS 1993 is compared with the data of the TDHS 1998. Induced abortion rate per 100 pregnancies during the five-year period before the survey has decreased to 7.6 from 8.7 in the course of time [6,5]. A similar decrease was seen when the Turkey Reproduction Survey-1978 was compared with the TDHS 1998 [7]. In another study conducted in Turkey; abortion rate (both induced and spontaneous abortions) of ever married women was found to be 14.9% in 1991 [8]. In a resent study conducted in Manisa in 2000 induced abortion rate per 100 pregnancies during the five-year period before the survey was found to be 12.1% [9]. It is seen that the induced abortion rate is decreasing not only in the SEAP region but also in other regions of Turkey in the course of time. In a study conducted by Senlet et al. it is reported that there is a decline in induced abortion rates in Turkey [10].
However, this low lifetime induced abortion rates do not show a success because unintended pregnancies end with births in the region. As a matter of fact, 30.1% of the latest births of the women during the last five year period were not desired by both of the couples in the Southeast Anatolian region [11]. Also, total fertility rate of the women was 4.2 in the East Anatolian region [5]. The high fertility rate and the high rate of ending unintended pregnancies with births is the real cause of the low lifetime induced abortion rate in the region.
The rate of induced abortion was higher in urban areas than rural areas. This was similar with the TDHS 1998 [5].
Lifetime induced abortion rate was 7.15% among illiterate women, 10.53% among literate women and was higher among women graduated from primary school or higher (% 12.77, p < 0.05). In a study conducted by Akın et al. similar results have been found [2]. Education is a very important factor effecting induced abortion rate.
In the logistic regression analysis the total number of pregnancies of the women, the family planning method usage status of the women and the employment of the women have been evaluated as the independent factors affecting lifetime induced abortion. As the total number of pregnancies increases, lifetime induced abortion risk increases (odds ratio is 1.7). Women who have more than four children may be the target group of the studies planned on this subject. In a study conducted by Akın et al. a similar odds ratio (1.1) have been found [2].
Lifetime induced abortions were 4.1 times greater with unemployed women than working women. This was due to the fact that these women have lower family planning usage rates but their pregnancy rate was high.
These results remind us that unemployed women and women who have more than four children must be our target group in the campaign against induced abortions as a family planning method.
Lifetime induced abortions were 5.3 times greater with women using a family planning method than women not using family planning methods. I.e. the usage of family planning methods are 5.3 times higher among the women who have had an induced abortion in the past. In a study conducted by Akın et al. similar results were reported during the five-year period before the survey (odds ratio is 2.9) [2].
Lifetime induced abortions have usually taken place at a health facility and with the assistance of health personnel. After these lifetime induced abortions, a qualified family planning consultant can be appointed to these women and they can be encouraged to use a suitable contraceptive method. The rate of effective family planning method usage after induced abortion was 43.7% in our study. The same rate was 34.2% in the TDHS 1998 during the five-year period before the survey [5]. There is an increase in the rate of effective family planning method usage after lifetime induced abortion and this increase is pleasing but it is still insufficient. This increase is thought to be one of the reasons of the decrease in induced abortion rates. Similarly, Senlet and et al has reported that one of the reasons of decrease of the induced abortion rates in Turkey is due to this factor [10].
After lifetime induced abortion, 32.3% of the women were not using a family planning method in the study and this was nearly the same with the percentage evaluated in the TDHS 1998 during the five-year period before the survey (32.1%) [5]. There was no important change during the past four years. In another study in Turkey 25% of the women did not begin to use a family planning method after induced abortion [12]. In two other studies conducted in Turkey it has been found out that approximately 20% of the women did not begin to use a family planning method after induced abortion [13,14]. Also, 23.9% of them have begun to use an ineffective method in our study. These data shows that the family planning services are not adequate at the institutions where induced abortion is performed. Private Doctors (46.88%) and public hospitals (27.08%) were the fist two places where the women applied to have an induced abortion. Similar results have been found in the TDHS 1998 for the Eastern Anatolian provinces during the five-year period before the survey (68.4% and 19.7% respectively) [5]. Similar results were obtained in another study in our country and it has been found out that 50% of the induced abortions were made by private doctors and private doctors were the first place chosen for induced abortion [15]. Thus, priority must be given to educate private gynecologists.
After lifetime induced abortion, 67.71% of the women have begun to use a family planning method in our study.
The primary reason given for the last induced abortion was "wanting no more children" (64.5%) and this is similar with the data of the TDHS 1998 [5]. This is also another indicator for high unintended pregnancy rates and insufficient family planning services in the region. Similar results have been obtained in a different study in our country. In this study 47.6% of the women requested an induced abortion because they wanted no more children [16].
Although the rate of lifetime induced abortions are decreasing in the course of time it is still an important health problem in the SEAP region. Unintended pregnancy and total fertility rates of the region is still higher than the other regions of Turkey. Thus, family planning services, the educational level of women and the status of women need improvement.
Conclusions
Although 9.0% of the ever married women who had at least one pregnancy in their life have made at least one induced abortion and 2.45 induced abortion per 100 lifetime pregnancies occurred at the SEAP region, these rates are lower than the whole rate of Turkey. But, the high fertility rate shows us that family planning services are insufficient in the region. Also 32.29% have not begun to use a contraceptive method after their last induced abortion and 23.96% of them have begun to use an ineffective contraceptive method. This shows an important lack on this subject. After these lifetime induced abortions a qualified family planning consultant can be appointed to these women and they can be encouraged to use a suitable contraceptive method. Also to decrease lifetime induced abortions; women who have more than four children and unemployed women may be the target group of studies planned on this subject.
Competing interests
The author(s) declare that they have no competing interests.
Authors' contributions
AİB participated in the conception and design, provision of study materials, analysis of the data, statistical expertise, drafting the article and revision of the article. BÖ participated in the conception and design, collection and assembly of data, provision of study materials, analysis of the data, statistical expertise, drafting the article, revision of the article and final approval of the article. SÖ participated in the conception and design, collection and assembly of data, provision of study materials, analysis of the data, statistical expertise, drafting the article, revision of the article and final approval of the article. SŞ participated in the conception and design, collection and assembly of data, provision of study materials, analysis of the data, statistical expertise, drafting the article, revision of the article and final approval of the article. TŞ participated in the collection and assembly of data, provision of study materials, analysis of the data and statistical expertise. GS participated in the conception and design, collection and assembly of data, provision of study materials, analysis of the data and statistical expertise. AC participated in the conception and design, collection and assembly of data, provision of study materials, analysis of the data and statistical expertise. Eİ participated in the conception and design, collection and assembly of data, provision of study materials, analysis of the data, statistical expertise, drafting the article, revision of the article and final approval of the article. HA participated in the collection and assembly of data, provision of study materials, analysis of the data and statistical expertise. YP participated in the collection and assembly of data, provision of study materials, analysis of the data and statistical expertise. FA participated in the conception and design, collection and assembly of data, provision of study materials. MA participated in the conception and design, collection and assembly of data, provision of study materials.
Pre-publication history
The pre-publication history for this paper can be accessed here:
Acknowledgements
This project was supported by the SEAP Regional Development Management of Prime Ministry Republic of Turkey.
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| 15615588 | PMC544586 | CC BY | 2021-01-04 16:28:47 | no | BMC Public Health. 2004 Dec 22; 4:65 | utf-8 | BMC Public Health | 2,004 | 10.1186/1471-2458-4-65 | oa_comm |
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J Transl MedJournal of Translational Medicine1479-5876BioMed Central London 1479-5876-2-431558833110.1186/1479-5876-2-43ResearchEpitope definition by proteomic similarity analysis: identification of the linear determinant of the anti-Dsg3 MAb 5H10 Lucchese Alberta [email protected] Abraham [email protected] Mong-Shang [email protected] Darja [email protected] Animesh A [email protected] Department of Odontostomatology and Surgery, Faculty of Medicine, University of Bari, P.za G. Cesare 11, 70124 Bari, Italy2 Department of Medicine, New York Medical College, Valhalla, NY 10595, USA3 Department of Dermatology, Medical College of Wisconsin, Milwaukee 53226, USA4 Department of Biochemistry and Molecular Biology, University of Bari, Via Orabona 4, 70126 Bari, Italy5 Department of Dermatology, Weill Medical College of Cornell University, 1300 York Avenue, New York, NY 10021, USA2004 11 12 2004 2 43 43 15 10 2004 11 12 2004 Copyright © 2004 Lucchese et al; licensee BioMed Central Ltd.2004Lucchese 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
Walking along disease-associated protein sequences in the search for specific segments able to induce cellular immune response may direct clinical research towards effective peptide-based vaccines. To this aim, we are studying the targets of the immune response in autoimmune diseases by applying the principle of non-self-discrimination as a driving concept in the identification of the autoimmunogenic peptide sequences.
Methods
Computer-assisted proteomic analysis of the autoantigen protein sequence and dot-blot/NMR immunoassays are applied to the prediction and subsequent validation of the epitopic sequences.
Results
Using the experimental model Pemphigus vulgaris/desmoglein 3, we have identified the antigenic linear determinant recognized by MAb 5H10, a monoclonal antibody raised against the extracellular domain of human desmoglein-3. The computer-assisted search for the Dsg3 epitope was conducted by analyzing the similarity level to the mouse proteome of the human desmoglein protein sequence. Dot-blot immunoassay analyses mapped the epitope within the sequence Dsg349–60 REWVKFAKPCRE, which shows low similarity to the mouse proteome. NMR spectroscopy analyses confirmed the specificity of MAb 5H10 for the predicted epitope.
Conclusions
This report promotes the concept that low level of sequence similarity to the host's proteome may modulate peptide epitopicity.
Epitope mappingComputational biologyProteomicsDesmoglein 3Pemphigus vulgaris
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Introduction
In the last decades, several computer-driven algorithms have been devised to take advantage of the linear representation of protein sequence information to search for epitopic motifs [1-5]. These algorithms search the amino acid sequence of a given protein for characteristics believed to be common to antigenic peptides, locating regions that are likely to induce cellular immune response. Given the rapid expansion of proteomic sequence data, the application of these algorithms to disease-associated proteins may direct research to specific segments of disease-associated proteins and thus potentially reduce the time and effort needed to develop effective vaccines [6,7].
We are using in silico technology platforms to identify epitopic peptide sequence(s) from disease-associated-antigens by following the hypothesis that peptide epitopicity might be regulated by the peptide similarity level to the host's proteome, in addition to other factors such as MHC binding potential [8-10]. As a scientific rationale, we follow the criterion that immune system might be allowed/forced to respond only to rarely encountered/never seen antigenic sequences. Accordingly, we explain the non-immunogenicity of tumor-associated-antigens as due to high level of similarity of oncoprotein sequences to self-proteome [8,9,12].
In this context we have tested here the possibility that endogenous, normal, housekeeping self-proteins harbouring sequences with little or no similarity to the collective host proteome might be epitopic targets in autoimmune responses. Self-reactivity and autoimmunity are processes related to the breakage of self-tolerance that can be distinguished by their different clinical outcome. The transition from self-reactivity to the autoimmune pathology appears to be mediated by a complex network of overlapping phenomena that comprehend epitope spreading, uncovering of cryptic epitopes, natural autoantibodies production, cross-reactivity, microchimerism, altered B lymphocyte function, inflammation, etc. [13]. In our labs, we are studying the autoantibody profile in Pemphigus vulgaris (PV). PV is an autoimmune bullous skin disease characterized by autoantibodies to a desmosomal adhesion molecule, the cadherin desmoglein-3 (Dsg3) [14]. Dsg3 represents an optimal autoantigen for studying the relationship between similarity level and immune responses. Indeed the PVA Dsg3 is a highly conserved protein, and the human and mouse forms present 71.6% of identity. Therefore, this protein allows to analyze the sequence specificity of the reaction PVA-autoantibody by using murine monoclonal antibodies. Moreover, our interest to study the autoantibody response in this autoimmune disease also stems from the following considerations: i) so far, notwithstanding the Dsg3 linear structure, attention has been focused mainly on conformational Dsg3 epitopes [15-18] and there is a lack of data on the occurrence and fine molecular characterization of linear Dsg3 epitopes; ii) although the precise pathological implications of anti-Dsg autoantibodies are not fully elucidated [19-23], it seems likely that a spectrum of autoantibodies, differing in number and type, might contribute to the autoimmune pathology in PV [24]. Given these premises, the development of new experimental approaches that might lead to the exact definition of linear PVA epitopes is desirable. As a first attempt to a computer-driven individuation of defined linear epitopic sequences of Dsg3, we describe here the identification of the linear epitope of the MAb 5H10 raised in BALB/c mice against the extracellular domain (EC1/EC2, aa1-212) of human Dsg3.
Materials and methods
Computer-assisted analyses
The amino acid sequence corresponding to accession number P32926, SWISS-PROT, was used in the similarity analysis of the extracellular domain of human Dsg3 (EC1 to part of EC2, amino acid 1–212) to the mouse proteome. The Dsg3 sequence under analysis was dissected into pentamer motifs, that were probed for sequence similarity to mouse proteome by using PIR non-redundant reference protein database and peptide match program [25]. The search was conducted against mouse complete genome for a total of 78435 sequences, and includes hypothetical/unidentified proteins sequences.
Antibodies
Anti-Dsg3 MAb 5H10 and 5G11, raised in BALB/c mice, were a generous gift of Dr. Margaret Wheelock, University of Toledo, Ohio. Both MAbs recognize a linear determinant and have been described in detail [26]. Briefly, MAb 5H10 has been shown to recognize the amino terminal part of the extracellular domain of Dsg3 (EC1 to part of EC2, amino acid 1–212). MAb 5G11 reacts with the carboxyl terminus of the EC domain of Dsg3 (part of EC4 to EC5, amino acid 446–613). Horseradish peroxidase (HRP)-conjugated goat anti-mouse IgG-specific Abs was from Sigma, Chemical Co., St.Louis, Mo.
Synthetic peptides
Peptides were synthesized using standard Fmoc (N-(9-fluorenyl)methoxycarbonyl) solid phase peptide synthesis. Peptide purity (>90%) was controlled by analytical HPLC, and the molecular mass of purified peptides confirmed by fast atomic bombardment mass spectrometry. The EC1/EC2 Dsg336–45EEMTMQQAKR, Dsg349–60REWVKFAKPCRE, and Dsg3173–187 NSLVMILNATDADEP peptides were obtained from PeptidoGenic Research & Co., Livermore, CA., and used in dot-blot immunoassay experiments. Human recombinant Dsg3 protein was used as further control [27]. The 15N-labelled synthetic peptide Dsg349–60REWVKFAKPCRE (where 15N-labelled amino acid residues are underlined) was from Primm srl, Milan, Italy. Peptides were dissolved in 0.9% NaCl, aliquoted and stored at -20°C.
Dot immunoassay
Nitrocellulose membrane (0.2 μm pore size, BioRad Laboratories, Hercules, CA) was pretreated for 10 min with 1.0% glutaraldehyde. Synthetic peptides were spotted on the activated membranes, left to dry at RT and exposed to UV rays for 10 min. Membranes were incubated for 1 h in phosphate-buffered saline/0.05% (v/v) Tween 20 (PBST) containing 4% bovine serum albumin (BSA), and then with the primary MAb (1:800) for further 2 h. Membranes were washed for 10 min with PBST (x3) and incubated in PBST/4% BSA with HRP-conjugated affinity-purified goat anti-mouse IgG (1:1000) for 1 h. Membranes were washed in PBST for 5 min (x3), in PBS for 5 min (x3) and immunoblots were developed using the enhanced chemiluminescence detection assay (Renaissance, NEN™ Life Science Products, Boston, MA.) following supplier's instructions.
NMR spectroscopy
NMR spectra of the reaction between the synthetic 15N-labelled peptide Dsg349–60REWVKFAKPCRE (where 15N-labelled amino acid residues are underlined) and MAb 5H10 (raised against EC1/EC2 from Dsg3) or 5G11 (raised against EC4/EC5 from Dsg3) were recorded at 298°K on a Bruker Avance DRX500WB spectrometer. The spectra were acquired by heteronuclear single quantum correlation (HSQC) experiments that correlate the chemical shift of proton with the chemical shift of the directly bonded nitrogen. Specifically, the two-dimensional 1H-15N inverse detected correlation spectra were acquired by the gradient pulse sensitivity improved Bruker automation program INVIETGPSI [28]. The 1H acquisition dimension was collected with a spectral width of 5 ppm, centered at 7.6 ppm, using 4096 datapoints for each of the total 32 scans/expt. Spectral width in the indirect detected 15N dimension was 200 ppm, centered at 90 ppm, obtained with a total of 1024 points. The spectra were processed by XwinNMR program, version 2.6, running on an INDY R5000 Silicon Graphics Workstation. Chemical shift values were referenced to sodium tetrasilyl propionate (TSP) (1H; 0.000 ppm) and external neat nitromethane (15N; 380.23 ppm) standards. To avoid interferences of TSP standard with peptide samples, the instrument scale was preliminarily calibrated in parallel experiments with samples containing the peptide to be analysed plus 0.2 mg TSP. We used chemical shift statistics from the full BioMagResBank database, where the calculated statistics are derived from a total of 559392 chemical shifts (website: ). Sequence-specific correction factor tabulations were applied to backbone 1H and 15N resonances [29]. Two-dimensional correlated spectroscopy spectra of MAb-peptide complex were obtained using a 1:2 stoicheiometric molar ratio (peptide:MAb, 0.1:20, mg/mg). That is, NMR samples contained either 0.1 mg free REWVKFAKPCRE peptide, or 20 mg MAb 5H10 complexed with 0.1 mg REWVKFAKPCRE peptide, or 20 mg MAb 5G11 complexed with 0.1 mg REWVKFAKPCRE peptide, in 0.5 ml aqueous solution H2O/D2O (9:1, v/v).
Results
Selection of Dsg3 peptide sequences with low similarity to the mouse proteome
The EC1/EC2 domain (aa1-212) of human Dsg3 was dissected into 5-mer sequences and analyzed for similarity to mouse proteins. The pentamers used for epitope scanning were offset by one residue, i.e. overlapped by 4 residues: MMGLF, MGLFP, GLFPR, LFPRT, etc. The 5-mer sequences were probed in computer-assisted similarity analyses against the complete mouse genome sequences. Fig. 1 reports the profile we obtained by representing the number of matches to mouse proteome over the sequential pentamer peptides. It can be seen that the EC1/EC2 Dsg3 protein sequence presents stretches scarcely represented in the mouse proteome. Among the low-similarity EC1/EC2 Dsg3 peptide fragments, the Dsg349–60REWVKFAKPCRE sequence was the longest (12 amino acid residues long) with the lowest number of matches to the murine proteome (number of matches to murine proteome = 9). The Dsg349–60REWVKFAKPCRE sequence and a second low-similarity fragment, Dsg336–45EEMTMQQAKR (number of matches to murine proteome = 12), were selected to test our hypothesis.
Figure 1 Molecular mimicry between the EC1/EC2 of human Dsg3 and mouse proteome. The EC1/EC2 Dsg3 sequence (aa1-212) was scanned for matches to mouse protein sequences by using pentamers offset by one residue. The arrow indicates the longest stretch having the lowest number of matches.
Dot-blot immunoassay
The two low similarity peptides selected as described above were synthetised in order to be assayed in immunodot-blot analyses to identify the linear determinant of the MAb 5H10. In addition, the synthetic Dsg3173–187NSLVMILNATDADEP peptide (matches to murine proteome = 109) was available and served as a high similarity peptide control. The three synthetic peptides are described in Fig. 2. The MAb 5G11 with specificity to the terminal EC portion of Dsg3, amino acid 446–613 [26] was used as a primary antibody control. The immunodot-blot experimental result is illustrated in Fig. 3. It can be seen that MAb 5H10 recognized peptide n. 2, i.e. the Dsg349–60REWVKFAKPCRE sequence having a lowest level of similarity to the mouse proteome (see Fig. 1). No reaction was monitored using MAb 5G11.
Figure 2 Similarity scanning on the human Dsg3 peptide sequences selected for immunoassay analysis with murine anti-EC1/EC2 Mab 5H10. Matching analysis to the murine proteome was performed using as probes pentamers offset by one residue as described under Methods. Peptide: 1) Dsg336–45EEMTMQQAKR; 2) Dsg349–60REWVKFAKPCRE; 3) Dsg3173–187NSLVMILNATDADEP.
Figure 3 Identification of the epitopic sequence recognized by mouse anti-Dsg31–212 MAb 5H10. Dot-blot immunoassay was performed on nitrocellulose membrane spotted with human Dsg3 (10μg), or Dsg3 peptide (2.5μg) corresponding to the sequence: 1) Dsg336–45EEMTMQQAKR; 2) Dsg349–60REWVKFAKPCRE; 3) Dsg3173–187NSLVMILNATDADEP.
NMR spectroscopic immunoanalysis
As a further step, we carried out a parallel experimental confirmation because of the doubts of false negative/positive data intrinsic to immunoassay. To this aim, the binding of the predicted epitopic Dsg349–60REWVKFAKPCRE peptide and the mouse anti-Dsg3 5H10 MAb was further verified by NMR spectroscopy. NMR spectroscopy can be used in 1) defining structure and conformation; 2) defining structure-activity relationships; 3) monitoring chemical reactions. Informations are mainly derived by measuring NMR chemical shifts. The chemical shift of a nucleus is the difference between the resonance frequency of the nucleus and a standard. This quantity is reported in parts per million (ppm). The NMR standards are molecules as tetrasilyl propionate, the signal of which is set at 0 ppm by having shielded protons. The NMR chemical shift allows for distinguishing magnetically inequivalent nuclei in a molecule, i.e. chemical shifts are a measure of the motional freedom.
We utilized NMR spectroscopy in order to determine whether the predicted peptide specifically binds to the MAb 5H10. To this aim, a 15N-labelled REWVKFAKPCRE peptide(where 15N-labelled amino acid residues are underlined) was synthesized. Theoretical chemical shift values of the 15N-labelled residues in the Dsg349–60 REWVKFAKPCRE peptide were calculated as described under Methods, and then were compared to the experimental values. Table 1 lists the theoretical and experimental chemical shift values of 15N-labelled residues in the Dsg349–60 REWVKFAKPCRE peptide, and their values following addition of MAb 5H10 or MAb 5G11.
Table 1 Sequence-specific assignments in the 15N-labelled Dsg349–60REWVKFAKPCRE peptide, and chemical shift changes on MAb 5H10 or 5G11 binding.
15N-Amino acid Chemical Shift Values:
Theoretical:
Free peptide Experimental:
Free peptide
+MAb 5H10
+control MAb 5G11
1HN 15N 1HN 15N 1HN 15N 1HN 15N
Arg-1 6.78 77.6 6.95 81.0 n.d. n.d. 7.05 80.5
Val-4 8.65 120.8 8.19 122.8 n.d. n.d. 8.18 122.5
Phe-6 8.42 121.7 7.99 119.2 n.d. n.d. 8.00 118.9
Ala-7 8.29 122.3 8.40 120.0 n.d. n.d. 8.44 120.2
Arg-11 6.78 77.6 7.25 81.5 n.d. n.d. 7.26 81.0
8.14 119.6 8.16 118.1 n.d. n.d. 8.15 117.8
The 15N-amino acid position in the peptide is reported. Theoretical chemical shift values were derived from and corrected [29]. Experimental chemical shift values were derived from resonance spectra reported in Fig. 4. The average value of chemical shifts relative to the H and N atoms of α amino residue, and the H and N atoms of η amino residues are reported for Arg-11 (data from: ). The chemical shifts relative to the H and N atoms of α amino residue in Arg-1 are undetectable because of terminal position of the amino acid in the peptide. n.d., not detected.
The resonance spectra of the reaction between MAb 5H10 and the 15N-labelled Dsg349–60 REWVKFAKPCRE peptide are illustrated in Fig. 4. Each "spot" in the figure is an NMR signal representing the 1H-15N one-bond coupling of the labelled amino acid residues in the peptide. In Fig. 4A, the reported spots correspond to the Arg, Val, Phe and Ala selective 1H-15N correlation signals of the free peptide in aqueous solution. As already illustrated in Table 1, the expected signals are present and in basic agreement with theoretical data (see BioMagResBank database, ) following sequence-dependent correction of random coil NMR chemical shifts. [29]. The upper panel of Fig. 4A displays the two Arg cross-peak signal, i. e. the chemical shifts relative to the H and N atoms of amino residues. The lower panel in fig. 4A reports the cross-signal of 15N-Arg-11 whereas the 15N-Arg-1 was undetectable because of the terminal position in the peptide. Fig. 4B documents that the addition of MAb 5H10 caused the loss of 15N-labelled residue spectral resonance signals thus indicating a complete loss of Dsg349–60REWVKFAKPCRE peptide mobility. In contrast to the total signal deletion provoked by MAb 5H10, the chemical shift values of the Dsg349–60 REWVKFAKPCRE peptide remained unaltered following the addition of the anti-Dsg3 MAb 5G11 (Fig. 4C, upper and lower panels).
Figure 4 1H-15N NMR HSQC spectra and relative 1-D contour plots of the Dsg349–60REWVKFAKPCRE peptide 15N-labelled at residues 7, 10, 12 and 14 (A), plus MAb 5H10 (B), or 511G (C). Upper panels report portions of the HSQC spectra showing resonances from residues 1, 4, 6, 7, and 11. Lower panels report expanded region of the HSQC spectra showing resonances from the same residues 4, 6, 7, and 11.
Discussion
The present data appear of interest since, as a preliminary consideration, it has to be noted that historically the search for biologically relevant epitopic sequences has demanded and still demands complicated procedures and expensive technologies [30]. Here, the low-similarity principle we used has allowed the utilization of proteomic computational program for the exact epitope definition of a MAb directed towards a 212 amino acid sequence by using only 3 synthetic peptide reagents. Minimally, the screening of a library of at least 14 non-overlapping peptides, each 15 residues in length, would be necessary in the unassisted case. Moreover, the individuation of the Dsg349–60REWVKFAKPCRE motif as the antigenic epitope of the Dsg3 EC1/EC2 domain, aa1-212, might contribute to the understanding of the autoimmune mechanisms in PV. This immunogenic sequence is hosted in the NH2 terminal region of Dsg3, which has adhesive function in cadherins [31] and contains the major epitopes recognized by sera from PV patients [32]. Further, chimeric toxins containing the Dsg3 EC1/EC2 domain, aa1-212, have been demonstrated to downregulate anti-Dsg3 IgG-producing B cells in mice immunized with Dsg3 aa1-212 [26].
In a wider context, the epitope mapping approach here described might be helpful in defining peptide antigenicity in a wide spectrum of human diseases including cancer pathologies as well as a range of diverse autoimmune disorders such as insulin-dependent diabetes mellitus, multiple sclerosis, rheumatoid arthritis, and Pemphigus vulgaris. The fine profiling of the disease-associated epitopic peptide repertoire is of particular importance in the definition of qualities as antigenicity and immunodominance, and is an essential preliminary step towards effective immunotherapeutical treatments in cancer and autoimmune pathologies. In synthesis, given the caveat that only linear sequences can be defined by the analysis of amino acid motif sharing by two or more proteins, the epitope prediction model we report could form the basis of a rapid, inexpensive and computationally driven system for the individuation of antigenic sequences that are the targets of autoimmune responses. Consequently, this proteomic strategy may serve as a general method suitable to define/distinguish/screen disease-(ir)relevant epitopes within potential autoantigens with clinical application to wide ranging human diseases where the precise targets of self-directed attack are unknown [33].
Abbreviations
Dsg3, desmoglein 3; EC, extracellular; PVA, Pemphigus vulgaris autoantigen; PIR, Protein Information Resource; HSQC, heteronuclear single quantum correlation.
Acknowledgements
We thank Prof. F. P. Fanizzi, NMR Unit, CARSO Cancer Research Center, Regione Puglia, Italy, for precious help and discussion. This work was supported by: European Community funding to the PhD course "Carcinogenesis, Aging and Immunoregulation"; Zalmin A. Arlin Cancer Fund, NY, USA; Public Health Service Grant RO1-AI48427 to M.S.L.; Research Grant from the Dermatology Foundation to A.A.S.
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| 15588331 | PMC544587 | CC BY | 2021-01-04 16:39:24 | no | J Transl Med. 2004 Dec 11; 2:43 | utf-8 | J Transl Med | 2,004 | 10.1186/1479-5876-2-43 | oa_comm |
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BMC PsychiatryBMC Psychiatry1471-244XBioMed Central London 1471-244X-4-421558830710.1186/1471-244X-4-42Research ArticleThe Depression Network (DeNT) Study: methodology and sociodemographic characteristics of the first 470 affected sibling pairs from a large multi-site linkage genetic study Farmer Anne [email protected] Gerome [email protected] Shyama [email protected] Nick [email protected] Mike [email protected] Ania [email protected] Wolfgang [email protected] Lefkos [email protected] Ole [email protected] Mike [email protected] Julia [email protected] Martin [email protected] Marcella [email protected] Theodore [email protected] Lisa [email protected] Ian [email protected] Peter [email protected] Medical Research Council Social Genetic and Developmental Psychiatry Centre, Institute of Psychiatry, London, UK2 Glaxo Wellcome Research and Development, Greenford UK3 Dept of Psychological Medicine, University of Wales, College of Medicine, Cardiff UK4 Department of Psychiatry, Trinity Centre for Health Sciences, Dublin Eire5 Barts and The London, Queen Mary's School of Medicine and Dentistry, London UK6 Department of Psychiatry, University of Bonn, Bonn, Germany7 Department of Psychiatry, University of Aarhus, Aarhus, Denmark8 University of Lausanne, Faculty of Biology and Medicine, Hopital de Cery, Unite psychopathologie, Prilly, Switzerland9 Zentralinstitut for mental health, Mannheim, Germany10 Dept of Psychiatry, Washington University, St Louis, USA2004 9 12 2004 4 42 42 2 9 2004 9 12 2004 Copyright © 2004 Farmer et al; licensee BioMed Central Ltd.2004Farmer 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 Depression Network Study (DeNt) is a multicentre study designed to identify genes and/or loci linked to and/or associated with susceptibility to unipolar depression in Caucasian families. This study presents the method and socio-demographic details of the first 470 affected sibling pairs recruited from 8 different sites in Europe and the United States of America.
Methods
Probands fulfilling either the Diagnostic and Statistical Manual 4th edition (DSM-IV) or the International Classification of Diseases 10th edition (ICD-10) criteria for recurrent unipolar depression of moderate or severe degree and who had at least one similarly affected sibling were eligible for the study. Detailed clinical and psychological assessments were undertaken on all subjects including an interview using the Schedules for Clinical Assessment in Neuropsychiatry. Blood samples were collected from all participants to extract DNA for linkage analysis.
Results
The different sites used different recruitment strategies depending on local health care organisation but despite this there was remarkable similarity across sites for the subjects recruited. Although the Bonn site had significantly older subjects both for age of onset and age at interview, for the sample as a whole, subjects were interviewed in their mid-40s and had experienced the onset of their recurrent depression in their 20s. Preliminary genome screening was able to include 929 out of the 944 subjects (98.4%) typed at 932 autosomal and 544 X chromosome markers
Conclusions
This paper describes the methodology and the characteristics of the subjects from the 414 families included in the first wave of genotyping from the multi-site DeNT study. Ultimately the study aims to collect affected sibling pairs from approximately 1200 families.
Please note that Dr Reich died after the completion of this study.
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Background
Genetic risk factors are well established for major affective disorders and a recent twin study has suggested that unipolar depression has a stronger genetic influence than was previously thought. McGuffin and colleagues [1] have estimated that the heritability (i.e. the proportion of liability explained by genetic risk factors) may be over 70% in a clinically ascertained twin sample while a population based twin study resulted in a very similar estimate using a re-test method of assessing lifetime diagnosis [2].
The majority of studies suggest a relative risk to siblings (λs) of affective disorder is in the region of 3 [3]. However, a recent study comparing the siblings of unipolar depressives with the siblings of healthy controls using strict definitions of both depression and health found a substantially higher λs of over 9 [4].
The inheritance of unipolar depression is complex and involves an inter-play of genetic and environmental factors. For unipolar depression these include certain types of severe and threatening life events such as events associated with humiliation or loss [5,6].
Despite an excess of females to males of about 2 to 1 for unipolar depression, the heritability in a clinically ascertained sample was the same in men and women [1]. Some population based twin studies suggest at least some of the genes conferring liability differ between the sexes [7] while others do not [8]. Although it has been suggested that early onset depression is more clearly familial than later onset, this is not supported by a meta-analysis [9]. The only characteristics of probands associated consistently with higher familiality or heritability are recurrence of episodes and severity of disorder [1,9].
Previous linkage studies of unipolar depression
Most previous linkage studies have been carried out in families identified by a bipolar proband and where unipolar and bipolar relatives are frequently grouped together into a broad definition of affective disorders. Most such studies have focussed on multiple affected extended pedigrees on the assumption that there may be a sub-set segregating a gene of major effect. This approach has been successful in complex disorders such as early onset Alzheimer's disease and breast cancer. However, consistent evidence of major gene effects in bipolar disorder has not been forth-coming[10]. In addition, the unknown mode of inheritance creates inherent difficulties in classic linkage approaches and consequently sib-pair methods are attractive in the study of complex familial disorder. An affected sib pair genome scan study of recurrent depression has now been published suggesting that there is a depression susceptibility locus on chromosome 15q [11]. Another genome scan focusing on multiply affected families found the strongest evidence for linkage on chromosome 12q [12]. In addition a genome scan of multiply affected families with alcoholism and in whom some individuals had depression or co-morbid alcoholism and depression found evidence of a depression linked locus on chromosome 1p. Clearly these results require further scrutiny and replication.
Methods
Subjects
Sibling pairs affected with recurrent unipolar depression were recruited from 8 clinical sites: Aarhus, Denmark; Bonn, Germany; Dublin, Ireland; Lausanne, Switzerland; St Louis, USA and London, Cardiff and Birmingham, UK. In addition, where available, parents of the affected sibling pairs were also included in the study.
Subjects were identified from psychiatric clinics, hospitals, general medical practices and from volunteers responding to media advertisements. Caucasian subjects over the age of 18 were included if they had experienced 2 or more episodes of unipolar depression of at least moderate severity separated by at least 2 months of remission as defined by the Diagnostic and Statistical Manual 4th edition operational criteria (DSMIV) [13] or the International Classification of Diseases 10th edition operational criteria (ICD10), for unipolar depression [14]. Probands were all white and of white European parentage. They were included in the study if they had at least one biological sibling, not a monozygotic twin, over the age of 18 years meeting the same diagnostic criteria. Subjects were excluded if either sibling had ever fulfilled criteria for mania, hypomania or schizophrenia.
Subjects were also excluded if they experienced psychotic symptoms that were mood incongruent or present when there was no evidence of a mood disturbance. Other exclusion criteria were intravenous drug use with a lifetime diagnosis of dependency; depression occurring solely in relation to alcohol or substance abuse or depression only secondary to medical illness or medication, and a clear diagnosis of bipolar disorder, schizophrenia, schizo-affective disorder or acute or transient psychotic disorders in first or second-degree relatives.
Clinical assessment
All subjects were interviewed using the Schedules for Clinical Assessment in Neuropsychiatry (SCAN) [15,16]. Items of psychopathology in the SCAN interview were rated for presence and severity according to the worst and second worst episodes of depression identified by the subjects. For the purposes of rating severity, subjects were asked to identify within each of these episodes of depression a 4–6 week period when their symptoms were at their worst (peak intensity). The majority of the SCAN items were coded as follows; 0 – indicates absence of the item, 1 – the item was present but to a mild degree or intermittently throughout the peak intensity 4–6 weeks, 2 – item moderately severe and present for more the 50% of the peak intensity period or severe but present for less than 50% of the peak intensity period, 3 – item severe for more than 50% of the peak intensity period. The computerised version of the SCAN2.1 is built on top of the IShell system, which is a computer aided personal interviewing tool produced by the World Health Organisation [17] and which provides diagnoses according to DSMIV and ICD10 operational definitions.
Interviewer training and reliability across sites
All interviewers from each site attended a 4-day SCAN training course in the UK. Each site also undertook further inter-rater reliability meetings regularly and annually all interviewers from all sites took part in a joint inter-rater reliability exercise.
Ethical approval
All sites obtained ethical approval for the DENT study within their own countries and institutions. All study participants gave written informed consent for participation in the study.
Self-report questionnaires and other information collected from participants at interview
In addition to the SCAN interview all study participants completed the Eysenck Personality Questionnaire [18] and a detailed family history of psychiatric and physical disorders. For the 6 months prior to the worst episode and 6 months prior to the second worst episode as well as the 6 months prior to interview the Brief Life Events Questionnaire (BLEQ) identified which of 12 types of severe life events had occurred. These were based on the list proposed by Brugha and colleagues [19] to which childbirth was added. If such an event had occurred the subject was also asked to rate the impact of the event as; very distressing (a score of 3), moderately distressing (scored 2) or not very distressing (scored 1). The BLEQs therefore gave a summated impact score out of 39 for each 6-month time frame.
Blood samples
At the time of the SCAN interview interviewers obtained 25 ml of whole blood that was collected in 37.5 ml (EDTA containing) monovettes. In addition drops of blood were placed on a Guthrie blood spot card. The blood samples were labelled with a bar code, gently mixed and stored frozen upright in a -20 degree centigrade freezer pending DNA extraction.
Phenotypic data analysis
All phenotypic information from interviews and questionnaires was coded by assigning a number to each subject, and removing any personal identifying information. The same codes were used on the blood sample tubes using a bar code system. The phenotypic information was first entered on an EXCEL spread sheet after which a data file was created using Statistical Procedures for the Social Sciences (SPSS) version 10 for Windows for the statistical analyses.
Results
Inter-rater agreement for SCAN interview
All the interviewers from all sites took part in a joint inter-rater reliability exercise (in English) involving both audio-taped interviews with study subjects and videotaped interviews with actors, role-playing a depressed subject. Item by item kappa statistics for SCAN items, were calculated comparing each interviewer's ratings against AF's "master" rating. A mean item by item kappa coefficient across all the sites of 0.77 (range 0.63 – 0.89) was obtained indicating a substantial level of inter-rater agreement.
Number of subjects, age at interview, age at illness onset and gender by site
For inclusion in the first part of the linkage analysis, 944 affected subjects were genotyped from the 8 study sites as follows: Aarhus 48, Birmingham 146, Bonn 110, Cardiff 126, Dublin 154, Lausanne, 56, London 111 and St Louis 193. The age at interview and age of illness onset by gender of the subjects recruited at each site are shown in Table 1.
Table 1 Numbers of male and female subjects, age at interview and age of illness onset by site
Site Gender Number Age at interview (SEM) Age at illness onset (SEM)
Aarhus Female 30 43.53 (2.1) 21.39 (1.8)
Male 18 45.11 (2.0) 24.67 (1.9)
Birmingham Female 104 48.12 (1.3) 23.44 (1.1)
Male 42 49.24 (2.2) 26.19 (1.7)
Bonn Female 86 51.67 (1.3) 28.10 (1.9)
Male 24 50.67 (2.9) 24.24 (4.1)
Cardiff Female 85 43.55 (1.1) 23.76 (1.3)
Male 41 45.12(1.9) 22.92 (1.6)
Dublin Female 110 42.86 (1.2) 21.12 (1.0)
Male 44 44.43 (0.9) 24.97 (2.1)
Lausanne Female 43 48.67 (1.5) 24.69 (2.0)
Male 13 42.39 (2.0) 25.3 (2.1)
London Female 80 45.04 (1.1) 22.63 (1.4)
Male 31 47.29 (2.1) 20.71 (2.0)
St Louis Female 132 47.14 (1.0) 18.44 (0.9)
Male 61 47.15 (1.6) 17.58 (1.5)
SEM = Standard Error of the Mean
Mean age at interview for both sexes combined for each site were as follows: Aarhus 44.13 years (standard error of the mean (SEM)1.5), Birmingham 48.44 years (SEM 1.1), Bonn 51.46 years (SEM 1.2), Cardiff 44.06 years (SEM 0.9), Dublin 43.32 years (SEM 1.0), Lausanne 47.21 years (SEM 1.3), London 45.67 years (SEM 1.0), St Louis 47.14 years (SEM 0.9). These mean age differences were statistically significant (Analysis of variance(ANOVA): F = 6.26 degrees of freedom (df) 7, 936, p < 0.001. Tukey Post hoc test: Dublin, Cardiff, Aarhus, London, St Louis, Lausanne, Birmingham < St Louis, Lausanne, Birmingham, Bonn).
Mean age at illness onset for both sexes combined per site were as follows: Aarhus 22.67 years (SEM 1.3), Birmingham 24.30 years (SEM 0.8), Bonn 27.28 years (SEM 1.2), Cardiff 23.47 years (SEM 0.9), Dublin 22.27 years (SEM 0.8), Lausanne 24.85 years (SEM 1.4), London 22.08 years (SEM 1.1), St Louis 18.17 years (SEM 0.8). These mean age differences were statistically significant (ANOVA: F = 9.82 df 7, 841 p < 0.001. Tukey Post hoc test: St Louis, London, Dublin, Aarhus, < London, Dublin, Aarhus Cardiff, Birmingham, Lausanne < Cardiff, Birmingham, Lausanne, Bonn).
However, there were no significant differences between sites for the numbers of men and women recruited (see Table 1) (chi squared test = 6.83 df 7 p = non significant (ns)).
Number of probands, siblings and other relatives recruited by site
Although study participants were mainly affected proband/sibling pairs, there were a few families where parents were also included. The numbers of probands, siblings and parents recruited per site is shown in Table 2.
Table 2 Number of probands and siblings recruited from each site
Site Gender Number of probands Number of siblings Number of parents
Aarhus Female 11 17 2
Male 12 6 0
Birmingham Female 42 53 9
Male 18 21 3
Bonn Female 38 43 5
Male 12 12 0
Cardiff Female 41 42 2
Male 17 23 1
Dublin Female 54 51 5
Male 14 28 2
Lausanne Female 18 25 0
Male 10 3 0
London Female 37 41 2
Male 13 18 0
St Louis Female 54 63 15
Male 21 33 7
In total there were 369 families with 2 affected siblings, 36 families with 3 affected siblings, 7 families with 4 affected siblings, and 2 families with 5 affected siblings. In addition 53 parents were also interviewed and provided blood for DNA extraction. Thus there were 470 affected sibling pairs (calculated as number of pairs per family equals number of affected siblings minus 1).
Gender, age at interview, age at illness onset and marital status for all sites combined
Of the 944 subjects, 670 (71%) were female and 274 (29%) were males and hence, the female/male ratio was 2.45:1.
Mean age at interview for all female subjects was 45.40 years (SEM 0.5) and for all males subjects was 45.69 (SEM 0.8). There were no significant gender differences for age at interview (t = -0.33, df = 477.58, p = ns)
The mean age of illness onset for depressed male subjects was 22.61 years (SEM 0.7) compared to 22.52 years (SEM 0.4) for depressed female subjects. There was no significant sex difference for age of onset.(t = -0.11, df = 443.55 p = ns).
Fifty five percent of male subjects and 61 % female subjects were living with a partner (married or cohabiting), while 45 % male subjects and 39% female subjects were living alone (ie separated, widowed, divorced or never married). Female subjects were significantly more likely to be living with a partner compared to male subjects. (Chi squared test = 26.89 df = 1 p < 0.001).
Gender, age at interview, age of illness onset and marital status for probands, siblings and parents
There were 295 female and 117 male probands, 335 female and 144 male siblings and 40 female and 13 male parents included in the total sample. There were no significantly differences for the gender of probands, siblings or parents (chi squared test = 0.85, df = 2 p = ns).
The mean age at interview for probands was 45.94 years (SEM 0.6) and for siblings was 45.80 years (SEM 0.5). There were no significant differences for age at interview between probands and their siblings (t = 0.17, df = 872.95 p = ns)
Probands gave a mean age of illness onset of 20.22 years (SEM 0.6) while siblings reported a mean age of onset of 21.04 years (SEM 0.6). Again these differences were not statistically significant (t = -0.98, df = 882.93, p = ns)
There were also no significant differences between probands and their siblings for marital status; 161 probands and 170 siblings were living alone while 242 probands and 290 sibings were living with a partner (chi squared test = 0.81 df = 1 p = ns).
Genotyping checking
Genotyping was carried out by DeCode and the results checked for mis-specified relationships by the programs RELPAIR and Graphical Representation of Relationships (GRR) at the Institute of Psychiatry. RELPAIR compares the multipoint probability of the data conditional on the possible relationships, while GRR calculates the IBS mean and SD for each pair and plots these values, representing each type of relative pair using a different colour. Decisions about each problem pair were made on the basis of the results from both programs, although where there was discrepancy between the programs the GRR results were used.
To check genotypes with Mendelian and other pedigree errors the PEDSTAT and MERLIN programs were used.
These data cleaning processes resulted in 929 individuals being genotyped at 932 autosomal markers and 44 X chromosome markers. Success rates for the autosomal markers were above 61% and for 90% were above 86%. For the X chromosome the success rate was above 66%. For individuals the genotyping success rate was above 73% for autosomal markers and 61% for the X chromosome.
Discussion
Inter-site differences and similarities
The Depression Network study has recruited affected sibling pairs and some of their parents from 7 European and 1 North American site for a linkage analysis of recurrent unipolar depression. Because of differences in local service organisation, different recruitment strategies have been employed at the different sites. This may account for the significant differences for age at interview and age at illness onset between sites. The Bonn site recruited the oldest sibling pairs, both in terms of when subjects were interviewed and also when their illnesses had commenced. The Bonn subjects had a mean age at interview of 51.46 years compared to the Dublin subjects whose mean age at interview of 43.31 years was the youngest. Similarly the Bonn subjects mean age at illness onset was 27.28 years compared to a mean age of illness onset nearly a decade earlier for the St Louis subjects (18.17 years). It is noteworthy however that the St Louis sample included several large affected sibships. Subjects from families where there are many affected relatives may have a more genetic form of the disorder that might be contributing to an earlier age of onset.
Despite these inter-site differences, the results show that there are also considerable similarities across the sites for the subjects recruited. Subjects have been mainly interviewed in their mid 40s and have experienced the onset of their recurrent depression in their early to mid 20s. Consequently subjects had on average around 20 years of history of episodes of depression when interviewed.
Gender ratio and similarlities between probands and siblings
As expected the study has shown the same preponderance of female to male subjects as many other studies with a gender ratio of around 2.45:1 [4]. However compared to male subjects, female subjects were significantly more likely to be living with a partner rather than alone.
We would also not expect to find any significant differences between probands and siblings in terms of gender, age at interview, age at illness onset or marital status, which the results show is the case. Indeed for the purpose of finding genes for depression we would require siblings to have experienced similar forms of the illness.
Genotyping checking
Although some subjects were excluded following genotyping due to errors that could not be reconciled, this preliminary genome linkage screen was able to include 929 subjects (98.4%) genotyped at 932 autosomal and 544 X chromosome markers. The results of the whole genome screen will be presented in due course.
Conclusions
The Depression network study is the first co-ordinated international collaboration of its kind on the genetics of depression and one of the largest ever neuropsychiatric linkage study collection to use a uniform methodology to define and describe the phenotype. Despite taking place across eight sites and in six different countries good inter-rater agreement has been achievable as has good comparability of data.
The study has been designed to overcome the difficulties that have been encountered in linkage studies of other psychiatric disorders such as schizophrenia and bipolar disorder. These started out optimistically with the assumption that genes of large effect would exist in at least some multiply affected families. However, after over a decade of contradictory findings and non replications, there is now consensus that such families are very rare or perhaps nonexistent. Rather it seems likely that common familial psychiatric disorders result from the combined effect of multiple genes none of which is either necessary or sufficient to cause the condition [20]. Consequently large samples are required to have adequate power to detect genes of comparatively small effect, typically where the "risk genotype" confers a genotype relative risk of less than two. Some order is beginning to emerge as a result of meta-analyses of schizophrenia and bipolar data [21-23] however meta-analyses are fraught with difficulties resulting from differences in diagnostic methods, types of the family, genetic marker sets used and methods of ascertainment, in addition to the technical problems of how best to assess statistical significance. It is far preferable to have large diagnostically and ethnically homogenous data sets such as the one described here which will ultimately contain well over 1000 families. Family samples of a comparable size are also being collected elsewhere [11]. Until now studies of the genetics of unipolar depression have lagged behind those on schizophrenia and bipolar disorder but in doing so we have been able to learn from earlier mistakes. With hope therefore, uncovering the molecular genetic basis of unipolar depression promises to throw up less uncertainties and produce more consistency than has been characteristic of linkage and association studies in psychiatry.
Competing interests
This study was funded by 3 year research grants to each participating site from Glaxo Wellcome Research and Development.
Authors' contributions
AF & PMcG were overall study Principal Investigators (PIs), conceived the study and were co-ordinators of the study design, diagnostic reliability and data analysis. AF trained the interviewers from all the sites and wrote the paper. SB, LM and JP obtained the funding from Glaxo Wellcome, recruited site PIs and oversaw the quality of data collection, handling and analysis. GB analysed the genotyping data. The following authors were the individual site PIs in charge of all aspects of subject recruitment and data quality locally: OM Aarhus site, NC, LJ and IJ (joint) Birmingham site, MR and WM (joint) Bonn site, AK and MO (joint) Cardiff site, MG Dublin site, MP Lausanne site AF and PMcG (joint) London site and TR St Louis site. All authors have read and approved the contents of the final manuscript.
Pre-publication history
The pre-publication history for this paper can be accessed here:
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| 15588307 | PMC544588 | CC BY | 2021-01-04 16:33:00 | no | BMC Psychiatry. 2004 Dec 9; 4:42 | utf-8 | BMC Psychiatry | 2,004 | 10.1186/1471-244X-4-42 | oa_comm |
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J Circadian RhythmsJournal of Circadian Rhythms1740-3391BioMed Central London 1740-3391-2-61559600910.1186/1740-3391-2-6ResearchDaily oviposition patterns of the African malaria mosquito Anopheles gambiae Giles (Diptera: Culicidae) on different types of aqueous substrates Sumba Leunita A [email protected] Kenneth [email protected] Arop L [email protected] John [email protected] Bart GJ [email protected] John C [email protected] Ahmed [email protected] International Centre of Insect Physiology and Ecology (ICIPE), PO Box 30772, Nairobi, Kenya2 Department of Zoology, Egerton University, PO Box 536, Njoro, Kenya3 Entomology Unit, Agency's laboratories Seibersdorf, International Atomic Energy Agency, A-1400, Vienna, Austria4 University of Miami School of Medicine, Department of Epidemiology and Public Health. Highland Professional Building, 1801 NW 9th Ave., Suite 300 (D-93), Miami, FL 33136, USA2004 13 12 2004 2 6 6 31 8 2004 13 12 2004 Copyright © 2004 Sumba et al; licensee BioMed Central Ltd.2004Sumba 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
Anopheles gambiae Giles is the most important vector of human malaria in sub-Saharan Africa. Knowledge of the factors that influence its daily oviposition pattern is crucial if field interventions targeting gravid females are to be successful. This laboratory study investigated the effect of oviposition substrate and time of blood feeding on daily oviposition patterns of An. gambiae mosquitoes.
Methods
Greenhouse-reared gravid and hypergravid (delayed oviposition onset) An. gambiae sensu stricto and wild-caught An. gambiae sensu lato were exposed to three types of substrates in choice and no-choice cage bioassays: water from a predominantly anopheline colonised ground pool (anopheline habitat water), swamp water mainly colonised by culicine larvae (culicine habitat water) and distilled water. The daily oviposition pattern and the number of eggs oviposited on each substrate during the entire egg-laying period were determined. The results were subjected to analysis of variance using the General Linear Model (GLM) procedure.
Results
The main oviposition time for greenhouse-reared An. gambiae s.s. was between 19:00 and 20:00 hrs, approximately one hour after sunset. Wild-caught gravid An. gambiae s.l. displayed two distinct peak oviposition times between 19:00 and 20:00 hrs and between 22:00 and 23:00 hrs, respectively. During these times, both greenhouse-reared and wild-caught mosquitoes significantly (P < 0.05) preferred anopheline habitat water to the culicine one. Peak oviposition activity was not delayed when the mosquitoes were exposed to the less preferred oviposition substrate (culicine habitat water). However, culicine water influenced negatively (P < 0.05) not only the number of eggs oviposited by the mosquitoes during peak oviposition time but also the overall number of gravid mosquitoes that laid their eggs on it. The differences in mosquito feeding times did not affect the daily oviposition patterns displayed.
Conclusion
This study shows that the peak oviposition time of An. gambiae s.l. may be regulated by the light-dark cycle rather than oviposition habitat characteristics or feeding times. However, the number of eggs laid by the female mosquito during the peak oviposition time is affected by the suitability of the habitat.
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Background
Although An. gambiae s.l. mosquitoes are nocturnal in their feeding and oviposition activities, the probable time of oviposition is determined by many factors including ambient temperature and light conditions, and the time the mosquito obtains its blood meal [1,2]. In addition, we hypothesised that the availability of a suitable larval habitat would also affect the mosquito's predisposition to oviposit. Anopheles gambiae is discriminative in its oviposition behaviour [3]. Its preferred larval habitats are fresh water pools that are generally small, transient and sunlit, devoid of vegetation and often turbid [4-6]. Oviposition tendency might therefore be related to location and availability of such sites. In this study, we compared the daily oviposition patterns and the number of eggs laid by An. gambiae s.s. and wild-caught An. gambiae s.l. on aqueous collections from habitats colonised by anopheline or culicine larvae respectively, and distilled water.
Methods
Mosquitoes
Anopheles gambiae s.s. (MBITA strain; colonised since February 2001) mosquitoes from Mbita Point, western Kenya, were reared in a greenhouse [7] in water obtained from a natural ground pool colonised by anopheline larvae. Average temperatures and relative humidities were 31°C, 52 % during the day and 24°C, 72% at night. The mosquitoes were exposed to the natural photoperiod, 00° 25' South of the equator. A data logger (HOBO™) was used to record ambient conditions. Larvae were fed on Tetramin® fish food. Adult mosquitoes were kept in standard mosquito rearing cages (30 × 30 × 30 cm) made of a metal wire frame with a solid metal base and covered with white nylon mosquito netting. They were offered a 6% glucose solution soaked in white paper towel wicks. Three-to-four-day-old females were offered two blood meals, one each day at 18.00 hrs, from the forearm of a human volunteer. The unfed mosquitoes were removed from the cage after each blood meal. Fully engorged females were left in the cages until they were gravid or hypergravid. Gravid mosquitoes are those that were provided with oviposition substrates on the third evening after their first blood meal. Hypergravid mosquitoes were provided with oviposition substrates one day later. Wild, indoor-resting, blood fed anopheline mosquitoes were collected during early morning hours from houses in Lwanda village of Suba district, western Kenya, by means of aspirators. They were immediately transported to the greenhouse, sorted out to obtain An. gambiae s.l. females and provided with 6% glucose solution. They were used in periodicity experiments on the second evening after collection, as described below.
Oviposition substrates
Turbid water taken from a natural ground pool colonised by anopheline larvae (anopheline habitat water), yellow-brown water from a reed swamp colonised by culicine larvae (culicine habitat water), and distilled water were used as oviposition substrates. Presence of larvae was determined by making five random dips using a 350 ml standard dipper.
Oviposition substrate preference
The experiments were carried out under greenhouse conditions in 25 cm cubic Plexi®-glass cages, each fitted with a white netting top and a side sleeve opening. To determine oviposition substrate preference, individual gravid An. gambiae s.s. mosquitoes were exposed to 20 ml of each of the above substrates in a three-choice bioassay (n = 55). The substrates were held in black plastic oviposition cups (2 cm depth, 4 cm diameter), placed at equal distances from one another. Individual mosquitoes were released into the cages at about 17.00 hours and left overnight. The following morning, eggs oviposited on each substrate were counted under a dissection microscope. In subsequent replications, oviposition cups containing substrates were rotated such that they occupied different positions every time in the oviposition cages.
Daily oviposition patterns in a no-choice bioassay
Daily oviposition patterns of An. gambiae female mosquitoes on test oviposition substrates, which were offered individually, were determined as follows. Groups of five greenhouse-reared gravid and hypergravid An. gambiae s.s. females were held in separate cages into which anopheline or culicine habitat water or distilled water were introduced. Each mosquito and substrate combination treatment was replicated four times on each experimental day and the experiment repeated on three different days. At the end of the experiment, the mosquitoes that had laid in each group were identified by dissecting each under a dissection microscope and examining their ovaries for the presence of either retained eggs, coiled or uncoiled tracheolar skeins [8].
Daily oviposition patterns in a choice bioassay
Groups of five gravid and hypergravid An. gambiae s.s. (ten cages of each) were placed in separate cages and allowed to choose from the three types of oviposition substrates. Similarly, groups of five wild-caught An. gambiae s.l. mosquitoes were offered a choice of the three substrates and their daily oviposition patterns monitored. The experiment was replicated twice on each experimental day and repeated on five different days with new mosquito batches. Individual species within the wild-caught An. gambiae mosquitoes that had laid were identified using polymerase chain reaction (PCR) [9].
Effect of the time of blood feeding on daily oviposition patterns
The effect of the time of blood feeding of An. gambiae s.s. on its daily oviposition pattern was determined as follows. Four groups of three-to-four-day-old females were given two blood meals, one each day at 06.00 hrs, 18.00 hrs, 22.00 hrs or at 00.00 hrs, respectively. Unfed females were removed from the cages after every blood meal. Gravid mosquitoes were then provided with oviposition cups on the third day at 06.00 hrs and their daily oviposition patterns monitored.
In all experiments, the oviposition cups were removed from the cages after every hourly interval, for 24 hours, starting at 18.00 hrs and replaced with freshly prepared ones. The eggs laid on each substrate were counted under a dissection microscope. To minimise disturbance that might have been due to exposure to white light, red light was used at night while replacing the oviposition cups.
Data analysis
Since oviposition trends for gravid and hypergravid females were similar, data for the two were pooled for analysis. The differences in the number of eggs laid on different oviposition substrates were compared statistically by analysis of variance using the General Linear Model (GLM) procedure. The effect of oviposition substrate on the number of either gravid or hypergravid mosquitoes contributing to the total egg number was similarly compared. Means were separated by the least significant difference (LSD) procedure. Data were subjected to log10 (n+1) transformation to normalise their distribution. All the analyses were carried out using the SPSS® statistical package, version 11.0.
Results
Oviposition substrate preference
The mean number ± standard error (39.4 ± 6.1) of eggs oviposited on anopheline habitat water was significantly higher than that on the culicine (16.1 ± 4.6; P = 0.01) or distilled water (23.7 ± 5.3; P = 0.02).
Daily oviposition patterns
Daily oviposition patterns of An. gambiae s.s. on different substrates, offered in either no-choice or choice assays, are presented in Figures 1 and 2, respectively. In both cases, the main oviposition time was between 19:00 and 20:00 hrs, approximately one hour after sunset, followed by a steady reduction in the number of eggs laid as the night progressed. In the choice bioassays, the gravid mosquitoes showed significant preference for anopheline habitat water over distilled (P = 0.004) or culicine habitat water (P = 0.001) throughout the daily cycle. In the no-choice bioassay, although the total number of eggs laid throughout the cycle on the different substrates was different, this was not statistically significant (P = 0.4). However, during the peak oviposition time, the eggs laid on anopheline habitat water were significantly more than those on the culicine one (P = 0.01) but not significantly more than those on distilled water (P = 0.07). Egg-laying by mosquitoes of different ovary development stages was influenced considerably by the type of oviposition substrate (P = 0.02). The hypergravid/ anopheline habitat water combination had the highest average number of mosquitoes (4.4 ± 0.3) laying their eggs, whereas gravid/culicine combination yielded the lowest response (2.5 ± 0.4; Table 1).
Figure 1 Daily oviposition patterns of Anopheles gambiae s.s. on different oviposition substrates in a no-choice bioassay. Mean percentage (± SE) of the total eggs laid on each of three different oviposition substrates during 1-h time intervals. n = 24 cages containing five females each. Mosquitoes in each cage were exposed to one type of substrate under a natural LD cycle (sunset at 18:00).
Figure 2 Daily oviposition patterns of Anopheles gambiae s.s. on different oviposition substrates in a choice bioassay. Mean percentage (± SE) of the total eggs laid on each of the three different oviposition substrates during 1-h time intervals. n = 20 cages containing five females each. Mosquitoes could choose from different substrates placed in the same cage under a natural LD cycle (sunset at 18:00).
Table 1 The number of mosquitoes (Mean ± SE1) contributing to the total eggs laid in each mosquito/ substrate combination.
Mosquito/ Substrate Mean ± SE1
Gravid/ Distilled water 3.3 ± 0.4bc
Gravid/ Anopheline habitat water 3.5 ± 0.4ab
Gravid/ Culicine habitat water 2.5 ± 0.4c
Hypergravid/ Distilled water 3.8 ± 0.4ab
Hypergravid/ Anopheline habitat water 4.4 ± 0.3a
Hypergravid/ Culicine habitat water 3.6 ± 0.4ab
1SE: Standard Error. n = 12 cages each containing five mosquitoes. Any two means sharing a letter in common are not significantly different at 5% level (LSD test).
Unlike the greenhouse-reared An. gambiae s.s., the wild-caught An. gambiae s.l., which consisted of 23.9% An. gambiae s.s.,71.7% An. arabiensis and 4.4% unidentified gravid females (n = 46), displayed two main oviposition times early in the night, between 19:00 and 20:00 hrs and between 22:00 and 23:00 hrs, respectively (Figure 3). These mosquitoes also showed significant preference (P = 0.01) for anopheline habitat water over distilled or culicine habitat water.
Figure 3 Daily oviposition patterns of wild-caught Anopheles gambiae s.l. on different oviposition substrates in a choice bioassay. Mean percentages (± SE) of the total eggs oviposited on each of the three different oviposition substrate during 1-h time intervals. n = 10 cages containing five females each. Mosquitoes could choose from different substrates placed in the same cage under a natural LD cycle (sunset at 18:00).
An. gambiae s.s. females that obtained their blood meals later in the night displayed a somewhat broader oviposition peak time interval, ranging from 19:00 hrs to 22:00 hrs (Figure 4), than those that had fed earlier on, whose peak oviposition time interval was narrower (19:00 hrs to 21:00 hrs).
Figure 4 Daily oviposition patterns of Anopheles gambiae s.s. fed at different times. Mean number (± SE) of eggs oviposited during 1-h time intervals. n = 8 cages containing five females each. Mosquitoes were kept under a natural LD cycle (sunset at 18:00).
Discussion
In the present study, the daily oviposition patterns of greenhouse-reared An. gambiae s.s. were well defined with oviposition peak times between 19:00 and 20:00 hrs, regardless of the type of oviposition substrate used. Haddow and Ssenkubuge [10] obtained comparable results using An. gambiae s.s. (KISUMU strain, western Kenya): about half of the eggs were laid during the first three hours of the night (18:00 – 21:00 hrs). On the other hand, oviposition by wild-caught mosquitoes from the coast of Kenya used by McCrae [1], comprising mostly An. gambiae s.s., peaked much later at night in the hour following midnight. This suggests differences in oviposition patterns between our strain and that of Haddow and Ssenkubuge representing Lake Victoria populations, on one hand, and that used by McCrae representing the Kenyan coastal population, on the other. Studies of oviposition patterns of populations from different parts of eastern Africa may help shed further light on the question.
In the current study, wild-caught An. gambiae s.l., which were shown to contain a mixture of An. gambiae s.s. and An. arabiensis gravid females, displayed two distinct oviposition peak times in the first half of the night. The two peaks may be attributed to the two sibling species and suggests that this may also be an important factor in the diversity of oviposition patterns in the field in different geographical locations.
The differences in the mosquito feeding times did not affect the timing of peak oviposition, although females that obtained their blood meals later in the night displayed a somewhat broader oviposition peak interval. Peak oviposition consistently occurred approximately one hour after sunset; therefore, a fall in light intensity might be one of the important cues that trigger oviposition in female An. gambiae that are physiologically ready to oviposit. On the other hand, McCrae [1] observed that the time of oviposition was a function of the time of blood feeding and not a result of an endogenous rhythm. Given the uniform oviposition peak times of mosquitoes that were fed at different times, daily oviposition among An. gambiae s.l. may also be endogenously regulated. Detailed experiments to demonstrate a free-running oviposition periodicity would clarify this. There was no difference in oviposition patterns displayed by gravid and hypergravid mosquitoes. Since significantly more gravid females exposed to the preferred substrate oviposited their eggs than those exposed to the less preferred one, gravid females that fail to find a suitable oviposition site on the night they are due may retain their eggs and oviposit early the next night as hypergravids.
Gravid mosquitoes are generally attracted to water; however, the decision to oviposit may depend on additional olfactory signals [11] and /or contact stimuli received when the insects land on the water surface [12]. In this study and others [13], the gravid mosquitoes showed marked preference for the water taken from a site naturally inhabited by anopheline larval populations. This suggests 'memory' of similar information gathered by contact with the oviposition water at emergence or during larval period as in the case of Culex quinquefasciatus [14]. In this regard, gravid females might associate specific site characteristics from conspecific and heterospecific immatures, soil microbial activity [11], colour and turbidity of the oviposition substrate [13] with their suitability for sustaining progeny development.
Conclusions
This study shows that the peak oviposition time of An. gambiae s.l. may be regulated by the light-dark cycle rather than oviposition habitat characteristics or feeding times. However, the number of eggs laid during the peak oviposition time is affected by the suitability of the habitat. This suggests that there is a relationship between the investment made by the female mosquito with respect to the number of eggs laid in a given habitat and the potential fitness of the progeny. Females may use a series of site characteristics, including olfactory cues, to locate and oviposit at such sites. Our results on oviposition patterns differ from those reported on a coastal population, and suggest that a lot more work needs to be done to elucidate differences in this regard between different populations.
Competing interests
The authors declare that they have no competing interests.
Authors' contributions
LAS and KO conducted all the experimental work. AH, ALD, BGJK, JCB and JG co-ordinated and/or supervised the work. All authors actively contributed to the interpretation of the findings and development of the final manuscript and approved the final manuscript.
Acknowledgements
We thank, E. Obudho, J. Wauna and the staff of the malaria vector programme at ICIPE-Mbita for their support, P. Seda, J. Mutunga, J. Kongere and N. Gitonga for assistance with mosquito identification, and L. Gouagna, D. Impoinvil and D. Chadee for their comments on an earlier version of this manuscript. This research was supported by funds from the National Institutes of Health (NIH) grant U19 AI45511 and the ABC Fogarty through grant number D43TWØ1142. LS wishes to acknowledge the PhD scholarship from the German Academic Exchange Service (DAAD) through the African Regional Post-graduate Programme in Insect Science (ARPPIS). Approval for feeding the mosquitoes on human subjects was sought and obtained from the Kenya National Ethical Review Board, protocol number KEMRI/RES/7/3/1.
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| 15596009 | PMC544589 | CC BY | 2021-01-04 16:39:12 | no | J Circadian Rhythms. 2004 Dec 13; 2:6 | utf-8 | J Circadian Rhythms | 2,004 | 10.1186/1740-3391-2-6 | oa_comm |
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RetrovirologyRetrovirology1742-4690BioMed Central London 1742-4690-1-431560147210.1186/1742-4690-1-43Short ReportHIV-1 encoded candidate micro-RNAs and their cellular targets Bennasser Yamina [email protected] Shu-Yun [email protected] Man Lung [email protected] Kuan-Teh [email protected] Molecular Virology Section, Laboratory of Molecular Microbiology, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, Maryland 20892-0460, USA2 Laboratory of Experimental and Computational Biology, National Cancer Institute Center for Cancer Research, National Cancer Institute, Frederick, MD 21702, USA2004 15 12 2004 1 43 43 17 11 2004 15 12 2004 Copyright © 2004 Bennasser et al; licensee BioMed Central Ltd.2004Bennasser 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.
MicroRNAs (miRNAs) are small RNAs of 21–25 nucleotides that specifically regulate cellular gene expression at the post-transcriptional level. miRNAs are derived from the maturation by cellular RNases III of imperfect stem loop structures of ~ 70 nucleotides. Evidence for hundreds of miRNAs and their corresponding targets has been reported in the literature for plants, insects, invertebrate animals, and mammals. While not all of these miRNA/target pairs have been functionally verified, some clearly serve roles in regulating normal development and physiology. Recently, it has been queried whether the genome of human viruses like their cellular counterpart also encode miRNA. To date, there has been only one report pertaining to this question. The Epstein-Barr virus (EBV) has been shown to encode five miRNAs. Here, we extend the analysis of miRNA-encoding potential to the human immunodeficiency virus (HIV). Using computer-directed analyses, we found that HIV putatively encodes five candidate pre-miRNAs. We then matched deduced mature miRNA sequences from these 5 pre-miRNAs against a database of 3' untranslated sequences (UTR) from the human genome. These searches revealed a large number of cellular transcripts that could potentially be targeted by these viral miRNA (vmiRNA) sequences. We propose that HIV has evolved to use vmiRNAs as a means to regulate cellular milieu for its benefit.
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Findings
Initially discovered in Caenorhabditis elegans as regulators of temporal control of post-embryonic development [1,2], miRNAs are small RNAs involved in the specific regulation at the post-transcriptional level of cellular genes in various organisms such as flies, plants and mammals [3,4]. To date, more than two hundred human miRNAs have been described [5]. Structurally, miRNAs are 21 to 25 nucleotide RNAs derived from the maturation of a hairpin precursor transcript which can be encoded by the 3' untranslated region of genes, introns of genes, or by specific chromosomal regions composed of tandem clusters of miRNA sequences. Precursor RNAs for miRNAs are structured as imperfect RNA hairpins containing mismatches and bulges. In mammalian cells, the maturation of miRNA occurs in two steps consecutively involving two cellular RNase III proteins, the nuclear Drosha and the cytoplasmic Dicer [6]. Accordingly, a miRNA precursor is specifically recognized in the nucleus by Drosha which cleaves the RNA to release an imperfect stem-loop structure of ~ 70 nucleotides, the pre-miRNA. This structure is then exported by exportin-5 into the cytoplasm and further cleaved there by Dicer into corresponding imperfect RNA duplexes of 21 to 25 nucleotides, the miRNA [7]. Mechanistically, either one of the two strands of the mature miRNA can be incorporated into the RNA-induced silencing complex (RISC). miRNA-armed RISCs can then specifically recognize and interact via imperfect complementarity with RNA targets to induce repression of translation and (less frequently) mRNA cleavage. The precise molecular mechanism of translational silencing remains unclear; however, in such instances, it has been observed that protein synthesis is inhibited while the stability of the mRNA is not altered [8-10].
Recently, in addition to plants, insects, invertebrate animals, and mammals, Pfeffer et al. identified virus-encoded miRNA sequences in Epstein-Barr virus (EBV) infected cells [11]. They reported that EBV encodes five miRNAs each capable of regulating viral genes involved in latency as well as modulating the expression of host cell genes. Thus, it would appear that EBV has evolved to use the miRNA pathway for its replicative benefit. To query whether this stratagem might also be employed by other viruses, we have analyzed putative miRNA-encoding capacity of HIV-1.
We wondered if HIV-1 maintains RNA structures that resemble pre-miRNAs. As a proof-of-principle, we examined pre-miRNA structures in one specific example of HIV-1, the genome of the pNL4-3 molecular clone. Because HIV has well-described stem-loops such as TAR (trans-activation responsive RNA) and RRE (Rev-responsive element) [12], one might think that pre-miRNA structures would be prevalently found in this virus. However, when we set search parameters to include RNA structure of ~ 70 nucleotides in total size with an imperfect stem of 21 to 25 base-pairs, only a few thermodynamically reasonable candidates were revealed. Using a new scanning method StemEd [13], we uncovered 5 pre-miRNA candidates. As shown in figure 1a, these sequences (#1 to #5) are discretely separated in different regions of the HIV genome: near TAR, in capsid gag, near the gag-pol frameshift, in the nef gene, and in the 3'LTR [14,15]. The corresponding predicted folding for each candidate and their deduced mature virally-encode miRNA (vmiRNA) sequences are presented in figure 1b.
Figure 1 Sequences and localization of HIV-encoded miRNA candidates. a) Locations for 5 predicted pre-miRNAs candidates in the pNL4-3 genome are shown. b) The folded structures of the 5 viral pre-miRNAs from pNL4-3 (Accession Number AF 324493) [17] are illustrated. Folded pre-miRNAs and their corresponding predicted mature viral miRNA (red) are listed. Nucleotide positions (where 1 is the initiation of transcription) in the pNL4-3 genome are presented in the right column.
The 5 HIV-1 encoded pre-miRNA candidates can in principle yield 10 mature vmiRNAs. To ask, whether these putative vmiRNAs, if expressed in infected cells, could be used by HIV-1 to modulate host cell gene expression profiles (i.e. suppress the translatability of cellular mRNAs), we checked each vmiRNA sequence against a 3'UTR database for human genes. Because the exact rules governing suppression of translation based on miRNA complementarity to 3' UTR remain unclear, we collected all "hits" that had 6 or fewer mismatches with an additional constraint that the 5'-most nucleotide of the vmiRNA cannot be mis-matched with the target sequence. Surprisingly, based on the above criteria, a very large number of cellular targets were found (Figure 2). On average, it is suggested that each vmiRNA could target 50 to 100 cellular RNAs. If all 10 vmiRNAs were functionally competent, this would argue that HIV-1 could potentially modulate the expression of 500 to 1,000 cellular transcripts using this mechanism. Intriguingly, in the setting where a limited number of mis-matches are permitted, the same vmiRNA apparently can target functionally distant cellular proteins such as IkB-kinase-β and proteasome 26S subunit or macrophage colony-stimulating factor M-CSF and CDC42 effector protein 1 (Figure 2). This suggests that vmiRNAs could pleiotropically affect the expression pattern of cellular proteins.
Figure 2 Potential cellular targets for each of the vmiRNAs. The two deduced mature vmiRNAs predicted from each precursor miRNA are shown. The mature vmiRNA sequences were individually searched against a database of human 3'UTRs using imperfect complementarity criteria as described in the text. The number of potential candidate cellular RNA targets is enumerated. Most of the cellular targets are incompletely characterized expressed sequence tag (EST) clones, with a subset of targets being known genes. For each predicted vmiRNA, we list two examples of known cellular gene targets at the right. A full list of targets is available upon request.
Here, we introduce the concept that the HIV genome could reasonably encode 5 candidate pre-miRNAs. We further suggest that a large number of cellular transcripts could potentially be targeted if these 5 pre-miRNAs were processed into 10 predicted mature vmiRNAs (Figure 2). Studies are in progress to verify experimentally the expression of our candidate vmiRNAs in HIV-1 infected cells. If HIV-1 encoded vmiRNA candidates can be shown to be functional, their action could, in part, explain the frequently observed landscape changes in host cell gene expression profiles during HIV-1 infection as revealed by micro-array studies [16]. We are also currently examining how vmiRNAs might additionally affect HIV-1 gene expression.
Competing interests
The authors declare that they have no competing interests.
Authors' contributions
YB and KTJ conceived of the ideas and wrote the paper. SYL did the computation work for the study. MLY participated in the discussion of the data.
Acknowledgements
We thank Anthony Elmo for assistance with the preparation of the manuscript.
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| 15601472 | PMC544590 | CC BY | 2021-01-04 16:36:36 | no | Retrovirology. 2004 Dec 15; 1:43 | utf-8 | Retrovirology | 2,004 | 10.1186/1742-4690-1-43 | oa_comm |
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Cardiovasc UltrasoundCardiovascular Ultrasound1476-7120BioMed Central London 1476-7120-2-281558832110.1186/1476-7120-2-28ResearchMitral valve prolapse associated with celiac artery stenosis: a new ultrasonographic syndrome? Arcari Luciano [email protected] Guglielmo Marconi University – ASDAC (Updating and Teaching in Cardiology, Scientific Association), ARCA Lazio, Rome, Italy2004 10 12 2004 2 28 28 26 8 2004 10 12 2004 Copyright © 2004 Arcari; licensee BioMed Central Ltd.2004Arcari; 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
Celiac artery stenosis (CAS) may be caused by atherosclerotic degeneration or compression exerted by the arched ligament of the diaphragm. Mitral valve prolapse (MVP) is the most common valvular disorder. There are no reports on an association between CAS and MVP.
Methods
1560 (41%) out of 3780 consecutive patients undergoing echocardiographic assessment of MVP, had Doppler sonography of the celiac tract to detect CAS.
Results
CAS was found in 57 (3.7%) subjects (23 males and 34 females) none of whom complained of symptoms related to visceral ischemia. MVP was observed in 47 (82.4%) subjects with and 118 (7.9%) without CAS (p < 0.001). The agreement between MVP and CAS was 39% (95% CI 32–49%). PSV (Peak Systolic Velocity) was the only predictor of CAS in MPV patients (OR 0.24, 95% CI 0.08–0.69) as selected in a multivariate logistic model.
Conclusion
CAS and MVP seem to be significantly associated in patients undergoing consecutive ultrasonographic screening.
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Background
Celiac artery stenosis (CAS) may be caused by atherosclerotic degeneration, as observed in different vascular districts, or by extrinsic compression (ECCA) usually exerted by an abnormally developed arched ligament of the diaphragm [1-7]. ECCA may cause abdominal or epigastric pain triggered by meals (angina abdominis), a syndrome called Celiac Artery Compression Syndrome (CACS) [1,2]. Resolution of symptoms after surgical resection of the arched ligament has been frequently reported. However, some aspects, such as the vascular etiology of symptoms, are still controversial, as the improvements observed after surgery may be attributed to the concurrent destruction of the celiac ganglion rather than to the severance of the arched ligament.
Mitral Valve Prolapse (MVP) is the most common valvular disorder, with a prevalence of about 6–7% in the general population [8,9]. It consists of systolic displacement of one or both valvular leaflets into the left atrium, with or without mitral regurgitation. Patients with MVP may also be affected by other cardiac anomalies, such as prolapse of the aortic valve and/or of the tricuspid valve, ostium secundum interatrial sect defect, atrio-ventricular left accesses, and/or extra-cardiac anomalies, such as pectus excavatum and loss of the physiological curvature of the thoracic spine, with greater frequency with respect to the general population. An increased incidence of MVP has been demonstrated in connective tissue disorders, especially in Marfan's syndrome [10]. Particular genic anomalies have been associated with MVP [11-13].
The association between MVP and CAS has not been extensively investigated. Accordingly, this study was aimed at verifying this hypothesis based on a possible common origin of the two conditions, i.e. the abnormal development of connective tissue causing both ECCA exerted by the arched ligament of the diaphragm and exuberance of valvular mitral tissue.
Methods
Study population
The study population consisted of 1560 out of 3780 (41%) consecutive patients undergoing echocardiographic assessment of MVP, who also had celiac artery Doppler ultrasound performed between November 1999 to September 2004.
Echocardiographic examination
MVP was defined as clear-cut billowing of one or both mitral leaflets across the mitral annular plane in 2-dimensional parasternal long axis recording or >2 mm late systolic posterior displacement of mitral leaflets by M-mode. A >4 mm displacement defined a moderate-severe prolapse.
Ultrasonography (HP Sonos 1000, Tohsiba Corevision, Esaote Caris, Esaote Caris plus, Kontron Iris) was performed without intestinal preparation to limit meteorism. The digestive phase was taken into account, because it may influence visceral arterial flow. The evaluation of arterial flow in the celiac tract was recorded by continuous Doppler (CW) and, whenever possible, with the aid of the color-Doppler signal. No corrections of flow velocity by evaluation of the cosine of the insonorisation axis and the axis of the vessel were used: in this way, flow velocity can never be overestimated. The Doppler signal was collected in apnea during inspiration. During expiration, an increase in the compression of the celiac tripod by the arched ligament occurs in the cases of CAS caused by ECCA, with concurrent increase in flow velocity with respect to the velocity during inspiration [14]. An example is provided in fig. 1, 2, 3. Figures 456789 are examples of patients with CAS and MVP.
Figure 1 Continuous Doppler of Celiac Artery Stenosis, deep expiration.
Figure 2 Continuous Doppler of Celiac Artery Stenosis, modeste inspiration.
Figure 3 Continuous Doppler of Celiac Artery Stenosis, deep inspiration.
CAS was defined as severe in case of Peak Systolic Velocity (PSV) flow velocity in the upper celiac tract greater than 2.0 metres/second [15,16] and End Diastolic Velocity (EDV) greater than 0.5 metres/second. PSV between 2.0 and 2.5 metres/second defined moderate CAS, whilst those > 2.6 metres/second identified severe CAS.
Statistical analysis
Continuous variables are expressed as means ± 1 standard deviation (SD). Differences between groups were compared using Student's t-test and chi-square test, as appropriate. Association between CAS and MPV was performed using the Kappa statistics, estimated with 95% confidence interval.
Univariate odds ratio (OR) along with their corresponding 95% confidence intervals were computed for describing association of clinical variables with MPV in CAS patients, using a logistic regression model. Selection of variables significantly associated with MPV in CAS patients was made using a multivariate logistic model. Selection criterion was the Akaike Information Criterion, applied backward to the multivariate logistic model. The statistical significance was settled at a p value < 0.05. The R (release 1.9) statistical package and the Harrell's Design and Hmisc libraries were used for analysis.
Results
The incidence of MVP in patients with (10.6%) and without (9.3%) assessment of CAS did not differ significantly.
Among those undergoing both examinations, CAS was found in 57/1560 (3.6%) patients. The clinical characteristics of patients with and without CAS are reported in Table 1. The incidence of CAS was significantly higher among subjects with as compared to those without MVP (28% vs. 0.7%, OR 5.11 95% CI 3.43 to 7.61; p < 0.001). Likely, the incidence of MVP was significantly higher among subjects with as compared to those without CAS (82.4% vs 7.9 %, OR 55.11 95% CI 27.17 to 111.97; p < 0.001). Thus, the overall concordance between CAS and MVP was 39% (95% CI 0.28 to 0.49). MPV as indicator of CAS has a high sensitivity (0.82 95% CI 0.71 to 0.90) and specificity (0.92 95% CI 0.91 to 0.92).
Table 1 Clinical characteristics of patients with and without CAS.
CAS (n= 57) No CAS (n= 1503) P-value
Age 40 ± 21 46 ± 14 0.002
Male 23 (40%) 691 (46%) 0.400
MVP 47 (82.4%) 118 (7.9%) <0.001
No patient with CAS was complaining of symptoms related to visceral ischemia, i.e. abdominal or gastric pain in concurrence with food ingestion.
Thirteen (22.8 %) of the 57 subjects with CAS were suffering from cardiovascular disorder. In particular, a macrovascular atherosclerotic disorder (myocardial infarction, carotid plaques, abdominal aorta aneurism, peripheral arterial disease involving the lower limbs) was ascertained in 7 subjects (6 male and 1 female, mean age 71+14 years), whilst no sign of macrovascular atherosclerosis was found in the remaining 50 subjects (16 males and 34 females, mean age 35+12 years).
In patients with CAS, factors associated with MPV are age, sex, PSV and BMI. At multivariate analysis only PSV resulted as an independent factor associated with MPV (OR 0.24 95% CI 0.08 to 0.69).
Discussion
The incidence of CAS is not clearly established, the majority of the literature consisting of isolated reports [17-21]. Surprisingly, one study [22] evaluating the incidence of CAS in 400 asymptomatic subjects undergoing angiographic examination because of hepatic neoplasm, reported a 7.3% incidence of hemodinamically significant CAS. Its origin ECCA in 55% and atherosclerosis in 10%, whilst it remained not determined in 35% of cases.
The incidence of CAS and MVP in the present study dealing with an unselected population was 3.7% and 10.6%, respectively. In addition, this is the first report, to our knowledge, to demonstrate a strong association between the two conditions.
CAS may be caused by ECCA or atherosclerosis. Unfortunately, the ultrasound technique has not enough resolution to allow an etiological discrimination that may be difficult to get even with angiographic examination [22]. However, clinical information may be of help to hypotesize the etiology of CAS in our population. Indeed, CAS was likely due to atherosclerotic disease in the 7 subjects with macrovascular atherosclerotic disorders, whilst it could be secondary to ECCA in the 50 subjects with no feature of atherosclerosis. Of interest, MVP was found in no subject of the first group, whilst it was present in 47/50 (94%) of the second group. Characteristics of CAS subjects according to the presence of atherosclerotic markers are reported In Table 2. On the basis of these findings, it is logical to hypothesize that ECCA represents the factor explaining the association between CAS and MVP. The two pathological conditions may share a common genetic disorder causing a similar defect of the connective tissue at the two anatomic sites. Nevertheless, no correlation was found between CAS severity and degree of MVP.
Table 2 Characteristics of CAS patients according to the presence of atherosclerotic disorders. Univariate OR (95% CI) are computed for the association of each single variable with MPV.
N No MPV (N = 10) MPV (N = 47) Combined (N = 57) OR (95% CI)
Sex 57 70% (7) 34% (16) 40% (23) 0.22 (0.005,0.97)
Age 57 41.7/66.0/72.7 20.0/33.0/47.5 23.0/36.0/55.0 0.15 (0.04, 0.57)
PSV 53 2.6/2.8/3.5 2.1/2.4/2.8 2.2/2.5/2.9 0.24 (0.08, 0.69)
EDV 53 0.9/1.0/1.2 0.7/0.85/1.0 0.7/0.9/1.1 0.43 (0.18, 1.05)
BMI 48 23.8/27.4/30.3 19.0/21.5/23.0 18.9/21.2/23.2 0.10 (0.01, 0.69)
Conclusions
The results of this study demonstrate that CAS is a relatively frequent finding among patients undergoing Doppler sonography of the celiac artery and is frequently associated to MVP. On the basis of these findings, further investigations are warranted aimed at determining the exact incidence of ECCA associated with MVP, the family distribution of the association between MVP and ECCA, or the prognostic implication of ECCA in subjects with MVP. Additionally, genetic studies could be advisable in subjects presenting with MVP associated to ECCA.
List of abbreviations
CAS Celiac Artery Stenosis
MVP Mitral Valve Prolapse
ECCA Extrinsic Compression of Celiac Artery
CACS Celiac Artery Compression Syndrome
PSV Peak Systolic Velocity
EDV End Diastolic Velocity
Figure 4 Continuous Doppler of Celiac Artery Stenosis
Figure 5 Mitral valve Prolapse, M-mode, patient of fig. 4
Figure 6 Color Doppler of aorta: celiac artery stenosis (longitudinal proiection)
Figure 7 Color Doppler of aorta : celiac artery stenosis (longitudinal proiection)
Figure 8 Continuous Doppler of Celiac Artery Stenosis.
Figure 9 Mitral valve Prolapse, M-mode patient of fig. 8
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| 15588321 | PMC544591 | CC BY | 2021-01-04 16:38:29 | no | Cardiovasc Ultrasound. 2004 Dec 10; 2:28 | utf-8 | Cardiovasc Ultrasound | 2,004 | 10.1186/1476-7120-2-28 | oa_comm |
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Virol JVirology Journal1743-422XBioMed Central London 1743-422X-1-151561324110.1186/1743-422X-1-15ResearchIn vivo dose-response of insects to Hz-2V infection Burand John P [email protected] Christopher P [email protected] Department of Entomology, University of Massachusetts at Amherst, Amherst, Massachusetts, USA2 Department of Microbiology, University of Massachusetts at Amherst, Amherst, Massachusetts, USA2004 21 12 2004 1 15 15 9 12 2004 21 12 2004 Copyright © 2004 Burand and Rallis; licensee BioMed Central Ltd.2004Burand and Rallis; 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
Hz-2V infection of female Helicoverpa zea moths is manifested as insects that are either sterile "agonadal" individuals with malformed reproductive tissues or fertile asymptomatic carriers which are capable of transmitting virus on to their progeny. Virus infected progeny arising from eggs laid by asymptomatic carrier females may themselves be either sterile agonadals or asymptomatic carriers.
Results
By injecting virus into female moths, a correlation was established between virus doses administered to the females and the levels of resulting asymptomatic and sterile progeny.
Conclusions
The results of these experiments indicate that high virus doses produced a higher level of agonadal progeny and lower doses produced higher levels of asymptomatic carriers.
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Background
The insect virus, Hz-2V originally named gonad-specific virus (GSV) [1] was first identified in moths from a colony of corn earworms, Helicoverpa zea originating at the USDA-ARS in Stoneville, MS [1,2]. Insects infected with this virus were found to have malformed and missing reproductive tissues and were sterile, a condition that has been referred to as "agonadal". The examination of infected moths revealed that this virus replicated in a variety of male and female reproductive tissues including the common and lateral oviducts. Hence the tropism and replication of the virus is not specific to gonadal tissues. This rod shaped, enveloped, DNA virus has been more appropriately named Hz-2V since it resembles Hz-1V in size, pathology in vitro and in genome structure and size [3-5].
While examining progeny from eggs laid by infected female moths, Hamm et al. [2] identified individuals that appeared healthy and were capable of transmitting Hz-2V to their progeny. Using PCR analysis Lupiani et al. [6], were able to detect viral DNA sequences in feral corn earworms from wild populations that appeared healthy. These apparently healthy, infected moths are asymptomatic carriers of Hz-2V. The ability of this virus to persist in these asymptomatic carriers is a key feature of the biology of this virus. Since productive replication of Hz-2V results in the gross malformation of reproductive tissues and sterility of infected adult moths, persistence in asymptomatic carrier moths allows the virus to be maintained in insect populations such as the Stoneville colony.
Hamm et al. [2] presented evidence from experimental matings involving asymptomatic female moths and uninfected males that showed the proportion of agonadal progeny arising from eggs laid on successive oviposition days increased rapidly with each oviposition day, suggesting a change in viral activity in the asymptomatic female. They proposed that the outcome of virus infection in progeny was related to virus dose, such that eggs laid on early oviposition days received a low virus dose resulting in more asymptomatic virus carrier moths, whereas those arising from later oviposition days received a high virus dose and developed into agonadal moths. These findings indicate that Hz-2V is able to exist in a persistent or latent state in some corn earworms and become induced into productive replication at a specific time in the development of the insect. During their experiments, Hamm et al. [2] were unable to accurately determine and control the virus dose female moths received and they were unable to directly detect females that were asymptomatic carriers of the virus.
Raina et al. [7] showed that it was possible to inject Hz-2V into healthy female corn earworm moths, and upon mating with healthy male moths, produce asymptomatic carrier and agonadal progeny. They found that about half of all of the progeny produced by females that were infected with a moderate virus dose exhibited the agonadal condition and that about 90% of the remaining apparently healthy progeny actually carried viral DNA sequences detectable by PCR. This data suggests that adult females can be injected with virus to experimentally produce females that mimic the asymptomatic carrier females described by Hamm et al. [2].
In this study we have used the approach of injecting virus into healthy female moths to examine the relationship between virus dose and the level of infected, agonadal and asymptomatic carrier progeny insects hatching from eggs laid on successive ovipostion days. The results presented here demonstrate that virus dose affects both the level of infected progeny and the kind of infection found in insects hatching from eggs laid by virus infected females, indicating a direct correlation between virus dose received by females and the level of infected progeny they produce. Also demonstrated here is the fact that for each virus dose, as the level of agonadal insects hatching from eggs laid on successive oviposition days increase, the level of asymptomatic carrier progeny decreases.
Results
A total of 1856 progeny moths resulting from approximately116 eggs laid on each of the first four oviposition days by females infected with 2 × 105, 2 × 106, 2 × 107, or 2 × 108 TCID50 units of Hz-2V were dissected and the reproductive tissues examined for signs of virus pathology. The PCR products of DNA samples from reproductive tissues of all apparently healthy progeny moths were examined for the presence of Hz-2V DNA via slot blot hybridization (figure 1), and the size of the PCR products of representative samples was determined by agarose gel electrophoresis. The results of agarose gel electrophoresis of PCR products from representative samples of agonadal, asymptomatic carriers and apparently healthy moths are shown in figure 2.
Figure 1 Slot blot hybridization results of DNA extracted from reproductive tissues of corn earworm moths. DNA was extracted, amplified via PCR, transferred onto a nylon membrane, and hybridized with a DIG-labeled viral DNA probe. Dark blots are indicative of DNA from asymptomatic carrier moths (As). Blots of DNA samples from insects from the healthy colony (H) and from insects that were determined to be apparently healthy (Ah) were blank or very light.
Figure 2 Agarose gel of PCR products from DNA extracted from the reproductive tissues of corn earworm moths. The first lane is from a sample containing purified Hz-2V DNA (V). Lanes 2 denotes a sample from normal, healthy moth (H) from our insect colony. Lane 3 contains a DNA sample from an apparently healthy (AH) progeny moth arising from and infected female. Lanes 4–8 contain DNA samples extracted from asymptomatic progeny corn earworm moths (AS).
Moths that had reproductive tissues that appeared to be normal but tested positive for Hz-2V DNA by PCR analyses were considered asymptomatic carriers of the virus. For each virus dose tested the number of agonadal moths, asymptomatic carriers, infected individuals (the sum of agonadal and asymptomatic carriers), and uninfected progeny moths hatching from eggs laid on each oviposition day was recorded.
The analysis of these results showed that the percentage of total infected progeny (asymptomatic carriers and agonadal moths) at all doses tested increased with each successive oviposition day, and the level of infected progeny increased as virus dose increased from 2 × 105 to 2 × 108 TCID50 units (figure 3). For individuals hatching from eggs on oviposition day one, the highest percentage of infected progeny (approximately 80%) was produced by females infected with the two highest virus dose (2 × 107 and 2 × 108), whereas the lowest percentage (about 60%) was produced by females infected with the lowest doses of virus (2 × 105 and 2 × 106 TCID50).
Figure 3 Mean percentages of infected (agonadal and asymptomatic carriers) progeny arising from eggs laid by female moths infected with 2 × 105, 2 × 106, 2 × 107, or 2 × 108 TCID50 units of Hz-2V.
Virus infected progeny moths arising from eggs laid on each oviposition day by females infected with different virus doses were divided into agonadal and asymptomatic carriers and these results are presented in figure 4. No agonadal insects arose from eggs laid on oviposition day one by females infected at the lowest virus doses, whereas approximately 15% of the progeny females from eggs laid at this time by females infected at the two highest doses were agonadal. At all of the viruses doses tested, between 70 and 90% of the individuals hatching from oviposition day one eggs were asymptomatic carriers (figure 4).
Figure 4 Mean percentages of all (male and female) agonadal (AG) and asymptomatic carrier (AS) F1 moths arising from eggs laid by females infected with 2 × 105, 2 × 106, 2 × 107, or 2 × 108 TCID50 units of Hz-2V.
For all F1 insects hatching from eggs laid on day two, (figure 4) the percentage of agonadal moths increased with increasing virus dose and the percentage of asymptomatic carriers at each dose declined (figure 4). The highest number of agonadal moths (approximately 70%) hatching from eggs laid on day two came from females that received the highest virus dose. At the two lowest doses the level of agonadals hatching from day two eggs was between 5 and 20%. At all doses almost 100% of the eggs laid on days three and four gave rise to agonadal moths.
In order to better illustrate the relationship between the two types of infections and to emphasize the effects of virus dose upon the proportions of asymptomatic and agonadal infections, percentages of asymptomatic carriers and agonadal progeny for only the highest and lowest dose are presented in figure 5. The trend in the two types of infected progeny insects follows the same general pattern for both virus doses relative to oviposition day. That is, at both doses the percentage of agonadal progeny increases with ovipostion day as the percentage of infected insects that are asymptomatic carriers of Hz-2V decreases. At the highest dose, the proportion of agonadal insects starts out higher (~ 10%) on the first oviposition day than that of agonadal progeny of females infected at the lowest dose (0%), and rises more quickly to ~ 70% of the progeny from eggs laid on oviposition day two. This is compared to only about 5% of the progeny arising from day two eggs laid by females infected at the lowest dose. Interestingly the reverse is the case for asymptomatic progeny hatching from oviposition day one eggs. Whereas approximately 90% of the infected oviposition day one individuals from females infected at the lowest virus does are asymptomatic only about 10% of the individuals from females infected at the highest dose are asymptomatic.
Figure 5 Mean percentages of all (male and female) agonadal (AG) and asymptomatic carrier (AS) F1 moths arising from eggs laid by females infected at the highest and lowest doses of Hz-2V.
Discussion
Injecting Hz-2V into female moths results in experimentally infected insects that resemble asymptomatic females and females that have become infected with the virus during copulation, not unlike the females infected during mass-matings by infected males in transmission experiments conducted by Hamm et al. [2]. These infected moths appear healthy, are fertile, and can transmit the virus to progeny that result from mating. Some of the progeny moths arising from these infected females do not carry any detectable Hz-2V DNA sequences, others are sterile with malformed reproductive tissues, and still others are fertile, asymptomatic carriers of the virus. This variety of infections suggests that the virus is not initially present in all of the eggs laid by infected females, but is transmitted transovarially to some of the eggs at sometime time prior to oviposition. This idea is important in that it suggests that the dose or titer of virus transmitted from the parent female moth to the developing oocytes is not constant, and that the virus dose that each oocyte receives determines the outcome of the infection when these progeny insects mature into adult moths. The precise molecular mechanism that determines which infected individuals become agonadal and which will maintain the virus in the population as asymptomatic carriers has yet to be determined.
The results presented in figure 4, demonstrate that the percentage of agonadal progeny resulting from eggs laid on oviposition day one by female moths infected with Hz-2V increased as the dose of Hz-2V used to infect female moths increased. Progeny arising from eggs laid on oviposition day two also exhibited this correlation between virus dose and percent agonadal progeny. This indicates that the titer of the virus present in the experimentally infected female moths determines the amount of virus that is transmitted to eggs, and is directly correlated to the percentage of agonadal progeny arising from eggs laid by the infected females. As the dose of Hz-2V used to infect a female moth is increased, a corresponding increase is observed in total agonadal progeny arising from all eggs laid by the infected female.
The percentage of agonadal progeny also increases with each successive oviposition day, approaching 100% agonadal progeny by day three at all virus doses tested, and all progeny moths arising from eggs laid on oviposition day four in all groups were agonadal. Based on the correlation between virus titer and percent agonadal progeny observed in these experiments, the increase in agonadal progeny per oviposition day is likely due to an increase in the titer of virus transmitted to the eggs, suggesting that the titer of virus increases in the parent female moths with each successive oviposition day.
Studies of Hz-2V replication in vitro revealed a rapid increase in virus titer by 24 hours post infection in Tn-368 and Ld-652Y cells [4,5]. Hz-2V replication in vivo in the epithelial cells of agonadal female oviduct tissue has been described previously by Rallis and Burand [8]. The level of detectable virus in these tissues increased dramatically between 8 days post pupation (dpp), measured from the day the last larval exuviae was shed, and 10 dpp. It is likely that the large increase in virus over a 24 hour cycle observed in vitro also occurs in vivo, resulting in a significant daily increase in the titer of Hz-2V in these experimentally infected female moths. Although the precise site of virus replication in these experimentally infected females is not known, the increase in virus titer in these individuals almost certainly results in an increase in virus being transmitted to the progeny with each successive oviposition day and ultimately in the patterns of infection reported here.
If, as we have proposed, low virus doses result in asymptomatic carrier moths, and high virus doses produce agonadal progeny, then asymptomatic carrier progeny would likely arise from eggs produced on the earliest oviposition days and decrease with each day, as the virus titer in the egg-laying female moth increases. In fact, the percentage of asymptomatic carrier progeny in these experiments does decrease with each successive oviposition day to 0% by day four. The percent asymptomatic carriers is highest in progeny that receive the lowest virus dose, specifically progeny from oviposition day one and progeny arising from the parent female moths that were experimentally infected with the lowest dose of virus. This is directly opposite of what is observed for agonadal progeny, which is at its highest level at the highest virus dose, specifically on the later oviposition days (days three or greater) and in progeny arising from parent female moths that were infected with the highest virus dose. Interestingly, the lowest percentage of asymptomatic carrier progeny arose from eggs laid by the group of female moths that received the highest virus dose of Hz-2V (figure. 3). These data suggest that the virus dose transmitted by infected female moths to their developing eggs determines whether the progeny develop the agonadal condition or become asymptomatic carriers of Hz-2V.
The results presented here clearly show that there is a direct correlation between virus dose and the relative percentage of agonadal and asymptomatic progeny. That is, increasing the virus dose causes an increase in the percentage of agonadal progeny, but a decrease in the percentage of asymptomatic progeny. At the present time, it is unknown how the development of an infected individual into an agonadal adult or an asymptomatic carrier is regulated. It is likely that a minimum titer of Hz-2V is needed at a key point in larval development to produce a viral factor(s) within the larval tissues at a threshold level required to reprogram the development and differentiation of the reproductive tissues into the agonadal structures. If this threshold is equaled or exceeded at this point in development, the progeny will exhibit the agonadal condition. However, if this threshold level is not attained, then the reproductive tissues are not reprogrammed and the infected insect becomes an apparently healthy, fertile, asymptomatic carrier of Hz-2V.
Conclusions
The evolution of Hz-2V infection in H. zea has resulted in the ability of the virus to produce two different types of infections in the insects that enable the virus to replicate to high titers in the reprogrammed reproductive tissues in sterile agonadal moths, while maintaining itself in a population in asymptomatic carrier moths. This replication strategy appears to be essential for the continued existence of Hz-2V, since the development of the sterile, agonadal condition in all infected moths would lead to the extinction of the insect host, and the possibly the virus as well. The production of asymptomatic carrier moths ensures that some fertile, infected moths exist that can mate and produce infected progeny, enabling an Hz-2V-infected population to sustain itself, as in the case of the Stoneville colony.
Methods
Source of insects and virus
Corn earworm larvae used to start a laboratory colony of healthy H. zea were obtained from the USDA-ARS in Stoneville, MS. Insects were reared on artificial diet and maintained as outlined previously [9].
Hz-2V for infecting female moths was prepared as described previously and purified via sucrose gradient centrifugation [4].
Injection of adults
Newly emerged adult female moths were prepared and injected with Hz-2V as outlined by Rallis and Burand [8]. The female moths were divided into four dose groups, and 9 or 10 insects were infected with Hz-2V at one of the following concentrations of 2 × 105, 2 × 106, 2 × 107, and 2 × 108 TCID50 units.
TCID50 assays
Tn368 cells were cultured as per Burand & Lu [4] and 100 ul of cell culture medium containing 8 × 104 Tn368 cells were seeded into each well of a 96-well plate. Between 6 and 13 serial dilutions were made from each virus sample assayed and 10 or 20 wells were plated with 10 ul for each dilution. Plates were incubated at 27°C for 3 to 4 days and examined for the appearance of cytopathic effect (CPE). The numbers of wells with CPE were counted and the TCID50 calculated [9].
DNA extraction and purification of viral DNA
DNA was extracted from the reproductive tissues of adult moths by first homogenizing dissected tissues in 200 ul of TE buffer (10 mM Tris, pH 7.4, 1 mM EDTA, pH 8.0) followed by a 2-minute incubation in a boiling water bath. The homogenate was then chilled on ice, after which Ribonuclease A (10 ug/ul) was added to each sample, which was then incubated at room temperature for 15 min. The samples were then clarified by centrifugation at 15,600 × g for 2 min.
Viral DNA used as template for PCR reactions was extracted from purified virus using 1% SDS in TE containing 1 mg/ml Protease K as outlined by Burand and Lu [4].
PCR amplification of viral DNA sequences
Two sets of primers were used to amplify Hz-2V genomic DNA to prepare a probe for use in slot blot analysis of insect reproductive tissues. The first set (P4-1, 5'-GCACGATTCGTAATGTTC-3'; and P4-2, 5'-GCACACCTATCAATCACC-3') was designed to amplify a 434 bp sequence of the Hz-2V genome [6]. PCR reactions using P4-1 and P4-2 primers were brought to a final volume of 20 ul using the Bioneer AccuPower® PCR reagent premix kit with 1 unit of Taq DNA polymerase. Each reaction was carried out in10 mM Tris-HCl (pH 9.0), 1.5 mM MgCl2 and 40 mM KCl, containing 250 uM of each of the four dNTP's, with 100 pM of P4-1 forward and P4-2 reverse primers, and 10 ng of purified viral DNA as template. These primer set and reaction conditions were also used to amplify viral DNA sequences in approximately 100 ng of DNA from reproductive tissues of moths thought to be asymptomatic carriers of Hz-2V.
The second set of primers (P4-3, 5'-GCTGTGCTGTACAAGTGC-3'; and P4-4, 5'-CCCTTGACGATCCCTTTTG-3') was designed to amplify a 350 bp region directly interior to that of the P4-1 and P4-2 amplified sequence. These primers were used to generate a DIG-labeled probe for Hz-2V to be used in slot blot hybridization assays. PCR reactions for production of the DIG-labeled probe were carried out in a final volume of 50 ul using the Boehringer Manheim DIG High Prime DNA Labeling and Detection Kit, with 1X concentrations of Taq Polymerase buffer (100 mM Tris-HCl pH 8.0, 500 mM KCL pH 8.3, and 25 mM MgCL2), 100 pM of both P4-3 and P4-4 primers, a hexanucleotide mixture containing DIG-labeled dUTP (2 mM dATP, dCTP, dGTP, 1.3 mM dTTP, and 0.7 mM alkali labile DIG-11 dUTP pH 7.0), and 100 pM of Hz-2V genomic DNA. The DIG-labeled PCR product was purified on a 0.8% agarose gel using the Qiagen gel electrophoresis purification kit.
Both PCR reactions for amplification of the viral DNA in tissue samples and for the production of the viral DNA probe consisted of 30 cycles of a DNA denaturation step at 95°C for 1 min., a primer annealing step for 1 min. at 55°C, and a 1 min. primer extension step at 72°C.
Detection of a viral DNA sequence by slot blotting
To prepare the DNA for slot blot analysis, 15 ul of the P4-1 and P4-2 PCR amplified DNA from insect samples was denatured by incubating with NaOH (0.4 M)/ EDTA (10 mM, pH 8.2) at 100°C for 10 min., then applied to a Hybon-N+ membrane prewashed with 500 ul 5X SSC buffer (0.6 M NaCl, 60 mM Na citrate pH 7.0) in a Manifold II slot blotter (Schleicher & Schuell). After applying the DNA, the membrane was baked at 88°C for 2 hrs under vacuum and prehybridized for 6 hrs. at 42°C in 50% formamide prehybridization buffer (5X SSC, 0.1% (w/v) N-laurylsarcosine, 1% (w/v) Na2-Dodecylsulfate, 2% Blocking reagent (Boehringer-Manheim), and 50% Formamide). Slot blots were hybridized with 150 ng DIG-labeled Hz-2V probe at 37°C for 12–14 hrs. Following washing, chemiluminescent detection was carried out as recommended by the DIG High Prime Labeling and Detection Kit Manual for DNA Hybridization (Boehringer Mannheim).
Analysis of PCR products by agarose gel electrophoresis
In order to confirm that the PCR products that hybridized to the viral DNA probe contained an amplified DNA fragment of the appropriate size (434 bp), representative samples were analyzed by electrophoresis on 0.8% agarose gels with 0.5X TBE buffer at 100 volts for approximately 1 hr, then stained with EtBr to visualize DNA bands under ultraviolet light.
Competing interests
The author(s) declare that they have no competing interests.
Authors' contributions
CPR participated in the design of the study, carried out the work with the insects, coordinated the project and assisted in the molecular analysis and drafting of the manuscript. JPB conceived the study, designed and supervised the experimental work and drafted the manuscript. All authors read and approved the final manuscript.
Acknowledgements
This project was funded in part by the Lotta Crabtree Graduate Fellowship in Agriculture, by USDA NRICGP grant #2001-35302-10885, and by Project # MAS00802 of the Massachusetts Agricultural Experiment Station.
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| 15613241 | PMC544592 | CC BY | 2021-01-04 16:38:32 | no | Virol J. 2004 Dec 21; 1:15 | utf-8 | Virol J | 2,004 | 10.1186/1743-422X-1-15 | oa_comm |
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Cardiovasc UltrasoundCardiovascular Ultrasound1476-7120BioMed Central London 1476-7120-2-301560691610.1186/1476-7120-2-30ResearchEchocardiographic AV-interval optimization in patients with reduced left ventricular function Melzer C [email protected] AC [email protected] F [email protected] WS [email protected] W [email protected] G [email protected] H [email protected] I Medizinische Klinik mit Schwerpunkt Kardiologie, Angiologie und Pulmologie, Charité, Campus Mitte, Berlin Germany2 Klinik für Nuklearmedizin, Charité, Campus Mitte, Berlin Germany3 Medtronic Inc., Minneapolis, USA2004 17 12 2004 2 30 30 5 10 2004 17 12 2004 Copyright © 2004 Melzer et al; licensee BioMed Central Ltd.2004Melzer 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
Ritter's method is a tool used to optimize AV delay in DDD pacemaker patients with normal left ventricular function only. The goal of our study was to evaluate Ritter's method in AV delay-interval optimization in patients with reduced left ventricular function.
Methods
Patients with implanted DDD pacemakers and AVB III° were assigned to one of two groups according to ejection fraction (EF): Group 1 (EF > 35%) and Group 2 (EF < 35%). AV delay optimization was performed by means of radionuclide ventriculography (RNV) and application of Ritter's method.
Results
For each of the patients examined, we succeeded in defining an optimal AV interval by means of both RNV and Ritter's method. The optimal AV delay determined by RNV correlated well with the delay found by Ritter's method, especially among those patients with reduced EF. The intra-class correlation coefficient was 0.8965 in Group 1 and 0.9228 in Group 2. The optimal AV interval in Group 1 was 190 ± 28.5 ms, and 180 ± 35 ms in Group 2.
Conclusion
Ritter's method is also effective for optimization of AV intervals among patients with reduced left ventricular function (EF < 35%). The results obtained by RNV correlate well with those from Ritter's method. Individual programming of the AV interval is fundamentally essential in all cases.
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Background
Since introduction of the DDD pacemaker in the early 1980s, researchers have repeatedly attempted to optimize the atrioventricular (AV) interval, for the purpose of maximizing patient hemodynamic performance. Cannon waves may be induced by programming excessively short AV intervals, and diastolic mitral regurgitation may occur with excessively long programmed AV intervals. The AV interval is considered optimal (AVopt) if it allows maximum cardiac output.
The duration of the optimal AV interval varies throughout a wide range among individuals, primarily the result of appreciable differences in interatrial conduction [1-4].
An extensive variety of techniques has been employed to optimize AV delay, including acquisition and analysis of essential hemodynamic parameters by means of aortic-valve Doppler signals, impedance cardiography [9-11], Swan-Ganz catheterization [12-15], and especially the stroke volume [5-8]. Leman et al. [16] have demonstrated that it is also possible to utilize measurement of left ventricular ejection fraction and stroke volume by myocardial thallium scintigraphy as a means of AV interval optimization. A further possibility involves detection of left atrial depolarization by an esophageal electrode recording [17,18]. During recent years, the use of Doppler echocardiography in conjunction with the mitral valve inflow profile has been investigated as means of AV interval optimization: i.e., Ritter's method [19]. Previous investigations have evaluated Ritter's method in patients with normal left ventricular ejection fractions. During recent years, cardiac resynchronization therapy (CRT) has increasingly gained in significance for patients with chronic heart failure (CHF) [20]. In cases without ventricular desynchronicity, normal DDD pacemakers (or ICDs with DDD pacemaker function) will in future continue to be implanted in patients with reduced left ventricular ejection fraction. The goal of our study was accordingly to apply Ritter's method – until now validated only for patients with normal EF – for patients with reduced left ventricular ejection fraction (EF < 35%).
Methods
We studied 20 DDD pacemaker patients within the context of in-office follow-up. Table 1 shows the baseline characteristics and Table 2, the inclusion criteria. We classified patients into two groups, according to left ventricular ejection fraction results obtained by echocardiography. Group 1 consisted of 10 patients with normal left ventricular ejection fraction, or with moderately reduced EF (EF > 35%). Group 2 comprised 10 patients with appreciably reduced left ventricular ejection fraction (EF < 35%).
Table 1 Clinical characteristics of the patients
Characteristics Group 1 (EF > 35%) (n = 10) Group 2 (EF < 35%) (n = 10)
Age 68.5 ± 4.5 65.7 ± 6.3
Male sex (%) 60 100
Left ventricular ejection fraction (%) 58 ± 9.7 22 ± 7.4
Left ventricular end-diastolic dimension (mm) 47 61
Coronary artery disease (%) 30 50
Dilated cardiomyopathy (%) 0 50
Hypertension (%) 30 0
Pharmacologic therapy (%)
ACE inhibitor 40 100
Beta-blocker 50 90
Loop diuretic 0 100
Spironolactone 0 70
Table 2 Inclusion criteria
DDD pacemaker by AVB III° with permanent atrial and ventricular pacing
No left bundle-branch block or possible indication for CRT
Pacemaker implantation at least 4 months beforehand
NYHA Class I or II
We performed ejection fraction analysis by RNV and Ritter's method to achieve AV optimization, for 5 AV intervals in the range of 100 to 250 ms. We performed all measurements within 15 minutes of AV interval programming for every patient. All patients were permanently stimulated in the right atrium and right ventricle (binodal disease). Patient heart rate remained constant during the measurement period at programmed pacemaker lower rate (60 – 70 beats/min).
Analysis of left ventricular ejection fraction by RNV
We performed radionuclide ventriculography (RNV) after in vivo marking of erythrocytes with tin DTPA and 10 MBq/kg KG Tc-99m, using a single-head Gamma camera (CGR gammatome 2, General Electrics, Paris, France) with a high-resolution, medium-energy collimator. For RNV we applied the equilibrium technique at 16 frames per cycle with patients at rest, and ventricular pacing at programmed AV intervals. We acquired a minimum of one million counts per image, and stored the data in a 64 × 64 matrix.
We calculated left ventricular ejection fraction (LVEF) semi-automatically after spatial and temporal smoothing and background subtraction. After Fourier analysis of ventricular stimulation progression, we recorded (with examiner definition) a region of interest (ROI) around the end-diastolic contour of the left ventricle and calculated the LVEF as follows:
Ritter's method
By 1994 a method developed by Ritter et al. had become established for optimizing the AV interval [19]. A prerequisite for application of Ritter's method is Doppler-echocardiographic measurement of the mitral inflow profile.
Ritter's method employs the following formula for calculation of the optimal AV interval:
AVopt = AVlong - (a - b)
We applied the following procedure in application of this formula (see Fig. 1):
Figure 1 Ritter's method: The first step is determination of "a" for a nonphysiologically short AV interval (e.g. 125 ms), followed be determination of "b" for a nonphysiologically long AV interval (e.g. 250 ms).
Step 1
The first step involves programming for the pacemaker a nonphysiologically short AV interval, followed by determination of "a". This value "a" is the temporal interval between the ventricular contraction spike and the end of the A wave. "a" designates the electromechanical delay between right ventricular stimulation and the beginning of the left ventricular systole (i.e., closure of the mitral valve).
Step 2
The next step is programming for the pacemaker a long AV interval (AVlong), followed by determination of "b". This value "b" is the temporal interval between the ventricular contraction spike and the end of the A wave. AVlong - b defines the duration of the undisturbed maximal diastolic left ventricular filling.
The purpose of AV interval optimization in accordance with Ritter is to allow the ventricular systole to begin immediately subsequent to maximum, undisturbed diastolic ventricular filling and, in turn to prevent the occurrence of Cannon waves as well as diastolic mitral regurgitation.
Statistics
We applied intra-class correlation in performing statistical evaluation.
Results
Group 1
In a given patient, our results indicated that it was possible to define an optimal AV interval for every patient: both by RNV as well as by Ritter's method. The mean optimal AV interval was 190 ± 28.5 ms. The correlation between RNV and Ritter's method is good: the intra-class quotient is 0.8965 (see Fig. 2). In results calculated by RNV, the mean percent difference in left ventricular ejection fraction between the hemodynamically best and worst AV intervals was 11 ± 4% (see Fig. 4).
Figure 2 The correlation of the results of the RNV and Ritter methods, with respect to the optimal AV interval for Group 1.
Figure 4 The maximum difference in left ventricular EF, determined by RNV and as a function of the programmed AV interval, for each of the patients examined.
Group 2
In a given patient, we likewise succeeded in defining the optimal AV interval for every patient: both by RNV as well as by Ritter's method. The mean optimal AV interval was 180 ± 35 ms. In Group 2 as well, there was good correlation between RNV and Ritter's method: the intra-class quotient was 0.9228 (see Fig. 3). In results calculated by RNV, the mean percent difference in left ventricular ejection fraction between the hemodynamically best and worst AV intervals was 28 ± 11% (see Fig. 4).
Figure 3 The correlation of the results of the RNV and Ritter methods, with respect to the optimal AV interval for Group 2.
Discussion
A number of studies have documented the importance of AV synchronization for maximizing the left ventricular ejection fraction in pacemaker patients [21-24]. Despite CRT, the implantation of a DDD pacemaker (or ICD with DDD-pacemaker function) is still justified for patients with reduced left ventricular function and a lack of ventricular asynchrony.
The goal of our study was accordingly to apply Ritter's method for patients with reduced left ventricular ejection fraction. In every subject of Groups 1 and 2, it was possible on the basis of the left ventricular ejection fraction to define the optimal AV delay by means of RNV. The method of AV delay optimization by RNV has been previously verified [16]. The cost and complexity of this method, however, have hindered its extensive clinical application. On the basis of minimal inter- and intraobserver variability this method is nevertheless very well suited as a reference method.
Our application of Ritter's method enabled definition of the optimal AV interval for all patients. We further determined that Ritter's method can be reliably employed even in cases of reduced left ventricular systolic function. The AV interval calculated by Ritter's method correlated well with data obtained by RNV: both for normal (with intra-class coefficient of 0.8965) as well as for reduced left ventricular EF (intra-class coefficient of 0.9228).
Since Ritter's initial publication in 1994, AV interval optimization on the basis of the mitral valve inflow profile has been reported in one additional study [19]. In 1997 Kindermann et al. compared results calculated from Ritter's formula with those obtained from impedance cardiography [10]. This study established a high degree of correlation between the results for the optimal AV interval determined by the two different methods. The mean deviation in optimal AV interval between the results from Ritter's formula and determination of stroke volume by impedance cardiography was ± 26 ms for the atrial-triggering mode, and ± 30 ms for the AV sequential mode. Kindermann et al. criticized the fact that it is possible to apply Ritter's method only for patients with ventricular stimulation.
In comparison to time-consuming and expensive RNV, and AV-interval optimization by Swan-Ganz catheterization (with the associated risks of an invasive procedure), Ritter's method offers the following advantages: it is non-invasive and can be quickly performed (approx. 5 min.). It does not require long years of echo experience, and it is cost-effective. Even with patients not readily amenable to sonographic detection, the mitral valve inflow profile is almost always qualitatively satisfactory enough to allow application of Ritter's method. The only noteworthy disadvantage of this method is the necessity for continuous ventricular stimulation: which means that it can be used only for patients with a complete AV block. Patients with only intermittent high-grade AV blocks are accordingly not suited for Ritter's method.
In our patients, the mean optimal AV interval in Group 1 (EF > 35%) was 190 ± 28.5 ms. In comparison, the optimal AV interval among the patients with chronic heart failure in Group 2 was 180 ± 35 ms. Data in the literature are not consistent on the duration of the optimal AV delay. Kindermann [10] considers AVopt = 88 ms ± 35 ms with atrial triggering, and AVopt = 143 ms ± 41 ms for the AV sequential mode. Knorre [18] has determined AVopt = 100.5 ± 27.8 ms for atrial triggering, and AVopt = 169 ± 24.5 ms for the AV sequential mode. Haskel [5] has established the best AV interval to be 150 ms. Janosik [6] considers AVopt = 144 ± 48 ms with atrial triggering, and AVopt = 176 ± 44 ms for the AV sequential mode. Ishikawa [15] has determined AVopt = 161 ± 26 ms.
Our results on the length of the optimal AV delay lie within the range found in the literature. The variance in data observed in some cases emphasizes the highly individual nature of the optimal AV delay: indeed, it results from the interatrial conduction period specific to each patient, and the potential delays induced by pacing versus intrinsic depolarization and conduction in a given patient [1-4].
As a result, the mean optimal AV intervals determined by us cannot be applied to other patient cohorts with the same basic disease. Individual programming of the AV interval is therefore necessary.
Conclusion
In summary, our findings confirm that Ritter's method can be reliably applied for patients with normal and with reduced left ventricular pump function. The only prerequisite is a continuous ventricular stimulation.
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| 15606916 | PMC544593 | CC BY | 2021-01-04 16:38:29 | no | Cardiovasc Ultrasound. 2004 Dec 17; 2:30 | utf-8 | Cardiovasc Ultrasound | 2,004 | 10.1186/1476-7120-2-30 | oa_comm |
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Reprod Biol EndocrinolReproductive biology and endocrinology : RB&E1477-7827BioMed Central London 1477-7827-2-831561323710.1186/1477-7827-2-83ResearchRetinol improves bovine embryonic development in vitro Livingston Tracy [email protected] Dawn [email protected] J Lannett [email protected] James [email protected] Department of Biology, Georgetown College, Georgetown, KY 40324 USA2 AviGenics, Inc., 111 Riverbend Road, Athens, GA 30605 USA3 Department of Animal Science, University of Tennessee, Knoxville, TN 37996 USA2004 21 12 2004 2 83 83 15 10 2004 21 12 2004 Copyright © 2004 Livingston et al; licensee BioMed Central Ltd.2004Livingston 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.
Retinoids are recognized as important regulators of vertebrate development, cell differentiation, and tissue function. Previous studies, performed both in vivo and in vitro, indicate that retinoids influence several reproductive events, including follicular development, oocyte maturation and early embryonic development. The present study evaluated in vitro effects of retinol addition to media containing maturing bovine oocytes and developing embryos in both a low oxygen atmosphere (7%) and under atmospheric oxygen conditions (20%). In the first experiment, abbatoir collected bovine oocytes were matured in the presence or absence of varying concentrations of retinol. After a 22–24 hour maturation period the oocytes were fertilized, denuded 18 hours later and cultured in a modified synthetic oviductal fluid (mSOF) in a humidified atmosphere at 38.5 degrees C, 5% CO2, 7% O2 and 88% N2. Cleavage rates did not differ among control and retinol-treated oocytes in all three experiments. Addition of 5 micromolar retinol to the maturation medium (IVM) tended (p < 0.07) to increase blastocyst formation (blastocyst/putative zygote; 26.1% +/- 2.2%) compared to the controls (21.9% +/- 1.9%). Further analysis revealed when blastocyst development rates fell below 20% in the control groups, 5 micromolar retinol treatment dramatically improved embryonic development, measured by blastocyst/putative zygote rate (14.4 +/- 2.1 vs 23.7 +/- 2.5; p < 0.02). The 5 micomolar retinol treatment also enhanced the blastocyst/cleaved rate by nearly 10% (23.7% vs 34.6%; p < 0.02). In the second and third experiments addition of 5 micromolar retinol to the embryo culture medium (IVC) under low oxygen conditions did not significantly improve cleavage or blastocyst rates, but 5 micromolar retinol significantly increased blastocyst development under 20% O2 conditions (p < 0.001). These studies demonstrate that supplementation of 5 micromolar retinol to the maturation medium may improve embryonic development of bovine oocytes indicated by their increased blastocyst rate. A significant improvement in the blastocyst development with the 5 micromolar retinol treatment under atmospheric conditions suggests a beneficial antioxidant effect during embryo culture.
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Background
Vitamin A is essential for reproduction, and deficiencies and excesses may result in embryonic loss and/or congenital defects [1]. Retinol (vitamin A alcohol) is the parent vitamin A compound and metabolites, analogs, and derivatives are known collectively as retinoids. Results from several studies, in a variety of species, have indicated that retinoid administration may function in very early events associated with reproductive success, including follicular development, oocyte maturation and early embryonic development. Retinol concentration in bovine follicular fluid was shown to be an indicator of follicular quality and was highest in healthy follicles, lowest in atretic follicles and highly correlated with estradiol concentrations [2,3]. Retinol or β-carotene administration has been shown to prevent fetal resorption in rats [4], increase the number of births in rabbits [5], and increase litter size in swine [6]. Retinol administration to ewes, in combination with superovulation followed by natural service was shown to improve the competence of resultant 1–4 cell and morula stage embryos, collected from the oviduct and uterus, respectively, to develop to the blastocyst stage when cultured in vitro [7]. In cattle, retinol injection improved the estimated quality of embryos collected from superovulated animals but did not increase the number recovered [8].
Retinol is transported systemically and intercellularly bound to retinol-binding protein (RBP). Cellular retinol-binding proteins (CRBP) and cellular retinoic acid-binding proteins function in intracellular vitamin A transport, metabolism and homeostasis [9]. All-trans and 9-cis retinoic acid (RA) are natural cellular metabolites of retinol and mediate biological activity through interaction with nuclear retinoic acid receptors (RAR) and retinoid X receptors (RXR), respectively. Ligand-bound RARs and RXRs influence transcription by interacting with response elements in the promoter regions of retinoid-regulated genes [10]. Within the ovary, RBP and CRBP are expressed in thecal and granulosa cells, and facilitate the transport of retinol from the blood into developing follicles [3]. Concentrations of RBP, and its ligand retinol, are highest in the follicular fluid of large preovulatory bovine ovarian follicles, compared to smaller and/or atretic follicles [3]. RBP synthesis and secretion increase in the oviduct and uterus coincident with the transport of the egg or embryo into these organs [11,12].
The cumulus oocyte complex (COC) may be a target for retinol, since the cells that nurture and communicate with the oocyte, contain transcripts and protein for several RARs and RXRs, RBP and retinaldehyde-2 dehydrogenase (RALDH-2) a metabolizing enzyme [13]. Bovine oocytes and embryos from the 2-cell to hatched blastocyst stage, also express transcripts for several RARs, RXRs, RBP and RALDH-2, and the inner cell mass and trophectoderm of blastocysts express immunoreactive protein for RAR and RXR [14]. It has been shown recently that addition of 9-cis RA to in vitro oocyte maturation medium affects trophectoderm differentiation, total cell number and inner cell mass-trophoblast cell ratios, following fertilization in cattle oocytes [15,16]. Together, these studies suggest that the reproductive tract delivers retinol to the oocyte and early embryo which possess key elements of retinoid metabolizing and signaling mechanisms; thus, influencing gene expression, differentiation, and development.
The mechanism by which retinol or retinoic acid administration influences oocyte maturation and positively impacts early embryonic development is not known and is the subject of much investigation. Retinoic acid may influence oocyte maturation through its effects on FSH or LH receptor expression as demonstrated in porcine [17] and rat [18] granulosa cells. Alternatively, it has been suggested that retinoic acid may increase mRNA quality and processing during maturation mediated by increases in polyadenylation [19]. Expression of several growth factors is influenced by RA [20]. Midkine [16], a member of the heparin-binding growth/differentiation family, is induced by RA and has been shown to improve bovine oocyte and embryonic developmental competence [21]. In addition, retinoids may promote development through participation in an endogenous oxidative-stress protection mechanism [22].
In the present study, we investigated the effects of retinol administration to in vitro matured oocytes, and cultured bovine embryos under atmospheric O2 and reduced O2 conditions. Results suggest beneficial effects of retinol administration during maturation especially to less competent oocytes, and improved development of embryos cultured under atmospheric oxygen conditions, indicating protection from oxidative stress.
Materials and Methods
Reagents and Media
All chemicals were purchased from Sigma Chemical Company, St. Louis, MO unless otherwise noted. Bovine oocyte collection medium (OCM) was composed of modified M199, 4.2 mM NaHCO3, 12 mM HEPES, and supplemented with 2 mM glutamine, 2% fetal bovine serum (FBS, BioWhittaker, Baltimore, MD), and penicillin/streptomycin (Specialty Media, Phillipsburg, NJ). Oocyte maturation medium (OMM) consisted of bicarbonate buffered TCM-199 supplemented with 50 μg/mL of gentamycin, purchased from Specialty Media, 5 μg/mL of FSH purchased from Vetrepharm Canada, Inc. (Ontario, Canada), 0.3 μg/mL of luteinizing hormone (LH) that was generously provided by the USDA, Beltsville, MD, 10% FBS, 0.2 μM sodium pyruvate and 2 mM glutamine. Modified Tyrode's Albumin Lactate Pyruvate (TALP) media used in sperm preparation (SP-TALP), removal of cumulus cells from oocytes (HEPES-TALP) and in vitro fertilization (IVF-TALP) were prepared as described by Parrish et al. [29]. In vitro culture (IVC) medium was a modified synthetic oviductal fluid (mSOF) [30] supplemented with 3 mg/mL of BSA, 0.6 mM sodium pyruvate, 2% (v/v) BME essential amino acids, 1% (v/v) MEM non-essential amino acids, and 100 μg/mL of penicillin and streptomycin.
All-trans retinol was dissolved in 100% ethanol, appropriate dilutions made, and aliquots were stored at -80°C until use. Retinol was prepared fresh each month and checked on a spectrophotometer for accuracy. The concentration of ethanol during maturation or culture was less than 0.1%.
Collection and in vitro maturation (IVM) of oocytes
Ovaries from mature, cycling cattle were obtained from an abbatoir and pooled. Cumulus oocyte complexes (COCs) were quickly harvested by slicing follicles (2–5 mm) with a sterile surgical blade, and collecting them in OCM. Intact COCs with homogeneous ooplasm and two or more layers of cumulus cells were selected, washed, and approximately 50 were transferred to 500 μl of pre-equilibrated OMM, and matured for 22–23 hours in a 38.5°C incubator with an atmosphere of 5.0% CO2, ambient air, and saturated humidity.
In vitro fertilization (IVF)
Fertilization (Day 0) was performed with combined semen from two bulls of proven fertility prepared according to the method by Parrish and coworkers [29]. Briefly, spermatozoa were washed in a discontinuous Percoll gradient (45%/90%) by depositing semen on top of the Percoll layers and centrifuged for 15 minutes at 960 g. The pellet was removed and resuspended in SP-TALP and centrifuged for 8 minutes at 460 g. After removal of the supernatant, the sperm sample was reconstituted in 500 μL of IVF-TALP for a final concentration of 1 × 106 spermatozoa/mL. The plate was incubated for 22 hours at 38.5°C in an atmosphere of 5.0% CO2 and ambient air with saturated humidity.
In vitro culture (IVC)
Approximately 18 hours after fertilization putative zygotes were denuded of cumulus cells by vortexing in 500 μl of HEPES-TALP for four minutes (Day 1). Putative zygotes (approximately 35–40) were cultured in 500 μL of mSOF for eight days in a 38.5°C incubator in an atmosphere of 5% CO2, 7% O2 and 88% N2 (first and second experiments) with saturated humidity. The mSOF medium was changed every 48 hours. Cleavage was assessed on Day 3 and blastocyst rate was calculated on Day 8.
Experimental Design
In the first experiment maturation medium alone was supplemented with all-trans retinol (0, 1.0, 5.0, or 10.0 μM) and embryos were allowed to develop under low oxygen conditions. In the second experiment all-trans retinol was added only to embryo culture medium (0, 1.0, 2.0, 5.0, or 10μM) on days 1, 3, 5, and 7, and the embryos developed in a low oxygen atmosphere. In the third experiment embryos were cultured under atmospheric oxygen conditions (air and 5% CO2) and all-trans retinol (0 or 5μM) was added to embryo culture medium on days 1, 3, 5, and 7.
Data Analysis
Data were analyzed as an incomplete block design (experiments 1, 2, and 3), or a randomized block design (experiment 4), blocked on plate using mixed model procedures of SAS [31]. At least six replicates were completed for each experiment. Fisher's protected least significant differences were used for separating least square differences for experiments 1, 2, 3, and a two-tailed Student's T-test was performed on data from experiment 4. Least square means ± S.E.M. are expressed as the proportion of putative zygotes. All data were subjected to a normality test (Shapiro-Wilk, > 0.90) and were found to be normally distributed.
Results
In the first experiment addition of 5μM retinol during IVM tended to improve (p < 0.07) embryonic development to the blastocyst stage, compared to controls (Table 1). The control blastocyst rate was 21.9% compared to 26.1% in 5μM retinol. Addition of 1μM retinol to the maturation medium did not appear to affect embryonic development compared to controls. Retinol (10μM) increased blastocyst development, although not significantly. Cleavage rates did not differ among the four maturation treatments.
Table 1 Effect of all-trans retinol addition to bovine oocyte maturation medium (mean ± S. E. M.). Embryos were cultured under low oxygen conditions.
Retinol concentration (μM) Putative zygote (n) Cleavage Blastocyst/ putative zygote Blastocyst/ cleaved
0 1095 66.7 ± 2.7 21.9 ± 1.9a 32.8 ± 2.2a
1.0 464 65.5 ± 3.9 20.4 ± 2.6a 31.7 ± 3.1a
5.0 1069 68.3 ± 3.2 26.1 ± 2.2b 37.1 ± 2.5b
10.0 508 70.1 ± 3.9 24.2 ± 2.7ab 33.8 ± 3.1ab
Values are listed as percentages. abMeans in the same column with different superscripts approach significance (p < 0.07).
Further analysis of the maturation data (Table 2) revealed that when development to the blastocyst stage of controls was below 20%, 5μM retinol dramatically improved (p < 0.02) embryo development (14.4 % vs. 23.7%). When expressed as blastocyst/cleaved the 5μM retinol treatment also showed a significant improvement in blastocyst development (p < 0.02). Neither 1μM nor 10μM retinol treatment improved embryonic development when compared to those controls that did not achieve a 20% blastocyst rate.
Table 2 Effect of all-trans retinol addition to bovine oocyte maturation medium on embryo development among replicate groups where less than 20% of control embryos reached the blastocyst stage (mean ± S. E. M.).
Retinol concentration (μM) Putative zygote (n) Cleavage Blastocyst/ putative zygote Blastocyst/ cleaved
0 516 62.7 ± 3.9 14.4 ± 2.1a 23.7 ± 2.6a
1.0 185 60.0 ± 6.0 15.9 ± 3.4ab 26.1 ± 4.2ab
5.0 530 65.9 ± 4.6 23.7 ± 2.5b 34.6 ± 3.1b
10.0 183 63.3 ± 6.7 17.6 ± 3.8ab 26.7 ± 4.6ab
Values are listed as percentages. abMeans in the same column with different superscripts were significantly different (p < 0.02).
Further experiments were conducted during IVC under both low and atmospheric oxygen tensions. Under low oxygen conditions concentrations of 1, 2, and 5μM retinol were not statistically different from controls, and 10μM was deleterious (Table 3). Preliminary dose-response studies were performed under atmospheric conditions (data not shown), and additional experiments were continued with the 5μM retinol treatment. Under atmospheric oxygen conditions the 5μM concentration significantly improved blastocyst development compared to controls (p < 0.001) (Table 4). Cleavage rates did not differ significantly among embryos treated with and without retinol during culture under low or high oxygen (Tables 3 &4). Fertilization rates did not differ significantly among all experiments (data not shown).
Table 3 Effect of all-trans retinol addition to the culture medium (mean ± S. E. M.). Embryos were cultured under low oxygen conditions.
Retinol concentration (μM) Putative zygote (n) Cleavage Blastocyst/ putative zygote Blastocyst/ cleaved
0 567 86.1 ± 2.5 26.5 ± 2.4a 30.7 ± 2.6a
1.0 312 84.7 ± 3.2 27.1 ± 3.2ab 32.1 ± 3.5a
2.0 414 85.3 ± 2.9 28.8 ± 2.8a 34.1 ± 3.1a
5.0 303 80.8 ± 3.2 20.2 ± 3.2a 25.4 ± 3.5a
10.0 388 81.2 ± 3.0 13.5 ± 3.0c 16.2 ± 3.3b
Values are listed as percentages. abcMeans in the same column with different superscripts were significantly different (p < 0.05).
Table 4 Effect of all-trans retinol addition to the culture medium (mean ± S. E. M.). Embryos were cultured under atmospheric oxygen conditions.
Retinol concentration (μM) Putative zygote (n) Cleavage Blastocyst/ putative zygote Blastocyst/ cleaved
0 400 73 ± 4.6 14 ± 2.3a 19.2 ± 3.2a
5.0 400 74 ± 1.5 28.8 ± 3.0b 38.9 ± 3.9b
Values are listed as percentages. abMeans in the same column with different superscripts were significantly different (p < 0.001).
Discussion
In the present study, over 3000 bovine oocytes were used to evaluate effects of retinol supplementation during IVM and IVC on embryonic development to the blastocyst stage. Retinol administration during the maturation period alone resulted in concentration-dependent effects. Whereas the presence of 1μM retinol had no effect on development, 5μM retinol tended to improve blastocyst rate of development, at the p < 0.07 level, compared to controls. At a concentration of 10μM, retinol did not significantly improve embryo development compared to controls. In preliminary studies, higher concentrations (100μM) were observed to be cytotoxic (data not shown). Similarly, exposure of bovine oocytes to low concentrations of 9-cis retinoic acid was shown to improve subsequent blastocyst development but high concentrations were detrimental [16].
A more striking effect on embryonic development (p < 0.02) was observed by supplementation of 5 μM retinol to groups of oocytes with reduced developmental competence in which development of control oocytes to blastocyst was less than 20%. These results indicate that retinol supplementation during maturation may not benefit oocytes competent to progress, but rather, it improves the viability of oocytes that are developmentally challenged. In support of this, we have shown previously that retinol supplementation during maturation improves developmental competence of bovine oocytes compromised by heat stress [32].
Since most transcription in the oocyte occurs prior to maturation during preovulatory development, in vitro culture deprives oocytes of much of this activity. Meiotic inhibitors have been used as a potential means of investigating regulation of oocyte transcription and mRNA processing in vitro [33]. Treatment of cumulus-enclosed oocytes with 9-cis RA during meiotic arrest was observed to improve cortical granule migration, increase subsequent blastocyst development and increase total cell number [34]. Gomez and co-workers [19] suggested that retinoid administration may improve mRNA quality based on the observation that 9-cis RA increased poly-(A) mRNA content in meiotically arrested oocytes. Poly-(A) mRNA content of oocytes treated with 9-cis RA or ethanol vehicle was greater in matured oocytes than in oocytes prematured in the presence of 9-cis RA and then matured.
Retinol supplementation of embryo culture medium dramatically improved development to the blastocyst stage (p < 0.001) when cultured in an atmosphere of approximately 20% O2 (air and 5% CO2) but not in an atmosphere of low O2 (7% O2, 5% CO2 and 88% N2). The present study, and all previous in vitro studies demonstrating a positive effect of retinoid administered during maturation, were performed in an atmosphere of approximately 20% O2 [15,16,34], a practice common to most laboratories. Together, these data indicate that retinoids may protect embryos from oxidative damage, which has been identified as a leading cause of embryonic wastage, especially in vitro [22].
Mammalian cells, including the oocyte and those of the early embryo, have evolved several mechanisms to protect against ROS damage and maintain appropriate balances in REDOX reactions. Antioxidants present in the oocyte, embryo and/or its environment include vitamins A (retinol), C and E, pyruvate, glutathione (GSH), hypotaurine, taurine, and cysteamine [22]. Antioxidant enzymes produced by oocytes and embryos include, copper, zinc superoxide dismutase (Cu, Zn-SOD), manganese-SOD (Mn-SOD), glutathione peroxidase (GPX), glutamyl cysteine synthase (GCS), glutathione reductase (GR), catalase and others [22]. Lonergan and co-workers [36] have shown that expression of several antioxidant enzymes are up-regulated during in vitro oocyte maturation compared to in vivo maturation indicating that the former environment creates oxidative stress and oocytes respond by activating internal defense mechanisms. Addition of antioxidants to culture medium or culture of embryos in an atmosphere of reduced O2 has been demonstrated to be beneficial to in vitro survival of embryos from a variety of species [22].
Retinoids participate in a biological antioxidant network, and have been implicated as important regulators of redox signaling pathways [23,24]. Carotenoids and retinol can quench single oxygen molecules and interact with other antioxidant compounds [23]. Retinoic acid has been shown to protect against oxidative stress-induced apoptosis by inhibition of the c-jun N-terminal kinase (JNK) activator protein 1 (AP-1) pathway in glomerular [26] and mesangial cells [37]. In addition, anti-apoptotic effects of RA were mediated by both nuclear receptor-dependent and independent pathways [37].
Retinoids may also protect against oxidative damage by maintaining adequate endogenous levels of antioxidant compounds and enzymes. Glutathione is the major non-protein sulphydryl compound found in mammalian cells responsible for strong basal ROS scavenging activity [35]. Maintenance of adequate GSH levels is essential for oocyte maturation, fertilization and embryonic development [22]. Retinoic acid inhibited staurosporine-induced GSH depletion in neuronal cells, preventing oxidative damage and apoptosis [25]. A retinoic acid response element (RARE) has been identified in the promoter region of a specific isoform of glutathione S-transferase-pi (GSTp) in glioblastoma cells [38] and GPX2 [28], an enzyme necessary for the conversion and utilization of GSH. RA has also been shown to significantly increase survival, reduce ROS content and increase protein levels of Cu-Zn SOD and Mn-SOD in neuronal cells treated with staurosporine [27]. Recently, microarray analysis revealed that three genes which encode enzymes involved in GSH synthesis and utilization were RXRα-target genes in mouse liver [39]. The same study showed that in hepatocytes of RXRα-deficient mice there was a significant reduction in GSH synthesis rate and GSH content [39]. Together, these data provide strong evidence that in several cell systems, retinoids support and improve endogenous antioxidant defense mechanisms.
Conclusions
Results from the present study indicate that retinol administration during in vitro maturation particularly improved embryonic development in those oocytes that may have been developmentally compromised. Moreover, retinol addition during in vitro culture, under atmospheric conditions, also improved embryonic development compared to those embryos incubated in a 7% oxygen atmosphere. The mechanisms by which retinoids affect the developmental capacity of oocytes and early embryos may include modulation of expression of growth factors and other developmental genes, improving mRNA quality, and direct and/or indirect affects on antioxidant defense mechanisms.
Authors' contributions
TL Performed the experiments under low oxygen conditions, helped to coordinate experiments, and drafted the manuscript.
DE Performed the experiments under high oxygen conditions and helped to coordinate experiments.
JE Coordinated and assisted in the experimental design.
JG Conceived of and coordinated the experiments and drafted the manuscript.
Acknowledgements
The authors thank Mary P. Roberts for her technical help, Dr. Arnold Saxton and George Livingston for their statistical advice, and Scott MacKenzie, Heather King, Lisa McCann, TJ Wilson, Carmen Dorado, and Jenifer Miller for help collecting the embryos.
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| 15613237 | PMC544594 | CC BY | 2021-01-04 16:36:43 | no | Reprod Biol Endocrinol. 2004 Dec 21; 2:83 | utf-8 | Reprod Biol Endocrinol | 2,004 | 10.1186/1477-7827-2-83 | oa_comm |
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Hum Resour HealthHuman Resources for Health1478-4491BioMed Central London 1478-4491-2-171559834410.1186/1478-4491-2-17ResearchThe migration of physicians from sub-Saharan Africa to the United States of America: measures of the African brain drain Hagopian Amy [email protected] Matthew J [email protected] Meredith [email protected] Karin E [email protected] L Gary [email protected] WWAMI Center for Health Workforce Studies, Department of Family Medicine, University of Washington, Seattle, Washington, USA2 Department of Primary Health Care, University of Oxford, Oxford, UK2004 14 12 2004 2 17 17 9 6 2004 14 12 2004 Copyright © 2004 Hagopian et al; licensee BioMed Central Ltd.2004Hagopian 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 paper is to describe the numbers, characteristics, and trends in the migration to the United States of physicians trained in sub-Saharan Africa.
Methods
We used the American Medical Association 2002 Masterfile to identify and describe physicians who received their medical training in sub-Saharan Africa and are currently practicing in the USA.
Results
More than 23% of America's 771 491 physicians received their medical training outside the USA, the majority (64%) in low-income or lower middle-income countries. A total of 5334 physicians from sub-Saharan Africa are in that group, a number that represents more than 6% of the physicians practicing in sub-Saharan Africa now. Nearly 86% of these Africans practicing in the USA originate from only three countries: Nigeria, South Africa and Ghana. Furthermore, 79% were trained at only 10 medical schools.
Conclusions
Physician migration from poor countries to rich ones contributes to worldwide health workforce imbalances that may be detrimental to the health systems of source countries. The migration of over 5000 doctors from sub-Saharan Africa to the USA has had a significantly negative effect on the doctor-to-population ratio of Africa. The finding that the bulk of migration occurs from only a few countries and medical schools suggests policy interventions in only a few locations could be effective in stemming the brain drain.
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Background
Doctors migrate from developing countries to wealthier countries in order to further their careers, or improve their economic or social situation. The World Health Organization (WHO) has long recognized that migration of health personnel from developing to developed countries creates unfortunate imbalances in the global health workforce [1].
America's physician workforce has been significantly infused with foreign-trained international medical graduates (IMGs) since World War II. The purpose of this paper is to describe a sub-population of IMGs in the USA, those who have trained in one of the 47 African subcontinent nations.
African governments have been very clear about their objections to the wholesale migration of their physicians to rich countries. In 1996, South Africa's then-Deputy President Thabo Mbeki implored the World Health Assembly to take measures to stop the flow of physicians from poor countries to rich ones. In 1995, South Africa itself banned the recruitment of doctors from other Organization of African Unity countries [2].
Nonetheless, large numbers of African-trained physicians leave home upon completion of their medical school training in search of careers in higher-income countries. They leave behind health systems in sub-Saharan Africa that are severely stressed: life expectancy is only 50 years, 162 children in 1000 die before reaching their fifth birthdays, and only half have access to clean water sources [3]. Further, AIDS prevalence among those 15 to 49 years old is estimated to be 8.4% [4], and in four countries, adult HIV prevalence exceeds 30% [5]. While health improvements in Africa will require a broad agenda of development activities, access to an educated workforce of health professionals is also essential [6].
African country health systems and workforce data are poor, making it difficult to estimate the effects of physician migration on sending countries. The World Bank has documented this data gap, noting "Quantitative data on the health workforce is notoriously unreliable in most countries...In poor countries, government and professional information systems are weak, when they exist at all, and are rarely comprehensive (often there is no information on the private sector) and up-to-date" [7]. Indeed, the way many African country ministries of health learn about the extent of their own emigration is through gleaning data presented by destination countries [8]. This paucity of sending-country data makes it difficult to fully describe the impact of migration on countries of origin.
The 47 nations of sub-Saharan Africa have a total of 87 medical schools, although 11 countries have no medical school at all and 24 have only one each (see Table 1). The population of the subcontinent totals over 660 million people, with a ratio of fewer than 13 physicians per 100 000 population, or a total of 82 949 doctors [9]. By comparison, the United Kingdom (UK) has 164 physicians per 100 000 and the USA has over 279 physicians per 100 000 (or almost 800 000 doctors for a population of 284 million).
Table 1 Physician workforce distribution and number of medical schools by African country
Country Population (in 1000s) Physicians per 100 000 population Total number of physicians FAIMER number of medical schools
Angola 10 132 7.7 780 1
Benin 6428 5.7 366 1
Botswana 1578 23.8 376 0
Burkina Faso 12 217 3.4 415 1
Burundi* 5714 6 343 1
Cameroon 14 792 7.4 1095 1
Cape Verde 0.04 17.1 68 0
Central African Republic 3501 3.5 123 1
Chad 8419 3.3 278 1
Comoros 0.578 7.4 43 0
Congo 2809 25.1 705 1
Congo (DR) 51 810 6.9 3575 3
Côte d'Ivoire 15 866 9 1428 1
Equatorial Guinea 0.474 24.6 117 0
Eritrea 4232 3 127 0
Ethiopia* 62 651 2 1253 3
Gabon* 1223 20 245 1
Ghana 19 509 6.2 1210 3
Guinea 8642 13 1123 1
Guinea-Bissau 1278 16.6 212 1
Kenya 30 310 13.2 4001 2
Lesotho 1847 5.4 100 0
Liberia 3149 2.3 72 2
Madagascar 15 506 10.7 1659 3
Malawi* 10 874 2.3 250 1
Mali 10 665 4.7 501 1
Mauritania 2668 13.8 368 0
Mauritius 1179 85 1002 1
Mozambique# 16 934 2.57 435 1
Namibia 11 826 29.5 3489 0
Niger 10 174 3.5 356 1
Nigeria 123 750 18.5 22 894 16
Rwanda* 7405 4 296 1
Sao Tome & Principe 0.16 46.7 75 0
Senegal 9784 7.5 734 2
Seychelles 0.08 132.4 106 1
Sierra Leone 5203 7.3 380 1
So. Africa 42 351 56.3 23 844 8
Somalia 7253 4 290 1
Sudan 35 080 9 3157 14
Swaziland 1,120 15.1 169 0
Tanzania 33 768 4.1 1384 4
The Gambia 1367 3.5 48 0
Togo 5033 7.6 383 1
Uganda* 23 496 3 705 3
Zambia 9799 6.9 676 1
Zimbabwe 12 186 13.9 1694 1
TOTAL/AVG 663 529 12.5 82,949 1.8 avg
Avg. physicians per country 1765
*Indicates countries for which physicians/100 000 data came from World Bank instead of World Health Organization
# Mozambique data come from Stephen S. Gloyd, as no data are available from WHO or World Bank
Note on calculations: Average physicians per 100 000 population in Africa overall = total number of doctors (82 949)/total population (663 529)
Sources: Population data from United States Census Bureau IDB Summary international Data Base (United Nations and National Statistics Offices)
Number of physicians from the World Health Organization
Number of FAIMER (Foundation for the Advancement of International Medical Education and Research) medical schools comes from
The dependence of the United States on IMGs is encoded in various policies, most specifically Medicare's financial support for significantly more residency positions than we have domestic medical school graduates [10]. Additionally, the USA will waive the exchange visitor requirement that would otherwise return IMGs to their home countries after residency training in exchange for agreements to practice in underserved USA settings. Further, the USA will grant permanent residency status to IMGs under a variety of conditions [11].
The UK has initiated efforts to meet its own health workforce planning needs while paying attention to global equity considerations by adopting a formal "code of practice" that prohibits its National Health Service employers from recruiting health professionals from a long list of developing countries [12]. While this code has not resulted in a reduction in nurse recruitment, the number of physicians migrating to the UK has declined for a brief period (but is now back up) [8,13]. Recently, two prominent medical journals in the UK, the Lancet and the British Medical Journal, have editorialized on the effects of the brain drain in poor countries, recommending an international code of ethics prohibiting the recruitment of developing world health professionals by rich countries [14,15].
While the UK has a centralized health system well positioned to address these issues, both within its health care system and with representatives of other nations, the USA, in contrast, has a fractured health system that is less able to engage these issues. Agencies of the USA government have been reluctant, unable or unwilling to impede free-market driven physician migration.
United States policies have always been quite friendly to physician migration, even taking into account toughened medical licensing examinations and tightened immigration rules over the past four or five decades. Furthermore, even though some types of immigration have been more restricted since September 11, 2001, Congress subsequently expanded the number of foreign physicians who will be granted favorable immigration status (HR 2215, passed 10/3/02 increases the number of J-1 visa waivers allocated to state health departments from 20 to 30; further, the Department of Health and Human Services took over the role formerly played by the USA Department of Agriculture in handling applications of J-1 waivers, thereby ensuring additional foreign physicians will have access to waivers.).
One of the most common initial points of entry for IMG physicians into the USA medical workforce is residency training program enrollment, even if physicians have already completed postgraduate training in their home countries. The reliance of many inner-city hospitals on IMGs has thwarted calls by medical policy organizations, such as the Council on Graduate Medical Education, to reduce the number of IMGs admitted to residency programs as a means of narrowing the IMG pipeline to the USA
There is little debate within the USA government or other institutions about the social justice implications of obtaining health professionals from poor countries [16]. Typically, research on the issues surrounding the role(s) of IMGs in the USA has focused on 1)whether IMGs practicing here contribute to a surplus of physician labor (which could tend to lower physician salaries and/or drive up health care costs) [17-19]; 2) the quality of care delivered by IMGs [20]; and 3) the contribution of IMGs to the "health safety net" in rural or underserved areas [21].
The ethics of health professional migration from poor countries to rich ones is complicated by the competition of legitimate interests – each country's need for an adequate health workforce as opposed to each individual's human right to travel. When health professionals travel to receive training and then return to apply their skills, there are advantages to the home country. Additionally, emigrants of all social classes from poor countries typically send funds home to relatives, although sub-Saharan African remittances, at less than USD 5 billion, comprise the lowest dollar amounts of any other poor world region [8]. Further, it must be noted that individuals having benefit of public funds for their medical training are sending their remittances home to private parties with no direct gain for the health or education systems.
Immigration theory informs us that "push factors" prompt professionals to leave poor countries in favor of settling in higher income countries [22]. Negative factors in the sending countries include insufficient suitable employment, lower pay, unsatisfactory working conditions, poor infrastructure and technology, lower social status and recognition, and repressive governments. Simultaneously, "pull factors" in wealthier countries systematically attract physicians. These include training opportunities, higher living standards, better practice conditions and more sophisticated research conditions.
The "world systems framework theory" stresses the more permeable barriers between and among countries created by the standardized curriculum and English language used in world medical schools, the use of common research methods and shared scientific knowledge, the easy articulation of requirements of practice across countries, and the weakened nationalism that occurs as a result of professional training [23]. Other theories characterize migration as a decision of family units, rather than individuals, emphasizing the insurance nature of establishing what are, in effect, "branch offices" in multiple locations [24].
Given the enduring migration from poor countries to rich ones, only likely to increase with the international liberalization of trade in health services [25], concerns for global health require the maintenance of an adequate health workforce in poor countries.
Methods
To describe the numbers and types of physicians practicing in the USA who earned medical degrees in Africa, we performed a cross-sectional study using the 2002 American Medical Association Physician Masterfile [26]. This data set contains detailed information on all 771 491 active physicians who were licensed to practice medicine in the year 2002 (excluding those physicians employed by federal entities such as the Veterans Administration, federal prisons or the military).
We reviewed these data for all physicians in the USA who received their training in sub-Saharan Africa (those 47 countries south of the Saharan desert on the African continent). These data included year of birth, gender, year of medical school graduation, name of medical school, current practice location, specialty of practice, and practice activity (office-based, hospital-based, in residency, or conducting teaching or research). Birth country information is missing for 68% of those who graduated from a sub-Saharan African medical school, so we did not analyze birth country data. To detect changes in migrant waves over time, we analyzed the data by cohorts, categorizing physicians who had graduated from medical school during four periods: before 1970, during the 1970s, during the 1980s, and 1990 and beyond.
We linked geographic data about practice locations to a four-category, rural-to-urban status and taxonomy, a condensed version of the Rural-Urban Commuting Area (RUCA) codes, to determine whether these physicians are practicing in rural or urban areas. RUCAs are a census tract-based classification scheme, that have also been adapted for zip codes, combining USA Census population data with work commuting information to characterize the types of rural and urban status [27].
Research colleagues in Canada and the UK provided some data on sub-Saharan African physicians in their countries, as well.
Results
A total of 179 978 (23.3%) of the 771 491 active non-federal physicians in the USA in the year 2002 received their medical qualification in another country. The largest portion of these, or 115 835 physicians, originate from low and lower-middle income nations, as defined by the World Bank. Indeed, the most frequent countries of origin of IMGs in the USA include India (36 634), the Philippines (17 755), Mexico (10 404), and Pakistan (8563). Canadian physicians conventionally are not included in the IMG count because the body that accredits USA medical schools (the Liaison Committee on Medical Education (LCME)) offers reciprocal accreditation to Canadian medical schools (accredited by the Committee on Accreditation of Canadian Medical Schools). Canadians are, however, still subject to relevant immigration requirements.
Sub-Saharan African medical schools in 22 countries have trained approximately 5334 physicians currently practicing in the USA. Only nine nations, however, have lost more than 40 physicians each (see Table 2). Some 86% are from three countries (Nigeria, South Africa and Ghana). Nigeria, with more than twice the population of any other country in the region and 16 medical schools, has lost 2158 physicians who are now practicing in the USA; South Africa, with eight medical schools, has lost 1943 physicians; and Ghana, with three medical schools, has lost 478 physicians to the USA. By region, West Africa lost 2697 physicians and Southern Africa 1943. It is also suspected there are many more physicians from these countries working in the USA, although they are not licensed as physicians.
Table 2 Country of medical school of sub-Saharan African international medical graduates (IMGs) in the United States and Canada
Country of training Number of African-trained IMGs in USA1 Number of African-trained IMGs in Canada2 Number of physicians remaining in home country3 % of total African-trained now in USA or Canada4
Nigeria 2158 123 22 894 9
South Africa 1943 1845 23 844 14
Ghana 478 37 1210 30
Ethiopia 257 9 1564 15
Uganda 133 42 722 20
Kenya 93 19 4001 3
Zimbabwe 75 26 1694 6
Zambia 67 7 676 10
Liberia 47 8 72 43
Other 12 countries* 83 35 12 912 1
Total/Average 5334 2151 69 589 10
1. American Medical Association: Physicians' professional record (AMA-PPD). 2002
2. Buske, Lynda. Associate director of research, Canadian Medical Association. Personal communication. February 3, 2003.
3. Number of physicians from the World Health Organization. Available at:
Calculation: [(Col. 1 + Col. 2)/ (Col. 1 + Col. 2 + Col. 3)]*100 = percent
* Other 12 countries with at least one graduate in the United States.
An analysis by school indicates only ten medical schools produced 79.4% of the sub-continent's graduates who are practicing in the USA. The medical schools most frequently attended by Sub-Saharan African IMGs in the USA include the University of the Witwatersrand (South Africa, 1053 physicians), the University of Cape Town (South Africa, 655), the University of Ibadan (Nigeria, 643), the University of Lagos (Nigeria, 429), the University of Nigeria (Nigeria, 394), the University of Ghana (Ghana, 389), Addis Ababa University (Ethiopia, 200), the University of Benin (Nigeria, 183), the University of Ife (Nigeria, 156), and the University of Pretoria (South Africa, 132), for a total of 4234 physicians.
An analysis of the numbers of sub-Saharan African IMGs coming to the USA in each of the last decades illustrates that it takes some time between graduation and emigration. The number of recent graduates currently in a USA residency program is higher than those in previous decades because of the obvious correlation between age and career stage.
Among sub-Saharan physicians in the USA, 78.3% are male. The picture is changing over time, however. Of the cohort who were trained in 1969 or earlier, 90% were male, but now only 66.3% of those who graduated from medical school in 1990 or later are male (see Table 3).
Table 3 Characteristics of sub-Saharan African international medical graduates in the United States by graduation year cohort
1969 or earlier 1970–1979 1980–1989 1990–2000 OVERALL
Number 720 1167 2268 1179 5334
Currently in residency (%) 0 2.3 17.6 59.7 21.2
Gender (% male) 90.0 86.5 76.7 66.3 78.3
Generalists (%) 19.0 28.3 47.8 57.9 41.9
current practice location:
Urban (%) 95.3 94.1 93.7 95.7 94.4
Large rural (%) 2.4 3.3 3.6 2.6 3.1
Small rural (%) 1.7 2.1 1.8 1.1 1.7
Isolated rural (%) 0.7 0.6 0.9 0.6 0.7
Note: includes residents.
Source of data: American Medical Association: Physicians' professional record (AMA-PPD) 2002.
The average age of sub-Saharan African physicians in the USA is 43 years, compared to 46 years for all USA physicians. Forty two percent of sub-Saharan African physicians in the USA are under 40 years, and another 32% are between 40 and 50. Among the large contributing countries, Nigerian physicians are the youngest cohort (63% are under 40), and South Africans are the oldest (only 20% are under 40).
A higher proportion of sub-Saharan physicians were in residency training programs (21.2%) than were USA physicians (14.1%), because many emigrate specifically for that reason. While 41.9% are in generalist specialty areas, compared to 34.7% of USA-trained physicians, the number has been rising with each new cohort. Table 3 illustrates that 57.9% of those trained in the 1990s selected a generalist practice specialty, compared to 28.3% of those trained in the 1970s. This apparently rising interest in generalist practice may be an artifact, however, as a prerequisite to internal medicine specialization is training in general internal medicine.
While 31.6% of all sub-Saharan African physicians in the USA are identified as family practitioners or general internists, it may be that this ratio of generalists will increase, as 45.4% of those in residency programs are in those two specialties. The next largest specialty groups are pediatrics (9.7%), psychiatry (5.5%), anesthesiology (5.4%), obstetrics and gynecology (3.3%) and general surgery (3.0%).
Urban areas attracted 93% of sub-Saharan African physicians (compared to 90.9% of other IMGs), even after excluding residents, who are typically based in urban teaching hospitals. Graduates of USA medical schools distribute themselves similarly, with 87% of USA-trained physicians in urban areas, even though a smaller 81% of the population lives in urban areas [27]. The states attracting the largest numbers of sub-Saharan Africa physicians include New York, California, Texas, Maryland, Illinois, Georgia, Pennsylvania, and New Jersey (see Figure 1). These are the same states that draw the largest portion of immigrant physicians generally.
Figure 1 Origin and distribution of African-trained physicians in the 11 US states with the most such physicians
The 1943 physicians trained in South African medical schools are somewhat different from their fellow African trainees from the subcontinent. They are older, more of them are male, they are typically white (94%) and they are more often in a subspecialty practice. This may reflect a particular wave of physicians seeking subspecialty training and practice opportunities in the USA during the political turmoil South Africa experienced in the 1970s and 1980s. It is unlikely they are seeking training abroad that is unavailable in their home country, as the medical training opportunities in South Africa are quite comprehensive.
Discussion
The 5000-plus physicians trained in sub-Saharan Africa who have migrated to the USA comprise only a small proportion of the total number of IMGs practicing in the USA. However, their relatively small numeric addition to the USA medical workforce contrasts markedly with the impact of their migration on the medical workforce in sub-Saharan Africa. Moreover, in absolute terms, the USA has drawn more of the medical workforce of Africa than either Canada, with 2151 African graduates (Lynda Buske, Canadian Medical Association, personal communication, 2/3/03), or the UK, with 3451 (Bonnie Sibbald, University of Manchester, personal communication, 1/24/03), largely because of the relative size of the health care system in the USA.
Including the USA, the UK, and Canada, then, 10 936 physicians trained in sub-Saharan Africa are practicing in the three countries, a number that represents 12% of all African physicians. We expect there are many more African physicians who are in the UK, as well, as our figures from there include only those who arrived post-1992. While some of the physicians in residency training will return home, there are unknown others who could be practicing medicine at home but were not able to get licenses abroad and therefore are engaged in other occupations. Almost all the medical schools sending graduates to the USA provide their instruction in English.
Our study provides some measures of sub-Saharan Africa physician migration to the USA. A next step is to collect data from other developed countries and begin to create a physician migration data set from multiple countries. Migration from country to country within the subcontinent is also worthy of further examination, and it would be useful to identify the relationship between country of birth and country of training. There are likely to be some medical schools that draw students from several African countries. It should be noted, as well, that there are physicians practicing in Africa who did not train there, most prominently Cubans.
Some sub-Saharan countries lose a larger proportion of their physicians to the USA than others. For example, while Ghana has a reported 1210 practicing physicians in its country, 478 graduates of Ghanaian medical schools are practicing in the USA. Without even considering those who have migrated to other countries, these 478 Ghanaian graduates in the USA represent 30% of Ghana's potential medical workforce (see Table 2). If none of those had come to the USA, the physician-to-population ratio in Ghana would rise from 6.2 to 8.7 per 100 000, or a 40% increase. By comparison, South Africa has lost 14% of its potential workforce to the USA and Canada.
The migration of physicians from sub-Saharan Africa represents a lost investment of significant training costs, since graduates of medical schools in Africa are likely to have contributed financially to only a small portion of the costs of their medical education [28]. Medical education is estimated to cost Ghana about USD 9 million per year and Nigeria USD 20 million (Hagopian & Ofosu, et al. The flight of physicians from West Africa: views of African physicians and implications for policy, unpublished 2003).
The United Nations Commission for Trade and Development has estimated that each professional leaving Africa costs the continent USD 184 000, or USD 4 billion a year – one third of official development funds to Africa [29]. The loss of trained health personnel also contributes to a general decline in average incomes, as physicians generate skilled health system jobs beyond their own. Lost tax revenues from absent physicians represent significant losses as well.
Ostensibly, the USA welcomes IMGs for two purposes. First, as a form of foreign aid, it provides specialty training that physicians can take back to their home countries for the benefit of residents of those nations. Second, IMGs fill positions in specialties and locations that are less attractive to their USA counterparts, and may help to correct physician maldistribution in some rural or underserved areas of the USA. (There are several federal agencies, along with state health departments who "sponsor" physicians who have completed their residency training in the USA on J-1 exchange – or "student"- visas. These sponsorships allow foreign national physicians to gain approval from the State Department and the USA Citizenship and Immigration Services to waive J-1 visa requirements that would otherwise require them to return to home countries for at least two years. In exchange for this waiver, physicians find employment with a health agency or private physician in a health professional shortage area.)
Longitudinal tracking of physicians entering the USA has indicated, however, that few IMGs ever leave the USA after arriving for residency training [30], and there is conflicting evidence about whether IMGs are more likely to practice in safety net practices for low-income and underinsured people [21,31,32].
Attracting physicians to rural practice in most countries is difficult, and is accomplished only through a careful set of policies designed to provide incentives for rural service. In most poor as well as rich countries, physicians are concentrated around urban hospitals that offer tertiary care, even though more rational service delivery systems might focus on a geographically decentralized system of primary and preventive care. Poor countries that offer medical training to produce too many physicians with highly technical skills, some of whom who cannot find satisfying jobs, may further contribute to physician migration [33]. India and the Philippines, for example, clearly over-produce physicians who are intended for an international market.
Our findings show African physicians are unlikely to select small or remote rural practice opportunities in either their home countries or in the USA, but the preponderance of African physicians in American inner-city underserved areas may to some extent be helpful to USA needs by boosting the number of minority physicians in the urban health workforce. The growing number of African immigrant female physicians follows the trend for increasing numbers of female physicians trained in the USA, and probably has similar implications. Some researchers have found, for instance, that female physicians are less likely to practice in rural areas [34].
While the sub-Saharan Africa region as a whole loses many of its physicians, it is apparent that a small handful of medical schools is the sources of the majority of this migration. Ten medical schools in four countries – South Africa, Nigeria, Ghana and Ethiopia – produce 79.4% of the émigré physicians to the USA, out of a total of 87 medical schools in the region. This suggests policy approaches to reducing the "brain drain" from Africa could be targeted at only these few countries or medical schools, a less daunting task than addressing the problem in 47 different countries.
Medical migration results from the complex interaction of myriad social, legal and economic forces. Single country policies are unlikely to alter the flows significantly. Even if the USA acknowledges that that it benefits from luring medical professionals here for whose medical school training we do not pay, solutions that would be compatible with social justice principles are not clear. Furthermore, if all African doctors returned to their home countries today, they would not necessarily find satisfactory employment opportunities in cash-strapped health systems.
Conclusions
The 57th World Health Assembly, in 2004, adopted a resolution to urge member countries to develop strategies to mitigate the adverse effects of migration of health workers; to develop policies that could provide incentive for health workers to remain in their countries; and, among other issues, requests WHO to help countries set up information systems to monitor the movement of health resources for health, and to include human resources for health development as a top-priority program at WHO from 2006 to 2015 [35].
In an ideal world, freedom of movement is a universal right for individuals, as there is ostensibly no rational reason why anyone would have a stronger right to be in any place more than anyone else [36]. Today, however, differences in wealth between countries create flows of educated people seeking better opportunities far from home. One result is that resource-strapped African (and other poor) countries have invested significant resources in educating health professionals who will never serve the populations that were taxed (or took out high-interest loans from international lenders) to pay for their training.
Importing health professionals from poor countries to provide care in rich countries is not consistent with a rational workforce policy rooted in social justice principles. In the short run, Mullan [37] and others are right to recommend that the USA expand its incentives to USA graduates to practice in rural and underserved areas through the National Health Service Corps and other programs. Grumbach [18] recommends reducing the number of excess residency training positions by limiting the Medicare subsidy.
In response to a recent report by the USA Physicians for Human Rights [38], The New York Times editorialized that the "obvious long-term solution to the medical brain drain is for wealthier countries to reimburse Africa's health and educational systems for the cost of poaching their professionals, and to greatly increase the financing and technical help for Africa's health systems" [39]. This unprecedented attention to the issue of the African medical brain drain in a major USA publication, coupled with a radical call for reparations, suggests USA policy makers may be called to address this issue.
The same Physicians for Human Rights report that prompted The New York Times response made a strong recommendation that the International Monetary Fund, World Bank, and other donors refrain from withholding loans or grants from countries that increase their spending on "health, education and other sectors and activities needed to promote human development, including to enhance salaries to health staff or to hire new health personnel."
One of the major limitations African nations face in addressing their health workforce problems is the lack of reliable data on how many health workers have graduated from their schools, how many are working in the country and in what locations, and how many have emigrated. It is urgent that poor countries put together the information systems required to track these data, as a basis for workforce policy and investment decisions.
And, finally, the fact that so few medical schools generate the vast majority of emigrants creates an opportunity to focus attention in a strategic way. These schools might be enticed to redirect their missions towards producing graduates who intend to serve their own countries. This would likely require curriculum changes, admissions policy changes, and a change in faculty culture to ensure that emigration is not promoted as a mark of prestige.
Competing interests
The author(s) declare that they have no competing interests.
Authors' contributions
AH conceived the project, designed the research and wrote the paper. MJT and KEJ actively participated in the conceptualization and re-writing of the paper. MF conducted the data management and analysis. LGH provided guidance, advice and editing assistance. All authors read and approved the final manuscript.
Acknowledgements
We are grateful to Bonnie Sibbald, PhD, professor of health services research at University of Manchester in the United Kingdom for data relating to African-trained physicians in the United Kingdom. We also appreciate data shared by Lynda Buske at the Canadian Medical Association. Thanks also to Richard Biritwum, Maureen Mackintosh, Carolyn Watts, Oscar Gish, and Allen Cheadle, who reviewed this paper. The WWAMI Center for Health Workforce Studies, Department of Family Medicine, University of Washington, Seattle, Washington, USA, is funded by the United States Health Resources and Services Administration's National Center for Health Workforce Analysis, Bureau of Health Professions, Cooperative Agreement #6 U79 HP 00003-04-05. WWAMI stands for Washington, Wyoming, Alaska, Montana and Idaho.
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| 15598344 | PMC544595 | CC BY | 2021-01-04 16:37:43 | no | Hum Resour Health. 2004 Dec 14; 2:17 | utf-8 | Hum Resour Health | 2,004 | 10.1186/1478-4491-2-17 | oa_comm |
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Biomagn Res TechnolBiomagnetic Research and Technology1477-044XBioMed Central London 1477-044X-2-71556657010.1186/1477-044X-2-7ReviewMagnetic techniques for the isolation and purification of proteins and peptides Safarik Ivo [email protected] Mirka [email protected] Laboratory of Biochemistry and Microbiology, Institute of Landscape Ecology, Academy of Sciences, Na Sadkach 7, 370 05 Ceske Budejovice, Czech Republic2 Department of General Biology, University of South Bohemia, Branisovska 31, 370 05 Ceske Budejovice, Czech Republic2004 26 11 2004 2 7 7 10 11 2004 26 11 2004 Copyright © 2004 Safarik and Safarikova; licensee BioMed Central Ltd.2004Safarik and Safarikova; 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.
Isolation and separation of specific molecules is used in almost all areas of biosciences and biotechnology. Diverse procedures can be used to achieve this goal. Recently, increased attention has been paid to the development and application of magnetic separation techniques, which employ small magnetic particles. The purpose of this review paper is to summarize various methodologies, strategies and materials which can be used for the isolation and purification of target proteins and peptides with the help of magnetic field. An extensive list of realised purification procedures documents the efficiency of magnetic separation techniques.
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Introduction
Isolation, separation and purification of various types of proteins and peptides, as well as of other specific molecules, is used in almost all branches of biosciences and biotechnologies. Separation science and technology is thus very important area necessary for further developments in bio-oriented research and technology. New separation techniques, capable of treating dilute solutions or solutions containing only minute amounts of target molecules in the presence of vast amounts of accompanying compounds in both small and large-scale processes, even in the presence of particulate matter, are necessary.
In the area of biosciences and biotechnology the isolation of proteins and peptides is usually performed using variety of chromatography, electrophoretic, ultrafiltration, precipitation and other procedures, affinity chromatography being one of the most important techniques. Affinity ligand techniques represent currently the most powerful tool available to the downstream processing both in term of their selectivity and recovery. The strength of column affinity chromatography has been shown in thousands of successful applications, especially in the laboratory scale. However, the disadvantage of all standard column liquid chromatography procedures is the impossibility of the standard column systems to cope with the samples containing particulate material so they are not suitable for work in early stages of the isolation/purification process where suspended solid and fouling components are present in the sample. In this case magnetic affinity, ion-exchange, hydrophobic or adsorption batch separation processes, applications of magnetically stabilized fluidized beds or magnetically modified two-phase systems have shown their usefulness.
The basic principle of batch magnetic separation is very simple. Magnetic carriers bearing an immobilized affinity or hydrophobic ligand or ion-exchange groups, or magnetic biopolymer particles having affinity to the isolated structure, are mixed with a sample containing target compound(s). Samples may be crude cell lysates, whole blood, plasma, ascites fluid, milk, whey, urine, cultivation media, wastes from food and fermentation industry and many others. Following an incubation period when the target compound(s) bind to the magnetic particles the whole magnetic complex is easily and rapidly removed from the sample using an appropriate magnetic separator. After washing out the contaminants, the isolated target compound(s) can be eluted and used for further work.
Magnetic separation techniques have several advantages in comparison with standard separation procedures. This process is usually very simple, with only a few handling steps. All the steps of the purification procedure can take place in one single test tube or another vessel. There is no need for expensive liquid chromatography systems, centrifuges, filters or other equipment. The separation process can be performed directly in crude samples containing suspended solid material. In some cases (e.g., isolation of intracellular proteins) it is even possible to integrate the disintegration and separation steps and thus shorten the total separation time [1]. Due to the magnetic properties of magnetic adsorbents (and diamagnetic properties of majority of the contaminating molecules and particles present in the treated sample), they can be relatively easily and selectively removed from the sample. In fact, magnetic separation is the only feasible method for recovery of small magnetic particles (diameter ca 0.1 – 1 μm) in the presence of biological debris and other fouling material of similar size. Moreover, the power and efficiency of magnetic separation procedures is especially useful at large-scale operations. The magnetic separation techniques are also the basis of various automated procedures, especially magnetic-particle based immunoassay systems for the determination of a variety of analytes, among them proteins and peptides. Several automated systems for the separation of proteins or nucleic acids have become available recently.
Magnetic separation is usually very gentle to the target proteins or peptides. Even large protein complexes that tend to be broken up by traditional column chromatography techniques may remain intact when using the very gentle magnetic separation procedure [2]. Both the reduced shearing forces and the higher protein concentration throughout the isolation process positively influence the separation process.
Separation of target proteins using standard chromatography techniques often leads to the large volume of diluted protein solution. In this case appropriate magnetic particles can be used for their concentration instead of ultrafiltration, precipitation etc. [3].
The purpose of this review is to summarize various methodologies and strategies which can be employed for the isolation and purification of target proteins and peptides with the help of magnetic materials. An extensive list of realised purification procedures documents the efficiency of magnetic separation techniques. All these information will help the scientists to select the optimal magnetic material and the purification procedure.
Necessary materials and equipment
The basic equipment for laboratory experiments is very simple. Magnetic carriers with immobilized affinity or hydrophobic ligands, magnetic particles prepared from a biopolymer exhibiting affinity for the target compound(s) or magnetic ion-exchangers are usually used to perform the isolation procedure. Magnetic separators of different types can be used for magnetic separations, but many times cheap strong permanent magnets are equally efficient, especially in preliminary experiments.
Magnetic carriers and adsorbents can be either prepared in the laboratory, or commercially available ones can be used. Such carriers are usually available in the form of magnetic particles prepared from various synthetic polymers, biopolymers or porous glass, or magnetic particles based on the inorganic magnetic materials such as surface modified magnetite can be used. Many of the particles behave like superparamagnetic ones responding to an external magnetic field, but not interacting themselves in the absence of magnetic field. This is important due to the fact that magnetic particles can be easily resuspended and remain in suspension for a long time. In most cases, the diameter of the particles differs from ca 50 nm to approx. 10 μm. However, also larger magnetic affinity particles, with the diameters up to millimetre range, have been successfully used [4]. Magnetic particles having the diameter larger than ca 1 μm can be easily separated using simple magnetic separators, while separation of smaller particles (magnetic colloids with the particle size ranging between tens and hundreds of nanometers) may require the usage of high gradient magnetic separators.
Commercially available magnetic particles can be obtained from a variety of companies. In most cases polystyrene is used as a polymer matrix, but carriers based on cellulose, agarose, silica, porous glass or silanized magnetic particles are also available. Examples of magnetic particles used (or usable) for proteins and peptides separation can be found elsewhere [5-7].
Particles with immobilised affinity ligands are available for magnetic affinity adsorption. Streptavidin, antibodies, protein A and Protein G are used most often in the course of protein and peptides isolation. Magnetic particles with above mentioned immobilised ligands can also serve as generic solid phases to which native or modified affinity ligands can be immobilised (e.g., antibodies in the case of immobilised protein A, protein G or secondary antibodies, biotinylated molecules in the case of immobilised streptavidin).
Also some other affinity ligands (e.g., nitrilotriacetic acid, glutathione, trypsin, trypsin inhibitor, gelatine etc.) are already immobilised to commercially available carriers. To immobilise other ligands of interest to both commercial and laboratory made magnetic particles standard procedures used in affinity chromatography can be employed. Usually functional groups available on the surface of magnetic particles such as -COOH, -OH or -NH2 are used for immobilisation, in some cases magnetic particles are available already in the activated form (e.g., tosylactivated, epoxyactivated etc).
In the laboratory magnetite (or similar magnetic materials such as maghemite or ferrites) particles can be surface modified by silanization. This process modifies the surface of the inorganic particles so that appropriate functional groups become available, which enable easy immobilisation of affinity ligands [8]. In exceptional cases enzyme activity can be decreased as a result of usage of magnetic particles with exposed iron oxides. In this case encapsulated microspheres, having an outer layer of pure polymer, will be safer.
Biopolymers such as agarose, chitosan, kappa carrageenan and alginate can be easily prepared in a magnetic form. In the simplest way the biopolymer solution is mixed with magnetic particles and after bulk gel formation the magnetic gel formed is mechanically broken into fine particles [9]. Alternatively biopolymer solution containing dispersed magnetite is dropped into a mixed hardening solution [4] or water-in-oil suspension technique is used to prepare spherical particles [10].
Basically the same procedures can be used to prepare magnetic particles from synthetic polymers such as polyacrylamide, poly(vinylalcohol) and many others [11].
In another approach used standard affinity or ion-exchange chromatography material was post-magnetised by interaction of the sorbent with water-based ferrofluid. Magnetic particles accumulated within the pores of chromatography adsorbent thus modifying this material into magnetic form [12,13]. Alternatively magnetic Sepharose or other agarose gels were prepared by simple contact with freshly precipitated or finely powdered magnetite [12,14].
Magnetoliposomes (magnetic derivatives of standard liposomes), either in the original form or after immobilization of specific proteins, have the potential for the separation of antiphospholipid antibodies [15], IgG antibodies [16] and other proteins of interest [17].
Recently also non-spherical magnetic structures, such as magnetic nanorods have been tested as possible adsorbent material for specific separation of target proteins [18].
Magnetic separators are necessary to separate the magnetic particles from the system. In the simplest approach, a small permanent magnet can be used, but various magnetic separators employing strong rare-earth magnets can be obtained at reasonable prices. Commercial laboratory scale batch magnetic separators are usually made from magnets embedded in disinfectant-proof material. The racks are constructed for separations in Eppendorf micro-tubes, standard test tubes or centrifugation cuvettes, some of them have a removable magnetic plate to facilitate easy washing of separated magnetic particles. Other types of separators enable separations from the wells of microtitration plates and the flat magnetic separators are useful for separation from larger volumes of suspensions (up to approx. 500 – 1000 ml). Examples of typical batch magnetic separators are shown in Fig. 1.
Figure 1 Examples of batch magnetic separators applicable for magnetic separation of proteins and peptides. A: Dynal MPC -S for six microtubes (Dynal, Norway); B: Dynal MPC – 1 for one test tube (Dynal, Norway); C: Dynal MPC – L for six test tubes (Dynal, Norway); D: magnetic separator for six Eppendorf tubes (New England BioLabs, USA); E: MagneSphere Technology Magnetic Separation Stand, two position (Promega, USA); F: MagnaBot Large Volume Magnetic Separation Device (Promega, USA); G: MagneSphere Technology Magnetic Separation Stand, twelve-position (Promega, USA); H: Dynal MPC – 96 S for 96-well microtitre plates (Dynal, Norway); I: MagnaBot 96 Magnetic Separation Device for 96-well microtitre plates (Promega, USA); J: BioMag Solo-Sep Microcentrifuge Tube Separator (Polysciences, USA); K: BioMag Flask Separator (Polysciences, USA); L: MagneSil Magnetic Separation Unit (Promega, USA); M: MCB 1200 processing system for 12 microtubes based on MixSep process (Sigris Research, USA); N: PickPen magnetic tool (Bio-Nobile, Finland). Reproduced with the permission of the above mentioned companies; the photos were taken from their www pages.
Flow-through magnetic separators are usually more expensive, and high gradient magnetic separators (HGMS) are the typical examples. Laboratory scale HGMS is composed from a column packed with fine magnetic grade stainless steel wool or small steel balls which is placed between the poles of an appropriate magnet. The suspension is pumped through the column, and magnetic particles are retained within the matrix. After removal the column from the magnetic field, the particles are retrieved by flow and usually by gentle vibration of the column.
For work in dense suspensions, open gradient magnetic separators may be useful. A very simple experimental set-up for the separation of magnetic affinity adsorbents from litre volumes of suspensions was described [19].
Currently many projects require the analysis of a high number of individual proteins or variants. Therefore, methods are required that allows multiparallel processing of different proteins. There are several multiple systems for high throughput nucleic acid and proteins preparation commercially available. The most often used approach for proteins isolation is based on the isolation and assay of 6xHis-tagged recombinant proteins using magnetic beads with Ni-nitriloacetic acid ligand [20]. The commercially available platforms can be obtained from several companies such as Qiagen, USA (BioRobot and BioSprint series), Tecan, Japan (Te-MagS) or Thermo Electron Corporation, USA (KingFisher).
Basic principles of magnetic separations of proteins and peptides
Magnetic separations of proteins and peptides are usually convenient and rapid. Nevertheless, several hints may be helpful to obtain good results.
Proteins and peptides in the free form can be directly isolated from different sources. Membrane bound proteins have to be usually solubilized using appropriate detergents. When nuclei are broken during sample preparation, DNA released into the lysate make the sample very viscous. This DNA may be sheared by repeated passage up and down through a 21 gauge hypodermic syringe needle before isolation of a target protein. Alternatively, DNase can be added to enzymatically digest the DNA.
Magnetic beads in many cases exhibit low non-specific binding of non-target molecules present in different samples. Certain samples may still require preclearing to remove molecules which have high non-specific binding activity. If preclearing is needed, the sample can be mixed with magnetic beads not coated with the affinity ligand. In the case of immunomagnetic separation, magnetic beads coated with secondary antibody or with irrelevant antibodies have been used. The non-specific binding can also be minimised by adding a non-ionic detergent both in the sample and in the washing buffers after isolation of the target.
In general, magnetic affinity separations can be performed in two different modes. In the direct method, an appropriate affinity ligand is directly coupled to the magnetic particles or biopolymer exhibiting the affinity towards target compound(s) is used in the course of preparation of magnetic affinity particles. These particles are added to the sample and target compounds then bind to them. In the indirect method the free affinity ligand (in most cases an appropriate antibody) is added to the solution or suspension to enable the interaction with the target compound. The resulting complex is then captured by appropriate magnetic particles. In case antibodies are used as free affinity ligands, magnetic particles with immobilised secondary antibodies, protein A or protein G are used for capturing of the complex. Alternatively the free affinity ligands can be biotinylated and magnetic particles with immobilised streptavidin or avidin are used to capture the complexes formed. In both methods, magnetic particles with isolated target compound(s) are magnetically separated and then a series of washing steps is performed to remove majority of contaminating compounds and particles. The target compounds are then usually eluted, but for specific applications (especially in molecular biology, bioanalytical chemistry or environmental chemistry) they can be used still attached to the particles, such as in the case of polymerase chain reaction, magnetic ELISA etc.
The two methods perform equally well, but, in general, the direct technique is more controllable. The indirect procedure may perform better if affinity ligands have poor affinity for the target compound.
In most cases, magnetic batch adsorption is used to perform the separation step. This approach represents the simplest procedure available, enabling to perform the whole separation in one test-tube or flask. If larger magnetic particles (with diameters above ca 1 μm) are used, simple magnetic separators can be employed. In case magnetic colloids (diameters ranging between tens and hundreds of nanometres) are used as affinity adsorbents, high-gradient magnetic separators have usually to be used to remove the magnetic particles from the system.
Alternatively magnetically stabilised fluidised beds (MSFB), which enable a continuous separation process, can be used. The use of MSFB is an alternative to conventional column operation, such as packed-bed or fluidised bed, especially for large-scale purification of biological products. Magnetic stabilisation enables the expansion of a packed bed without mixing of solid particles. High column efficiency, low pressure drop and elimination of clogging can be reached [21,22].
Also non-magnetic chromatographic adsorbents can be stabilized in magnetically stabilized fluidized beds if sufficient amount of magnetically susceptible particles is also present. The minimum amount of magnetic particles necessary to stabilize the bed is a function of various parameters including the size and density of both particles, the magnetic field strength, and the fluidization velocity. A variety of commercially available affinity, ion-exchange, and adsorptive supports can be used in the bed for continuous separations [23].
Biocompatible two phase systems, composed for example from dextran and polyethylene glycol, are often used for isolation of biologically active compounds, subcellular organelles and cells. One of the disadvantages of this system is the slow separation of the phases when large amounts of proteins and cellular components are present. The separation of the phases can be accelerated by the addition of fine magnetic particles or ferrofluids to the system followed by the application of a magnetic field. This method seems to be useful when the two phases have very similar densities, the volumetric ratio between the phases is very high or low, or the systems are viscous. Magnetically enhanced phase separation usually increases the speed of phase separation by a factor of about 10 in well-behaved systems, but it may increase by a factor of many thousands in difficult systems. The addition of ferrofluids and/or iron oxide particles was shown to have usually no influence on enzyme partioning or enzyme activity [24,25].
Proteins and peptides isolated using magnetic techniques have to be usually eluted from the magnetic separation materials. In most cases bound proteins and peptides can be submitted to standard elution methods such as the change of pH, change of ionic strength, use of polarity reducing agents (e.g., dioxane or ethyleneglycol) or the use of deforming eluents containing chaotropic salts. Affinity elution (e.g., elution of glycoproteins from lectin coated magnetic beads by the addition of free sugar) may be both a very efficient and gentle procedure.
Examples of magnetic separations of proteins and peptides
Magnetic affinity and ion-exchange separations have been successfully used in various areas, such as molecular biology, biochemistry, immunochemistry, enzymology, analytical chemistry, environmental chemistry etc [26-29]. Tables 1, 2, 3, 4, 5, 6, 7, 8, 9 show some selected applications of these techniques for proteins and peptides isolation.
Table 1 Examples of proteinases and peptidases purified by magnetic techniques
Purified enzyme Source Magnetic carrier Affinity ligand Further details Reference
Aminopeptidase Arabidopsis Amine-terminated magnetic beads N-1-Naphthylphthalamic acid KCl gradient elution [54]
Angiotensin-converting enzyme Pig lung membranes Dynabeads Polyclonal antibodies [57]
Bromelain Commercial preparation Polyacrylic acid – iron oxide magnetic nanoparticles Elution with KCl solution [59]
Caspase (recombinant, histidine-tailed) Human cells Magnetic agarose Ni-NTA Elution with SDS-PAGE buffer [60]
Chymotrypsin Commercial preparation Magnetic chitosan beads Elution with N-acetyl-D-tryptophan [62]
NIa-protease (recombinant, histidine-tagged) Plum Pox Virus Magnetic core and nickel-silica composite matrix Ni2+ Elution with imidazole containing buffer [36]
Proteinases Commercial sources Magnetic cross-linked erythrocytes Elution with low pH buffer [46]
Proteinase, bacterial (Savinase) Bacillus clausii Silanized magnetite particles Bacitracin [84]
Trypsin Porcine pancreatin Silanized magnetite particles p-Aminobenzamidine Elution with low pH buffer [50]
Porcine pancreatin Magnetic polymer particles Soybean trypsin inhibitor Elution with low pH solution [86]
Commercial preparation Silanized ferrite powder Soybean trypsin inhibitor [87]
Commercial preparation Magnetic κ-carrageenan particles Soybean trypsin inhibitor Elution with low pH solution, MSFB [88][89]
Commercial preparation Magnetic polyacrylamide beads Soybean trypsin inhibitor Magnetically stabilized fluidized beds [90]
Commercial preparation Magnetic chitosan particles Aprotinin Elution with low pH solution [91]
Commercial preparation Magnetic cross-linked erythrocytes Elution with low pH buffer; separation from large volume sample [19]
Urokinase Crude urokinase preparation Magnetic dextran, agarose, polyvinyl alcohol, polyhydroxyethyl methacrylate microspheres p-Aminobenzamide, L-arginine methyl ester, guanidine hexanoic acid or guanidine acetic acid [93]
Table 2 Purification of lysozyme by magnetic techniques
Purified enzyme Source Magnetic carrier Affinity ligand Further details Reference
Lysozyme Hen egg white Magnetic chitin Elution with 0.01 M HCl [71]
Hen egg white Magnetic acetylated chitosan Elution with 0.01 M HCl [9]
Commercial preparation Magnetic poly(2-hydroxyethyl methacrylate) Cibacron Blue F3GA Elution with 1 M KSCN [72]
Magnetic chitosan beads Magnetically stabilized fluidized bed [73]
Ornithodoros moubata Magnetic chitin Elution with alkaline, high salt buffer [74]
Commercial preparation Magnetic cross-linked polyvinylalcohol Cibacron blue 3GA Elution with high salt buffer [52]
Magnetite – polyacrylic acid nanoparticles Ion-exchange separation [75]
Magnetic cross-linked polyvinylalcohol beads Adsorption study [76]
Commercial preparation Magnetic agarose beads Cibacron blue 3GA Magnetically stabilized fluidized bed [77]
Magnetic chitosan Cibacron blue 3GA Study of adsorption properties [78]
Commercial preparation Ferrofluid modified sawdust Elution with 0.5 M NaCl [79]
Commercial preparation Nano-sized magnetic particles Elution with NaH2PO4 and NaSCN [80]
Lysozyme (recombinant, histidine-tailed) T4 BioMag, amine terminated Iminodiacetic acid charged with Cu2+ Elution with low pH buffer [81]
Table 3 Examples of polysaccharide and disaccharide hydrolases purified by magnetic techniques
Purified enzyme Source Magnetic carrier Affinity ligand Further details Reference
α-Amylases Porcine pancreas, Bacillus subtilis, wheat germ Magnetic alginate beads Elution with 1 M maltose [4]
Bacillus amyloliquefaciens, porcine pancreas Magnetic alginate microbeads Elution with 1 M maltose [10]
β-Amylase Sweet potato Magnetic alginate beads Elution with 1 M maltose [55]
β-Galactosidase Escherichia coli homogenate Silanized magnetite p-Aminophenyl-β-D-thiogalactopyranoside Elution with borate buffer, pH 10 [58]
β-Galactosidase (fusion protein comprising the DNA-binding lac repressor) Bacterial lysate Magnetic beads DNA containing Escherichia coli lac operator Elution with lactose analogue [64]
Glucoamylase Aspergillus niger Magnetic alginate beads Elution with 1 M maltose [55]
Pectinase Commercial preparation Magnetic alginate beads [82]
Pullulanase Bacillus acidopullulyticus Magnetic alginate beads Elution with 1 M maltose [55]
Table 4 Examples of other enzymes purified by magnetic techniques
Purified enzyme Source Magnetic carrier Affinity ligand Further details Reference
Alcohol dehydrogenase Yeast homogenate Magnetic cross-linked polyvinylalcohol Cibacron blue 3GA Elution with high salt buffer [52]
Saccharomyces cerevisiae extract PEG with bound Cibacron blue Magnetic two-phase system [53]
Aldolase (recombinant, histidine tagged) Pea Magnetic core and nickel-silica composite matrix Ni2+ Elution with imidazole containing buffer [36]
AngioI-TEM-β-lactamase Escherichia coli cells extracts Magnetic agarose beads Iminodiacetic acid charged with Zn2+ Elution with low pH buffer [56]
Asparaginase Escherichia coli homogenate Magnetic polyacrylamide gel particles D-Asparagine Elution with D-asparagine solution [58]
Carbonic anhydrase Model mixture Magnetic agarose beads Sulfanilimide Elution with high salt buffer [14]
Catalase Bovine liver, commercial preparation Magnetic poly(EGDMA-MAH) beads Fe3+ Elution with NaSCN solution [61]
Cytochrome c Horse, Candida krusei Amine terminated iron oxide particles Iminodiacetic acid charged with Cu2+ Binding studies [63]
Commercial preparation Au@magnetic particles MALDI MS analysis [31]
Horse heart Magnetic agarose beads Iminodiacetic acid charged with Cu2+ Elution with EDTA containing buffer [56]
Bovine heart Magnetic ion-exchange particles Protein binding studies [12]
Glucose-6-phosphate dehydrogenase Ferrofluid modified Sepharose 4B ADP [65]
Saccharomyces cerevisiae extract PEG with bound Cibacron blue Magnetic two-phase system [53]
Hexokinase Escherichia coli homogenate PEG with bound Cibacron blue Magnetic two-phase system [53]
Lactate dehydrogenase Beef heart Ferrofluid modified Sepharose 4B AMP Elution with 1 mM NADH [13]
Porcine muscle Magnetic agarose beads Reactive Red 120 Column elution with NaCl gradient [66]
Lactoperoxidase Sweet whey Magnetic cation exchanger HGMS [67,68]
Luciferase (histidine-tagged) Escherichia coli homogenate MagneHis™ system Ni2+ [69,70]
Phosphatase, alkaline Human placenta Dynabeads M-450 Specific antibody Activity of bound enzyme measured [83]
Phosphatase, alkaline (fusion protein comprising the DNA-binding lac repressor) Bacterial lysate Magnetic beads DNA containing Escherichia coli lac operator Elution with lactose analogue [64]
Phosphofructokinase Saccharomyces cerevisiae extract PEG with bound Cibacron blue Magnetic two-phase system [53]
6-Phosphogluconate dehydrogenase Ferrofluid modified Sepharose 4B ADP Elution with 1 mM NADP [13]
Thioredoxin (recombinant, histidine-tagged) Escherichia coli Magnetic agarose Ni-NTA Elution with imidazole containing buffer [20]
tRNA methionyl synthetase (recombinant, histidine-tagged) Escherichia coli MagneHis™ system Ni2+ Rapid detection and quantitation of isolated protein [85]
Uricase (recombinant, histidine-tailed) Bacillus Ion-chelating magnetic agarose beads Ni2+ Elution by cleavage with proteinase K [92]
Table 5 Examples of antibodies purified by magnetic techniques
Purified antibody Source Magnetic carrier Affinity ligand Further details Reference
Anti-BODIPY-fluorescein antibodies Magnetoliposomes BODIPY-fluorescein [94]
Anti-DNA antibody Systemic lupus erythematosus patient plasma Magnetic poly(2-hydroxyethyl-methacrylate) beads DNA Desorption with 1 M NaSCN solution [95]
Anti-human chorionic gonadotropin antibody Murine ascites supernatants Magnetic cellulose beads Human chorionic gonadotropin [96]
Antibody (from rat) Sample from affinity chromatography Dynabeads M-280 Sheep anti-rabbit IgG Antibody concentration [3]
Antibody Rabbit serum Dynabeads M-280 Sheep anti-rabbit IgG Elution with 0.5 M acetic acid [97]
Monoclonal antibodies Mouse hybridoma culture broth Magnetite particles Protein A [98]
Anti-bovine serum albumin antibodies Thermosensitive magnetic microspheres Bovine serum albumin Immobilization by the carbodiimide method [99]
Immunoglobulin G, human Commercial preparation Magnetic poly(ethylene glycol dimethacrylate-N-methacryloly-L-histidine-methylester) beads Elution with 1 M NaCl [100]
Immunoglobulin G Blood serum Carboxyl-terminated magnetic particles MproteinAG [101]
IgE antibodies Allergic patients sera Magnetoliposomes Antigenic proteins [16]
Murine anti-fibroblast growth factor receptor 1 IgM Ascites Polystyrene magnetic beads Rat anti-mouse IgM monoclonal antibody [102]
Table 6 Examples of DNA/RNA/oligonucleotide/aptamer binding proteins purified by magnetic techniques
Purified protein Source Magnetic carrier Affinity ligand Further details Reference
CUG binding proteins Human myoblasts or fibroblasts Dynabeads M-280 streptavidin Biotinylated(CUG)10 Elution with 1 M NaCl [103]
Transcription factor τ Saccharomyces cerevisiae Dynabeads M-280 streptavidin Biotinylated tRNAGlu gene fragment Elution with high salt buffer [104,105]
DNA-binding proteins Crude tissue extract Magnetic phospho cellulose particles [106]
DNA-binding proteins Escherichia coli Magnetic phospho cellulose particles [107]
DNA-binding proteins HeLa nuclear extracts Dynabeads M-280 streptavidin Biotin-labelled DNA fragment Elution with 2 M NaCl [108]
Vaccinia virus early transcription factor Vaccinia virions Dynabeads M-280 streptavidin Biotinylated double-stranded DNA Elution with high salt buffer [109]
Ecdysteroid receptor Drosophila melanogaster nuclear extract Dynabeads M-280 streptavidin Biotinylated double-stranded oligonucleotide Elution with 0.4 M KCl [110]
NanR protein (recombinant) Escherichia coli μMACS streptavidin MicroBeads Biotin-labelled DNA fragment Elution with 1 M NaCl [111]
p27 Rabbit hepatocytes Dynabeads M-280 streptavidin Guanine-rich single-stranded DNA Elution with NaCl solution [112]
Pigpen protein Endothelial cells Magnetic streptavidin beads Biotinylated aptamer Elution with 1 M NaCl [113]
RNA binding proteins Saccharomyces cerevisiae μMACS streptavidin MicroBeads Biotin-labelled RNA probe Elution with 1 M NaCl [114]
Single-stranded telomere binding protein (sTBP) Nuclei from vertebrate tissues Dynabeads M-280 streptavidin Biotinylated single stranded TTAGGGn repeats Elution with high salt buffer [115]
Transcription proteins Human myeloid cells Dynabeads M-280 streptavidin Biotinylated serum inducible element (hSIE) Elution with high salt buffer [116]
Transcription factor γRF-1 Human monocytes and epidermal cells Dynabeads M-280 streptavidin Biotinylated DNA containing γRF-1 sequences Elution with 0.6 M KCl [117]
Protein factor MS2 Murine skeletal myotubes Dynabeads Double-stranded DNA Elution with 100 mM sodium acetate, pH 4.2 [118]
Guide RNA binding protein Trypanosoma brucei mitochondria Dynabeads M-450 goat anti-mouse IgG Monoclonal antibody Elution with low pH buffer cont. SDS [119]
RNA binding proteins Pollen grains Streptavidin MagneSphere particles Biotinylated oligonucleotides Elution with SDS buffer [120]
DNA binding protein Schistosoma mansoni Dynabeads M-280 streptavidin Biotinylated DNA Elution with sodium acetate buffer [121]
ssDNA binding proteins Transfected mouse fibroblasts Dynabeads anti-rabbit IgG Rabbit antibody Indirect method [122]
Tenascin-C Glioblastoma cells Dynabeads streptavidin Biotinylated aptamer Elution with high salt buffer [123]
Thermostable brain factor (ThBF) Rat brain Streptavidin magnetic particles Biotinylated oligonucleotides Elution with 0.7 M KCl [124]
TTF1 protein Escherichia coli lysate Dynabeads M-280 streptavidin Biotinylated aptamer Elution with DNase [125]
Table 7 Purification of albumin and haemoglobin by magnetic techniques
Purified protein Source Magnetic carrier Affinity ligand Further details Reference
Albumin, bovine serum Commercial preparation Magnetic agar beads Cibacron blue3GA Adsorption experiments [126]
Commercial preparation Magnetic cross-linked polyvinylalcohol Cibacron blue3GA Adsorption experiments [76,127]
Magnetic chitosan microspheres Cibacron blue3GA [78]
Commercial preparation Magnetic poly(glycidyl methacrylate-triallyl isocyanurate-divinylbenzene) particles Anion exchange separation [128]
Commercial preparation Magnetic poly(ethylene glycol dimethacrylate-co-N-methacryloyl-(L)-histidine methyl ester) microbeads Cu2+ Elution with 1.0 M NaSCN [129]
Albumin, human serum Commercial preparation Magnetic poly(2-hydroxyethylmethacrylate) beads Iminodiacetic acid charged with Cu2+ Elution with 1.0 M NaSCN [130]
Human plasma Magnetic poly(2-hydroxyethyl methacrylate) beads Cibacron blue F3GA Elution with 0.5 M NaSCN [131]
Commercial preparation Magnetic particles covered with thermosensitive polymer - Desorption by decreasing temperature [132,133]
Albumin, human serum (recombinant, FLAG tagged) Yeast cells Magnetic glass beads Anti-FLAG antibody Elution with EDTA containing buffer [1]
Glycated haemoglobin Human blood Magnetic poly(vinyl alcohol) beads m-Aminophenyl-boronic acid Elution with sorbitol [138]
Haemoglobin Bovine, commercial preparation Amine terminated iron oxide particles Iminodiacetic acid charged with Cu2+ Elution with imidazole containing buffer [63]
Haemoglobin A1c Human blood Magnetic particles isolated from Magnetospirillum magneticum AMB-1 m-Aminophenyl-boronic acid used for affinity immunoassay [150]
Table 8 Examples of other proteins purified by magnetic techniques
Purified protein Source Magnetic carrier Affinity ligand Further details Reference
Aprotinin Bovine pancreatic powder Magnetic chitosan particles Trypsin Elution with low pH buffer [134]
Concanavalin A Jack bean extract Magnetic particles Dextran [68,135]
Solanum tuberosum lectin Potato tuber Magnetic chitosan Elution with low pH buffer [136]
Green fluorescent protein (histidine tagged) Magnetic nanoparticles Ni-NTA Elution with imidazole containing buffer [137]
SIRT2 protein (recombinant, histidine tailed) Human Magnetic agarose beads Ni-NTA Elution with imidazole containing buffer [139]
Elongation factor (recombinant, histidine tailed) Caenorhabditis elegans Magnetic agarose beads Ni-NTA Elution with imidazole containing buffer [140]
Protein A Recombinant Escherichia coli Magnetic Eudragit Human IgG Magnetic two-phase system [141]
Tumor necrosis factor (TNF) Dynabeads M-280 Mouse monoclonal antibody Solid phase immunoassay [142]
Anti-MUC1 diabody fragment Recombinant Escherichia coli Magnetic agarose beads Specific peptide [143]
MHC class II molecules MDCK cells Dynabeads M-450 rat anti-mouse IgG1 Specific antibodies Elution with SDS-PAGE buffer [144]
Lamin B3 Xenopus egg extracts Dynabeads Specific antibodies Elution with 6 M urea [44]
6x-His-tagged proteins Human fibroblasts Magnetic agarose beads Ni-NTA Elution with imidazole containing buffer [145]
Estrogen receptor Adipose tissue Dynabeads M-280 streptavidin Biotinylated monoclonal mouse anti-human estrogen receptor antibody Indirect method [146]
Thiol-reactive chromatin restriction fragments Mouse fibroblasts Mercurated agarose magnetic beads p-Hydroxymercuribenzoate Elution with 0.5 M NaCl and 20 mM dithiothreitol [147]
L1 coat protein Human papillomavirus Magnetic polyglutaraldehyde particles Iminodiacetic acid charged with Cu2+ Elution with imidazole containing buffer [41]
Insulin receptor Rat muscle or liver extract Dynabeads M-450 Anti-P5 antibody SDS PAGE analysis [148]
Stat3 DER cells Dynabeads Biotinylated tyrosine phosphorylated peptides SDS PAGE analysis [149]
Transferrin receptor Human Dynabeads M-450 sheep anti-mouse IgG1 Anti-human transferrin receptor monoclonal antibody SDS analysis [151]
Prion protein PrPSc Brain extract Dynabeads M-280 tosyl activated Plasminogen SDS analysis [39,40]
Biotinylated proteins from extracellular matrix Bipolaris sorokiniana Dynabeads Streptavidin SDS analysis [152]
Cryoprotectin Leaves of cold-acclimated cabbage (Brassica oleracea) Dynabeads-protein A Specific antibody [153]
Prostate specific antigen Serum from a prostate cancer suffering patient Streptavidin-coated magnetic beads Biotinylated monoclonal antibody Elution with low pH solution [154,155]
Estrogen receptor In vitro translation Magnetic beads Antibody Elution with SDS buffer [156]
VHDL receptor Helicoverpa zea Streptavidin-coated magnetic beads VHDL-biotin ligand [157]
Fructosyllysine-specific binding protein U937 cells Dynabeads M-280 tosylactivated Poly-L-lysine-glucose conjugate Two proteins isolated [158]
Ubiquitin (histidine tagged) Nickel-gold nanorods Elution with acidic buffer [18]
Table 9 Examples of peptides purified by magnetic techniques
Purified peptide Source Magnetic carrier Affinity ligand Further details Reference
Biotinylated peptides Model mixtures Dynabeads M-280 streptavidin Streptavidin Used in MALDI-TOF mass analysis [159]
(His)6-Ala-Tyr-Gly Synthetic peptide Dynabeads M-280 tosylactivated Aminocaproic nitrilotriacetic acid charged with Ni2+ Elution with imidazole solution [160]
Synthetic pentapeptides against fructose-1,6-biphosphate aldolase Synthetic mixture Streptavidin-coated magnetic beads Biotin labelled fructose-1,6-biphosphate aldolase of T. brucei Pentapeptides were anchored on polystyrene beads [161]
Tryptic digest products of cytochrome c Trypsin digested cytochrome c Au@magnetic particles - Ion-exchange separation followed by MALDI MS analysis [31]
Glutathione Gold and iron oxide nanocomposites [162]
Nisin Z Lactobacillus lactis EDC activated magnetic beads Anti-nisin antibody Elution with 6 M urea [163]
In the case of proteins and peptides purifications, no simple strategy for magnetic affinity separations exists. Various affinity ligands have been immobilised on magnetic particles, or magnetic particles have been prepared from biopolymers exhibiting the affinity for target enzymes or lectins. Immunomagnetic particles, i.e. magnetic particles with immobilised specific antibodies against the target structures, have been used for the isolation of various antigens, both molecules and cells [5] and can thus be used for the separation of specific proteins.
Magnetic separation procedures can be employed in several ways. Preparative isolation of the target protein or peptide is usually necessary if further detailed study is intended. In other cases, however, the magnetic separation can be directly followed (after elution with an appropriate buffer) with SDS electrophoresis. Magnetically separated proteins and peptides can also be used for further mass spectroscopy characterization [30,31]. The basic principles of magnetic separations can be used in the course of protein or peptide determination using various types of solid phase immunoassays. Usually immunomagnetic particles directly capture the target analyte, or magnetic particles with immobilised streptavidin are used to capture the complex of biotinylated primary antibody and the analyte. The separated analyte is then determined (usually without elution) using an appropriate method. A combination of magnetic separation with affinity capillary electrophoresis is also possible [32].
Enzyme isolation is usually performed using immobilised inhibitors, cofactors, dyes or other suitable ligands, or magnetic beads prepared from affinity biopolymers can be used (see Tables 1, 2, 3, 4).
Genetic engineering enables the construction of gene fusions resulting in fusion proteins having the combined properties of the original gene products. To date, a large number of different gene fusion systems, involving fusion partners that range in size from one amino acid to whole proteins, capable of selective interaction with a ligand immobilized onto magnetic particles or chromatography matrices, have been described. In such systems, different types of interactions, such as enzyme-substrate, receptor-target protein, polyhistidines-metal ion, and antibody-antigen, have been utilized. The conditions for purification differ from system to system and the environment tolerated by the target protein is an important factor for deciding which affinity fusion partner to choose. In addition, other factors, including protein localization, costs for the affinity matrix and buffers, and the possibilities of removing the fusion partner by site-specific cleavage, should also be considered [33,34]. As an example, isolation of recombinant oligohistidine-tagged proteins is based on the application of metal chelate magnetic adsorbents [35,36]. This method has been used successfully for the purification of proteins expressed in bacterial, mammalian, and insect systems.
Antibodies from ascites, serum and tissue culture supernatants can be efficiently isolated using magnetic particles with immobilized Protein A, Protein G or anti-immunoglobulin antibodies. Protein A, isolated from Staphylococcus aureus, binds the Fc region of IgG of most mammalian species with high affinity, leaving antigen specific sites free. Protein G, isolated from Streptococcus sp., reacts with a larger number of IgG isotypes. It has a higher binding affinity to immunoglobulins than Protein A, however, it also interacts with the Fab regions of IgG, although the affinity is ten times lower than for the Fc region [37]. Antiphospholipid antibodies were successfully isolated using magnetoliposomes [15].
Aptamers are DNA or RNA molecules that have been selected from random pools based on their ability to bind other molecules. Aptamers binding proteins can be immobilised to magnetic particles and used for isolation of target proteins.
DNA/RNA binding proteins (e.g., promoters, gene regulatory proteins and transcription factors) are often short-lived and in low abundance. A rapid and sensitive method, based on the immobilization of biotinylated DNA/RNA fragments containing the specific binding sequence to the magnetic streptavidin particles, can be used. The bound DNA/RNA binding proteins are usually eluted with high salt buffer or change of pH [38].
Other types of proteins were isolated using specific affinity-based procedures. For example, plasminogen immobilized on magnetic particles was used to separate scrapie and bovine spongiform encephalopathy associated prion protein PrPSc from its conformer which is a cellular protein called PrPC. In fact, plasminogen represents the first endogenous factor discriminating between normal and pathological prion protein. This unexpected property may be exploited for diagnostic purposes [39,40].
Magnetic separation was also successfully used for the recovery of proteins expressed in the form of inclusion bodies, involving at first chemical extraction from the host cells, then adsorptive capture of the target protein onto small magnetic adsorbents, followed by rapid collection of the product-loaded supports with the aid of high gradient magnetic fields [41].
A new approach for analytical ion-exchange separation of native proteins and proteins enzymatic digest products has been described recently [31]. Magnetite particles were covered with a gold layer and then stabilized with ionic agents. These charged stabilizers present at the surface of the gold particles are capable of attracting oppositely charged species from a sample solution through electrostatic interactions. Au@magnetic particles having negatively charged surfaces are suitable probes for selectively trapping positively charged proteins and peptides from aqueous solutions. The species trapped by the isolated particles were then characterized by matrix-assisted laser desorption/ionization mass spectrometry (MALDI MS) after a simple washing.
Magnetic solid phase extraction (MSPE) enables to preconcentrate target analytes from larger volumes of solutions or suspensions using relatively small amount of magnetic specific adsorbent. Up to now this procedure was used for preconcentration of low-molecular weight xenobiotics [42,43] but using suitable magnetic adsorbents the MSPE could be used to preconcetrate target proteins and peptides as well.
Sometimes the removal of certain proteins will reveal functions involving the depleted proteins or will help in the course of subsequent protein isolation. As an example, Dynabeads have been used to remove involved proteins from Xenopus egg extracts for analyses of the cell mitosis mechanisms [44,45]. Rapid removal of contaminating proteolytic enzymes from the crude samples could increase yields of sensitive proteins due to the limitation of their proteolysis [46].
A combination of mechanical cell disintegration and magnetic batch affinity adsorption was used to simplify the isolation of intracellular proteins. Magnetic glass beads were used because of their hardness and rigidity [1].
An example of quite different protein purification strategy can also be mentioned. Proteins associated with the endocytic vesicles of Dictyostelium discoideum were separated after magnetic isolation of the vesicles that was accomplished by feeding the amoebae with dextran-stabilized iron oxide particles. The cells were broken, the labelled vesicles were magnetically separated and then disrupted to release proteins which were resolved by SDS-PAGE. After „in-gel“ digestion with endoproteinase Lys-C or Asp-N the generated peptides were used for amino acid sequencing. This strategy allowed the identification of the major protein constituents of the vesicles [47]. Analogous procedure was used for the separation and study of peroxisomes proteins when at first peroxisomes were separated using magnetic beads with immobilized specific antibodies and then the protein content of the separated peroxisomes was analysed [48].
Conclusions
Standard liquid column chromatography is currently the most often used technique for the isolation and purification of target proteins and peptides. Magnetic separation techniques are relatively new and still under development. Magnetic affinity particles are currently used mainly in molecular biology (especially for nucleic acids separation), cell biology and microbiology (separation of target cells) and as parts of the procedures for the determination of selected analytes using magnetic ELISA and related techniques (especially determination of clinical markers and environmental contaminants). Up to now separations in small scale prevail and thus the full potential of these techniques has not been fully exploited.
It can be expected that further development will be focused at least on two areas. The first one will be focused on the laboratory scale application of magnetic affinity separation techniques in biochemistry and related areas (rapid isolation of a variety of both low- and high-molecular weight substances of various origin directly from crude samples thus reducing the number of purification steps) and in biochemical analysis (application of immunomagnetic particles for separation of target proteins from the mixture followed by their detection using ELISA and related principles). Such a type of analysis will enable to construct portable assay systems enabling e.g. near-patient analysis of various protein disease markers. New methodologies, such as the application of chip and microfluidics technologies, may result in the development of magnetic separation processes capable of magnetic separation and detection of extremely small amount of target biologically active compounds [49].
In the second area, larger-scale (industrial) systems are believed to be developed and used for the isolation of biologically active compounds directly from crude culture media, wastes from food industry etc., integrating three classical steps (clarification, concentration and initial purification) into a single unit operation [50]. It is not expected that extremely large amounts of low cost products will be isolated using magnetic techniques, but the attention should be focused onto the isolation of minor, but highly valuable components present in raw materials. Of course, prices of magnetic carriers have to be lowered and special types of low-cost, biotechnology applicable magnetic carriers and adsorbents prepared by simple and cheap procedures have to become available. The existence of inexpensive and effective magnetic separators enabling large-scale operations is necessary, as well.
In the near future quite new separation strategies can appear. A novel magnetic separation method, which utilizes the magneto-Archimedes levitation, has been described recently and applied to separation of biological materials. By using the feature that the stable levitation position under a magnetic field depends on the density and magnetic susceptibility of materials, it was possible to separate biological materials such as haemoglobin, fibrinogen, cholesterol, and so on. So far, the difference of magnetic properties was not utilized for the separation of biological materials. Magneto-Archimedes separation may be another way for biological materials separation [51].
It can be expected that magnetic separations will be used regularly both in biochemical laboratories and biotechnology industry in the near future.
Acknowledgements
The research is a part of ILE Research Intention No. AV0Z6087904. The work was supported by the Ministry of Education of the Czech Republic (Project No. ME 583) and Grant Agency of the Czech Academy of Sciences (Project No. IBS6087204).
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| 15566570 | PMC544596 | CC BY | 2021-01-04 16:37:41 | no | Biomagn Res Technol. 2004 Nov 26; 2:7 | utf-8 | Biomagn Res Technol | 2,004 | 10.1186/1477-044X-2-7 | oa_comm |
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Virol JVirology Journal1743-422XBioMed Central London 1743-422X-1-141561055910.1186/1743-422X-1-14ResearchMurine leukemia virus (MLV) replication monitored with fluorescent proteins Sliva Katja [email protected] Otto [email protected] Alexandra [email protected] Barbara S [email protected] Institute for Biomedical Research, Georg-Speyer-Haus, Paul-Ehrlich-Str. 42-44, 60596 Frankfurt/Main, Germany2 Paul-Ehrlich-Institute, Paul-Ehrlich-Str. 51-59, 63225 Langen, Germany2004 20 12 2004 1 14 14 26 11 2004 20 12 2004 Copyright © 2004 Sliva et al; licensee BioMed Central Ltd.2004Sliva 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
Cancer gene therapy will benefit from vectors that are able to replicate in tumor tissue and cause a bystander effect. Replication-competent murine leukemia virus (MLV) has been described to have potential as cancer therapeutics, however, MLV infection does not cause a cytopathic effect in the infected cell and viral replication can only be studied by immunostaining or measurement of reverse transcriptase activity.
Results
We inserted the coding sequences for green fluorescent protein (GFP) into the proline-rich region (PRR) of the ecotropic envelope protein (Env) and were able to fluorescently label MLV. This allowed us to directly monitor viral replication and attachment to target cells by flow cytometry. We used this method to study viral replication of recombinant MLVs and split viral genomes, which were generated by replacement of the MLV env gene with the red fluorescent protein (RFP) and separately cloning GFP-Env into a retroviral vector. Co-transfection of both plasmids into target cells resulted in the generation of semi-replicative vectors, and the two color labeling allowed to determine the distribution of the individual genomes in the target cells and was indicative for the occurrence of recombination events.
Conclusions
Fluorescently labeled MLVs are excellent tools for the study of factors that influence viral replication and can be used to optimize MLV-based replication-competent viruses or vectors for gene therapy.
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Background
Efficient and long-lasting gene delivery is the major challenge in the development of vectors for gene therapy. Replication-competent retroviruses (RCRs) encoding suicide genes linked via an internal ribosome entry site (IRES) offer a significant advantage over replication-deficient vectors in cancer gene therapy, since they are able to spread efficiently in vivo [1-4]. Uncontrolled virus spread is, however, associated with serious risk of adverse events due to viral-integration mutagenesis. Therefore, for a therapeutic application, RCRs have to be equipped with additional safety features, e.g. transcription controllable by exogenous agents or viral entry restricted to the diseased cells. The selective delivery of a therapeutic gene by targeting retroviral entry would immensely reduce unfavorable side effects and ease the clinical application of gene therapy. The ecotropic MLV envelope protein does not recognizes receptors on human cells. An obvious challenge has been to extend the host range of vectors carrying the ecotropic envelope glycoprotein to a predetermined human cell type. This change in host range requires the inclusion of a novel attachment site and the induction of fusion via a novel receptor interaction. It has been shown before that it is possible to modify ecotropic Env and change its binding specificity, however, the efficient triggering of the membrane fusion or the escape from endosomes of viral particles targeted to e.g. epidermal growth factor (EGF)-receptor is still missing [5,6]. The further development of such targeted vectors requires the understanding of the mechanisms that are involved in adsorption and internalization of retroviruses.
Investigating murine leukemia virus (MLV) replication is technically inconvenient because MLV infection does not cause a cytopathic effect in the infected cell. Viral replication can only be studied by immunostaining, measurement of reverse transcriptase activity or syncytia formation. We have developed a tool to simplify these analyses. We generated an MLV tagged with a fluorescent envelope protein, which allows viral replication and Env attachment to target cells to be followed by flow cytometry. This method will be useful for optimizing RCRs or retroviral vectors for gene therapy.
Results
Construction of GFP-tagged MLVs and their replication
We previously constructed a modified ecotropic murine leukemia virus (Mo-MLV) bearing the green fluorescent protein (GFP) from Aequoria victoria in its envelope. A replication competent ecotropic MLV variant was generated (GFP-EMO) that had the 53 aas of the epidermal growth factor (EGF) fused to the N-terminus of Env and the GFP sequences inserted into the proline-rich region (PRR) [7]. We deleted the EGF sequences by replacing a Pfl MI fragment of GFP-EMO with wt sequences. This resulted in a replication-competent virus expressing the chimeric GFP-Env protein (GFP-MOV) (Fig. 1A). NIH3T3 cells were transfected with 10 μg plasmid DNA encoding GFP-MOV or GFP-EMO using the calcium-phosphate procedure and were cultured for 13 days. Viral replication was monitored as GFP-positive cells by flow cytometry. As indicated in Figure 1B, both viruses replicate with similar kinetics. Untransfected NIH3T3 cells did not show green fluorescence.
Figure 1 Generation and replication of the GFP-Env-tagged viruses. (A) Schematic representation of the GFP-Env-tagged viruses. EGF, epidermal growth factor; PRR, proline rich region; GFP, green fluorescent protein; L, signal peptide.(B) Viral replication kinetic in transfected NIH3T3 cells monitored by the percentage of GFP-positive cells.(C) PCR analysis of genomic DNA from FLY-Jet cells transfected with GFP-EMO. The N-terminal sequences of the EGF-Env gene were analyzed by PCR using the primers MLV-5'-Env and BS-5. GFP-EMO plasmid DNA was used as a positive control and gave rise to a 900 bp fragment. Predominantly faster migrating fragments were amplified from genomic DNA (gDNA) of GFP-EMO transfected FLY-Jet cells 32 days after transfection.
Sequestering of EGF-Env-containing viral particles has been described before [8,9]. Viral particles containing EGF-Env were rapidly trafficked to endosomes and became degraded. This effect was dominant over the normal entry pathway, because mouse cells expressing the ecotropic receptor and the EGF-receptor showed a severely decreased infectivity of EGF-Env containing vectors [8]. We were interested, if replication competent GFP-EMO might be useful to select viral variants able to escape the degradation in the endosomes. Transfection of GFP-EMO into cells expressing only the EGF-receptor (A431, COS-7) did not result in viral replication (data not shown). Therefore, GFP-EMO and GFP-MOV were transfected into FLY-Jet cells [10], which express the human EGF-receptor and the receptor for ecotropic MLV. Viral replication of GFP-EMO could be observed in FLY-Jet cells, although strongly delayed, after 10 days only 7.4 % of the cells were GFP-positive. After 38 days, all cells were GFP-positive and the N-terminus of the Env gene was analyzed by PCR amplification of genomic DNA isolated from infected cells. Predominantly a band migrating faster than the GFP-EMO fragment was amplified (Figure 1C), which was verified by sequence analysis to contain wt Env sequences. The less abundant, slower migrating fragments still contained the EGF sequences in Env. This confirms the sequestering of EGF-Env containing retroviral particles via the EGF-receptor. The selection of viruses able to escape the endosomal degradation was not possible and shows that degradation of viral particles in the endosomes favors the selection of wt Env-containing MLV, which escapes the sequestering by EGF-receptor.
Cell binding of GFP-tagged MLV
Viral entry is initiated by the binding of the envelope protein (Env) to the retrovirus receptor at the target cell surface. To test whether labeling of Env with GFP allows viral attachment to be monitored, we incubated supernatants of NIH3T3 cells producing GFP-EMO or GFP-MOV with cells that either express mCAT, the receptor for ecotropic MLV [11] (NIH3T3), do not express it (293T, A431) or do express the human EGF receptor (A431). As illustrated in Figure 2A, NIH3T3 cells incubated with cell culture supernatants showed a shift to green fluorescence, indicating specific binding of GFP-tagged Env to mCAT. The shift to green fluorescence could not be increased by larger amounts of viral supernatants or longer incubation times (data not shown), which shows that already after 5 min. all receptors are occupied by Env. For GFP-MOV supernatants a shift in fluorescence was only observed with mCAT-expressing cells, while GFP-EMO supernatants also produced a shift with A431 cells. This indicates additional specific binding to the EGF receptor. The shift was more pronounced on A431 cells than COS-7 cells, correlating with the amount of EGF receptor expressed by the target cells (data not shown).
Figure 2 Binding of GFP-Env to cells. (A) Supernatants of GFP-EMO- or GFP-MOV-infected NIH3T3 cells were incubated with the indicated target cells and analyzed by flow cytometry. Binding of GFP-Env was detected by a shift to green fluorescence (FL-1).(B) Supernatants from GFP-MOV-infected NIH3T3 cells were incubated with the indicated target cells and analyzed by flow cytometry. Soluble receptor binding domains of the ecotropic or the amphotropic MLV Env (E-sRBD, A-sRBD) were added prior to the virus, as supernatants from 293T cells transfected with the expression constructs. After 5 mins., supernatants of GFP-MOV-infected NIH3T3 cells were added for an additional 5 mins. Binding of GFP-Env was detected by a shift to green fluorescence (FL-1). NIH3T3i-MLV: chronically MLV-infected NIH3T3 cells.
The specificity of cell staining by supernatants containing GFP-MOV was further examined using chronically Mo-MLV-infected NIH3T3 cells (NIH3T3i-MLV). These cells have only negligible numbers of mCAT molecules on the cell surface, because Env expression leads to their retention within the cell (receptor interference). As expected, NIH3T3i-MLV cells produced no shift when incubated with GFP-MOV supernatants (Fig. 2B). Furthermore, binding of GFP-MOV supernatants could be inhibited by preincubation of NIH3T3 target cells with a soluble Env fragment containing the receptor binding domain (sRBD) derived from the ecotropic Env [12], but not with the equivalent sRBD derived from the amphotropic Env [12], which binds to a different receptor (Fig. 2B). This shows that GFP-tagging can be used to investigate Env-binding properties by flow cytometry.
Replication of semi-replicative retroviral vectors
The size of a retroviral genome is limited to roughly 11 kb. The capacity for the insertion of a therapeutic gene for gene therapy is, however, increased by the use of semi-replicative retroviral vectors (SRRVs), where the gag/pol and env genes are split between two viral genomes. We constructed split viral genomes and used fluorescent proteins to monitor the replication of the resulting SRRVs.
A packagable MLV Gag/Pol expression vector, GAG/POL-RFP, was generated by deleting of the env gene and replacing it with the red fluorescent protein (RFP) (Fig. 3). RFP is encoded by the spliced mRNA and its expression can be monitored by red fluorescence (Fig. 4C). The GFP-Env protein was cloned into the retroviral vector pczCFG5 IEGZ (Lindemann, unpublished) (Fig. 3). This vector has additional GFP sequences linked via an IRES element, but GFP expression derived from IRES-GFP in transduced cells is barely detectable. GFP expressing cells always showed staining of the endoplasmatic reticulum (ER)/Golgi and plasma membrane but not of the nucleus. This is the expected pattern for Env, indicating that the green fluorescence detected derived from GFP-Env (Fig. 4B). Co-transfection of equal amounts of both plasmids into NIH3T3 cells resulted in the spread of both genomes, which was detecteable by the appearance of green and red fluorescence (Fig. 4A, green, red and double positive). Separation of the viral genomes strongly delayed viral growth and we did not observe 100% double-positive cells in any of the transfections. Since the expression of Env in the target cell leads to receptor down-regulation (receptor interference), Env-expressing cells should no longer be transducible. This could explain the selected appearance of GFP-positive cells, but their rapid increase starting day 12 also points towards the generation of full-length MLV genomes containing GFP-Env. We therefore, analyzed the integrity of the viral genomes by PCR. Both split genomes were co-transfected in different ratios into NIH3T3 cells and genomic DNA was isolated at the time points indicated in Figure 5. Primers derived from the pol and the env regions (p1, p2; Fig. 3) were used to study the generation of full-length MLV from the split genomes. As indicated in Figure 5A, lane 3, a 600 bp fragment can be amplified from full-length MLV DNA using these primers. The split genomes do not give rise to a DNA fragment, because the primer binding sites are on separate genomes (Fig. 5A, lane 2). After 13 days of culture, the appearance of a full-length MLV recombinant could be observed when the vector genomes were co-transfected in a ratio of 1:1 (gag/pol:env) (Fig. 5A, lane 5) and after 32 days, wt MLV could be detected in all samples (Fig. 5A, lanes 9, 10 and 11). This illustrates that full-length MLV was generated from the split viral genomes after prolonged passage.
Figure 3 Schematic representation of fluorescently labeled semi-replicative retroviral vectors. The env open reading frame was replaced with the gene for red fluorescent protein (RFP) in the gag/pol-expressing construct, GAG/POL-RFP, and GFP-tagged Env was expressed from a packagable vector (GFP-Env). Positions of primers used to analyze the appearance of replication-competent viruses and the stability of the inserted GFP sequences by polymerase chain reactions (PCR) are indicated as p1 to p4. SA: splice acceptor site; SD: splice donor site.
Figure 4 eplication of semi-replicative retroviral vectors. (A) Replication of semi-replicative retroviral vectors in transfected NIH3T3 cells, monitored by detection of green, red or double fluorescent cells by flow cytometry.(B) NIH3T3 cells expressing GFP-Env. The green fluorescence of the GFP-Env fusion protein can be detected in regions surrounding the nucleus (ER/golgi) and in the plasma membrane.(C) NIH3T3 cells expressing GAG/POL-RFP. RFP expression can be detected all over the cell, since RFP is not fused to a viral protein and is able to freely diffuse.
Figure 5 PCR analysis of genomic DNA from NIH3T3 cells transfected with semi-replicative retroviral vectors. (A) The generation of full-length MLV genomes was analyzed by PCR using the primers p1 and p2 (see Fig. 4). Full-length MLV generates an 800 bp PCR fragment, semi-replicative retroviral vectors should not give rise to a DNA fragment because the primers do not bind to the same genome. DNA was transfected in different molar ratios as indicated. The first number indicates the molar ratio of the gag/pol plasmid and the second the Env encoding plasmid.(B) The stability of the GFP sequences inserted into the Env gene was analyzed by PCR using the primers p3 and p4 (see Fig. 4). The gfp-env sequence gives rise to a 1.5 kb fragment and wt env to an 800 bp fragment. Untransfected NIH3T3 cells were cultured in parallel and analyzed identically. The data are given as negative at days 13 and 32. NTC, no template control.
In addition, we examined the stability of the GFP-tagged Env in the split genome approach. As shown in Figure 5B, PCR analysis with primers flanking the GFP sequences in Env (p3, p4; Fig. 3) clearly demonstrated that GFP-Env is stable and the GFP sequences were not deleted from the viral genome after 32 days of culture (Fig. 5B, lanes 5, 6 and 7).
Discussion
Our data demonstrate that labeling the MLV Env with a fluorescent protein is an easy method of monitoring MLV replication and the attachment of Env to target cells. This is especially useful for the development of novel cancer gene therapies that use replication-competent MLV encoding a cytotoxic gene [3]. Labeling Env with GFP in the PRR leaves the 3' untranslated region at the Env boundary available for the insertion of IRES-linked therapeutic genes [1]. These recombinant viruses could be monitored by GFP expression and would allow the study of replication kinetics in vitro and in vivo. The biodistribution of replication-competent viruses in animal models and their safety for cancer treatment could, thereby, be assessed.
A further improvement of replication-competent viruses would be tumor cell-specific entry. The inclusion of tumor-specific ligands into Env is one option to potentially expand the ecotropic host range of MLV to human tumor cells [6,5]. Ecotropic MLV containing GFP-tagged Env can be used to analyze the receptor-dependent binding of the viral Env proteins to target cells. Labeling Env in the PRR leaves the N-terminus or the receptor binding site [13] available for further insertions of ligands to target tumor cell specific receptors. The use of GFP-tagged Env to determine receptor binding is very simple and in addition GFP-tagged Envs are helpful for the identification of recombinant viruses from retroviral library screens. GFP-Env fusions will therefore be very useful for the development of targeted vectors and as a screening system for retroviral-receptor antagonists. However, selecting EGF-Env containing MLV on cells that express both receptors (EGF- and ecotropic receptor) did not permit the isolation of a virus with an EGF-receptor specific tropism. EGF sequences were deleted from the viral genome in this setting. EGF sequences in Env, however, did not alter the replication kinetics in mouse fibroblasts (Fig. 1), which further indicates that targeting retroviruses to membrane spanning receptor tyrosine kinases inactivates retroviral particles.
In our experiments using semi-replicative retroviral vectors, we found that a rapid increase in GFP-positive cells correlated with the appearance of recombinations and the formation of full-length MLV genomes. This indicates that semi-replicative vectors have to be improved to avoid intergenomic recombination before they can be considered to be used for gene therapy. The recombinants did contain the GFP-Env gene, providing further proof that insertion of GFP into the proline-rich region of Env did not interfere with viral fitness.
Conclusions
Fluorescently labeled MLVs are excellent tools for the study of factors that influence viral replication and can be used to optimize MLV-based vectors or viruses for gene therapy. This method is not limited to ecotropic Env, but can be extended to amphotropic MLV, since it has been shown recently that the amphotropic MLV Env can also be tagged with GFP [14].
Methods
Cell lines
NIH3T3, A431, 293T and COS-7 cells were grown in Dulbecco's modified Eagle's medium (Gibco) supplemented with 10% fetal calf serum, 4 mM L-glutamine, 100 U/ml penicillin and 100 μg/ml streptomycin at 37°C in 10% CO2.
Plasmids
The construction of GFP-EMO has been described previously [7]. GFP-MOV was generated by replacing a Pfl MI fragment of pGFP-EMO with wt MLV sequences using standard cloning procedures [15]. GAG/POL-RFP was generated starting with the genomic MLV clone, pKAΔenv-egfp, which contains a 30 nucleotide-linker with an Sfi I-site introduced at position 5893 (all positions according to GenBank Accession No. J02255) and an additional Sfi I-site at position 5389 removed by mutation. The start codon of MLV env (position 5777) was deleted to allow translation to start at the inserted GFP sequence [16]. We replaced GFP with RFP, which was introduced as a Sfi I-Cla I fragment. GFP and RFP sequences were derived from vectors purchased from Clontech (BD Biosciences Clontech, Heidelberg, Germany)
Transfections
Plasmids encoding the MLV genomes or soluble receptor binding fragments (sRBDs) [12] were transfected using the calcium phosphate procedure [15]. For the sRBDs, supernatant was collected two days after transfection, filtered through a 0.45 μm pore filter (Millipore, Eschborn, Germany) and 1 ml was used per binding assay.
Cell binding assay
Supernatants of tissue culture cells were collected, filtered through a 0.45 μm pore filter (Millipore, Eschborn, Germany) and added to target cells. After 5 min. at room temperature, the cells were spun down, redispersed in PBS and immediately monitored by fluorescence-activated cell sorting (FACScan, Becton Dickinson, Heidelberg) using the Cellquest software.
Fluorescence-activated cell sorter (FACS) analysis
Green fluorescence protein (GFP) expression was monitored by a shift to green fluorescence (FL-1) and red fluorescent protein (RFP) by a shift to red (FL-2). FACS analysis was performed with FACScan (Becton Dickinson, Heidelberg) using the Cellquest software.
Polymerase chain reaction (PCR)
Genomic DNA was isolated after proteinase K digestion and phenol/chloroform extraction. PCR was performed using the manufacturers protocol (Qiagen, Hilden, Germany).
N-terminal EGF-Env sequences were analyzed using the primers BS-5: 5'-TCT GAG TCG GAT CCC AAA TGT AAG and MLV-5'-Env: 5'-TAA CCC GCG AGG CCC CCT AAT CC, which amplified a 899 bp fragment from GFP-EMO and a 726 bp fragment from wt MLV. The generation of full-length genomes was analyzed using the primers p1: 5'-GAA TAG AAC CAT CAA GGA GAC and p2: 5'-CTC GAG AAG CTT AGT ACT GA, which amplify a 600 bp fragment from full-length MLV. No fragment should be amplified from the semi-replicative vectors, because the primers bind to genes on separate constructs. The stability of the GFP-Env fusion gene was analyzed using the primers p3: 5'-GTC AGT AAG CTT CTC GA and p4: 5'-GGT TTT GTC AGG ACT GGT GAG, which amplify a 1.5 kb fragment from gfp-env and an 800 bp fragment form wt env.
Competing interest
The author(s) declare that they have no competing interests.
Authors' Contributions
Katja Sliva and Alexandra Bittner performed the experiments. Katja Sliva, Otto Erlwein and Barbara Schnierle participated in the design of experiments, oversight of the conduction of the experiments, and in the interpretation of the results.
Acknowledgements
We thank C. Haynes for helpful discussions and critically reading the manuscript. We are grateful to D. Lindemann, K. Cichutek and F.-L. Cosset for kindly providing the plasmids pczCFG5 IEGZ, pKAΔenv-egfp, E-sRBD and A-sRBD.
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| 15610559 | PMC544597 | CC BY | 2021-01-04 16:38:33 | no | Virol J. 2004 Dec 20; 1:14 | utf-8 | Virol J | 2,004 | 10.1186/1743-422X-1-14 | oa_comm |
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BMC Med GenetBMC Medical Genetics1471-2350BioMed Central London 1471-2350-5-301561324510.1186/1471-2350-5-30Research ArticleCatechol-O-Methyltransferase (COMT) Val108/158 Met polymorphism does not modulate executive function in children with ADHD Taerk Evan [email protected] Natalie [email protected] Leila Ben [email protected] Philippe [email protected] Valentin [email protected] Rosherie [email protected] Adam [email protected] Marina Ter [email protected] Chantal [email protected] Ridha [email protected] Department of Psychiatry, McGill University and Douglas Hospital Research Centre, Montreal, Quebec, H4H 1R3, Canada2 Department Neurology and Neurosurgery, McGill University and Douglas Hospital Research Centre, Montreal, Quebec, H4H 1R3, Canada3 Department of Human Genetics, McGill University and Douglas Hospital Research Centre, Montreal, Quebec, H4H 1R3, Canada2004 21 12 2004 5 30 30 12 8 2004 21 12 2004 Copyright © 2004 Taerk et al; licensee BioMed Central Ltd.2004Taerk 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
An association has been observed between the catechol-O-methyltransferase (COMT) gene, the predominant means of catecholamine catabolism within the prefrontal cortex (PFC), and neuropsychological task performance in healthy and schizophrenic adults. Since several of the cognitive functions typically deficient in children with Attention Deficit Hyperactivity Disorder (ADHD) are mediated by prefrontal dopamine (DA) mechanisms, we investigated the relationship between a functional polymorphism of the COMT gene and neuropsychological task performance in these children.
Methods
The Val108/158 Met polymorphism of the COMT gene was genotyped in 118 children with ADHD (DSM-IV). The Wisconsin Card Sorting Test (WCST), Tower of London (TOL), and Self-Ordered Pointing Task (SOPT) were employed to evaluate executive functions. Neuropsychological task performance was compared across genotype groups using analysis of variance.
Results
ADHD children with the Val/Val, Val/Met and Met/Met genotypes were similar with regard to demographic and clinical characteristics. No genotype effects were observed for WCST standardized perseverative error scores [F2,97 = 0.67; p > 0.05], TOL standardized scores [F2,99 = 0.97; p > 0.05], and SOPT error scores [F2,108 = 0.62; p > 0.05].
Conclusions
Contrary to the observed association between WCST performance and the Val108/158 Met polymorphism of the COMT gene in both healthy and schizophrenic adults, this polymorphism does not appear to modulate executive functions in children with ADHD.
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Background
Attention Deficit Hyperactivity Disorder (ADHD) is a childhood psychiatric disorder characterized by symptoms of inattention, impulsivity and motor hyperactivity afflicting 6–8% of school-aged children in North America [1,2]. Although ADHD is a disorder with complex and heterogeneous etiology, genetic factors appear to play a significant role in predisposing and perpetuating the development of the disorder as evidenced by twin [3,4], family [5-7], and adoption studies [8]. Association studies have implicated several susceptibility loci including a 40-base pair (bp) allele of the Variable Number of Tandem Repeats (VNTR) polymorphism of the SLC6A3 gene [9] and a 48-bp repeat polymorphism of the DRD4 gene [10]. Attempts to replicate these findings have met with modest success possibly owing to the clinical heterogeneity characteristic of the disorder [11]. One method that may act to augment the strength of these associations would be to identify endophenotypic intermediates conferring risk for the development of ADHD rather than attempting to identify direct linkages between genetic variations and the behavioural manifestation of the disorder.
Theories of dysregulated dopamine (DA) pathways in ADHD have been supported by the efficacy of dopamine agonists in reducing the core symptoms of the disorder [12]. The mesocortical DA pathway appears to be integral to prefrontal cortex (PFC)-mediated cognitive functioning, specifically working memory [13], through the enhancement of task-related neural activity via D1 receptor activation [14]. Both PET [15] and SPECT [16] imaging studies support a neuromodulatory role for DA in the PFC during tasks of executive function. In addition, administration of DA agonists to the rat PFC acts to enhance working memory in these animals [17]. Consistent with this line of thinking, children with ADHD show deficits in performance of tasks of executive function [summarized in a meta-analysis by Sergeant et al. (2002)] [18] and significant improvement of performance under methylphenidate [19,20]. These findings have prompted the hypothesis that the overt symptoms of ADHD are the manifestation of an underlying deficiency in a range of PFC-mediated cognitive domains, including working memory, planning, and set shifting, collectively regarded as executive function [21-23].
The hypothesized role of a dysfunctional mesocortical dopaminergic pathway in the development of symptoms of ADHD has encouraged the investigation of candidate genes involved in this pathway including SLC6A3 [9], DRD4 [10] and, more recently, the catechol-O-methyltransferase (COMT) gene [24]. The COMT, encoded by a gene located on chromosome 22q11, catalyzes the degradation of catecholamines, most importantly DA [25]. A functional polymorphism of this gene, involving a substitution of Valine (Val) for Methionine (Met) at codon 108/158 (Val108/158 Met), results in a 4-fold variation in enzyme activity, with individuals homozygous for either the Val or Met allele exhibiting either reduced or preserved levels of DA respectively [26]. Although the dopamine transporter (DAT) is the predominant means of DA termination in most dopaminergic neurons [27], considerable evidence exists to suggest that the DAT may play a reduced role within the PFC [28-32], where other clearance mechanisms may be implicated. Comparison of DA metabolite levels within discrete brain loci in both rats [33] and monkeys [34], as well as the measurement of DA levels in COMT knock-out mice [35], suggest an important functional role for COMT in the PFC. If COMT is indeed inextricably linked to DA metabolism within the PFC, it is reasonable to assume that variations in enzyme activity, as dictated by the Val108/158 Met polymorphism, may modulate the performance of tasks of executive functioning in healthy individuals, as well as individuals with reduced PFC basal dopamine levels. In support of this assumption, associations have been reported between the Val108/158 Met polymorphism and performance on the Wisconsin Card Sorting Test (WCST) in healthy adults [36,37]. In adults with Schizophrenia, a disorder characterized by dopaminergic hypofrontality [38], associations have also been observed between the COMT polymorphism and WCST performance [39-41]. Although one study reported an association between the COMT polymorphism and ADHD using a haplotype relative risk design [24], this study failed to investigate any indices of executive function and several other studies failed to replicate this finding [3,42-44].
Given the putative role of COMT in DA metabolism within the PFC [33-35], we hypothesized that the Val108/158 Met polymorphism of the COMT gene will be associated with alterations in performance on tasks of executive function, a behavioural index of PFC integrity and function [45]. Since dysfunctional DA neurotransmission [46] and deficient neuropsychological task performance [18] are both characteristic of children with ADHD, we further hypothesized that this association would be evident within this particular clinical population. Specifically, ADHD children expressing the high enzymatic activity Val allele (H), resulting in reduced PFC DA neurotransmission [26], will show more pronounced deficits in neuropsychological task performance than their low enzymatic activity Met allele (L) counterparts. In order to test this hypothesis, we used three measures of executive function: the WCST [47], a measure of set-shifting ability capable of differentiating between ADHD children and controls [18] and associated with the COMT polymorphism in normal [36,37] and schizophrenic adults [39-41]; the Tower of London (TOL) [48], a measure of planning ability, which consistently differentiates ADHD children from controls [18], and the Self-Ordered Pointing Task (SOPT) [49], a measure of working memory also capable of differentiating between ADHD children and controls [18].
Methods
Subjects
118 children were recruited from the Disruptive Behaviour Disorders Program and the children outpatient clinic at the Douglas Hospital. They were referred to these specialized care facilities by school principals, community social workers, and paediatricians.
Inclusion criteria required children to be between the ages of 6 and 12 years of age, meeting DSM-IV diagnosis criteria for ADHD [50]. Diagnosis of ADHD was based on a structured clinical interview of parents using the DISC-IV (parental report) [51], school reports, teacher interviews, and clinical observation of the child. In the majority of cases, mothers were the primary informants for the collection of clinical information. Written reports from the child's school were also available in the majority of cases. Parents completed the Child Behavioural Checklist (CBCL) [52], a scale that assesses a variety of behavioural domains, and the Conners' Global Index for parents (CGI-P) [53]. Teachers also completed the Conners' Global Index (CGI-T) [54]. Assessments were made while children were free of medication. Exclusion criteria included a history of mental retardation, with an IQ less than or equal to 70 as measured by the WISC-III [55], and history of Tourette Syndrome, pervasive developmental disorder, psychosis or any medical condition or impairment that may interfere with the child's ability to complete the study.
Neurocognitive assessment
A comprehensive neuropsychological test battery assessing different aspects of the central executive functions was administered to all children by trained research personnel. All children were assessed subsequent to a one-week medication "wash-out" period. Children were permitted to take breaks upon request and, in some cases, testing was carried out over two sessions. On average, the testing procedure lasted 1.5 hours. The research protocol was approved by the Research Ethics Board of the Douglas Hospital. Parents were explained the study and provided written consent. Children were also explained the study and gave their assent to participate as well.
Tests were selected according to their ability to tap into various performance domains of executive function. We restricted the number of tests in each domain in order to balance comprehensiveness with the co-operation of patients. Abstraction and concept formation were evaluated by means of the WCST (perseverative errors) [47]. In this task, children are required to sort cards according to three different criteria (colour, number, or shape of signs presented on cards). Feedback on whether the child achieved a correct or incorrect match is given after each trial. The matching criterion changes after ten consecutive correct matches and the child has to identify the new matching criterion using the feedback (correct/incorrect) provided to them. Evidence of the reliability and validity of the WCST with various normal and clinical populations has been reported in several studies [18]. Planning capacity was evaluated using the TOL [48]. This test is used to assess planning and problem solving aspects of executive functioning. The validity and reliability of the TOL has been reported in numerous studies [18]. Standardized administration and scoring procedures as well as normative data have been developed for paediatric populations [56]. Visual Working Memory was evaluated using the abstract version of the SOPT [49]. In this task, series of matrices of 6, 8, 10, and 12 images are presented to the child. The child is asked to select, by pointing, one different image on each page. Errors occur when the child points to images previously selected on the preceding pages. Each set is presented to the child three times. Successful performance on this task involves working memory as well as planning and monitoring skills. Shue & Douglas (1992) have reported significant differences in performance between ADHD children and normal controls on the SOPT [57].
Molecular genetics
The Val108/158 Met polymorphism of the COMT gene was genotyped using a PCR based method as previously described [26]. The PCR was performed in a 25 μl total reaction volume containing 1X PCR buffer, 200 uM dNTPs, 200 ng of primers (5'-GCGATGGTGGCACTCCAAGC; 5'-TTGGAGAGGCTGAGGCTGAC), 1 unit of Taq DNA polymerase, and 100 ng of genomic DNA. PCR products were electrophoresed on agarose-TAE gel along with 1 kb ad 100 bp DNA ladders, visualized under UV-light and coded according to the length of the PCR product. Genotypes were called by two independent and experienced technicians who were blind to all clinical data. No disconcordance in any of the readings was noted. Children were stratified according to genotype only after all neuropsychological task data was collected.
Statistical analyses
The Val108/158 Met polymorphism consists of both the low-activity Met (L) and high-activity Val (H) alleles. Subjects were stratified into three groups: two homozygous genotype groups (LL, HH) and one heterozygous genotype group (HL).
A one-way analysis of variance (ANOVA) where genotype (LL, HL, HH) was the independent variable and neuropsychological task performance (standardized WCST perseverative error score, standardized TOL total item score) was the dependent variable was performed. For the SOPT, no normalized scores are available and testing procedures involve several levels of difficulty (4). We therefore used a two-way, repeated measure, mixed design analysis of covariance (ANCOVA), where genotype and level of task difficulty were the between and within subjects independent variables, respectively, neuropsychological task performance (SOPT raw error score) was the dependent variable, and age was the covariate. As the TOL also involves multiple levels of task difficulty (12), we repeated the analysis for this test using the same statistical approach as that applied to the SOPT. A one-way ANCOVA, where genotype was the independent variable and age was the covariate, was performed on all other non-standardized measures of neuropsychological task performance (WCST number of categories completed, WCST number of trials to first category, TOL number of problems solved).
An investigation of linkage and within-family association between quantitative phenotypes (standardized WCST perseverative error score, standardized TOL error score, and SOPT error score) was conducted utilizing the Quantitative Trait Disequilibrium Test (QTDT) statistical software package [58].
Results
Table 1 shows clinical and demographic information for the children stratified according to genotype [n = 23 for LL (19.5%), n = 66 for HL (56.0%) and n = 29 for HH (24.5%)]. The three groups were similar with regard to age, average household income, severity of behavioural problems as assessed by the CBCL, and mean number of inattention items, mean number of hyperactivity items and distribution of ADHD subtypes according to the DISC-IV. No significant differences existed between the groups in IQ as measured by the WISC-III. Our sample was characterized by a high prevalence of comorbid disorders, particularly oppositional defiant disorder and conduct disorder. The frequency of these disorders was equally distributed between the genotype groups. The proportion of subjects who had never received medication for ADHD within each genotype group was also remarkably similar. Although a significant effect of gender was observed between genotype groups (χ2 = 7.39; df = 2, p = 0.02), this result was treated as a type I error (false positive) due to the absence of female subjects with the HH genotype and given the relative lack of female representation across all genotype groups. However, given the previously observed association between gender and several polymorphisms at the COMT loci [59], increasing the sample size to achieve a more comparable gender representation and distribution would be a valuable revision to the present design.
Table 1 Demographic and clinical characteristics of children with ADHD separated according to COMT genotype
LL (23) HL (66) HH (29) p-value
Gender (M/F) 20/3 52/14 29/0 χ2 = 7.39, df = 2 p = 0.02
Age 9.2 (2.0) 9.0 (1.8) 9.3 (1.7) F2,115 = 0.21, p = 0.81
IQ 97.2 (13.7) 97.5 (13.5) 95.6 (13.8) F2,98 = 0.17, p = 0.84
CBCL (total score) 68.0 (9.8) 70.9 (10.4) 68.9 (8.9) F2,112 = 0.87, p = 0.42
Income (% less than 20 K) 32 % 42 % 48 % χ2 = 1.39, df = 2 p = 0.50
DISC-IV Inattention Items 7.3 (1.5) 6.9 (2.2) 7.2 (2.3) F2,113 = 0.46, p = 0.63
DISC-IV Hyperactivity Items 5.9 (2.4) 6.4 (2.3) 6.4 (2.7) F2,113 = 0.33, p = 0.72
DISC-IV ADHD Subtype (I/H/C) 10/3/10 14/13/39 7/3/19 χ2 = 5.68, df = 2 p = 0.22
Comorbid ODD 13/23 50/66 20/27 χ2 = 3.21, df = 2 p = 0.20
Comorbid CD 5/23 27/64 8/27 χ2 = 3.57, df = 2 p = 0.17
Never Medicated 11/22 38/62 18/28 χ2 = 1.17, df = 2 p = 0.56
CBCL = Child Behavioral Checklist. DISC-IV = Diagnostic Interview Schedule for Children fourth edition. ODD = Opposition Defiant Disorder, CD = Conduct Disorder. ADHD Subtypes: I = Inattentive, H = Hyperactive, C = Combined. Values are mean (SD).
The genotype distribution conformed to a Hardy-Weinberg equilibrium (χ2 = 0.42; df = 2, p = 0.81). 156 parents participated in the study and gave blood samples. Among these parents, 76 were heterozygous (M = 43 and F = 33) and transmitted the Val allele to their affected children in 28 occurrences, whereas this same allele was not transmitted in 29 occurrences [χ2 = 0.02; df = 1, p > 0.05 (transmission disequilibrium)]. Conversely, parents transmitted the Met allele to their affected children in 29 occurrences, whereas this same allele was not transmitted in 28 occurrences [χ2 = 0.02; df = 1, p > 0.05 (transmission disequilibrium)]. In addition, results from the QTDT revealed no evidence of linkage or within-family association between the three quantitative phenotypes and the COMT gene.
A one-way ANOVA performed on these data revealed no significant difference between the LL, HL, and HH genotypes according to WCST standardized perseverative error scores [F2,97 = 0.66, p > 0.05](Table 2) and TOL standardized total item scores [F2,99 = 0.97, p > 0.05](Table 2). A repeated-measure, mixed design ANCOVA performed on these data revealed no effect of genotype on SOPT raw error scores [F2,108 = 0.62, p > 0.05] (Table 2), TOL raw item scores [F2,107 = 0.35, p > 0.05], and TOL time to complete each trial [F2,108 = 0.04, p > 0.05]. No genotype by task interaction was observed for SOPT raw error scores [F6,327 = 0.39, p > 0.05], TOL raw item scores [F11,1199 = 1.63, p > 0.05], and TOL time to complete each trial [F11,1210 = 1.65, p > 0.05]. A one-way ANCOVA performed on these data revealed no effect of genotype on WCST number of categories completed [F2,96 = 1.94, p > 0.05], WCST number of trials to first category [F2,96 = 1.04, p > 0.05] and TOL number of problems solved [F2,112 = 1.04, p > 0.05]. No genotype effects were observed when the HL and HH genotype groups were combined into one category and contrasted with the LL genotype (recessive model) on WCST standardized perseverative error scores [F1,98 = 1.11, p > 0.05], WCST number of categories completed [F1,97 = 0.01, p > 0.05], WCST number of trials to first category [F1,97 = 0.36, p > 0.05], TOL standardized total item scores [F1,100 = 0.42, p > 0.05], TOL raw item scores [F1,108 = 0.22, p > 0.05], TOL time to complete each trial [F1,109 = 0.07, p > 0.05], TOL number of problems solved [F1,113= 1.33, p > 0.05] and SOPT raw error scores [F1,109 = 0.85, p > 0.05].
Table 2 Neuropsychological task performance in children with ADHD
LL (23) HL (66) HH (29) ES p-value
WCST 96.3 (15.1) 99.1 (11.8) 100.6 (12.2) 0.31 F2,97 = 0.67, p = 0.52
TOL 103.3 (16.5) 99.5 (15.1) 103.8 (12.6) 0.03 F2,99 = 0.97, p = 0.38
SOPT 13.5 (6.9) 15.1 (8.8) 15.8 (8.2) 0.31 F2,108 = 0.62, p = 0.54
WCST = Wisconsin Card Sorting Test standardized perseverative error score (LL: n = 21, HL: n = 56, HH: n = 23). TOL = Tower of London standardized score (LL: n = 20, HL: n = 55, HH: n = 27). SOPT = Self Ordered Pointing Task error score (LL: n = 23, HL: n = 63, HH: n = 26). ES = Effect size for LL vs. HH. Values are mean (SD).
Discussion
Previous studies have identified an association between the COMT polymorphism and a variety of indices reflecting executive control both in healthy [36,37] and schizophrenic adults [39-41]. The COMT appears to be important to the regulation of dopamine metabolism within the PFC [33-35]. Since the PFC and dopamine pathways have been hypothesized to play an important role in the pathogenesis of ADHD [9-11,60,61]), we conducted this study in an attempt to test whether the COMT Val108/158Met polymorphism, which is known to be associated with a significant change in the catabolic capacity of this enzyme, modulates the risk for ADHD or various indices of executive control. Contrary to our expectations and findings in both healthy [36,37] and schizophrenic adults [39-41], an association between the Val108/158 Met functional polymorphism of the COMT gene and neuropsychological task performance reflecting executive control was not observed in children with ADHD. This result is consistent with the findings of a recent case-control study conducted by Mills et al. (2004), which, to our knowledge, is the only other study to investigate the relationship between the COMT Val108/158Met polymorphism and neuropsychological task performance in children with ADHD [62]. However, this study did not include the WCST, the measure responsible for producing the most consistent results in the previous literature. In addition, we did not identify a biased transmission of either of the two alleles from parents to affected offspring.
The absence of an association between the COMT Val108/158Met polymorphism and behavioral indices of executive function in children with ADHD may be explained by the young age of the population of patients included in the present study. Indeed it is possible that, due to age-related changes in the functional importance of the COMT within the prefrontal cortex, this association is observable only in adults. This possibility is supported by data in both rats [63-65] and humans [66,67] suggesting that monoamine content and metabolism decrease with age. This age-related decrease may render functions dependent on monoamine content more prone to be dysfunctional at an older age. In addition, evidence from rat studies has indicated a positive correlation between aging and COMT activity [68-70]. This observation may suggest that the implication of the COMT in the catabolism of dopamine is developmentally regulated, with children relying less on this catabolic pathway than adults. Conversely, it has been reported that DAT density is inversely correlated with age [71]. Taken together, the presence of an inverse and direct correlation between age and DAT density on the one hand and COMT activity on the other hand, may suggest that dopamine metabolism relies more on the DAT than on COMT activity in children compared to adults. This hypothesis is compatible with the fact that several studies have identified an association between the DAT [9,60,72-74], but not the COMT, gene and ADHD.
It is also possible that the negative result observed in the present study is due to a type II error (false negative) secondary to the lack of power of our sample to detect an association. However, using results from the WCST, the variable for which relevant genetic data already exists, we conducted a power analysis and determined that our sample size has sufficient power (80% at α = .05) to detect a mean difference of 11.2 on this measure. Furthermore, it is possible that some of the tests used in our assessment are mediated by the PFC but insensitive to PFC DA levels [75].
An additional limitation of the present study is that some genotype groups included few subjects. Increasing the sample size to achieve larger genotype groups would be necessary to reach firmer conclusions. This is particularly true for female subjects who were significantly underrepresented in the study (as is common to most clinical studies of ADHD). In order to generalize these negative results to females, a more comparable gender representation is required, particularly in view of some previous research indicating that the allelic distribution of the COMT may be gender dependent [59].
Conclusions
This study does not support the involvement of the Val108/158 Met polymorphism of the COMT gene in increasing the risk for ADHD or in modulating several indices of executive functions in children with ADHD. This result is contrary to previous findings in both healthy and schizophrenic adults and may be related to developmental specificities.
Competing interests
The author(s) declare that they have no competing interests.
Authors' contributions
ET performed the data analysis and drafted the manuscript. NG was involved in the conception of the study and provided clinical support. LBA provided clinical support and aided in data collection. PL provided clinical support. VM aided in neuropsychological testing and data collection. RD and ATZ performed the genotyping for the study and aided in data management. MTS coordinated the clinical aspects of the study and was involved in data management. CB provided clinical support. RJ was responsible for the conception of the study, drafting of the manuscript, and supervision of the research project.
Pre-publication history
The pre-publication history for this paper can be accessed here:
Acknowledgements
This work was supported in part by grants from the Fonds de la Recherche en Santé du Québec, Réseau de Santé Mentale du Québec, and the Canadian Institutes of Health Research to RJ and ET. We thank Johanne Bellingham, Anna Polotskaia and Nicole Pawliuk for technical assistance.
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BMC GenomicsBMC Genomics1471-2164BioMed Central London 1471-2164-5-971561056510.1186/1471-2164-5-97Research ArticleThe association of Alu repeats with the generation of potential AU-rich elements (ARE) at 3' untranslated regions. An Hyeong Jun [email protected] Doheon [email protected] Kwang Hyung [email protected] Jonghwa [email protected] BioSystems Dept., Korea Advanced Institute of Science and Technology (KAIST) 373-1 Guseong-dong, Yuseong-gu, Daejeon 305-701, Republic of Korea2 NGIC, KRIBB, Daejeon, Korea3 BiO institute, Daejeon, Korea4 OITEK (Inc), Daejeon, Korea2004 21 12 2004 5 97 97 4 8 2004 21 12 2004 Copyright © 2004 Jun An et al; licensee BioMed Central Ltd.2004Jun An 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 significant portion (about 8% in the human genome) of mammalian mRNA sequences contains AU (Adenine and Uracil) rich elements or AREs at their 3' untranslated regions (UTR). These mRNA sequences are usually stable. However, an increasing number of observations have been made of unstable species, possibly depending on certain elements such as Alu repeats. ARE motifs are repeats of the tetramer AUUU and a monomer A at the end of the repeats ((AUUU)nA). The importance of AREs in biology is that they make certain mRNA unstable. Proto-oncogene, such as c-fos, c-myc, and c-jun in humans, are associated with AREs. Although it has been known that the increased number of ARE motifs caused the decrease of the half-life of mRNA containing ARE repeats, the exact mechanism is as of yet unknown. We analyzed the occurrences of AREs and Alu and propose a possible mechanism for how human mRNA could acquire and keep AREs at its 3' UTR originating from Alu repeats.
Results
Interspersed in the human genome, Alu repeats occupy 5% of the 3' UTR of mRNA sequences. Alu has poly-adenine (poly-A) regions at its end, which lead to poly-thymine (poly-T) regions at the end of its complementary Alu. It has been found that AREs are present at the poly-T regions. From the 3' UTR of the NCBI's reference mRNA sequence database, we found nearly 40% (38.5%) of ARE (Class I) were associated with Alu sequences (Table 1) within one mismatch allowance in ARE sequences. Other ARE classes had statistically significant associations as well. This is far from a random occurrence given their limited quantity. At each ARE class, random distribution was simulated 1,000 times, and it was shown that there is a special relationship between ARE patterns and the Alu repeats.
Table 1 Defined ARE classes. (Symbol marks are used in this study instead of full sequences.)
Symbol ARE sequence
Class I (AUUU)5A AUUUAUUUAUUUAUUUAUUUA
Class II (AUUU)4A AUUUAUUUAUUUAUUUA
Class III U(AUUU)3AU UAUUUAUUUAUUUAU
Class IV UU(AUUU)2AUU UUAUUUAUUUAUU
Class V U4AUUUAU4 UUUUAUUUAUUUU
Class VI W3UAUUUAUW3 WWWUAUUUAWWW
Conclusion
AREs are mediating sequence elements affecting the stabilization or degradation of mRNA at the 3' untranslated regions. However, AREs' mechanism and origins are unknown. We report that Alu is a source of ARE. We found that half of the longest AREs were derived from the poly-T regions of the complementary Alu.
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Background
Varying more than ten-fold, messenger RNA degradation is essential for the regulation of gene expression [1,2]. Differential mRNA decay rates were determined by specific cis-acting sequences within mRNA. For example, the mRNA sequences of yeast, many mammalians, and other eukaryotes contain AU-rich elements or AREs at their 3' untranslated regions (UTR) [3,4]. For example, in yeast, AREs stimulated the shortening of poly adenine (poly A), and two kinds of degradation pathways followed. One is 5'-to-3' exonuclease access by removal of the 5' cap structure. The other is 3'-to-5' digestion by a complex of exonucleases called exosome [5,6]. Genes required for these steps have been identified in yeast and were found to be conserved among eukaryotes. Although the mechanisms of AREs enhanced mRNA degradation are unknown, several groups provided evidence that 3'-to-5' degradation by the exosome may be the major pathway of decay for at least some mammalian mRNAs, including ARE-containing mRNA sequences [7-9]. The length of AREs also affected the half-life of mRNA. The nonamer UUAUUUAUU is a typical ARE, and the simple repeats, (AUUU)nA motif, is the well-known pattern of AREs. It has been shown that the number of ARE motifs correlated with the turnover of ARE-mRNAs such as GM-CSF [10,11]. Because of this, AREs are usually classified according to the number of the repeats [12].
It is known that the stabilization factor, such as HuD, is able to bind to AREs [13] and most AREs seem to function as destablizing factors. The overall importance of AREs in biology is that they can make certain critical gene products unstable. They include proto-oncogenes such as c-fos [14], c-myb [15], c-myc [16], and Pim-1 [17]. Another class of ARE-associated genes are immune response genes such as interferon [15,18] and interleukin [15,19-21]. Growth factors, such as Gro-α [22] and the vascular endothelial factor [23] in humans, are also known to be associated with AREs.
AREs consist of a great number of thymine (or uracil) and a few adenines. Alu repeats can be a source of poly-T regions in mRNA. Therefore, there is a possible link between ARE and Alu repeats.
Alu repeats are sequences of approximately 300 nucleotides (nt) transcribed by RNA polymerase III. The Alu region is then reverse-transcribed and inserted into a new location in the genome [24]. It can reach a copy number in excess of 500,000 in the human genome [25]. Alu repeats were thought to be inserted very early in primate evolution, approximately 65 million years ago (mya). Alu amplification appears to have reached a maximum rate between 35 and 60 mya, and is currently amplifying at only 1% of the maximum rate [26]. Statistical analyses have identified key diagnostic nucleotide positions in Alu sequences that define 12 subfamilies. J class is the oldest one, S class is intermediate, and Y class is the newest. The majority of Alu retrotranspositions were completed at least 30 mya when the Alu-Sx subfamily, which accounts for half of all human Alu sequences, and the Alu-Sp and Alu-Sq subfamilies became unable to replicate [27-30]. Alu repeats account for 6–13% of the human genome [31] and were identified in 5% of 1,616 human full-length cDNA. Of the 5%, 82% were found in the 3' UTR, while 14% were located in the 5' UTR, and very rarely in the coding region [32]. The common role of Alu at 3' UTR has not been reported, although there is one specific case that the chemical, PMA, can bind to Alu at 3' UTR and increased mRNA half-life [43].
We investigated the link between Alu sequence and the potential AREs (that have not been experimentally verified but contain ARE sequence patterns), and suggest that the complementary poly-adenine regions of Alu is one of the sources of AREs at the 3' UTR of mRNA. Figure 1A shows that the poly-adenine regions of Alu contained in the anti-sense strand on DNA complemented the poly-thymine regions in the sense strand; therefore, the poly-thymine regions on DNA transcribed the poly-uracil regions on mRNA (Figure 1B). We propose a mechanism on how Alu has been converted to AREs gradually. When adenine was inserted at a regular interval in the poly-T(U) regions, it eventually led to the generation of potential AREs. It is not clear why such a regular insertion occurs, but the phenomenon has also been found in other ARE-like sequences. Figure 1C shows transcribed ARE on mRNA [33,34].
Figure 1 The schematic diagram of poly-thymine (poly-T) generation by Alu.(A) Alu contains poly-adenine (poly-A) region at the end. It is shown as 'aaaaaaaa'. The poly-A of Alu at anti-sense becomes poly-T (complement of poly-A) at sense strand on DNA. It is shown as 'tttttttt'. (B) mRNA now contains a poly-uracile (poly-U) region after the transcription of poly-T region. (C) AU-rich elements are found in this poly-U region in (B).
Results
The results from the method are shown in Figure 2. In the ARE class I, marked as (AUUU)5A pattern in Table 1, 26 AREs were found in all 21,121 mRNA 3' UTR. 38.5% of 26 AREs included in the class I, were detected in Alu sequences at 3' UTR. When we did a simulation test for the 26 AREs and 1,504 Alu sequences by 1,000 times, with a 95% confidence interval (C.I.) threshold, it was statistically significant (see the statistical analysis of the search results in the Methods section). In other words, 38.5% occurrences were out of the likelihood for random overlaps of Alu and ARE patterns in the human genome. In the ARE class II (Table 1, (AUUU)4A pattern), 41 were found in all 3' UTR, and 7 were detected in Alu sequences among them (17.1%). The simulation results showed the 17.1% was less than the maximum random range of 7.3%. Therefore, class II data also showed a significance between ARE patterns and Alu. In class III (Table 1), 94 AREs were discovered from all 3' UTR. 15 out of 94 AREs were located in Alu sequences (16.0%). 16% was also statistically significant with the given sample size. In classes IV and V, 5% and 6.1% of ARE were found in Alu, respectively. These results were still out of the random chance distribution, although they were relatively less significant than the previous classes. In class VI, only 85 out of 8,649 AREs were detected in Alu (1%), and it is an insignificant hypothesis that the class VI pattern is associated with Alu sequences.
Figure 2 ARE found in Alu at each class (Table 1).The numbers of ARE found in all 3' UTR, the number of ARE found in the Alu sequence, the ratio between them, and the randomly simulated results among 1,000 times at each ARE class (Table 1). Only the maximum possible ratios of the randomly simulated range at 95% confidence interval (C.I.) were shown. X-axis is for ARE patterns in all the classes. The left Y-axis is for the number of AREs, and the right Y-axis is for the overlap ratios.
Discussion
The possible mechanism of how AREs originated from Alu is as follows: Alu is a special sequence that contains a poly-adenine (poly-A) region at its end. The poly-A region plays an important role in the retroposition mechanism of Alu [35]. It is known that the products of LINE (L1) transposon bind the poly-A of Alu. This enables Alu to retroposition [36,37]. When Alu with poly-A are inserted as above, it is in the double helix form with the complementary poly-T. Therefore, the poly-T regions produce poly-uracil (poly-U) regions in mRNA when transcribed (Figure 1). We hypothesized that the poly-U regions generated from the Alu are the source of AREs after either random or directed mutation.
With this hypothesis, we suggest a new role for Alu was involved in the 3' UTR. It is well known that Alu affected gene expression at the 5' of genes and alternative splicing at the intron region [38,39]. However, no Alu role at the 3' UTR has been suggested yet. We could have applied the same test to Alu at 5' UTR region, but there were too few data sources [32].
Conclusion
AREs are mediating sequences that affect the stabilization or degradation of biologically important genes' mRNA. However, their origin in evolution has not been clear. This report presents a hypothesis and statistical evidence that Alu was one of the sources of ARE generation or origin. A possible mechanism of ARE generation from Alu via retroposition and regular pattern mutation is suggested.
Methods
Human 3' UTR sequences
We used the RefSeq database from the National Center for Biotechnology Information (NCBI) for human 3' UTR sequences [41]. We extracted 3' UTR of CDS (coding sequence) from all the annotated mRNA sequences (mRNA_Prot, 2004.9.13). The number of 3' UTR was 21,121 and the average length was 996 bp. We used the Biojava package [42] to extract only 3' UTR with Genbank's feature information. The number of 3' UTR was 21,121 and the average length was 996 bp.
Alu sequence and AU-rich element (ARE) pattern detection
AREs were searched for in the all 3' UTR (Table 1). An in-house java program was used to search for these AREs. While the number of AUUUA repeats decreased, the T flank region increased to 21 bp. Each ARE was allowed within one base mismatch. This is a stricter mismatch criterion than the one of AU-rich elements database (ARED) (the ARED trained experimental ARE data allow 10% of ARE length mismatch [24]). The RepeatMasker program was used for finding Alu. It is a program for finding repeat sequences [25]. After finding Alu sequences using RepeatMasker at 3'UTR, for each Alu, we recorded the position information (RefSeq ID, start and end position) for the next step analysis.
Comparison between two search results
We compared the positions of 3' UTR Alu and ARE sequences. If an ARE was discovered within an Alu sequence, this ARE was regarded found in 3' UTR Alu. For example, if an Alu was found between 100–400 bp and an ARE was found between 99–129 bp, this ARE was in 3' UTR Alu in the same 3' UTR. If less than 50% of an ARE length was discovered in an Alu, we further check if there is 7 bp TSD (Target Site Duplication) between the Alu's end and the ARE's end [4]. For example, if an Alu is between 100–400 bp and an ARE between 80–110 bp, about 10 bp (33%) of the ARE belongs to the Alu. In this case, we check if there is 7 bp TSD between upstream region from 80 bp and downstream from 400 bp.
Statistical analysis of the search results
To validate the significance of the searches, we calculated the random chance of the ARE and Alu sequence overlap at each class (Table 1).
Hypothesis
H0: ARE occurs in human 3'UTR independently from Alu.
Random sequence generation for statistical validation
The average length of 3' UTR of 21,121 human sequences was 996 bp. Within the long theoretical sequence of 21,121 × 996 bp, we generated 1,504 Alu (300 bp) and ARE sequences (21–13 bp). For example, 1,504 Alu and 26 (21 bp) AREs in ARE class I (Table 1) were generated following a uniform distribution as a control set. 1,504 and the number of AREs for ARE classes were the actual numbers of Alu and AREs found by our method. This random sequence generation was done 1,000 times with a 95% significance threshold.
Test results
In the ARE class I (Table 1), the significance range at a 5% error range was 0.0–11.5% (Figure 2) for the random chance of association between ARE patterns and Alu sequences. The results in other ARE classes are also shown in Figure 2. Our result of a 38.5% – 6.1% overlap between AREs and Alu, depending on ARE classes, was statistically significant. Therefore, hypothesis H0 was rejected.
Authors' contributions
HJA conceived of this study, carried out the tests, and drafted the manuscript. JB participated in the design of the study and drafted the manuscript. KWL and DL amended and improved the design of the study. All authors read and approved the final manuscript.
Acknowledgements
This work was supported by Korea Research Foundation Grant (KRF-2003-041-D20490). JB is supported by IMT-2000-C4-3 grant of ministry of information and communication of Korea and BioGreen21 project of Korea. We would like to thank CHUNG Moon Soul Center for BioInformation and BioElectronics, and the IBM SUR program for providing research and computing facilities. We thank Maryana Huston for editing this manuscript and Dr. Kim, Ho at SNU for his statistical expertise.
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BMC GenomicsBMC Genomics1471-2164BioMed Central London 1471-2164-5-991561323810.1186/1471-2164-5-99Research ArticleArrays of ultraconserved non-coding regions span the loci of key developmental genes in vertebrate genomes Sandelin Albin [email protected] Peter [email protected] Sara [email protected]öm Pär G [email protected] Joanna M [email protected] Wyeth W [email protected] Johan [email protected] Boris [email protected] Center for Genomics and Bioinformatics, Karolinska Institutet, Stockholm, Sweden2 Department of Cell and Molecular Biology, Karolinska Institutet, Stockholm, Sweden3 Department of Biosciences at Novum, Karolinska Institutet, Stockholm, Sweden4 Centre for Molecular Medicine, Department of Medical Genetics, University of British Columbia, Vancouver, Canada2004 21 12 2004 5 99 99 2 12 2004 21 12 2004 Copyright © 2004 Sandelin et al; licensee BioMed Central Ltd.2004Sandelin 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
Evolutionarily conserved sequences within or adjoining orthologous genes often serve as critical cis-regulatory regions. Recent studies have identified long, non-coding genomic regions that are perfectly conserved between human and mouse, termed ultra-conserved regions (UCRs). Here, we focus on UCRs that cluster around genes involved in early vertebrate development; genes conserved over 450 million years of vertebrate evolution.
Results
Based on a high resolution detection procedure, our UCR set enables novel insights into vertebrate genome organization and regulation of developmentally important genes. We find that the genomic positions of deeply conserved UCRs are strongly associated with the locations of genes encoding key regulators of development, with particularly strong positional correlation to transcription factor-encoding genes. Of particular importance is the observation that most UCRs are clustered into arrays that span hundreds of kilobases around their presumptive target genes. Such a hallmark signature is present around several uncharacterized human genes predicted to encode developmentally important DNA-binding proteins.
Conclusion
The genomic organization of UCRs, combined with previous findings, suggests that UCRs act as essential long-range modulators of gene expression. The exceptional sequence conservation and clustered structure suggests that UCR-mediated molecular events involve greater complexity than traditional DNA binding by transcription factors. The high-resolution UCR collection presented here provides a wealth of target sequences for future experimental studies to determine the nature of the biochemical mechanisms involved in the preservation of arrays of nearly identical non-coding sequences over the course of vertebrate evolution.
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Background
Comparative genome sequence analysis, often termed phylogenetic footprinting, has proven successful for the identification of cis-regulatory regions[1,2]. Recent computational and experimental studies have identified a small number of large, highly conserved enhancers, or 'global control regions', associated with the regulation of important developmental genes such as DACH [3], SOX9 [4], Dlx bigene [5,6], and HOX-D [7,8] clusters. These regulatory regions can act at distances of several hundred kilobases from their target genes, while at the same time conferring an equivalent expression pattern to reporter genes over much shorter distances (e.g. [3]). A recent computational analysis proves that such highly conserved elements (termed ultra-conserved elements (UCRs)) are occurring far more often than expected [9]. In the study by Bejerano et al., UCRs are defined as regions perfectly conserved between human and mouse longer than 200 base pairs (bp). The study reports a significant association of a non-transcribed subset of those elements with DNA-binding proteins; an equivalent observation has been made independently by Boffeli et al.[10] for a limited number of most highly conserved elements between human and pufferfish. The stringent criteria for conservation applied in the two studies miss many known enhancer elements that are shorter than 200 bp, and highly conserved across all vertebrates. For instance, in a recently published study, Sabarinadh et al. [11] described a number of non-transcribed regions flanking the genes of HoxD gene cluster that are highly conserved across vertebrate genomes.
In this paper, we define a set of UCRs using high-resolution criteria that detect segments conserved between the human, mouse and pufferfish genomes. Analysis of this set provides insights into a previously unrealized organizational structure of UCRs in vertebrate genomes. We conclusively show that clusters of UCRs are globally associated with many of the genes that act as master regulators during vertebrate development. The clustered distribution of these regions along chromosomes and, importantly, around their presumptive target genes suggests that gene regulation involves the coordinated action of numerous, widely dispersed elements.
Results
Definition and genomic environment of ultra-conserved non-coding regions (UCRs)
We initiated this study by applying comparative genomics to identify putative regulatory regions for a number of evolutionary conserved homeodomain transcription factors that control neural cell fate determination [12,13]. When we examined the genomic landscapes surrounding homeodomain gene loci, we consistently found non-coding regions that exhibited a striking degree of sequence conservation between human and mouse over a minimum of 50 bp. Many of these regions are at least partially conserved over extended periods of evolution. The observed nucleotide identities between human and mouse sequences exceed even those of exon sequences encoding identical proteins. Such striking sequence conservation has previously been anecdotally associated with long-range enhancers for several developmental genes [3-8].
To test whether the association of UCRs with regulatory genes reflected a global genomic trend, we identified a comprehensive set of human/mouse/pufferfish UCRs for detailed analysis. We defined minimum requirements for a UCR (see Methods) and performed a genome-scale computational analysis that retrieved 3583 human/mouse/pufferfish UCRs. Since one of the requirements is that the UCRs are not overlapping actively transcribed genomic regions, they would belong to type II UCRs defined by Bejerano et al. [9].
The median UCR length was 125 bp, but extreme lengths (>1000 bp) were observed. Qualitative assessment of "genescapes", the gene structures, surrounding UCRs revealed them to be present either in introns, in dense clusters around a group of genes or in 'gene deserts' (up to several thousands kilobases from known genes). There appeared to be a strong association between locations of our set of UCRs and genes encoding transcription factors – even stronger than that reported by Bejerano et al.[9] [see Additional file 1 and 2]. This observation will be proven in the subsequent analysis.
UCRs are strongly associated with DNA-binding proteins
To quantitatively assess the characteristics of genes proximal to UCRs, we analyzed the over-representation of gene annotations. We retrieved the InterPro [14] domain annotation for all genes adjacent to or containing UCRs. A statistical assessment (Fisher's exact test) of the observed domain biases for these genes was performed to assess the probability that the domain distributions were the same for the UCR genes as compared to the set of all genes. Even with a conservative (Bonferroni) correction for multiple testing [15], structural domains of transcription factors are significantly over-represented (P-value 9.33e-66) within the gene annotations (Table 1) [all domains are listed in Additional file 3 and 4]. In order to obtain robust results, we chose the four domains from Table 1 present in the highest number of proteins (homeobox, C2H2 zinc finger, forkhead and nuclear steroid receptor). We examined the extent to which all known genes containing each of these four transcription factor domains co-localize with UCRs (Figure 1). We found that a high proportion of these genes (163/1084; P-value 7.33e-11) are in genomic neighborhoods (<8 kb) of UCRs: more than 30% of all homeodomain-encoding genes have an UCR within 8 kbp (90/237; p-value 8.67e-11), and more than 55% have one within 100 kb (133/237, P-value 7.78e-11). The UCR association rates (the fraction of genes with an UCR closer than 8 kb, compared to the expected value) for genes encoding forkhead (8/31, P-value 6.6e-11), nuclear steroid receptor (9/38, P-value 2.81e-9) or zinc finger domains (56/751 P-value 8.12e-11) were noted as significant as well. These data provide strong evidence that the UCRs are spatially associated with genes encoding regulatory proteins.
Table 1 Over-representation of protein domains in genes flanking UCRs. Bonferroni-corrected and uncorrected Fisher Exact Test p-values are shown for the 16 most over-represented InterPro domains. Typical transcription factor domains (DNA binding domains) are indicated in bold. A full list of all InterPro domains with P-values is given in [Additional file 3].
Domain description INTERPRO ID Fisher test P value Corrected P value
HTH_lambrepressr IPR000047 6.40E-20 5.36E-17
Homeobox IPR001356 1.60E-12 1.34E-09
Antennapedia IPR001827 1.37E-10 1.15E-07
Paired_box IPR001523 2.39E-05 2.00E-02
HLH_basic IPR001092 2.40E-05 2.01E-02
POU_domain IPR000327 3.06E-05 2.56E-02
Homeo_OAR IPR003654 3.08E-05 2.58E-02
TF_Fork_head IPR001766 6.15E-05 5.15E-02
Znf_C4steroid IPR001628 7.45E-05 6.23E-02
Hormone_rec_lig IPR000536 1.06E-04 8.86E-02
HMG_12_box IPR000910 1.81E-04 1.51E-01
Stdhrmn_receptor IPR001723 2.63E-04 2.20E-01
COUP_TF IPR003068 7.62E-04 6.38E-01
LIM IPR001781 1.10E-03 9.18E-01
RtnoidX_receptor IPR000003 1.28E-03 1.07E+00
FN_III IPR003961 2.57E-03 2.15E+00
Figure 1 Spatial correlation of transcription factor gene families to UCRs in the human genome. A. Cumulative distribution of distances to the closest UCR for selected subsets of genes. Distance to the closer end of the transcript mapping (either 3' or 5'). Majority of major classes of transcription factors are closer to UCRs than random genes. B, C. Occurrence of UCRs around selected subsets of genes. This plot summarizes the distribution of distances to all UCRs on the same chromosome for each gene in the subset. There is a visible over-representation of UCRs up to 300 kb from homeobox genes, and up to 150 kb from C2H2 zinc finger genes.
UCRs clusters encompass the entire gene loci of key developmental genes
In order to visualize the distribution of UCR locations across the human genome, we generated a UCR density map for each chromosome [see Additional file 5]. Figure 2a shows such a map for chromosome 2. Visual inspection reveals an obvious qualitative tendency of UCRs to occur in large clusters, which was validated by a quantitative comparison of the distributions of nearest-neighbor distances between UCRs and a neutral background model (P-value 8.02e-16; Kolmogorov-Smirnov test). There is no observed correlation between regions of high gene density and UCRs, consistent with previously reported observations that larger conserved regions can be located in gene deserts [3]. As previously noted, many of the UCRs are adjacent to homeobox protein-encoding genes (Figure 1a, Figure 2b). It is interesting to note that the over-representation of UCRs near homeobox genes extends up to 300 kbp away from the transcription start site (Figure 1b). This is consistent with numerous observations that control regions need not be proximal to targeted genes, but can be located hundreds of kilobases from the transcription start site [3,7,16]. A similar trend is observed for UCRs near C2H2 zinc finger genes, with over-representation of UCRs extending up to 150 kbp away (Figure 1c). Large clusters of UCRs can span regions of several hundred kilobases around inferred target genes. For the 50 largest UCR clusters we generated comprehensive views of the chromosomal neighborhood (Figure 3). We find that 41 of the 50 clusters span one or more genes known to be expressed in embryonal development, including fundamental master regulator genes (i.e. the HoxD cluster, Nkx6.1, Nkx2.2 and Pbx3) [for detailed annotated lists of genes associated with UCR clusters, see Additional files 6 and 7]. To provide access to the entire set of UCRs, we have implemented a basic UCR browser with links to the UCSC genome browser [17].
Figure 2 Genomic distributions of UCRs and transcription factor genes. A. Distribution of UCRs on human chromosome 2 is shown in yellow, and total gene density along the chromosome is shown in blue (top track). Note the lack of correlation between gene density and UCR density. Positions of homebox-domain containing genes locations are marked in red, and generally coincide with local maxima of UCR density. The remaining UCR density peaks coincide with genes for transcription factors belonging to structural classes other than homeobox. B. Close-up of a UCR cluster coinciding with the HoxD gene cluster. The HoxD cluster coincides with one of the larger UCR density peaks on chromosome 2, and is associated with nine UCRs. UCR locations are shaded in yellow.
Figure 3 Genomic landscape surrounding the most prominent UCR clusters in the human genome. UCRs were counted by sliding a 500 kb window along the chromosomes. Overlapping UCR-containing windows were merged into a single cluster span. Each of the regions shows a 4 MB region around the corresponding UCR cluster. The cluster span coordinates correspond to the human genome NCBI build 33 (UCSC hg15, April 2003). Transcription factor genes are colored according to structural class. UCR clusters are visibly correlated with transcription factor genes; other developmental regulators that do not contain any of the probed protein domains were located manually (boxed), such as the autism susceptibility gene (chromosome 7, number 37) and the DACH gene (chromosome 13, number 10). The numbers correspond to annotations in [Additional file 6 and 7]. The figure was created with the help of the Bio::Graphics Perl library[27].
Rare duplications of UCRs across evolution
We performed a global pairwise comparison of all UCRs, in order to determine if UCR duplication was common across evolution. We discovered only five sets of duplicated UCRs, all of which are adjacent to corresponding duplicated genes. For example, duplicated UCRs are present in the introns of SOX5 (on chromosome 12) and SOX6 (on chromosome 11), two highly similar genes involved in chondrocyte differentiation [18]. Of special interest is the conservation of UCRs in the Iroquois (IRX) gene clusters. IRX genes are situated in two clusters of three genes each, present on human chromosomes 5 and 16 [19]. Similarly positioned arrays of UCRs are present in each of the four intergenic regions between the IRX genes (Figure 4). The great majority of UCRs, while conserved across vertebrate evolution, show no similarity between the clusters within the species. An intriguing exception is the set of four UCRs that are highly similar in both cluster position and nucleotide sequence.
Figure 4 Sets of UCRs sharing high sequence similarity are involved in regulation of related genes: the case of Iroquois gene clusters. Four similarly positioned UCRs are located within the two Iroquois gene clusters at chromosomes 5 and 16. Block arrows indicate significant sequence similarity. The arrow width is inversely proportional to the alignment BLASTN E-value. There are additional shorter blocks of similarity between the two three-gene clusters; however, most UCRs have diverged between the two clusters, while still preserved across vertebrates.
Discussion
The human genome contains numerous ultra-conserved regulatory sequences that are shared broadly across vertebrates. These UCRs occur in arrays of highly conserved regulatory elements spanning large chromosomal regions. The clusters are co-localized with genes encoding key proteins for the regulation of development, with a particular correlation with genes encoding transcription factors. The strength of association between UCRs and diverse classes of DNA binding transcription factors validates that a relatively simple definition of UCRs captures a biologically meaningful set of functional sequences. The presence of non-coding UCRs is predictive for the presence of genes implicated in development, differentiation and malignancies. The list presented in [Additional file 6] hints at potentially crucial roles of currently uncharacterized transcription factor genes, while the collection of reported UCRs provides a wealth of regulatory locations for further study.
Exceptional mechanisms are brought to bear to retain UCRs over hundreds of millions of years of parallel evolution. UCRs are more strongly conserved than sequences encoding identical proteins, and exhibit sequence identity exceeding essentially all known cis-regulatory sequences. The retention properties suggest that UCRs have important functions in the vertebrate genome.
The observed UCRs could fall into multiple functional categories, including enhancers of transcription, regulators of chromatin structure and unknown genes for non-coding transcripts. A small subset of UCRs have been identified previously as enhancers of transcription [7,3].
The high conservation and length of UCRs compared to binding sites for single transcription factors suggests that the mode of regulation must involve more than the binding of small number of transcription factors. Homeotypic clusters of binding sites, as seen in developmental genes in Drosophila melanogaster [20], represent one regulatory mechanism that could explain the occurrence of long, conserved non-coding regions. However, as transcription factors tolerate considerable variation between functional binding sites, a homeotypic cluster of binding sites as such cannot warrant the extreme level of conservation observed in UCRs. Alternatively, the recent emergence of the role of microRNAs in regulation suggests that there could be additional non-coding genes in the human genome, perhaps at the sites of ultra-conservation.
The clustering of UCRs suggests that UCR-mediated transcriptional regulation may involve molecular events on a greater scale, possibly involving chromatin structure. This potential link to chromatin structure is suggested by the striking pattern of UCRs in the IRX gene clusters. Most of the UCRs have no similarity between the two clusters, with the exception of a set of four UCRs that have retained both mutual sequence similarity and spatial position (Figure 4). It is tempting to assume that the retention of their mutual similarity is a consequence of IRX cluster co-regulation, the mechanism of which remains unknown.
Based on the preservation of nearly identical sequences over ~450 million years of vertebrate evolution, it is reasonable to postulate the influence of exceptional biochemical mechanisms. Numerous hypotheses could account for the observed data, broadly falling into two categories – active mechanism(s) resulting in the decrease of mutational frequency in UCRs, or negative pressure consistent with evolutionary selection against such mutations. Given the breadth of possibilities, we leave postulation until further data emerges.
Conclusion
Since Bejerano et al.[9] focused on larger regions (200 bp) of perfect nucleotide identity compared to our more permissive settings (95% sequence identity over 50 bp), the genomic arrangement of UCR-containing regions with respect to their presumptive target genes was not fully realized. Our findings include critical new information about UCR clusters, particularly with regards to patterns of conservation, their genomic organization, and the insights they provide into potential chromatin regulating mechanisms. These mysterious regions retained over hundreds of millions of years of evolution appear to contribute to a novel mechanism of developmental regulation. Detailed studies of UCRs that will ensue from the discoveries reported here promise to advance our understanding of vertebrate development.
Methods
Definition of UCRs applied in this study
We defined UCRs as non-protein coding genomic regions having a sequence identity > 95% over a 50 bp sliding window of length in human/mouse comparison (based on the tight alignments track from the UCSC genome browser database[17], using human and mouse assemblies hg15 and mm3, respectively). As a further constraint, an UCR must overlap with sequences conserved between the human and pufferfish genomes, as defined in the UCSC genome browser databases (a BLAT [21] alignment between human and pufferfish with a minimum BLAT score of 20). In order to avoid inclusion of coding sequence, we required that a UCR must not overlap a mouse or human cDNA mapped to the genome (based on cDNA tracks from from the UCSC genome browser database[17]) or overlap putative coding regions predicted by GenScan [22].
Calculation of UCR and gene distributions
The distribution of UCRs in the genome was calculated by counting the number of UCRs within a 500 kilobase (kb) window which was progressively slid over each chromosome in 100 kb intervals. The same approach was used to estimate the gene density; specifically by summing the number of bases within the window that aligned with human mRNA (from the UCSC Genome Browser database).
Gene-UCR distance calculation
Distances between a given gene and UCR on the same chromosome were defined as the shortest distance between the starting points and/or endpoints of UCR and gene in the human genome (UCSC assembly hg15), using EnsEMBL [23] gene annotation. Genes based solely on ESTs or computational predictions were not included.
Estimation of significance of Gene-UCR distances
The distances from genes within a set (for instance, all forkhead domain-containing genes) to the closest UCRs were calculated as above. The expected fraction of gene-UCR distances smaller than 8 kb was estimated by simulation: UCR genome coordinates were randomly chosen and distances measured as above. The simulation process was repeated 1000 times and the average fraction reported. In order to estimate if the observed distribution was significantly different from the expected, we used the chi-squared test.
Estimation of domain over-representation in genes closest to UCRs
For each UCR, the closest upstream and downstream gene within 2 Mbp was identified (UCRs inside introns of genes were analyzed separately). EnsEMBL InterPro [14] domain annotation was used to tabulate a contingency table consisting of the positive sample counts (number of genes in the set containing a certain domain), negative sample counts (number of remaining genes in the set), background positives (number of genes containing the same domain in the genome) and background negatives (remaining genes). For clarity, a given gene was only counted once, and multiple occurrences of the same domain within the same protein were not counted.
For each domain found in the UCR-proximal genes, we tested the null-hypothesis that the sample and background sets are drawn from the same population versus the alternative hypothesis that the sample set has a higher frequency of the domain, using Fisher's Exact Test [24] from the R statistical package . Since the number of tests is considerable, we corrected for multiple sampling using the conservative Bonferroni method [15], in which the number of tests is multiplied with the P-value from the Fisher test with the number of unique domains tested (837). An analogous analysis was performed with genes containing one or more UCRs within their introns [see Additional file 4].
Estimation of clustering tendency
We used the distances between consecutive UCRs as a statistic indicating clustering. A neutral background distance distribution was created by assigning UCRs genome coordinates randomly, and subsequently measuring distances between consecutive UCRs. This process was repeated 1000 times. We compared the distance distribution between naturally occurring UCRs and the background using the Kolmogorov-Smirnov test [25], which assigns a probability that two distributions are similarly shaped.
UCR sequence similarity analysis
All possible pairs of UCRs were aligned using NCBI BLASTN [26] with standard settings. For any pair to be reported as near-identical, we required an HSP of at least 50 bp and a pairwise sequence identity exceeding 75%.
Abbreviations
UCR – ultraconserved non-coding region; bp – basepairs; kbp – 103 base pairs
Authors' contributions
AS collected the data and performed most steps of bioinformatic and statistical analysis presented in the paper. He produced all the Figures in the paper and Table 1, and co-wrote the manuscript. PB made initial analyses of putative regulatory elements on selected genes involved in neural tube development. He discovered a number of super-conserved regions in the process, which helped create the rules for their genome-wide computational detection. He also co-wrote the first versions of the manuscript. SB participated in the annotation of the gene set and in the creation of software for the visualization of results. PE prepared genome sequence and annotation data for human, mouse and pufferfish. He and AS designed the statistical tests applied in the study. JK participated in the initial analyses and data extraction with PB. WWW participated in result interpretation, design of statistical tests, and writing later versions of the manuscript. JE initiated and co-supervised the study, which has the roots in his research in developmental neurobiology. He also co-wrote the manuscript. BL designed and supervised the bioinformatic study, developed the initial framework for the analysis of the genomic sequences, made an independent observation about high incidence of clustering of super-conserved regions around genes encoding DNA-binding proteins, and annotated the UCR clusters with co-localizing genes. He also co-wrote the manuscript.
Supplementary Material
Additional File 1
Genescape around 50 randomly selected UCRs. Selected UCRs are shown as yellow triangles, other UCRs as light yellow triangles. Genes are colored after domain (red = Homeobox, green = C2H2 Zink fingers in green, pink = Nuclear receptors, Blue = forkhead).
Click here for file
Additional File 2
Genescape around 50 randomly selected genes. UCRs are shown as as light yellow triangles. Color coding of genes as above.
Click here for file
Additional File 3
Complete list of protein domains in genes flanking UCRs. Each tested domain is listed along with corrected and uncorrected P-value as in Table 1.
Click here for file
Additional File 4
Complete list of protein domains in genes with UCR(s) in intron(s) Each tested domain is listed along with corrected and uncorrected P-value as in Table 1.
Click here for file
Additional File 5
UCR distribution in the human genome UCR density (pink) and gene density (blue) is shown for each chromosome. Densities are calculated as described in Methods.
Click here for file
Additional File 6
Genes associated with enumerated UCR clusters from Figure 3. UCRs were counted by sliding a 500 kb window along the chromosomes. Overlapping UCR-containing windows were merged into a single cluster span. The cluster span coordinates correspond to the human genome NCBI build 33 (UCSC hg15, April 2003). A more exhaustive list is found in [Additional file 7]
Click here for file
Additional File 7
Extended list of UCR clusters An extended, but less annotated, version of in [Additional file 6]
Click here for file
Acknowledgements
AS and BL were supported in part by funding from Pharmacia Corporation (now Pfizer). JE is supported by the Royal Swedish Academy of Sciences, by a donation from the Wallenberg Foundation, The Swedish Foundation for Strategic Research, The Wallenberg Foundation, The Swedish National Research Council and the EC network grants: Brainstem Genetics: QLRT-2000-01467 and Stembridge: QLG3-CT-2002-01141. W.W. is supported by the Michael Smith Foundation for Health Research and the Canadian Institutes of Health Research.
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| 15613238 | PMC544600 | CC BY | 2021-01-04 16:39:23 | no | BMC Genomics. 2004 Dec 21; 5:99 | utf-8 | BMC Genomics | 2,004 | 10.1186/1471-2164-5-99 | oa_comm |
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BMC NeurolBMC Neurology1471-2377BioMed Central London 1471-2377-4-201557162310.1186/1471-2377-4-20Research ArticleHomozygosity for a missense mutation in the 67 kDa isoform of glutamate decarboxylase in a family with autosomal recessive spastic cerebral palsy: parallels with Stiff-Person Syndrome and other movement disorders Lynex Clare N [email protected] Ian M [email protected] Jack P [email protected] Rajgopal [email protected] Simon [email protected] Eamonn R [email protected] C Geoffrey [email protected] David T [email protected] Alex F [email protected] Molecular Medicine Unit, University of Leeds, Clinical Sciences Building, St James's University Hospital, Leeds, UK2 Neonatal Medical Unit, St Mary's Hospital, Manchester, UK3 Department of Paediatrics and Child Health, Section of Medical and Molecular Genetics, The Medical School, University of Birmingham, Birmingham, UK2004 30 11 2004 4 20 20 5 5 2004 30 11 2004 Copyright © 2004 Lynex et al; licensee BioMed Central Ltd.2004Lynex 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
Cerebral palsy (CP) is an heterogeneous group of neurological disorders of movement and/or posture, with an estimated incidence of 1 in 1000 live births. Non-progressive forms of symmetrical, spastic CP have been identified, which show a Mendelian autosomal recessive pattern of inheritance. We recently described the mapping of a recessive spastic CP locus to a 5 cM chromosomal region located at 2q24-31.1, in rare consanguineous families.
Methods
Here we present data that refine this locus to a 0.5 cM region, flanked by the microsatellite markers D2S2345 and D2S326. The minimal region contains the candidate gene GAD1, which encodes a glutamate decarboxylase isoform (GAD67), involved in conversion of the amino acid and excitatory neurotransmitter glutamate to the inhibitory neurotransmitter γ-aminobutyric acid (GABA).
Results
A novel amino acid mis-sense mutation in GAD67 was detected, which segregated with CP in affected individuals.
Conclusions
This result is interesting because auto-antibodies to GAD67 and the more widely studied GAD65 homologue encoded by the GAD2 gene, are described in patients with Stiff-Person Syndrome (SPS), epilepsy, cerebellar ataxia and Batten disease. Further investigation seems merited of the possibility that variation in the GAD1 sequence, potentially affecting glutamate/GABA ratios, may underlie this form of spastic CP, given the presence of anti-GAD antibodies in SPS and the recognised excitotoxicity of glutamate in various contexts.
Table 4 GAD1 single nucleotide substitutions detected on mutation analysis and occurring in sequences submitted to NCBI SNP database and in the literature. This is not a definitive list, but includes those described at the time of the mutational analysis. *Nucleotide positions were not provided by Maestrini et al. [47].
Source SNP position in mRNA, from the translational start site (bp) Gene position of SNP(bp) Amino acid change
(A)Lappalainen et al. (2002) A(-478)Del Exon 0 (73) No substitution
(B)Lappalainen et al. (2002) G(-147)A Exon 0 (404) No substitution
(C)Lappalainen et al. (2002) A(-39)C Exon 1 (25) No substitution
(D)Spastic CP patients family B G(36)C Exon 1 (97) Ser(12)Cys
(E)NCBI collated resource G(48)C Exon 1 (104) Pro(17)Ala
(F)Control samples & family A NCBI collated resource T(110)C Exon 2 (29) No substitution
(G)Kure et al. (1998) T(315)C Exon 4 (14) No substitution
(H)Bu and Tobin (1994) Kure et al. (1998) A(407)G Exon 4 (105) No substitution
(I)Maestrini et al. (2002)* G/C Intron 4 No substitution
(J)NCBI collated resource C(696)T Exon 6 (56) No substitution
(K)Lappalainen et al. (2002) T/Del Intron 7 (35) No substitution
(L)In control samples Lappalainen et al. (2002) T/C Intron 8 (185) No substitution
(M)Maestrini et al. (2002)* C/T Intron 9 No substitution
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Background
Cerebral palsy (CP) is a term used to define a group of disorders [1] characterized by a non-progressive abnormality of posture and movement, resulting from defects in the developing nervous system [2]. Approximately 1 in 250 to 1000 live births presents with CP, making it one the commonest congenital disabilities [3]. Many different aetiological factors have been implicated. Among preterm infants, the incidence of CP generally increases with decreasing gestational age and the origin in most cases may be traced to post/peri-partum periventricular leukomalacia and intraventricular/periventricular haemorrhage [4]. Conversely in term infants perinatal causes can only confidently be attributed where there is documented perinatal hypoxia/acidosis and clinical encephalopathy in the early neonatal period [5]. Prenatal risk factors in the aetiology of CP include low birth-weight, intrauterine infection and exposure to teratogens during pregnancy [6,7]. The cause in a large proportion of cases remains obscure.
Depending on the overall clinical picture, CP can be sub-classified into a number of phenotypic groups [8,9]. Dyskinetic CP accounts for ~20% of all cases, which may be further divided into choreoathetotic (5%) and dystonic (15%) forms. Ataxic CP (~10% of all cases) can also be sub-divided into two forms, simple (congenital) ataxia (5%) and ataxic diplegia (5%). Spastic CP is the most prevalent sub-type (~70%) and was the phenotype of the probands in this study [10]. It is characterised by muscular hypertonicity and pronounced rigidity of the affected limbs. Spastic CP can be sub-classified according to the topography of the affected limbs as hemiplegic (20%), monoplegic (<1%), diplegic (40%), or quadriplegic (10%) [11].
Kuban and Leviton [3] suggested that CP could be genetic in origin, as well as the result of environmental insult at any point during CNS development. Most estimates place the proportion of CP cases with a genetic aetiology at between one and two percent of the total [12]. Among infants and children with spasticity, symmetry of neurological signs has been identified as a strong indicator of a probable genetic aetiology [13,14]. The proportion of cases demonstrating Mendelian inheritance varies among the different sub-types of CP [2,13,7]. X-linked, autosomal dominant and recessive inheritance patterns have been described for non-progressive CP. An ataxic diplegic autosomal recessive trait (OMIM:605388) [15] has been mapped to chromosome 9p12-q12. Progressive spastic paraplegia (SPG) has a similar pathology to CP. SPG displays autosomal dominant (SPG3A at 14q11-q21 encoding the atlastin GTPase; SPG4 at 2p21-22 encoding spastin, an AAA family ATPase/chaperonin; SPG6 at 15q11.1; SPG8 at 8q23-q24; SPG9 at 10q23-q24; SPG10 at 12q13; SPG12 at 19q13; and SPG13 at 2q24 encoding the HSP60 mitochondrial chaperonin), recessive (SPG5A at 8cen; SPG7 at 16q24.3 encoding paraplegin, an AAA family ATPase/inner mitochondrial membrane chaperonin; SPG11 at 15q13-q15; SPG14 at 3q27-q28; SPG15 at 14q22-q24; and SPG17 at 11q12-q14), or X-linked inheritance patterns (SPG1 at Xq28 encoding the L1CAM adhesion molecule; SPG2 at Xq22 encoding proteolipid protein-1; and SPG16 at Xq11.2).
A non-progressive, autosomal recessive, symmetrical spastic CP locus has been mapped to a 5 cM region between D2S124 and D2S333, at 2q24-31.1 (LOD score of 5.75) in consanguineous families originating from the Mirpur region of Pakistan (OMIM:603513) [10]. Affected individuals had no identifiable perinatal cause of CP, or underlying diagnosis and presented with developmental delay, mental retardation and sometimes epilepsy as part of the phenotype. We initially performed detailed physical mapping of the 5 cM region, so as to accurately define the marker order and to refine the linkage interval. The positions of a large number of genes and ESTs were defined accordingly, allowing the rapid identification of candidate disease genes. The minimum region of homozygosity was reduced to 0.5 cM by typing large numbers of microsatellite markers in the families. Subsequently, portions of this region have been sequenced in the human genome project. Within the region we have concentrated on the positional candidate GAD1, which codes for the 67 kDa isoform of L-glutamate decarboxylase (GAD EC:4.1.1.15).
GAD requires the cofactor pyridoxal 5'-phosphate (PLP) and catalyses the production of Gamma-aminobutyric acid (GABA) from glutamate [16]. Two separate, independently-regulated genes, GAD1 and GAD2 (at chromosome 10p11, encoding a 65 kDa GAD isoform), have presumably arisen by duplication and been conserved during evolution, as indicated by their sequence homology [17] and the retention of common intron-exon boundary splice sites [18]. Their N-termini demonstrate ~23% homology, but their C-termini, which contain the catalytic site, have ~73% amino acid sequence identity between the isoforms [19]. GABA and glutamate are the most abundant amino acid neurotransmitters in the brain. GABA, an inhibitory neurotransmitter, and excitatory glutamate, both play important roles in synaptic plasticity and neuroendocrine function [20]. Both isoforms of GAD are also involved in intermediary metabolism, participating in the GABA shunt, which bypasses two steps of the TCA cycle [21]. GAD activity of both isoforms, is ubiquitous, but highest in the brain and pancreatic islets of Langerhans. We therefore performed detailed mutational screening of GAD1 in familial spastic CP probands and unaffected family members.
Methods
Features and pedigrees of ascertained families
Family A (4718/4719)
The oldest affected male diagnosed with non-progressive, spastic CP, demonstrated global developmental delay, with no associated neurological abnormalities and moderate mental retardation. His affected younger sister was also diagnosed with spastic CP, global developmental delay and moderate mental retardation.
Family B (4578/4579/4581/4679)
The oldest non-progressive, spastic CP female has severe mental retardation, and is confined to a wheelchair. The next oldest spastic CP male has severe mental retardation, mild hypertonia and ataxia of the upper limbs. The next oldest spastic CP male is a dizygotic twin born by Caesarean section. This patient on presentation demonstrated developmental delay, mild hypertonia and ataxia of the upper limbs. The youngest affected female is not able to walk or stand unaided and has severe developmental delay. Details of the clinical picture in these pedigrees have been described previously [10,14].
Physical mapping of candidate region
ICI and CEPH YAC libraries were screened by PCR amplification of STSs that were mapped between D2S2157 and D2S385. The positive clones (CEPH human mega YAC clones: 761-G10, 797-G4, 842-G1, 842-G3, 910-G12, 945-C12; ICI human YAC clones: 13I-E10, 13I-G11, 14I-G12, 16F-H2, 1E-F6, 21E-G5, 30A-D10, 30H-D2, 33D-C4, 35B-D2, 18B-E3, 31H-A4, 40D-E8, 8D-E12, 9H-F10) were obtained from the UK HGMP Resource Centre . Clones were grown up in casamino acid selective broth overnight and harvested by centrifugation. The pellet was then washed twice in 0.5 ml 100 mM Tris-HCl, pH7.5, 0.5 M EDTA buffer. After a second round of centrifugation the pellet was resuspended in molten 1% LMP agarose in 5 mM Tris-HCl, pH7.5, 0.05 M EDTA, 10 mM NaCl with 100 μg of Zymolase. The agarose was cooled and the resultant plugs were incubated overnight at 37°C in 50 ml of 0.5 M EDTA, 10 mM Tris-HCl, pH7.5, 10 mM NaCl. The buffer was replaced with fresh solution to which 100 μl of 40% Sarkosyl NL30 and 50 μl Proteinase K (1 mg/ml) was added and the plugs were incubated overnight at 50°C.
The YAC chromosomal DNA was purified and separated for sizing by CHEF electrophoresis. The switching angle was 120°, using a CHEF™ electrophoresis tank (Bio Rad), run for 16 hours at 6 V/cm, 10°C with an initial pulse time of 30 sec to a final pulse time of 90 sec. DNA was stained with (20 mg/ml) ethidium bromide solution (BDH) for 2 hours and visualized under ultra-violet illumination. Southern blotting and membrane hybridisation of CHEF gels were performed on Hybond-N™ membranes (Amersham). DNA was immobilised by heating the membrane to 80°C in vacuum for 1 hour in a gel dryer (Bio-Rad). Radio-labelled YAC vector-specific probes were generated using the Megaprime random primer kit (Amersham) as described in the manufacturer's instructions, using PCR products as a template. Sizes were estimated based on comparisons with the known sizes of the native yeast chromosomes.
Genetic mapping
The microsatellite markers: Cen-D2157, D2S124, D2S2330, CHLC.GATA71B02 (D2S1776), D2S2345, CHLC.GATA71D01, D2S294, AFMA109YC1, D2S376, D2S2284, D2S2177, D2S2194, D2S333, D2S2302, D2S2381, D2S326, AFMA304WB1, D2S138, D2S148, D2S300-Tel, were amplified using fluorescently labelled primers (Lifetech) previously designed by the Whitehead Institute or on the GDB database , , . These primers were used to amplify microsatellite marker alleles from individuals by PCR. Individual alleles were identified by denaturing polyacrylamide gel electrophoresis on an ABI Prism 377 sequencer and analysed using Genescan™ and Genotyper™ (version 1.1.1) software (Applied Biosystems).
Single-strand conformational polymorphism analysis
GAD1 exon sequences were amplified by PCR using the primers described in Table 1. SSCP was performed on GeneGel Excel (Amersham), using a 12.5/24 gel (14°C, 600 V, 25 mA, 15 W for 80 min) and the DNA was visualised by silver staining as per the manufacturer's instructions.
Table 1 Oligonucleotide primer used in the amplification of GAD1 exons.
Primer Forward Reverse
Exon 1 dGCCCCATTTATTTCCCAGCC dGCACAGCTCTCGCTTCTCTT
Exon 2 dGAAAACCATTGTCCTCCACC dGCCTGTCGGCTCACAGATT
Exon 3 dACCAGCTTCTTGTGCCATAG dATCTACTGGCTAGCATGGGG
Exon 4 dATTCCATGTCTGAGCAGCCT dACTGTTACTGCCCAAGCTTG
Exon 5 dGCCGTTTGCCTTCAAGATAG dAGAACCACTGGGACTGAACT
Exon E dACCAGTATCTCCTCGCCATG dTTGGGAGGCCCCTGGAAATT
Exon 6 dACCCAACTACAAATACTAAACC dAATAGGAAGTCAGGGTATCC
Exon 7 dGAGACACCAGCTCAGCGTTC dCTGCAACAAACAGAGGCTCG
Exon 8 dGTCGGGGATGCTTTCTCCATG dCTCAGTACATTGTGCCAAGC
Exon 9 dCAAGCTGCTAATGGTCTGTT dGTCTCATATTATCAAGGACTG
Exon 10 dCACAATTCTTCTTCCTGTGA dTGGGGAGGAGCTTGAGGCAA
Exon 11 dACAATCAGTGTGGGCTGAAC dGAAGCAAACTTAGACCGAAA
Exon 12 dCTTGAGTTGGAATGGGTGTT dACTGCAAAGAGACCCCACGT
Exon 13 dTCCTTCCAAGCAGCCTAGTT dGTGATATATCTTTGCCCCTC
Exon 14 dGACAGCATAGCCTTCCCAAA dCATGTTGCCAGAAGCTTCAG
Exon 15 dGGTTTGGGAACAGCTTTCTC dTTCCCCCACTAGAAAGGCAC
Exon 16 dGTTAAAAAGAGAGGGTGTTC dCCCTCAATGAAATGGCCTGT
Sequencing of GAD1 exon sequences
Primers used to amplify the exons of GAD1 were designed from the sequence of BAC RP11-570c16 and obtained from Lifetech (Table 1). PCR products were purified from agarose gels using QIAquick gel extraction kits (Quiagen), sequenced using ABI PRISM Big Dye Terminator Cycle Sequencing Ready Reaction Kits (Applied Biosystems) and then analysed using an ABI Prism 377 automated sequencer.
Results
Physical map of the spastic CP locus
An integrated YAC (ICI and CEPH mega YAC libraries) and RP-9 PAC (HGMP Resource Centre) contig for the interval D2S2157 to D2S385 was constructed (Figure 1). This map provided physical continuity connecting 25 loci from centromere to telomere spanning the entire 2q24.3-31.1 cytogenetic band region. These data were combined with BAC contigs, constructed at Washington University , in an attempt to form an ordered BAC/YAC contig across the minimum region of interest. Selected YACs from this contig were sized (Table 2) enabling the physical length of the region to be estimated. The partial contig map included 3 identified CEPH mega-YAC contigs (contig 1: 945-C12; 912-B6; 744-G6; 797-G4; 761-G10; contig 2: 752-G9; 757-E1; 807-H5; 842-G1; 842-G3 and contig 3: 964-H5; 935-E10; 855-H2; 785-G8; 963-D11; 751-H3). The size of the region incorporating the three contigs was estimated to be at least 2870 kb. The sizes of the ICI YACs were estimated to encompass a minimum locus of 2940 kb. Microsatellite markers, ESTs and known genes, mapped to this region by NCBI, were then located on the physical contig by PCR and BLAST sequence homology searches of the Washington University BAC contigs. The expression profiles of unidentified EST clusters were determined and used to form EST "bins". These contained groups of ESTs, which mapped to adjacent locations and showed common expression profiles, suggesting that they might represent different exons of the same gene. This placement of known genes and ESTs onto the physical map provided an annotation of the gene content of the region, at the time was constructed before any such facility was available from the HGP, as the focus for candidate disease gene selection.
Figure 1 An integrated physical YAC contig spanning the human chromosome 2 spastic CP locus. This was constructed against a framework of microsatellite and STS markers, to incorporate the region of linkage identified by genotyping data. The positions of microsatellite and STS markers are represented numerically left to right from centromere to telomere. These loci 1–25 were mapped arbitrarily equi-distant onto the contig, in the following order: Cen-(1) D2S2157; (2) D2S382; (3) WI-18792; (4) D2S124; (5) D2S111; (6) D2S2384; (7) D2S2330; (8) D2S399; (9) D2S2345; (10) D2S294; (11) D2S2188; (12) D2S2284; (13) D2S2177; (14) D2S335; (15) D2S326; (16) D2S2381; (17) D2S2302; (18) D2S2307; (19) D2S2257; (20) D2S2314; (21) D2S138; (22) D2S148; (23) D2S2173; (24) D2S300; (25) D2S385-Tel.
Table 2 Approximate sizes of YAC clones spanning the 2q24-31.1 autosomal recessive spastic CP disease gene locus, used to estimate the minimum physical size of the region (kb). The CEPH MEGA and ICI YACs were sized using CHEF PFGE compared against the native yeast chromosomes. This confirmed the estimated size ranges of the YAC inserts, predicted by the Whitehead Institute (WI) database of YAC clones.
CEPH human MEGA-YAC clones ICI human YAC clones
Clone Size (kb) Clone Size (kb)
910-G12 1630 40D-E3 290
744-G6 1120 35B-D2 260
912-B6 1190 33D-C4 120, 480
945-C12 1540 30H-D2 250
842-G3 1330 30A-D10 260
797-G4 1000 18B-E3 120
807-H5 1680, 1290 16F-H2 220
752-G9 1740 13I-G11 230
785-G8 690, 1060 13I-E10 460
842-G1 1380 8D-E12 280
757-E1 1100
812-G1 1540
863-H12 1550
935-E10 1360
785-G8 1060
751-H3 1780
963-D11 1670, 890
Genetic mapping data
From the PAC/YAC contig, 20 polymorphic microsatellite markers were identified that span the chromosome 2q24-31.1 CP critical region. These were then used, with informed consent and local research ethics committee approval, in the detailed mapping of previously linked families [10] (Figure 2). These data did not support the presence of a founder mutation for autosomal recessive spastic CP, in that families did not share a common haplotype across the minimal homozygous region between D2S2345 and D2S326.
Figure 2 Annotation of two pedigrees of spastic autosomal recessive CP families and corresponding linkage mapping data. The markers shown are those that demonstrate the minimal homozygous region between the affected individuals of both families.
Sequence analysis of GAD1
GAD1 was sequenced in affected and unaffected individuals in both families ascertained. In order to differentiate possible disease-causing mutations from polymorphisms, 100 control individuals were screened by SSCP to detect any GAD1 sequence variations in the normal population. SSCP variants were then sequenced to identify the underlying nucleotide substitutions. An homozygous G(36)C (Figure 3A,3B) nucleotide change was observed in 4 affected patients, which generated a Ser(12)Cys amino acid substitution. No obligate carriers were identified for this mutation. This variant has not been previously described and was not present in 200 normal chromosomes. A number of other sequence changes were detected, but none of these resulted in amino acid changes. These variants and all those recorded previously in the literature are presented in Table 4 and Figure 4. For those rare variants in the databases, which result in amino acid changes, no homozygous or compound heterozygous individuals have yet been described.
Figure 3 Electropherograms of the sequence of the exon 1 SNP of GAD1 identified in the process of mutational analysis. (A) and (B) show the normal C variant in the forward and reverse directions, respectively. (C) and (D) show the alternative G variant in the forward and reverse directions, respectively. This variant was only found in affected individuals of family B. No heterozygous individuals were identified for this nucleotide variant.
Table 3 Autozygosity mapping data generated by genotyping eight members of the two autosomal recessive spastic CP families. Subjects 4718, 4719, 4720 and 4722 represent family A; subjects 4578, 4579, 4581 and 4679 represent family B as demonstrated in Figure 2. This Table is organised according to the definitive marker order determined from current databases and the physical contig mapping undertaken. The minimum homozygous region is highlighted. *Denotes an unaffected family member.
Marker Base position 4718 4719 4722* 4720* 4578 4579 4581 4679
D2S2157 AFMA119YH5 166029867 145 145 145/147 145 145/147 145/147 145/147 145/147
D2S124 AFM094ZC9 166347755 160 160 160 160 160/163 160/163 160/163 160/163
D2S2330 AFMC015YD9 166900134 156 156 156/158 156/158 160/168 160/168 160/168 160/168
CHLC.GATA71B02 167624503 240/256 240/256 240/256 240/256 240/256 240/256 240/256 240/256
D2S2345 AFM080XG9 168922932 156 156 150/156 150/156 152 152 152 152
CHLC.GATA71D01 169848018 193 193 193 193 193 193 193 193
D2S294 AFM205XF12 170579380 186 186 186 186 208 208 208 208
AFMA109YC1 171576976 256 256 254/256 256 254 254 254 254
D2S376 AFM319XG1 171576985 235 235 235 235 235 235 235 235
D2S2284 AFMB314YE1 171696071 166 166 166 166 166 166 166 166
D2S2177 AFMA155TF9 171790203 118 118 118 118 128 128 128 128
D2S2194 AFMA222XB9 171884139 141 141 141 141/143 145 145 145 145
D2S333 AFM2702E9 172592103 189 189 189/191 189/191 189 189 189 189
D2S2302 AFMB342ZD9 172758604 204 204 199/204 199/204 204 204 204 204
D2S2381 AFMA082TF5 172861090 222 222 226 222/226 224 224 224 224
D2S326 AFM266VE1 173299492 92 92 92 92 92 92 92 92
AFMA304WB1 176057526 130/132 122/132 130/132 122/132 124 124 124 124
D2S138 AFM176XD4 177947395 108 108 113 108/113 111/117 111 111 111
D2S148 AFM200WA11 178434054 184/188 184/188 184/194 184/194 188/190 186 186 186
D2S300 AFM214XC3 178826338 87/89 87/89 87/89 87/89 87/89 89 89 89
Figure 4 An annotation of the distribution of single nucleotide substitutions identified in the open reading frame of GAD1. The approximate positions with respect to intron-exon of the open reading frame structure are illustrated. These were determined by sequencing of the probands in this study, from published data and from the NCBI collated database of SNPs. The letters refer to the SNPs listed in Table 4. Upper case letters refer to SNPs in the cDNA and lower case letters indicate SNPs in the genomic DNA. A: G(36)C, B: G(210)A, C: G(253)C, D: T(315)C, E: A(407)G, F: C(696)T, G: C(1506)T, H: C(1575)T, i: T(1625)G, J: C(1654)T, k: A(1659)G, l: G(1799)A, m: C(1899)A.
Discussion
CP is a term used as a collective definition for a group of neurological disorders [3]. The pathophysiology in most cases is poorly understood, but includes genetic syndromes, congenital malformation, infective intra-uterine encephalitis, cerebral haemorrhage or infarction, ischemic damage, periventricular leukomalacia (PVL), and non-infarctive telencephalic leukomalacia [11]. The contribution of Mendelian inherited cases of CP accounts for approximately 2% of the total number [2,22]. A non-progressive form of autosomal recessive spastic CP has been identified [13]. McHale et al. [10] succeeded in identifying a 5 cM region on chromosome 2q24-31.1, which segregated with disease in consanguineous families. Linkage analysis identified a locus between markers D2S124 and D2S333, which produced a LOD score of 5.75, sufficient to warrant the further investigation described herein.
To refine and confirm the genetic marker order across a region, which was incompletely sequenced at the time, we used YAC and PAC clones to construct a physical framework and performed PCR to map ESTs and microsatellite markers to clones within the partial contig (Figure 1). When this contig was integrated with the BAC sequence contigs, rearrangement of the BAC order was necessary. With each subsequent DNA sequence update, the degree of inconsistency was reduced and this led to revision of microsatellite order compared with that used previously [10]. Using the YAC sizes, the sizes of gaps in the BAC contig could be estimated. Having generated a detailed map spanning the region, we selected microsatellite markers at evenly spaced intervals across the locus. These markers were used to refine the minimum region homozygous by descent in linked families, to between the markers D2S2345 and D2S326. The physical size of the region between these markers is approximately 0.5 cM. There was no suggestion of a founder haplotype common to the two families (Figure 2).
The Goldenpath Human Genome Working Draft Assembly 2001, is an annotation of the Washington BAC contig, combining sequence data of BACs, ESTs, known genes and hypothetical genes. We mapped ESTs and known, uncharacterised or hypothetical genes on the basis of sequence homology (NCBI , Whitehead and Goldenpath databases), onto our YAC/PAC contig. ESTs were then collated according to their expression profiles to produce a "binned" EST map, on which candidate gene selection could be based. This reduced the number of hypothetical genes in the region and allowed the combination of genetic, physical mapping and expression data, into a single comprehensive map.
One interesting candidate within the minimal region was GAD1, which encodes GAD67. Expression of its transcript is ubiquitous, including the CNS. The main function of GAD67 is to catalyze the conversion of the excitatory amino acid and neurotransmitter glutamate to GABA, the main inhibitory neurotransmitter in the CNS [23]. In the developing CNS, GABA has an important role in neuronal differentiation and the control of plasticity [21]. GABA has also been implicated in the pathogenesis of various seizure and movement disorders [20].
Vertebrates have two separate genes coding for GAD, which produce distinct forms of the enzyme. GAD1 and GAD2 have diverged relatively recently in evolution, as indicated by their degree of sequence homology and the retention of common intron-exon boundary splice sites [17] (Figure 5A). The variants of GAD differ in molecular weight, cellular and sub-cellular localisation, and their interaction with the cofactor PLP [18,20,24].
Figure 5 Three illustrations of the genomic, protein and comparative sequence homologies of the different species of GAD. (A) The genomic structures of GAD1/GAD25/GAD2 and Drosophila Gad1. (B) Comparative protein domain structures of GAD65/GAD25/GAD67 and Drosophila Gad1. (Numbers represent approximate amino acid residues). (C) Schematic illustrating the relative homology of the protein structures of GAD67/GAD65 and Drosophila Gad1.
GAD2, located at 10p13-p11.2, is transcribed to produce a 5.6 kb mRNA in islets and brain, encoding a 65 kDa protein (585 AA residues). The 67 kDa (594 AA residues) form [17] is localised to 2q25-26 and encoded by a 3.7 kb transcript (GAD1) [23]. There is also evidence for a 25 kDa inactive protein (GAD25) produced from an alternatively spliced GAD1 transcript of 2 kb that contains an in-frame stop codon. This GAD1 splice variant has only been found in human islets, testis and adrenal cortex, although the homologue is present in fetal mouse brain [25]. GAD67 and GAD65 consist of two major sequence domains (Figure 5B). The N-termini (AA residues 1–94 in GAD65 and 1–101 in GAD67) demonstrate ~23% homology. These N-terminal domains are thought to be responsible for sub-cellular targeting and the formation of GAD65–GAD67 heterodimers [26]. The C-terminal domains (AA residues 96–585 in GAD65 and 102–594 in GAD67) contain the catalytic site, with ~73% sequence identity between the isoforms [19] (Figure 5).
In the CNS, GAD65 appears to be preferentially distributed in axon terminals and the associated synaptic vesicles, whereas GAD67 is also located in the cell bodies and more uniformly distributed throughout the neuron [24]. This suggests that each GAD isoform is involved in the synthesis of GABA in different sub-cellular compartments [21]. This is supported by the discovery that GAD65 is the main source of apoGAD (an inactive reservoir), which responds to short-term changes in neuronal activity and is more responsive to levels of PLP [18]. On the other hand, GAD67 predominantly exists bound to the PLP cofactor (holoGAD), providing a constitutive level of GABA production [20]. Bond et al. [27] showed that GAD25 is expressed in a temporally controlled manner, in the developing striatum and cortex in rodents, suggesting this may provide a mechanism of regulating GABA production in differentiating neurons.
Asada et al. [28] undertook the selective elimination of each GAD isoform in order to determine their respective roles. Gad2-/- mice are slightly more susceptible to seizures, consistent with an excitatory increase in the relative ratio of glutamate/GABA. However, they showed no obvious overall change in neuronal GABA content. Therefore GAD67 alone appears to produce sufficient GABA for effective neurotransmission [21]. Gad1-/- mice demonstrated a decrease of ~20% in total glutamate decarboxylase activity at birth. This was assayed by the conversion of 14C-labelled glutamate to 14CO2 in the presence of PLP. There was also a marked (7%) reduction in total GABA content in cerebral cortex homogenate measured by liquid chromatography [28]. Unfortunately, these mice died neonatally of severe cleft palate, masking any potential neurological dysfunction and also illustrating a role for Gad67 in non-neural tissues [21]. It is of interest that mice with mutations in the β-3 GABA receptor (GABRB3) at the Angelman syndrome (OMIM:105830) locus, also display cleft palate, implying a key role for GABA signalling in normal palate development [29,30].
Pyridoxine-dependent epilepsy (PDE) is a rare autosomal recessive disorder (OMIM:266100), characterized by generalized seizures during the first hours of life. The associated pathology may result from an alteration in the binding of the co-factor PLP to GAD. Interestingly epilepsy is commonly associated with CP and grand mal epilepsy developed at age six months in the two linked pedigrees described here [10]. GAD1 mutation was previously suspected of being the cause of PDE. Linkage of pyridoxine-dependent epilepsy has however been reported to 5q31.2-31.3, with GAD1 and GAD2 excluded [31]. Decreased levels of brain and CSF GABA, increased levels of CSF and cortical glutamate, and decreased levels of PLP in the frontal cortex, have been described in this condition.
GAD65 and GAD67 have been identified as auto-antigens in "Stiff Person Syndrome" (SPS, OMIM:184850), and in cerebellar ataxia [32-34]. GABA-mediated synaptic transmission is thought to be functionally impaired by the production of autoantibodies to GAD65 and GAD67 [35-37]. This results in a reduction in brain levels of GABA, prominent in the motor cortex, which can be demonstrated by Magnetic Resonance Imaging (MRI) in SPS patients. SPS is a disabling disorder characterised by muscle rigidity and episodic spasms of the musculature, thought to be due to autoimmune-mediated dysfunction of supraspinal GABAergic inhibitory neurons [38]. Hyperexcitability of the motor cortex in SPS has been demonstrated by transcranial magnetic stimulation [39].
Anti-GAD65 auto-antibodies in the CSF of ataxic and SPS patients selectively suppress GABA-mediated transmission in cerebellar Purkinje cells, without affecting glutamate-mediated transmission [37,40]. Low CSF levels of GABA have been reported in patients with Kok disease (OMIM:149400 also known as hyperexplexia/exaggerated startle reaction/startle disease) [41]. The exact mechanism by which autoantibodies target these intracellular GAD antigens is not clear. However, it is interesting that SPS may also arise in individuals with autoantibodies to gephyrin, a cytosolic protein concentrated at the postsynaptic membrane of inhibitory synapses where it is associated with GABAA receptors [42]. This provides a further example of chronic rigidity and spasm possibly secondary to disruption of the inhibitory synapses.
Mutations in the CLN3 gene are thought to be responsible for the neurodegenerative disorder Batten disease (OMIM:204200). In cln3-knockout mice autoantibodies to GAD65 have been reported to be associated with brain tissue and result in inhibition of GAD activity [43]. These mice also demonstrate elevated brain glutamate levels as compared with controls, which may have a causative role in the astrocytic hypertrophy evident in cln3-knockout mice and along with anti-GAD65 autoantibodies in Batten disease patients may contribute to the associated preferential loss of GABAergic neurons.
Drugs, which potentiate the action of GABA, such as benzodiazepines and baclofen, ameliorate muscle rigidity and spasticity. These GABA agonists are thought to counter disinhibition of the velocity-dependent increase in skeletal muscle during stretch reflexes, observed in spasticity, which is the result of inadequate presynaptic inhibition of the muscle spindles [44,35,37]. γ-Vinyl-γ-aminobutyric acid (GVG) is used to treat neurological disorders including epilepsy, tardive dyskinesia and spasticity. It has been reported that it is the GABA-elevating effect of this compound that is responsible for its anti-convulsive properties [20].
We have identified a GAD1 sequence change G(36)C, which segregates with autosomal recessive spastic CP in 4 affected siblings. This nucleotide substitution causes a missense mutation, changing serine (12) to a cysteine in the N-terminal domain. This serine residue is conserved between all mammals (human/mouse/rabbit/pig) for which data are available. The association of GAD67 with membranes requires formation of heteromeric links with GAD65, which are mediated via their N-terminal domains. The N-terminus of GAD65 is palmitoylated and binds to the cellular membrane. The first 27 amino acids appear to be essential in this function [32]. The palmitoylation of cysteines 30 and 45 of GAD65, and the inability of residues 1–29 of GAD67, to substitute for this region, highlights the potential impact on cellular localisation of a nucleotide substitution in this domain [20]. GAD65 also undergoes phosphorylation of the first four serine residues in the N-terminal domain. These post-translational modifications highlight the importance of the flexibility and accessibility of this domain. N-terminal epitopes of GAD65, in the region corresponding to the residue, which undergoes mutation in GAD67, are particularly prominent autoantigens [45].
S(12)C amino acid substitution may thus produce subtle effects on cellular localisation, protein-protein interactions and/or protein processing, with a subsequent effect on GABA production. This is not inconsistent with the mouse Gad1 knockout where complete loss of Gad1 enzymatic function (~20% reduction of total Gad activity in the cerebral cortex) resulted in a cleft palate phenotype and neonatal death [28,30]. There is redundancy of GABA production as a result of the presence of two GAD proteins, and the precise function of each isoform may differ between man and mouse. It is interesting to note that the GAD25 splice variant of GAD67, also contains the S(12)C amino acid substitution in affected CP patients. This truncated variant is identical to the first 213 amino acids of GAD67, with the addition of an extra 11 C-terminal residues. It lacks the binding site for the cofactor PLP and is believed to lack any GAD activity [25]. The function of GAD25 is not known, but it may compete with GAD67 for incorporation into protein complexes. Therefore the presence of an N-terminal mutation would affect both GAD25 and GAD67, and may disrupt a complex regulatory mechanism for GAD67.
Conclusions
This study illustrates the difficulty of gene cloning in rare autosomal recessive diseases mapped in small, consanguineous pedigrees. Identification of an ancestral haplotype allows refinement of the locus, but this has not been possible in this example. Within the minimal linkage region, any sequence change will segregate with the disease phenotype. Detection of a nonsense mutation leading to a protein truncation would provide compelling support for a mutation as causative. However, in the present example we have not seen such a mutation in the candidate gene so far examined. We are now expressing the variant forms of GAD67 (S12C) and GAD25 (S12C), as recombinant proteins, to assess catalytic activity and binding properties with respect to their normal counterparts. However, it may well prove difficult to detect subtle effects based on sub-cellular localisation changes in the mutant proteins, or changes in post-translational modification patterns.
Stability of the mRNA transcripts from these variants will be assessed by transfection studies in neuronal cells. Eventually, it would be of interest to attempt knock-in experiments with GAD1 (G36C), into the Gad1-/- mouse to see if this can rescue the cleft palate phenotype and reveal a CP-like picture. These experiments will be reported elsewhere. However, the possibility that reduced GAD67 activity may cause CP in the patients studied herein, in a manner reminiscent of that seen in SPS, leads us to report our findings at this stage. The success reported in treating SPS with intravenous immunoglobulin [40,46], suggests further evaluation of GABA agonists in the management of this difficult clinical problem.
Abbreviations
BAC Bacterial artificial chromosome
BLAST Basic local alignment search tool
CEPH Centre d'Etude du Polymorphisme Humain
CHEF Contour-clamped homogenous electric field
CNS Central nervous system
CP Cerebral Palsy
CSF Cerebrospinal fluid
DNA Deoxyribonucleic acid
EST Expressed sequence tag
GABA Gamma-aminobutyric acid
GAD L-Glutamate decarboxylase
GVG Gamma-vinyl-gamma aminobutyric acid
HGMP Human genome mapping project
IDDM Type 1 Insulin-Dependent Diabetes Mellitus
MRI Magnetic resonance imaging
NCBI National Centre for Biotechnology Information
PAC Plasmid artificial chromosome
PCR Polymerase chain reaction
PDE Pyridoxine-dependent epilepsy
PFGE Pulse field gel electrophoresis
PLP Pyridoxal 5'-phosphate
PVL Periventricular leukomalacia
SPG Spastic paraplegia
SPS Stiff Person Syndrome
SSCP Single-strand conformational polymorphism
STS Sequence tagged site
YAC Yeast artificial chromosome
Competing interests
The author(s) declare that they have no competing interests.
Authors' contributions
CNL carried out the molecular genetic studies, sequencing and drafted the manuscript. The YAC mapping work was undertaken by JPL and CNL. AFM, DTB and IMC conceived the study and participated in the design and coordination, they also secured financial sponsorship from the Wellcome Trust and MRC. RA, SM, ERM and CGW recruited and gained consent from the families detailed in 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
Research in the authors' laboratory is supported by the Wellcome Trust, MRC, CRUK and Yorkshire Cancer Research. CNL has an MRC PhD studentship. This paper is dedicated to the late Prof. S. Bundey, University of Birmingham.
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| 15571623 | PMC544830 | CC BY | 2021-01-04 16:28:50 | no | BMC Neurol. 2004 Nov 30; 4:20 | utf-8 | BMC Neurol | 2,004 | 10.1186/1471-2377-4-20 | oa_comm |
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Biomed Eng OnlineBioMedical Engineering OnLine1475-925XBioMed Central London 1475-925X-3-421554832410.1186/1475-925X-3-42ResearchTransport lattice models of heat transport in skin with spatially heterogeneous, temperature-dependent perfusion Gowrishankar TR [email protected] Donald A [email protected] Gregory T [email protected] James C [email protected] Harvard-MIT Division of Health Sciences and Technology, Massachusetts Institute of Technology, Cambridge, MA 02139, USA2 Thermal Technologies, Inc., Cambridge, MA 02139, USA2004 17 11 2004 3 42 42 23 4 2004 17 11 2004 Copyright © 2004 Gowrishankar et al; licensee BioMed Central Ltd.2004Gowrishankar 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
Investigation of bioheat transfer problems requires the evaluation of temporal and spatial distributions of temperature. This class of problems has been traditionally addressed using the Pennes bioheat equation. Transport of heat by conduction, and by temperature-dependent, spatially heterogeneous blood perfusion is modeled here using a transport lattice approach.
Methods
We represent heat transport processes by using a lattice that represents the Pennes bioheat equation in perfused tissues, and diffusion in nonperfused regions. The three layer skin model has a nonperfused viable epidermis, and deeper regions of dermis and subcutaneous tissue with perfusion that is constant or temperature-dependent. Two cases are considered: (1) surface contact heating and (2) spatially distributed heating. The model is relevant to the prediction of the transient and steady state temperature rise for different methods of power deposition within the skin. Accumulated thermal damage is estimated by using an Arrhenius type rate equation at locations where viable tissue temperature exceeds 42°C. Prediction of spatial temperature distributions is also illustrated with a two-dimensional model of skin created from a histological image.
Results
The transport lattice approach was validated by comparison with an analytical solution for a slab with homogeneous thermal properties and spatially distributed uniform sink held at constant temperatures at the ends. For typical transcutaneous blood gas sensing conditions the estimated damage is small, even with prolonged skin contact to a 45°C surface. Spatial heterogeneity in skin thermal properties leads to a non-uniform temperature distribution during a 10 GHz electromagnetic field exposure. A realistic two-dimensional model of the skin shows that tissue heterogeneity does not lead to a significant local temperature increase when heated by a hot wire tip.
Conclusions
The heat transport system model of the skin was solved by exploiting the mathematical analogy between local thermal models and local electrical (charge transport) models, thereby allowing robust, circuit simulation software to obtain solutions to Kirchhoff's laws for the system model. Transport lattices allow systematic introduction of realistic geometry and spatially heterogeneous heat transport mechanisms. Local representations for both simple, passive functions and more complex local models can be easily and intuitively included into the system model of a tissue.
==== Body
Background
Heat transfer in biological systems is relevant in many diagnostic and therapeutic applications that involve changes in temperature. For example, in hyperthermia the tissue temperature is elevated to 42–43°C using microwave [1,2], ultrasound [3], or laser light [4]. There has also been long standing interest in thermal properties of skin [5] in order to understand conditions leading to thermal damage (burns) to skin, usually involving contact of skin to hot objects [6], in which local thermal conduction and heat capacity are dominant. Investigation of such bioheat transfer problems requires the evaluation of temporal and spatial distributions of temperature. This class of problems has been traditionally addressed using the Pennes bioheat equation [7,8]. Here we show that a transport lattice approach [9] can solve bioheat problems. This method is illustrated by solving models for skin contact heating used in transcutaneous blood gas monitoring and for spatially distributed heating due to 10 GHz microwave radiation.
Contact heating is used in transcutaneous blood gas monitoring, in which oxygen is transported out of the vasodilated capillary bed to a surface mounted oxygen sensor. Heating is used to achieve vasodilation. In 1851 it was already known that "skin breathing" occurs, in which oxygen diffuses out of ambient air into the body, supplying of order 1% of the body's oxygen uptake [10]. Typically the ambient air temperature even with clothing insulation causes the skin surface temperature to be significantly cooler than body core temperature. Much later, in 1957, it was shown that elevated skin temperature caused an outward diffusive flux of oxygen, so that oxygen could be measured at the surface of the skin [11]. The basic idea is that contact surface heating results in heat transport into the body, such that the outer portion of the dermis (the site of the outermost blood capillaries) experiences a significant increase in perfusion. This temperature-dependent perfusion "arterializes" the blood content of the capillaries, such that the oxygen concentration is closer to that of arterial blood, because to a good approximation capillary flow increases faster than oxygen transport out of the capillaries.
Initial clinical demonstration with neonates occurred in 1969 when a polarographic electrode placed on the head was used to measure oxygen partial pressure [12]. Since the early studies significant development has taken place [13-16]. A basic issue of safety is involved, as sensor contact is often prolonged (1–8 h) in which a heated sensor (typically 45°C) with contacting material of high thermal diffusivity is placed against the skin.
Spatially distributed heating of skin and deeper tissue by electromagnetic fields and ultrasound is also of established interest [17-20]. Microwave electromagnetic radiation is incident on tissue under a variety of exposure conditions. As an example, we consider 10 GHz microwave exposure. In this case, the penetration depth is approximately 3 mm so that most of the power is deposited within the outer region of the skin. Accurate prediction of the temperature distribution in skin exposed to microwave radiation is important in understanding both beneficial and harmful effects [21-23].
In hyperthermia, tissue is heated to enhance the effect of conventional radio- or chemotherapy. By delivering thermal energy, the tissue is stimulated to increase the blood flow by thermoregulation in order to remove the excess heat. The common method to produce local heating in the human body is the use of electromagnetic waves.
Many of the bioheat transfer problems have been modeled using the Pennes equation, which accounts for the ability of tissue to remove heat by both passive conduction (diffusion) and perfusion of tissue by blood. Perfusion is defined as the nonvectorial volumetric blood flow per tissue volume in a region that contains sufficient capillaries that an average flow description is considered reasonable. Most tissues, including much of the skin and brain, are highly perfused, with a perfusion coefficient denoted by ω (traditionally with units of 100 ml/100 g min = 1 ml g -1 min -1). Alternatively, ω can be replaced by ωm, the nondirectional mass flow associated with perfusion. Perfusion is valid on the spatial scale of ~100 μm. The contributions of heat conduction and perfusion are combined in the Pennes bioheat equation [7,8], which we use in a form [24] that employs ωm (SRI units of kgm-3 s-1),
Here, ρ, c, k are the density, specific heat and thermal conductivity of tissue, respectively and cb, is the specific heat of blood, ρb is the density of blood, T is local tissue temperature, Ta is a reference temperature (arterial blood), t is time, Qm is the metabolic heat production per volume, and P(z, t) is the heat deposited per volume due to spatially distributed heating. In this general form, ωm is a function of temperature to include the specific case of temperature dependent perfusion. Vascularized tissue generally experiences increased perfusion as temperature increases [25,26]. Because of thermoregulation skin blood flow rises 15 fold to 100 ml l00 g min-1, often with a time lag of minutes.
Prediction of heat transport has long been carried out by both analytical and numerical methods [27,28]. The temperature rise for constant (temperature independent) perfusion has been predicted by traditional analytical methods based on Eq. 1, which can be solved analytically for simple geometries [22,29] or by finite element models for more realistic, complicated tissue geometry [30-32]. Models which include temperature-dependent increases in perfusion are more difficult to solve, but the case of a linear temperature dependence have been described using analytical expressions [33] and numerical simulations [24]. The bioheat transfer (Eq. 1) has been used in a wide range of applications to describe heat transport in blood perfused tissues, [34] and solved by a variety of methods. An adaptive finite element method was used to optimize the nonlinear bioheat equation for optimizing regional hyperthermia [35]. Two-dimensional biothermal models of ultrasound applicators based on the bioheat equation were solved by finite difference equation [36]. The boundary element and finite difference methods have also been used to solve the bioheat equation [37-41]. Recently, closed form analytical solutions to the bioheat transport problems with space and transient heating were reported using Green's function method [42].
Here we show that the transport lattice approach can be used to model transport of heat by conduction and temperature-dependent blood perfusion. This method employs a network of locally interacting transport, storage and source models that are solved as a system model by Kirchhoff's laws. Although Kirchhoff's laws can be used to describe transport of heat (and of molecules), usually charge transport is treated. Indeed, there is an extensive literature and robust methodology for solving large electric circuits [43]. For this reason, we use electrical circuits which provide mathematical analogs to heat transport and storage. Transport lattices allow systematic introduction of realistic geometry and spatially heterogeneous heat transport mechanisms. One attribute of a transport lattice model is that local representations for both simple, passive functions (e.g. heat storage via fixed heat capacitance and thermal conduction via fixed thermal conductivity) and more complex local models (e.g. nonlinear, temperature-dependent perfusion and spatially non-uniform perfusion in which the time lag of perfusion onset can be selected) can be easily and intuitively included into the system model of a tissue. It is a fundamentally modular approach in which local models can be introduced or removed.
Methods
Here we extend the transport lattice modeling approach previously demonstrated for electrical fields and currents in single and multiple cells [9] and supra-electroporation of cells by submicrosecond pulses [44] to describe heat transport within a multilayered skin model. A related approach has been described for analysis of calorimeters to measure specific heat of liquids [45]. Circuit analysis has long been used to solve problems that can be described by differential equations [45-49]. Here we use a modular approach in which the skin is represented by three layers, each with many interconnected local models that account for the local heat storage (heat capacity) and local transport by both conduction and perfusion (Fig. 1). The different parameters employed in the model and their values are listed in Table 1. We model two cases of skin heating: surface contact heating and spatially distributed heating.
Figure 1 Transport lattice method – Geometry with transport, source and storage models. The one-dimensional model of human skin (top) is represented by a lattice of conduction models (Mc) and source, storage and sink models (Ms). The subscripts denote the different layers of the model, namely, a: air, e: epidermis, d: dermis and s: subcutaneous tissue. The equivalent circuit models are shown for each layer: Rc represents heat conduction, Rp represents heat removal by perfusion (not present in epidermis) and C represents heat storage. The conduction model, Mc, is represented by Rc while the storage, perfusion and power input model, Ms, is represented by the combination of Rp, C and I (Table 2). (a) Surface Contact Heating: The surface temperature Ts is elevated from 33°C to 45°C at t = 0. (b) Spatially Distributed Heating: A layer of air contacting the skin is added to the model with the air temperature (Tair) held at 25°C. The local microwave power dissipation is represented by the current source (I) at each node. The arterial reference temperature Ta is represented by a common node. A ladder-like network of variable resistors, Rp, represents the temperature dependent perfusion in dermis and subcutaneous tissue.
Table 1 Thermal and electrical property values assigned to different layers of skin.
Air
ta thickness 500 μm
Na number of lattice elements 100
ℓ lattice node spacing 5 μm
ka thermal conductivity 0.0263 Wm-1 °C-1
ρa density 1.3 kg m-3
ca specific heat 1004 Jkg-1 °C-1
Epidermis
te thickness 80 μm [52]
Ne lattice elements 80
ℓ lattice node spacing 1 μm
σe electrical conductivity 8.01 Sm-1 [70]
εe relative permittivity 31.3 [70]
ηe penetration depth 3.8 mm [70]
λe wavelength 5.2 mm [70]
ke thermal conductivity 0.23 Wm-1 °C-1 [52]
ρe density 1200 kg m-3 [52]
ce specific heat 3590 Jkg-1 °C-1 [52]
ωe perfusion rate 0 m3s-1m-3 tissue [52]
Dermis
td thickness 2000 μm [52]
Nd lattice elements 100
ℓ lattice node spacing 20 μm
σd electrical conductivity 8.01 Sm-1 [70]
εd relative permittivity 31.3 [70]
ηd penetration depth 3.8 mm [70]
λd wavelength 5.2 mm [70]
kd thermal conductivity 0.45 Wm-1 °C-1 [52]
ρd density 1200 kg m-3 [52]
cd specific heat 3300 Jkg-1 °C-1 [52]
ωd perfusion rate 1.25 × l0-3 m3s-1m-3 tissue [52]
Subcutaneous Tissue
ts thickness 18000 μm [52]
Ns lattice elements 100
ℓ lattice node spacing 180 μm
σf electrical conductivity 0.585 Sm-1 [70]
εf relative permittivity 4.60 [70]
ηf penetration depth 19.6 mm [70]
λf wavelength 13.9 mm [70]
ks thermal conductivity 0.19 Wm-1 °C-1 [52]
ρs density 1000 kg m-3 [52]
cs specific heat 2675 Jkg-1 °C-1 [52]
ωs perfusion rate 1.25 × 10-3 m3s-1m-3 tissue [52]
Blood
cb specific heat 3770 Jkg-1 °C-1 [52]
ρb density 1060 kg m-3 [52]
Surface contact heating
The case of a fixed skin surface temperature is relevant to transcutaneous blood gas sensors, in which a skin-contacting sensor with controlled temperature, and a local source of heat of up to 45°C are employed to significantly increase perfusion within the outer capillary bed [50], thereby "arterializing" the capillary blood. This situation also represents heating at the skin surface by a heat source, or the skin contacting a hot object with a large thermal diffusivity, such as in thermal injury [51,52]. Surface heating may be either essentially constant (long duration) or transient (short duration). The latter is relevant to laser pulse application or flash skin burns. We model surface contact heating by considering step heating of skin surface to different temperatures at t = 0. The core temperature is assumed to be constant at the ambient temperature (Ta). The boundary conditions are shown in Fig. 1.
Spatially distributed heating
Spatially distributed heating occurs in skin exposed to penetrating, dissipative radiation such as microwave, ultrasound and laser light [51,53]. These heating methods often involve an exponentially decaying power transmission accompanied by reflection at the interface of regions with different electrical properties. We consider a uniform plane wave incident normally upon the skin surface, with a layer of air included to model the reflection at the skin/air interface. We also account for interference from reflections at the interface of dermis and subcutaneous fat and at the skin/air interface. The average absorbed power density, P(z, t), in epidermis and dermis (of thickness d = te + td), in the range 0 <z ≤ d is given by
where
and
and the average absorbed power density in the subcutaneous fat layer (z >d) is given by
where
where P(0, t) is the power density incident on the skin surface at time t, E(0, t) is the corresponding electric field amplitude, (z, t) is the propagating electric field in the epidermis and dermis, E(d, t) is the electric field at the dermis-subcutaneous fat interface, ηd and ηs are the penetration depths for dermis and subcutaneous fat, λd and λs are the wavelengths in dermis and subcutaneous fat, and Za, Zd and Zs are the intrinsic impedances of air, dermis, and subcutaneous fat, respectively. Note that the incident power, P(0, t), is expressed as an area density whereas the absorbed power density, P(z, t), in the skin is expressed as a volume density. The summation of (z, t) in Eq. 2 was carried out to 10 terms, although only the first two terms are significant. The reflection (Γ) and transmission (T) coefficients at the skin/air (sa) and for skin/fat (sf) interfaces and the intrinsic impedances are given by [54]
where σd and σs are the conductivities of dermis and subcutaneous fat and εd and εs are the permittivities of dermis and fat, respectively (listed in Table 1), μ0 is the permeability of free space, ε0 us the permittivity of free space, and f = 1010 Hz.
Circuit model of heat conduction
A motivation for the transport lattice for heat conduction is the electrical equivalence of heat transport (a diffusion process [27]). We consider the well known equivalence of electrical and thermal conduction. Heat conduction is described using a thermal resistance, R, which relates the heat flow per unit time Q to the temperature difference ΔT as Q = (1/R)ΔT. In the case of heat conduction across a cube of thickness ℓ and area ℓ2, Rc = ℓ/(kℓ2) = (kℓ)-1 where k is the thermal conductivity of the slab material. Heat storage is described by the thermal capacitance, C, which for a slab is C = ρcpℓ·ℓ2 = ρcpℓ3 where ρ is the density of the slab material and cp is its specific heat. The associated thermal relaxation time is τQ = Q/ = (ρcpℓ3)/(kℓ) = ℓ2/α, where α = k/(ρcp) is the thermal diffusivity. The heat conduction models for different layers of skin are shown in Fig. 1 as Rcand C with subscripts identifying the particular skin layer.
Circuit model of perfusion
Pennes bioheat equation provides an approximate description of heat transport by tissue conduction and by blood flow using a local temperature dependent conduction path to perfusing blood. This additional heat removal is proportional to the local temperature difference T - Ta. Here, local heat removal by perfusion is described by a thermal resistor, Rp = (ℓ3ωmcbρb), connected to a reference node at ambient temperature (Fig. 1) where cb is specific heat of blood, ρb is the density of blood.
Circuit model of surface heat loss
Unoccluded skin often transports heat across its outer surface via a combination of conduction into a boundary layer of air, convective movement of air, and black or gray body radiation. Because our emphasis here is on conduction and perfusion, we lump these surface transport mechanisms into a single heat transfer coefficient. The numerical value of this coefficient was determined by requiring the initial skin surface temperature to be Ts = 33°C (before contacting the skin to a heated surface or applying microwave radiation). The surface heat loss for microwave heating is represented by a series of conduction models (Rca in Fig. 1). For contact heating the surface is initially set to 33°C and then raised to 45°C at t = 0.
Circuit model of spatially distributed power deposition
Spatially distributed power deposition from 10 GHz radiation is modeled by representing Eqs. 2 and 3 by an equivalent local current (heat flux) source, Iz = P(z, t)ℓ3, at each node (Fig. 1). That is, each node has a local power input based on Eqs. 2 and 3 multiplied by the local volume.
Metabolic heat generation
Metabolic heat generation can also be represented by local sources, but these are set to zero in the present models. In a transport lattice model an additional heat source can be introduced at each node to represent heat generation by metabolism. Here, metabolism is assumed to maintain the baseline temperature at a constant value equal to the arterial blood temperature. However, metabolism could also be made a function of temperature.
Thermal damage to tissue
An Arrhenius rate constant relationship is widely used to estimate cumulative thermal damage associated with burns of tissue, including skin [55-60]. This is equivalent to describing the conversion of a native form molecule to a denatured form by overcoming an energy barrier [61]. The Arrhenius rate constant-based expression for accumulation of irreversible thermal damage describes the process in terms of a rate at which the native form of a molecule moves to a transition state atop the energy barrier and then a final, denatured state. This simple description assumes that a single damage process, with Ω a dimensionless indicator of accrued tissue damage [56,62,63]:
where A = 2.9 × 1037 s-1 is the attempt rate, ΔE = 2.4 × 105 J mol-1 is the effective activation energy, ℜ = 8.31 J mol-1 K-1 is the universal gas constant, and T(z, t) is the absolute temperature at a given location (here depth).
According to Lee and co-workers [57,58], the approximate threshold for the onset of thermal damage is 42°C. We therefore estimate the accumulated thermal damage using
where z is the depth into the tissue and texp is the duration of the exposure. The cumulative damage index, Ω, has been related to tissue damage but can also be interpreted as the fraction of hypothetical indicator molecules that are denatured. Complete epidermal necrosis corresponds to Ω = 1. Although the heat induced damage to skin involves many processes, Eq. 7 is a simple model with zeroth-order kinetics [61].
Initial and boundary conditions
Surface heating
The temperature of the surface node is elevated to the indicated temperature at t = 0 for a specified duration. The temperature of the core node deep in the skin is held constant at the ambient temperature of 37°C. In the 2-D case, all the lattice nodes at the skin surface are elevated to the indicated initial temperature at t = 0.
Spatially distributed heating
The far (left) boundary of the air layer away from the skin is held at 25°C while the core temperature is fixed at 37°C. The thermal current sources with different values is a function of z (Eq. 2), representing power deposition at different nodes, are turned on at t = 0 for a specified duration. This accounts for the spatially distributed power dissipation.
Transport lattice solution
The transport lattice method employs locally interacting functional models to describe heat conduction, heat sources, removal of heat by perfusion and heat storage that are solved by Kirchhoff's Laws. We use electrical circuits which are mathematically analogous to the thermal processes (Fig. 1). The resulting electrical circuits were solved by Kirchhoff's laws using Berkeley SPICE version 3f5 [43,47], yielding currents and voltages of lattice elements. The voltages are converted to equivalent temperatures and displayed as temperature plots and images using Matlab (MathWorks, Natick, MA). A Pentium based computer (2 GHz CPU, 4 GB RAM) was used to obtain the solutions.
Results
We demonstrate the use of a transport lattice approach to solve bioheat problems, using surface contact heating and spatially distributed heating of skin as illustrations.
Method Validation
The transport lattice approach is validated theoretically by comparison to an one-dimensional analytical solution of the perfusion equation for a single medium with a homogeneous sink (equivalent to uniform perfusion). In this validation case, the surface of the medium was instantaneously changed to T1 (= 45°C) while the core was maintained at T2 (= 37°C). The initial condition assumed that the tissue temperature throughout was 37°C. The steady-state analytical solution to the bioheat equation (Eq. 1) for these conditions is the same as a spatially distributed uniform sink given by the equation [27]
Here L is the length of the model geometry (= 10 cm), ρb is the density of blood, cb is the specific heat of blood, ωm is the perfusion rate and k is the thermal conductivity of the tissue.
The analytical result of Eq. 8 is compared with solutions of a one-dimensional transport lattice model with dermis tissue values assigned to the local models. For validation comparisons, the tissue (10 cm in length) is represented by a lattice with different number of nodes. The transport lattice temperature profiles agree remarkably well with Eq. 8 for different perfusion levels (Fig. 2). The performance of the transport lattice method was quantified by the maximum deviation of the transport lattice temperature profile from the analytical result normalized by (T1-37). As seen in Fig. 2, the numerical error is less than 1% when the geometry is represnted by only 40 nodes and becomes progressively better as more nodes are used.
Figure 2 Validation using one-dimensional geometry. The validation model is a homogeneous section of a material (here dermis) with uniform perfusion (homogeneous sink), 10 cm in length and 100 μm × 100 μm in area. The surface of the tissue was elevated to 45°C at t = 0 s. The perfusion level (in ml/100 g min) was varied as shown in inset. The solid line represents the analytical solution (Eq. 8) and the symbols represent the transport lattice solution. Top: Steady-state temperature as a function of depth. The 10 cm long tissue is represented by 100 lattice elements, but the temperature profile is shown only to the depth of 6 cm. Bottom: The 10 cm long tissue is now represented by different number of lattice elements. Maximum % deviation as a function of the number of nodes used to represent the tissue is shown. The deviation is computed as the maximum discrepancy between the simulated temperature and the corresponding analytical value normalized to the step increase in temperature (here = 8°C).
Surface Contact Heating – Transcutaneous Application
In this case, the skin surface temperature is approximated as increasing instantaneously from 33°C to 45°C at t = 100 s. This situation is encountered in transcutaneous blood gas sensing and, for more extreme heating, in skin burns due to a hot metal object and flash fire exposure.
Temporal distribution
The spatial distribution of temperature and the resulting tissue damage from surface contact heating is shown in Fig. 3. The steady-state temperature distribution shows an exponential fall off with spatial decay constants dependent on the thermal properties of different layers of skin. Accumulated tissue damage is shown for different perfusion levels in Fig. 3. As expected intuitively, when the surface contact temperature is elevated, outer layers experience more damage than deeper regions of skin. For typical transcutaneous blood gas sensing conditions the estimated damage is small, even with prolonged skin contact to a 45°C surface.
Figure 3 Temperature distribution with surface contact heating. The surface of the skin was elevated from 33°C to 45°C at t = 0 s for 3600 s. This approximates contact heating in which a metal (large thermal diffusivity) -encased heater with controlled temperature is held against the skin. Top: the temperature distribution as a function of skin depth with 10 ml/100 g min perfusion is shown for four different time points (inset in s). The four curves show the temperature profile before the application, immediately after the application, before the removal, and after the removal of the surface heating. Bottom: Tissue damage indicator predicted for the transcutaneous heating for four different perfusion levels (inset in ml/100 g min).
Temperature-dependent perfusion
Experiments have shown that heat stress causes a temperature-dependent response of the vasculature in tissues [64]. The blood flow in skin and muscle increases significantly for temperatures up to 43°C. Here temperature dependent blood perfusion in dermis and subcutaneous tissue is represented by ω0(1 + γT) where ω0 is the baseline perfusion and γ is the linear coefficient of temperature dependence. Figure 4 shows the temporal distribution of temperature close to skin surface for different values of γ for 1-hr exposure. As expected, increased perfusion causes a decline in local temperature. The accumulated tissue damage (Fig. 4) is also lower if the blood perfusion has a higher temperature coefficient, because the temperature rise is constrained.
Figure 4 Temperature-dependent perfusion distributions for contact heating. The surface of the skin was elevated to 45°C at t = 100 s for 1 h. The perfusion level was dependent on local temperature with a temperature coefficient shown in inset. The basal perfusion rate was 10 ml/100 g min. Top: Temperature of skin close to surface as a function of time. Bottom: Tissue damage as a function of depth integrated over time (only the damage for two smallest perfusion values are discernible, hence the curves for higher perfusion rates are not seen in the figure).
Spatially Distributed Heating by Microwave Exposure
The spatially distributed heating case illustrated here relates to heat generation (power dissipation) decaying exponentially with the distance within each skin tissue layer. We analyzed the case of an exposure to 10 GHz microwave for 3 s duration (a short-duration and high power microwave [HPM] exposure [21]).
Applied power level
Figure 5 shows the change in skin surface temperature over time for different incident power levels. The skin is exposed to a 1 to 10 W cm-2 10 GHz pulse for 3 s. The layer of air farthest from the skin was set at 25°C and the core (2 cm below the surface) was set to 37°C. This resulted in the skin/air interface having a steady-state temperature of 34°C before the microwave exposure. The skin/air interface has a power transmission coefficient (|Tsa|2Re{Za/Ze}) of 0.49 at 10 GHz. Applying 10 GHz microwave results in an essentially linear rise in temperature, in agreement with prediction using other methods. When the input power level is less than 5 W cm-2, the peak surface temperature is less than 42°C. When the microwave exposure is turned off, relaxation of the skin temperature occurs over a time scale of several seconds. Onset of tissue damage occurs when the local tissue temperature reaches 42°C. The distribution of tissue damage with depth is shown for different power densities (Fig. 5). Even for an incident power density as high as 10 W cm-2, the accumulated tissue damage for a 3 s exposure is far less than 1, even in the epidermis and dermis. Because of the large difference in the conductivity and permittivity of the dermis and subcutaneous fat, over 20% of the power deposited at the interface is reflected back into the dermis resulting in reduced power deposition in the fat layer. For power densities 5 W cm-2 and less, the tissue temperature remains less than 42°C and the tissue damage indicator is negligible throughout the skin. This suggests that the tissue suffers no damage during this exposure.
Figure 5 Effect of applied power level for spatially distributed heating. Response to a 10 GHz microwave pulse of 3 s duration with four different power densities (inset in W cm-2). The layer of air farthest from the skin (2 mm) was at 25°C, the skin surface was at 34°C before the pulse was applied and the core temperature was fixed at 37°C. The blood perfusion level was assumed to be 10 ml/100 g min. Top: Surface temperature as a function of time. Bottom: Tissue damage indicator, Ω, as a function of depth. Only the two highest levels of power generate noteworthy values of Ω (the plots for lower power levels are, therefore, not visible in the figure).
Perfusion level
The peak surface temperature is shown in Fig. 6 for different blood flow rates. The basal perfusion levels in dermis and subcutaneous tissue were varied from 2.5 ml/100 g min to 20 ml/100 g min. The surface temperature distribution was nearly identical for this range of blood flow rate, a level of 20 ml/l00 g min is already at the high end of physiologic range for skin. This is consistent with the same skin temperature increases at different blood flow rates at 100 GHz reported by Nelson et al. [21].
Figure 6 Effect of blood perfusion level. A 10 GHz microwave pulse of 3 s duration with power density of 5 W cm-2 was considered for illustrative purposes. Blood perfusion levels in units of ml/100 g min are shown in inset. Skin surface temperature as a function of time is shown for these different perfusion levels. The different plots essentially overlap, showing that blood perfusion has negligible effect on temperature distribution in the case of a 3-sec 10 GHz exposure.
Temperature distribution dynamics
Change in the spatial temperature distribution over time due to a 10 GHz pulse is shown in Fig. 7. The temperature of the outer layers of skin is below the core temperature of 37°C before the microwave exposure. During the pulse, the temperature of epidermis and dermis layers increases rapidly compared to deeper subcutaneous tissue. The temperature in the subcutaneous fat layer does not increase appreciably from its initial temperature because only a fraction of the incident power is transmitted into this region of the skin, and although heat absorbed in outer layers is removed partially by conduction, heat in the outer layers is mainly intercepted and removed by perfusion.
Figure 7 Temperature distribution dynamics. A 10 GHz microwave pulse of 3 s duration with a power density of 5 W cm-2 was applied at t = 1 s. The layer of air farthest from the skin (2 mm) was set to 25°C, the skin surface was at 34°C before the RF field was applied and the core temperature (here 20 mm deep) was at 37°C. The temperature-independent blood perfusion level was assumed to be 10 ml/100 g min. Temperature change from baseline as a function of distance from skin surface is shown for different time points (2, 3 and 4 s).
Skin heterogeneity
The local elevated temperature at the interface of dermis and subcutaneous tissue observed in the spatial distribution of temperature during a 10 GHz exposure is due to different thermal properties of the homogeneous slabs that comprise the model. The effect of skin heterogeneity on temperature distribution is shown in Fig. 8. The specific heat and thermal conductivity of epidermis and subcutaneous tissue were varied relative to each other using a range of published values. In agreement with qualitative expectations, the temperature distribution prior to the end of microwave pulse shows that the larger the difference between the specific heat of the two layers, the larger the magnitude of the locally elevated temperature. However, most of the temperature increase is confined to the epidermis and dermis, as most of the incident power is deposited in those layers. It is expected that at higher frequencies, the temperature distribution in subcutaneous layers will be uniform because most of the RF energy will be deposited closer to the surface of the skin as the penetration depth diminishes.
Figure 8 Effect of skin layer parameters on temperature distribution for 10 GHz exposure. Temperature change due to a 10 GHz pulse of 3 s duration with an incident power density of 5 W cm-2. The layer of air farthest from the skin was at 25°C, the skin surface was at 34°C before this RF field was applied and the core temperature was at 37°C. The blood perfusion level was assumed to be 10 ml/100 g min. The thermal conductivity and specific heat of dermis and subcutaneous tissue were varied relative to each other as shown in the inset.
Two-dimensional temperature distribution
The use of transport lattice approach for predicting heat transport in spatially heterogeneous structures is further illustrated with a two-dimensional model of the skin. The model is derived from an image of a histological section of skin (Fig. 9). The temperature distribution from a thermally insulated wire (20 μm diameter, 60 μm length) with a hot tip that is inserted into the epidermis is also modeled. The model assumes that the tip of the metal wire (kw = 200 W m-1°C-1; ρw = 8900 kg m-3; cw = 383 J kg-1°C-1) is enclosed in a thermally insulating material (kp = 0.15 W m-1°C-1; ρp = 1200 kg m-3; cp = 2010 J kg-1°C-1). The skin model contains stratum corneum, epidermis and dermis. As before, the core temperature (37°C) is fixed at 2 cm from the skin surface by extending the subcutaneous layer. Before heating the wire conducts heat outwardly to the air, consistent with the isotherms. The temperature at the hot wire tip is increased to 45°C at t = 10 s. The temperature contours at different time points is shown in Fig. 9. The heterogeneity in skin structure is seen in the temperature contours immediately after the tip is heated, but then diminishes with time because the thermal properties of different skin regions differ only slightly. As intuitively expected, the thermal contours show a temperature gradient into the surrounding air when the wire tip is heated.
Figure 9 Two-dimensional distribution of tissue temperature. Top Left: Image of skin cross-section used in generating the simulation geometry. Scale bar: 50 μm. (Top Right). The Cartesian grid (ℓ = 1 μm) is superimposed on the model geometry to create the simulation geometry. Stratum corneum was assigned the same thermal properties as the epidermis. The hot wire is seen as dark blue region with a red tip. Center: A part of the 216 × 188 lattice is shown. At each node, five functional models are connected, four conduction models (Mc) to the neighboring nodes and a fifth model (Ms) representing heat storage and perfusion-transported heat to a reference temperature, Ta. Depending on the type of underlying tissue (or air), the transport model at any node is one of four models shown in Fig. 1. The tip of the hot wire was elevated to 45°C at t = 10 s for 50 s with a rise and fall time of 5 s. Bottom Panels: Temperature contours at different time points: before the hot wire tip temperature is raised; just before the hot wire is turned off; immediately after the hot wire is turned off; 10 s later.
Discussion
Temperature-dependent perfusion
Both in vivo and in vitro studies have shown that the tissue response to heat stress is strongly temperature-dependent [33,64,65]. When heated to 41–43°C, temperatures that are commonly used in clinical hyperther-mia, the blood flow in normal tissues increases significantly [35]. In order to demonstrate the use of transport lattice approach to model temperature-dependent perfusion, we considered the simplest case of perfusion varying linearly with temperature. The perfusion was assumed to include a temperature-independent basal component and a temperature-dependent vasodilatory component.
As shown in Fig. 4, increased perfusion resulting from temperature dependence results in a lower peak temperature close to the skin surface. An increase in perfusion causes greater heat loss from the tissue into the blood, thus reducing the peak temperature. In addition, the temperature decays faster for a larger temperature coefficient after the removal of external heat source. The coefficient of temperature dependence could also utilize a non-linear function of temperature in the transport lattice method. This could reflect a decrease in perfusion at temperatures over 45°C resulting from heat-induced damage to blood capillaries.
A more comprehensive non-linear temperature dependent perfusion model has been applied in modeling hyperthermia. Tompkins et al. [66] used temperature-dependent models to show that blood perfusions initially increase with tissue temperature and then decrease at higher temperatures. Erdmann et al. [35] employed a Gaussian profile for temperature-dependence of perfusion increase between 37°C and 45°C, and a plateau for temperatures above 45°C. Our simpler linear dependence of perfusion on temperature is intended to demonstrate the use of a transport lattice method for heat transport in skin.
Skin heterogeneity
We present a modular approach to modeling in which the skin is represented by three homogeneous layers, each with many interconnected local, steady state models that account for the local heat storage (heat capacity), local heat dissipation (local microwave heating) and local transport by both conduction and perfusion (Fig. 1). The existence of different thermal properties in adjacent layers of the model is particularly important for spatially distributed heat sources that extend through skin layers. In the case of a 10 GHz microwave radiation, the penetration depth is approximately 3 mm. Therefore, an exposure to 10 GHz radiation will cause a non-uniform temperature distribution within the skin.
As shown in Fig. 8, differences in thermal conductivity and specific heat of different layers of the skin create different temperature profiles, especially in the subcutaneous fat layer. However, with higher frequency RF radiation, the penetration depth decreases, and the difference in temperature profiles in the skin will diminish.
Tissue damage
Prolonged exposure to elevated temperatures can cause tissue damage by, for example, protein alteration or denaturation, often followed by recognizable changes in the optical properties of tissue [67]. The rate of the transition from natural to denatured states is governed approximately by the Arrhenius rate equation (Eq. 7). The lipid bilayer components of the cells are most vulnerable to thermal damage because they are held together only by forces of hydration [68]. Exposure to ambient microwave fields has been shown to cause tissue damage. The rate of tissue heating has a large dependence on the density of dipoles, resulting in a much slower microwave heating in fatty tissues [69].
When skin is exposed to a 10 GHz pulse of 3 s duration, the tissue damage indicator near the skin surface may be as high as 0.08, which suggests some damage at high power levels (Fig. 5). This exposure generates a surface temperature of approximately 51°C. Human pain perception studies have shown that the threshold for perception corresponds to a significantly lower mean skin temperature of 44°C [22]. Thus, a relatively non-damaging exposure might cause significant pain.
Conclusions
Transport of heat by conduction, and by temperature-dependent, spatially heterogeneous blood perfusion, is predicted using a transport lattice model. This approach uses interconnected, local, steady state models for transport and storage, to together represent the Pennes bioheat equation. The thermal system model of the skin was solved by exploiting the mathematical analogy between local thermal models and local electrical (charge transport) models, thereby allowing robust, circuit simulation software to obtain solutions to Kirchhoff's laws for the system model. The skin model has a nonperfused viable epidermis, and deeper regions of dermis and subcutaneous tissue with perfusion that was constant or temperature-dependent. Spatially distributed heating and surface heating cases were considered. Accumulated thermal damage was estimated by using an Arrhenius type relation at locations where viable tissue temperature exceeds 42°C. Prediction of spatial temperature distributions was also illustrated with a two-dimensional model of skin created from an image. Validation of the transport lattice approach using experimental data is necessary for practical application of this method.
Authors' Contributions
TRG constructed and solved the several transport lattice models and wrote much of the manuscript. DAS computed the reflected and transmitted power in the skin layers, contributed to construction and solution of the models, and to writing of the manuscript. GTM provided guidance and advice with respect to thermal modeling, and helped write the manuscript. JCW conceived the local transport lattice model for solving the bioheat equation, provided overall guidance and helped with interpretation of results and writing the manuscript. All authors read the final manuscript.
Table 2 Definition of model parameters used in the transport lattice simulations. The parameter values were obtained from the sources cited in the rightmost column.
Air
Rca conduction model = (kaℓ)-1
Ca storage model = ρcaℓ3
Epidermis
Rce conduction model = (keℓ)-1
Ce storage model = ρceℓ3
Ie distributed local power = P(z, t)ℓ3
Dermis
Rcd conduction model = (kdℓ)-1
Cd storage model = ρcdℓ3
Rpd conduction model = ℓ3ωmcbρb
Id distributed local power = P(z, t)ℓ3
Subcutaneous Tissue
Rcs conduction model = (ksℓ)-1
Cs storage model = ρcsℓ3
Rps conduction model = ℓ3ωmcbρb
Is distributed local power = P(z, t)ℓ3
Acknowledgments
Supported by NIH grant RO1-GM63857. We thank P. A. Mason, R. C. Lee, K. Foster, A. Esser, Z. Vasilkoski, K. Smith, and H. F. Bowman for stimulating discussions, K. G. Weaver for computer support, and anonymous reviewers for comments that led to important manuscript revisions.
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| 15548324 | PMC544831 | CC BY | 2021-01-04 16:37:31 | no | Biomed Eng Online. 2004 Nov 17; 3:42 | utf-8 | Biomed Eng Online | 2,004 | 10.1186/1475-925X-3-42 | oa_comm |
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Proteome SciProteome Science1477-5956BioMed Central London 1477-5956-2-91559835510.1186/1477-5956-2-9MethodologyDevelopment and standardization of multiplexed antibody microarrays for use in quantitative proteomics Perlee LT [email protected] J [email protected] R [email protected] B [email protected] S [email protected] M [email protected] W [email protected] M [email protected] VT [email protected] DD [email protected] SF [email protected] Molecular Staging Inc., 300 George St., New Haven, CT 06511 USA2 Thurston Arthritis Research Center and Department of Medicine, University of North Carolina, 3330 Thurston Building, Chapel Hill, NC 27599 USA3 National Center for Genome Resources, 2935 Rodeo Park Drive East, Santa Fe, NM 87505 USA2004 15 12 2004 2 9 9 30 7 2004 15 12 2004 Copyright © 2004 Perlee et al; licensee BioMed Central Ltd.2004Perlee 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
Quantitative proteomics is an emerging field that encompasses multiplexed measurement of many known proteins in groups of experimental samples in order to identify differences between groups. Antibody arrays are a novel technology that is increasingly being used for quantitative proteomics studies due to highly multiplexed content, scalability, matrix flexibility and economy of sample consumption. Key applications of antibody arrays in quantitative proteomics studies are identification of novel diagnostic assays, biomarker discovery in trials of new drugs, and validation of qualitative proteomics discoveries. These applications require performance benchmarking, standardization and specification.
Results
Six dual-antibody, sandwich immunoassay arrays that measure 170 serum or plasma proteins were developed and experimental procedures refined in more than thirty quantitative proteomics studies. This report provides detailed information and specification for manufacture, qualification, assay automation, performance, assay validation and data processing for antibody arrays in large scale quantitative proteomics studies.
Conclusion
The present report describes development of first generation standards for antibody arrays in quantitative proteomics. Specifically, it describes the requirements of a comprehensive validation program to identify and minimize antibody cross reaction under highly multiplexed conditions; provides the rationale for the application of standardized statistical approaches to manage the data output of highly replicated assays; defines design requirements for controls to normalize sample replicate measurements; emphasizes the importance of stringent quality control testing of reagents and antibody microarrays; recommends the use of real-time monitors to evaluate sensitivity, dynamic range and platform precision; and presents survey procedures to reveal the significance of biomarker findings.
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Background
Traditional immunoassay platforms have very limited multiplexing capability and high sample volume requirement. The development and application of high throughput, multiplex immunoassays that measure hundreds of known proteins in complex biological matrices, is becoming a significant tool for quantitative proteomics studies, diagnostic discovery and biomarker-assisted drug development [reviewed in [1-4]]. Two broad categories of antibody microarray experimental formats have been described: [1] direct labelling, single antibody experiments, and [2] dual antibody, sandwich immunoassays [4]. In the direct labelling method, all proteins in a complex mixture are tagged, providing a means for detecting bound proteins following incubation on an antibody microarray. In the sandwich immunoassay format, proteins captured on an antibody microarray are detected by a cocktail of detection antibodies, each antibody matched to one of the spotted antibodies. In addition, a variety of microarray substrates have been described, including nylon membranes, plastic microwells, planar glass slides, gel-based arrays and beads in suspension arrays. Much effort has been expended in optimizing antibody attachment to the microarray substrate. Finally, various signal generation and signal enhancement strategies have been employed in antibody arrays, including colorimetry, radioactivity, fluorescence, chemiluminescence, quantum dots and other nanoparticles, enzyme-linked assays, resonance light scattering, tyramide signal amplification and rolling circle amplification. Each of these formats and procedures has distinct advantages and disadvantages, relating broadly to sensitivity, specificity, dynamic range, multiplexing capability, precision, throughput, and ease of use [1-4]. In general, multiplexed microarray immunoassays are ambient analyte assays [5]. Given the heterogeneity of antibody array formats and procedures currently in use in proteomics studies, and the absence of a "gold standard", there exists an urgent need for development and adoption of standards that permit platform comparisons and benchmarking.
Unique, general considerations in assembling multiplexed immunoassays include: Requirements for elimination of assay cross-reactivity; configuration of multi-analyte sensitivities; achievement of dynamic ranges appropriate for biological relevance when performed in diverse matrices and biological states; and optimization of reagent manufacturing and chip production to achieve acceptable reproducibility. In contrast to traditional monoplex enzyme-linked immunoassays, generally agreed specifications and standards for antibody microarrays have not yet been formulated. A number of recent articles have started to examine certain of these issues [3,6,7].
Microarray immunoassays performed on planar glass slides and employing signal enhancement with rolling circle amplification (RCA), have been developed by several groups and have demonstrated usefulness in measurements of temporal and dose-dependent changes in a variety of immunological model systems and human diseases [[1,2,8-16]; Patel, D.D. et al. Submitted]. In general, these RCA microarray immunoassays have utilized indirect sandwich immunoassays featuring five steps (Figure 1):
Figure 1 Schematic layout of antibody microarray slide and RCA immunoassay. At the far left is an illustration of the 1" × 3" slide platform containing sixteen individual sample wells with an etched barcode. Within each of the wells, a 16 × 16 configuration of printed capture antibodies is arrayed. Each of the capture antibodies is capable of binding analytes from applied samples and undergoing RCA signal amplification. Finally, the fluorescently labeled signal, detected through conventional laser scanning, is quantified.
I. Analytes in an applied sample bind to capture antibodies immobilized on a silanized glass surface.
II. Applied secondary biotinylated detector antibodies bind to captured analytes, creating a highly specific immune complex.
III. Biotinylated detector antibodies bound to the immune complex are detected with a universal anti-biotin antibody. The latter is conjugated to primer oligonucleotides that are pre-annealed to a complementary circular oligonucleotide.
IV. DNA polymerase extends the 3' ends of primers around the circles, resulting in long, single stranded RCA products that remain attached to the complex.
The RCA product, composed of tandem DNA repeats complementary to the circle sequence, is detected by hybridization with cyanine 5 (Cy5)-labeled complementary oligonucleotides.
The present report describes initial development of standardized operating procedures, quality controls and standards for microarray immunoassays performed on planar glass slides using signal enhancement with RCA. These metrics have been tested for use in generation of data with adequate sensitivity, reproducibility and assay performance for biomarker discovery [[12-14,16], Patel D.D et al., submitted]. Initial specifications and standards are also described for the addition of new analytes to antibody microarrays, which are needed to ensure that a high level of performance is maintained. While certain of these recommendations and standards are specific to RCA immunoassays, others represent generally applicable first generation standards for benchmarking antibody array platforms that enable interoperability of data generated in proteomics studies.
Results
Data Quality
To demonstrate the feasibility of using a multiplex immunoassay system to measure protein levels in complex biological matrices, the performance of dual-antibody, sandwich immunoassay arrays performed on planar glass slides with RCA signal enhancement was evaluated for specificity, sensitivity, reproducibility and accuracy using standardized titrations, spiked biological matrices and clinical samples. Array performance was evaluated based on ability to: measure analytes across a broad dynamic range at sufficiently low coefficients of variation (CVs); detect proteins at levels requisite to capture biologically relevant expression differences; confirm reliability of methods to normalize data to minimize platform imprecision and demonstrate the utility of generating standard curves to convert analyte MFI (mean fluorescence intensity) data into mass unit information.
Data Redaction
An advantage of arrays is the ability to measure each analyte multiple times, enhancing precision. Capture antibody spots were printed in quadruplicate on planar glass slides providing redundancy of individual analyte measurements. Data redaction was applied to raw immunoassay data to improve data quality by eliminating outlier data points. Outliers were identified by employing two subsequent statistical approaches in a step-wise manner.
First, the Bland-Altman plot was used. Bland-Altman plots are often used in DNA microarray analysis to identify differences and/or replicate outliers. This involves plotting the difference between the logarithm of intensities of two replicates (M) versus the average of logarithm of intensities (A) for each analyte within an individual array (see Material and Methods). Thus, there will be 6 MvA plots for each data set to reflect the 170 analytes positioned across 6 arrays. Each MvA plot will contain 3*Ns*Na points, where 3 reflects the number of possible unique pair wise combinations of the three replicates, Ns represents the number of samples and Na defines the number of analytes measured on a given array. An example of an MvA plot produced in a project comprising 150 clinical serum samples for Array 4 with 37 analytes is shown in Figure 2 (panel a). This plot contains 3*150*37 = 16650 data points. The quadruplicate measurements within an arrayfor each anlayte are represented as a mean replicate value.. Lines represent 99% confidence intervals for individual data points. Data points outside of 99% confidence intervals are considered outliers. The quality and /or intensity of individual spots are manually investigated for each outlier by using proprietary visualization software, which allows examination of individual spot image/quality at every data processing step. Outliers are redacted by removing aberrant spots from the data set. The resulted MvA plot is shown on Figure 2 (panel b).
Figure 2 An example of raw data quality and outlier removal. (panel a, top) Raw data (37 analytes) from array 4 containing all sample replicates shown on an MvA plot (a typical microarray data plot of the log ratio vs. the log difference for each pair of intensities. See: Dudoit, S., Yang, Y. H, Callow, M. J., and Speed, T. P. (2002) Statistica Sinica 12, 111–140). The dashed lines indicate a 99% confidence interval around the data and outliers of this interval are shown in red, black or magenta. (panel b, bottom) Redacted data with 1% of outlier data removed (all points outside of the displayed confidence interval).
The second step of data reduction involves a linear correlation analysis. Pair-wise correlation analysis is done between all replicates of individual sample. Figure 3 shows the three scatter plots generated for the three replicates of a representative sample. The correlation coefficient (R2) is examined for each plot. Each plot contains Na data points, where Na reflects the number of analytes. Plots with R2 values <0.95 are examined to identify the cause of the poor correlation. We have identified two major sources of poor correlation: incorrect positioning of the capture grid during image quantitation and general aberrations in image spot quality. Assigning the specific source of low correlation is accomplished by tracing back to the image data. In the case of grid misplacement, suspect data images are re-quantified. Poor correlations due to aberrant spot morphology/intensity are manually examined and removed from data set. If the R2 value does not improve as a result of outlier removal, the replicate is redacted from the data set. The sample is considered passed if there are two replicates with R2 >= 0.95. The pass rate is defined as the number of passed samples divided by the total number of samples. A run of an array is considered to be passed if 85% of the samples have two or more passed replicates.
Figure 3 Pair wise scatter plots between three replicates of a sample. Each replicate was assayed on a different slide. Solid lines represent linear regression fits. Regression equation is indicated within legend box along with the individual slide barcodes for this particular assay. R2 value of the fit is indicated in the title. Both X and Y-axes indicate mean fluorescence signal Log2(MFI).
In our experience, applying the MvA statistical approach first, followed by the linear correlation analysis is an efficient process to identify outliers without compromising data throughput. Since, MvA plots can be generated quickly, it allows for relatively fast redaction of significant outliers using an objective semi-automated approach. In contrast, the sample correlation analysis is considerably more labor intensive and currently requires manual investigation of each scatter plot with R2 values < 0.95. This process reduces throughput of data redaction, particularly on large data sets. Table 1 shows the impact of using MvA plot analysis as a first step approach for outlier removal in a clinical project containing 106 samples. Each assay in Table 1 represents a single sample replicate, with a total possible number of assay points equal to 418 (106*3 = 418). The table reflects an assay count of 417 due to one sample having only two replicates due to a shortfall in sample volume. The correlation analysis performed on all sample replicates increased the pass rate by 8% following outlier removal. This improvement was due to the elimination of individual analyte replicates having a negative impact on total sample correlation derived from all analyte replicates within an array (Table 1).
Table 1 Improved sample pass rates achieved through individual analyte data reduction
Before After Total
Assays (#) 357 393 417
Assays (%) 86 94 100
In general, for data sets with more than 40 samples, outlier removal only demonstrated small improvements in reducing average CVs across all samples. The most significant impact of outlier removal is on improving reproducibility across the three replicates of the individual samples. In our experience, outlier removal has been shown to reduce individual sample replicate CVs by 2–3 fold. This effect is directly related to improving sample correlation pass rates by by 10–20%.
Normalization
Many systematic factors can modify spot intensity during the process of measurement. Normalization is the process of reducing the effects of systematic variation on spot intensity. Normalization in DNA microarrays typically involves adjusting distributional summaries of data (mean, median) from each chip to common reference values. For example, one assumption could be that the average signal from each protein chip should be the same, as with DNA microarrays and the difference between replicate values is due to systematic variability in the measurement process. Unfortunately, the nature of protein antibody microarrays, configured with a multiplex of individual capture and detector antibodies, is more specialized and differentiated than that of a DNA microarray. Use of a single reference factor derived from a global value is not sufficiently refined to take into account the difference in platform configuration. In the current report, the organization of protein microarrays allows the measurement of up to 16 samples per slide (chip). This is very different from DNA microarrays where one chip represents the total collection of measured values for an individual sample.
To accommodate the differences inherent to the platform, we have applied a normalization strategy based on the three major sources of technical variability observed in our system. The first type of variability relates to spot-to-spot differences observed between quadruplicate spots of the individual analytes printed within a sample well. The second level of variability can be described as the difference in measurements between wells within the same slide. The third element of variability represents the variability observed between sample wells compared across different slides. We found that slide-to-slide variability is the largest source of variation accounting for more than 70% of the total measurement imprecision (data not shown). Thus the goal of normalization is to reduce the imprecision of slide-to-slide measurement error since this represents the major source of platform variability.
Normalization is performed using a system of standard controls to reduce the effect of slide-to-slide variability. A series of four standard control samples (see "Anchor Point Calibrators" in Methods and Materials) are run in 4 wells of each slide. Each control sample represents a cocktail of the full repertoire of analytes for the given array tittered at 4 specific concentrations. The standards have been optimized at concentrations (12 pg/ml, 111 pg/ml, 333 pg/ml and 1000 pg/ml) to capture measurements across the linear range of detection for each analyte. The global average of total analyte signal for the four prepared controls is calculated across all slides run in a batch. An adjustment factor is created for each slide that reflects the difference between global intensity average for all slides and the individual intensity average based on the controls from the individual slide. The averaged pixel intensity of each spot on the slide is scaled by the adjustment factor.
As an example, the average value of the adjustment factor was evaluated across a batch of 33 slides and found to have a value of 1.33+/- 0.47. The primary benefit of normalization was in reducing the replicate sample CVs. Figure 4 contains two panels revealing the impact of normalization on individual analyte CVs across a series of samples for a given analyte. The upper panel shows the variation in raw MFI signal intensities on a logarithm scale observed between the 3 replicate measurements for each of the 11 samples. The lower panel reveals the impact of normalization on reducing variability. Normalization typically reduced sample replicate CVs an average of 5% without producing rank order changes in analyte MFI.
Figure 4 Effect of Normalization. The top panel reveals the raw data, shown as the Log2(MFI), for the analyte Monokine induced by interferon gamma (MIG) across three replicates with 11 patient samples (numbered on the X-axis). The bottom panel reflects the impact of normalization in reducing variation in intensity within each sample and hence the replicate MFI CV.
Assessment of Platform Precision
A 15-point series of standardized titrations containing recombinant proteins diluted in buffer were used to evaluate platform precision. This assessment was used in the quality control of each slide lot prior to release, as well as within each client project to verify run-time analyte performance. Six replicates for each point were run in the quality control testing of each slide lot and six replicates of each point were run within each client study to generate standard curves. CVs were evaluated for each concentration of analyte across six slides. Average CVs were calculated for each analyte. Statistical summaries of CV distribution across all array 2 analytes using the standardized 15-point standard titration series are shown in Table 2. The mean CVs of the control titration replicates were typically in the 10–15% range following normalization. Collectively evaluating mean, median and interquantile range CVs served to identify measurements significantly influenced by outlier values producing a skewed distribution. In general, CVs obtained for the quadruplicate within-well analyte measurements were 5–9% for the prepared controls. Replicate sample CVs obtained from biological samples tended to be somewhat higher than prepared controls with quadruplicate within well measurements at 10–15% post normalization and 20–25% average CVs for replicates samples positioned in wells across different slides. Table 3 reveals CVs obtained in a project containing 110 clinical serum samples run across the 6 arrays. Each sample was tested in triplicate generating 3 replicates measurements obtained from 3 different slides. The average CVs were 18%, 20%, 17%, 20%, 16% and 17 % for Arrays 1, 2, 3, 4, 5 and 6 respectively. The data reduction rate was less than 5% of all data points. This reduction rate is typical of what we have observed across more than 30 clinical projects.
Table 2 Mean and standard deviation of analyte MFI CVs from titration standards for a given array
%CV
Conc. (pg/ml) N* Maximum Mean Minimum Std. Dev.
12 26 19.4 14.0 9.4 2.4
111 26 15.8 9.4 6.1 2.9
333 26 15.3 9.4 4.5 2.5
1,000 26 29.1 12.0 2.6 5.8
3,000 26 26.4 9.7 2.5 6.2
9,000 26 22.2 7.8 2.8 4.3
27,000 26 16.3 8.2 2.5 4.0
81,000 26 19.7 7.8 2.6 4.1
* N reflects 26 analytes measured on array 2.
Table 3 Mean and standard deviation of MFI CVs from clinical samples
Array N* <CV> Std Dev
1 3856 18.1 11.2
2 3752 19.7 12.3
3 3891 16.9 10.6
4 5211 20.4 15.4
5 3750 16.2 11.2
6 4274 17.4 10.9
* N reflects all analyte measurements for all QC passed sample replicates (project contained 110 samples tested in triplicate).
Variance Decomposition
A variance decomposition analysis was performed to reveal the extent to which platform error influenced the ability to identify biomarkers. The variance component assigned to platform error was typically found to be an order of magnitude lower than the average inter-individual variation. Figure 5 reveals the contribution of platform error on the total variance observed in a given project for each analyte across array 1. These results indicated the system variability was sufficiently low to capture moderate expression level differences that were reflective of biological change.
Figure 5 Variance Decomposition. Example of a variance decomposition analysis performed on the analytes for array 1 in a client study. The X-axis corresponds to analyte name and y-axis corresponds variance. Red blocks reflect platform variation, while green and blue blocks represent inter-patient and inter-treatment variance respectively.
Average lower limit of quantitation (LLQ)/ upper limit of quantitation (ULQ) values and analyte dynamic range
The left panel of Figure 6 shows a typical dose response curve of MFI (mean fluorescence intensity) versus analyte concentration, generated for a single analyte based on the 3 replicate measurements from the 15-point titration series containing a multiplex of recombinant analytes spiked into buffer at fixed concentrations. Each titration point was replicated across 3 control slides generating 3 replicate measurements. The vertical lines defined the LLQ and ULQ as well as the dynamic range of the individual analyte within a 30% CV of analyte concentration. The right panel of Figure 6 shows the corresponding clinical sample values obtained in the same run revealing the sample values that fell within, above and below the linear range of detection as defined by the standard titrations. Table 4 contains a summary of the average dynamic range obtained for the 170 analytes surveyed over 8 independent runs.
Figure 6 LLQ/ULQ. (Left) Plot of 3 replicate points from a 15-point titration series of IL-8. The LLQ is indicated by the green vertical line and the ULQ indicated by the rightmost black vertical line. The zero point was removed from the curve fitting procedure since the data undergoes a log transformation. The right panel reveals sample values that fell within and above dynamic range of assay. Here, the majority of tested points for IL-8 fell within the LLQ/ULQ dynamic range.
Table 4 Average dynamic range achieved across each of the production arrays
Detectable1 ((W+A)>50%) > 1 log > 1.5 log > 2 log > 2.5 log > 3 log
Array 1 59% 96% 78% 41% 0% 0%
Array 2 65% 100% 92% 77% 12% 0%
Array 3 67% 100% 70% 33% 7% 0%
Array 4 92% 97% 92% 54% 16% 3%
Array 5 100% 92% 80% 48% 20% 0%
Array 6 85% 100% 74% 41% 7% 0%
Average 78% 98% 81% 49% 10% 1%
Data shown is based on a summary of 8 independent project runs. 98% of the analytes demonstrated at least 1 log dynamic range, 49% of the analytes demonstrated at least a 2 log dynamic range, 10% at least a 2.5 log dynamic range and 1% with at least a 3 log dynamic range.
1 See legend table 5–10 for explanation.
Performance assessment
A performance assessment of individual analytes was conducted to determine the utility of each analyte across multiple projects covering diverse disease areas. Each analyte was evaluated according to the percentage of clinical samples that fell within (W), below (B) or above (A) the linear range of detection. (Tables 5,6,7,8,9,10) Analytes were considered to be detectable if the percentage of samples that fell in the W+A categories was greater than 50%. The projects surveyed across 8 independent studies containing over 1,000 clinical samples. The disease areas included rheumatoid arthritis, osteoarthritis, systemic lupus erythematosus (SLE), chronic obstructive pulmonary disease (COPD), asthma, diabetes and ovarian cancer. The average percentage of detectable analytes was 56% for array 1, 62% for array 2, 67% for array 3, 73% for array 4, 81% for array 5 and 85% for array 6 across the 8-project survey group. A limited number of analytes (<5%) revealed high endogenous concentrations, producing assay saturation where >90% of the measured samples fell above the linear range of detection. In most cases, this could be resolved by re-running a sample dilution or scanning at a lower gain. Although there were analytes that had detectable percentages below 50%, in many cases these reflected analytes that were only detected under up-regulated conditions associated with specific disease states or conditions of drug induction, revealing value within specific disease or therapeutic areas. Tables 5,6,7,8,9,10 also reveal the average LLQ/ULQ values of the 170 analytes within a 30% CV of concentration obtained from the control titrations run in parallel with the clinical samples.
Table 5 Array 1: Averaged LLQs/ULQs in pg/mL obtained from 15 point standard titrations
Feature B W A W+A LLQ (pg/mL) ULQ (pg/mL)
ANG 1 6 93 99 7 1262
BLC 7 93 0 93 62 4614
EGF 32 65 3 68 347 1184
ENA-78 8 81 12 92 161 9225
Eot 19 81 0 81 186 4454
Eot2 2 83 15 98 23 2313
FGF-7 28 72 0 72 218 19367
FGF-9 67 33 0 33 463 21049
Fas 16 84 0 84 290 39248
GDNF 66 34 0 34 48 7715
GM-CSF 67 33 0 33 67 3626
IL-13 29 71 0 71 29 4458
IL-15 73 27 0 27 4682 52224
IL-1ra 45 54 0 55 71 8960
IL-2sRa 3 94 3 97 20 3975
IL-3 58 42 0 42 852 19428
IL-4 81 19 0 19 43 3548
IL-5 67 33 0 33 13 2438
IL-6 59 34 7 41 14 2291
IL-7 73 27 0 27 32 2396
IL-8 21 65 13 79 6 916
MCP-2 13 84 4 87 42 2144
MCP-3 45 54 1 55 58 3205
MIP-1a 51 46 3 49 464 10315
MPIF-1 9 91 0 91 293 7410
OSM 73 27 0 27 78 8511
PlGF 46 54 0 54 61 3080
Data shown is from titrations run in 8 client projects (1000 clinical samples surveyed) covering 8 diverse disease areas. The percentage of samples falling below (B), within (W) or above (A) the linear range of detection are presented. The analyte was considered to be detectable if the W+A percentage was above 50%.
Table 6 Array 2: Averaged LLQs/ULQs in pg/mL obtained from 15 point standard titrations
Feature B W A W+A LLQ (pg/mL) ULQ (pg/mL)
AR 73 27 0 27 48 5599
BDNF 8 87 5 92 26 3956
Flt3Lig 2 98 0 98 17 9835
GCP-2 26 74 0 74 132 13153
HCC4 8 46 46 92 112 7907
I-309 30 70 0 70 20 5142
IL-17 95 5 0 5 330 7921
IL-1a 69 31 0 31 10 2943
IL-1b 46 54 0 54 4 2932
IL-2 89 7 4 11 31 3671
M-CSF 55 45 0 45 86 6761
MCP-1 17 69 14 83 82 2065
MIG 8 87 5 92 13 5030
MIP-1b 29 56 16 71 16 2399
MIP-1d 10 72 18 90 192 6969
NT-3 75 25 0 25 160 22314
NT-4 60 40 0 40 128 19170
PARC 4 25 70 96 12 1870
Rantes 0 13 87 100 5 1302
SCF 29 71 0 71 69 20306
TARC 15 85 0 85 21 3134
TNF-R1 2 97 1 98 108 16285
TNF-a 71 29 0 29 56 6901
TNF-b 88 12 1 12 221 7617
VEGF 86 14 0 14 702 79564
sgp130 0 63 37 100 334 38573
Data shown is from titrations run in 8 client projects (1000 clinical samples surveyed) covering 8 diverse disease areas. The percentage of samples falling below (B), within (W) or above (A) the linear range of detection are presented. The analyte was considered to be detectable if the W+A percentage was above 50%.
Table 7 Array 3: Averaged LLQs/ULQs in pg/mL obtained from 15 point standard titrations
Feature B W A W+A LLQ (pg/mL) ULQ (pg/mL)
BTC 84 16 0 16 980 25361
DR6 7 93 0 93 1200 86864
FGF1 60 40 0 40 162 53854
FasL 79 21 0 21 4300 82436
Fractalkine 78 22 0 22 662 12170
GROb 6 91 3 94 66 2505
HCC1 3 28 70 97 164 3610
HGF 70 30 0 30 1039 80079
HVEM 6 94 0 94 636 105560
ICAM-3 0 100 0 100 477 126635
IGFBP2 0 40 60 100 1523 46325
IL2Rg 71 29 0 29 429 41325
IL5Ra 63 37 0 37 1945 57281
IL-9 36 64 0 64 4506 156041
L-Selectin 0 47 53 100 161 28007
Leptin 5 69 26 95 1322 58408
MCP4 21 74 6 79 93 2508
MIP3b 18 82 0 82 40 9211
MMP7 0 99 1 100 70 21138
MMP9 0 51 49 100 3043 163481
PECAM1 6 93 1 94 1033 52935
RANK 17 83 0 83 223 87693
SCF R 3 97 0 97 445 38465
ST2 51 49 0 49 558 66234
TIMP1 0 77 23 100 2010 90647
TRAIL R4 87 13 0 13 4945 122116
VEGF R2 17 83 0 83 1254 138750
Data shown is from titrations run in 8 client projects (1000 clinical samples surveyed) covering 8 diverse disease areas. The percentage of samples falling below (B), within (W) or above (A) the linear range of detection are presented. The analyte was considered to be detectable if the W+A percentage was above 50%.
Table 8 Array 4: Averaged LLQs/ULQs in pg/mL obtained from 15 point standard titrations
Feature B W A W+A LLQ (pg/mL) ULQ (pg/mL)
ALCAM 0 100 0 100 991 145528
CD27 10 90 0 90 508 148468
CD30 53 47 0 47 2460 128280
CTACK 0 100 0 100 43 10691
Eot-3 37 63 0 63 130 28149
FGF-2 31 67 2 69 102 5814
FGF-4 57 43 0 43 260 14563
Follistatin 8 92 0 92 138 57120
GRO-g 12 75 13 88 59 4344
I-TAC 11 89 0 89 16 6191
ICAM-1 1 76 23 99 289 29018
IFN-g 39 60 1 61 14 7365
IFN-w 37 58 5 63 1177 42508
IGF-II 0 85 15 100 46 13538
IGF-1R 33 67 0 67 330 91601
IGFBP-1 2 72 26 98 272 85578
IGFBP-3 0 7 93 100 5530 30760
IGFBP-4 0 80 20 100 410 22880
IL-1 sR1 30 70 0 70 534 54857
IL-10rb 12 88 0 88 28 11331
IL-16 28 72 0 72 724 86874
IL-1 srII 19 81 0 81 509 76440
IL-2rb 71 29 0 29 10277 107828
LT bR 14 86 0 86 34 37957
Lymphotactin 28 72 0 72 166 9216
M-CSF R 0 91 9 100 1951 121318
MIP-3a 16 84 0 84 13 3389
MMP-10 10 90 0 90 158 42033
PDGF-Ra 30 66 5 70 8112 151722
PF4 0 23 77 100 67 5458
TGF-a 33 67 0 67 35 2678
TIMP-2 0 12 88 100 130 25224
TRAIL R1 33 67 0 67 21 10956
VAP-1 1 23 76 99 4624 150226
VE-cadherin 1 94 5 99 2652 146826
VEGF-D 34 66 0 66 1370 100539
b-NGF 27 73 0 73 141 12400
Data shown is from titrations run in 8 client projects (1000 clinical samples surveyed) covering 8 diverse disease areas. The percentage of samples falling below (B), within (W) or above (A) the linear range of detection are presented. The analyte was considered to be detectable if the W+A percentage was above 50%.
Table 9 Array 5: Averaged LLQs/ULQs in pg/mL obtained from 15 point standard titrations
Feature B W A W+A LLQ (pg/mL) ULQ (pg/mL)
4-1BB 46 54 0 54 334 92233
ACE-2 37 63 0 63 1138 128330
AFP 4 96 0 96 17 11483
AgRP 18 82 0 82 72 13198
CD141 0 62 38 100 780 14852
CD40 30 70 0 70 101 19145
CNTF Ra 18 82 0 82 46 21212
CRP 4 28 68 96 201 12376
D-Dimer DD5 0 4 96 100 12070 65452
E-Selectin 0 85 15 100 89 21531
HCG 40 59 0 60 345 18736
IGFBP-6 0 2 98 100 855 38589
IL-12p40 44 56 0 56 2505 159213
IL-18 0 100 0 100 5 3743
LIF Ra 45 54 1 55 5350 117637
MIF 3 84 13 97 9753 132966
MMP-8 0 82 18 100 111 48374
NAP-2 0 19 81 100 103 9114
Neut Elast 0 66 34 100 376 24009
P-Selectin 0 82 18 100 2128 93967
PAI-II 28 72 0 72 251 115374
Prolactin 0 96 4 100 1133 77120
Protein C 0 83 17 100 1266 154054
Protein S 0 1 99 100 10808 64646
TSH 43 57 0 57 81 15614
Data shown is from titrations run in 8 client projects (1000 clinical samples surveyed) covering 8 diverse disease areas. The percentage of samples falling below (B), within (W) or above (A) the linear range of detection are presented. The analyte was considered to be detectable if the W+A percentage was above 50%.
Table 10 Array 6: Averaged LLQs/ULQs in pg/mL obtained from 15 point standard titrations
Feature B W A W+A LLQ (pg/mL) ULQ (pg/mL)
6Ckine 0 100 0 100 152 27541
ACE 0 60 40 100 3283 93777
CA125 19 81 1 81 273 120834
CNTF 67 33 0 33 2562 109254
ET-3 9 83 8 91 881 25735
Endostatin 0 17 83 100 432 9966
ErbB1 1 95 4 99 3864 86553
ErbB2 12 88 0 88 2293 118586
FGF R3 (IIIb) 47 53 0 53 455 89028
FGF R3 (IIIc) 53 47 0 47 214 51823
FGF-6 42 58 0 58 220 18535
G-CSF 69 31 0 31 1487 49342
HB-EGF 41 59 0 59 47 3143
IFN-a 41 59 0 59 20 5021
LIF 52 48 0 48 655 52660
MMP-1 3 97 0 97 537 103951
MMP-2 0 99 1 100 1446 154542
OPN 0 97 3 100 496 60615
PAI-1 0 10 90 100 22 8289
PDGF Rb 14 86 0 86 645 59309
PEDF 0 19 81 100 2599 63199
TGF-b RIII 10 50 40 90 595 13518
Tie-2 43 57 0 57 6005 147662
VEGF R3 10 90 0 90 466 37150
uPA 12 88 0 88 89 16374
uPAR 11 89 0 89 1259 125972
VCAM-1 0 49 51 100 1577 150401
Data shown is from titrations run in 8 client projects (1000 clinical samples surveyed) covering 8 diverse disease areas. The percentage of samples falling below (B), within (W) or above (A) the linear range of detection are presented. The analyte was considered to be detectable if the W+A percentage was above 50%.
Validation of Array Performance
The development of an antibody array featuring 25–40 novel immunoassays requires extensive validation related to the comprehensive assessment of antibody cross reactivity, definition of analyte minimal detection limits (MDL) and establishing robust assay performance. Each antibody array must be validated for use with several matrices, since the latter may have different ambient analyte levels (and therefore, different ideal MDL) or cross-reactivity profiles.
Analyte sensitivity
Analyte sensitivity was assessed to identify analytes lacking adequate performance for retention on an array. Additional experiments were performed to determine the endogenous levels of each analyte. For analytes without previously reported biological values, the "0 × n" assays indicated the approximate ambient analyte level. Testing across multiple biological matrices was required, since different matrices affected the detection of analyte specific signals. The "0 × n" experiments also revealed the level of non-specific background which was influenced by the total concentration of antibody load in the detector mix. In our experience, certain plasma matrices were also more likely to generate high background when compared to matched serum samples. The impact of high generalized background is a reduced sample pass rate. When background was observed, the total detector antibody concentration could often be reduced to minimize background noise. Ultimately, a balance between reduction in background and enhancement of sensitivity was required to achieve maximal analyte performance in a mutiplex configuration.
Analyte cross reaction
The results of the 1 × (n-1) assays identified analytes that demonstrated cross-reaction between the captured analyte and the complex detector mix prepared without the cognate detector antibody. Binding between the spiked analyte and the cognate capture that generated signal, indicated a cross-reaction to one or more non-cognate detector antibodies contained within the complex mix. In cases where non-cognate detector signal was observed, an additional series of experiments were run with the corresponding analyte tested against each of the individual detectors to identify the cross-reacting detector antibody. Since cross reaction is an additive process, the outcome of the cross reaction assessment allowed for adjustments to be made to achieve a balance between maximizing content with multiplexed array specificity. The 0 × (n-1) assays were run to provide a baseline of MFI values to compare to the results obtained in the 1 × (n-1). In addition, the 0 × (n-1) experiments also served to screen the various biological matrices for cross-reactivity with endogenous proteins.
Analyte performance under multiplexed conditions
Serum MDLs were typically found to be higher than buffer MDLs due to the presence of endogenous analyte, potential analyte-binding proteins present in the biological matrix and other possible matrix-related interferences. The assay conditions used to stress test the system under conditions of high analyte load were designed to identify cross-reaction thresholds for each of the individual analytes. MFI cut off values were used to identify significant increases in non-cognate signal that warranted removal of a feature from the array. The results provide a certain utility in predicting array performance under conditions where sample analyte concentrations exceed reported biological levels. Examples might include patient samples tested under diseased states, elevated analytes produced in stimulated cell culture supernatants or in samples exhibiting a strong drug response. The final validation involved measuring the accuracy of the multiplex assay when challenged with a high concentration of analyte. Figure 7 shows the correlations of signal intensities obtained between (1 × n) compared to (n × n) experiments at 50x MDL levels. High R2 values obtained between the two conditions provided a measurement of the accuracy of the multiplexed system.
Figure 7 New array validation. Stress testing at 50X MDL analyte concentration. The pink line reveals the specific MFI signal for each analyte at 50X MDL in the presence of all detectors (n × n). The blue line shows the signal for each analyte under conditions where all analytes are added at 50X MDL along with all detector antibodies minus the cognate detector antibody (n × (n-1)) to reveal non-specific signal contributed by non-cognate detectors.
Discussion
Thirty years of widespread use of conventional, monoplex immunoassays has established firm benchmarks for performance in protein measurement. In the present paper, we have examined several, unique but general considerations in assembling multiplexed immunoassays with performance similar to conventional monoplex immunoassays. These include development of a comprehensive validation program to identify and minimize antibody cross reaction under highly multiplexed conditions; application of standardized statistical approaches for data handling for highly replicated assays; inclusion of standardized samples in each run to normalize sample replicate measurements; quality control of reagents and antibody microarrays; implementation of real-time monitors to evaluate sensitivity, dynamic range and platform precision; and initial procedures for identification of specific, significant immunoassay results in biomarker discovery projects involving clinical samples. Each of these will be discussed briefly.
Requirement for a comprehensive array validation program
An array validation program represents the foundation of tests required to establish robust assay performance in a multiplexed environment. The most significant component in array validation is the comprehensive evaluation of cross reactivity. The vast majority of the ~5000 commonly available antibody pairs available today have not previously been evaluated for cross reactivity in a multiplexed environment. Therefore, the recommended program should include procedures that identify analytes demonstrating cross reactivity with immobilized capture antibodies as well as cross reaction that might manifest between the secondary detector antibody with a non-cognate analyte or non-specific binding to an immobilized capture agent.
The performance of analytes in a multiplexed configuration should be benchmarked against the baseline, monoplex performance. This multiplexed immunoassay comparison with baseline performance, together with minimal standards for multiplexed cross-reactivity, permits determination of the practical, optimal number of array elements that can be successfully combined. In our experience, using dual-antibody, sandwich immunoassays, planar glass slides and RCA signal amplification, protein micorarrays can generally accommodate multiplexing of 25–35 analytes without an appreciable drop in individual analyte sensitivity or performance. Specifically, we have described development of six different dual-antibody sandwich immunoassay arrays, each containing 25–37 sandwich immunoassays. Since cross reactivity is an additive process, the ultimate goal is to achieve a balance between maximal multiplexing and monoplex-like performance. With exhaustive selection for antibodies without cross-reactivity in multiplexed format, it is possible to multiplex 50 sandwich immunoassays. However, this exercise is very expensive. In our experience, suspension arrays, alternative microarray surface substrates and attachment chemistries do not offer significant advantages in multiplexing while maintaining performance. We have not evaluated the impact of novel, affinity ligands on multiplexing.
Additional array validation for cross-reactivity should include "stress-testing" under high analyte load to reflect conditions where analytes may be significantly over-expressed. In our experience, levels of induction of proteins in common biological matrices can be very large following drug administration or in disease states, and may induce cross-reactivity that is not observed in testing within normal biological analyte levels.
Finally, array validation should be performed across all common sample matrices to examine effects on assay performance associated with endogenous analyte, matrix specific analyte binding proteins or other matrix-specific inhibitors. Absence of cross-reactivity for an immunoassay in one matrix does not always imply absence of cross-reactivity in others. The matrices for which the antibody microarrays described herein have been validated include isotonic buffers, serum, citrate plasma, heparin plasma, EDTA plasma, cell culture supernatants, amniotic fluid, sputum, and exhaled breath condensates. Several of the arrays have also been validated for use with ex vivo treated whole blood. EDTA plasma and ex vivo treated whole blood had higher levels of background signal and lower sample pass rates than other matrices.
Applying Standardized Approaches to Data Redaction
A significant advantage of array-based immunoassays is the ability to measure each analyte in a sample many times. Removal of outlier replicates is obligatory for microarray assays due to signal-related and morphology-based artefacts typically associated with dispersing small volumes of material on a solid substrate in a microarray format. Application of standardized statistical approaches for data redaction is superior to manual inspection and removal of outliers since operator-dependent subjectivity is minimized and throughput is greatly increased. The data redaction procedures described herein employed two, separate steps: Bland-Altman plots and linear correlation analysis. Bland-Altman plots were employed first and identified 99% confidence intervals for all collected data points. This enabled rapid identification and elimination of the outlying 1% of the data with minimal human intervention. This was determined to be an objective, reproducible redaction procedure that greatly reduced time and effort associated with the subsequent, second data redaction step of linear correlation analysis. Linear correlation analysis required performance of 3 replicate assays on each sample, and manual inspection of the series of 3 scatter plots generated from pair-wise correlations of these 3 sample replicates. Individual replicate points for each specific analyte that fell outside the R2> 0.95 range were eliminated. In order for data from sample replicates to pass and be admitted into the final data set, the overall sample replicate-to-replicate correlation for the 25–37 analytes of the array was required to have an R2> 0.95. Experience in multiplexed immunoassay measurements in samples across more than 30 research projects indicated the R2 value >0.95 to be routinely achievable and associated with high quality replicate data. In each project, the data lost through these two sequential redaction procedures was typically less than 5% of the total original data. An additional quality metric to assess the overall run performance was that at least two of the three replicates must have passed for 85% of the total samples. Runs falling short of this metric were failed and subject to repeat. The typical run fail rate was less than 3%.
Within-Run Controls to Normalize Data
Within-run controls were employed to account for the effects of systematic variation in replicate measurements. Variation was identified at three levels based on the unique configuration of the 16 sample well microarray chip. The lowest level of variability was observed between the quadruplicate spots of an individual analyte measured within a single sample well. The next level of variation was described as the difference between replicate analyte values measured in different wells located within the same slide. The highest level of variation was associated with measurements taken from a single sample applied to multiple wells positioned across different slides within a run. Since slide-to-slide variation demonstrated the highest system variation, a series of four controls were designed to minimize the impact on sample replicate measurements. The four controls contained all analytes for that array at four concentrations spread across the dynamic range. The four controls were run on every slide within a project and used to generate a global average of total analyte signal. Based on the global average, each individual slide was assigned an adjustment factor to compensate for the slide specific intensity bias. The analyte signal from each individual slide could then be scaled by the adjustment factor to normalize the intensity values between the sample replicates positioned across different slides. In addition, it is possible to use a blocking experimental design, intentionally positioning sample replicates across different slides and different slide locations to eliminate the potential for a slide-specific or location-specific intensity bias. An example of the latter might have been the well at the corner of a slide. Replicate measurements in conjunction with a mechanism to normalize systemic variation results in the production of high quality data required for maximal sensitivity in the identification of significant differences between samples in multiplexed immunoassays. An additional benefit of inclusion of standardized controls run across all slides of every project is the ability to standardize data, for example in mass units, and enable data comparisons between runs, between days and between projects. Such comparisons are necessary when projects constitute large numbers of samples or when it is desired to create a relational database of assay results. Our platform described herein, for example, can perform triplicate measurements on up to 200 samples in a single run.
Stringent Quality Control of Reagents and Arrays
Quality control of approximately 1200 individual reagents is necessary in order to provide consistent performance of 170 immunoassays on the array platform described herein. These reagents, unfortunately, have widely different shelf life and storage conditions. Stringent quality control procedures specifying performance metrics associated with these reagents were required to achieve reproducible array performance across hundreds of slide lots and reagent sets. Each new lot of a given component was benchmarked to an earlier lot to verify performance. Analyte intensity, dose-response curve, LLD/ULD absolute values, dynamic range and background signal were evaluated in fuctional tests performed on all assay components. Historical performance was monitored by comparing running averages obtained from earlier lots to prevent performance change over time. These procedures were made practical by assembling cocktails of reagents for each step in an assay, dispensing these in single-use aliquots, establishing optimal storage conditions and shelf life, and performing regular (typically weekly) quality checks on aliquots. Implementation of such procedures required use of a laboratory information management system.
Real-Time Monitors of Platform Performance
The utility of integrating real-time platform performance monitors cannot be understated. Given the complex nature and potential instability of biological reagents associated with a multiplex antibody array, it is critical have a program in place to evaluate performance beyond the quality control release. Real time monitors measure performance of controls under conditions identical to the test samples and reflect a second level verification of assay performance. Our test system employed a series of monitors to capture precision metrics that would create a flag to review the data if the specifications were not met. The requirements included mean coefficients of variation of assay values for controls be less than 15% and for sample replicates be less than 25% for project samples run within a batch. Failure to achieve these metrics indicated a problem related to the performance of the manufactured slides and/or reagents or a technical failure associated with sample handling or assay execution. 15-point standardized titrations were also performed on 6 slides in every run in order to captured detail related to analyte dynamic range, LLQ/ULQ values, dose response behaviour, and background signal that provided a comprehensive assessment of real time platform performance. The detail of the performance assessment was included in final reports for each project to verify data quality and generate confidence in the data generated from a highly complex assay.
Evaluating the Utility of Multiplexed Immunoassays in Quantitative Proteomics
Evaluating data generated from multiplexed immunoassays for utility in systematic identification of significant differences between samples, or "biomarker discovery", is an important step in understanding the true platform performance. One of the procedures that revealed the sensitivity of the platform for biomarker discovery was variance decomposition analysis for each project. Variance decomposition analysis examines the magnitude of individual components of platform variation and how they compare to analyte variation between samples or individuals. In our experience the platform error of the system described herein was generally an order of magnitude lower than the heterogeneity observed between samples or individuals of the same test group. The utility of this test is in revealing the extent to which platform error impacts the ability to discover moderate expression level differences between samples that are reflective of biological change. Platforms with lower precision will have less sensitivity for detection of relevant differences between samples and will discovery only a subset of the markers that would have been identified with a more precise system.
Finally, a global performance assessment should be performed across multiple projects covering diverse disease areas to gain a solid understanding of the platform utility. An evaluation of this type can be used to identify assays that will not identify differences in expression between samples because they are not sufficiently sensitive, unable to generate sufficient dynamic range given the window of expression, or reveal high endogenous abundance producing assay saturation artefacts. In addition, specific assays that have appropriate sensitivity and dynamic range may be constitutively expressed and therefore poor biomarker candidate analytes for certain disease or treatment effect studies. This analysis may be used to direct efforts to continue to optimize the survey platform in order to generate the highest value in identifying biomarkers using a quantitative proteomic approach.
Conclusions
Protein microarrays offer the ability to simultaneously survey multiple protein markers in an effort to develop expression profile changes across multiple protein analytes for potential use in diagnosis, prognosis, and measurement of therapeutic efficacy. The current report details certain minimal standards, use of which was found to be necessary to generate the requisite specificity, sensitivity and reproducibility to discover biomarkers. Results revealed that a multiplex system could be operated with high analyte specificity, adequate detection sensitivity and sufficiently broad dynamic range to capture expression differences across diverse disease and therapeutic areas.
Methods
Slide Manufacture
Glass inspection
Raw soda-lime glass slides (1" × 3") prepared with a Teflon mask configured to provide 16 individual sample wells and an etched barcode for traceability were subjected to visual inspection to identify imperfections that might translate into printing and/or scanning artifacts. Slides with scratches, surface contamination or defects in the applied Teflon mask were identified through a visual examination using a long wavelength inspection lamp equipped with a 532 nm filter. The inspection also failed slides that did not meet stringent dimensional specifications, required for downstream printing and automated assay conditions.
Surface activation
Slides passing the visual inspection were silanized with 3-cyanopropyltriethoxysilane according to procedures previously described [17]. Measurements of water contact angle were taken at six discrete locations across the slide surface over a 2% batch sampling to evaluate the uniformity of the applied surface. Since the mean value of contact angle measurements can be influenced by external factors, the deviation in measurements within a batch was also evaluated as an indicator of surface uniformity. Slide batches achieving a mean contact angle value of 52 ± 5 degrees and an average standard deviation of less than 3 degrees were considered suitable for printing.
Printing arrays
Capture antibodies prepared as previously described [11] were printed onto coated slides using a PerkinElmer SpotArray Enterprise piezoelectric, non-contact arrayer housed in a class 10,000 controlled access cleanroom. Quadruplicate spots of ~350pL of each capture antibody were applied to each of the 16 wells within a slide generating 256 printed elements per well, 4096 spots per slide and 108 slides per print batch.
Controls
Printed features
Each printed array contained 256 spots representing 64 individual elements printed in quadruplicate. Each array contained 25–40 capture antibodies spread across production chips 1–6, generating a panel of 170 survey analytes. The balance of the elements was reserved for internal assay controls. Each printed array contained multiple copies of an element called BLANK, containing the components used in capture antibody preparation. Blanks were used to survey non-specific sample background within each well. Other printed controls included a series of biotinylated mouse IgG calibration standards to monitor RCA signal amplification and a third control that acted as a monitor for spot contamination resulting from carry-over between sequentially printed features.
15-point standard titration calibrators
Preparations of standardized multiplex analyte titration series were manufactured using recombinant analytes diluted in buffer that covered the range from 12 pg/mL up to 81 ng/mL at 14 discrete points along with two zero analyte buffer blanks. These titration points were distributed among the sixteen available wells on a slide (Figure 1). The standard titrations, designed to overlap the linear range of detection for each individual analyte, were used to generate standard curves from which sample analyte concentrations were determined. The standardized titrations were utilized in both the quality control testing performed on each print lot prior to release, as well as within each client project to verify real-time analyte performance. Six replicates for each point were run in the quality control testing of each slide lot and three replicates of each point were run within each client study to generate standard curves.
Anchor point calibrators
The three replicates tested for each study sample were positioned across different slides to avoid slide specific signal bias. Four of the fifteen standard titration points identified as "anchor" points were run across four wells of each sample slide to allow for data normalization of the replicates. The four specific points selected for each array were intended to capture the linear range of detection across the dose response curves for the individual panels of analytes. The remaining 12 wells of the slide were reserved for study samples.
Slide Qualification
Microscopic Examination
Microscopic inspection of printed spots was performed on a 10% sampling of slides within each print batch. Slide selection was biased to interrogate slides located at critical positions on the arrayer deck, reflecting the beginning, middle and end of the print run. Printed slides were examined under a light microscope to evaluate spot positioning, morphology and print grid alignment within each well. Print lots demonstrating features with poor spot morphology, missing spots or misaligned features were not released for use.
Positional confirmation
Slides were subject to a full assay function test to confirm the proper location of the printed capture antibodies. Each printed slide lot was tested to confirm the position of the individual feature by spiking in purified analytes in groups of 1–3 per well at a fixed analyte concentration, and performing the full RCA assay to confirm signal at the appropriate printed location. Slide lots revealing signals in inappropriate locations, printing defects or missing signals failed the positional QC test and were not released for use.
Performance assessment
Functional testing was performed on a 10% sampling of each slide lot to evaluate the performance of each analyte. Replicates of the 15-point standardized cytokine titration series, were run to evaluate analyte dose-response, average fluorescent signal intensity, replicate spot variability, replicate sample correlations and LLQ/ULQ values to establish functionality of individual features as well as overall array performance for each slide lot produced. Values obtained for the various metrics were compared to historical averages to identify deviations in performance for the individual analytes. Slide lots were failed if analyte dose-response curves produced sample correlations with R2 values less than 0.90, if average replicate spot-to-spot CVs were >15%, or if RCA signal amplification as measured by the biotinylated mouse IgG calibration standards fell below predefined MFI cutoffs.
Assay
Assay Automation
The manual RCA microarray immunoassay reported previously was modified to optimize performance on an automated platform (Protedyne BioCube). Manual immersion washes were substituted with pipette delivered solutions finely tuned to control pipette tip aspiration and delivery position above printed slide wells and to carefully control liquid application and aspiration speeds to minimize disruption to the assembled immunosandwich complex. Incubation times were increased from 30 to 45 minutes for two of the assay steps (RCA signal amplification and detector incubation) and the number and volume of washes between steps increased from 2 to 4–5 and from 20 uL to 30 uL respectively. A Tecan LS200 unit was used to scan the slides. Microarray images were quantified using image capture software (ImageGrabber) developed in-house.
Clinical Samples
Sample procurement/processing
Frozen serum samples from over 800 clinical patients were thawed, centrifuged to remove particulate matter and mixed with 0.25 mg/ml Heteroblock (Omega), 0.25 mg/ml IIR (Bioreclamation) and 0.1% Tween-20 prior to the assay. Twenty microliters of serum was applied to each well.
Data processing
Outlier removal
Data points producing outlier events as a result of missing spots, spots with poor morphology, or printed features demonstrating high pixel outliers were removed using a combination of automated and manual methods. MvA plots, were generated by plotting the difference of the log intensities (M=log2(Rep1/Rep2) versus the average of the log intensities (A=log2((Rep1*Rep2))/2) for each of the replicates across all analytes. Patterns were visualized using fitted curves from robust local regression with applied visual cues to identify a 99% confidence interval. All outliers in the MvA plot outside of the interval (having a p-value < 0.01) were automatically removed from analysis. The MvA scatter plots also allowed the user to highlight subsets of points on the plot and investigate patterns of intensity differences observed between replicate values. In cases where redaction of an entire replicate (comprised of 4 individual spots) was too stringent, individual spots could be removed using an in-house developed software tool (Terminator) to visually inspect aberrant data points. Data redaction using this method was performed on a limited basis to remove individual spot outliers with poor circularity, non-uniform pixel intensity or missing spots.
Sample replicate correlation QC
As a quantitative QC measure, data review included a sample replicate correlation assessment with a predefined correlation coefficient (R2) value cutoff. An ideal microarray, when compared to its identical replicate, would have a R2 value of 1. Any comparison producing values lower than the cutoff would result in at least one failed replicate. Individual sample correlations were generated by plotting analyte MFI values (on a Log2 scale) from each replicate against the other replicates individually covering all combinations of replicates over the 25–37 analytes within the array. The R2 values obtained for the three plots were manually reviewed to identify failed sample replicates. Only sample replicates with R2 values >0.95 for replicates run within a day or R2 values >0.90 for replicates run across multiple days passed the correlation QC. A summary of the overall sample replicate pass rate monitored the number of failed replicates observed across each of the individual arrays. Project performance specifications required that >85% of all study samples had at least 2 reported replicates.
Data Normalization
Individual sample values were normalized using linear regression of the anchor points run across 4 wells of each sample slide to reduce assay imprecision observed among replicates. A four-point standard titration was run on every slide for normalization and quality control purposes. Fluorescence intensities of the four spot replicates for each analyte within a well were averaged on a logarithmic (base two) scale to generate within-slide titration curves. Linear regression coefficients (slope and intercept) were calculated between individual titration curves from each slide to generate an "average" titration curve. Calculated slope and intercept were used to transform averaged analyte values for each sample well. Data normalization was performed on the data set after outlier removal.
Precision assessment
A standardized precision assessment was performed on each run to monitor assay performance with respect to; within well variation (based on mean coefficient of variation (CV) observed between quadruplicate printed spots for all features across all sample wells of a project) and between-slide variation (reflecting the average CV observed between all sample replicates across all samples in a study). The mean and median CVs with standard deviations were also metrics included in the precision assessment. The precision assessment was performed as a quality control using the 15-point titration calibrators to qualify new slide lots and generate quality metrics for each client project.
LLQ/ULQ determinations
Mean fluorescent intensity (MFI) values, on a logarithmic scale, from the 3 replicate measurements of the 15 point standard titration series were used to generate precision profiles to define the upper and lower limit of quantitation (ULQ, LLQ) within a predefined concentration CV [18]. To do this, a dose-response curve was fitted to the 15-point calibrators using 4-paramter logistic curve fitting procedures. The MFI standard deviation (SD) of the triplicate measurements was converted to concentration SD for the 15 concentration units by dividing by the slope of the dose-response at each concentration point. The conversion provides the relative SD or %CV as a function of analyte concentration to define the precision of the assay for each analyte throughout the working range.
Variance decomposition analysis
The VARCOMP procedure of SAS (SAS Institute), was used to obtain estimates of the variance components in a mixed model. The fixed effect variable represents variance observed in different groups in the study, for example groups of healthy versus diseased individuals. Random effects were represented by unique sample identifiers nested within levels of a fixed variable. This component of variance represented within-group differences associated with patient-to-patient variability or disease heterogeneity. The residual variance represented the platform error.
New Array Validation
Establishing analyte sensitivity
Assay sensitivity was determined in two series of experiments. Initial testing to identify analyte cross-reactivity was performed in a configuration where all printed capture antibodies are surveyed in a "1 × n" format, representing a single recombinant analyte tested against all (n) detectors across multiple matrices (serum, heparin plasma, citrate plasma, EDTA plasma and buffer). Capture antibodies that revealed binding to non-cognate antigens were removed or replaced with suitable alternatives. Analytes that demonstrated low signal across all matrices were removed. If signals were low in buffer, a comparison was made with signals obtained in serum or plasma to determine if the endogenous analyte level was detectable and determine if depressed signals were due to analyte instability in a non-biological matrix. Assessment of analyte endogenous level was performed using a "0 × n" format where unadulterated serum and plasma (heparin, citrate, EDTA) are assayed with the full complement of detector antibodies for a given array.
Evaluating cross reaction
Two conditions were examined to study potential cross-reactivity between the complex detector antibody mixture and the immobilized capture analyte. The first condition included a 1 × (n-1) format in which 1 analyte was tested in the presence of all detectors minus the detector antibody specific to the added analyte (n-1). In the case of an array containing 40 printed features, 40 unique detector antibody cocktails are prepared containing 39 of the detector antibodies found in the complex mix, with each mix containing all but one of the 40 corresponding detectors. The 40 individual reaction mixes are added to specific arrays after the arrays were incubated with the antigen corresponding to the missing detector. The second condition represented the 0 × (n-1) format where no analyte was added in the presence of all detectors minus the detector specific to the analyte under examination. In each case the analytes were spiked in buffer, serum, and plasma (heparin, citrate, EDTA) at a fixed analyte concentration of 50 ng/ml.
Stress testing
Single analyte titrations were prepared in buffer, serum and plasma (heparin, citrate, EDTA) to assign a minimum detection limit (MDL) for each analyte based on a 95% confidence interval above background. The format of the experiments included an n × n design, where all analytes were run in the presence of all detectors. Then, using a 1 × n format, where only one antigen was added to an assay containing all detectors (n), each analyte was tested at 0X, 10X, 50X, and 100X MDL across the same test matrices to identify non-cognate cross-reaction under high analyte load. Additional antigen titration experiments were run to compare the performance achieved in the presence of a single antigen (1 × n) to one in which all analytes were present (n × n).
List of Abbreviations
MFI: Mean Fluorescence Intensity.
RCA: Rolling Circle Amplification.
CV: Coefficient of Variation
R2: correlation coefficient
LLQ: lower limit of quantitation
ULQ: upper limit of quantitation
MDL: minimal detection limits
SD: standard deviation
Competing interests
L.T. Perlee, J. Christiansen, B. Grimwade, S. Lejnine, V. Tchernev and M. Sorette were employees of Molecular Staging, Inc. and D.D. Patel and S.F. Kingsmore have received consulting fees.
Authors' contributions
RD oversaw the manufacturing of reagents, JC established standardized quality control testing procedures, MS implemented assay automation and oversaw all clinical testing. BG was responsible for data curation, SL performed statistical analysis. MM and WS contributed to array develoment. SFK, VTT and DDP were involved in study design, LTP in project execution and LTP, SL, JC and SFK in manuscript preparation.
Acknowledgements
We would like to thank David Ward and Peter Fuller for continued support and long standing scientific contribution to Molecular Staging.
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| 15598355 | PMC544832 | CC BY | 2021-01-04 16:37:15 | no | Proteome Sci. 2004 Dec 15; 2:9 | utf-8 | Proteome Sci | 2,004 | 10.1186/1477-5956-2-9 | oa_comm |
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Mol CancerMolecular Cancer1476-4598BioMed Central London 1476-4598-3-371560692510.1186/1476-4598-3-37ResearchIdentification of a three-gene expression signature of poor-prognosis breast carcinoma Bièche Ivan [email protected] Sengül [email protected] Igor [email protected] Rosette [email protected] Laboratoire d'Oncogénétique – INSERM E0017, Centre René Huguenin, St-Cloud, France2 Laboratoire de Génétique Moléculaire – UPRES EA 3618, Faculté des Sciences Pharmaceutiques et Biologiques, Université René Descartes – Paris V, Paris, France2004 20 12 2004 3 37 37 17 6 2004 20 12 2004 Copyright © 2004 Bièche et al; licensee BioMed Central Ltd.2004Bièche 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 clinical course of breast cancer is difficult to predict on the basis of established clinical and pathological prognostic criteria. Given the genetic complexity of breast carcinomas, it is not surprising that correlations with individual genetic abnormalities have also been disappointing. The use of gene expression profiles could result in more accurate and objective prognostication.
Results
To this end, we used real-time quantitative RT-PCR assays to quantify the mRNA expression of a large panel (n = 47) of genes previously identified as candidate prognostic molecular markers in a series of 100 ERα-positive breast tumor samples from patients with known long-term follow-up. We identified a three-gene expression signature (BRCA2, DNMT3B and CCNE1) as an independent prognostic marker (P = 0.007 by univariate analysis; P = 0.006 by multivariate analysis). This "poor prognosis" signature was then tested on an independent panel of ERα-positive breast tumors from a well-defined cohort of 104 postmenopausal breast cancer patients treated with primary surgery followed by adjuvant tamoxifen alone: although this "poor prognosis" signature was associated with shorter relapse-free survival in univariate analysis (P = 0.029), it did not persist as an independent prognostic factor in multivariate analysis (P = 0.27).
Conclusion
Our results confirm the value of gene expression signatures in predicting the outcome of breast cancer.
Breast cancerGene expression profilingReal-time RT-PCR quantificationPrognostic value
==== Body
Background
Breast carcinoma is the most common female cancer and is showing an alarming year-on-year increase. Most patients do not die as a result of the primary tumor but from metastatic invasion. The mean 5-year relapse-free survival rate is about 60% overall, but differs significantly between patients with forms that rapidly metastasize and those with less aggressive forms.
Current clinical, pathological and biological parameters, i.e. age, menopausal status, lymph-node status, macroscopic tumor size, histological grade and estrogen receptor status, fail to accurately predict clinical behavior.
Breast cancer initiation and progression is a process involving multiple molecular alterations, many of which are reflected by changes in gene expression in malignant cells. Many clinical studies have attempted to identify correlations between altered expression of individual genes and breast cancer outcome, but often with contradictory results. Examples of such genes include ERBB2, CCDN1, MYC, UPA and PAI1 [1-3]. It is thus likely that these genes have limited predictive power when considered in isolation, but that their clinical relevance may be increased when several genes are considered together.
The recent development of effective tools for monitoring gene expression on a large scale is providing new insights into the involvement of gene networks and regulatory pathways in various tumor processes [4]. It has also led to the discovery of new diagnostic and prognostic indicators, and to the identification of new molecular targets for drug development [5]. These tools include cDNA microarrays, which can be used to explore the expression of thousands of genes at a time, and real-time RT-PCR assays for more accurate and quantitative studies of the expression of a smaller number of selected candidate genes.
In this study, we used real-time quantitative RT-PCR assays to quantify the mRNA expression of 47 candidate prognostic molecular markers in a series of 100 ERα-positive breast tumor samples. We identified a three-gene expression signature (BRCA2, DNMT3B and CCNE1) associated with poor clinical outcome. We then tested this "poor prognosis" signature on an independent panel of ERα-positive breast tumor samples from a well-defined cohort of 104 postmenopausal breast cancer patients treated with primary surgery followed by adjuvant tamoxifen alone with known long-term follow-up.
Materials and Methods
Patients and samples
We analyzed samples from two series of women with primary unilateral ERα-positive breast carcinoma. ERα-positive status was determined at both the protein level by the Dextran-coated charcoal method until 1988 and enzymatic immuno-assay thereafter, and at the mRNA level by real-time quantitative RT-PCR assay [6].
The first series consisted of 100 women whose breast tumors were excised at Centre René Huguenin from 1977 to 1987. The patients (mean age 58.1 years, range 34–91) were pre- or post-menopausal (37 and 63 patients, respectively). Sixty patients received adjuvant therapy, consisting of chemotherapy alone in 14 cases, hormone therapy alone in 15 cases, and both treatments in 31 cases. The standard prognostic factors are presented in Table 1. The median follow-up was 9.3 years (range 1.4–16.2 years). Thirty-seven patients relapsed within 10 years after surgery. The first relapse events consisted of local and/or regional recurrences in 11 patients, metastases in 22 patients, and both events in four patients.
Table 1 Characteristics of the first series of 100 ERα-positive breast tumor patients, and relation to RFS
RFS
Number of patients Number of events (%)a P valueb
Age NS (0.68)
≤50 32 11 (34.3)
>50 68 26 (38.2)
SBR histological gradec NS (0.14)
I 16 3 (18.7)
II 51 21 (41.1)
III 26 13 (50.0)
Lymph node status 0.042
Node-negative 34 7 (20.5)
Node-positive 66 30 (45.4)
Macroscopic tumor sized NS (0.97)
≤30 mm 69 26 (37.6)
>30 mm 24 10 (41.6)
a: First relapses (local and/or regional recurrences, and/or metastases).
b: Log-rank test. NS, not significant.
c: Scarff Bloom Richardson classification. Information available for 93 patients.
d: Information available for 93 patients.
The second series consisted of 104 post-menopausal women whose breast tumors were excised at Centre René Huguenin from 1980 to 1994. The patients (mean age 70.9 years, range 54–86) all received post-operative adjuvant hormone therapy consisting of tamoxifen (20 mg daily for 3–5 years) and no other treatment. The standard prognostic factors are reported in Table 2. The median follow-up was 5.9 years (range 1.4–18.1 years). Thirty-one patients relapsed within 10 years after surgery. The first relapse events consisted of local and/or regional recurrences in five patients, metastases in 24 patients, and both events in two patients.
Table 2 Characteristics of the second series of 104 ERα-positive postmenopausal breast tumor patients, and relation to RFS
RFS
Number of patients Number of events (%)a P valueb
Age NS (0.92)
≤70 52 17 (32.6)
>70 52 14 (26.9)
SBR histological gradec 0.0005
I 13 0
II 67 17 (25.3)
III 23 13 (56.5)
Lymph node status NS (0.17)
Node-negative 17 2 (11.7)
Node-positive 87 29 (33.3)
Macroscopic tumor sized 0.015
≤30 mm 71 16 (22.5)
>30 mm 31 14 (45.1)
a: First relapses (local and/or regional recurrences, and/or metastases)
b: Log-rank test. NS, not significant.
c: Scarff Bloom Richardson classification. Information available for 103 patients.
d: Information available for 102 patients.
Complete clinical, histological and biological information was available for the two series of breast cancer patients; no radiotherapy or chemotherapy was given before surgery, and full follow-up took place at Centre René Huguenin. The histological type of the tumor and the number of positive axillary nodes were established at the time of surgery. The malignancy of infiltrating carcinomas was scored according to Scarff Bloom and Richardson's (SBR) histoprognostic system.
Both series of tumor samples were placed in liquid nitrogen until total RNA extraction immediately following surgery.
Real-time RT-PCR
(1) Theoretical basis
Quantitative values are obtained from the cycle number (Ct value) at which the increase in fluorescent signal associated with an exponential growth of PCR products starts to be detected by the laser detector of the ABI Prism 7700 Sequence Detection System (Perkin-Elmer Applied Biosystems, Foster City, CA) using the PE Biosystems analysis software according to the manufacturer's manuals.
The precise amount of total RNA added to each reaction (based on optical density) and its quality (i.e. lack of extensive degradation) are both difficult to assess. We therefore also quantified transcripts of the gene TBP (Genbank accession NM_003194) encoding for the TATA box-binding protein (a component of the DNA-binding protein complex TFIID) as an endogeneous RNA control, and normalized each sample on the basis of its TBP content.
Results, expressed as N-fold differences in target gene expression relative to the TBP gene, termed "Ntarget", were determined by the formula: Ntarget = 2ΔCt sample, where ΔCt value of the sample was determined by subtracting the average Ct value of the target gene from the average Ct value of the TBP gene.
The Ntarget values of the samples were subsequently normalized such that the Ntarget value of the tumor sample which contained the smallest amount of target gene mRNA in each tumor series would equal a value of 1.
(2) Primers and probes
Primers and probes for TBP and the 47 target genes were chosen with the assistance of the computer programs Oligo 5.0 (National Biosciences, Plymouth, MN). We conducted searches in dbEST, htgs and nr databases to confirm the total gene specificity of the nucleotide sequences chosen for the primers and probes, and the absence of single nucleotide polymorphisms. In particular, the primer pairs were selected to be unique when compared with the sequences of the closely related family member genes or of corresponding retropseudogenes. To avoid amplification of contaminating genomic DNA, one of the two primers or the probe was placed at the junction between two exons. Agarose gel electrophoresis allowed us to verify the specificity of PCR amplicons. The list of the 47 target genes tested in this study is indicated in Table 3.
Table 3 List of the 47 target genes selected
Genesa Genbank accession number Chromosomal location Description
AR NM_000044 Xq11.2-q12 Androgen receptor
AREG NM_001657 4q13-q21 Amphiregulin
ARHC/RhoC NM_175744 1p13.1 Ras homolog gene family, member C
BCL2 NM_000633 18q21.3 B-cell CLL/lymphoma 2
BRCA1 NM_007294 17q21 Breast cancer 1, early onset
BRCA2 NM_000059 13q12.3 Breast cancer 2, early onset
CAV1 NM_001753 7q31.1 Caveolin 1
CCND1 NM_053056 11q13 Cyclin D1
CCNE1 NM_001238 19q12 Cyclin E1
CD44 NM_000610 11p13 CD44 antigen
CDH1 NM_004360 16q22.1 Cadherin 1 (E-cadherin)
CGA NM_000735 6q12-q21 Glycoprotein hormones, alpha polypeptide
CGB NM_000737 19q13.32 Chorionic gonadotropin, beta polypeptide
CP/Ceruloplasmin NM_000096 3q23-q25 Ceruloplasmin
CXCL12 NM_000609 10q11.1 Chemokine (C-X-C motif) ligand 12
CXCR4 NM_003467 2q21 Chemokine (C-X-C motif) receptor 4
DNMT3B NM_006892 20q11.2 DNA (cytosine-5-)-methyltransferase 3 beta
EGFR/ERBB1 NM_005228 7p12 Epidermal growth factor receptor
ERBB2 NM_004448 17q21.1 ErbB2
ERBB3 NM_001982 12q13 ErbB3
ERBB4 NM_005235 2q33.3-q34 ErbB4
ESR1/ERα NM_000125 6q25.1 Estrogen receptor 1 (alpha)
ESR2/ERβ NM_001437 14q Estrogen receptor 2 (beta)
ETV4/PEA3/E1AF NM_001986 17q21 Ets variant gene 4
HAS2 NM-005328 8q24.12 Hyaluronan synthase 2
HMMR/RHAMM NM_012484 5q33.2-qter Hyaluronan-mediated mobility receptor
KRT19 NM_002276 17q21.2 Keratin 19
MKI67 NM_002417 10q25-qter Antigen identified by monoclonal antibody Ki-67
MYC NM_002467 8q24.12-q24.13 c-myc oncogene
p14/ARF NM_058195 9p21 Alternative reading frame p14 (p14ARF)
p15/CDKN2B NM_004936 9p21 Cyclin-dependent kinase inhibitor 2B (p15 CDK inhibitor)
p16/CDKN2A NM_000077 9p21 Cyclin-dependent kinase inhibitor 2A (p16 CDK inhibitor)
PGR/PR NM_000926 11q22-q23 Progesterone receptor
PLAU/UPA NM_002658 10q24 Plasminogen activator, urokinase
PTGS2/COX2 NM_000963 1q25.2-q25.3 Prostaglandin-endoperoxide synthase 2
PTTG1/Securin NM_004219 5q35.1 Pituitary tumor-transforming 1
RB1 NM_000321 13q14.2 Retinoblastoma 1
SERPINB2/PAI2 NM_002575 18q21.3 Plasminogen activator inhibitor type 2
SERPINB5/Maspin NM_002639 18q21.3 Maspin
SERPINE1/PAI1 NM_000602 7q21.3-q22 Plasminogen activator inhibitor type 1
SPP1/Osteopontin NM_000582 4q21-q25 Secreted phosphoprotein 1
SRC NM_005417 20q12-q13 c-src oncogene
TERT NM_003219 5p15.33 Telomerase reverse transcriptase
TFF1/pS2 NM_003225 21q22.3 Trefoil factor 1
TIAM1 NM_003253 21q22.11 T-cell lymphoma invasion and metastasis 1
TOP2A NM_001067 17q21-q22 Topoisomerase (DNA) II alpha 170 kDa
XLKD1/LYVE-1 NM_006691 11p15 Extracellular link domain containing 1
aLocusLink symbol
(3) RNA extraction
Total RNA was extracted from frozen tumor samples by using the acid-phenol guanidinium method. The quality of the RNA samples was determined by electrophoresis through agarose gels and staining with ethidium bromide, and the 18S and 28S RNA bands were visualized under ultraviolet light.
(4) cDNA Synthesis
Reverse transcription of total RNA was done in a final volume of 20 μL containing 1X RT buffer (500 μM each dNTP, 3 mM MgCl2, 75 mM KCl, 50 mM Tris-HCl pH 8.3), 20 units of RNasin RNase inhibitor (Promega, Madison, WI), 10 mM DDT, 100 units of Superscript II RNase H- reverse transcriptase (Invitrogen, Cergy Pontoise, France), 3 μM random hexamers (Pharmacia, Uppsala, Sweden) and 1 μg of total RNA. The samples were incubated at 20°C for 10 min and 42°C for 30 min, and reverse transcriptase was inactivated by heating at 99°C for 5 min and cooling at 5°C for 5 min.
(5) PCR amplification
All PCR reactions were performed using a ABI Prism 7700 Sequence Detection System (Perkin-Elmer Applied Biosystems). PCR was performed using either the TaqMan® PCR Core Reagents kit or the SYBR® Green PCR Core Reagents kit (Perkin-Elmer Applied Biosystems). The thermal cycling conditions comprised an initial denaturation step at 95°C for 10 min and 50 cycles at 95°C for 15 s and 65°C for 1 min.
Statistical Analysis
The distributions of the gene mRNA levels were characterized by their median values and ranges. Relationships between mRNA levels of the different target genes and comparison between the target gene mRNA levels and the clinical parameters were estimated using nonparametric tests: the Mann-Whitney U test (link between 1 qualitative parameter and 1 quantitative parameter) and the Spearman rank correlation test (link between 2 quantitative parameters). Differences between the two populations were judged significant at confidence levels greater than 95% (p < 0.05).
To visualize the efficacy of a molecular marker to discriminate two populations (in the absence of an arbitrary cutoff value), we summarized the data in a ROC (receiver operating characteristic) curve [7]. This curve plots the sensibility (true positives) on the Y axis against 1 – the specificity (false positives) on the X axis, considering each value as a possible cutoff value. The AUC (area under curves) was calculated as a single measure for the discriminate efficacy of a molecular marker. When a molecular marker has no discriminative value, the ROC curve will lie close to the diagonal and the AUC is close to 0.5. When a test has strong discriminative value, the ROC curve will move up to the upper left-hand corner (or to the lower right-hand corner) and the AUC will be close to 1.0 (or 0).
Hierarchical clustering was performed using the GenANOVA software [8].
Relapse-free survival (RFS) was determined as the interval between diagnosis and detection of the first relapse (local and/or regional recurrences, and/or metastases).
Survival distributions were estimated by the Kaplan-Meier method [9], and the significance of differences between survival rates was ascertained using the log-rank test [10]. Cox's proportional hazards regression model [11] was used to assess prognostic significance.
Results
mRNA expression of 47 genes in 100 ERα-positive breast tumors
The results for the 47 genes are summarized in table 4, with medians and ranges of mRNA levels in patients who relapsed (n = 37) and those who did not (n = 63).
Table 4 Relationships between the prognostic (+/- relapses) and the mRNA levels of the 47 selected genes in 100 ERα-positive breast tumors
GENES Tumors without relapses (n = 63) Tumors with relapses (n = 37) Pa ROC-AUCb
BRCA2 4.6 (1.0–12.4)c 7.1 (1.9–18.8) 0.0011 0.696 (0.59–0.80)d
DNMT3B 3.0 (1.0–13.6) 4.6 (1.2–17.4) 0.0015 0.690 (0.58–0.80)
CCNE1 6.2 (1.0–36.9) 8.9 (3.1–82.5) 0.0038 0.674 (0.57–0.78)
HMMR/RHAMM 18.9 (1.0–163.5) 30.1 (3.8–186.5) 0.0068 0.663 (0.55–0.77)
MKI67 9.1 (1.0–49.8) 14.4 (1.7–54.9) 0.016 0.645 (0.53–0.75)
TERT 18.7 (1.0–121.9) 22.1 (1.8–135.8) 0.049 0.618 (0.50–0.73)
TOP2A 40.9 (1.0–306) 55.6 (6.1–317) NS 0.605 (0.49–0.72)
PLAU/UPA 4.6 (1.0–36.4) 5.8 (1.4–34.0) NS 0.588 (0.47–0.70)
CGB 4.2 (1.0–31.2) 6.4 (1.4–32.8) NS 0.579 (0.46–0.70)
ERBB2 14.0 (1.0–175) 16.3 (4.2–179.8) NS 0.573 (0.46–0.69)
BRCA1 11.9 (1.0–44.5) 14.3 (1.8–62.5) NS 0.569 (0.45–0.69)
CXCR4 6.5 (1.0–40.5) 7.5 (1.5–71.5) NS 0.569 (0.45–0.68)
PTTG1/Securin 1.9 (1.0–26.9) 1.9 (1.2–33.1) NS 0.566 (0.45–0.68)
SRC 2.6 (1.0–4.3) 2.9 (1.4–10.2) NS 0.561 (0.45–0.67)
p16/CDKN2A 3.4 (1.0–107.4) 4.4 (1.1–136.7) NS 0.560 (0.44–0.68)
AREG 89.3 (1.0–5667) 110.1 (3.1–3301) NS 0.555 (0.44–0.67)
SERPINE1/PAI1 3.8 (1.0–21.4) 4.5 (1.3–21.8) NS 0.554 (0.44–0.67)
ERBB3 2.6 (1.0–10.7) 3.3 (1.2–13.4) NS 0.552 (0.44–0.67)
SERPINB5/Maspin 12.6 (1.0–321) 16.4 (1.0–718) NS 0.551 (0.43–0.67)
CDH1 11.3 (1.0–32.6) 13.9 (1.5–33.3) NS 0.549 (0.43–0.67)
p15/CDKN2B 3.5 (1.0–16.2) 4.2 (1.0–34.9) NS 0.548 (0.43–0.67)
SPP1/Osteopontin 43.3 (1.0–1403) 56.8 (2.1–941) NS 0.548 (0.42–0.68)
ETV4/PEA3/E1AF 5.1 (1.0–49.3) 6.9 (1.8–62.0) NS 0.545 (0.43–0.66)
CP/Ceruloplasmin 33.5 (1.0–9815) 81.5 (1.0–33943) NS 0.545 (0.42–0.67)
SERPINB2/PAI2 13.0 (1.0–498) 15.3 (1.0–1652) NS 0.535 (0.42–0.65)
TIAM1 13.6 (1.0–55.9) 13.3 (3.9–83.2) NS 0.526 (0.41–0.64)
RB1 4.2 (1.0–7.4) 4.3 (1.5–7.7) NS 0.520 (0.40–0.64)
AR 54.2 (1.0–219) 64.8 (1.0–211) NS 0.518 (0.40–0.64)
HAS2 6.5 (1.0–40.8) 6.4 (1.4–31.9) NS 0.516 (0.40–0.63)
TFF1/pS2 1772 (1.0–138 545) 1783 (3–55 878) NS 0.509 (0.39–0.62)
ESR2/ERβ 28.2 (1.0–368) 25.3 (1.4–219) NS 0.500 (0.38–0.62)
ERBB4 141 (1.0–1489) 143 (2.1–1062) NS 0.483 (0.37–0.60)
KRT19 14.4 (1.6–99.1) 10.8 (1.0–57.1) NS 0.482 (0.36–0.60)
ESR1/ERα 25.5 (1.0–508) 21.7 (1.2–498) NS 0.479 (0.36–0.60)
CXCL12 12.1 (1.3–36.1) 9.6 (1.0–30.5) NS 0.464 (0.35–0.58)
MYC 8.1 (1.0–35.5) 7.5 (1.0–51.2) NS 0.464 (0.35–0.58)
EGFR/ERBB1 8.3 (1.2–108) 6.2 (1.0–66.8) NS 0.462 (0.34–0.58)
ARHC/RhoC 6.9 (1.0–192) 6.3 (1.0–17.2) NS 0.458 (0.34–0.58)
p14/ARF 4.9 (1.4–68.1) 4.4 (1.0–61.2) NS 0.457 (0.34–0.57)
XLKD1/LYVE-1 4.5 (1.4–10.9) 3.7 (1.0–10.7) NS 0.448 (0.33–0.57)
CD44 3.1 (1.2–9.6) 2.7 (1.0–8.4) NS 0.440 (0.32–0.56)
CGA 17.6 (1.0–16 552) 6.4 (1.0–5 836) NS 0.425 (0.31–0.54)
CAV1 7.4 (1.1–30.7) 5.6 (1.0–26.6) NS 0.422 (0.31–0.54)
BCL2 4.9 (1.2–13.3) 3.2 (1.0–11.8) NS 0.415 (0.30–0.53)
PGR/PR 277 (1.0–8 034) 97 (1.0–4 551) NS 0.412 (0.29–0.53)
PTGS2/COX2 4.6 (1.0–154) 3.0 (1.0–14.8) NS 0.397 (0.28–0.51)
CCND1 6.3 (1.2–45.3) 4.0 (1.0–21.3) 0.042 0.378 (0.26–0.50)
a:P value, Mann-Whitney U test ; NS, not significant
b:ROC (Receiver Operating Characteristics) – AUC (Area Under Curve) analysis
c:Median (range) of gene mRNA levels
d:AUC value (95% confidence interval)
Seven genes showed significantly different expression according to relapse status (P < 0.05), namely BRCA2, DNMT3B, CCNE1, HMMR/RHAMM, MKI67, TERT and CCND1. The prognostic performance of these 7 genes was also assessed using ROC-AUC analysis. BRCA2 emerged as the most discriminatory marker of relapse status (ROC-AUC, 0.696). The mRNA expression of this gene, as well as DNMT3B, CCNE1, HMMR/RHAMM, MKI67 and TERT, was higher in patients who relapsed than in patients who did not relapse, while only CCND1 mRNA expression was lower in patients who relapsed.
On hierarchically clustering the tumor samples according to the expression of the three most discriminatory genes i.e. the genes with the highest ROC-AUC values (BRCA2, DNMT3B and CCNE1, ROC-AUC: 0.696, 0.690 and 0.674, respectively), the patient population fell into two subgroups (65 and 35 subjects, respectively) with significantly different relapse-free survival curves (log-rank test, P = 0.007; Figure 1A) (5-year RFS rate 66.9% ± 8.1 versus 83.9% ± 4.6; 10-year RFS rate 41.0% ± 8.7 versus 67.0% ± 6.6).
Figure 1 Relationship between RFS and the three-gene expression signature in the initial series of 100 ERα-positive breast tumor samples (A) and in an independent series of 104 ERα-positive postmenopausal breast tumor samples (B).
The prognostic value of a two-gene expression signature based on only BRCA2 and DNMT3B was lower than that of the three-gene expression signature. The addition of HMMR/RHAMM and/or MKI67 to the three-gene signature provided no additional prognostic value.
Using a Cox proportional hazards model, we also assessed the prognostic value, for RFS, of parameters that were significant or near-significant (P < 0.2) in univariate analysis, i.e. SBR grade, lymph-node status (Table 1) and the three-gene expression signature (Figure 1A). Only the prognostic significance of the three-gene expression signature persisted [P = 0.006; regression coefficient = 0.86; relative risk (95% confidence interval) = 2.37 (1.27–4.43)]. The prognostic significance of these three parameters for RFS, calculated in terms of the relative risk, did not change after adjustment for age and macroscopic tumor size (data not shown).
Validation of the three-gene expression signature in an independent series of 104 ERα-positive postmenopausal breast tumor samples
The results for each of the three genes are summarized in table 5, with medians and ranges of mRNA levels in the 31 patients who relapsed and the 73 patients who did not relapse, as well as ROC-AUC values. As in the initial tumor series, BRCA2, DNMT3B and CCNE1 mRNA levels were significantly higher in patients who relapsed than in those who did not relapse.
Table 5 Relationships between the prognostic (+/- relapses) and the mRNA levels of BRCA2, DNMT3B and CCNE1 in 104 ERα-positive postmenopausal breast tumors
GENES Tumors without relapses (n = 73) Tumors with relapses (n = 31) Pa ROC-AUCb
BRCA2 4.7 (1.0–23.6c 7.5 (1.4–38.4) 0.0018 0.694 (0.58–0.80)d
DNMT3B 3.6 (1.0–27.8) 6.2 (2.3–74.1) 0.00052 0.716 (0.61–0.82)
CCNE1 6.4 (1.0–46.2) 9.0 (1.3–62.8) 0.028 0.636 (0.51–0.76)
a:P value, Mann-Whitney U test.
b:ROC (Receiver Operating Characteristics) – AUC (Area Under Curve) analysis
c:Median (range) of gene mRNA levels
d:AUC value (95% confidence interval)
On hierarchical clustering of the samples, the three-gene expression signature dichotomized the 104 patients into two subgroups (n = 30 and n = 74, respectively) of similar sizes to those of the initial patient population (n = 35 and n = 65, respectively).
The "poor prognosis" signature was again associated with shorter relapse-free survival in this independent tumor series (log-rank test, P = 0.029; Figure 1B) (5-year RFS 59.2% ± 9.1 versus 80.7% ± 4.8; 10-year RFS 51.2% ± 9.50 versus 70.4% ± 6.5).
Multivariate analysis based on a Cox proportional hazards model showed that, among the parameters that were significant or near-significant (P < 0.2) in univariate analysis, i.e. SBR grade, lymph-node status, macroscopic tumor size (Table 2) and the three-gene expression signature (Figure 1B), only SBR grade was an independent predictor of RFS (P = 0.00023); the three-gene expression signature only showed a trend towards significance (P = 0.27).
Discussion
We used real-time quantitative RT-PCR assays to quantify the mRNA expression of 47 genes previously identified as candidate prognostic molecular markers in 100 ERα-positive breast tumor samples. We identified a three-gene expression signature (BRCA2, DNMT3B and CCNE1) with independent prognostic significance in breast cancer (P = 0.007 by univariate analysis; P = 0.006 by multivariate analysis). This "poor prognosis" signature was then tested on an independent set of 104 ERα-positive breast tumors from a well-defined cohort of postmenopausal breast cancer patients treated with primary surgery followed by adjuvant tamoxifen alone. It was found to be significant in univariate analysis (P = 0.029), but not in multivariate analysis (P = 0.27). We have previously published individual data for 18 of these 47 genes, namely ERBB1-4 [12]; MYC [13]; TERT [14]; CCND1 [15]; CGB, CGA, ERα, ERβ, PR, PS2 [16]; AR [17]; DNMT3B [18], PAI1, PAI2 and UPA [19], obtained using the same real-time RT-PCR method but in a heterogeneous series of 130 ERα-positive and ERα-negative breast tumors.
Large-scale real-time quantitative RT-PCR is a promising complement and/or alternative to cDNA microarrays for molecular tumor profiling. CDNA microarrays have been used to identify gene expression profiles associated with poor outcome in breast cancer [20-26], but discrepancies have been reported. For example, only 2 of 456 genes identified by Sorlie et al. [21] was among the 70 genes identified by van de Vijver et al. [24].
These discrepancies may be due to the clinical, histological and ethnic heterogeneity of breast cancer, but also to the fact that breast tumors consist of many different cell types – not just tumoral epithelial cells, but also additional epithelial cell types, stromal cells, endothelial cells, adipose cells, and infiltrating lymphocytes. Real-time RT-PCR requires smaller starting amounts of total RNA (about 1–2 ng per target gene) than do cDNA microarrays, making it more suitable for analyzing small tumor samples, cytopuncture specimens and microdissected samples. Real-time RT-PCR also has a linear dynamic range of at least four orders of magnitude, meaning that samples do not need to contain equal starting amounts of RNA. Real-time RT-PCR is also more suitable than cDNA microarrays for analyzing weak variations in gene expression and weakly expressed genes (e.g. TERT as in the present study), and for distinguishing among closely related family member genes or alternatively spliced specific transcripts (e.g. the gene cluster p14/ARF, p16/CDKN2A and p15/CDKN2B as in the present study). Finally, real-time quantitative RT-PCR assay is a reference in terms of its performance, accuracy, sensitivity and throughput for nucleic acid quantification, and is more appropriate for routine use in clinical laboratories, being simple, rapid and yielding good inter-laboratory agreement and statistical confidence values.
In this study, we chose to include well known genes involved in breast carcinogenesis reported in the literature and representing a broad range of cellular functions, such as cell cycle control, cell-cell interactions, signal transduction pathways, apoptosis and angiogenesis (Table 3). Many important genes were not studied, but our results nevertheless demonstrate the usefulness of real time RT-PCR by identifying a potentially useful gene expression signature with prognostic significance.
The comparison of median target gene mRNA levels between patients who did and did not relapse provided two interesting results: (a) ERBB2 mRNA levels were very similar between the two subgroups, with ROC-AUC values close to 0.5 (ROC-AUC, 0.573), confirming that the ERBB2 mRNA expression level is not a major prognostic factor in breast cancer; (b) ESR1/ERα mRNA levels were not different between the two subgroups (ROC-AUC, 0.530), suggesting that the ESR1/ERα mRNA expression level in ERα-positive tumors is not predictive of outcome.
The three-gene expression signature predictive of subsequent relapse status comprised genes involved in cell cycle control (CCNE1), DNA methylation (DNMT3B) and DNA damage repair (BRCA2). This gene expression signature is an interesting candidate for routine clinical use, especially as the three genes encode well-characterized proteins for which specific antibodies are already commercially available. Furthermore, the three protein products are amenable to pharmacological control.
CCNE1 codes for cyclin E, a protein involved in regulating the early G1 to late G1 phase "restriction point traversal", an irreversible commitment to undergo one cell division [27]. We found that high CCNE1 mRNA levels were associated with poor outcome, confirming published data suggesting that cyclin E upregulation may be a major prognostic marker in breast cancer [28-31].
BRCA2 codes for a ubiquitously expressed tumor suppressor protein involved in processes fundamental to all cells, including DNA repair, DNA recombination and cell cycle checkpoint control [32]. We found that high BRCA2 mRNA levels were associated with poor outcome and correlated positively and strongly with cell proliferation. By hierarchical clustering analysis of the 47 genes, we identified BRCA2 as the leading gene in a cluster of proliferation genes also including TERT, BRCA1, HMMR/RHAMM and MKI67 (data not shown). We also observed a strong positive link between BRCA2 and MKI67, which encodes the proliferation-related Ki-67 antigen (Spearman rank correlation test: r=+0.670, P < 10-7). The observed strong associations between BRCA2, HMMR/RHAMM and MKI67 mRNA expression explain why four- and five-gene expression signatures, comprising HMMR/RHAMM alone or together with MKI67, showed no additional prognostic value relative to the three-gene signature.
Our results for BRCA2 expression ex vivo are in keeping with reports from several authors [33,34] showing that BRCA2 mRNA expression is upregulated in rapidly proliferating cells in vitro. Our results are also in agreement with those of Egawa et al. [35] showing that high BRCA2 expression carries a poor prognosis in breast cancer. This link between BRCA2 overexpression and poor outcome should be taken into account when evaluating future BRCA2-based therapeutic approaches to breast cancer.
Finally, DNMT3B, the third gene in our expression signature, codes for one of the three functional DNA methyltransferases (DNMT1, DNMT3A and DNMT3B) that catalyze the transfer of methyl groups to the 5-position of cytosine (DNA methylation). We previously showed that, among the three DNA methyltransferases (DNMT1, DNMT3A and DNMT3B), only DNMT3B overexpression is associated with poor outcome in breast cancer [18]. DNMT3B (like DNMT3A) is known to be a de novo methylator of CpG sites. Abnormal DNA methylation is thought to be a major early event in the development of tumors characterized by widespread genome hypomethylation leading to chromosome instability and localized DNA hypermethylation; the latter may be important in tumorigenesis by silencing tumor suppressor genes [36].
Conclusions
In conclusion, by studying the expression of 47 genes previously identified as candidate prognostic markers in breast cancer, we identified a three-gene expression signature (BRCA2, DNMT3B and CCNE1) with prognostic significance. The practical value of this signature remains to be validated in large prospective randomized studies.
Abbreviations
ERα, estrogen receptor alpha; RT-PCR, reverse transcriptase-polymerase chain reaction.
Authors' contributions
Real-time RT-PCR have been carried out by ST and IG. IB and RL interpreted the result, performed bioinformatics and statistical analyses.
Acknowledgements
We thank the staff of Centre René Huguenin for assistance in specimen collection and patient care. We also thank Dr. Kamel Hacène (Département de Stastistiques Médicales, Centre René Huguenin, 92211 St-Cloud, France) for helpful contributions.
This work was supported by the Comité des Hauts-de-Seine de la Ligue Nationale Contre le Cancer.
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| 15606925 | PMC544833 | CC BY | 2021-01-04 16:36:34 | no | Mol Cancer. 2004 Dec 20; 3:37 | utf-8 | Mol Cancer | 2,004 | 10.1186/1476-4598-3-37 | oa_comm |
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RetrovirologyRetrovirology1742-4690BioMed Central London 1742-4690-1-451561323910.1186/1742-4690-1-45ResearchInhibition of HIV-1 replication in primary human monocytes by the IκB-αS32/36A repressor of NF-κB Palmieri Camillo [email protected] Francesca [email protected] Antimina [email protected] Giuseppe [email protected] Giuseppe [email protected] Ileana [email protected] Department of Clinical and Experimental Medicine, University of Catanzaro "Magna Graecia", Via T. Campanella 115, 88100 Catanzaro, Italy2 Department of Biochemistry and Medical Biotechnology, University of Naples "Federico II", Via S. Pansini 5, 80131 Naples, Italy2004 21 12 2004 1 45 45 13 12 2004 21 12 2004 Copyright © 2004 Palmieri et al; licensee BioMed Central Ltd.2004Palmieri 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 identification of the molecular mechanisms of human immunodeficiency virus type 1, HIV-1, transcriptional regulation is required to develop novel inhibitors of viral replication. NF-κB transacting factors strongly enhance the HIV/SIV expression in both epithelial and lymphoid cells. Controversial results have been reported on the requirement of NF-κB factors in distinct cell reservoirs, such as CD4-positive T lymphocytes and monocytes. We have previously shown that IκB-αS32/36A, a proteolysis-resistant inhibitor of NF-κB, potently inhibits the growth of HIV-1 and SIVmac239 in cell cultures and in the SIV macaque model of AIDS. To further extend these observations, we have generated NL(AD8)IκB-αS32/36A, a macrophage-tropic HIV-1 recombinant strain endowed to express IκB-αS32/36A.
Results
In this work, we show that infection with NL(AD8)IκB-αS32/36A down-regulated the NF-κB DNA binding activity in cells. NL(AD8)IκB-αS32/36A was also highly attenuated for replication in cultures of human primary monocytes.
Conclusions
These results point to a major requirement of NF-κB activation for the optimal replication of HIV-1 in monocytes and suggest that agents which interfere with NF-κB activity could counteract HIV-1 infection of monocytes-macrophages in vivo.
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Background
HIV-1 infection is characterized by a long period of clinical latency followed by the development of acquired immunodeficiency syndrome, AIDS. During latency and when viral replication is being controlled in patients treated with antiretroviral therapy, HIV-1 is present in cellular reservoirs and continues to replicate, with each ensuing round of replication giving rise to escape mutants, which further replenish viral reservoirs [1,2]. This grim picture calls for novel targeted therapies for eradicating virus-infected cells and for preventing new infections.
Initial infection in vivo by HIV-1 is thought to occur in CD4-positive, CCR5-positive lymphocytes and monocytes. Accordingly, when HIV-1 envelope protein in its oligomerized g160 form contacts the cell surface receptor a signalling cascade is triggered that results in transcriptional activation of specific gene arrays, such as the inflammatory cytokines IL-1 β, IL-6, IL-8, TNF-α, TGF-β; these cytokines, in turn, function to enhance the transcriptional activity of the proviral long terminal repeat (LTR) promoter [3,4]. This cytokine-driven inflammatory-like setting is mediated molecularly by the NF-κB family of transcription factors [5,6]; thus, it serves to reason that preventing NF-κB activation would attenuate HIV-1 replication. Indeed, the LTR of HIV-1 does contain two tandem NF-κB sites [7] and three repeated Sp1 sites [8] upstream of the TATAA box with an additional NF-κB site located in the 5' untranslated region of viral genome [9]. Both sets of NF-κB sequences enhance HIV-1 transcription in response to various signals [9]. However, the Sp1 sites and TATAA box can redundantly sustain the Tat-mediated transactivation of the HIV-1 LTR in the absence of NF-κB sites [10]. It is controversial whether NF-κB cellular factors are required for the HIV-1 replication. Mutant HIV-1 carrying deletions or base-pair substitutions in the NF-κB enhancer in the LTR have been shown to be either competent or incompetent for replication [11-13]. These divergent observations are likely explained by differing cellular contexts, such as primary cells versus immortalized cell lines, and varying levels of cellular activation.
IκB inhibitors regulate NF-κB activity [14]. In response to activating stimuli, IκB proteins become phosphorylated, ubiquinated and degraded by proteasomes. This releases cytoplasmic-sequestered NF-κB to enter the nucleus to activate the transcription of responsive genes [14]. The mutant IκB-αS32/36A is defective for serine 32- and serine 36-phosphorylation and is resistant to proteolysis. IκB-αS32/36A acts as a potent inhibitor of the NF-κB-dependent gene transcription, including those from the HIV-1 genome [15]. To verify the requirement of NF-κB in the replication of HIV-1 in primary cells, we previously designed HIV-1 and SIV molecular clones containing the IκB-αS32/36A cDNA positioned into the nef region of the respective viral genome [16,17]. We found that these recombinant viruses were highly attenuated for replication in T cell lines as well as in human and simian PHA-activated peripheral blood mononuclear cells, PBMCs [16,17]. These findings supported an interpretation that in these cellular contexts NF-κB is required for efficient viral replication. We also showed that a recombinant SIV which expressed IκB-αS32/36A inhibitor was also highly replication attenuated in vivo in rhesus macaque [17]. Here, we have extended our analysis of IκB-αS32/36A function in HIV-1 replication to primary monocytes. We report that a macrophage-tropic derivative of NL4-3 strain that expresses the proteolysis-resistant IκB-αS32/36A inhibitor of NF-κB replicated poorly in cultured primary human monocytes.
Results
Construction of pNL(AD8)IκB-αS32/36A
To generate a macrophage-tropic HIV-1 expressing the IκB-αS32/36A cDNA fused to the FLAG epitope, the CXCR4-tropic envelope of pNLIκB-αS32/36A [16] was replaced with the CCR5-tropic envelope from pNL(AD8) [18]. Briefly, the 2.7 Kb EcoR1-BamH1 fragment of pNL(AD8) was religated to the 13.1 Kb EcoR1-BamH1 fragment of pNLIκB-αS32/36A or pNLIκB-antisense, thus generating pNL(AD8)IκB-αS32/36A and pNL(AD8)IκB-antisense, respectively (Fig. 1A). Both molecular clones are Nef-minus because our cloning strategy deleted the first 39 amino acids from the N terminus of Nef and engineered a translational frameshift into the remaining Nef-encoding codons [16]. The respective molecular clones were transfected into 293T cells to analyse for the expression of HIV-1 proteins and IκB-αS32/36A polypeptide by immunoblotting (Fig. 1 B, C). As expected the IκB-αS32/36A-FLAG protein was expressed by pNL(AD8)IκB-αS32/36A (Fig. 1C, lane 4).
Figure 1 Genome structure and expression of recombinant pNL(AD8)IκB-αS32/36A and pNL(AD8)IκB-antisense molecular genomes. Panel A shows the structure of pNL(AD8) derivatives that carry the IκB-αS32/36A-FLAG insert into the nef region in sense (pNL(AD8)IκB-αS32/36A) or antisense (pNL(AD8)IκB-antisense) orientations. Panel B shows the immunoblot analysis using hyperimmune AIDS patient serum of total extracts (10 μg) from 293T cells 24 hours after transfection with the indicated viral plasmids (10 μg). Panel C shows the immunoblot analysis using an anti-FLAG monoclonal antibody of total extracts (10 μg) from 293T cells 24 h after transfection with the indicated viral plasmids (10 μg).
Inhibition of NF-κB activity by pNL(AD8)IκB-αS32/36A
To assess the functional impact of IκB-αS32/36A expressed from the recombinant NL(AD8) genome, 293T cells were transfected individually with pNL(AD8), pNL(AD8)IκB-αS32/36A or pNL(AD8)IκB-antisense, and the respective nuclear extracts were evaluated for NF-κB (Fig. 2A) and Sp1 DNA binding activity (Fig. 2B). A significant reduction in NF-κB DNA binding activity was observed upon transfection of pNL(AD8)IκB-αS32/36A (Fig. 2A, lane 5) as compared to the other viral transfections (Fig. 2A, lanes 3,4). The specificity of the IκBαS32/36A-mediated inhibition of NF-κB was verified by the demonstration that Sp1 binding to DNA was unaffected (Fig. 2B). These results support the interpretation that IκBαS32/36A expressed from the recombinant viral genome functionally inhibited NF-κB activity.
Figure 2 Reduced NF-κB DNA binding activity in cells transfected with pNL(AD8)IκB-αS32/36A. Panel A shows the NF-κB binding activity of nuclear extracts (5 μg) from 293 T cells transfected with the indicated viral plasmids (10 μg) or were mock-transfected. Panel C shows the Sp1 binding activity of the same nuclear extracts as in panel A. Binding competitions were performed with 100-fold molar excess of the respective unlabelled oligonucleotide.
Attenuation of pNL(AD8)IκB-αS32/36A in primary monocytes
We next analyzed the replication properties of the recombinant HIV-1 genomes in cultured human monocytes from different individuals. Based on normalized amounts of input virus, we found that NL(AD8)IκB-αS32/36A was highly attenuated for replication when compared to NL(AD8) and NL(AD8)IκB-antisense (Fig. 3 A-B). Accordingly, virus-induced syncitium formation was also strongly inhibited in monocytes infected with NL(AD8)IκB-aS32/36A (Fig. 4 A, B). Taken together, our results underscore a critical contribution of NF-κB to HIV-1 growth in monocytes.
Figure 3 Attenuated replication of NL(AD8)IκB-αS32/36A in primary human monocytes. Panels A and B show the growth NL(AD8), NL(AD8)IκB-antisense and NL(AD8)IκB-αS32/36A in cultures of primary human monocytes. Cells (105) were infected with equal amounts of viruses normalized based on RT counts of 106 cpm (A) or 105 cpm (B). A representative experiment of three independent infections of monocytes from different individuals is shown.
Figure 4 Reduced syncitia formation by NL(AD8)IκB-αS32/36A in infection of primary human monocytes. Panel A shows the kinetics of syncitia generation upon infection of primary human monocytes with 105 cpm RT activity of the indicated viral stocks. The average of syncitia observed per optical field is reported. Panel B shows the picture of primary human monocytes at 14 days post-infection with 105 cpm RT activity of the indicated viral stocks (original magnification × 430).
Discussion
Substantial numbers of monocytes are preserved in infected individuals even at later clinical stages of AIDS, when T cell numbers are dramatically reduced. Consistently, in animal models of HIV-1 infection, monocytes are the major reservoir after acute depletion of CD4-positive T cells [19,20]. This indicates that these cells are long lasting infected moieties that shuttle from mucosal sites to lymph nodes and could function as a major HIV-1 reservoir in vivo. In addition, monocytes are programmed to produce a large amount of inflammatory cytokine, including IL1-β, IL-6, TNF-α, which are strong inducers of HIV-1 replication [5]. Indeed, HIV-1 envelope binding to CCR5 receptor activates an intracellular signalling cascade that promotes high levels of transcription factors, including NF-κB, which sustain the initial rounds of viral replication and induce the production of inflammatory cytokines which activate surrounding cells to become more susceptible to virus infection [3,4].
Based on the published literature, the role of NF-κB in HIV-1 replication has been controversial [13,16,21]. For instance, the deletion of NF-κB binding sites from HIV-1 and SIV LTRs [22] has suggested that NF-κB activity may not be required for HIV-1 LTR-directed transcription. Moreover, deletion of NF-κB sequences in the LTR has also been reported not to affect HIV-1 replication in defined cellular settings [11,12]. These latter studies relied on short-term infections of immortalized cells that may not express a physiologic concentration of transcription factors. To address this issue, we have developed a novel HIV-1 strain, NL(AD8)IκB-αS32/36A, which was engineered to express a proteolysis-resistant IκBαS32/36A, and is a strong inhibitor of NF-κB activity. This recombinant virus expresses the envelope of the AD8 strain, a macrophage-tropic virus. Our findings show that NL(AD8)IκB-αS32/36A replication profile is different from that of the NL(AD8)IκB-antisense control. NL(AD8)IκB-αS32/36A failed to produce a productive infection in primary monocytic cells over a thirty-days acute infection (Fig. 3). These results were correlated with a strong inhibition NF-κB activity in NL(AD8)IκB-αS32/36A-infected cells (Fig. 2), indicating that in the setting of HIV infection of primary monocytes NF-κB plays a non-redundant role. These results are in agreement with the evidence that IκB-αS32/36A negatively affected the replication of HIV and SIV in PBMC cultures and in monkeys [16,17].
Because IκB-αS32/36A constitutively inhibits NF-κB [15], the potent inhibition of HIV/SIV replication could be due to repression of the NF-κB-dependent activation of HIV/SIV transcription. However, additional mechanisms might explain the potent inhibition of HIV/SIV replication by IκB-αS32/36A. In this regard, IκB-α regulates the transcriptional activity of NF-κB-independent genes by interacting with nuclear co-repressors, histone acetyltransferases and deacetylases [23,24]. Further studies are required to clarify novel activities of IκB-α in the modulation of the transcriptional machinery. Our results underscore a central role for IκB-α as a potent inhibitor of the replication of HIV-1 in both T cells [16] and monocytes (this study), and point to the NF-κB/IκB network as a suitable target for therapeutic intervention of AIDS.
Conclusions
In this study we have addressed the role of NF-κB/IκB proteins in the replication of HIV-1 in primary human monocytes. We show a strong attenuation in the replication of a macrophage-tropic HIV-1 strain expressing the IκB-αS32/36A repressor of NF-κB in primary cultures of human monocytes. These results are consistent with previous evidence of HIV/SIV inhibition by IκB-αS32/36A in PBMCs and in macaques [16,17]. In addition, these findings further support a role of NF-κB inhibitors in blocking HIV-1 replication and suggest novel strategies for the development of anti-viral therapy that targets NF-κB factors.
Methods
Transfections and Viral stocks
293T cells were cultured in Dulbecco's modified Eagle's medium supplemented with 10% v/v heat-inactivated fetal bovine serum and 3 mM glutamine. Viral stocks were produced by transfecting 293T cells (106) with viral plasmids (10 μg) using calcium phosphate. Forty hours later, the cell culture supernatant was passed through a 0.45-μm filter and measured for RT activity as previously described [16].
Immunoblotting analysis
293T cells were transfected with viral plasmids (10 μg) and lysed in RIPA buffer (150 mM NaCl, 1 % Nonidet P-40, 0.5 % sodium deoxycholate, 0.1% sodium dodecyl sulfate, 50 mM Tris-HCl pH 8.0) 24 hours later. Proteins (10μg) were separated by electrophoresis in 10% SDS-polyacrylamide gel and transferred to Immobilon-P (Millipore). Filters were blotted with an AIDS patient serum or with anti-FLAG monoclonal antibody by using Western-Light Chemiluminescent Detection System (Tropix, Bedford, MA).
Electrophoretic Mobility Shift Assays
Nuclear extracts and gel retardation assays were performed as described previously [9]. Briefly, cells were harvested, washed twice in cold phosphate-buffered saline, and resuspended in lysing buffer (10 mM Hepes, pH 7.9, 1 mM EDTA, 60 mM KCl, 1 mM DTT, 1 mM phenylmethylsulfonyl fluoride, 0.2% v/v Nonidet P-40) for 5 min. Nuclei were collected by centrifugation (500 × g, 5 min), rinsed with Nonidet P-40-free lysing buffer, and resuspended in 150 μl of buffer containing 250 mM Tris-HCl, pH 7.8, 20% glycerol, 60 mM KCl, 1 mM DTT, 1 mM phenylmethylsulfonyl fluoride. Nuclei were then subjected to three cycles of freezing and thawing. The suspension was cleared by centrifugation (7000 × g, 15 min), and aliquots were immediately tested in gel retardation assay or stored in liquid phase N2 until use. The HIV-1 NF-κB oligonucleotide probe was 5'-CAAGGGACTTTCCGCTGGGGACTTTCCAG-3'; the Sp1 oligonucleotide probe was 5'-GGGAGGTGTGGCCTGGGCGGGACTGGGGAGTGGCG-3'. The probes were end-labelled with [γ-32P]ATP (Amersham Int., Buckinghamshire, UK) using polynucleotide kinase (New England Biolabs, Beverly, MA). Equal amounts (5 μg) of cell extracts were incubated in a 20 μl reaction mixture containing 10% glycerol, 60 mM KCl, 1 mM EDTA, 1 mM DTT, and 2 μg of poly [d(G-C)] (Boehringer Mannheim, Germany) for 5 min on ice. One μl of [γ32P]-labelled double-stranded probe (0.2 ng, 5 × 104 cpm) was then added with or without a 100-fold molar excess of competitor oligonucleotide. The reactions were incubated at room temperature for 15 min and run on a 6% acrylamide:bisacrylamide (30:1) gel in 22.5 mM Tris borate, 0.5 mM EDTA. Gels were dried and autoradiographed.
Monocytes cultures and infections
Human monocytes were isolated from PBMC by elutriation, cultured in RPMI, 10% FCS and GMCSF (20 ng/ml) for 48 hours. Infections were performed with viral stocks measured by reverse-transcriptase (RT) activity [16]. Usually, cell cultures (105 cells) were infected with 105 - 106 cpm of RT activity. The cell culture supernatants were collected every two days and replaced with fresh medium. The viral production was measured as RT activity in the culture supernatants as previously described [16]. The syncitia formation in cell cultures was evaluated by calculating the average number of syncitia in at least six optical fields.
List of abbreviations used
NF-κB, nuclear factor kappa B
IκB, inhibitor of nuclear factor kappa B
IL-1, interleukin-1
IL-6, interleukin-6
IL-8, interleukin-8
TNF-α, tumor necrosis factor alpha
TGF-β, transforming growth factor-beta
cpm, counts per minute
FCS, fetal calf serum
GMCSF, granulocyte-macrophage colony-stimulating factor
Competing interests
The author(s) declare that they have no competing interests.
Authors' contributions
CP carried out the analysis of viral growth and DNA band-shift assays. FT was responsible for cell cultures. AP performed the immunoblotting analysis. GF produced the viral plasmids and viral stocks, and performed the artwork of the paper. GS participated in the design of the study and discussion of results. IQ designed this study and edited the manuscript.
Acknowledgements
We thank K. T. Jeang for helpful discussions, and E. Freed for providing pNL(AD8). This work was supported by Ministero della Sanità-Istituto Superiore della Sanità-Programma Nazionale di Ricerca sull'AIDS, and Ministero dell'Istruzione, dell'Università e della Ricerca. C.P and A.P were recipients of FIRC fellowships.
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| 15613239 | PMC544834 | CC BY | 2021-01-04 16:36:37 | no | Retrovirology. 2004 Dec 21; 1:45 | utf-8 | Retrovirology | 2,004 | 10.1186/1742-4690-1-45 | oa_comm |
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Cardiovasc DiabetolCardiovascular Diabetology1475-2840BioMed Central London 1475-2840-4-11563163210.1186/1475-2840-4-1Case ReportIsolated low high density lipoprotein-cholesterol (HDL-C): implications of global risk reduction. Case report and systematic scientific review Hayden Melvin R [email protected] Suresh C [email protected] Department of Family and Community Medicine, University of Missouri Columbia, Missouri, PO BOX 1140 Lk. Rd. 5-87, Camdenton, Missouri 65020 USA2 Department of Physiology and Biophysics, 500 South Preston Street, University of Louisville, Louisville, Kentucky 40292 USA2005 4 1 2005 4 1 1 8 12 2004 4 1 2005 Copyright © 2005 Hayden and Tyagi; licensee BioMed Central Ltd.2005Hayden and Tyagi; 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 importance of low high-density lipoprotein cholesterol (HDL-C), elevated non HDL-C (as part of the metabolic syndrome, prediabetes, and type 2 diabetes mellitus), and an isolated low HDL-C is rapidly emerging. The antiatherosclerotic roles of reverse cholesterol transport and the pleiotropic antioxidant – anti-inflammatory mechanistic effects of HDL-C are undergoing rapid exponential growth.
Case presentation
In 1997 a 53-year-old Caucasian male presented with a lipoprotein profile of many years duration with an isolated low HDL-C and uric acid levels in the upper quintile of normal. He developed an acute myocardial infarction involving the right coronary artery and had percutaneous transluminal coronary angioplasty with stenting of this lesion. He also demonstrated a non-critical non-flow limiting lesion of the proximal left anterior descending coronary artery at the time of this evaluation.
Following a program of global risk reduction this patient has done well over the past 7 years and remains free of any clinical signs and symptoms of atherosclerosis. His HDL-C and uric acid levels are currently in the normal physiological range.
Conclusion
Low HDL-C and isolated low HDL-C constitute an important risk factor for atherosclerosis. Therapies that lead to a return to normal physiologic range of HDL-C may result in the delay of atherosclerotic progression.
Apo A-1ABCA1AtheroscleropathyAtherosclerosisantioxidantanti-inflammatorylipoprotein Aredox stressfibratesniacinezetimibestatins.
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Case presentation
MRH, a 53-year-old Caucasian male (physician) developed an acute inferior myocardial infarction (MI) associated with bradycardia and occasional PVCs. Emergency medication included aspirin, nitroglycerin and a bolus of TPA.
The cardiology team preformed PCTA at the site of near complete blockage of the right coronary artery with successful stent placement. At this time a non-critical 40% lesion located in the proximal left anterior descending coronary artery was noted, which was not manipulated. The patient was discharged following 24 hours of stable monitoring.
Past Medical History
Relapsing fever 1971 full recovery, spontaneous left pneumothorax times two (1982–83), lumbar fusion back surgery 1985, and Herpes Simplex encephalitis 1989 with full recovery.
Family History
Mother with CVA (cerebellar) age 58 full recovery. Died of Hodgkin's lymphoma 64. Brother with type 1 diabetes mellitus with onset at age 29 (known PAD and aorto-femoral bypass age 49) died in sleep age 51.
Father with CVA (vertebrobasilar) age 75 with full recovery, COPD, died in sleep while recovering from TIA and pneumonia age 84.
Grandparents lived to their 80s and died of old age.
Social History
High stress family physician who seldom drank alcohol and smoked a pipe occasionally. Blood pressure at times of high stress would elevate to 140/85–88 and return to 120–130s/ 70–75 at times of non-stress in the office. He was physically active with no dedicated exercise program
Laboratory Values
Five months prior to MI and reflective of numerous metabolic profiles over the preceding decades.
Total cholesterol 198 mg/dL
Triglycerides 154 mg/dL
HDL-C 34 mg/dL. HDL-C (1970–1973 32 mg/dL and 34 mg/dL)
LDL-C calculated 120 mg/dL
Non HDL-C = (198-34) = 164
Total Chol/HDL ratio = 6.2 > than 5 and is high
Uric acid 6.5 mg/dL
Blood sugar non-fasting 102 mg/dL
Homocysteine first week post MI fasting: 28 mcmol/L
LFTs, electrolytes, calcium and phosphorus, serum iron, renal function, and CBC were all in normal range.
Patient started a program reflecting the global risk reduction approach described in the RAAS acronym (table 1) and is currently taking an angiotensin receptor blocker, aspirin, beta blocker, folic acid, and a statin. Patient was intolerant of ACE inhibitor therapy due to cough and fatigue and has been unable to tolerate niacin on numerous attempts both pre and post MI due to incapacitating headaches.
Table 1 The RAAS acronym: global risk reduction
R Reductase inhibitors (HMG-CoA). Decreasing modified LDL-cholesterol, i.e. oxidized, acetylated LDL-cholesterol. Decreasing triglycerides and increasing HDL-cholesterol Improving endothelial cell dysfunction. Restoring the abnormal Lipoprotein fractions. Thus, decreasing the redox and oxidative stress to the arterial vessel wall and myocardium.
Redox stress reduction.
A AngII inhibition or blockade:
ACEi-prils. ARBS-sartans. Both inhibiting the effect of angiotensin-II locally as well as systemically. Affecting hemodynamic stress through their antihypertensive effect as well as the deleterious effects of angiotensin II on cells at the local level – injurious stimuli -decreasing the stimulus for O2. production. Decreasing the A-FLIGHT toxicities. Plus the direct-indirect antioxidant effect within the arterial vessel wall and capillary. Antioxidant effects.
Aspirin antiplatelet, anti-inflammatory effect.
Adrenergic (non-selective blockade) in addition to its blockade of Prorenin→Renin
Amlodipine with its calcium channel blocking antihypertensive effect, in addition to its direct antioxidant effects.
Redox stress reduction.
A Aggressive control of diabetes to HbA1c of less than 7. (This usually requires combination therapy with the use of: Insulin secretagogues, insulin sensitizers (thiazolidinediones), biguanides, alpha-glucosidase inhibitors, and ultimately exogenous insulin.) Decreasing modified LDL cholesterol, i.e. glycated – glycoxidated LDL cholesterol. Improving endothelial cell dysfunction. Also decreasing glucotoxicity and the oxidative – redox stress to the intima and pancreatic islet.
Aggressive control of blood pressure, which usually requires combination therapy, including thiazide diuretics to attain JNC 7 guidelines.
Aggressive control of dyslipidemias, which frequently requires combination therapy (especially in the metabolic syndrome and T2DM), including TLC, statins, fibrates, selective cholesterol inhibitors such as ezetimibe, and niacin
Aggressive control of Hcy with folic acid with its associated additional positive effect on re-coupling the eNOS reaction by restoring the activity of the BH4 cofactor to run the eNOS reaction and once again produce eNO.
Redox stress reduction.
S Statins. Improving plaque stability (pleiotropic effects) independent of cholesterol lowering. Improving endothelial cell dysfunction. Plus, the direct – indirect antioxidant anti-inflammatory effects within the islet and the arterial vessel wall promoting stabilization of the unstable, vulnerable islet and the arterial vessel wall. Style: Lifestyle modification: lose weight, exercise, and change eating habits. Stop Smoking
Redox stress reduction
Current Laboratory Values 2004:
Total cholesterol: 138 mg/dL
Triglycerides: 94 mg/dL
HDL-C: 45 mg/dL
LDL-C calculated: 74 mg/dL
Non HDL-C: (138-45) = 93
Total Chol/HDL ratio = 3.0
Uric acid: 6.5 mg/dL
Blood sugar: Fasting 80 mg/dL, 2 hour post prandial 118 mg/dL
Homocysteine: 7.2 mcmol/L
Lp(a): 4.2 mg/dL in normal range immediate post MI and again at this time: 4.3 mg/dL.
hs-CRP: 0.7 mg/L.
LFTs, electrolytes, calcium and phosphorus, serum iron, renal function, and CBC are all in normal range.
This patient has done well over the past seven years and remains free of any clinical signs and symptoms of cardiovascular disease. While this patient will always remain a CHD risk, his current laboratory values remain in a normal physiological range. As noted above his HDL-C and uric acid levels are currently in the normal physiological range and his hs-CRP remains in the second quartile.
Comment
According to Framingham risk scores associated with the National Cholesterol Education Program Adult Treatment Panel III (NCEP ATP III) guidelines [1], few would have recommended any treatments other than therapeutic lifestyle changes (TLC) and possibly niacin, which our patient was intolerant both pre and post event in 1997.
If we score this patient according to the estimate of 10-year risk for men he gets 6 points for age 53, 2 points for total cholesterol 160–199 age 53, 3 points for being a pipe smoker, 2 points for HDL being < 40 mg/dL, and 1 point for systolic blood pressure 140–159 untreated. This totals 14 points and results in an estimated 10-year risk for men of 16%, which is less than the 20% recommended for more aggressive therapy.
Even if we apply the NCEP ATP III guidelines of having two plus risk factors: Male sex, hypertension, smoking, and low HDL-C with a 10 risk < or = to 20% we obtain the following recommendations: LDL-C goal < 130 mg/dL, initiation of TLC if LDL-C is = or > 130 mg/dL, consideration of drug therapy if LDL-C is > or = to 130 mg/dL after three months of TLC. It is important to note that our patient had a LDL-C of 120 mg/dL prior to his event. Even if we look at the non HDL-C levels, which are allowed to be 30 mg/dL higher than LDL-C goals we have a patient with a non HDL-C of only 164. MRH became a CHD risk patient within a short period of time of 5 months.
Discussion
The importance of low HDL-C and cardiovascular disease associated with the lipid triad (Low HDL-C, elevated triglycerides, and increased small dense LDL-C) found in the metabolic syndrome (metS) and overt type 2 diabetes mellitus (T2DM) and a contributing factor to the elevated non HDL-C discussed in the current NCEP ATP III guidelines or the patients with isolated low HDL-C is rapidly evolving.
The accelerated atherosclerosis (atheroscleropathy) associated with the metS and T2DM has been previously reviewed and is definitely a serious problem associated with the current epidemic of obesity – diabesity and T2DM [2-4].
Both isolated low HDL-C and elevated non HDL-C (total cholesterol minus HDL-C) levels are difficult to get to known NCEP ATP III recommendations and this task usually requires combination therapy. These therapies consist of therapeutic life style changes and pharmacotherapy including statins, fibrates, selective cholesterol inhibitors such as ezetimibe, and niacin in addition to a global risk reduction of all non HDL-C existing risk factors (table 2) [5].
Table 2 Effects of drugs on HDL-C levels
DRUG PERCENT INCRESE
Nicotinic acid (niacin) 15% – 35%
Fibrates 10% – 15%
Estrogens 10% – 15%
Statins
Coupled Dual Effect
Associated with potent LDL-C reduction, which make the statins "shine" 5% – 10%
Alpha blockers 10% – 20%
Alcohol (in moderation) 10%
Ezetimibe 3%
In this case report a focus on isolated low HDL-C is appropriate. This case report demonstrates a marked improvement of all lipid parameters including his low HDL-C. However, this marked improvement is not always as simple as this case and therefore, both the patient and the clinician need to be very patient, as well as, creative in order to achieve global risk reduction [5].
Isolated low HDL-C
In 1977 the Tromso Heart Study demonstrated that CAD patients have HDL-C levels 35% lower than controls and those patients with low HDL-C are three times more likely to develop CAD than those with elevated LDL-C [6]. These early views certainly support the concept that an isolated low HDL-C is a common antecedent of clinical CHD, as well as being important in accelerating the progression of atherosclerosis.
The inverse relation of HDL-C to CHD events has been widely discussed since the original publication of data from the Framingham study (1986) [7,8]. Castelli WP et al. were able to show an inverse association of high HDL-C and low coronary risk was as statically as strong as the direct association of high LDL-C and high coronary risk in a cohort of men and women age 40–82 followed for 12 years who were free from CAD at study entry. At any level of cholesterol low HDL-C increases the rate of CHD [1]
The NCEP ATP III guidelines clearly defines a level < 40 mg/dL as an independent risk factor for CHD [1]. Raising HDL-C is not a target for either primary or secondary prevention at this time, however its importance as a tertiary target is rapidly emerging.
Michael Miller has stated: "Low HDL-C is the most common lipoprotein abnormality in patients with CHD and is predictive of CHD events, even when total cholesterol levels are normal" [9].
Goldbourt U et al., found that the prevalence of isolated low HDL-C as a risk factor for CHD mortality to be present in one out of six or 16.6 % while studying a 21-year follow up of 8000 men [10]. Furthermore, they found that an excess CHD risk associated with isolated low HDL-C appeared particularly increased in men with diabetes mellitus, whose death rate was 65% higher than in diabetics with HDL-C > 0.9 mmol/L or 36 mg/dL.
There are at least eight secondary causes for low HDL-C (table 3) and at least seven drugs that have a positive effect on raising HDL-C (table 2). As demonstrated in our case report, the beneficial effects of raising HDL-C with statin therapy and a program of global risk reduction have been positive in preventing the progression of atherosclerosis and recurrent acute coronary syndromes (table 4).
Table 3 Secondary causes of low HDL-C
1. Elevated triglycerides. (Component of metS)
End stage renal disease
Hypothyroidism [also increased total Chol/HDL-C ratio.
2. Obesity and Overweight. – [waist measurement] – (Component of metS)
[Visceral obesity in particular]
For every 3 kg. (7 lbs.) weight loss HDL-C increased 1 mg/dL.
3. Prediabetes and overt Type 2 Diabetes Mellitus. (Component of metS)
4. Physical inactivity (lifestyle choice).
5. Smoking (lifestyle choice).
6. Very high carbohydrate intakes > 50–60% of energy (lifestyle choice).
[Especially Fructose Containing Soft Drinks.]
7. Metabolic Syndrome: As potent a risk factor as smoking.
8. Drugs, such as beta-blockers, anabolic steroids, and progestational agents.
Table 4 Beneficial effects of HDL-C
REVERSE CHOLESTEROL TRANSPORT
Accepts cholesterol from the macrophage and tissues and transports it back to the liver for disposal in the bile (figure 1).
Acts a an apoprotein donor to the other lipoproteins
ANTIOXIDANT
Antioxidant activity (through intimal paraoxonase, and redox -sensitive methionine residues of apo A-1)
Increases eNOS and endothelial nitric oxide
ANTIINFLAMMATORY
Downregulates adhesion molecule expression on endothelium: (I-CAM, V-CAM and MCP-1)
Inhibits neutrophil degranulation
ANTITHROMBOTIC
Antithrombotic activity via its ability to block TxA2 and potentiates activity of proteins: C and S.
Stimulates prostacyclin production (antithrombotic and vasodilitory).
ENDOTHELIAL PROTECTION PROTERTIES
Acts as an endothelial mitogen and inhibits endothelial cell apoptosis: This would help to decrease the incidence of plaque erosion and promote plaque stabilization
Stimulates endothelial nitric oxide (eNO and its enzyme eNOS) and prostacyclin production with vasodilatation, antioxidant, and anti-inflammatory properties.
HDL-C is synthesized in the intestine and liver and is extremely important in reverse cholesterol transport from the tissues to the liver for disposal. It works in conjunction with the ABCA1 cholesterol transporter within the intimal macrophages (figure 1).
Figure 1 Reverse Cholesterol Transport. This figure demonstrates the process of reverse cholesterol transport. It begins in the arterial vessel wall and with the assistance of the ATP binding cassette transporter A-1 (ABCA-1) and in collaboration with the Apo A-1 protein attached to the outer shell of the nascent HDL-C lipoprotein particle free cholesterol is internalized within the HDL-C lipoprotein particle. The enzyme lecithin cholesterol acyltransferase (LCAT) esterifies free cholesterol (FC) via a lipidation process and internalizes it within the HDL-3, which matures to a larger HDL-2 lipoprotein particle. From this point in time the HDL-3 and 2 particles can enter the hepatic cycle via the Scavenger Receptor B-1 and subsequently excreted in the bile. The alternative pathway is for the larger HDL-C apoA-1 lipoprotein particles to undergo a transference of the cholesterol esters through an exchange process with triglycerides via cholesterol ester transfer protein (CETP) to the ApoB-100 lipoprotein particles and enter the liver for further metabolism via the low density lipoprotein receptor (LDLR) to be subsequently excreted in the bile.
This important dual interaction of HDL-C and the ABCA1 transporter is of great importance and recently we have learned that certain gene polymorphism of ABCA1 transporter may have a profound effect on HDL-C in addition to the well known abnormality of Tangier disease [11]. Probst MC et al., have even set aside a website to list all of the known ABCA1 gene polymorphisms [11].
Oxidative stress and the reductive stress (redox stress) associated with overt T2DM and multiple risk factors associated with accelerated atherosclerosis may result in a damaging effect to HDL-C and interfere with the ABCA1 transporter in reverse cholesterol transport via a mechanism of oxidation and nitration of tyrosine residues on the apo A-1 lipoprotein outer shell of HDL-C lipoprotein [12]. This biochemical alteration of the apo A-1 lipoprotein could disable the process of reverse cholesterol transport and aggravate an underlying isolated low HDL-C level.
Lifestyle changes that are important in raising low HDL-C consist of smoking cessation, weight loss, exercise, and the use of alcohol in moderation.
HDL-C has numerous positive effects on the endothelium and arterial vessel wall, which decrease non-diabetic atherosclerosis and the accelerated atherosclerosis – atheroscleropathy associated with metS and overt T2DM (table 4).
Emerging novel risk markers of atherosclerosis
NCEP ATP III allows the clinician to factor in the additional risks associated with novel, emerging risk markers such as our patients elevated homocysteine. Other risk markers would be the lipid markers: Elevated triglyceride, remnant lipoproteins, lipoprotein (a), an abnormal TC/HDL-C ratio, small dense LDL particles, HDL subspecies, and apolipoprotein A and B. The non lipid markers would include: An elevated glucose, inflammatory markers (elevated hs-CRP and the emerging importance of the various interleukins and in particular IL-6, which is the driving force behind hs-CRP elevation), coagulation markers (elevated PAI-1, Lp(a), and fibrinogen), the emerging roles of matrix metalloproteinases (MMPs) and of course the established risk marker of an elevated homocysteine. It is interesting to note that Qujeq D et al., noted a negative correlation between total homocysteine and HDL-C levels (p < 0.05, r = 0.93) in a study evaluating 126 patients (67 male and 59 females, aged 29–73 mean of 48.65 +/- 5.79) with unequivocal changes of acute myocardial infarction in the electrocardiogram as compared to 135 normal healthy controls, while noting a positive correlation between total homocysteine and LDL-C levels (p < 0.05, r = 0.98) [13]
The reader may note that the patients' uric acid level was quite elevated prior to his acute coronary event and that this level returned to a very normal level following global risk reduction and aggressive therapy for his multiple risk factors in addition to his isolated low HDL-C. Although not a considered a risk factor or even an emerging, novel risk marker, uric acid may be a quite sensitive marker of underlying redox and oxidative stress. Uric acid levels greater than 4 mg/dL may be considered a red flag in those patients, such as our case report, with high risk for CHD [14].
The Atherosclerotic Kitchen Sink
When viewing the sources for atherosclerosis it is important to note that there are two routes for accumulation of atherogenic lipoproteins (input and outflow) within the arterial intima and subsequent remodeling of the arterial vessel wall.
The atherosclerosis equation: Lipoprotein Accumulation (retention) in the arterial vessel wall = Lipoprotein in - lipoprotein out. L-A avw = L-in - L-out.
L-in, would equal the net lipoproteins derived from the GI tract (absorption) plus that synthesized by the liver. Lipoprotein out is strictly via reverse cholesterol transport to the liver and secretion via bile into the gut. L-in is primarily the beta lipoproteins or apolipoprotein B containing lipoprotein particles, whereas L-out depends primarily on the alpha lipoproteins, apolipoprotein A or HDL-C. The beta lipoproteins are atherogenic and the alpha lipoproteins are antiatherogenic. From this analogy one can see can see why non HDL-C was so important in the recent NCEP ATPIII guidelines: non HDL-C = total Cholesterol – HDL-C (reflecting the total atherogenic burden).
This is also why the recent global (52 countries) INTERHART study found the ApoB/ApoA-1 ratio (the ratio of atherogenic lipoproteins to non atherogenic lipoproteins) to be the best predictor of CHD (odds ratio of 3.25 for top verses lowest quintile) as compared to the other eight other risk factors (table 5) [15].
Table 5 Nine risk factors account for up to 90 % of MIS worldwide in both sexes, all ages, and in all regions
RISK FACTOR ODDS RATIO
Abnormal lipids: ApoB/ApoA-1 3.25
Smoking 2.87
Diabetes 2.37
Hypertension 1.91
Abdominal obesity
Reason for such a high OR: This could aggravate smoking, diabetes, hypertension, obesity, alcohol abuse and even nutrition (eating aggressively) 1.12
Psychosocial Factors 2.67
Alcohol use 0.91
Physical Activity 0.86
Consumption of fruits and vegetables 0.70
L-Aavw = ApoB/ApoA-1 ratio of the INTERHART study
L-in, would be comparable to the faucet (GI tract and Liver) delivering the atherogenic apoB lipoproteins. While the kitchen sink would represent the accumulation of atherogenic lipoproteins within the arterial vessel wall or L-Aavw.
In a like manner, the DRAIN would represent L-out or HDL-C or apoA-1 lipoproteins. From this analogy it can easily be seen that if there is inadequate HDL-C or apoA-1 the atherogenic kitchen sink will overflow and result in acute coronary syndromes as happened in our case report (figure 2).
Figure 2 The Atherosclerotic Kitchen Sink. This image portrays the importance of the HDL-C drain in maintaining a certain level of atherogenic lipoproteins within the arterial vessel wall to prevent accumulation and the undesirable possibility of an acute event with overflow or acute coronary syndromes. This simple analogy of homeostasis points to an important concept: That being the frequent need for combination therapy in order to control the various components of the atherogenic lipoprofile. Isolated low HDL-C is certainly a red flag regarding the development of atherosclerosis and CHD and additionally the elevation of low HDL-C levels may have a DRANO-LIKE effect to open a clogged drain in an atherosclerotic arterial vessel wall.
Conclusion
While the treatment of isolated HDL-C may seem overwhelming at times, it will be rewarding for both the clinician and the patient as demonstrated by the our case study. This patient has done well for seven years and it is anticipated he will continue to do well with his laboratory values now in a sustained, normal physiologic range.
Additional tests by nuclear magnetic resonance spectroscopy (NMR LipoProfile) would assist us in knowing the LDL particle number (LDL-P) and would assist us in even more aggressive therapy. In addition to his current goals he has met, he should have an LDL-P under 1000 micromol/L and small LDL-P under 700 micromol/L.
Even though we have discussed LDL-C from a quantity perspective, due to an isolated low HDL-C, we should additionally be aware that there exists and equally important role for the quality of HDL-C [12]. Recently, the Apo A-1Milano and Apo A-1Paris have resulted in a marked increase in research interest for the HDL-C lipoprotein particle and its future manipulation [16]. In the near future we may be utilizing gene transfer utilizing variations of the Milano and Paris forms, as well as the newer apoA-1 mimetics such as L-4F [17]. Recently there has been increased interest in CETP inhibitors and Phase II studies are underway with torcetrapib and the combination of torcetrapib and atorvastatin [18]. Additional attention to the PPAR agonists and atherosclerosis and the liver X receptor alpha (LXR alpha) agonists is being employed at the present and the positive dual effects on HDL-C and atherosclerosis is being actively investigated. This dual agonism of PPAR alpha, gamma, and possible delta, as well as the dual effects of PPAR alpha and LXR alpha are quite exciting and we will learn a great deal regarding their effects on atherosclerosis and HDL-C in the near future [19].
Recently John Snow, M.D. (1813–1858), a legendary figure in the field of epidemiology, of London, England was honored [20]. He hypothesized that Cholera was transmitted by water rather than miasma (bad air). He suspected the water from the Broad Street pump was the source of the disease and subsequently had the pump handle removed in 1854 (150 years ago) [21].
Could low HDL-C be the "pump handle" of atherosclerosis and CHD?
List of abbreviations
ABCA-1: ATP binding cassette transporter A-1
BMI: body mass index
CHD: coronary heart disease
CAD: coronary artery disease
hs-CRP: highly sensitive C reactive protein
T2DM: type 2 diabetes mellitus
metS: metabolic syndrome
HDL-C: high density lipoprotein cholesterol
LDL-C: low density lipoprotein cholesterol
LFTs: liver function tests
PCTA: percutaneous transluminal coronary angioplasty
VLDL-C: very low density lipoprotein cholesterol
TC: total cholesterol
TLC: therapeutic lifestyle changes
TPA: tissue plasminogen activator
Competing interests
The author(s) declare that they have no competing interests.
Author contribution
MRH conceived the idea to write this manuscript. MRH and SCT wrote, and edited this manuscript together.
Acknowledgements
The patient and family described in this case report have given permission to publish this case report. The patient is the submitting author: Melvin R Hayden, M.D.
The authors would like to acknowledge the kind, gentle and considerate interventional cardiologists of the University of Missouri – Columbia, Missouri: Drs. HK Reddy, M.D. and DJ Voelker, M.D. currently of Popular Bluff Heart Center: Popular Bluff, Missouri.
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Miller NE Thelle DS Forde OH Mjos OD The Tromso heart-study. High-density lipoprotein and coronary heart-disease: a prospective case-control study Lancet 1977 1 965 968 67464 10.1016/S0140-6736(77)92274-7
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Probst MC Thumann H Aslanidis C Langmann T Buechler C Patsch W Baralle FE Dallinga-Thie GM Geisel J Keller C Menys VC Schmitz G Screening for functional sequence variations and mutations in ABCA1 Atherosclerosis 2004 175 269 279 15262183 10.1016/j.atherosclerosis.2004.02.019
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| 15631632 | PMC544835 | CC BY | 2021-01-04 16:38:10 | no | Cardiovasc Diabetol. 2005 Jan 4; 4:1 | utf-8 | Cardiovasc Diabetol | 2,005 | 10.1186/1475-2840-4-1 | oa_comm |
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Ann Clin Microbiol AntimicrobAnnals of Clinical Microbiology and Antimicrobials1476-0711BioMed Central London 1476-0711-3-271561056310.1186/1476-0711-3-27ResearchIn vitro activity of antiamoebic drugs against clinical isolates of Entamoeba histolytica and Entamoeba dispar Bansal Devendra [email protected] Rakesh [email protected] Yogesh [email protected] Ramesh Chander [email protected] Nancy [email protected] Department of Parasitology, Post Graduate Institute of Medical Education & Research, Chandigarh, India2 Department of Hepatology, Post Graduate Institute of Medical Education & Research, Chandigarh, India2004 21 12 2004 3 27 27 22 9 2004 21 12 2004 Copyright © 2004 Bansal et al; licensee BioMed Central Ltd.2004Bansal 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
Amoebiasis is a major public health problem in tropical and subtropical countries. Although a number of antiamoebic agents are used for its treatment, yet the susceptibility data on clinical isolates of Entamoeba histolytica and Entamoeba dispar are not available. Therefore, the present study was aimed to assess the in vitro susceptibility of clinical isolates of E. histolytica and E. dispar to metronidazole, chloroquine, emetine and tinidazole.
Methods
A total of 45 clinical isolates (15 E. histolytica and 30 E. dispar) were maintained in polyxenic cultures followed by monoxenic cultures. In vitro drug sensitivity (IC50) of clinical isolates and standard reference strain of E. histolytica (HM1: IMSS) was assessed by nitro blue tetrazolium (NBT) reduction assay after exposure to various concentrations of each drug.
Results
The results showed that all clinical isolates had a higher IC50 compared to reference strain to all the four drugs. E. histolytica isolates appeared to be more susceptible [IC50 (μm) 13.2,26.3,31.2 and 12.4] compared to E. dispar isolates [IC50(μm) 15.6,28.9,32.8 and 13.2] and the reference strain of E. histolytica [IC50 (μm) 9.5, 15.5, 29.9 and 10.2] to the metronidazole, chloroquine, emetine and tinidazole respectively.
Conclusions
The results indicate that till date, Entamoeba isolates in India do not seem to be resistant to the commonly used antiamoebic drugs.
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Background
Entamoeba histolytica, is the etiological agent of amoebic dysentery and amoebic liver abscess (ALA). Worldwide, 40–50 million symptomatic cases of amoebiasis occur annually and 70,000 to 100,000 deaths due to this infection [1]. There are two distinct, but morphologically identical species of Entamoeba: Entamoeba histolytica, which is pathogenic and Entamoeba dispar, which is non-pathogenic. E. histolytica, has the capacity to invade intestinal mucosa resulting in intestinal amoebiasis and cause extra intestinal amoebiasis [amoebic liver abscess (ALA)] [2].
Infection is primarily treated by instituting antiamoebic therapy. Drugs of choice for invasive amoebiasis are tissue active agents, like metronidazole, tinidazole and chloroquine or the more toxic emetine derivatives, including dehydroemetine. Metronidazole and tinidazole are derived from 5-nitroimdazole which kill the trophozoites by alterations in the protoplasmic organelles of the amoeba, but are ineffective in the treatment of cyst passers. Chloroquine is derived from 4-aminoquinolines, which acts on the vegetative forms of the parasite and kills it by inhibiting DNA synthesis. Emetine, a plant alkaloid, kills the trophozoites of E. histolytica mainly by inhibiting protein synthesis.
Indiscriminate use of drugs has led to an increase in the minimum inhibitory concentration (MIC) of these therapeutic agents [3]. Although, drug resistance to E. histolytica does not appear to be a serious problem, there are occasional reports of failure with metronidazole suggesting that this could probably be heralding the development of drug resistance clinically [4]. Recurrence of ALA even after treatment with metronidazole has been reported and parasites may survive in spite of adequate treatment [5]. However, differences in drug sensitivity between strains of E. histolytica have been reported, indicating that there may be a small percentage of amoebae which are either resistant to the drug or may even eventually become resistant due to abuse of antiamoebic agents [6]. Although, earlier studies have been focused on in vitro sensitivity of the only axenic strains of E. histolytica [7-9], yet to the best of our knowledge, studies on in vitro drug susceptibility studies on clinical isolates of E. histolytica and E. dispar have not been reported. Therefore, in the present study an attempt has been made to assess the in vitro activity of antiamoebic drugs (emetine, chloroquine, metronidazole and tinidazole) against clinical isolates of E. histolytica and E. dispar.
Methods
Clinical isolates
Forty-five isolates from patients attending the Out Patient Departments of Nehru hospital, attached to the Post Graduate Institute of Medical Education & Research, Chandigarh, India, identified earlier [10] as either E. histolytica (15) or E. dispar (30) by hexokinase isoenzyme analysis and by Techlab ELISA were used in the present study. These have been cultured in modified Boeck and Drbohlav (NIH) medium [11] followed by Robinson's medium [12].
Standard reference strain (HM1: IMSS)
Reference strain of E. histolytica (HM1: IMSS) maintained axenically in TYI-S-33 medium was included as control [13].
Preparation of antimicrobial agents
The drugs (metronidazole, chloroquine, emetine dihydrochloride and tinidazole) used in the study were procured as pure salt from Sigma-Aldrich Co., St. Louis, MO., 63178 USA. The stock solutions of drugs (each 0.1 M) were prepared in dimethyl sulphoxide (DMSO) [14] and stored at -20°C till use. The stock solutions were diluted in medium to the required concentration. A starting concentration used was 200 μM, which yielded a maximum concentration in the assay of 17.1 μg/ml metronidazole, 51.59 μg/ml chloroquine, 55.3 μg/ml emetine, and 24.7 μg/ml tinidazole.
In vitro drug sensitivity assay
Drug sensitivity to all the compounds was carried out by nitroblue tetrazolium (NBT) reduction method [15]. Each clinical isolate was tested in duplicate along with the reference E. histolytica strain (HM1: IMSS). Amoebae were harvested from 24 hour old cultures and suspended in medium. The parasite count was adjusted to 3 × 105 parasites/ml in medium by haemocytometer [15].
The assay was carried out in microtiter plates (Grenier bio-one, Germany). Briefly, in row A 200 μl of drug and in all other rows (B-H) medium was added and doubling dilutions of the drug were performed down the plate. Final drug concentration in rows A-H was as follows: 100, 50, 25, 12.5, 6.25, 3.12, 1.6 and 0.8 (μM). Further 100 μl of parasite suspension (3 × 105/well) was added to all the rows (A-H). Each test included control (without drug) and blank wells (medium only). The plates were incubated at 37°C for 4 hrs. The contents of the plates were discarded and washed with pre warmed Hank's balanced salt solution (HBSS pH 7.2). Thereafter, 100 μl of NBT/well in HBSS was added and the plates were incubated at 37°C for 45 min. followed by aspiration of the contents. Plates were then washed with HBSS twice and 200 μl/well of DMSO (100% v/v) was added. Following incubation at 37°C for 10 min, the optical density (OD) was measured in an ELISA recorder at 540 nm.
The percentage of non-viable organisms, which failed to metabolize NBT and therefore did not produce the dark blue formazan product, was determined by applying the following formula:
Percentage of non-viable organisms at each drug conc. =
Statistical analysis
The mean IC50 values of all clinical isolates against the four drugs were compared with corresponding IC50 values of the reference E. histolytica strain (HM1: IMSS). Standard deviation (SD) was used to indicate the extent of variation around group mean values. The p value was calculated using the student's-t test.
Results
The IC50 values of emetine, chloroquine, metronidazole and tinidazole for the 45 clinical isolates [15 E. histolytica and 30 E. dispar] and the reference strain HM1: IMSS were determined by the NBT reduction assay. The mean IC50 values were significantly higher (P < 0.001) in E. dispar isolates to all the four antiamoebic drugs as compared to the E. histolytica isolates and the reference E. histolytica strain (Table 1 & Figures 1–4).
Figure 1 Percentage inhibition of E. histolytica (HM1: IMSS) and clinical isolates of E. histolytica and E. dispar by metronidazole
Figure 2 Percentage inhibition of E. histolytica (HM1: IMSS) and clinical isolates of E. histolytica and E. dispar by chloroquine
Figure 3 Percentage inhibition of E. histolytica (HM1: IMSS) and clinical isolates of E. histolytica and E. dispar by emetine
Figure 4 Percentage inhibition of E. histolytica (HM1: IMSS) and clinical isolates of E. histolytica and E. dispar by tinidazole
Discussion
Treatment failure among amoebiasis patients often raises the possibility of drug resistance [16]. In the present study the 15 E. histolytica and 30 E. dispar clinical isolates maintained by in vitro cultivation in monoxenic medium were subjected to drug susceptibility tests against four antiamoebic drugs: metronidazole, chloroquine, emetine and tinidazole by NBT reduction assay. E. histolytica reference strain (HM1: IMSS) was also included in each set of experiments.
Results showed a significant difference in drug sensitivity in clinical isolates as compared to the reference strain with all the four drugs. The mean IC50 values (μm) of the E. histolytica/E. dispar isolates against metronidazole, chloroquine, emetine and tinidazole were 13.2/15.6, 26.3/28.9, 31.2/32.8 and 12.4/13.2 respectively. The IC50 values (μm) of the reference strain against all the four respective drugs were 9.5, 15.5, 29.9 and 10.2. Recently Upcroft & Upcroft [14] have reported that the MIC values of metronidazole ranges from 12.5–25 μm for laboratory-passaged E. histolytica strains. Adagu, et.al. [9] have shown the mean metronidazole IC50 value as 18.47 μm for the most susceptible isolates of E. histolytica with a > 30 μm value as the cut off for resistance. Burchard & Mirelman, studied in vitro sensitivity to metronidazole and emetine of non-pathogenic zymodemes and showed that all were similarly sensitive to both the drugs (1–10 μg/ml) [6]. In the present study, clinical isolates maintained in monoxenic culture were used to detect the in vitro sensitivity as earlier it has been concluded that bacterial flora associated with the amebae did not significantly interfere with the test performance and sensitivity values [6].
Although resistance to metronidazole has been reported against Trichomonas vaginalis [17], Giardia lamblia [18] and Leishmania donovani [19], yet to the best of our knowledge there is no documented resistance among clinical isolates of E. histolytica and E. dispar.
Conclusion
The results of the present study are in agreement with previous findings [6,9,14], except that there was a significantly higher IC50 value of all four drugs to the clinical isolates as compared to the reference strain. E. dispar isolates showed higher IC50 values when compared to E. histolytica or reference strain. This is the first report of in vitro drug sensitivity pattern to clinical isolates of E. histolytica and E. dispar. There is definitely a need to monitor the random drug susceptibility among clinical isolates especially in context to widespread use of metronidazole and tinidazole, which are available over the counter in many countries. Increased awareness and continued surveillance for the possible emergence of resistance among clinical isolates is necessary for the ultimate prevention and control of amoebiasis.
Authors' contributions
DB, carried the practical work mentioned in the manuscript.
RS, was responsible for formulation of the project and provided guidance time to time.
YC, he is clinician and carried clinical examination and proposed clinical diagnosis of the patient.
RCM, he guided the proposed work related to differentiation of Entamoeba histolytica and Entamoeba dispar and in vitro drug sensitivity.
NM, proposed the concept for this manuscript and guided the practical work and writing of the manuscript.
Table 1 Comparison between 1C50 value of clinical isolates (E. histolytica and E. dispar) vs reference strain (HM1: IMSS)
COMPOUND MEAN IC50 OF CLINICAL ISOLATES (μm ± SD) IC50 VALUE OF REFERENCE STRAIN (μm ± SD)
E. histolytica E. dispar
Metronidazole 6.5 ± 0.81***a 15.6 ± 2.12***c 9.5 ± 1.53***b
Chloroquine 18.9 ± 1.39***a 28.9 ± 2.45***c 21.5 ± 1.26***b
Emetine 26.8 ± 1.27***a 32.8 ± 1.68***c 28.0 ± 2.62***b
Tinidazole 8.2 ± 1.09***a 13.2 ± 1.43***c 10.2 ± 0.43***b
Results expressed as mean ± SD from two experiments conducted in duplicate
Student's t-test [*** P < 0.001]
a = Eh Vs Ed Eh – E. histolytica
b = Eh Vs C Ed – E. dispar
c = Ed Vs C C – Reference strain (HM1: IMSS)
Acknowledgements
Financial assistance from the Indian Council of Medical Research (ICMR), New Delhi, is gratefully acknowledged.
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Int Semin Surg OncolInternational seminars in surgical oncology : ISSO1477-7800BioMed Central London 1477-7800-2-11563162910.1186/1477-7800-2-1Case ReportSimultaneous two organ metastases of the giant basal cell carcinoma of the skin Copcu Eray [email protected] Alper [email protected] Plastic and Reconstructive Surgery Department, Medical Faculty, Adnan Menderes University, Aydin, Turkey2 Plastic and Reconstructive Surgery Department, Samsun State Hospital, Samsun, Turkey2005 4 1 2005 2 1 1 9 10 2004 4 1 2005 Copyright © 2005 Copcu and Aktas; licensee BioMed Central Ltd.2005Copcu and Aktas; 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
Basal Cell Carcinoma (BCC) is the most common carcinoma in humans. It accounts for 20% of carcinomas in men and 10–15% of carcinomas in women. Despite its high incidence, metastatic events are exceedingly rare. The reported frequency of metastatic dissemination is estimated at 0.0028–0.5 percent. Once metastasis is detected, there is a high mortality rate of 50% within 8 months.
Methods
In this study, we present a case of simultaneous lung and parotid metastases of giant BCC primary located on the right medial canthus of a 62 year old female.
Results
Examination of the tumor located on the medial canthus obtained showed "adenoid BCC". Computed tomography (CT) was performed to evaluate parotid region for evaluation of parotid gland and chest. Parotid and lung metastasis were detected in CT. Routine labarotory tests and radiological investigations were done. There was no abnormal finding. We also investigated this patient with a bone scan (normal), abdominal and cranial CT scans (also normal).
Conclusion
Although metastasis of BCC is a very rare condition, this study reports a case of simultaneous parotid gland and lung metastasis originating from a giant BCC primary that was located on the right inner canthus of a 62 year old female.
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Background
Basal cell carcinoma (BCC) is the most common carcinoma in humans and accounts for 20% of carcinomas in men and 10–15% of carcinomas in women. Approximately 75–86% of primary BCCs are found on the head or neck. The most common location on the head is the nose, specifically the nasal tip and alae. It constitutes 90% of periorbital malignancies [1,2]. Sun exposure is the primary etiologic agent for the development of BCC. The tumors are more frequent in individuals with fair complexions.
BCC arising on the medial canthus tends to be deep and invasive and may result in perineural extension and loss of optic nerve function. Pieh et al reported that the highest recurrence rates of BCC following attempted excision, (approximately 60%), was seen with lesions arising from the medial canthus since these lesions tend to be more invasive and difficult to manage [3]. Reclusive patients or patients who neglect the lesions for long periods of time are more likely to have giant, invasive tumors [4]. Giant BCC, defined as lesions more than 5 cm at its largest diameter, are rare forms of BCC [4]. Giant BCCs more commonly appear on the trunk and display a more aggressive behavior, resulting in local invasion and metastasis. The reported incidence of metastatic BCC ranges from 0.03 % to 0.55 [5]. We report a case of simultaneous lung and parotid gland metastases of giant BCC located on medial canthus.
Case report
A 62-year-old woman was referred to the Plastic and Reconstructive Surgery Department for treatment of a bleeding exophytic tumor located on the right inner canthus. She had had the lesion for approximately 11 years. Initially, the patient was treated with excision and primary closure ten years ago. At this time the tumor had a diameter of 5 cm. The tumor was diagnosed as adenoid BCC microscopically and surgical margins were tumor-positive. The patient was operated on two years later when the diameter of the recurrent tumor was 15 mm. Histological examination of this second specimen revealed an "adenoid BCC" with clear surgical margins.
Although the tumor recurred again after the second excision, the patient neglected medical advice and did not undertake any treatment (Figure 1). More recently, however, the tumor began growing rapidly and became hemorrhagic. On examination the lesion was located on the right inner canthus and involved 1/3 of the eyelid. The size of tumor was approximately 55 mm × 45 mm. Visual functions of the patient were normal. However, a fixed mass developed in the patient's periauricular area six months ago (Figure 2), although there were no palpable cervical nodes. We therefore investigated this region with computed tomography (CT), which revealed a tumor involving the right orbital structures extending to the ethmoidal cells. The tumor also involved the right parotid gland and multiple cervical lymph nodes.
Figure 1 Giant BCC located on the inner canthus
Figure 2 Involvement of the parotid gland of the patient
We also investigated this patient with a bone scan (normal), abdominal and cranial CT scans (also normal) and a thoracic CT. Multiple metastatic lesions were seen in the chest CT (Figure 3).
Figure 3 CT scan of the chest of the patient. Multiple metastatic lesions were seen.
Examinations of the cardiovascular, gastrointestinal, neurological, urogenital and hematological systems and other parts of the skin were performed by physical and routine laboratory and radiological techniques. There were no abnormal findings. Biopsy was performed from the tumor located on the inner canthus and revealed "adenoid basal cell carcinoma" (Figure 4). Also, Fine needle biopsies were performed on the parotid and pulmonary lesions which confirmed the presence of adenoid BCC in these regions.
Figure 4 Histological view of the specimen showed "adenoid BCC". (Hematoxylene eosin staining, × 50 magnification)
The patient did not accept the offer of surgical treatment for the tumor on the inner canthus. She was referred to the Oncology Department and treated with radiotherapy and chemotherapy. The patient received approximately 6000 cGy of external beam radiation over 3 weeks totally. Also, chemotherapy was initiated with cisplatin and 5-fluoruracil. She was followed up with physical examination and CT scans for six months and there were no metastases to other organs. She is still being followed.
Discussion
Spates et al. noted that metastatic BCC was first reported in 1894 [6]. As outlined by Lattes and Kessler, metastatic basal cell carcinoma is defined by the following criteria:
1) the primary tumor must occur in skin containing hair follicles and not the mucous membranes;
2) metastasis cannot be by simple extension, but occurring at a site distant from the primary tumor;
3) the primary tumor and the metastasis must have similar histologic appearances of basal cell carcinoma; and
4) squamous cell features must not be present in the lesions [7,8]. The case presented here satisfied these three criteria. More than 300 cases of metastatic BCC have been reported in the literature. Two-thirds of metastatic BCCs arise from primary tumors on the face, with the ear being the most common location. Higher rates of metastasis also occur from primary lesions on the scalp and genitalia [9-11]. Primary BCC metastasizes through hematogenous and lymphatic routes. As was the case with our patient, metastasis to the lymph nodes has been estimated to occur in 70% of cases. The most common organs involved in hematogenous spread are lungs, bone, and skin [12,13].
While the usual BCC that gives rise to metastases is a large, ulcerated, locally invasive BCC of the head and neck that recurs despite repeated surgical procedures or radiotherapy, these features are not absolute prerequisites for metastasis [14]. Immunosupression may be a factor in the pathogenesis of the metastasis of BCC, but there was no finding suggestive of immune system abnormality in the case presented here [14]. Some authors have suggested that immunosuppression or impaired cell-mediated immunity (including AIDS) may predispose to BCC and BCC metastasis [15-17]. BCC usually has multiple skin recurrences before metastases become evident as in the case presented here [18]. BCCs with any of the following: long duration, location in the mid face or ear, a diameter larger than 2 cm, with aggressive histological subtype, previous treatment, neglected, or a history of radiation exposure, should be considered "high risk [19]. Although giant BCC is commonly located on the trunk, Takemato et al reported a case of with giant basal cell carcinoma which invaded the orbital tissue and anterior skull base [4]. They concluded that giant basal cell carcinomas have aggressive character to destroy tissue and more metastatic potential. Many investigators have reported that radical treatment with a wide excision of the tumor at an early stage is essential to treat a potentially aggressive BCC. Takemato et al used a free rectus abdominis musculocutaneous flap in the treatment of giant BCC which invaded the orbital tissue [4].
Tumors greater than 3 cm in diameter have a 2 % incidence of metastasis and/or death. This increases to 25% in those lesions more than 5 cm in diameter and to 50% in lesions more than 10 cm in diameter [20]. The prognosis of metastatic BCC is extremely poor. Once metastasis is detected, there is a high mortality rate of 50% within 8 months [20]. This poor outcome has led to the use of systemic chemotherapy in a number of individual cases. Several chemotherapeutic agents that have been used in metastatic BCC, including fluorouracil and combination of vincristine, bleomycine and prednisone [18].Kaufman suggested that cisplatin, alone or in combination is probably the most active in metastatic BCC [21]. Recently, Jefford et al presented their experience in the treatment of metastatic BCC [22]. According to their study, systemic chemotherapy with cisplatin and paclitaxel provided palliative benefit to their patient with acceptable toxicity and conclude that the regimen is a reasonable choice for the rare patient presenting with metastatic BCC. About half of metastatic BCCs have metastasis to lymph nodes as the first site, but hematogenous route to lung and bone is also equally represented [23]. Robinson and Dahiya reported a case of BCC with pulmonary and lymph node metastasis causing death [14]. BCC located on the nose, eyebrow, ear, nose, and temple frequently metastasizes to the parotid and lymph nodes of neck [24]. To the best of our knowledge this report is the first to present simultaneous lung and parotid gland metastases of giant BCC.
Competing interests
The author(s) declare that they have no competing interests.
Authors' contributions
EC, conceived the study and coordinated the write-up and submission. AA participated in the writing of the manuscript. All authors read and approved the final manuscript.
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| 15631629 | PMC544837 | CC BY | 2021-01-04 16:38:37 | no | Int Semin Surg Oncol. 2005 Jan 4; 2:1 | utf-8 | Int Semin Surg Oncol | 2,005 | 10.1186/1477-7800-2-1 | oa_comm |
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Microb Cell FactMicrobial Cell Factories1475-2859BioMed Central London 1475-2859-4-11562906410.1186/1475-2859-4-1ReviewSoluble expression of recombinant proteins in the cytoplasm of Escherichia coli Sørensen Hans Peter [email protected] Kim Kusk [email protected] Danish Technological Institute, Holbergsvej 10, 6000 Kolding, Denmark2 Laboratory of BioDesign, Department of Molecular Biology, Aarhus University, Gustav Wieds Vej 10C, 8000 Aarhus C, Denmark2005 4 1 2005 4 1 1 12 11 2004 4 1 2005 Copyright © 2005 Sørensen and Mortensen; 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.
Pure, soluble and functional proteins are of high demand in modern biotechnology. Natural protein sources rarely meet the requirements for quantity, ease of isolation or price and hence recombinant technology is often the method of choice. Recombinant cell factories are constantly employed for the production of protein preparations bound for downstream purification and processing. Eschericia coli is a frequently used host, since it facilitates protein expression by its relative simplicity, its inexpensive and fast high density cultivation, the well known genetics and the large number of compatible molecular tools available. In spite of all these qualities, expression of recombinant proteins with E. coli as the host often results in insoluble and/or nonfunctional proteins. Here we review new approaches to overcome these obstacles by strategies that focus on either controlled expression of target protein in an unmodified form or by applying modifications using expressivity and solubility tags.
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Introduction
Microorganisms like the enterobacterium Escherichia coli are outstanding factories for recombinant expression of proteins. An expression system for the production of recombinant proteins in E. coli usually involves a combination of a plasmid and a strain of E. coli [1]. The main purpose of recombinant protein expression is often to obtain a high degree of accumulation of soluble product in the bacterial cell. This strategy is not always accepted by the metabolic system of the host and in some situations a cellular stress response is encountered. Another response encountered in recombinant systems is the accumulation of target proteins into insoluble aggregates known as inclusion bodies. These aggregated proteins are in general misfolded and thus biologically inactive [2].
Under normal cellular conditions a subset of cytoplasmic proteins are able to fold spontaneously [3] while aggregation prone proteins require the existence of a number of molecular chaperones that interact reversibly with nascent polypeptide chains to prevent aggregation during the folding process [4]. Aggregation of recombinant proteins overexpressed in bacterial cells could therefore result either from accumulation of high concentrations of folding intermediates or from inefficient processing by molecular chaperones. No universal approach has been established for the efficient folding of aggregation prone recombinant proteins [1].
The literature describes a number of methods for the redirection of proteins from inclusion bodies into the soluble cytoplasmic fraction (Figure 1). Overall they can be divided into procedures where protein is refolded from inclusion bodies [5] and procedures where the expression strategy is modified to obtain soluble expression. In this review we focus on methods developed for soluble expression in the E. coli cytoplasm. Refolding from inclusion bodies is in many cases considered undesireable, but is however sometimes the method of choice. The major obstacles are the poor recovery yields, the requirement for optimization of refolding conditions for each target protein and the possibility that the resolubilization procedures could affect the integrity of refolded proteins. In addition, the purification of highly expressed soluble protein is less expensive and time consuming than refolding and purification from inclusion bodies. Maximizing the production of recombinant proteins in a soluble form is therefore an attractive alternative to in vitro refolding procedures. The methods used to mediate soluble expression can be divided into procedures where target modification is avoided and procedures where the target sequence is engineered (Figure 1).
Strategies where target modification is avoided
Some proteins directly influence the cellular metabolism of the host by their catalytic properties, but in general expression of recombinant proteins induces a "metabolic burden". The metabolic burden is defined as the amount of resources (raw material and energy), which are withdrawn from the host metabolism for maintenance and expression of the foreign DNA [6]. The formation of inclusion bodies occurs as a response to the accumulation of denatured protein. The metabolic burden and inclusion body formation are not directly linked but are both among the main factors to determine the ability of cells to produce soluble recombinant protein. Since the accumulation of denatured protein and the metabolic burden can be controlled by a number of environmental factors, we are partially able to control the formation of soluble protein in vivo.
Protein expression at reduced temperatures
A well known technique to limit the in vivo aggregation of recombinant proteins consists of cultivation at reduced temperatures [7]. This strategy has proven effective in improving the solubility of a number of difficult proteins including human interferon α-2, subtilisin E, ricin A chain, bacterial luciferase, Fab fragments, β-lactamase, rice lipoxygenase L-2, soybean lypoxygenase L-1, kanamycin nuclotidyltransferase and rabbit muscle glycogen phosphorylase (see [8] and references cited therein).
The aggregation reaction is in general favored at higher temperatures due to the strong temperature dependence of hydrophobic interactions that determine the aggregation reaction [9]. A direct consequence of temperature reduction is the partial elimination of heat shock proteases that are induced under overexpression conditions [10]. Furthermore, the activity and expression of a number of E. coli chaperones are increased at temperatures around 30°C [11,12]. The increased stability and potential for correct folding at low temperatures are partially explained by these factors.
However, a sudden decrease in cultivation temperature inhibits replication, transcription and translation [13]. Traditional promoters used in vectors for recombinant protein expression are also strongly affected in terms of efficiency [14]. A similar transcriptional effect is achieved when a moderately strong or weak promoter is used or when a strong promoter is partially induced. Low induction levels have been found to result in higher amounts of soluble protein [15]. This is a result of the reduction in cellular protein concentration which favors folding. However, bacterial growth is decreased, thus resulting in a decreased amount of biomass.
Different strategies aimed at optimizing the expression of recombinant proteins at low temperature are as follows.
A system based on the cspA promoter was developed for the expression of proteins at low temperature [16]. The cspA promoter is highly induced at low temperature and is well repressed at and above 37°C. A sequence encoding the TolAI-β-lactamase fusion protein which is toxic to E. coli and rapidly degraded at 37°C was placed under the control of the cspA promoter. Temperature downshift to 15 or 23°C abolished degradation of the fusion protein and the toxic phenotype associated with expression at 37°C was suppressed. It was suggested that this system is a valuable tool for the production of proteins containing membrane-spanning domains or otherwise unstable gene products in E. coli.
A principle that allows for protein expression and folding at 4°C was presented recently [17]. This principle is based on co-expression of the target protein with chaperones from a psychrophilic bacterium. The two chaperones (Cpn60 and Cpn10 from Oleispira antarctica RB8T) allow E. coli to grow at high rates at 4°C [12]. An esterase from O. antarctica RB8T was co-expressed with Cpn60 and Cpn10 in E. coli at 4°C. This procedure increased the specific activity of the purified esterase 180 fold as compared to enzyme prepared from cultivations at 37°C. It was concluded that the low temperature was beneficial to folding and the system was suggested as a tool for expression and correct folding of recombinant proteins in the cytoplasm of E. coli.
E. coli strains used to improve soluble expression
Numerous specialized host strains have been developed to overcome the metabolic burden related to high level protein expression.
Two E. coli mutant strains have contributed significantly to the soluble expression of difficult recombinant proteins. C41(DE3) and C43(DE3) are mutants that allow over-expression of some globular and membrane proteins unable to be expressed at high-levels in the parent strain BL21(DE3) [18]. Expression of the F1Fo ATP synthase subunit b membrane protein in these strains, in particular C43(DE3), is accompanied by the proliferation of intracellular membranes and inclusion bodies are absent [19]. These strains are now commercialized by Avidis and a high number of reports on their use in expression of difficult proteins have been published [20-23]. A recent work reports that the stability of plasmids encoding toxic proteins is increased in C41(DE3) and especially in C43(DE3) [24].
Cysteines in the E. coli cytoplasm are actively kept reduced by pathways involving thioredoxin reductase and glutaredoxin. The disulfide bond dependent folding of heterologous proteins is improved in the Origami strains from Novagen. Disruption of the trxB and gor genes encoding the two reductases, allow the formation of disulfide bonds in the E. coli cytoplasm. The trxB (Novagen AD494) and trxB/gor (Novagen Origami) negative strains of E. coli have been selected in several expression situations [25-27]. Folding and disulfide bond formation in the target protein, is enhanced by fusion to thioredoxin in strains lacking thioredoxin reductase (trxB) [28]. Overexpression of the periplasmic foldase DsbC in the cytoplasm stimulates disulfide bond formation further [27].
Modification of cultivation strategies to obtain soluble protein
The simplest way to produce a recombinant protein is by batch cultivation. Here all nutrients required for growth are supplied from the beginning and there is a limited control of the growth during the process. This limitation often leads to changes in the growth medium such as changes in pH and concentration of dissolved oxygen as well as substrate depletion. Furthermore inhibitory products of various metabolic pathways accumulate. Cell densities and production levels are only moderate in batch cultivations.
In fed batch cultivations, the concentration of energy sources can be adjusted according to the rate of consumption. Several other factors can also be regulated in order to obtain the maximal production level in terms of target protein per biomass. The formation of inclusion bodies can be followed in fed batch cultivations by monitoring changes in intrinsic light scattering by flow cytometry [29]. This allows for real time optimization of growth conditions as soon as inclusion bodies are detected even at low levels and inclusion body formation can potentially be avoided [30].
Folding of some proteins require the existence of a specific cofactor. Addition of such cofactors or binding partners to the cultivation media may increase the yield of soluble protein dramatically. This was demonstrated for a recombinant mutant of hemoglobin for which the accumulation of soluble product was improved when heme was in excess [31]. Similarly, a 50% increase in solubility was observed for gloshedobin when E. coli recombinants were cultivated in the presence of 0.1 mM Mg2+ [32]. An important factor in soluble expression of recombinant proteins is media composition and optimization. Although this is attained mostly by trial and error, it nevertheless may be beneficial.
Molecular chaperones drive folding of recombinant proteins
A possible strategy for the prevention of inclusion body formation is the co-overexpression of molecular chaperones. This strategy is attractive but there is no guarantee that chaperones improve recombinant protein solubility. E. coli encode chaperones, some of which drive folding attempts, whereas others prevent protein aggregation [4,11,33]. As soon as newly synthesized proteins leave the exit tunnel of the E. coli ribosome they associate with the trigger factor chaperone [34]. Exposed hydrophobic patches on newly synthesized proteins are protected by association with trigger factor from unintended inter- or intramolecular interactions thus preventing premature folding. Proteins can start or continue their folding into the native state after release from trigger factor. Proteins trapped in non-native and aggregation prone conformations, are substrates for DnaK and GroEL. DnaK (Hsp70 chaperone family) prevents the formation of inclusion bodies by reducing aggregation and promoting proteolysis of misfolded proteins [11]. A bi-chaperone system involving DnaK and ClpB (Hsp100 chaperone family) mediates the solubilization or disaggregation of proteins [35]. GroEL (Hsp60 chaperone family) operates the protein transit between soluble and insoluble protein fractions and participates positively in disaggregation and inclusion body formation. Small heat shock proteins lbpA and lbpB protect heat denatured proteins from irreversible aggregation and have been found associated with inclusion bodies [36,37].
Simultaneous over-expression of chaperone encoding genes and recombinant target proteins proved effective in several instances. Co-overexpression of trigger factor in recombinants prevented the aggregation of mouse endostatin, human oxygen-regulated protein ORP150, human lysozyme and guinea pig liver transglutaminase [38,39]. Soluble expression was further stimulated by the co-overexpression of the GroEL-GroES and DnaK-DnaJ-GrpE chaperone systems along with trigger factor [39]. The chaperone systems are cooperative and the most favorable strategies involve co-expression of combinations of chaperones belonging to the GroEL, DnaK, ClpB and ribosome associated trigger factor families of chaperones [40-42].
Interaction partners and protein folding
Protein insolubility in the E. coli cytoplasm is partially related to the distribution of hydrophobic residues on the surface of the protein. The soluble expression of subunits of hetero multimeric proteins therefore sometimes suffers from inclusion body formation in the absence of an appropriate binding partner.
Soluble expression in E. coli of the bacteriophage T4 gene 23 product (major capsid protein) required the co-expression of gene product 31 (phage co-chaperonin gp31) [43]. Expression of the correct interaction partner enabled gp23 to fold correctly and form long regular structures in the cytoplasm of E. coli.
Another study reports the purification of a heterodimeric complex by expression of each subunit (pheromaxein A and C) as a fusion to thioredoxin [44]. Each subunit remained soluble in solution, when thioredoxin was proteolytically removed, only in the presence of the other.
Conclusively, interaction partners potentially favour in vivo solubility of target proteins. New systems for co-expression of multiple proteins involved in complex structures enable such strategies [1].
Strategies involving engineered target protein
Target proteins are not always expressed in a soluble form by the strategies described above. The last part of this review discusses how misfolded proteins can be engineered or pushed to evolve and selected to gain soluble expression.
Fusion protein technology
The use of affinity tags in recombinant protein purification has a long tradition. Not only have they been exploited for the development of generic purification strategies. Affinity tags have been observed to improve protein yield, to prevent proteolysis and to increase solubility in vivo [1,45].
Among the most potent solubility enhancing proteins characterized to date are the E. coli maltose binding protein (MBP) and the E. coli N-utilizing substance A (NusA). MBP (40 kDa) and NusA (54.8 kDa) act as solubility enhancing partners and are especially suited for the expression of proteins prone to form inclusion bodies. Although many proteins are highly soluble, they are not all effective as solubility enhancers. E. coli MBP proved to be a much more effective solubility partner than the highly soluble GST and thioredoxin proteins in a comparison of solubility enhancing properties [46]. Solubility enhancement is a common trait of maltodextrin-binding proteins (MBPs) from a number of organisms and some of them are even more effective than E. coli MBP [47]. A precise mechanism for the solubility enhancement of MBP has not been found. However, MBP might act as a chaperone by interactions through a solvent exposed "hot spot" on its surface which stabilizes the otherwise insoluble passenger protein [48,49]. The ability of MBP to promote the solubility of fusion partners can be improved by addition of supplemental tags. Different configurations for MBP fusion proteins have been suggested for high-throughput protein expression and purification [50].
Wilkinson and Harrison proposed a model for the theoretical calculation of solubility percentages of recombinant proteins expressed in the E. coli cytoplasm [51]. A webserver for the calculation of this index is found at . The Wilkinson-Harrison model along with experimental data identified NusA as a highly favorable solubility partner [52]. The major advantage of NusA, in addition to the good solubility characteristics, is its high expressivity. Both MBP and NusA have been used for the solubilization of highly insoluble ScFv antibodies in the cytoplasm of E. coli [48,53]. Numerous examples of MBP and NusA as functional solubility enhancers are found in the literature [54-57].
Natural molecular chaperones that have been used as solubility enhancers include prolyl cis trans isomerases (PPIases) [58], thioredoxin [59] and dsbA [60].
Fusion partners such as MBP and NusA are relatively large proteins. We recently suggested the use of a highly soluble N-terminal fragment of translation initiation factor IF2 (17.4 kDa) as a solubility partner [61]. The use of a small partner reduces the amount of energy required to obtain a certain number of molecules, diminishes steric hindrance and simplify downstream applications such as NMR. Another relatively small protein, barnase was suggested to exert chaperone like functions both in vivo and in vitro when fused to the C-terminus of the light chain variable domain of an IgG [62].
In a recent study it was shown that a 17 residue C-terminal extension of Pfg27 resulted in several fold enhancement of soluble expression [63]. Several studies have shown that the nature of terminal residues in proteins can play a role in recognition and subsequent action by proteases [64,65]. The terminal extension of proteins might therefore indirectly protect them from the denaturaturation/misfolding associated to partial proteolytic degradation. It has also been suggested that large net charges of peptide extensions increases electrostatic repulsion between nascent polypeptides and therefore enhances their correct folding [66].
Screening strategies have been employed to select for favorable fusion partners in a high throughput manner. In such a system more than 80% of the proteins tested showed high levels of expression of soluble products with at least one of eight fusion partners including NusA, intein, thioredoxin, His-tag, MBP, calmodulin binding protein and glutathione-S-transferase [67]. These results were supported by another similar study [68].
Screening for and selection of soluble variants
Structural and functional genomics and proteomics are important elements in the evaluation of gene function. The expression and purification of properly folded proteins in a high throughput manner are key elements in these studies. A number of different approaches to the high throughput screening of soluble expression products have been described recently.
The intrinsic folding yield, stability and solubility of target proteins can be improved by engineering the target protein. When structural information is available, the solubility of the expressed protein has been improved by rational site directed mutagenesis [69]. A more general approach is to find more soluble variants by directed evolution. Libraries generated in this context include random point mutants, deletions and fragments [70]. The generated mutants are screened for solubility either by the function of the protein of interest or by more general screens. A screen based on biological activity implies that a new assay has to be developed for every new protein studied. Moreover, in many cases the protein or protein domain studied does not display any known activity at all. The general screens include fusion reporter methods, stress reporter methods and direct methods and are therefore usually preferred for high-throughput approaches.
Fluorescence of E. coli cells expressing target genes fused to the GFP-gene is related to the solubility of the target gene expressed alone [71]. Hence, protein folding in E. coli can be improved by directed evolution approaches for a certain target protein by screening for fluorescing mutants. This approach evolved three insoluble proteins including Pyrobaculum aerophilum methyl transferase, tartrate dehydratase β-subunit and nucleoside diphosphate kinase to be 50%, 95% and 90% soluble respectively [72]. The GFP reporter system was further used to screen for solubilizing interaction partners to insoluble targets. Fusion of integration host factor β upstream to GFP resulted in aggregation, whereas co-expression of the binding partner (integration host factor α) increased fluorescence dramatically [73].
A similar approach is the use of selective pressure. By fusing target proteins with chloramphenicol acetyl transferase (CAT) more soluble fusion protein mutants were selected on media containing progressively higher levels of chloramphenicol [74]. Furthermore, selective pressure (fusion to kanamycin phosphotransferase) was used in a system aiming at the obtainment of soluble proteins encoded by cDNA fragments in a high throughput approach [75].
Another fusion reporter method use the β-galactosidase α peptide as fusion partner in a screen for lacZα complementation in a system where inactive lacZΩ is supplied in trans. Active β-galactosidase can be detected when the α peptide becomes soluble and restore enzyme activity by binding to lacZΩ [76].
An innate host cell response is induced when recombinantly expressed proteins are misfolded. This response can be monitored by the transcription from E. coli promoters that are up-regulated when misfolded proteins are expressed. It was found that the promoter for the small heat shock protein ibpA could be fused to lacZ and used as a reporter for misfolded protein [77]. This reporter could discriminate soluble, partially soluble and insoluble recombinant proteins. Genetic screens and directed evolution is further reviewed elsewhere [78].
Soluble fusion proteins are not necessarily biologically active and properly folded. Several reports have demonstrated that soluble preparations of fusion proteins have low biological activity as compared to the non-fused protein [79]. It was shown that a fusion of HPV oncoprotein E6 to MBP formed soluble multimeric aggregates composed of folded MBP and misfolded E6. These "soluble inclusion bodies" could be avoided by optimization of the expression conditions by screening for monodispersity [79].
Alternative strategies
A few strategies that are radically different from the conventional fusion partner and selection approaches have been developed for the potential rescuing of recombinant proteins from misfolding in the E. coli cytoplasm.
A system based on artificial oil bodies was developed and illustrated by a fusion protein composed of oleosin and GFP [80]. The expressed fusion protein was found in the insoluble cellular fraction but could be reconstituted as oil-bodies by addition of triacylglycerol and phospholipids to the purified inclusion bodies. GFP could subsequently be separated from the oil bodies using an engineered factor Xa cleavage site and centrifugation.
An in vivo rescuing system based on the E. coli ribosome was recently presented [81]. Target proteins are rescued from in vivo aggregation by fusing them to ribosomal protein L23. The fusion protein is expressed in a strain of E. coli deficient in the essential L23 ribosomal protein. This allows for the covalent coupling of target proteins to the highly soluble ribosomal particles. Ribosomes with coupled target protein can subsequently be isolated by centrifugation methods and the target protein released in a highly enriched form by site specific protease cleavage.
Conclusions
We have reviewed the most recent improvements in obtaining soluble and functional protein preparations from E. coli recombinants. A subset of the methods focus on relieving the cellular stress that is a response to the extreme metabolic situation experienced by the host cell during the process of hyperexpression of a single or a few proteins. A second subset of methods focus on improving the solubility and structural stability of the expressed protein, by the combination of the target protein with specific peptide tags. A common trait in modern expression strategies is the skillful combination of the utensils in the genetic toolbox, but also a constant reconsideration of the accepted paradigms in trade of protein expression.
Acknowledgements
K.K.M is funded by grants from the Danish Natural Science Research Council and Carlsberg (grants no. 21-03-0592, 21-04-0149 ANS-0987/40 and ANS-1649/40).
Figures and Tables
Figure 1 Downstream applications employed to obtain soluble proteins from recombinant E. coli. As a common trait the in vivo strategies aims at lowering the metabolic burden associated with recombinant expression. Some of the mentioned strategies have therefore merely indirect influence on folding such as the use of tRNA complementation plasmids and stabilization of mRNA (see text and ref [1] for details).
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Malar JMalaria Journal1475-2875BioMed Central London 1475-2875-3-501560147110.1186/1475-2875-3-50MethodologyOptimized expression of Plasmodium falciparum erythrocyte membrane protein 1 domains in Escherichia coli Flick Kirsten [email protected] Sanjay [email protected] Arnaud [email protected] Maria Teresa [email protected] Qijun [email protected] Microbiology and Tumor Biology Centre (MTC), Karolinska Institutet, Stockholm, Sweden2 Center for Infectious Medicine, Department of Medicine, Karolinska University Hospital, Stockholm, Sweden3 Swedish Institute for Infectious Disease Control, Box 280, 171 77, Stockholm, Sweden2004 15 12 2004 3 50 50 25 10 2004 15 12 2004 Copyright © 2004 Flick et al; licensee BioMed Central Ltd.2004Flick 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 expression of recombinant proteins in Escherichia coli is an important and frequently used tool within malaria research, however, this method remains problematic. High A/T versus C/G content and frequent lysine and arginine repeats in the Plasmodium falciparum genome are thought to be the main reason for early termination in the mRNA translation process. Therefore, the majority of P. falciparum derived recombinant proteins is expressed only as truncated forms or appears as insoluble inclusion bodies within the bacterial cells.
Methods
Several domains of PfEMP1 genes obtained from different P. falciparum strains were expressed in E. coli as GST-fusion proteins. Expression was carried out under various culture conditions with a main focus on the time point of induction in relation to the bacterial growth stage.
Results and conclusions
When expressed in E. coli recombinant proteins derived from P. falciparum sequences are often truncated and tend to aggregate what in turn leads to the formation of insoluble inclusion bodies. The analysis of various factors influencing the expression revealed that the time point of induction plays a key role in successful expression of A/T rich sequences into their native conformation. Contrary to recommended procedures, initiation of expression at post-log instead of mid-log growth phase generated significantly increased amounts of soluble protein of a high quality. Furthermore, these proteins were shown to be functionally active. Other factors such as temperature, pH, bacterial proteases or the codon optimization for E. coli had little or no effect on the quality of the recombinant protein, nevertheless, optimizing these factors might be beneficial for each individual construct. In conclusion, changing the timepoint of induction and conducting expression at the post-log stage where the bacteria have entered a decelerated growth phase, greatly facilitates and improves the expression of sequences containing rare codons.
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Background
Qualitative and quantitative production of proteins in heterologous systems is essential for the characterization of any molecule, from determination of antigenicity, functional and structural analysis to vaccine development. Malaria antigens are among the most difficult proteins to express with in vitro methods because of their extreme genetic codon usage. Different organisms have been applied for the production of malaria proteins, including Escherichia coli [1,2], baculovirus [3,4], yeast (Pichia pastoris and Saccharomyces cerevisiae) [5-8], transgenic tobacco plants [9] and transgenic mice [10]. Among these, the E. coli expression system is the most attractive and most frequently used, because it quickly produces large amounts of biomass without sophisticated laboratory equipment and at low costs. However, the quality of many proteins expressed in E. coli has not been satisfactory. In many cases, the recombinant proteins are either expressed as truncated forms or precipitate in insoluble inclusion bodies in the bacterial cells. Although methods have been developed to obtain correctly folded proteins from these inclusion bodies, the process of refolding cannot be successfully applied to all proteins [11,12].
Proteins expressed in insect cells using the baculovirus system are generally correctly folded [4]. However, so far only a few proteins have been successfully produced using this system because many proteins turned out to be toxic to the insect cells. In addition, the system achieves limited yields, which makes large-scale production cost ineffective. In recent years, expression of malaria proteins in yeast cells including P. pastoris and S. cerevisiae has been established in several laboratories [5-8]. Recombinant CSP, MSP-119, MSP-1-AMA-1 hybrid proteins and the cysteine-rich inter-domain region (CIDR) of a Plasmodium falciparum erythrocyte membrane protein 1 (PfEMP1) have been produced in P. pastoris for malaria vaccine studies in either primates or pre-clinical trials in humans [13]. However, for expression in P. pastoris, the codon sequences of these antigens need to be optimized. In most cases, sequences encoding for the amino acids of potential glycosylation sites have to be removed. So far, this system is the most promising one and might be the favourite choice when it comes to the production of recombinant malaria proteins under GMP conditions. It is nevertheless unlikely that this system will replace E. coli as a routine bench bioreactor due to its complicated manipulation and relatively long cultivation times. The use of long synthetic peptides (LSP) has been explored in malaria vaccine antigen production in recent years [14,15]. The advancing technology of peptide biosynthesis has made it possible to produce LSP with a high degree of homogeneity and purity. Furthermore, LSP can be designed in a way that they contain a large number of T cell epitopes, which leads to the generation of stronger CTL-mediated immune responses. However, this technology also has its limitations: it is still difficult to manufacture peptides that are more than 100 amino acids in length and proteins with multiple disulfide bonds are generally complicated to produce.
The P. falciparum genome is one of the most A/T-rich genomes. Surface exposed molecules expressed by the parasite such as members of the PfEMP1 family are positively charged, caused by the abundance of arginine and lysine residues in their sequences, which complicates their expression in heterologous systems such as E. coli. The high content of A/T repeats in the mRNA template is a reason for early translation termination and results in heterogeneity of recombinant proteins. Members of the PfEMP1 family are of great interest since they are virulence factors that mediate adhesion of P. falciparum-infected erythrocytes in the post-capillary microvasculature, a process that leads to severe malaria [16]. Each PfEMP1 molecule is composed of several Duffy-binding like domains (DBL) and CIDR domains. Both DBL and CIDR domains have a distinct number of cysteine residues and several lysine and arginine motifs [17]. Molecular characterization, including antigenic analysis of this protein family, relies in most cases on the successful in vitro production of the correctly folded protein, and production of these proteins in the native conformation remains particularly difficult.
In most studies where E. coli was used as a bioreactor, it has been the main goal to reach as high expression levels of the recombinant protein as possible. However, high expression levels will not guarantee a high quality of the final product. Efficient expression of heterologous proteins in E. coli is impaired by the rarity of certain tRNAs that are abundant in the organisms from which the heterologous protein is derived [1]. When the process of expression achieves high levels, the limited amounts of the tRNAs will quickly be exhausted. The lack of tRNAs will result in a drop-off of the ribosomal unit from the mRNA template and will terminate the translation process.
This study describes a simple method for optimizing the cultivation conditions and especially the timepoint of induction exemplified by the expression of several PfEMP1 domains in E. coli. Induction at the post-log stage of bacterial growth leads to the production of considerably larger amounts of soluble protein of a higher quality compared to standard conditions. Moreover, the method is easily applicable in laboratories where a sophisticated cultivation facility is not available.
Methods
Parasites
The P.falciparum parasite strains FCR3S1.2 and TM284S2 were cultured according to standard methods with 10% AB+ Rh+ serum added to the buffered medium (RPMI supplemented with Hepes, gentamycin and sodium bicarbonate). Genomic DNA from these parasites was purified using the EasyDNA purification kit (Invitrogen) according to the manufacturer's protocol.
Recombinant plasmids
Plasmid constructs for the expression of the recombinant proteins GST-DBL1α and GST-CIDR1α of FCR3S1.2var1PfEMP1, GST-DBL1α and GST-DBL2β of TM284S2var1PfEMP1 were generated as described earlier [18,19].
Codon optimization and gene resynthesis
The sequence of the DBL1α domain of FCR3S1.2var1PfEMP1 was optimized for codon adaptation in E. coli. The genes were re-synthesized chemically (GeneArt, Germany). The re-synthesized DBL1α was amplified with oligonucleotide primers (rDBL-1 5'-ATG GCT ACT TCC GGA GGA, rDBL-1.1 5'-TTC GAT AAG CAG AAG AAG TAC) and cloned into the pGEX4T-1 vector as described [20].
E. coli strain
The BL21-CodonPlus-RIL strain purchased from Stratagene (California, USA) was used for protein expression. This bacterial strain has been engineered to contain a high copy number of argenineU-, leucineW-and isoleucineY-tRNA genes for optimal expression of heterologous proteins of organisms with A/T-rich genetic sequences.
Expression of PfEMP1 domains in BL21-CodonPlus-RIL bacteria
BL21 competent cells were transformed with recombinant pGEX4T-1 plasmids containing FCR3S1.2 DBL1α, TM284S2 DBL1α or TM284S2 DBL2β as inserts. The transformed bacteria were selected on LB agar plates containing ampicillin (100 μg/ml). A single colony of the transformed bacteria was inoculated in 30 ml LB medium containing ampicillin (100 μg/ml) and chloramophenicol (50 μg/ml) for cultivation at 37°C overnight. Aliquots of the culture were inoculated into one litre LB medium with ampicillin (100 μg/ml). The cultivation was carried out with a shaking speed of 225 rpm. The pH value and the optical density at A600 of the cultures were monitored systematically. Aliquots (50 ml) of each culture were sequentially taken after the OD A600 reached 0.5 and IPTG (isoprophyl-b-D-thiogalactopyranoside) was added to a final concentration of 0.1 mM to induce the expression. The expression was carried out for three hours at 37°C and the bacteria were harvested afterwards by centrifugation at 4000 rpm for 15 minutes. The recombinant proteins were purified on Glutathione-sepharose (Amersham-Phamacia, Sweden) as described earlier [18,19].
SDS-PAGE analysis of the recombinant proteins
To analyse the recombinant proteins, aliquots of the soluble and insoluble fractions of the expressed proteins from each purification were mixed with an equal volume of SDS-PAGE loading buffer containing β-mercaptoethanol and boiled at 100°C for 5 min. The denatured proteins were resolved in 10% acrylamide gels containing 1% SDS and visualized by staining in Coomassie brilliant blue solution.
Binding to heparin and blood group A antigen
Purified recombinant DBL1α of FCR3S1.2 expressed with pGEX plasmids containing either the wild-type DBL1 sequence or the codon-optimized sequence was further passed through a heparin-HiTrap column (Amersham-Phamacia Biotech, Sweden). After washing with PBS tween-20 buffer, the bound protein was released from the column with 2M NaCl and dialyzed immediately against cold PBS. Aliquots of the eluted proteins were subjected to SDS-PAGE.
The binding of recombinant DBL1α of FCR3S1.2 to blood group A antigen was studied using a solid phase assay system as described earlier [20].
Results and discussion
The expression of three different DBL-domains and one CIDR-domain as recombinant proteins in E. coli was induced either at an OD A600 of 0.6, which is commonly recommended or at an OD A600 higher than 2.0. SDS-PAGE analysis (Figure 1) shows that most of the recombinant proteins of the cultures induced at a low OD A600 (Figure 1, lane 1, 3, 5, 7) were truncated at the C-terminal end displaying multiple bands of different molecular weights, while the intact protein represents only a small fraction of the overall protein yield. In contrast, if the expression was induced at a higher OD A600 (Figure 1, lane 2, 4, 6, 8), the dominant fraction of the protein was found to be the intact form, which proved to be true for all four domains tested although derived from different PfEMP1s.
Figure 1 Comparison of recombinant DBL, CIDR proteins expressed at mid-or post-log phase. Expression of GST-DBL1α and GST-CIDR1α of FCR3S1.2, GST-DBL1α and GST-DBL2β of TM284S2 was induced when the bacterial growth was at mid-log respectively post-log stages. The purified recombinant proteins were analysed in SDS-PAGE. Results presented in lane 1, 3, 5 and 7 are proteins purified from cultures where expressions was initiated at mid-log phase, while lane 2, 4, 6 and 8 show proteins after induction at post-log phase. The intact fraction of each expressed protein is marked with an arrow. Proteins were in addition verified by Western-Blot both with anti-GST-and anti-DBL1-antibodies [26].
In bacterial cultures, the growth will be at log phase between an OD A600 of 0.3 and 1.5. During the log phase, the number of bacteria in the culture doubles approximately every 20 minutes. Afterwards, the proliferation rate slows down due to the lack of nutrients. If the induction is initiated while the bacteria grow in log phase, the bacterial translation machinery will be highly active and the expression of the recombinant protein follows this profile, because once turned on, the promoter controlling the heterologous sequence on the vector does not underlie further control mechanisms. During expression, the rare codons of arginine, leucine, isoleucine and proline frequently found in PfEMP1 sequences will inhibit the translation process, most likely caused by the exhaustion of the tRNAs for these amino acids. It has been reported that the rare codons of arginine and proline are likely to cause frameshifts and with that undesired products in bacterial expression system [21-23]. The data reported here indicate that these problems mainly occur during the high-level expression stage, since proteins expressed at post-log growth stage are much less truncated.
Enzymatic digestion of heterologous proteins in E. coli is thought to be an additional reason for product heterogeneity of recombinant proteins [24]. The experiments of this study could not confirm degradation by bacterial proteases as one of the major causes, since the use of a protease inhibitor cocktail in the purification protocol did not affect the pattern of the expressed products (data not shown). In addition, expression was carried out using a BL21 Codon Plus bacterial strain (Stratagene) that is deficient in the OmpT and Lon bacterial proteases.
We have previously found that a large proportion of the recombinant proteins remain in the insoluble fraction whereas only small amounts appear in the soluble fraction (Figure 2 and data not shown) if expression is initiated at an OD A600 of 0.6. To check whether the bacterial growth status at the induction timepoint has any effect on protein solubility, induction of expression was carried out on aliquots of the same bacterial stock culture at different bacterial densities (OD A600 value). Both soluble and insoluble fractions of the same culture were compared. The results (Figure 2A–C) clearly show that the majority of the three recombinant proteins remain in the insoluble fraction when the expression was induced at an OD A600 below 2.0. If, on the other hand, the induction is initiated at an OD A600 greater than 2.0, almost the total amount of the recombinant proteins appears in the soluble fraction.
Figure 2 Impact of bacterial growth stages on protein solubility. The expression of the three recombinant proteins was induced at various timepoints chosen gradually from low OD A600 to higher OD A600. The comparison of the soluble and insoluble fractions of the recombinant proteins revealed that after initiation of expression at an OD A600 of greater than 2.0, the recombinant proteins are found almost completely in the soluble fraction. The amount of each protein loaded is not proportional to the size of the cultivation.
The solubility of a protein correlates with its correct structure that is formed during a post-translational folding process. Freshly synthesized polypeptides remain in a stage of intermediate form in the bacterial cytoplasma. After several enzymatic and biochemical processing steps, the peptides are folded into their functional form [24]. However, if proteins are folded incorrectly, they tend to accumulate as aggregates in the bacterial cell and, in order to avoid toxic effects on the host system, the bacteria store these aggregates in confined structures referred to as inclusion bodies.
Formation and accumulation of heterologous proteins as inclusion bodies is a common problem in protein expression. The exact mechanism of this process is still not understood. It has been suggested that factors such as culture pH, temperature and protein amino acid composition might affect the solubility of a recombinant protein [24]. The data reported here indicate that the expression speed and, with that, the subsequent folding process is the most important factor. Protein expression at the post-log phase resulted in high amounts of soluble protein, which indicates that at this stage the low bacterial growth rate implicates a biosynthesis process that is kept at low speed. The slow synthesis process will allow the protein processing machinery to efficiently assemble the freshly synthesized peptides into the correct structure. Correctly folded proteins are most likely to stay in the soluble form provided that the molecule does not contain large numbers of hydrophobic residues.
Although we found that the pH value of the growing culture is influenced by the amino acid composition of the expressed polypeptide, keeping a stable pH value in the bacterial culture does not affect the protein solubility (data not shown) and, therefore, has little influence on the quality of the expressed protein. However, temperature is an important factor to consider. Keeping the culture at 16°C before and after induction slightly improves the protein quality (data not shown), but, on the other hand, slows down bacterial growth considerably and therefore minimizes the final yield of the recombinant protein.
It has been reported that codon-optimized sequences for the use in E. coli will improve expression quality. Here we show that C/G versus A/T contents of the heterologous gene sequence are not among the most important factors that determine the quality of the recombinant protein. The expression of GST-DBL1α of FCR3S1.3 (Figure 2A) optimized for expression in E. coli shows a very similar expression pattern compared to those ones of GST-DBL1α and GST-DBL2β of TM284S2 which were expressed using the wildtype P. falciparum sequences (Figure 2B,C). This indicates that sequence composition is not always a determinant factor for expression quality.
We have previously found that the DBL1α domain of FCR3S1.2var1 PfEMP1 binds to the human erythrocyte surface through heparan sulfate [20,25]. Further, the recombinant GST-DBL1α of FCR3S1.2 protein can be purified through binding to heparin-sepharose. In this study, the same amount of GST-DBL1α purified from cultures of expression started at an OD A600 of 0.6 and greater than 2.0 was tested for its ability to bind to heparin. Although the truncated forms of the DBL1 display binding to heparin due to the presence of heparin-binding motifs in these peptides, there is a remarkable difference in terms of binding affinity between the proteins expressed at different bacterial densities as shown in Figure 3. Proteins expressed at a high OD A600 are not only more intact and more soluble, but also display higher affinity to heparin.
Figure 3 Binding to heparin. Recombinant GST-DBL1α of FCR3S1.2 was purified from cultures with induction at mid-log (lane 1) and post-log stage (lane 2) and bound to heparin. Protein expressed by bacteria at post-log stage showed considerably higher affinity to heparin.
To further demonstrate functionality of the proteins expressed at high OD A600 the DBL1α of FCR3S1.2 was subjected to a blood group A binding assay, which confirmed the specific interaction between the DBL1α and the blood group A antigen (data not shown).
The expression of P. falciparum derived proteins, especially membrane-bound proteins is still a great challenge due to the high content of amino acids encoded by rare codons in the P. falciparum genome. The method reported here presents an easily applicable tool to express sequences containing rare codons. The key factor for the expression of such proteins is to decelerate the translation machinery inside the bacteria. Low expression speed will not only allow the ribosomal unit to smoothly pass through the mRNA templates and synthesize full-length polypeptide chains, but also enable the proteins to slowly transfer from the unstable intermediate phase to the correctly folded phase. The described expression approach will result in a final product that is soluble, intact and functional, nevertheless, additional factors might influence the expression and need to be optimized for each individual construct.
The expression of eukaryotic genes in E. coli is one of the most frequently used tools in modern science. Numerous approaches have aimed at achieving the highest possible level of expression by having a maximum amount of protein expressed per bacterial cell. Our studies suggest on the contrary that increasing the number of bacterial cells in the culture while at the same time keeping the expression process at a low profile, might considerably improve the quality and quantity of the protein. That way, high level expression can simply be achieved by increasing the bacterial density of a culture, whereby problems in form of truncated or insoluble protein factions are almost completely eliminated.
Authors' contributions
KF carried out the expression assays. SA and AC participated in the expression and optimization experiments. MTB participated in sequence design. QC coordinated the experiments and helped to draft the manuscript. All authors read and approved the final manuscript
Acknowledgements
This work was funded by the grants from the European Malaria Vaccine Consortium the Swedish Research Council; SIDA/SAREC and the EU Malaria Network of Excellence program (BioMalPar).
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| 15601471 | PMC544839 | CC BY | 2021-01-04 16:37:27 | no | Malar J. 2004 Dec 15; 3:50 | utf-8 | Malar J | 2,004 | 10.1186/1475-2875-3-50 | oa_comm |
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Filaria JFilaria Journal1475-2883BioMed Central London 1475-2883-3-101562740010.1186/1475-2883-3-10ResearchHomologs of the Brugia malayi diagnostic antigen BmR1 are present in other filarial parasites but induce different humoral immune responses Noordin Rahmah [email protected] Ros Azeana Abdul [email protected] Balachandran [email protected] Institute for Research in Molecular Medicine and School of Medical Sciences, Universiti Sains Malaysia, Malaysia2 Division of Immunology, Regional Medical Research Centre, Indian Council of Medical Research, Bhubaneswar, India2004 31 12 2004 3 10 10 27 3 2004 31 12 2004 Copyright © 2004 Noordin et al; licensee BioMed Central Ltd.2004Noordin 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 recombinant antigen BmR1 has been extensively employed in both ELISA and immunochromatographic rapid dipstick (Brugia Rapid) formats for the specific and sensitive detection of IgG4 antibodies against the lymphatic filarial parasites Brugia malayi and Brugia timori. In sera of individuals infected with Wuchereria bancrofti the IgG4 reactivity to BmR1 is variable, and cross-reactivity of sera from individuals infected with Onchocerca volvulus or Loa loa was observed only in single cases. In order to characterize the homologs of the BmR1 antigen in W. bancrofti (Wb-BmR1), O. volvulus (Ov-BmR1) and L. loa (Ll-BmR1) the cDNA sequences were identified, the protein expressed and the antibody reactivity of patients' sera was studied.
Methods
PCR methodology was used to identify the cDNA sequences from cDNA libraries and/or genomic DNA of W. bancrofti, O. volvulus and L. loa. The clones obtained were sequenced and compared to the cDNA sequence of BmR1. Ov-BmR1 and Ll-BmR1 were expressed in E. coli and tested using an IgG4-ELISA with 262 serum samples from individuals with or without B. malayi, W. bancrofti, O. volvulus and L. loa infections or various other parasitic infections. BmR1, Ov-BmR1 and Ll-BmR1 were also tested for reactivity with the other three IgG subclasses in patients' sera.
Results
Wb-BmR1 was found to be identical to BmR1. Ov-BmR1 and Ll-BmR1 were found to be identical to each other and share 99.7% homology with BmR1. The pattern of IgG4 recognition of all serum samples to BmR1, Ov-BmR1 and Ll-BmR1 were identical. This included weak IgG4 reactivities demonstrated by L. loa- and O. volvulus-infected patients tested with Ov-BmR1 and Ll-BmR1 (or BmR1). With respect to reactivity to other IgG subclasses, sera from O. volvulus- and L. loa-infected patients showed positive reactions (when tested with BmR1, Ov-BmR1 or Ll-BmR1 antigens) only with IgG1. No reactivity was observed with IgG2 or with IgG3. Similarly, ELISAs to detect reactivity to other anti-filarial IgG subclasses antibodies showed that sera from individuals infected with B. malayi or W. bancrofti (active infections as well as patients with chronic disease) were positive with BmR1 only for IgG1 and were negative when tested with IgG2 and with IgG3 subclasses.
Conclusions
This study demonstrates that homologs of the BmR1 antigen are present in W. bancrofti, O. volvulus and L. loa and that these antigens are highly conserved. Recognition of this antigen by patients' sera is similar with regard to IgG1, IgG2 and IgG3, but different for IgG4 antibodies. We conclude that the BmR1 antigen is suitable for detection of IgG4 antibodies in brugian filariasis. However, its homologs are not suitable for IgG4-based diagnosis of other filarial infections.
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Background
Lymphatic filariasis (LF) caused by Brugia malayi and Brugia timori is endemic in several Asian countries and infects approximately 13 million people. In May 2000, The Global Program for Elimination of Lymphatic Filariasis (GPELF ) was officially formed with the goal of eliminating the disease as a public health problem by the year 2020. To this end, sensitive and specific field-applicable diagnostic tools are required for mapping the distribution of the disease and monitoring the various phases of the program. Many areas endemic for LF are remote and have poor access to well-equipped laboratories, thus a rapid and field-applicable diagnostic test is important to ensure that it can be easily be performed by field workers and reliable, reproducible, results can be obtained. For bancroftian filariasis caused by Wuchereria bancrofti, the ICT antigen card test (Binax Inc., USA ) is widely used for this purpose. This test is based on the detection of a circulating adult worm antigen of W. bancrofti. Although this antigen is also present in Brugia [1], a reliable antigen detection test for human B. malayi infection is not available. Therefore, despite its inconvenience and insensitivity, routine diagnosis of brugian filariasis is made by light microscopy of night blood.
Although PCR assays are highly sensitive, these mainly detect individuals with circulating microfilariae (mf); and they are both time consuming and labour-intensive requiring well-equipped laboratory facilities.
Detection of anti-filarial IgG4 antibody provides a good alternative diagnostic tool for brugian filariasis, as this antibody subclass has been shown to be elevated in active infection and decline post-treatment [2-9]. Recombinant antigen-based antibody assays would be preferable over assays based on parasite extracts since the former allow for unlimited supply of well-defined antigens.
The BmR1 recombinant antigen, expressed by gene pPROEXHT/Bm17DIII (GenBank accession no. AF225296), has been shown by us to be a highly specific and sensitive antigen for IgG4 assays to detect exposure to both B. malayi and B. timori infections. The antigen has been used in both ELISA and immunochromatographic rapid dipstick (Brugia Rapid) formats, and evaluation in various laboratories and field trials has revealed a sensitivity of 93%–100% in detecting microfilariaemic individuals [9-13]. Furthermore, in some endemic areas antibodies were also detected in amicrofilaraemic individuals, indicating the sensitivity of the assay in detecting sub-patent infections in brugian filariasis [10,13-15]. The BmR1 antigen is highly specific (99%–100%) with respect to reactivity with sera from non-filarial infections [11,12]. The highest prevalence of cross-reacting antibodies in other filarial infections was found in W. bancrofti, followed by Loa loa, while only one sample of nine patients with Dirofilaria infection was found to be reactive. No cross-reactivity was exhibited in patients infected with O. volvulus or Mansonella [11,16].
Due to its diagnostic significance, it is therefore important to characterize the BmR1 antigen more closely. The varying degree of BmR1 recognition in other filarial infection raises the question of whether the homologous antigen is also present in W. bancrofti, L. loa and O. volvulus. In the present study we have shown that BmR1 antigen is highly conserved (99–100% amino acid identity), and that almost identical antigens are present in the other human filarial parasites of public health importance. Interestingly, however, the ability of the hosts to mount IgG4 response to BmR1 homologs was found to be highly variable in some infections. In addition, the antibody responses of other IgG subclasses to BmR1 and its homolog were also investigated.
Materials & methods
cDNA and genomic DNA
W. bancrofti microfilaria (mf), adult male and adult female cDNA libraries were obtained from the Filarial Genome Project Resource Centre (Smith College, Northampton, Massachusetts, USA [email protected]). Genomic DNA of W. bancrofti mf were prepared from samples provided by Dr. B Ravindran, Division of Immunology, Regional Medical Research Centre, Indian Council of Medical Research, Bhubaneswar, India . The samples were comprised of two from individuals whose serum/blood samples were negative by the Brugia Rapid test and two from individuals who were positive by the Brugia Rapid test. L. loa L3 and O. volvulus mf cDNA libraries were kindly provided by Dr. P Fischer, Bernhard Nocht Institute for Tropical Medicine, Hamburg, Germany .
PCR
To amplify the entire Bm17DIII gene sequence the following PCR primers were used: RNF (24 mer) 5' ATT ACT GAT TAG TAT TTT ATC GTT 3' and RNR (24 mer) 5'ATG ATA AAA ATG AAT GAG AAA TAT 3'. λ phage plaques were amplified and the DNA was extracted using a λ DNA extraction kit (Qiagen, Germany ). PCR was then performed in a thermocycler (Perkin Elmer, USA ) at the following conditions: 94°C, 5 mins.; 55°C, 5 mins.; 35 cycles for 94°C, 45 sec; 55°C, 45 sec & 72°C, 90 sec; 72°C, 10 mins.
Genomic DNA from W. bancrofti mf was prepared using Genispin Tissue DNA Kit (BioSynTech, Malaysia ). PCR amplifications using the above primers were then performed using the following thermocycler conditions: 94°C, 5 mins; 35 cycles for 94°C, 1 min; 55°C, 1 min & 72°C, 1 min; 72°C, 10 mins.
TOPO cloning and DNA sequence analysis
For sequence analysis of the gene homologs in W. bancrofti, O. volvulus and L. loa, the PCR products were cloned into TOPO-TA vector (Invitrogen, USA ), then transformed into E. coli TOP10 host (Invitrogen, USA ). The recombinant plasmids were then amplified, purified using QIAprep® Spin Miniprep Kit (Qiagen, Germany ), and subsequently sent for sequencing (ACGT Inc, USA ). The results of the DNA sequences were analyzed using vector NTI software (Invitrogen, USA ).
Subcloning, expression and purification of Ov17DIII/Ll17DIII
The Bm17DIII gene homologs in O. volvulus and L. loa were subcloned into a bacterial expression vector, pPROEX-HT which contain 6-His tag (Life Technologies, USA ), then transformed into E. coli TOP 10 host cells.
The recombinant bacteria were cultured in Terrific broth and placed in a shaker incubator at 37°C until the optical density reached 0.5. The culture was then induced with 1 mM IPTG (isopropyl β-D-thiogalactopyranoside) for 3 hrs at 30°C. The bacterial pellet was reconstituted with lysis buffer containing 50 mM Tris HCl (pH 8.5), 5 mM 2-mercaptoethanol and a cocktail of protease inhibitors (Roche Diagnostics, Germany ). The suspension was sonicated at 200 W for 10 minutes, followed by centrifugation at 12 000 g for 30 minutes. The resulting supernatant was purified using Ni-NTA resin (Qiagen, Germany ) and buffers containing imidazole. The protein-containing fractions were then pooled.
ELISA
The methodology employed was as previously reported [9]. Briefly, microtiter wells (Nunc, USA ) were coated with 100 μl of either BmR1 (20 μg/ml) or the homologous recombinant antigens (5, 10 or 20 μg/ml) in NaHCO3 buffer (pH 9.6). After a blocking step, serum samples (1:20 or 1:50 or 1:100) were incubated for 2 h, followed by 0.5 h incubation with the secondary antibody HRP conjugated to monoclonal anti-human IgG1 (1:6000), IgG2 (1:1000, 1:2000), IgG3 (1:1000, 1:2000) or IgG4 (1:4500) (CLB Sanquin Blood Supply Foundation, Netherlands ). Subsequently ABTS substrate (Roche Diagnostics, ) was added for 30 minutes before the optical densities (OD) were read at 410 nm with an ELISA spectrophotometer (Dynatech (now DYNEX Technologies), USA ).
Serum samples were from existing serum banks, collected according to the ethical requirements of each institution. The samples were as follows: O. volvulus, L. loa, W. bancrofti, B. malayi and other parasitic infections. In addition serum samples from endemic normals (healthy and Brugia Rapid negative individuals from endemic areas in Malaysia) and non-endemic normals (healthy blood donors from Malaysia) were also tested. The O. volvulus sera were from microfilaremic from western Uganda [17]. L. loa sera were from microfilaremic individuals from the clinical department of the Bernhard Nocht Institute for Tropical Medicine, Hamburg, Germany. W. bancrofti sera samples were from India; while sera from B. malayi infections, endemic normals, non-endemic normals and other parasitic infections were from Malaysia. Infections with other parasites comprised of patients from Malaysia:
• whose stool specimens were positive for parasite ova/larva (single or mixed infections with Ascaris lumbricoides, Trichuris trichiura, hookworm, Strongyloides stercoralis)
• with clinical presentation and serology consistent with toxocariasis and amoebiasis
• with Gnathostoma spinigerum isolated from the eye (one patient)
Results
Identification of the BmR1 homolog in W. bancrofti, O. volvulus and L. loa
In order to explain the pattern of antigen recognition in patients with other filarial infection we identified the homologs of the BmR1 antigen in W. bancrofti (Wb-BmR1), O. volvulus (Ov-BmR1) and L. loa (LI-BmR1). PCR of W. bancrofti cDNA libraries and W. bancrofti genomic DNA (from all 4 mf samples) produced a single band of 618 bp. PCR products were eluted from band 618 and cloned into TOPO vector. A total of 12 recombinant clones from six TOPO reactions (2 from mf cDNA, 1 from adult female cDNA, 1 from adult male cDNA and 2 from mf genomic DNA) were sequenced. A total of 31 DNA sequencing reactions were analyzed and all obtained sequences were identical. Comparison of the obtained nucleotide sequence showed that it was identical to cDNA sequence of BmR1, irrespective whether the template DNA came from cDNA libraries, or microfilaria originated from individuals positive or negative for Brugia Rapid.
For identification of the cDNA of the Ov-BmR1 and Ll-BmR1, a total of 5 and 3 recombinant clones were sequenced, respectively (comprising a total of 20 reactions). The Ov-BmR1 and Ll-BmR1 were 100% identical to each other, and only two base pairs were different from BmR1 and Wb-BmR1 i.e. at 97 bp and 483 bp. When the amino acid sequences were compared only one amino acid difference was observed: the uncharged polar isoleucine at position 33 was substituted by a neutral threonine (Figure 1).
Figure 1 a) Nucleotide sequence of Bm17DIII and its homologs in W. bancrofti, O. volvulus and L. loa. Top sequence data shows DNA sequences of Bm17DIII and its homologs in W. bancrofti, O. volvulus and L. loa. b) Amino acid sequence of BmR1 and its homologs in W. bancrofti, O. volvulus and L. loa. Bottom sequence data shows amino acid sequence of BmR1 and its homologs in W. bancrofti (Wb-BmR1), O. volulus (Ov-BmR1) and L. loa (Ll-BmR1). Comparison between Bm17DIII DNA sequence and its DNA homologs in O. volvulus and L. loa showed only two bases difference at 98 and 483. BmR1 homologs of the amino acid sequence was identical with W. bancrofti. However, with O. volvulus and L. loa a difference occurred only at one amino acid coded by bases 97–99 i.e. a change from Ile (ATC) to Thr (ACC).
Antibody reactivity to BmR1, Ov-BmR1 and Ll-BmR1 recombinant antigens
For IgG4-ELISA, serum samples that demonstrated average optical density (OD) readings of ≥0.300 were considered to be positive [9]. The comparison of IgG4 reactivites with BmR1 and its homologs (Ov-BmR1 and Ll-BmR1) using a panel of 201 sera samples from individuals with various parasitic infections and 61 healthy controls (29 endemic normals and 32 non-endemic normals) indicated that the exchange of one amino acid had no influence on the reactivity of IgG4 antibodies. The IgG4-ELISA results showed all recombinant antigens were identical in reactivity with the various categories of sera (Table 1).
Table 1 Comparison among IgG4 reactivities of BmR1, Ov-BmR1 and Ll-BmR1 using a panel of serum samples from patients with various parasitic infections and healthy controls (endemic and non-endemic normals). BmR1 is the antigen expressed by Bm17DIII DNA sequence; while Ov-BmR1 and Ll-BmR1 are the antigens expressed by the homologs of Bm17DIII DNA sequence in O. volvulus and L. loa respectively.
Serum type No Positive by BmR1 (%) Positive by Ov-BmR1 and Ll-BmR1 (%)
O. volvulus, mf positive 70 1 (1.43) 1 (1.43)
L. loa, mf positive 14 6 (42.86) 6 (42.86)
W. bancrofti, mf positive 33 8 (24.24) 8 (24.24)
B. malayi, mf positive 28 28 (100) 28 (100)
Trichuris trichiura 8 0 0
Ascaris lumbricoides 8 0 0
Mixed infection with T. trichuris, A. lumbricoides and hookworm 8 0 0
Entamoeba histolytica (invasive) 11 0 0
Toxocara 14 0 0
Gnathostoma spinigerum 1 0 0
Strongyloides stercoralis 6 0 0
Endemic normals (healthy controls) 29 0 0
Non-endemic normals (healthy controls) 32 0 0
TOTAL 262
Reactivities of BmR1 and its homologs (Ov-BmR1 and Ll-BmR1) with serum antibodies of the other three IgG subclasses (IgG1, IgG2 and IgG3) using samples from O. volvulus and L. loa infected individuals showed positive reactions with only IgG1. Most IgG1 positive samples had an OD >1.000. Similarly, the reactivities of anti-filarial IgG1, IgG2 and IgG3 antibody subclasses with BmR1 on serum samples from active and chronic cases of W. bancrofti and B. malayi showed positive reactions only with IgG1. It is also noted that sera from non-endemic normals and soil-transmitted infections also showed similar reactivities i.e. IgG1 positive and IgG2- & IgG3-negative (Table 2).
Table 2 Results of ELISAs to detect IgG1, IgG2 and IgG3 anti-filarial antibodies in serum samples from patients with various helminthiasis and healthy controls (non-endemic normals) using BmR1, Ov-BmR1 and Ll-BmR1. All antigens (tested separately) demonstrated identical results with all serum samples.
Type of serum sample Number of positive results out of number of samples tested
IgG1-ELISA IgG2-ELISA IgG3-ELISA
O. volvulus mf+ 47/47 0/21 0/21
L. loa mf+ 14/14 0/14 0/14
W. bancrofti mf+ 6/6 0/6 0/6
W. bancrofti chronic 6/6 0/6 0/6
B. malayi mf+ 10/10 0/10 0/10
B. malayi chronic 14/14 0/14 0/14
Soil-transmitted helminth infections 10/10 0/10 0/10
Non-endemic normals (healthy controls) 10/10 0/10 0/10
Discussion
BmR1, a recombinant B. malayi antigen of ~30 kDa expressed by Bm17DIII DNA coding sequence (cds), has been consistently shown to be a sensitive and specific antigen for the immunodiagnosis of brugian filariasis in studies employing either ELISA or immunochromatographic rapid test (Brugia Rapid) formats [9,11-13,15]. When compared with the DNA sequences in GenBank, Bm17DIII cds exhibited 94% homology with the reported EST sequence derived from B. malayi microfilaria cDNA (GenBank AW244981). Southern blot hybridization assays performed on cDNA libraries of L3, L4, mf, adult male and adult female B. malayi showed that it is present in all of the above stages (Rahmah et al., unpublished data). Bands of the correct molecular weight were observed in a Western blot of B. malayi mf, adult male and adult female soluble antigens probed with monopurified antibody to BmR1 (Rahmah et al., unpublished data).
Multicenter evaluations performed with Brugia Rapid showed variable reactivity of BmR1 to sera of W. bancrofti-infected patients. Reactivity to sera from Chennai, India was 54.5% (12/22); from Indonesia was 70% (14/20) and from the Cook Islands was 90% (9/10) [12,15]. The wide variation in the reactivity of the assay in Bancroftian filariasis in the above three geographical areas prompted us to undertake the current investigation. The present study has shown that the homolog in W. bancrofti is identical to the cDNA of BmR1 – irrespective of the source of the parasites – whether the mf were isolated from the individuals whose sera showed positive or negative reactivity with the Brugia Rapid test. Thus the observed differences in the reactivity of BmR1 antigen with W. bancrofti sera collected from different geographical regions does not appear to be due to genotypic variability between different isolates of mf. Further studies are currently underway to determine if the variability in the expression of the gene could account for the variability in the Brugia Rapid results with serum samples collected from W. bancrofti infected individuals.
PCR experiments were performed on the W. bancrofti genomic DNA samples to obtain an amplicon with a size greater than 618 bp (since an intron is expected to be present in genomic material). However, only one prominent band of 618 bp was obtained (very occasionally a faint band of >1 kb was observed which was shown later to be due to unspecific amplification). PCR on W. bancrofti genomic DNA to amplify the intron sequence (using primers based on the Bm17DIII intron) produced a sequence that shared ~75% homology to the intron of Bm17DIII. This is believed to be an amplification on another part of W. bancrofti genome, since PCR using a pair of internal primers that flank the possible intron site produced a PCR product of ~300 bp (a size that is expected if there was no intron). Conversely, amplification of B. malayi genomic material produced two kinds of amplicons: 618 bp and 1010 bp. The latter was comprised of an intron (393 bp) and two flanking exons (237 bp and 381 bp), the sequences of which were consistent with B. malayi data at TIGR website . Thus at Universiti Sains Malaysia, genomic DNA of Wb17DIII was found to be intronless, whereas genomic DNA of Bm17DIII was shown to have two variants (i.e one with and one without an intron). These results, though seemingly controversial, were a result of exhaustive efforts with appropriate PCR controls. Data from other laboratories will hopefully confirm these results.
Anti-BmR1 IgG4 was detected in 84.6% (44/52) of L. loa sera but generally not detected in O. volvulus serum samples [11,16]. Ov-BmR1 and Ll-BmR1 were identical to each other and 99.7% similar to BmR1 (and to Wb-BmR1) on the nucleotide level (Figure 1). Ov-BmR1 and Ll-BmR1 were found to display identical reactivity compared to BmR1 when tested with IgG4-ELISA on a panel of serum samples (Tables 1 &2). Therefore, the difference of one amino acid between BmR1 and its homologs (Ov-BmR1 and Ll-BmR1) did not alter their antigenicity. It is interesting to note that although IgG4 has been shown to be elevated in onchocerciasis with assays using other recombinant antigens [18,19], the IgG4 reactivity to BmR1 or Ov-BmR1 in O. volvulus was generally negative. One possible explanation for this is that adult worms mostly express Ov-BmR1 and the immune response to O. volvulus is predominantly due to mf [20]. This may explain the very poor IgG4 response to BmR1 and Ov-BmR1. It is possible that the uptake of antigen from lymphatic filariae by antigen presenting cells is significantly different compared to O. volvulus (where adult worms and mf reside either in sub-dermal nodules or in the skin).
The BmR1, Ov-BmR1 and Ll-BmR1 recombinant antigens were also used to determine if IgG1, IgG2 or IgG3 antibodies in O. volvulus and L. loa serum samples were reactive with the recombinant proteins. In addition, the three IgG subclasses were also tested with BmR1 on assays using sera collected from patients with B. malayi and W. bancrofti infections. In all cases only anti-filarial IgG1 was reactive, while anti-filarial IgG2 and IgG3 assays were consistently negative. It is important to note that IgG1 antibodies to BmR1 and its homologs are unspecific and without any diagnostic value. The BmR1 antigen obviously contains widespread epitopes that are recognized by IgG1 antibodies.
Thus based on the current study, BmR1 and its homologs in W. bancrofti, O. volvulus and L. loa induce IgG antibody responses restricted to IgG1 and IgG 4 subclasses only. Unlike the anti-filarial IgG4 response in B. malayi infection, the IgG4 response to BmR1 in W. bancrofti and L. loa was not consistently detected in all infected individuals, indicating that this recombinant antigen will not be of much use in the diagnosis of these two filarial infections. Although IgG1 response to BmR1 was observed in all the filarial infections tested, it lacks specificity since it was also positive when tested with serum samples from normal individuals and with those infected with other parasites.
Conclusions
The study demonstrates the presence of identical and almost identical homologs of the diagnostic BmR1 antigen in other filarial parasites. However, they do not seem to induce consistent antibody responses in all infected subjects. Thus the immunogenicity of BmR1 in brugian filariasis appears to be clearly different from that of bancroftian filariasis, onchocerciasis and loiasis.
Competing interests
The author(s) declare that they have no competing interests.
Authors' contributions
RN – was the principle researcher, designed the study, supervised the experiments and result analysis, wrote the first draft of the manuscript.
RAAA – performed the experiments and participated in the analysis of the data.
BR – provided parasite materials, collected patients' sera, edited the paper.
Acknowledgements
These studies received financial support from Malaysian Government IRPA grant no 06-02-05-1007PR0016/06-05 and European Commission (EC) grant, No. ICA4-CT-2001-10081. We would also like to thank Dr. Peter Fischer from Bernhard Notch Institute for Tropical Medicine, Hamburg, Germany for providing the parasite material, sera samples and helpful comments.
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| 15627400 | PMC544840 | CC BY | 2021-01-04 16:38:24 | no | Filaria J. 2004 Dec 31; 3:10 | utf-8 | Filaria J | 2,004 | 10.1186/1475-2883-3-10 | oa_comm |
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Comp HepatolComparative Hepatology1476-5926BioMed Central London 1476-5926-3-111561757510.1186/1476-5926-3-11ResearchEarly increases in plasminogen activator activity following partial hepatectomy in humans Mangnall David [email protected] Kirsty [email protected] Nigel C [email protected] Ali W [email protected] Liver Research Group, Division of Clinical Sciences South, K Floor, Royal Hallamshire Hospital, Sheffield S10 2JF, UK2004 23 12 2004 3 11 11 28 9 2004 23 12 2004 Copyright © 2004 Mangnall et al; licensee BioMed Central Ltd.2004Mangnall 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
Increases in urokinase-like plasminogen activator (uPA) activity are reported to be amongst the earliest events occurring in remnant liver following partial hepatectomy in rats, and have been proposed as a key component of the regenerative response. Remodelling of the extracellular matrix, conversion of single chain hepatocyte growth factor to the active two-chain form and a possible activation of a mitogenic signalling pathway have all been ascribed to the increased uPA activity. The present study aimed to determine whether similar early increases in uPA activity could be detected in the remnant liver following resection of metastatic tumours in surgical patients.
Results
Eighteen patients undergoing partial hepatectomy for the removal of hepatic metastases secondary to primary colonic tumours were studied. Increased plasminogen activator activity was found in the final liver samples for the group of patients in whom the resection size was at least 50%. For smaller resections, the increased activity was not observed. The increased activity did not correlate with the age of the patient or with the time between the start of resection and the end of the operation. There was, however, a negative correlation between plasminogen activator activity and the time for which blood supply to the liver was clamped.
Conclusions
Our findings are in accordance with those from experimental animal models and show, for the first time, that rapid increases in plasminogen activator activity can occur following similarly large liver resection in humans. Thus, increases in plasminogen activator activity are an early event in the remnant liver following major liver resection in man. Our observations provide support for the contention that increases in plasminogen activators play a key role in the initiation of hepatic regeneration in man.
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Background
Urokinase-like plasminogen activator (uPA), initially recognised by its ability to convert plasminogen to plasmin and to participate in the fibrinolytic cascade, is now considered to have a wider role, which encompasses metastatic invasion by tumour cells and liver regeneration. In regeneration of the liver following partial hepatectomy, uPA has a number of potential roles. These include initiating the remodelling of the extracellular matrix to allow cell division, activation of extra-cellular pro-metalloproteases and the release of the bound single-chain form of hepatocyte growth factor (HGF) from the extracellular matrix (ECM). In vitro uPA and tissue-like plasminogen activator (tPA) have been shown to convert single chain inactive HGF into the active two chain form [1] in cultures of hepatocytes. In normal rodent liver, both the inactive and active forms of HGF can be detected, with the predominance of the inactive form [2]. Following partial hepatectomy in the rat there is an early net decrease in the total amount of HGF in the liver, but the relative proportion of the single chain, inactive form, is decreased and the active two-chain form increased [2]. This implies an early proteolytic conversion, possibly mediated by the plasminogen activators. The importance of the uPA-plasminogen system to liver repair has been further demonstrated by the inability of plasminogen deficient animals to form regenerative nodules in response to acute liver injury [3]. As discussed by Mangnall et al. [4], uPA may also activate a signalling pathway leading to mitosis of the hepatocyte.
Increases in uPA activity are amongst the earliest documented changes following partial hepatectomy in rats [5]. Raised uPA activity was detected in the remnant liver at one-minute post-hepatectomy and continued to increase for at least one hour, although there were no changes in the total amount of uPA protein detectable by Western blotting. The binding of uPA to the uPA receptor (uPAR) is also associated with an increase in uPA enzymatic activity [6]. In the rat partial hepatectomy model, the increase in uPA activity is thought to be due to an increase in the level of uPAR and subsequent binding and activation of uPA. In the remnant liver, increases in the amount of uPAR have been detected by Western blotting also as early as 1 min post hepatectomy and more clearly at 1 hour. This had decreased by 6 h and was back to basal levels by 24 h [5]. The mechanism underlying these changes remains unclear.
Additional support for a role for uPA in the hepatic regenerative process comes from studies of uPA-deficient (uPA-/-) mice. In these animals, uptake of [3H]-thymidine into DNA and mitotic index were reduced by almost half at 44 h post-hepatectomy (the peak time for control mice), suggesting a slower hepatocyte growth response [7]. In a separate study uPA-/-mice were treated with anti-Fas monoclonal antibody to induce extensive hepatocyte apoptosis. Fas (a member of the TNF-receptor superfamily) is present in the inactive state as a monomer, but on binding the appropriate ligand (in this case the antibody) the receptors aggregate and activate apoptosis leading to cell destruction. In these uPA-deficient animals, the regeneration response following anti-Fas treatment was delayed relative to normal control animals [8]. Generation of mature HGF and time of peak levels were delayed in the uPA-/-mice and peak levels of proliferating cell nuclear antigen at 96 h were also delayed relative to controls, which peaked at 48 h. Treatment of the uPA-/-mice with the uPA gene by lipofection reversed these effects. The results support a role for uPA in the generation of mature HGF and in the regeneration after Fas-mediated liver damage.
More recently, studies with uPA or plasminogen deficient mice confirmed the requirement for plasminogen activation in liver regeneration and also showed a need for plasminogen in regeneration-associated hepatic angiogenesis [9]. Collectively, these studies strongly suggest that a very early increase in uPA activity is a key feature of the liver regenerative response in rodents. It is generally assumed that regeneration in the human liver follows a similar course but the relative paucity of studies in humans means that, at present, it is unclear whether a similar role for uPA exists in the regeneration of human liver.
Though not necessarily identical, it is clear from the literature that regeneration in humans and rodents share similar mechanisms. Many of the cytokines and growth factors essential for regeneration in rodents [reviewed in [4]] are also found in increased amounts in the regenerating human liver, implying once more similar mechanisms.
However, clear differences between species do exist; a notable example being the differences in the time at which DNA synthesis peaks in the remnant liver. In rats, this is at about 24 h; in mice, at about 40 h; and in man, at 180–200 h following hepatectomy. In the case of the human studies, this may partially reflect the relatively greater age of the patients since the rate of regeneration slows with age. Such age related effects are less likely in the rodent studies where the timing of hepatocyte entry into DNA synthesis following partial hepatectomy has been shown to be an intrinsic, cell-autonomous, feature [10]. Thus, although the basic mechanisms may be fundamentally similar, there are inherent differences between species (such as the timing of the cell cycle clock) which underscores the need not to assume that all aspects of regeneration operate identically in all mammals.
The unique sensitivity of the human hepatocyte to TRAIL (tumour necrosis factor-related apoptosis-inducing ligand) [11] likewise emphasises the need for caution when extrapolating from rodent liver to human liver.
The vast majority of the literature concerns regeneration in rats and mice and much less information is available from human studies since the opportunity to study liver regeneration in humans is generally limited to units specialising in liver surgery and is necessarily constrained by ethical considerations. Surgical removal of liver metastases affords the opportunity to obtain small samples of liver at the start, time of resection and time of wound closure approximating to the early sampling times in the animal studies. In this vein, the aim of the present study was to determine whether very early increases in uPA activity occur in the remnant liver following resection in man.
Results
The basal uPA activity associated with the membrane preparations showed a wide patient to patient variation ranging from 4 to 24 nmol/min/mg protein with a mean of 9.94 nmol/min/mg protein (n = 18, SD = 5.06). This variation in basal activity correlated neither with the age of the patient – linear regression analysis gave a slope of -0.03 and correlation analysis gave a Spearman coefficient of -0.097 (p = 0.7), nor there was any difference between the values for male patients (mean = 9.14 nmol/min/mg, SD = 3.84, n = 7) and female patients (mean = 10.45, SD = 5.83, n = 11) with a non-significant unpaired Student's t test (p = 0.61).
The uPA activity associated with the membrane fractions prepared from samples taken during the operation is shown in Figure 1, for all the patients studied. The activity of the final remnant fraction taken at the end of the operation was increased significantly above the activity of the other fractions. The increased activity of the final remnant fraction was, almost exclusively, confined to those patients who had undergone a resection estimated at 50% or greater (Figure 2A) and there was no increase in those patients in whom the resection was less than 50% (Figure 2B). The percentage change in uPA activity as a function of the resection size for the individual patients is shown in Figure 2C. There was no correlation between uPA activity and the size of the resection below about 50% resection, but a positive correlation was observed when the resection size was 50% or greater.
Figure 1 Membrane associated plasminogen activator activity (nmol/min/mg protein) in the whole patient group. Samples were: Start (control sample taken at start of operation); 1 = Res 0; 2 = Rem 0; 3 = Res End; and 4 = Rem End, as described in the Methods. Values are means and the error bars are 95% confidence limits. Student's paired t test analysis showed the Start and Rem End samples to be statistically significantly different (p = 0.01, n = 18). There were no statistical differences between start and any of the other samples.
Figure 2 Membrane associated plasminogen activator activity (nmol/min/mg protein) in the group for whom the estimated resection size was 50% or greater and for the group where the resection size was less than 50%. Samples were: Start (control sample taken at start of operation); 1 = Res 0; 2 = Rem 0; 3 = Res End; and 4 = Rem End; as described in the Methods. Values are means and the error bars are 95% confidence limits. For the 50% and greater group (Figure 2A), Student's paired t test analysis showed the Start and Rem End samples to be statistically significantly different (p = 0.002, n = 8). There were no statistical differences between start and any of the other samples. For the less than 50% group (Figure 2B), there were no statistical differences between any of the samples (n = 10). The relationship between increased uPA activity in the Rem End samples and extent of resection is shown in Figure 2C. There was no statistical correlation below 50% resection (Spearman correlation coefficient = -0.22, p = 0.268) but for 50% resection and higher a positive statistical correlation was observed (Spearman correlation coefficient = 0.67, p = 0.025, n = 9)
Figure 3 shows examples of the zymography gels confirming the presence in the membrane fractions of plasminogen-dependent proteolytic activities. Minor bands in the plasminogen-free control gel (indicating proteolysis which was not plasminogen dependent) were occasionally seen. The activity in these bands was always very much less than the plasminogen dependent activities and plasminogen-free gels had to be more extensively destained in order to visualise these minor bands. Figures 3A and 3B show samples from a patient in whom the estimated resection size was 60% and Figures 3C and 3D show samples from a 15% resection. In both cases, major bands corresponding to high molecular weight uPA and tPA were clearly demonstrable in all the membrane fractions together with minor bands of higher molecular weight. The lanes in plasminogen-free control gel for the patient with the major resection all showed a single very faint band of approximately uniform intensity corresponding to a protein larger than tPA (Figure 3A and 3B). In Figure 3C, a high molecular weight band present only in the lane corresponding to the remnant end sample appeared with approximately equal intensity in the plasminogen-free gel (Figure 3D) indicating that this material was not a plasminogen activator. Although the final remnant sample in Fig 3A had increased uPA activity relative to the other samples, as determined by the fluorometric assay, the major bands of activity in the gel, corresponding to the uPA and tPA markers, showed little change. In our experience, this is a reflection of the qualitative nature of the plasminogen activator zymography. We find that with purified uPA and tPA proteins, an increase of about one order of magnitude is necessary before the bands produced in the gels are notably different.
Figure 3 Zymography gels for patients with greater than 50% resection and less then 50% resection. Figures 3A and 3B are greater than 50% and Figures 3C and 3D are less than 50%. The gels in Figures 3A and 3C contained plasminogen and the gels in Figures 3B and 3D are the corresponding plasminogen-free control gels. The samples run were, Lane 1- uPA (low molecular weight standard 33 kD, 0.6 ng); Lane 2- tPA standard (65 kD, 1 ng); Lane 3- uPA (high molecular weight standard 54 kD, 0.75 ng); Lane 4–10 μl SeeBlue Plus2 pre-stained standard markers; Lanes 5–9 were washed membrane preparation (30 μg protein / lane); Lane 5 – start; Lane 6 – Res 0; Lane 7 – Rem 0; Lane 8 – Res End; and Lane 9 – Rem End.
There were apparent increases in band size associated with the higher molecular weight minor bands in the remnant end sample. Although, at present, the precise nature of these high molecular weight bands is uncertain, similar high molecular weight plasminogen activators have been previously reported [12,13]. The most logical explanation would be an association with either uPAR or plasminogen activator inhibitor type 1 (PAI-1), and on the basis of the present observations such a complex is as likely to contain tPA as uPA. These were not observed in the samples from the patient with a smaller resection. At present, the biological significance of these changes in what appear to be relatively minor bands is uncertain, but there was an association with the large resection size and increased uPA activity, as measured by fluorimetry.
Increases in uPA activity did not correlate with patient age (Figure 4A) or the elapsed time interval between taking the remnant start and the remnant end samples (Figure 4B). There was, however, a statistically significant negative correlation between the increase in activity and the total time for which the blood supply to the liver had been clamped during the operative procedure (Figure 4C), suggesting a link between increased proteolytic activity and hepatic perfusion.
Figure 4 Relationship between the increase in membrane associated uPA activity (nmol/min/mg protein) and (A) Age, (B) Elapsed time between the time of the resection and the time at which the final sample was obtained, and (C) the total time for which the portal vein was clamped during operation. Spearman correlation analysis showed no correlation for A (p = 0.44, Spearman correlation coefficient = -0.071, n = 8), or B (p = 0.27, Spearman correlation coefficient = 0.26, n = 8). In C however, a statistically significant correlation was observed (p = 0.012, Spearman correlation coefficient = -0.89, n = 7).
Discussion
Increased uPA activity and increased levels of uPAR are among the earliest reported events in the remnant liver, following 70% partial hepatectomy in rodents [5]. Since then, several studies have emphasised the importance of the plasminogen system to hepatocyte proliferation and angiogenesis in the regenerating rodent liver [6-9]. We have shown here for the first time in humans that increases in plasminogen activator activity occur following hepatectomy. Increased activity was only seen in remnant liver at the end of the operative procedure when, at least, 15 min had elapsed between the time at which the resection was completed and the last remnant sample taken, and where the magnitude of the resection was estimated to be at least 50% of total liver volume. If, as proposed by Mars et al. [5], increased uPA activity is an essential feature at the start of regeneration, then these observations confirm the findings of animal studies that the magnitude of the regenerative response is dependent on the extent of the hepatectomy [14,15]. The present studies suggest that removal of, at least, half the liver mass is necessary to generate the biological signal that results in increased plasminogen activator activity.
The lack of any increase in uPA in response to resections less than 50% compared to the positive correlation between increased uPA activity and increased resection above 50% suggests a threshold event around the 40 to 50% level. The plot shown in Figure 2C bears a striking resemblance to the data in the review by Bucher [14] showing incorporation of tritiated thymidine into DNA following hepatectomy in mature rats. A similar threshold point at about 40% resection, with no correlation below this level and a positive correlation above, was clearly demonstrated in those studies also. The mechanism by which resections greater than about 50% increasingly result in elevated uPA activity and increased DNA synthesis remains elusive. It is still unclear whether the same trigger is responsible for the increases in both systems.
The possibility that the increased uPA activity seen here represents a response to injury rather than an early regenerative response cannot be totally discounted. However, in the rat partial hepatectomy model the anatomy allows removal of the major liver lobe without imposing surgical trauma on the remnant liver suggesting that increased uPA activity is not injury related.
Zymography clearly showed several plasminogen activators to be associated with the membrane fractions. As expected, the major bands corresponded to the high molecular weight uPA and tPA markers. Although uPA and its receptor uPAR have been implicated in the initiation of the liver regeneration process [5,16], no similar role has been ascribed to tPA. The latter binds to both liver endothelial cells (via the mannose receptor) and hepatocytes (by the LDL receptor-related protein) as part of the process by which tPA is rapidly cleared from the circulation by the liver. To date, however, there is no evidence from rodent studies to suggest that binding of tPA to receptors is, in any way, involved in the response to hepatectomy. However, the present study clearly shows tPA activity associated with the liver membrane preparations, and given the ability of tPA to generate active HGF in vitro [1] the possibility of a role for tPA in the response of human liver to partial hepatectomy needs to be borne in mind. We also found several minor bands of higher molecular weight, the nature of which is uncertain. These could potentially represent larger forms of the plasminogen activator or the plasminogen activator tightly bound to some other protein. The most likely candidates for such a complex would be uPA associating with either uPAR or PAI-1. The final remnant sample obtained after major resection showed increased amounts of these higher molecular weight components. High molecular weight forms of uPA have been observed in the rat prostate following castration [12] and also in cultured Kaposi sarcoma cells [13]. In the latter, it was suggested that the high molecular weight form of uPA contributed to the characteristic hyperproliferative and invasive phenotype of the Kaposi sarcoma lesions. Increased uPA activity associated with increased metastatic activity seems well accepted and uPA and other members of the urokinase plasminogen activator system (including uPAR and PAI-1) have been selected as novel targets for potential tumour therapies [17]. Whether the high molecular weight forms of uPA are also characteristic of an increased proliferative activity in the liver remains to be fully established.
Presently, the source of the increased uPA activity is uncertain. The very early increase in activity at 1 minute post-hepatectomy in the rat and the lack of any associated increase in mRNA for uPA, precludes any de novo protein synthesis [5]. In the present study, the increased activity at 15 minutes post-resection also seems too rapid for a mechanism requiring new protein synthesis. Mars et al. [5] suggest that the increased uPA activity seen in rats immediately following partial hepatectomy represents binding of uPA from the blood to the uPA receptor in the liver. The uPAR was undetectable on Western blots from rat liver prior to hepatectomy, but was present in the remnant liver as early as one minute post-hepatectomy and increased in amount during the next 60 minutes. It has been proposed that this increased uPAR binds uPA from the circulating blood resulting in the increased uPA activity within the liver. The suggestion that this is a key element in the initiation of the regeneration process highlights the need for adequate perfusion of the liver. Though the underlying molecular mechanisms remain unclear, interruption of hepatic perfusion generally has adverse effects on the regenerative response [4]. The present study supports the hypothesis that continued liver perfusion is important in the process by which increased uPA activity is generated. Firstly, increases in uPA activity did not occur in the liver that had been resected and removed from the circulation; secondly, for those patients in whom there was an increase in uPA activity, the magnitude of the increase was inversely related to the clamp time, i.e., the longer the liver perfusion was interrupted the smaller the response. Thus, in the present study, increased uPA activity was negatively correlated with total clamp time suggesting that hypoxia, which has been shown to induce uPAR expression in cells in culture [18-20], is not a likely mediator of the uPA increase seen here.
Finally, despite the proliferative capacity of hepatocytes and the ability of the liver to regenerate declining with age [14,15], we found no correlation between age and basal uPA activity and the increase in remnant liver uPA activity was also not age dependent.
Conclusions
In the present paper, we show early increases in uPA activity can be demonstrated in the remnant liver following resection of metastatic tumours in patients in whom the resection was estimated to be 50% or greater. To the best of our knowledge this is the first time this has been demonstrated. Such increases are amongst the earliest events following hepatectomy in rats, where they are considered to initiate changes in the extracellular matrix essential for subsequent hepatocyte division. Thus, our results support a similar role in the initiation of liver regeneration in man.
Methods
Patients
The South Sheffield Research Ethics Committee approved the research protocol and fully informed consent was obtained. Eighteen patients undergoing partial hepatectomy for the removal of hepatic metastases secondary to primary colonic tumours were studied. There were 7 males and 11 females with an age range from 24 to 78 years (median 67.5).
Operative Procedure
Standard operative procedures were followed. The liver was mobilised and the resection delineated with diathermy. The portal inflow was clamped while resection with an ultrasonic dissector was carried out. Typically, the portal inflow was released every 15 minutes for 5 minutes intervals to prevent ischaemic damage and the total clamping time was recorded. Resection margins were sent separately for histopathology. The magnitude of the resection was estimated as percentage of the total liver volume, by the surgeon.
The following samples were taken from tumour-free regions of the liver during the operation. A sample was obtained before the resection was started (this was labelled 'Start'). Immediately following resection samples were taken from the remnant liver (labelled 'Rem 0' for remnant liver at time 0) and from the resected liver as far away from the tumour as possible (labelled 'Res 0' for resected liver at time 0). The samples were placed in cryovials and immediately frozen in liquid nitrogen in the operating theatre. A second sample of the resected liver (labelled 'Res end') was kept at room temperature until the end of the operative procedure and was only transferred to liquid nitrogen when the final sample from the remnant liver was taken. The final sample ('Rem end' for remnant end) was taken from the remnant liver as late into the operation as possible and frozen immediately. The 'Res end' sample was also frozen at this time. The interval between the time of sampling 'Rem 0' and 'Rem end' ranged from 7 to 90 minutes. The median was 20.5 minutes and 15 of the 18 intervals were between 10 and 33 minutes. Samples were stored in liquid nitrogen in the laboratory and only thawed immediately prior to analysis.
Materials
Casein, plasminogen, uPA (high and low molecular weight forms) and tPA were purchased from Calbiochem (CN Biosciences Ltd., UK). The fluorometric substrates 7-amino-4-methylcoumarin (AMC), Z-Gly-Gly-Arg-AMC and EGR-CMK (Glu-Gly-Arg-Chloromethylketone) were from Bachem Ltd. (UK). Electrophoresis reagents were from BioRad and Geneflow Ltd. Other reagents were from Sigma-Aldrich Co Ltd. (Poole, UK).
Sample preparation
Liver samples were homogenised in a ten-fold volume of homogenisation buffer: 250 mM sucrose / 10 mM MOPS pH 7.4 containing the protease inhibitors E-64 (20 μM), Pepstatin A (20 μM), and EDTA (0.2 mM). Inhibitors against the serine proteases, which include uPA, were not included.
Membrane preparation
A membrane preparation was made by differential centrifugation of the homogenate in a TLS 55 swinging bucket rotor in a Beckman TL-100 bench top ultracentrifuge (Beckman Coulter Ltd., High Wycombe, UK). The homogenate was initially centrifuged at 40,000 g, for 20 minutes, to pellet large cell organelles such as nuclei and mitochondria. After centrifugation the fat at the top of each tube was removed with a piece of tissue and the supernatants transferred to clean tubes and recentrifuged at 105,000 g, for 1 hour. The membranous pellets were then washed twice by resuspending in homogenisation buffer and recentrifuged at 105,000 g, for 1 hour. All centrifugations were carried out at 4°C.
The protein content of the homogenates and membrane preparations was determined by the BCA (bicinchoninic acid) method [21] using a kit from Sigma-Aldrich.
uPA fluorometric assay
uPA activity was determined by a fluorimetric continuous rate assay of Z-Gly-Gly-Arg-AMC hydrolysis using a Perkin Elmer LS50B fluorimeter linked to an IBM compatible computer running the FLUSYS software [22]. Cleavage at the Gly-Arg bond by uPA releases the AMC from its quenched state [23] and the rate at which fluorescence is produced taken as a measure of uPA activity.
At the end of the assay, EGR-CMK (Glu-Gly-Arg-Chloromethylketone), a chemical inhibitor of uPA, was added to check that this compound inhibited the measured activity. Any activity still persisting was taken as not uPA-mediated and subtracted from the rate measured in the absence of EGR-CMK.
Since the biologically relevant fraction of uPA is generally considered to be associated with its receptor uPAR and therefore localised to the cell membrane, uPA activity measurements were performed with washed membrane preparations rather than with total liver homogenates. Preliminary experiments demonstrated the necessary linear response between the measured activity and the volume of membrane preparation assayed (data not shown).
All samples were assayed in triplicate at two sample volumes to ensure linearity of activity with amount of extract. The measured rates were then adjusted for the protein concentration of each sample to give a rate in nmoles/min/mg of protein.
Zymography
Zymography was carried out with 7.5% SDS PAGE two-substrate gels essentially as described by Bryson et al. [24]. The control, plasminogen-free, gels contained casein alone (final concentration of 6 mg/ml gel) and the test gels contained plasminogen at a final concentration of 9.3 μg (1.12 U) / ml gel in addition to the casein. Following electrophoresis gels were washed in 25% (v/v) Triton X-100, for 1 hour at room temperature, and then in 50 mM Tris (pH 7.6) for 16–20 hours and at 37°C, prior to staining with Coomassie blue. Purified uPA (high molecular weight and low molecular weight) and tPA (all from CalBiochem, CN Biosciences Ltd., Nottingham UK) and SeeBlue Plus2 Pre-stained standards (Invitrogen Life Technologies, Paisley, UK) were included on each gel as markers.
Graph plotting and statistical analysis
All Figures were generated and analysed with the GraphPad Prism package (version 3.0). Statistical analyses (Student's t tests, simple linear regression, Spearman correlations) were performed using the software in the cited package.
Authors Contributions
DM initiated the study, carried out the zymography experiments, prepared tissue extracts and drafted the manuscript. KS prepared tissue extracts and carried out the fluorometric assays. AWM and NCB participated in the design and coordination of the study. All authors have read and approved the final manuscript.
Acknowledgements
KAS was supported by a grant from Yorkshire Cancer Research.
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| 15617575 | PMC544841 | CC BY | 2021-01-04 16:38:23 | no | Comp Hepatol. 2004 Dec 23; 3:11 | utf-8 | Comp Hepatol | 2,004 | 10.1186/1476-5926-3-11 | oa_comm |
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Comp HepatolComparative Hepatology1476-5926BioMed Central London 1476-5926-3-111561757510.1186/1476-5926-3-11ResearchEarly increases in plasminogen activator activity following partial hepatectomy in humans Mangnall David [email protected] Kirsty [email protected] Nigel C [email protected] Ali W [email protected] Liver Research Group, Division of Clinical Sciences South, K Floor, Royal Hallamshire Hospital, Sheffield S10 2JF, UK2004 23 12 2004 3 11 11 28 9 2004 23 12 2004 Copyright © 2004 Mangnall et al; licensee BioMed Central Ltd.2004Mangnall 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
Increases in urokinase-like plasminogen activator (uPA) activity are reported to be amongst the earliest events occurring in remnant liver following partial hepatectomy in rats, and have been proposed as a key component of the regenerative response. Remodelling of the extracellular matrix, conversion of single chain hepatocyte growth factor to the active two-chain form and a possible activation of a mitogenic signalling pathway have all been ascribed to the increased uPA activity. The present study aimed to determine whether similar early increases in uPA activity could be detected in the remnant liver following resection of metastatic tumours in surgical patients.
Results
Eighteen patients undergoing partial hepatectomy for the removal of hepatic metastases secondary to primary colonic tumours were studied. Increased plasminogen activator activity was found in the final liver samples for the group of patients in whom the resection size was at least 50%. For smaller resections, the increased activity was not observed. The increased activity did not correlate with the age of the patient or with the time between the start of resection and the end of the operation. There was, however, a negative correlation between plasminogen activator activity and the time for which blood supply to the liver was clamped.
Conclusions
Our findings are in accordance with those from experimental animal models and show, for the first time, that rapid increases in plasminogen activator activity can occur following similarly large liver resection in humans. Thus, increases in plasminogen activator activity are an early event in the remnant liver following major liver resection in man. Our observations provide support for the contention that increases in plasminogen activators play a key role in the initiation of hepatic regeneration in man.
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Background
Urokinase-like plasminogen activator (uPA), initially recognised by its ability to convert plasminogen to plasmin and to participate in the fibrinolytic cascade, is now considered to have a wider role, which encompasses metastatic invasion by tumour cells and liver regeneration. In regeneration of the liver following partial hepatectomy, uPA has a number of potential roles. These include initiating the remodelling of the extracellular matrix to allow cell division, activation of extra-cellular pro-metalloproteases and the release of the bound single-chain form of hepatocyte growth factor (HGF) from the extracellular matrix (ECM). In vitro uPA and tissue-like plasminogen activator (tPA) have been shown to convert single chain inactive HGF into the active two chain form [1] in cultures of hepatocytes. In normal rodent liver, both the inactive and active forms of HGF can be detected, with the predominance of the inactive form [2]. Following partial hepatectomy in the rat there is an early net decrease in the total amount of HGF in the liver, but the relative proportion of the single chain, inactive form, is decreased and the active two-chain form increased [2]. This implies an early proteolytic conversion, possibly mediated by the plasminogen activators. The importance of the uPA-plasminogen system to liver repair has been further demonstrated by the inability of plasminogen deficient animals to form regenerative nodules in response to acute liver injury [3]. As discussed by Mangnall et al. [4], uPA may also activate a signalling pathway leading to mitosis of the hepatocyte.
Increases in uPA activity are amongst the earliest documented changes following partial hepatectomy in rats [5]. Raised uPA activity was detected in the remnant liver at one-minute post-hepatectomy and continued to increase for at least one hour, although there were no changes in the total amount of uPA protein detectable by Western blotting. The binding of uPA to the uPA receptor (uPAR) is also associated with an increase in uPA enzymatic activity [6]. In the rat partial hepatectomy model, the increase in uPA activity is thought to be due to an increase in the level of uPAR and subsequent binding and activation of uPA. In the remnant liver, increases in the amount of uPAR have been detected by Western blotting also as early as 1 min post hepatectomy and more clearly at 1 hour. This had decreased by 6 h and was back to basal levels by 24 h [5]. The mechanism underlying these changes remains unclear.
Additional support for a role for uPA in the hepatic regenerative process comes from studies of uPA-deficient (uPA-/-) mice. In these animals, uptake of [3H]-thymidine into DNA and mitotic index were reduced by almost half at 44 h post-hepatectomy (the peak time for control mice), suggesting a slower hepatocyte growth response [7]. In a separate study uPA-/-mice were treated with anti-Fas monoclonal antibody to induce extensive hepatocyte apoptosis. Fas (a member of the TNF-receptor superfamily) is present in the inactive state as a monomer, but on binding the appropriate ligand (in this case the antibody) the receptors aggregate and activate apoptosis leading to cell destruction. In these uPA-deficient animals, the regeneration response following anti-Fas treatment was delayed relative to normal control animals [8]. Generation of mature HGF and time of peak levels were delayed in the uPA-/-mice and peak levels of proliferating cell nuclear antigen at 96 h were also delayed relative to controls, which peaked at 48 h. Treatment of the uPA-/-mice with the uPA gene by lipofection reversed these effects. The results support a role for uPA in the generation of mature HGF and in the regeneration after Fas-mediated liver damage.
More recently, studies with uPA or plasminogen deficient mice confirmed the requirement for plasminogen activation in liver regeneration and also showed a need for plasminogen in regeneration-associated hepatic angiogenesis [9]. Collectively, these studies strongly suggest that a very early increase in uPA activity is a key feature of the liver regenerative response in rodents. It is generally assumed that regeneration in the human liver follows a similar course but the relative paucity of studies in humans means that, at present, it is unclear whether a similar role for uPA exists in the regeneration of human liver.
Though not necessarily identical, it is clear from the literature that regeneration in humans and rodents share similar mechanisms. Many of the cytokines and growth factors essential for regeneration in rodents [reviewed in [4]] are also found in increased amounts in the regenerating human liver, implying once more similar mechanisms.
However, clear differences between species do exist; a notable example being the differences in the time at which DNA synthesis peaks in the remnant liver. In rats, this is at about 24 h; in mice, at about 40 h; and in man, at 180–200 h following hepatectomy. In the case of the human studies, this may partially reflect the relatively greater age of the patients since the rate of regeneration slows with age. Such age related effects are less likely in the rodent studies where the timing of hepatocyte entry into DNA synthesis following partial hepatectomy has been shown to be an intrinsic, cell-autonomous, feature [10]. Thus, although the basic mechanisms may be fundamentally similar, there are inherent differences between species (such as the timing of the cell cycle clock) which underscores the need not to assume that all aspects of regeneration operate identically in all mammals.
The unique sensitivity of the human hepatocyte to TRAIL (tumour necrosis factor-related apoptosis-inducing ligand) [11] likewise emphasises the need for caution when extrapolating from rodent liver to human liver.
The vast majority of the literature concerns regeneration in rats and mice and much less information is available from human studies since the opportunity to study liver regeneration in humans is generally limited to units specialising in liver surgery and is necessarily constrained by ethical considerations. Surgical removal of liver metastases affords the opportunity to obtain small samples of liver at the start, time of resection and time of wound closure approximating to the early sampling times in the animal studies. In this vein, the aim of the present study was to determine whether very early increases in uPA activity occur in the remnant liver following resection in man.
Results
The basal uPA activity associated with the membrane preparations showed a wide patient to patient variation ranging from 4 to 24 nmol/min/mg protein with a mean of 9.94 nmol/min/mg protein (n = 18, SD = 5.06). This variation in basal activity correlated neither with the age of the patient – linear regression analysis gave a slope of -0.03 and correlation analysis gave a Spearman coefficient of -0.097 (p = 0.7), nor there was any difference between the values for male patients (mean = 9.14 nmol/min/mg, SD = 3.84, n = 7) and female patients (mean = 10.45, SD = 5.83, n = 11) with a non-significant unpaired Student's t test (p = 0.61).
The uPA activity associated with the membrane fractions prepared from samples taken during the operation is shown in Figure 1, for all the patients studied. The activity of the final remnant fraction taken at the end of the operation was increased significantly above the activity of the other fractions. The increased activity of the final remnant fraction was, almost exclusively, confined to those patients who had undergone a resection estimated at 50% or greater (Figure 2A) and there was no increase in those patients in whom the resection was less than 50% (Figure 2B). The percentage change in uPA activity as a function of the resection size for the individual patients is shown in Figure 2C. There was no correlation between uPA activity and the size of the resection below about 50% resection, but a positive correlation was observed when the resection size was 50% or greater.
Figure 1 Membrane associated plasminogen activator activity (nmol/min/mg protein) in the whole patient group. Samples were: Start (control sample taken at start of operation); 1 = Res 0; 2 = Rem 0; 3 = Res End; and 4 = Rem End, as described in the Methods. Values are means and the error bars are 95% confidence limits. Student's paired t test analysis showed the Start and Rem End samples to be statistically significantly different (p = 0.01, n = 18). There were no statistical differences between start and any of the other samples.
Figure 2 Membrane associated plasminogen activator activity (nmol/min/mg protein) in the group for whom the estimated resection size was 50% or greater and for the group where the resection size was less than 50%. Samples were: Start (control sample taken at start of operation); 1 = Res 0; 2 = Rem 0; 3 = Res End; and 4 = Rem End; as described in the Methods. Values are means and the error bars are 95% confidence limits. For the 50% and greater group (Figure 2A), Student's paired t test analysis showed the Start and Rem End samples to be statistically significantly different (p = 0.002, n = 8). There were no statistical differences between start and any of the other samples. For the less than 50% group (Figure 2B), there were no statistical differences between any of the samples (n = 10). The relationship between increased uPA activity in the Rem End samples and extent of resection is shown in Figure 2C. There was no statistical correlation below 50% resection (Spearman correlation coefficient = -0.22, p = 0.268) but for 50% resection and higher a positive statistical correlation was observed (Spearman correlation coefficient = 0.67, p = 0.025, n = 9)
Figure 3 shows examples of the zymography gels confirming the presence in the membrane fractions of plasminogen-dependent proteolytic activities. Minor bands in the plasminogen-free control gel (indicating proteolysis which was not plasminogen dependent) were occasionally seen. The activity in these bands was always very much less than the plasminogen dependent activities and plasminogen-free gels had to be more extensively destained in order to visualise these minor bands. Figures 3A and 3B show samples from a patient in whom the estimated resection size was 60% and Figures 3C and 3D show samples from a 15% resection. In both cases, major bands corresponding to high molecular weight uPA and tPA were clearly demonstrable in all the membrane fractions together with minor bands of higher molecular weight. The lanes in plasminogen-free control gel for the patient with the major resection all showed a single very faint band of approximately uniform intensity corresponding to a protein larger than tPA (Figure 3A and 3B). In Figure 3C, a high molecular weight band present only in the lane corresponding to the remnant end sample appeared with approximately equal intensity in the plasminogen-free gel (Figure 3D) indicating that this material was not a plasminogen activator. Although the final remnant sample in Fig 3A had increased uPA activity relative to the other samples, as determined by the fluorometric assay, the major bands of activity in the gel, corresponding to the uPA and tPA markers, showed little change. In our experience, this is a reflection of the qualitative nature of the plasminogen activator zymography. We find that with purified uPA and tPA proteins, an increase of about one order of magnitude is necessary before the bands produced in the gels are notably different.
Figure 3 Zymography gels for patients with greater than 50% resection and less then 50% resection. Figures 3A and 3B are greater than 50% and Figures 3C and 3D are less than 50%. The gels in Figures 3A and 3C contained plasminogen and the gels in Figures 3B and 3D are the corresponding plasminogen-free control gels. The samples run were, Lane 1- uPA (low molecular weight standard 33 kD, 0.6 ng); Lane 2- tPA standard (65 kD, 1 ng); Lane 3- uPA (high molecular weight standard 54 kD, 0.75 ng); Lane 4–10 μl SeeBlue Plus2 pre-stained standard markers; Lanes 5–9 were washed membrane preparation (30 μg protein / lane); Lane 5 – start; Lane 6 – Res 0; Lane 7 – Rem 0; Lane 8 – Res End; and Lane 9 – Rem End.
There were apparent increases in band size associated with the higher molecular weight minor bands in the remnant end sample. Although, at present, the precise nature of these high molecular weight bands is uncertain, similar high molecular weight plasminogen activators have been previously reported [12,13]. The most logical explanation would be an association with either uPAR or plasminogen activator inhibitor type 1 (PAI-1), and on the basis of the present observations such a complex is as likely to contain tPA as uPA. These were not observed in the samples from the patient with a smaller resection. At present, the biological significance of these changes in what appear to be relatively minor bands is uncertain, but there was an association with the large resection size and increased uPA activity, as measured by fluorimetry.
Increases in uPA activity did not correlate with patient age (Figure 4A) or the elapsed time interval between taking the remnant start and the remnant end samples (Figure 4B). There was, however, a statistically significant negative correlation between the increase in activity and the total time for which the blood supply to the liver had been clamped during the operative procedure (Figure 4C), suggesting a link between increased proteolytic activity and hepatic perfusion.
Figure 4 Relationship between the increase in membrane associated uPA activity (nmol/min/mg protein) and (A) Age, (B) Elapsed time between the time of the resection and the time at which the final sample was obtained, and (C) the total time for which the portal vein was clamped during operation. Spearman correlation analysis showed no correlation for A (p = 0.44, Spearman correlation coefficient = -0.071, n = 8), or B (p = 0.27, Spearman correlation coefficient = 0.26, n = 8). In C however, a statistically significant correlation was observed (p = 0.012, Spearman correlation coefficient = -0.89, n = 7).
Discussion
Increased uPA activity and increased levels of uPAR are among the earliest reported events in the remnant liver, following 70% partial hepatectomy in rodents [5]. Since then, several studies have emphasised the importance of the plasminogen system to hepatocyte proliferation and angiogenesis in the regenerating rodent liver [6-9]. We have shown here for the first time in humans that increases in plasminogen activator activity occur following hepatectomy. Increased activity was only seen in remnant liver at the end of the operative procedure when, at least, 15 min had elapsed between the time at which the resection was completed and the last remnant sample taken, and where the magnitude of the resection was estimated to be at least 50% of total liver volume. If, as proposed by Mars et al. [5], increased uPA activity is an essential feature at the start of regeneration, then these observations confirm the findings of animal studies that the magnitude of the regenerative response is dependent on the extent of the hepatectomy [14,15]. The present studies suggest that removal of, at least, half the liver mass is necessary to generate the biological signal that results in increased plasminogen activator activity.
The lack of any increase in uPA in response to resections less than 50% compared to the positive correlation between increased uPA activity and increased resection above 50% suggests a threshold event around the 40 to 50% level. The plot shown in Figure 2C bears a striking resemblance to the data in the review by Bucher [14] showing incorporation of tritiated thymidine into DNA following hepatectomy in mature rats. A similar threshold point at about 40% resection, with no correlation below this level and a positive correlation above, was clearly demonstrated in those studies also. The mechanism by which resections greater than about 50% increasingly result in elevated uPA activity and increased DNA synthesis remains elusive. It is still unclear whether the same trigger is responsible for the increases in both systems.
The possibility that the increased uPA activity seen here represents a response to injury rather than an early regenerative response cannot be totally discounted. However, in the rat partial hepatectomy model the anatomy allows removal of the major liver lobe without imposing surgical trauma on the remnant liver suggesting that increased uPA activity is not injury related.
Zymography clearly showed several plasminogen activators to be associated with the membrane fractions. As expected, the major bands corresponded to the high molecular weight uPA and tPA markers. Although uPA and its receptor uPAR have been implicated in the initiation of the liver regeneration process [5,16], no similar role has been ascribed to tPA. The latter binds to both liver endothelial cells (via the mannose receptor) and hepatocytes (by the LDL receptor-related protein) as part of the process by which tPA is rapidly cleared from the circulation by the liver. To date, however, there is no evidence from rodent studies to suggest that binding of tPA to receptors is, in any way, involved in the response to hepatectomy. However, the present study clearly shows tPA activity associated with the liver membrane preparations, and given the ability of tPA to generate active HGF in vitro [1] the possibility of a role for tPA in the response of human liver to partial hepatectomy needs to be borne in mind. We also found several minor bands of higher molecular weight, the nature of which is uncertain. These could potentially represent larger forms of the plasminogen activator or the plasminogen activator tightly bound to some other protein. The most likely candidates for such a complex would be uPA associating with either uPAR or PAI-1. The final remnant sample obtained after major resection showed increased amounts of these higher molecular weight components. High molecular weight forms of uPA have been observed in the rat prostate following castration [12] and also in cultured Kaposi sarcoma cells [13]. In the latter, it was suggested that the high molecular weight form of uPA contributed to the characteristic hyperproliferative and invasive phenotype of the Kaposi sarcoma lesions. Increased uPA activity associated with increased metastatic activity seems well accepted and uPA and other members of the urokinase plasminogen activator system (including uPAR and PAI-1) have been selected as novel targets for potential tumour therapies [17]. Whether the high molecular weight forms of uPA are also characteristic of an increased proliferative activity in the liver remains to be fully established.
Presently, the source of the increased uPA activity is uncertain. The very early increase in activity at 1 minute post-hepatectomy in the rat and the lack of any associated increase in mRNA for uPA, precludes any de novo protein synthesis [5]. In the present study, the increased activity at 15 minutes post-resection also seems too rapid for a mechanism requiring new protein synthesis. Mars et al. [5] suggest that the increased uPA activity seen in rats immediately following partial hepatectomy represents binding of uPA from the blood to the uPA receptor in the liver. The uPAR was undetectable on Western blots from rat liver prior to hepatectomy, but was present in the remnant liver as early as one minute post-hepatectomy and increased in amount during the next 60 minutes. It has been proposed that this increased uPAR binds uPA from the circulating blood resulting in the increased uPA activity within the liver. The suggestion that this is a key element in the initiation of the regeneration process highlights the need for adequate perfusion of the liver. Though the underlying molecular mechanisms remain unclear, interruption of hepatic perfusion generally has adverse effects on the regenerative response [4]. The present study supports the hypothesis that continued liver perfusion is important in the process by which increased uPA activity is generated. Firstly, increases in uPA activity did not occur in the liver that had been resected and removed from the circulation; secondly, for those patients in whom there was an increase in uPA activity, the magnitude of the increase was inversely related to the clamp time, i.e., the longer the liver perfusion was interrupted the smaller the response. Thus, in the present study, increased uPA activity was negatively correlated with total clamp time suggesting that hypoxia, which has been shown to induce uPAR expression in cells in culture [18-20], is not a likely mediator of the uPA increase seen here.
Finally, despite the proliferative capacity of hepatocytes and the ability of the liver to regenerate declining with age [14,15], we found no correlation between age and basal uPA activity and the increase in remnant liver uPA activity was also not age dependent.
Conclusions
In the present paper, we show early increases in uPA activity can be demonstrated in the remnant liver following resection of metastatic tumours in patients in whom the resection was estimated to be 50% or greater. To the best of our knowledge this is the first time this has been demonstrated. Such increases are amongst the earliest events following hepatectomy in rats, where they are considered to initiate changes in the extracellular matrix essential for subsequent hepatocyte division. Thus, our results support a similar role in the initiation of liver regeneration in man.
Methods
Patients
The South Sheffield Research Ethics Committee approved the research protocol and fully informed consent was obtained. Eighteen patients undergoing partial hepatectomy for the removal of hepatic metastases secondary to primary colonic tumours were studied. There were 7 males and 11 females with an age range from 24 to 78 years (median 67.5).
Operative Procedure
Standard operative procedures were followed. The liver was mobilised and the resection delineated with diathermy. The portal inflow was clamped while resection with an ultrasonic dissector was carried out. Typically, the portal inflow was released every 15 minutes for 5 minutes intervals to prevent ischaemic damage and the total clamping time was recorded. Resection margins were sent separately for histopathology. The magnitude of the resection was estimated as percentage of the total liver volume, by the surgeon.
The following samples were taken from tumour-free regions of the liver during the operation. A sample was obtained before the resection was started (this was labelled 'Start'). Immediately following resection samples were taken from the remnant liver (labelled 'Rem 0' for remnant liver at time 0) and from the resected liver as far away from the tumour as possible (labelled 'Res 0' for resected liver at time 0). The samples were placed in cryovials and immediately frozen in liquid nitrogen in the operating theatre. A second sample of the resected liver (labelled 'Res end') was kept at room temperature until the end of the operative procedure and was only transferred to liquid nitrogen when the final sample from the remnant liver was taken. The final sample ('Rem end' for remnant end) was taken from the remnant liver as late into the operation as possible and frozen immediately. The 'Res end' sample was also frozen at this time. The interval between the time of sampling 'Rem 0' and 'Rem end' ranged from 7 to 90 minutes. The median was 20.5 minutes and 15 of the 18 intervals were between 10 and 33 minutes. Samples were stored in liquid nitrogen in the laboratory and only thawed immediately prior to analysis.
Materials
Casein, plasminogen, uPA (high and low molecular weight forms) and tPA were purchased from Calbiochem (CN Biosciences Ltd., UK). The fluorometric substrates 7-amino-4-methylcoumarin (AMC), Z-Gly-Gly-Arg-AMC and EGR-CMK (Glu-Gly-Arg-Chloromethylketone) were from Bachem Ltd. (UK). Electrophoresis reagents were from BioRad and Geneflow Ltd. Other reagents were from Sigma-Aldrich Co Ltd. (Poole, UK).
Sample preparation
Liver samples were homogenised in a ten-fold volume of homogenisation buffer: 250 mM sucrose / 10 mM MOPS pH 7.4 containing the protease inhibitors E-64 (20 μM), Pepstatin A (20 μM), and EDTA (0.2 mM). Inhibitors against the serine proteases, which include uPA, were not included.
Membrane preparation
A membrane preparation was made by differential centrifugation of the homogenate in a TLS 55 swinging bucket rotor in a Beckman TL-100 bench top ultracentrifuge (Beckman Coulter Ltd., High Wycombe, UK). The homogenate was initially centrifuged at 40,000 g, for 20 minutes, to pellet large cell organelles such as nuclei and mitochondria. After centrifugation the fat at the top of each tube was removed with a piece of tissue and the supernatants transferred to clean tubes and recentrifuged at 105,000 g, for 1 hour. The membranous pellets were then washed twice by resuspending in homogenisation buffer and recentrifuged at 105,000 g, for 1 hour. All centrifugations were carried out at 4°C.
The protein content of the homogenates and membrane preparations was determined by the BCA (bicinchoninic acid) method [21] using a kit from Sigma-Aldrich.
uPA fluorometric assay
uPA activity was determined by a fluorimetric continuous rate assay of Z-Gly-Gly-Arg-AMC hydrolysis using a Perkin Elmer LS50B fluorimeter linked to an IBM compatible computer running the FLUSYS software [22]. Cleavage at the Gly-Arg bond by uPA releases the AMC from its quenched state [23] and the rate at which fluorescence is produced taken as a measure of uPA activity.
At the end of the assay, EGR-CMK (Glu-Gly-Arg-Chloromethylketone), a chemical inhibitor of uPA, was added to check that this compound inhibited the measured activity. Any activity still persisting was taken as not uPA-mediated and subtracted from the rate measured in the absence of EGR-CMK.
Since the biologically relevant fraction of uPA is generally considered to be associated with its receptor uPAR and therefore localised to the cell membrane, uPA activity measurements were performed with washed membrane preparations rather than with total liver homogenates. Preliminary experiments demonstrated the necessary linear response between the measured activity and the volume of membrane preparation assayed (data not shown).
All samples were assayed in triplicate at two sample volumes to ensure linearity of activity with amount of extract. The measured rates were then adjusted for the protein concentration of each sample to give a rate in nmoles/min/mg of protein.
Zymography
Zymography was carried out with 7.5% SDS PAGE two-substrate gels essentially as described by Bryson et al. [24]. The control, plasminogen-free, gels contained casein alone (final concentration of 6 mg/ml gel) and the test gels contained plasminogen at a final concentration of 9.3 μg (1.12 U) / ml gel in addition to the casein. Following electrophoresis gels were washed in 25% (v/v) Triton X-100, for 1 hour at room temperature, and then in 50 mM Tris (pH 7.6) for 16–20 hours and at 37°C, prior to staining with Coomassie blue. Purified uPA (high molecular weight and low molecular weight) and tPA (all from CalBiochem, CN Biosciences Ltd., Nottingham UK) and SeeBlue Plus2 Pre-stained standards (Invitrogen Life Technologies, Paisley, UK) were included on each gel as markers.
Graph plotting and statistical analysis
All Figures were generated and analysed with the GraphPad Prism package (version 3.0). Statistical analyses (Student's t tests, simple linear regression, Spearman correlations) were performed using the software in the cited package.
Authors Contributions
DM initiated the study, carried out the zymography experiments, prepared tissue extracts and drafted the manuscript. KS prepared tissue extracts and carried out the fluorometric assays. AWM and NCB participated in the design and coordination of the study. All authors have read and approved the final manuscript.
Acknowledgements
KAS was supported by a grant from Yorkshire Cancer Research.
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| 15613235 | PMC544842 | CC BY | 2021-01-04 16:36:21 | no | Cytojournal. 2004 Dec 21; 1:6 | latin-1 | Cytojournal | 2,004 | 10.1186/1742-6413-1-6 | oa_comm |
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Respir ResRespiratory Research1465-99211465-993XBioMed Central London 1465-9921-6-11563162710.1186/1465-9921-6-1ResearchHuman lung cancer cells express functionally active Toll-like receptor 9 Droemann Daniel [email protected] Dirk [email protected] Johannes [email protected] Artur J [email protected] Detlev [email protected] Ekkehard [email protected] Klaus [email protected] Peter [email protected] Torsten [email protected] Medical Clinic, Research Center Borstel, D-23845 Borstel, Germany2 Department of Immunology and Cell Biology, Research Center Borstel, D-23845 Borstel, Germany3 Department for Thoracic Surgery, Krankenhaus Großhansdorf, D-22927 Großhansdorf, Germany4 Clinical and Experimental Pathology, Research Center Borstel, D-23845 Borstel, Germany5 Medical Clinic III, University of Lübeck, D-23538 Lübeck, Germany2005 4 1 2005 6 1 1 1 16 8 2004 4 1 2005 Copyright © 2005 Droemann et al; licensee BioMed Central Ltd.2005Droemann 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
CpG-oligonucleotides (CpG-ODN), which induce signaling through Toll-like receptor 9 (TLR9), are currently under investigation as adjuvants in therapy against infections and cancer. CpG-ODN function as Th-1 adjuvants and are able to activate dendritic cells. In humans TLR9 has been described to be strongly expressed in B-lymphocytes, monocytes, plasmacytoid dendritic cells and at low levels in human respiratory cells. We determined whether a direct interaction of bacterial DNA with the tumor cells themselves is possible and investigated the expression and function of TLR9 in human malignant solid tumors and cell lines. TLR9 expression by malignant tumor cells, would affect treatment approaches using CpG-ODN on the one hand, and, on the other hand, provide additional novel information about the role of tumor cells in tumor-immunology.
Methods
The expression of TLR9 in HOPE-fixed non-small lung cancer, non-malignant tissue and tumor cell lines was assessed using immunohistochemistry, confocal microscopy, in situ hybridization, RT-PCR and DNA-sequencing. Apoptosis and chemokine expression was detected by FACS analysis and the Bio-Plex system.
Results
We found high TLR9 signal intensities in the cytoplasm of tumor cells in the majority of lung cancer specimens as well as in all tested tumor cell lines. In contrast to this non-malignant lung tissues showed only sporadically weak expression. Stimulation of HeLa and A549 cells with CpG-ODN induced secretion of monocyte chemoattractant protein-1 and reduction of spontaneous and tumor necrosis factor-alpha induced apoptosis.
Conclusions
Here we show that TLR9 is expressed in a selection of human lung cancer tissues and various tumor cell lines. The expression of functionally active TLR9 in human malignant tumors might affect treatment approaches using CpG-ODN and shows that malignant cells can be regarded as active players in tumor-immunology.
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Background
The Toll gene, the expression of one of it's relatives we are reporting here concerning human malignant tumors, originally was characterized for its role in specifying dorsoventral polarity of the Drosophila embryo[1]. Since homologues of Toll are also present in plants, mammalian toll-like genes are products of an ancient evolutionary process beginning before the separation of animals and plants [2]. Within the genome of Drosophila thus far nine toll-like genes were identified, ten different human toll-like genes are currently described. In contrast to Drosophila, the mechanisms taking place in mammalian embryogenesis concerning TLR are widely unknown. The discovery of immune function for Toll in Drosophila led to a new understanding of innate immunity mechanisms.
Human TLR recognize pathogen-derived products, also termed pathogen-associated molecular patterns (PAMP) [3]. These are bacterial lipoproteins (sBLP) [4], viral double stranded RNA/poly (I:C) [5], lipopolysaccharides (LPS) [6], flagellin [7] and bacterial DNA [8], which engage TLR2, TLR3, TLR4, TLR5 and TLR9, respectively. All functionally characterized TLR signal via the cytoplasmic Toll/interleukin-1 receptor domain (TIR) leading to activation of transcription factors like activator protein-1 (AP-1) and nuclear factor-κB (NF-κB) [9]. TLR9, in contrast to the other TLR, is not located at the cell surface, but intracellularily and, therefore, inhibition of endocytosis or endosome formation completely ablates the effects of CpG-ODN [10].
Different studies show an immunostimulatory capacity of bacterial components which can mediate anti-tumor activity. The first reported use of such a therapy for a nonbacterial disease took place 1890, evaluating the anti-tumor activity of living streptococci directly injected into tumor masses [11]. Shimada demonstrated that bacterial DNA itself can stimulate the immune system [12]. Over the past years there has been an enormous increase in the understanding of the molecular and cellular effects of CpG-ODN [13], which have been found to function as Th-1 adjuvants [14], and are able to activate dendritic cells [15]. This led to the idea to utilize CpG-ODN for induction of anti-tumor immune response as an adjuvant therapeutic strategy [16-18].
In order to characterize possible interactions between malignant cells and CpG-ODN, we investigated whether TLR9 is present in malignant tumors. A variety of malignant solid tumors and cell lines were tested for TLR9 expression; in addition, we examined direct effects of CpG-ODN upon apoptosis and chemokine production of tumor cells.
Methods
Tissues
Samples of human tumors and tumor-free tissues were obtained from lobectomies because of lung cancer. Tumor-free tissues were taken at least 5 cm away from the tumor-border. The specimens were fixed and paraffin-embedded using the HOPE-technique [19]. Sections were cut, mounted, and deparaffinized as described elsewhere [20].
For increased comparability of the staining intensities in malignant and non malignant cells we additionally performed IHC on tumor-bearing and tumor free lung tissues which have been assembled on one slide by use of a mechanical tissue arrayer device (MTA1, Alphametrix, Germany).
Cell culture
A549 cells and HeLa cells were grown in 25 cm2 polystyrene flasks with Dulbecco's modified Eagle's medium DMEM (Sigma) with 10 % heat-inactivated fetal calf serum (PAA Laboratories), 100 μg/ml penicillin G, 100 μg/ml streptomycin and 2 mM L-glutamine (Sigma), maintained under 5 % CO2 by routine passage every 3 days. Cells were seeded in 35-mm dishes (Nunc).
For IHC cells were cytocentrifuged and treated by the HOPE-technique [21], the cell lines used were: A549, HeLa, NCI-H727, Jurkat, L428, CPC-N, Raji, H23, U937, H157, H125, L428, and DV90.
Preparation of the probes
Total RNA was extracted from lung tissues according to the manufacturer's recommendations (RNeasy, Qiagen). After destroying residual DNA with DNase (Invitrogen), cDNA was synthesized by reverse transcription [22]. PCR was performed targeting a 393 bp fragment of human TLR9-mRNA (TLR9 forward: AAC TGG CTG TTC CTG AAG TC; TLR9 reverse: TGC CGT CCA TGA ATA GGA AG). PCR-products were separated on 2 % agarose gels stained by ethidiumbromide. Cycle sequencing confirmed 100 % identity with the human TLR9 wild-type-sequence. Probes were labeled with digoxigenin using High-Prime (Roche) according to the manufacturer's recommendations [23].
ISH
Hybridization, detection of signals and controls were carried out as previously described (concentration of probe 2 ng/μl, hybridization temperature 46°C) [20,22].
IHC
Primary antibody (mouse anti-human TLR9, clone 26C593, Imgenex) was applied in a dilution of 1/100 in PBS for 16 h at 4°C. Negative controls comprised omission of the primary antibody. Detection was performed by horseradish-peroxidase labeled streptavidine-biotin technique (LSAB2, Dako) [24].
RT-PCR/Cell lines
A549, HeLa, BEAS 2b, U937, and NCI-H727 cell lines were used. RT-PCR was performed like described above using TLR9 specific primers (forward: 5'CATGCCCTGCGCTTCCTATTCA; reverse: 5'TGGGCCAGCACAAACAGCGTCTT) spanning an amplicon of 260 bp. Mononuclear cells were included as positive control as well as RT-PCR targeting glyceraldehyde-3-phosphate dehydrogenase (GAPDH) (forward: GTCATCATCTCCGCCCCTTCTGC; reverse: GATGCCTGCTTCACCACCTTCTTG) (not shown). PCR-products were separated along with a molecular weight marker (MW8, Roche) using 2 % agarose gels (Fig. 1).
Figure 1 Immunohistochemistry (IHC) (A-C) for TLR9 detected by a mouse monoclonal antibody. Adenocarcinoma of the lung (A). Squamous cell carcinoma of the lung (B). A549 cells (all 600 ×) (C). In situ hybridization (ISH) targeting mRNA of human TLR9 with a digoxigenin-labeled DNA-probe in a squamous cell carcinoma of the lung (600 ×) (D). Immunohistochemical staining of TLR9-expression-levels in nonmalignant (E) and malignant tissues (F) derived from the same lungs an stained by the use of tissue arrays. Results of RT-PCR targeting TLR9 in cell lines (G). M: molecular-weight marker (MW8, Roche). 1: negative control; 2: A549; 3: NCI-H727; 4: BEAS 2b; 5: Mononuclear cells from a healthy human donor. Confocal laser microscopy of A549 cells transiently transfected with a GFP-TLR9 plasmid: Cytoplasmic expression of TLR9 is observable in all cells, while successful transfection led to overexpression of TLR9 resulting in bright GFP signals completely superimposed by the TLR9 antibody signal (H). Nuclear counterstain was performed with TOTO3.
Transfection
A549-cells were seeded in 35-mm glass bottom dishes (MatTek Corp.) overnight. Cells were transfected with GFP-huTLR9 using Polyfect (Qiagen) according to the manufacturer's instructions or incubated in medium.
Confocal Microscopy
Cells were washed in tris-buffered-saline, containing 0.2 % Tween 20 (TTBS), fixed with 4 % paraformaldehyde in phosphate-buffered-saline (PBS) for 10 min on ice, and permeabilized with 0.25 % Triton-X100 (Roche) in PBS for 10 min. Cells were washed with TTBS, blocked with 10 % bovine-serum-albumine (BSA) in TBS for 20 min, and incubated with primary antibody (clone 26C593, Imgenex) or isotype (Mouse IgG1, Jackson ImmunoResearch Laboratories) 1:150 in TBS 10 % BSA for 30 min. Cells were washed with TTBS, incubated for 30 min with Alexa-568/goat-anti-MouseIgG1 (Molecular Probes Inc.) 1:500 in TBS containing 10 % BSA, and washed with TTBS. Counterstaining was achieved using TOTO-3 1:500 in TBS containing 10 % BSA. Cells were washed with TTBS, fixed again as above, mounted and analyzed using a confocal laser microscope. The GFP-TLR9 plasmid was kindly provided by Terje Espevik, Trondheim, Norway.
Treatment Protocols
For CpG-ODN stimulation the M362 sequence was used in a concentration of 1 μM; as control M383 was used as described by Hartmann et al. [25] (MWG-Biotech). Human tumor necrosis factor-alpha (TNF-α, Roche) in PBS containing 0.5 % bovine serum albumin was added to the cultures in a concentration of 10 ng/ml. CHX (Sigma) was dissolved in PBS and added in a concentration of 10 μM.
Flow cytometry
Annexin-V FITC apoptosis kit I and PE-conjugated active caspase-3 apoptosis kit I were used according to the manufacturer's instructions (BD Pharmingen). TLR9 antibody and isotype control (eBioscience, clone: eB72-1665) were stained after fixation and permeabilization using Intraprep (Beckmann Coulter) according to the manufacturer's instructions. Flowcytometric data (FACS Calibur) collected from 10,000 cells are reported as percentages of positive cells (Becton Dickinson).
Cytokine assays
Cell culture supernatant (50 μl per sample) was analyzed using the Bio-Plex system and a Luminex 100TM analyzer (BioRad) according to manufacturer's instructions.
Stimulation of tumor-tissues and RT-PCR
Tissue blocks from lung cancer specimens (edge length approximately 0.5 cm) were cultivated in RPMI 1640 at 37°C and 5 % CO2 for 24 h, and either stimulated or not stimulated with 1 μM of CpG-ODN (M362 sequence). These blocks from adjacent locations of the same lung-tumors were fixed using the HOPE-technique and paraffin embedded. RT-PCR was carried out like described above using primers targeting human MCP-1 (forward: AAAGCACCAGTCAACTGGAC; reverse: AGCGCTTGGTGATGTGCTTT) resulting in a 149 bp PCR-product and GAPDH (forward: AGAACGGGAAGCTTGTCATC; reverse: TGCTGATGATCTTGAGGCTG) resulting in a 257 bp PCR-product. PCR products were separated on 2 % agarose gels along with a molecular weight marker (pBR322-Msp1) and the results displayed in figure 4b.
Figure 4 MCP-1 secretion in response to CpG-ODN-stimulation in the presence or absence of TNF-α by HeLa and A549 cells (A). Data are expressed as the mean ± SD (n = 6). Student's t test was used for statistical analysis. RT-PCR targeting mRNA of MCP-1 in human non-small cell lung cancer tissue stimulated with CpG-ODN for 24 h (B) (M = pBR322-Msp1). Lanes 2 and 3, as well as lanes 4 and 5 respectively show results of tissue samples from the same tumors either in the absence or presence of CpG-ODN.
Results
Expression of TLR9 in malignant tumors
To investigate the expression of TLR9 in human lung tumors and lung tumor cell lines we used the recently described HOPE-fixation method. HOPE-fixed [19] specimens showed superior preservation of morphology after in situ hybridization (ISH). The generation of TLR9-signals was achieved within 10 minutes, whereas unspecific signals were not detected in the control preparations. We found high signal intensities for TLR9 transcripts in the cytoplasm of tumor cells in the majority of lung cancer specimens. Immunohistochemistry (IHC) revealed strong TLR9 protein expression within tumor cells of tissues and cell lines. In contrast normal lung tissues sporadically showed weak expression of TLR9 mainly in cells revealing morphological characteristics of alveolar macrophages and alveolar epithelial cells as displayed in figure 1. Negative control specimens did not display signals. The results are summarized in table 1; some representative results of ISH and IHC are displayed in figure 1. To confirm the results obtained by ISH we analyzed TLR9-transcripts in tumor cell lines by RT-PCR. As shown in figure 1, we found that all tumor cell lines indeed express TLR9.
Table 1 Summarized results of immunohistochemistry (IHC) targeting TLR9 in tumor tissues and cell lines.
Entity N* No expression Weak expression Strong expression
Adenocarcinoma of the lung 21 1 7 13
Squamous cell carcinoma of the lung 23 1 14 8
Large cell carcinoma of the lung 3 0 2 1
Cell lines** 13 0 1 12
Total 60 2 24 34
* Number of analyzed specimens
** See methods
A cytoplasmic localization of TLR9 was confirmed by confocal microscopy (fig. 1). This finding is in agreement with previous studies on the distribution of TLR9 in RAW264.7 cells [10]. Furthermore, immunostaining of GFP-TLR9 transfected A549 cells verified the specificity of the TLR9 antibody: Only those cells which were successfully transfected as demonstrated by the GFP-dependent fluorescence also stained brightly with the TLR9 antibody.
CpG-ODN stimulation reduces spontaneous and tumor necrosis factor-alpha (TNF-α)/Cycloheximide (CHX)-induced apoptosis
The expression of TLR9 in tumor cells and cell lines rises up the question, whether this receptor is functional active in these cells. As shown in figure 2a, CpG-ODN decrease the rate of spontaneous and induced apoptosis in HeLa and A549 cells after treatment with TNF-α and CHX. Representative histograms demonstrate the detection of annexin in the presence or absence of CpG-ODN and TNF-α/CHX (Fig. 2b and 2c). The induction of apoptosis after stimulation with TNF-α/CHX was further verified by the expression of active caspase 3 as shown in figure 2d. In the presence of CpG-ODN the expression was reduced analogous to the reduction of annexin-staining (Fig. 2e).
Figure 2 CpG-ODN-stimulation decreases apoptosis in HeLa and A549 cells. Cells were stained with Annexin-V after CpG-ODN-stimulation in the presence or absence of TNF-α and CHX after 24 h (A). Data are expressed as the mean ± SD (n = 6). Student's t test was used for statistical analysis. Representative histograms are shown from experiments with HeLa cells after CpG-ODN-stimulation in the absence (B) or presence (C) of TNF-α and CHX. Caspase 3 expression in HeLa cells is shown after incubation with TNF-α and CHX (D). In the presence of CpG-ODN the expression is decreased (E). The percentage of positive cells in each sample is indicated.
Influence of induced apoptosis on TLR9 expression
Here we investigated, whether CpG-ODN can modulate their own receptor. We found no differences in TLR9 expression with and without CpG-ODN stimulation. However, in the presence of TNF-α/CHX the expression of TLR9 was strongly reduced, whereas CpG-ODN stimulation counteracted this downregulation (Fig. 3a and 3b).
Figure 3 TLR9 expression after CpG-ODN-stimulation in HeLa cells: There is no difference in TLR9 expression with and without CpG-ODN-stimulation after 24 h (A). CpG-ODN partially inhibit downregulation of TLR9 which is induced by TNF-α and CHX (B). FI = fluorescence intensity.
Secretion of MCP-1 in response to CpG-ODN and TNF-α
In order to obtain further information about the functional activity of TLR9 in tumors we studied cytokine release upon CpG-ODN stimulation. The measurement of cytokines from stimulated HeLa and A549 cells revealed a significantly enhanced release of monocyte chemoattractant protein-1 (MCP-1) after 24 h of stimulation in response to CpG-ODN or TNF-α (Fig. 4a). The production was further enhanced when stimulated with a combination of CpG-ODN and TNF-α (Fig. 4a). There was no effect of CpG-ODN on TNF-α production (data not shown). To verify the induction of MCP-1 by CpG-ODN in cell lines we additionally analyzed human tumor tissues by RT-PCR; the results are shown in figure 4b. The relative amounts of RT-PCR-signals for MCP-1 in relation to GAPDH were higher in the specimens treated with CpG-ODN if compared with the controls confirming the results obtained in cell culture experiments on the tissue level.
Discussion
By application of a novel fixation technique we specify for the first time the expression of TLR9 protein and mRNA in a selection of human non small cell lung cancer tissues as well as cell lines. Stimulation of the TLR-9 expressing cell lines A549 and HeLa with CpG-ODN showed a marked antiapoptotic effect. In addition, there was substantially enhanced release of MCP-1 from the cell lines upon CpG-ODN stimulation which was also shown in ex vivo experiments. We conclude the expression of a functionally active TLR9 in human malignant tumors.
The presence of molecules involved in ontogenesis e.g. the carcinoembryonic antigen (CEA) is frequently observed in malignant tumors suggesting a kind of "shift-back" towards earlier developmental stages [26]. The significance and underlying mechanisms of this phenomenon are poorly understood; nevertheless, the detection of such molecules is used for diagnostic purposes in cancer [27]. The role of TLR in mammalian embryogenesis is unknown, and thus far there is no evidence for an endogenous TLR9 ligand homologous to Spaetzle. Such a ligand could play a role for the activation of human TLR9. Whether the expression of TLR9 in human malignant cells takes advantage of TLR9-function in embryogenesis therefore remains unclear.
On the other hand TLR9 in malignant cells could have similar functions as in cells of the innate and adaptive immune system. In humans TLR9 has been described to be mainly expressed in B-lymphocytes, monocytes and plasmacytoid dendritic cells [28]. In addition Platz et al. reported a weak expression in respiratory epithelial cell lines and primary epithelial cells [29].
The CpG-ODN sequence M362 used in our study is known to potently activate TLR9-expressing immune cells in humans including plasmacytoid dendritic cells and B cells as shown by Hartmann et al. [25] B cells are induced to proliferate and secrete immunoglobulin in response to CpG-ODN, dendritic cells produce a wide array of cytokines and apoptosis is inhibited [30,31].
These mechanisms are both reflected in the results we obtained in our study after CpG-ODN stimulation of malignant cells:
Firstly, stimulation of the A549 and HeLa cells with CpG-ODN showed an antiapoptotic effect. This was demonstrated for spontaneous as well as induced apoptosis with TNF-α and CHX after 24 h. Our observation is consistent with previous evidence in other cell lines. Yi et al. demonstrated antiapoptotic effects of CpG-ODN in a mouse B lymphoma cell line [32], and similar changes were described in chronic lymphocytic leukemia cells [33,34]. Previous data of systemic administration of bacterial DNA as a single agent in vivo showed anti-tumor effects. However, this anti-tumor effect appears to be effective indirectly and is related to enhanced NK cell activity. In a murine model of lymphoma the immunostimulatory effect of CpG-ODN was demonstrated to be responsible for the observed anti-tumor effects [35]. Carpentier et al. have shown that CpG-ODN in vivo induced rejection of neuroblastoma xenografts [36]. In contrast CpG-ODN had no effect on survival in mice inoculated with the 38C13 murine B cell lymphoma. However, a single injection of CpG-ODN enhanced the response to anti-tumor antibody therapy [37]. To what extent the antiapoptotic effects of CpG-ODN on tumor cells demonstrated in our study affect the tumorbiology in vivo requires further investigation.
Secondly, tumor cell lines (A549 and HeLa) stimulated with CpG-ODN showed strong secretion of the CC chemokine MCP-1. Furthermore a similar effect was observed in the investigated tumor tissues. Immunostimulatory properties together with anti-tumor activity of bacterial DNA were initially reported for a DNA fraction derived from mycobacteria by Tokunaga and coworkers [38]. It is known that such DNA induces enhanced production of various cytokines with anti-tumoral activity in NK cells, B cells, monocytes, macrophages and dendritic cells, such as TNF-α, IL-12, and IFN-γ [39]. In our study a substantial costimulatory effect in addition to CpG-ODN was achieved using TNF-α. MCP-1 has various biological activities including the induction of increased cytotoxic activity of monocytes and NK cells. Transfection of MCP-1 into a human malignant glioma cell line tested on nude mice did not reduce the tumor mass but was associated with the infiltration of large numbers of NK cells and monocytes at the tumor site [40]. A further study by Nokihara et al. performed with transfection of the MCP-1 gene into human lung adenocarcinoma cells showed reduced systemic spread of transfected cells inoculated i.v. in NK cell-intact severe combined immunodeficient (SCID) mice. These findings suggest that locally produced MCP-1 suppresses tumor progression by a NK cell-mediated mechanism [41]. Thus, apart from the direct activation of immune cells, the effect of CpG-ODN stimulation on the secretion of MCP1 by TLR9 expressing tumor cells could possibly lead to anti-tumoral effects due to an increase of local MCP1 production which then might lead to attraction of immune cells. The costimulatory effect of TNF-α as demonstrated in vitro in this study could further enhance this scenario.
Regarding TLR9 expression in nonmalignant lung tissue our data confirm the findings of low TLR9 expression in respiratory cells of Platz et al. [29], who have been working on single cell preparations. However TLR9 expression was only seen sporadically weak in nonmalignant lung tissue.
Biological explanations for the TLR9 expression in malignant cells require further investigations. Three possibilities are conceivable: Either this could represent a bystander phenomenon, a side effect of a pathway functional to a different purpose. Secondly the upregulation of TLR9 could be beneficial to the tumor, promoting tumor cell survival. Thirdly, it even might help immune control strategies of the organisms an element of a pathway directing defense mechanisms against malignantly transforming cells. While the first possibility seems unlikely in the light of our findings of a functionality of the receptor in various in vitro and ex vivo experiments, our data provide evidence for the second as well as the third possibility; the sum effect of these two counteracting mechanisms in an in vivo setting can not be estimated from these experiments and could even differ from tumor entity to tumor entity.
Conclusions
In conclusion, we showed in a selection of samples that human malignant tumors express functionally active TLR9 and respond to CpG treatment with prolonged survival and chemokine release. This might influence the effects of CpG-ODN based anti-tumor therapies. Broad screening approaches will be worthwhile to further substantiate these initial results.
While recent strategies in tumor-immunology mainly target a strengthening of the host-defense, we provide evidence that the malignant cells themselves can be regarded active players in the complex struggle between tumor and host. In any case CpG-ODN based anti-tumor therapies should be reconsidered in the light of our findings since CpG-ODN products are currently in Phase I/II clinical trials both as a monotherapy and as part of multi-drug regimens.
Author's contributions
DD carried out the flow cytometry and cytokine assays and was involved in the design and coordination of the study and drafting the manuscript. DA and AJU carried out the confocal microscopy, RT-PCR with cell lines and were involved in drafting the manuscript. JG was involved in immunohistochemistry of cell lines and the design of the study. DB conducted the surgical part of the study. EV conducted the pathological part of the study and was involved in the design of the study. KD and PZ conducted the clinical part of the study and were involved in the design and coordination of the study. TG performed the immunohistochemistry, in situ hybridization and RT-PCR with tissues and conceived of the study. All authors read and approved the final manuscript.
Acknowledgements
The authors thank H. Kühl, D. Bubritzki, S. Adrian, J. Hofmeister and S. Ross for excellent technical assistance, Elvira Richter for sequencing the PCR-products and Maria Manoukian for help with the confocal microscopy.
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| 15631627 | PMC544843 | CC BY | 2021-01-04 16:36:27 | no | Respir Res. 2005 Jan 4; 6(1):1 | utf-8 | Respir Res | 2,005 | 10.1186/1465-9921-6-1 | oa_comm |
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Biomed Eng OnlineBioMedical Engineering OnLine1475-925XBioMed Central London 1475-925X-4-11563163510.1186/1475-925X-4-1ResearchEvolutionary autonomous agents and the nature of apraxia Borrett Donald S [email protected] Frank [email protected] Hon C [email protected] Division of Neurology, Toronto East General Hospital, Toronto, Canada2 Department of Physiology, University of Toronto, Toronto, Canada2005 4 1 2005 4 1 1 7 10 2004 4 1 2005 Copyright © 2005 Borrett et al; licensee BioMed Central Ltd.2005Borrett 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
Evolutionary autonomous agents are robots or robot simulations whose controller is a dynamical neural network and whose evolution occurs autonomously under the guidance of a fitness function without the detailed or explicit direction of an external programmer. They are embodied agents with a simple neural network controller and as such they provide the optimal forum by which sensorimotor interactions in a specified environment can be studied without the computational assumptions inherent in standard neuroscience.
Methods
Evolutionary autonomous agents were evolved that were able to perform identical movements under two different contexts, one which represented an automatic movement and one which had a symbolic context. In an attempt to model the automatic-voluntary dissociation frequently seen in ideomotor apraxia, lesions were introduced into the neural network controllers resulting in a behavioral dissociation with loss of the ability to perform the movement which had a symbolic context and preservation of the simpler, automatic movement.
Results
Analysis of the changes in the hierarchical organization of the networks in the apractic EAAs demonstrated consistent changes in the network dynamics across all agents with loss of longer duration time scales in the network dynamics.
Conclusion
The concepts of determinate motor programs and perceptual representations that are implicit in the present day understanding of ideomotor apraxia are assumptions inherent in the computational understanding of brain function. The strength of the present study using EAAs to model one aspect of ideomotor apraxia is the absence of these assumptions and a grounding of all sensorimotor interactions in an embodied, autonomous agent. The consistency of the hierarchical changes in the network dynamics across all apractic agents demonstrates that this technique is tenable and will be a valuable adjunct to a computational formalism in the understanding of the physical basis of neurological disorders.
evolutionary autonomous agentsapraxiaembodimentgenetic algorithmdynamical system theory
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Background
The conceptual framework by which a neurological syndrome such as apraxia is presently explained is based on a computational understanding of brain function. Briefly, this framework assumes the existence of well-defined, determinate perceptual representations and motor programs that interact in a fashion similar to the way that symbols are manipulated in a computer. Recent theoretical and applied work in natural and artificial systems, however, has conspired to shift the emphasis in neuroscience from the computational paradigm to a dynamical understanding of brain function [1-5]. This approach emphasizes that cognition occurs in an embodied agent interacting dynamically with its environment and avoids the assumptions of determinate motor programs and perceptual representations implicit in the computational framework [6,7]. The identification of the dynamical changes in an apractic nervous system may provide a causally mechanistic explanation for the syndrome that may complement the higher-level descriptive explanation of the standard computational approach.
The apraxias constitute a spectrum of movement disorders in which there is impairment in the performance of a skilled, learned movement that cannot be attributed to an elementary motor or sensory deficit. Based of the pioneering work of Liepmann [8], the apraxias have traditionally been divided into ideational, ideomotor and limb-kinetic apraxia. Limb-kinetic apraxia, which is felt by many to not be a true apraxia, manifests as slowness or clumsiness of distal limb movements with preservation of knowledge of the appropriate action to perform. Ideational apraxia is characterized by loss of knowledge of how an object is used or as impairment in the sequencing of constituent movements in a complex movement. Ideomotor apraxia is usually diagnosed on the basis of spatiotemporal errors that occur on transitive gesture tasks requiring demonstration of the pantomime appropriate to specific object use [9]. Asking a patient to demonstrate how they would use a comb or a hammer would be typical transitive gesture tasks used in the assessment of the presence or absence of ideomotor apraxia. In many cases of ideomotor apraxia, the spatiotemporal errors improve when the object is actually used rather than when its use is pantomimed. In addition, the movements are often performed normally when they occur spontaneously but are impaired when the patient is instructed to perform the movement. The patient may scratch his nose spontaneously but may be unable to perform this task to command. This voluntary-automatic dissociation, although frequently seen in ideomotor apraxia, is not universal [10].
The standard conceptualization of apraxia is based on a two-system model of action: a conceptual system, located in the dominant parietal lobe, and a production system localized to the frontal lobe [11]. Dysfunction of the former would lead to ideational apraxia and dysfunction in the latter would result in ideomotor or limb-kinetic apraxia. With improved knowledge of the multiple frontoparietal circuits that subserve visuospatial transformations for reaching, somatosensory transformations for postural adjustments and the coding of peripersonal space for limb and neck movements, it has been possible to analyze the deficits which occur in apraxia in more detail than afforded by the standard conceptualization. Based primarily on primate studies, specialized circuits responsible for more detailed properties of action have been identified and provide a framework by which the idiosyncratic deficits evident in the apractic patient that defy explanation by the simple two-system model have been explained [11].
Regardless of whether the standard two-system scheme of action or the more detailed model based on multiple, specialized parietofrontal circuits is used, the standard conceptualization of the origin of apraxia still conforms to the computational paradigm in which a specific motor program is activated based on the conceptual framework in place in the dominant parietal lobe. Similarly, the usual explanation for the automatic-voluntary dissociation frequently seen in ideomotor apraxia relies on this computational framework. This framework postulates that a verbal command establishes a conceptual bias in the parietal lobe that activates the appropriate motor program in the frontal lobe. In ideomotor apraxia there would be a disconnection between the instruction and the effector mechanisms frontally. To explain the preservation of the corresponding automatic movement, alternative pathways not dependent on the parietal lobe would need to remain functional.
Evolutionary autonomous agents (EAAs) are robots or robot simulations whose controller is a dynamical neural network and whose evolution is guided by a genetic algorithm. They are embodied agents-either software programs living in a virtual environment or true robots that function in a specified environment. These agents function autonomously in their environments with the agents performing such functions as navigation around obstacles, gathering food, seeking prey or mating partners. [12] Their development is guided by evolutionary algorithms which utilizes a fitness function to select the most appropriate agents for propagation. Motor or sensory activity, in particular, evolves autonomously in response to the constraints of the fitness function without the organizational restrictions imposed by the notions of determinate motor programs or perceptual representations. Such agents provide a system in which the organization of motor or perceptual activity can be followed and analyzed. Because its nervous system is limited to a small number of neuron-like elements, the analysis of the network dynamics of these agents is also more tractable. Their primary value for the neurosciences is that they provide simple systems unencumbered by the assumptions inherent in present day neurosciences that can serve as a test-bed for thinking about neural processing and techniques for deciphering these processes [13].
To model the voluntary-automatic dissociation seen in ideomotor apraxia, a lesioned EAA needs to demonstrate a behavioral dissociation between a movement that the agent does automatically and an identical movement that has a symbolic context. An analysis of the change in network dynamics that occurs in the apractic EAA will provide information on the physical basis of the dissociation without the assumptions inherent in a computational formalism. The extrapolation of results in the EAA to human brain function is based on a principle concerning the organization of complex systems that has been emphasized by Herbert Simon. He suggested that the organization of self-organizing complex systems is dependent only on the behavioral characteristics of the system and not the nature of the constituent elements of the system. "My central theme is that complexity takes the form of hierarchy and that hierarchic systems have some common properties independent of their specific content" [14]. Regardless of whether the system consists of 100 billion interacting living cells or a small number of computer generated input-output units, it is not inconceivable that the organizational structure of the systems will be similar if their demands are identical and if they are allowed to evolve autonomously. In the absence of an evolved agent with language capabilities, a paradigm is needed that captures the essential elements of an inability to move to command with preservation of that same movement if performed spontaneously. It is felt that the ability to move to a target location without ongoing visual feedback from the target represents the simplest activity upon which the dynamics and connectivity of a robotic system could develop cognitive functions such as off-line reasoning [15]. Since predicative activity such as language may originate in this type of network activity, this particular movement paradigm may be used as a surrogate for a verbal command.
The presence of multiple time scales in the dynamics of a neural network is indicative of a temporal hierarchical structure. Simon discussed the presence of higher and lower frequency dynamics in complex systems and associated more executive function with the lower frequency components. He stated that "it is generally believed that the relevant planning horizon of executives is longer, the higher their location in the organizational hierarchy" and that "the average interval between interactions are greater at higher than lower levels [14]." In the nervous system, it is also expected that such a multiple time scale framework would occur with a hierarchical structure requiring more executive function reflecting lower frequency dynamics evolving as tasks become more complicated. In this paper, the temporal hierarchical structure of the dynamics of the neural network controller of an EAA will be assessed by the analysis of the power spectral distribution and Hurst exponent of all nodes in the network [16]. The analysis will be applied to an EAA model of the voluntary-automatic dissociation seen in ideomotor apraxia in an attempt to causally explain its physical origin.
Methods
A simulation platform, WEBOTS (Cyberbotics, Switzerland), was used to simulate the movement of a Khepera robot (K-TEAM Corporation, Switzerland) in a dark 1 m square arena without walls or obstacles. The Khepera is a two-wheeled mobile robot with eight ambient light sensors and a rotation encoder for each wheel. The Khepera also has eight infrared proximity sensors but these were not activated in the present study. The neural network controller was composed of a layer of 5 fully connected radial basis function units or neurons. In addition, each unit projected to the two linear units or motor neurons and received inputs from the eight light sensors and the two wheel encoders.
A combined genetic and adaptation algorithm was used similar to that described by Urzelai and Floreano [17]. Briefly, the first generation at the beginning of the evolution was composed of a hundred individuals with randomly assigned synaptic weights and adaptation rules. A chromosome was made up of all the synaptic weights and their associated adaptation rules. A standard genetic algorithm with cross over and mutation operators were used with the best twenty individuals selected. The roulette wheel selection method, wherein the probability of an individual being selected for a new generation is the individual's fitness as a fraction of the total population fitness, was used. One of five Hebbian adapation rules were associated with each weight, the standard Hebbian, presynaptic, postsynaptic, covariance [17] and no change. Although the synaptic weights were altered by the adaptation rule during the life of each individual, it was the pre-adaptation weights and rules that were transmitted to the next generation. One individual had a lifespan of 75 seconds. Each sensorimotor cycle was 64 ms allowing adaptation to occur over 1172 iterations.
The arena was maintained dark, except at random times when a light of constant intensity would appear at random locations. The robot had to attempt to reach the light. There were two tasks for the robot during each trial. In the first task, the robot simply had to go to the light when it appeared. The light remained on and was extinguished when the robot moved to within 2 body diameters, or 7 cm of the light. After a 1–3 s of random delay (in darkness) another light would appear in the arena at another random location requiring the robot to approach that light. This would continue for a total of 4–6 constant lights. The second task began after this series of constant lights. With the second task the robot had to go to the location of a brief light flash (1 s or 16 sensorimotor cycles in duration) that did not persist while the robot approached the location. Since the robot could not use light intensity to continuously guide its movement to the location, successful completion of this task required the robot to have some notion of objective orientation and distance. Each trial lasted a total of 75 seconds.
The fitness function F was defined as:
F = T (s)·n
where T (s) = 1 for s ≤ 1 and
T (s) = s for s > 1,
n the number of lights reached, df the average distance of the robot from the flash point once the flash occurred, tf the time between flash onset and end of simulation, and ts the duration of simulation. Mutation rate was in the 0.2 – 0.5% range.
Evolution was continued until the fitness plateaued. Three populations were evolved requiring up to 1300 generations before there was a clear plateau. Individuals in each population were screened for their ability to accomplish the tasks. Each screening trial was the same as the trials in which the robots were evolved, that is, a 75 second epoch during which the robot had to approach several sustained light locations followed by a single unsustained light flash. Invariably, individuals appeared first that were able to go to the sustained light but failed to respond to the light flash. Eventually, individuals with high fitness were found that were able to successfully complete both tasks. From this latter group, lesions were introduced into the neural networks and the subsequent individual was screened with the same 75 s trial. Individuals were subsequently identified in whom the lesion resulted in the loss of the ability to move toward the brief light flash with preservation of the ability to move toward the sustained light.
The lesions that were introduced into the networks were inactivations of single synapses. Typically, an individual that could successfully accomplish both tasks was screened for apraxia by lesioning each synapse in the network and observing the subsequent behavior of that individual. Although individual neurons could have been inactivated, this approach was avoided because the network only had 5 neurons and the resulting lesioned individual often demonstrated gross motor deficits.
Once individuals were identified which could successfully accomplish both tasks and in whom a specific lesion resulted in a dissociation between movement towards the sustained light and movement towards the light flash, their network dynamics were characterized in two ways. First, a fast Fourier transform (FFT) was performed on the activation pattern of all 5 neurons in the network. The time window chosen in the calculation of the FFT was the entire test epoch. The Hurst exponent was then calculated by the average wavelet coefficient method [18] for all 5 neurons in the network again using the entire test epoch.
Results
Five agents were identified which were able to successfully accomplish both tasks and in whom a lesion resulted in the dissociation between the two tasks. Analysis was performed on all 5 of the neurons in each individual. Five trials were used in each individual to allow statistical analysis of the Hurst exponent data.
Figures 1 and 2 demonstrate prototypical data obtained for one individual which was able to accomplish both tasks prior to lesioning and demonstrated a dissociation after lesion. In figure 1, before lesioning, only the trajectories to the last constant light stimulus followed by the brief light flash are shown. In figure 2, after lesioning, the last two trajectories to the sustained light stimuli are shown with the agent failing to approach the brief light flash. Individual neuron activity of all 5 neurons for the entire test epoch before and after lesioning along with the FFT data of each neuron before and after lesioning is also shown. The Hurst exponent for the entire epoch for each neuron is also indicated. Visual inspection of the FFT data showed a consistent trend in all neurons with a relative loss of low frequency components in the apractic robots compared to the normal situation.
Figure 1 (a) Trajectory of a robot which was able to accomplish both tasks; only the last 3 movements are shown. The robot begins at the open triangle. It then moves to position 1 (sustained light) and then to position 2 (brief light flash). (b) Activity from each neuron of the network and from the light sensor; data for the entire epoch is shown which constituted movements to 5 different sustained lights followed by 1 brief light flash. (c) FFT data for all five neurons calculated over the entire epoch; the value of the corresponding Hurst exponent for each neuron is on the top right.
Figure 2 (a) Trajectory of a lesioned robot which lost the ability to move to the brief light flash; only the last 3 successful movements are shown. The robot begins at the open triangle. It then moves to position 1 (sustained light) and then to position 2 (sustained light). The robot remained at position 2 despite the occurrence of a brief light flash at position 3. (b) Activity from each neuron of the lesioned network and from the light sensor; data for the entire epoch is shown which constituted movements to 6 different sustained lights and inability to move to the brief light flash. (c) FFT data for all five neurons of the lesioned robot over the entire epoch. The value of the corresponding Hurst exponent of each neuron is on the top right.
We computed the Hurst exponents of the 25 neurons from the 5 individuals drawn from 3 populations. Five trials were executed for each individual. When comparing the Hurst exponents for each neuron before and after the lesion, highly significant differences were obtained (p < 0.03 to 10-7) for 24 of the 25 neurons, with the remaining neuron showing the same trend but not reaching statistical significance. The grand average of the 125 Hurst exponents (5 trials each for 25 neurons) was1.0957 ± 0.2325 for the normal condition, and 0.8292 ± 0.1833 for the apractic condition.
Both the obvious frequency changes in the FFT data and the Hurst exponent calculations show that the loss of the ability to perform a movement that has a symbolic context is associated with a loss in the longer time scales (low frequency components) in the hierarchical organization of the neural network Since the calculation of the Hurst exponent is based on the full test epoch and since most of the robot movement during this period was the movement to the constant light rather than the light flash, this result also demonstrates that the strategy for both movements was influenced by the lesion despite maintained ability to move to the constant light.
Discussion
Despite significant advances in the understanding of the anatomy and physiology of normal movement and the pathophysiology of movement disorders, their conceptual framework still relies on the computational paradigm used by cognitive science and neuroscience for decades. This approach assumes the existence of determinate structures in the CNS that are manipulated in the same fashion that a computer manipulates symbols or language combines words. In the last 10 years, there has been a shift in focus with appreciation of the limitations of the computational approach and the realization of the importance of embodiment in conceptualization of motor behavior. When the social and the environmental influences on behavior are analyzed without prejudice, it becomes clear that indeterminacy is a hallmark of our functioning [19] and the idea of determinate perceptual representations and motor programs is an assumption commensurate with the approach of the natural sciences in general. This indeterminacy is captured in the formalism of dynamical systems theory. EAAs provide a system by which development of cognitive structures evolve in an embodied agent constrained only by the nature of the environment and the definition of fitness. They assume no structural characteristics of perception or movement but provide a forum by which sensorimotor structures arise as the organism self organizes in response to its interaction with the environment. Because they possess a simple network controller, the analysis of their development and architecture is more tractable than a similar analysis of the human nervous system.
We have demonstrated that in an evolved agent with two modes of environmental interaction that can be viewed as automatic and symbolic, lesions that produce the equivalent of the automatic-voluntary dissociation seen in ideomotor apraxia cause a change in the hierarchical organization of its dynamical neural network controller with a loss of the longer duration time scales corresponding to the loss of symbolic function. Two techniques were utilized to demonstrate these changes in the network dynamics, a Fourier analysis of the activity of all 5 neurons in the network and the calculation of the their Hurst exponents. With Fourier analysis, EAAs that were able to successfully perform both tasks demonstrated a 1/f power spectral distribution. This type of scale free distribution has been encountered in a number of physical and biological systems including heart rate variability, electrical currents and reaction times in human cognition [16,20]. The 1/f distribution has also been associated with self-organized criticality, a theory of the internal interactions of large systems that has been postulated to govern such diverse natural phenomena as the size of avalanches and earthquakes [21]. This 1/f pattern was altered in the apractic EAAs indicating that this abnormal behavior was associated with a loss of the lower frequency components in the network dynamics. The calculation of the Hurst exponent is based on long range correlations over all time scales in the signal of interest and is a measure of persistence or memory, that is, how long a given fluctuation in a time series will be reflected in future values of the series [18]. The larger the value of the exponent, the more dominant are low frequencies in the time signal. EAAs that could successfully perform both tasks had a larger value of the Hurst exponent compared to the lesioned, apractic agents again indicating the loss of low frequency components in the network dynamics of the apractic agents.
Does the EAA paradigm described capture the essential features necessary to causally explain the automatic-voluntary dissociation seen in ideomotor apraxia? Ideally, to most reliably model this phenomenon, an EAA would need to have language capabilities. Barring that, it is argued that the ability to move to a target site without the continued visual presence of that target captures the minimal requirements necessary to extrapolate to the case of a verbal command. Although controversy exists concerning the conceptual or operational definition of internal representations in evolved agents [22,23], Clark has argued that the notion of representation remains valuable in evolved robotic systems and can be defined well enough to discuss the physical basis of cognition [15]. He distinguished between what he called weak and strong representations. A weak representation in a robotic system is the dynamics and connectivity of the network which is associated with a behavior requiring ongoing sensory feedback. A movement that is done spontaneously and automatically would be a type of behavior whose underlying network would use a weak representation. A strong representation in a robotic system is the network dynamics and connectivity associated with a behavior that does not require ongoing sensory feedback for its successful execution. He argued that the kind of network organization that supports movement in the absence of visual feedback, such as the movement of an EAA to a target flash location, is the simplest kind of strong representation and is prototypical of representations in robotic systems that could support more sophisticated cognitive processes including off-line reasoning. Since language capability would require the presence of strong representations, it was felt that the movement of an EAA to a target location without continued visual presence of the target was an acceptable surrogate for a verbal command.
It was clear while individual EAAs were being observed as they performed the trial in the normal and apractic conditions that the apractic robots often demonstrated minor deficits even in the case in which they were approaching the sustained light target. Usually, this took the form of a mild slowness in movement although other deficits were seen such as altered trajectories toward the sustained light source. It has been assumed that patients with ideomotor apraxia function normally in the environments to which they are accustomed and in which automatic behavior is expected. This has also been suggested as a reason why apraxia is not detected as frequently as its true incidence predicts [10]. This clear cut dissociation would also be predicted by a computational formalism since distinct and determinate motor programs would lend themselves to inactivation of one with maintenance of the other. Recently, this idea has been disputed and, in fact, careful examination of apractic patients reveals deficits even in movements that are performed automatically [10]. The results in our EAA simulation corroborates this more recent analysis. Since determinate motor programs or perceptual representation do not exist in the EAAs, it would be expected that a lesion in the network would have some effect on all aspects of the agent's motor performance.
The notion that a lesion that causes apraxia has a global effect on the patient that is not restricted to those functions that can be considered symbolic was elaborated in detail by Merleau-Ponty [19]. He performed an extensive existential analysis of one patient, Schneider by name, who had suffered a shell injury to his brain. Merleau-Ponty suggested that the standard empirical or cognitive explanations of the nature of apraxia are limited because they fail to take into consideration the phenomenology of apraxia. According to Merleau-Ponty, "Beneath the intelligence as an anonymous function or as a categorical process, a personal core has to be recognized, which is the patient's being, his power of existing. It is here that illness has its seat". He argued that it is "ridiculous to think that the shell splinter directly struck symbolic consciousness" but rather "any pathological degeneration should affect the whole of consciousness." This phenomenological insight was mirrored in the observation that lesions that resulted in a behavioral dissociation in the EAAs had an influence on the dynamics of the network even in the tasks that were behaviorly unchanged. By avoiding the assumptions inherent in the computational framework and by evolving hierarchical self-organization, including symbolic thought, from the most basic level of sensorimotor interactions in an embodied agent, EAAs provide the optimal framework by which these phenomenological observations on the nature of apraxia can be modeled. In fact, taking phenomenological accuracy in addition to physiological plausibility as a constraint in the selection of those EAAs that successfully perform a behavioral task will help to discover those networks whose architecture and function most closely parallel those of the human nervous system [24].
Conclusion
In an embodied agent with two modes of environmental interaction which can be viewed as automatic and symbolic, lesions that produce the experimental equivalent of voluntary-automatic dissociation of ideomotor apraxia cause a change in the hierarchical organization of its dynamical neural network controller with a loss of the longer duration time scales corresponding to the loss of symbolic function. The identification of the dynamical changes in an apractic nervous system may provide a causally mechanistic explanation for the syndrome that may complement the higher-order descriptive explanation of the standard computational approach. The methodology of EAAs is just beginning to be recognized and it is hoped that further studies can be performed with this methodology to further understand the physical origin of normal and pathological brain function.
This study was funded by a research grant from the Toronto East General Research Foundation.
We thank Weyland Cheng and Owen Wong for assistance in the research.
Author's contributions
DSB conceived the study, participated in its design and the data analysis and drafted the manuscript. FJ carried out all computer simulations, participated in the study design and the data analysis and contributed to the manuscript draft. HCK conceived the study, participated in its design and the data analysis and contributed to the manuscript draft. All authors read and approved the final manuscript.
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| 15631635 | PMC544844 | CC BY | 2021-01-04 16:37:33 | no | Biomed Eng Online. 2005 Jan 4; 4:1 | utf-8 | Biomed Eng Online | 2,005 | 10.1186/1475-925X-4-1 | oa_comm |
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J CarcinogJournal of Carcinogenesis1477-3163BioMed Central London 1477-3163-3-161561757010.1186/1477-3163-3-16ResearchNitrosative stress induces DNA strand breaks but not caspase mediated apoptosis in a lung cancer cell line Bentz Brandon G [email protected] Neal D [email protected] James A [email protected] G Kenneth [email protected] Department of Surgery, Division of Otolaryngology-Head and Neck Surgery, University of Utah, 3362 Huntsman Cancer Institute, 2000 Circle of Hope, Salt Lake City, UT, 84112-5550, USA2 Center for Molecular Biology of Oral Disease (MC860), College of Dentistry, University of Illinois at Chicago, USA and the Jesse Brown VAMC, 801 South Paulina Street, Chicago, IL, 60612-7213, USA3 Department of Pathology, Northwestern University Medical Center, W127 Ward 6-223, 303 East Chicago, Ave., Chicago, IL, 60611, USA2004 23 12 2004 3 16 16 10 6 2004 23 12 2004 Copyright © 2004 Bentz et al; licensee BioMed Central Ltd.2004Bentz 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
Key steps crucial to the process of tumor progression are genomic instability and escape from apoptosis. Nitric oxide and its interrelated reactive intermediates (collectively denoted as NOX) have been implicated in DNA damage and mutational events leading to cancer development, while also being implicated in the inhibition of apoptosis through S-nitrosation of key apoptotic enzymes. The purpose of this study was to explore the interrelationship between NOX-mediated DNA strand breaks (DSBs) and apoptosis in cultured tumor cell lines.
Methods
Two well-characterized cell lines were exposed to increasing concentrations of exogenous NOX via donor compounds. Production of NOX was quantified by the Greiss reaction and spectrophotometery, and confirmed by nitrotyrosine immunostaining. DSBs were measured by the alkaline single-cell gel electrophoresis assay (the COMET assay), and correlated with cell viability by the MTT assay. Apoptosis was analyzed both by TUNEL staining and Annexin V/propidium iodine FACS. Finally, caspase enzymatic activity was measured using an in-vitro fluorogenic caspase assay.
Results
Increases in DNA strand breaks in our tumor cells, but not in control fibroblasts, correlated with the concentration as well as rate of release of exogenously administered NOX. This increase in DSBs did not correlate with an increase in cell death or apoptosis in our tumor cell line. Finally, this lack of apoptosis was found to correlate with inhibition of caspase activity upon exposure to thiol- but not NONOate-based NOX donor compounds.
Conclusions
Genotoxicity appears to be highly interrelated with both the concentration and kinetic delivery of NOX. Moreover, alterations in cell apoptosis can be seen as a consequence of the explicit mechanisms of NOX delivery. These findings lend credence to the hypothesis that NOX may play an important role in tumor progression, and underscores potential pitfalls which should be considered when developing NOX-based chemotherapeutic agents.
Nitric OxideDNA Strand Breaks
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Background
Nitric oxide (NO•) is a ubiquitous nitrogen radical species that has been found to exert protean influences on physiologic and pathophysiologic processes in a wide variety of organ systems. Vastly increasing the functional consequences of NO• production is its interrelationship between the nitroxyl anion (NO-) and the nitrosonium cation (NO+) depending upon the redox environment in which NO• is being produced. Each of these interrelated redox species demonstrates its own biological consequences. Collectively, nitric oxide biology attributable to the integrated actions of these three species (referred to as NOX) demonstrates broad reaching consequences.
It is generally thought that well regulated levels of NOX production is important to numerous physiologic processes, while NOX overproduction is increasingly being implicated in pathophysiologic processes via nitrosative stress. One molecular mechanism underlying these pathophysiologic processes is NOX-mediated genomic damage inducing apoptosis in susceptible cells [1]. This induction of apoptosis is thought to be dependent upon an intact p53-pathway in response to genotoxicity [2].
While the field of NOX in cancer biology is a new and developing area of research, the intricacies of NOX influence are beginning to be elucidated. We have previously demonstrated widespread increases in expression of nitric oxide synthase isoforms within human primary tumors when compared to surrounding normal tissues [3-6]. Both cancer promotive and cancer protective roles have been ascribed to NOX. Cancer promoting effects include: 1) the capacity to trigger mutagenesis, 2) enhance growth, invasion, angiogenesis and metastasis of tumors, 3) select for increasingly virulent tumor cell clones, and 4) suppress the host anti-tumor immune response [7,8]. Cancer protective actions are manifest in the ability of immune-mediated NOX production to attenuate tumor cell respiration and DNA synthesis as well as to trigger apoptosis. The mechanistic basis of these differences remains unknown, but may be explained by: 1) differences in the relevant levels of NOX-species, 2) the redox environment of NOX production, 3) the presence and proximity of downstream response elements within cells under study, 4) the susceptibility of these NOX targets, 5) as well as the rate of NOX detoxification.
If DNA repair mechanisms are overwhelmed or are incapable of adequate repair, endogenous cell death is triggered via apoptosis. This "programmed cell death" thus prevents damaged cells from undergoing replication perpetuating mutational events. Chronic nitrosative stress is implicated in inhibiting apoptosis. Potential mechanisms for this inhibition of apoptosis include the alteration of protein transcription and translation, or post-translational control of protein function [9]. NOX can alter protein function through post-translational modification of thiol groups via S-nitrosation, a key motif within many enzymes and structural proteins [10]. One target of this type of post-translational modulation of apoptotic enzymatic activity are the caspases [11].
Thus, nitrosative stress can on one hand induce DNA damage while inhibiting apoptosis, on the other. This lends mechanistic support to the hypothesis that chronic nitrosative stress may be cancer promotive in specific situations. Based on these data, the purpose of this study was to investigate whether cultured tumor cells exposed to exogenously administered nitrosative stress undergo NOX-mediated DNA damage. Furthermore, we investigate the relationship between this ongoing DNA damage and cell death via apoptosis within our cell lines of interest.
Methods
Cell Lines
The human lung adenocarcinoma cell line (A549) and the control SV40 transformed human fibroblast cell line (WI38) were obtained from the American Type Culture Collection (Manassas, VA). A549 was grown in RPMI media, while WI38 cells were grown in MEM (Gibco, Paisley, UK). All media was supplemented with 10% fetal calf serum, penicillin, streptomycin, L-glutamine, and fungizone. For various tests, cells were harvested after trypsin-EDTA treatment, washed with Dulbecco's PBS, and resuspended in serumless media.
Chemicals
Cells were incubated with one of four NOX-donor compounds that differed in their mode of donation of NO-equivalents and their half-lives. Two of these compounds were NONOate donor compounds [diethylenetriamine-NONOate (DETA-NONOate, Sigma Chemical Company, St. Louis, MO) and Spermine-NONOate (Oxis International, Portland, OR)] which donate NO• its pure form into the aqueous environment. The other two compounds were thiol-based nitric oxide donors [(±)-S-Nitroso-N-acetypenicillamine (SNAP) and N-(β-D-Glucopyranosyl)-N2-acetyl-S-nitroso-D,L-penicillamide) glyco-SNAP (Oxis International, Portland, OR)], which preferentially donate NO+-equivalents to other thiols. Additionally, these donors differed in their stability. Based upon our spectrophotometric analysis at 37°C and a pH of 7.4, Spermine-NONOate has t1/2 = 5 hours, DETA-NONOate a t1/2 ≈ 24 hours, SNAP a t1/2 = 10 hours, and glyco-SNAP a t1/2 = 28 hours [data not shown]. Furthermore, NONOate-based donors donate two molar equivalents of NOX per mole of donor compound, whereas the thiol donors deliver only one mole of NOX per mole of donor compound. These donor compounds were chosen to assess whether differences in the concentration, mode, or the rate of NOX delivery alter the genotoxicity of this radical species.
Nitrite Production in Media
Nitric oxide donors were added to media without cells or to cell supernatants at increasing concentrations from 75 to 600 μM. After 24 hours of incubation the amount of nitrite produced in the media was assayed by the Greiss reaction as previously described [12,13]. Briefly, 50 μl of media was added to 50 μl of 1% sulfanilamide in 2.5% H3PO4. Then 50 μl of 0.1% napthylethylenediamine dihydrochloride in 2.5% H3PO4 was added in a 96-well microtiter plate. After incubation at room temperature for 30 minutes, the absorbance was measured on a microplate reader (Molecular Devices, Sunnyvale, CA) at 540 nm. The concentration of nitrite in the media was quantified as derived from standard curves created by adding known concentrations of NaNO2 from 50 to 300 μM sodium nitrite. NOX delivery kinetics was confirmed by determining changes in λ maximum of each compound over time on a spectrophotometer (Beckman Coulter DU530, Fullerton, CA).
Single Cell Gel Electrophoresis (COMET assay)
The COMET assay was performed according to the procedure of Singh et al. with a few modifications [14]. Briefly, 120 μl of 0.5% normal melting point agarose in Ca+2 and Mg+2-free phosphate buffer at 56°C were quickly layered onto a fully frosted slide and immediately covered with a cover-slip. The slides were kept at 4°C to allow the agarose to solidify. After gently removing the cover-slip a 50 μl aliquot of cell suspension was mixed with an equal volume of 1% low melting point agarose (Sigma, St. Louis, MO) at 37°C and quickly pipetted onto the first agarose layer in the same manner. Finally, 70 μl of 0.5% LMP agarose was added to cover the cell layer. The slide sandwiches without cover-slips were immersed in freshly prepared, cold lysing buffer [2.5 mol/l NaCl, 100 mmol/l Na2EDTA, 10 mmol/l Tris, 1% N-Lauroyl sarcosine sodium salt, pH 10, with 1% Triton X-100 added just before use] and kept at 4°C for 45 min to 1 hour.
The slides were placed on a horizontal gel electrophoresis platform and covered with cold alkaline buffer [300 mmol/l NaOH, and 1 mmol/l Na2EDTA] for 8 to 20 minutes in the dark at 4°C to allow DNA unwinding and expression of the alkali-labile sites. The timing for lysis and unwinding was determined empirically for each cell line. Electrophoresis was conducted at 4°C in the dark for 20 min at 25 V and 300 mA. The slides were then rinsed gently twice with neutralizing buffer (0.4 mol/l Tris, pH 7.5). Each slide was stained with 120 μl of propidium iodine (Sigma) at a concentration of 5 μg/ml and covered with a cover-slip.
COMET tail lengths were quantified as the distance from the centrum of the cell nucleus to the tip of the tail in pixel units, with the mean tail length being determined as the mean length of twelve tails.
TUNEL Assay for Apoptosis
A terminal deoxynucleotidyl transferase (TdT)-mediated dUTP labeling (TUNEL) method was used for the detection of apoptotic cell death. For this purpose, the Apop Tag-Plus in-Situ Apoptosis Detection Kit (peroxidase) (Oncor, Gaithersburg, MD) was used. The staining was performed according to the manufacturer's recommended procedure. In brief, cell cultures of unexposed or exposed cells were initially treated with proteinase K (20 μg/ml) for 15 minutes at room temperature prior to using TdT to label the 3'-OH ends of DNA with digoxigenin-labeled nucleotides (1 hour incubation at 37°C). The slides were then treated with anti-digoxigenin antibody-peroxidase conjugate for 30 minutes at room temperature, stained with 3'-3 diaminobenzidine tetrahydrochloride (DAB) for 5 minutes to produce the characteristic brown color of positive cells, counterstained with hematoxylin, and mounted. Sections included in the kit were stained and served as positive controls. Consecutive oil immersion (100× objective) fields were counted on an Olympus BX40 microscope. A minimum of 1000 cells were counted and the apoptotic index was calculated as the percentage of staining cells. Cells were defined as apoptotic when the whole nuclear area was labeled or when occasional labeled globular bodies (apoptotic bodies) could be observed in the cytoplasm. Apoptosis found in the untreated cell cytospins at this time point were found to be 1%. Negative controls were cytospins of our cell lines in their logarithmic phase of growth supplemented with conditioned media and FCS. No apoptotic cells were demonstrated in these cytospins.
Annexin V-FITC/propidium iodine FACS
Flow cytometric determination of apoptosis was performed using a commercially available (R&D Systems, Minneapolis, MN) Annexin V-FITC/propidium iodine apoptosis detection kit. Untreated and treated cells were collected after 24 hours of incubation by trypsinization and centrifugation at 500 × g for 5–10 minutes at room temperature. Cells were washed and resuspended in ice cold PBS and pelleted by centrifugation. Cells were then resuspended in Annexin V incubation reagent at a concentration of 1 × 106 cells/100 μl, and incubated in the dark for 15 minutes at room temperature. Binding buffer was then added to each sample. Samples were then analyzed within one hour by flow cytometry, and evaluated based on the percentage of the population of cells staining low or high for Annexin V (apoptotic cells) and propidium iodide (necrotic cells).
Caspase Activity Assay
Activities of caspase-3 and -9 were determined using the corresponding caspase activity detection kits (R&D Systems, Minneapolis, MN) as described previously [15]. Briefly, 100 μg of total protein was added to 50 μl of reaction buffer, and 5 μl of substrates DEVD-pNA and LEDH-pNA were used to analyze the activity of caspase-3 and -9 respectively. Samples were incubated at 37°C for 3 hours and the enzyme-catalyzed release of pNA was quantified at 405 nm using a microtiter plate reader. The values of treated samples were normalized to corresponding untreated controls allowing determination of the fold increase or decrease in caspase activity. Alterations in enzymatic activity directly attributable to NOX were determined by the change in caspase activity in the presence or absence of 1 mM DTT.
Cell Viability Assay
Cell viability was determined via a modified MTT assay as previously described [16]. In brief, cells were incubated with the various NOX donors for 24 hours at 37°C prior to cell viability assay. The media was aspirated and replaced with 0.2 ml of MTT (1 mg/ml in PBS), followed by incubation at 37°C for 5 hours. MTT was then aspirated and the wells were air dried for 5 minutes. The crystals were dissolved with 200 μl of DMSO, until the solution turned purple and absorbance analyzed in an enzyme-linked immunosorbent assay (ELISA) plate reader at 540 nm.
Nitrotyrosine Staining
Pathologic nitric oxide exposure was confirmed by immunostaining using an anti-nitrotyrosine (Upstate Biotechnology, Lake Placid, NY) monoclonal antibody by immunoperoxidase as previously described [17]. The nitrotoyrosine Mab have previously demonstrated specific activity against human nitrotyrosine.
Statistical Analysis
Statistical evaluation was performed using Prism-3.00 (GraphPad Software Inc., San Diego, CA). A p-value of <0.05 was considered significant. Values listed represented mean ± the standard error of the mean unless otherwise indicated.
Results
Increasing Concentrations of NOX donors Increases Nitrosative Stress
Our first aim was to confirm that adding nitric oxide donors into aqueous media increased NOX production in a concentration dependent fashion. To investigate this, we determined the accumulation of NO2- in the supernatants based on the Greiss reaction. Figure 1 demonstrates the concentration of nitrite in media in the absence of and in the presence of our two cell lines. In the absence of cells, we demonstrated an increasing concentration of nitrite within the media after 24 hours of incubation of various concentrations of all four NOX donors. This increase in nitrite accumulation was the same when NOX donors were added to the media alone or to the supernatants of our two cell lines. These data proved that our NOX donors were liberating NOX in a concentration dependent fashion.
Figure 1 Nitrite Production in Media and Supernatants in the Presence of Increasing Concentrations of NOX Donors: A. Nitrite production in media without cells, B. Nitrite production in WI38 supernatants, C. Nitrite production in A549 Supernatants. Measurements of nitrite production in media alone or the supernatant of WI38 and A549 as measured by the Greiss reaction demonstrate increasing concentrations of nitrite production in the presence of increasing concentrations of NOX donors. This confirms that our NOX donors are liberating NOX in a concentration-dependent fashion. Interestingly, the curves for SNAP and glycol-SNAP were shifted toward the right in the supernatants of both WI38 and A549 possibly reflecting the possibility that these thiol-based NOX donors were preferentially donating their NOX to intracellular thiols.
We then confirmed that the published half-lives of our donor compounds were consistent with the kinetic release of NOX in our system. Using λ maximum specific for each donor compound, we found that the decay in absorbance to half-maximum roughly reflected the published t1/2 for each of our donor compounds [data not shown].
To confirm that our cell lines were indeed being exposed to increasing amounts of NOX resulting from the addition of our NOX donors, we examined immunostaining for nitrotyrosine [Table 1]. In the absence of NOX donors, both cell lines demonstrated low levels of nitrotyrosine staining which increased with the addition of our donor compounds. The addition of exogenous NOX increased the staining intensity for nitrotyrosine in both cell lines, which appeared to saturate at a moderate level of immunostaining for nitrotyrosine. This result suggests ongoing low level of autologous nitration, and confirms that cells are being exposed to increasing nitrosative stress with the addition of NOX donors.
Table 1 Cell Line Nitrotyrosine Staining (Intensity)
NO• Donor Concentration (μM) Control SNAP Glyco-SNAP DETA Spermine
A549 0 +
75 ++ + + ++
150 ++ ++ ++ ++
300 ++ ++ ++ ++
WI38 0 +
75 + ++ + ++
150 + ++ ++ ++
300 ++ ++ ++ ++
Mean nitrotyrosine staining intensity per 100 cells: 0+-no staining, 1+-mild staining, 2+-moderate staining, 3+-intense staining.
DNA strand-breaks after exposure to NOX
In order to quantify the ability of exogenous NOX to cause mutational events, we performed the in-vitro single-cell gel electrophoresis assay (the COMET assay). Increasing concentrations of these NOX donors were added to the supernatants of our cell lines and DNA strand-breaks were measured. As seen in Figure 2, distinctly different susceptibility to NOX exposure was seen in WI38 when compared to the tumor cell lines A549. Increasing concentrations of NOX donors increased DNA strand breaks in a concentration dependent fashion in the lung adenocarcinoma cell line, A549. Exposure of WI38 cells to increasing concentrations of NOX failed to cause an increase in DNA fragmentation that was detectable by the COMET assay. Examples of COMET tail moments seen in untreated A549 cells, in cells exposed to low concentrations, and high concentrations of NOX donors are seen in Figure 3.
Figure 2 COMET Tail Moment Lengths After 24 hour Exposure to Various NOX Donors: A. COMET tail moment of WI38 after 24 hours of exposure to increasing concentrations of NOX, B. COMET tail moment of A549 after 24 hours of exposure to increasing concentrations of NOX. Measurements of the COMET tail moments for WI38 and A549 in the presence of increasing concentrations of NOX donors demonstrate that A549 was found to have increasing DNA strand breaks as nitrosative stress increased which was not seen in the WI38 control cell line. As seen in the graph, NOX donors with a long half-life [i.e. glyco-SNAP with a t1/2 = 28 hours and DETA-NONOate with a t1/2 of 20 hours] demonstrated significant increases in COMET tail moments only at the higher concentrations of NO donor, whereas NO donors with a shorter half-life [i.e. SNAP with a t1/2 of 10 hours and Spermine-NONOate with a t1/2 of 39 minutes] demonstrated significant increases in DNA strand breaks at lower concentrations. This confirms that A549 demonstrates more genomic instability in the presence of increasing nitrosative stress, which is not seen in control cells.
Figure 3 Example COMET Tail Moments for A549: These are example COMET tail moments for A549 in the presence of A.) 0 μM, B.) 75 μM, and C.) 150 μM Spermine-NONOate. Note that a significant increase in tail moment can be visualized with increasing concentrations of this NOX donor.
Determination of Cell Viability as a result of NOX-dependent DNA Fragmentation
We then utilized the MTT assay to explore if this NOX-donor compound induced DNA fragmentation caused a decrease in cell viability. As demonstrated in Figure 4, both cell lines were found to have a decrease in cell viability after exposure to increasing concentrations of either thiol based or NONOate-based NOX donors. Our control fibroblast cell line appeared to be more susceptible to NOX exposure, in that WI38 demonstrated a significant decrement in cell viability between 75 and 300 μM. Interestingly, the NONOate-based NOX donors were able to significantly decrease the cell viability of WI38 cells at a lower concentration than the thiol-based NOX donors. This is contrasted with our tumor cell line, A549. The decrement in cell viability for A549 was not significant until 600 μM of these NOX donors. In fact, a significant increase in cell growth was seen at the lowest concentration (75 μM) of SNAP in our A549 cells. Therefore, within the range of concentrations of NOX donor compounds studied, we have found that NOX has the ability to induce DNA fragmentation in A549 but not in the WI38 cells, while significantly decrease cell viability for WI38 relative to A549 cells.
Figure 4 Cell Viability as Determined by the MTT Assay with Exposure to Increasing Concentrations of NOX Donors: A. Cell viability of WI38 as measured by the MTT assay after exposure to increasing concentrations of NOX, B. Cell viability of A549 as measured by the MTT assay after exposure to increasing concentrations of NOX. The cell viability was significantly reduced in WI38 the presence of increasing concentrations of NOX donors at all concentrations greater than 150 μM (p < 0.007 for all) for DETA- and spermine-NONOate, whereas the thiol-based glycol-SNAP and SNAP did not significantly decrease cell viability until 300 μM concentrations. These results are contrasted with A549 cells which did not demonstrate a significant decrease in cell viability (p < 0.03) until 600 μM concentrations of either thiol-based or NONOate-based NOX donors. Interestingly, the lowest concentration of SNAP (75 μM) demonstrated a significant growth advantage (p = 0.01).
Determination of apoptosis after exposure to NOX donors
We then sought to determine whether NOX-mediated increases in DNA strand breaks correlated with apoptosis via the TUNEL assay. Since a decrease in cell viability was not seen until A549 cells were exposed to 600 μM NOX-donor, we examined apoptosis after exposure to this concentration. Cultured cells were exposed to 600 μM of the thiol-based NOX-donor, SNAP, or the NONO-ate based NOX donor, Spermine. Either NOX donor compound exposure for 24 hours did not significantly increase the percentage of TUNEL positive cells when compared with untreated controls [data not shown].
Because TUNEL positivity represents a late event in the apoptotic cascade, we wanted to ensure that the assay time frame did not represent assessment prior to detectable apoptosis. Therefore, we confirmed our apoptosis results via Annexin-V/propidium iodide FACS sorting. After exposure of our cell lines to 600 μM of SNAP or Spermine-NONOate, we confirmed the absence of any significant increase in apoptosis (Figure 5).
Figure 5 Annexin-V/Propidium Iodine FACS in A549 Cells Unexposed and Exposed to High Concentrations of NOX Donors: A. Untreated control, B. 600 μM Spermine NONOate treated, C. 600 μM SNAP treated. Annexin-V/Propidium Iodine FACS confirms the TUNEL results that demonstrate that no significant increase in apoptosis is noted in A549 with or without exposure to high nitrosative stress.
Determination of caspase activity after NOX exposure or inhibition of NOX production
In an attempt to explain these seemingly contradictory data, we examined the role of NOX in the modulation of caspase enzyme function via an in vitro caspase activity assay (figure 6). Using the artificial substrates DEVD-pNA and LEDH-pNA for assaying caspase-3 and -9 activity respectively, we demonstrated a basal inhibition of caspase-3 in A549 cells as demonstrated by a significant increase in caspase substrate cleavage with the addition of the NOX-specific inhibitor, NΩ-monomethyl-L-arginine (L-NMMA), when compared to control cells. This inhibition appears to be attributable to basal S-nitrosation of caspase-3, since the addition of 1 mM DTT also significantly increased caspase-3 activity. With the addition of exogenous NOX via our thiol-based donor compound, SNAP, a significant decrease in the activity of both caspases was found. In contrast, our NONOate-based NOX donor, Spermine, was unable to decrease either caspase-3 or -9 activities. The inhibition of both caspase's activity by SNAP was reversed with exposure to 1 mM DTT, suggesting that transnitrosation of these caspase enzymes may contribute to the inhibition of enzymatic activity. These data suggest that thiol-based NOX-mediated inhibition of caspase activity in A549 cells may cause a relative tolerance to ongoing DNA damage by inhibiting apoptosis.
Discussion
We have demonstrated that NOX genotoxicity is highly dependent upon the concentration and kinetics of delivery of NOX. In addition, tumor cells appear to have increased genomic instability but increased resistance to ongoing NOX-mediated genotoxicity when compared to more normal transformed cells which demonstrate less genomic instability but increased susceptibility to ongoing DNA damage. Moreover, the mechanism of NOX delivery appears to have profound consequences upon the function of downstream effector molecules such as caspases.
Over the past 20–30 years key proteins have been increasingly implicated as targets for nitric oxide signaling. With respect to its role in cancer, both carcino-protective [18] and carcinogenic roles [19] have been attributed to NOX. The seemingly paradoxic nature of NOX in cancer has made investigations in this area quite enigmatic.
Increasing evidence supports NOX's role in the induction and promotion of cancer [8,20]. Chronic inflammatory nitrosative stress has been implicated in carcinogenesis in a number of other organ systems [21,22]. Within this lung adenocarcinoma model, it is easy to hypothesize that either endogenous chronic inflammatory nitrosative stress as seen with idiopathic pulmonary fibrosis, or exogenous NOX as a result of cigarette smoking may predispose individuals to the development of this type of cancer.
One mechanism by which NOX has been hypothesized to be carcinogenic is through oxidative/nitrosative DNA damage and genotoxicity [23-25]. NOX can induce mutational events via DNA oxidation, deamination, point-mutations, and strand-breaks, thus contributing to the multi-step process of carcinogenesis. A number of papers have documented that NOX can cause DNA strand breaks in areas of chronic nitrosative stress [26-28]. Our data demonstrates that in the human lung adenocarcinoma cell line, A549, increasing concentrations of NOX donors increases DNA strand-breaks in a concentration dependent fashion. Moreover, the kinetics of NOX exposure also affects its genotoxicity. It is hypothesized that this ongoing DNA damage can lead to cancer development. In support of this notion, in a mouse model of inflammatory mediated lung carcinogenesis, studies have demonstrated that genetic ablation of the inducible isoform of nitric oxide synthase decreased the lung tumor multiplicity after exposure to the carcinogen, urethane [29].
The data presented herein of NOX-dependent DNA strand breaks in A549 cells is in stark contrast to NOX's inability to induce DNA fragmentation in our control fibroblast cell line, WI38. Normally, DNA damage is repaired by a complex series of DNA repair mechanisms. Defects in repair mechanisms can predispose people to cancer development [30]. An increasing body of evidence supports epigenetic modulation of enzymatic function, such as S-nitrosation [31]. As a result of nitrosation of key thiol groups within DNA repair enzymes, DNA-damage repair can be inhibited potentially perpetuating mutational events. One possible explanation for these opposing sets of data is that A549 may be capable of expressing unstimulated NOS activity [32], and therefore may have saturated such intracellular thiols as glutathione. In support of this hypothesis, immunostaining for nitrotyrosine in both cell lines found low levels of nitrotyrosine staining in the absence of exogenous NOX. Research is ongoing to determine whether tumor elicited NOX production may contribute to tumor progression.
If DNA damage overwhelms repair mechanisms, normal cells are triggered to undergo apoptosis. Redundant mechanisms for the induction of apoptosis exist, but the most common pathway involves the induction of p53. With the induction of p53, apoptosis is triggered through the release of cytochrome c and caspase activation. Despite possessing wild-type p53, our data demonstrated that in A549 cells we were unable to detect a decrease in cell viability as measured by the MTT assay until very high concentrations of donors, or an increase in cell apoptosis via either the TUNEL assay or Annexin V/propidium iodide FACS. There are conflicting results in the literature regarding the effects of exogenous NOX on cell viability and apoptosis. Certain studies have shown that NOX by itself does not cause A549 cells to die, but the addition of hyperoxia induces rapid cell death [33], perhaps through the production of peroxynitrite. On the other hand, other studies have demonstrated that the addition of the NOX donor SNAP decreased cell viability via apoptosis in a concentration dependent fashion [34]. This study went on to analyze apoptosis in cells other than A549, thus precluding a direct correlation with A549 and apoptosis after SNAP exposure.
Further confusing the interrelationship between DNA fragmentation and apoptosis is the fact that the prevailing opinion in the literature is that there exists a direct correlation between the fragmentation seen in the COMET assay and the fragmentation seen in apoptosis. With triggering of apoptosis, DNA strands are cleaved or nicked by nucleases, exposing 3'-hydroxyl ends. Nick-end cleavage may not necessarily equate with strand breaks seen with various mutagens. A growing body of evidence suggests that the fragmentation found in the COMET assay is not necessarily related to apoptotic fragmentation [35,36]. Studies have demonstrated that COMET tail moments of cells undergoing apoptosis are highly fragmented to the point of loosing nuclear architecture [37]. Furthermore, nitroso-compound related DNA damage appears to be independent of more commonly accepted measures of apoptosis, such as the TUNEL assay [38]. The mechanistic basis for these disparate measures remains to be determined, but appears to represent fundamental differences in the characteristics of exposed 3' ends seen with oxidative DNA damage versus apoptosis related DNA cleavage. Therefore, the COMET assay may be a measure of genotoxicity, without necessarily detecting apoptosis.
Lending credence to NOX's possible carcinogenic role is the implication from several lines of study that NOX can influence enzymatic function of such enzymes as caspases [11]. In this way, NOX may increase the threshold by which cells that have undergone a level of genotoxicity would be triggered to undergo apoptosis. If any member of the apoptotic pathway is lost or inhibited, the threshold for apoptosis would theoretically be increased. NOX has demonstrated a capability of inhibiting caspase activity through S-nitrosation of key thiols [15,39]. Our data supports the NOX-mediated inhibition of both caspase-3 and -9 in A549 cells with ongoing DNA mutational events. In fact, prior data has demonstrated that basal inhibition of mitochondrial caspases by cysteine nitrosation must be removed in order to activate the caspase-mediated apoptotic pathways [40]. Our data supports this in A549 in that the addition of the antioxidant, DTT, increased caspase activity detectable by our in fluorogenic assay. One potential mechanism for this inhibition of casapase activity is the S-nitrosation of essential thiol groups [40]. The fact that our thiol-based NOX donor appeared to inhibit caspase activity more efficiently that the NONOate donor further supports this notion, since it is well known that nitroso-thiols will preferentially transnitrosate other thiols [41]. In light of the increasing interest in NOX-donor compounds as cytotoxic cancer therapy, a careful consideration of potential downstream effects of the mode of NOX delivery should be undertaken. Caspases can be inhibited not only by direct protein S-nitrosation, but also indirectly by a cGMP-mediated pathways [39]. The return of caspase activity with the addition of DTT would further suggest that caspase inactivation was mediated by S-nitrosation in our lung adenocarcinoma model.
The implications of these data can be applied both to the understanding of tumor progression as well as the design of NOX-based chemotherapeutic strategies. It is well established that tumors demonstrate a high rate of cellular turnover, with the vast majority of cells undergoing cell death resulting from ongoing genomic instability. Only those clones possessing a survival advantage will be able to repopulate the tumor and thus contribute to increasing aggressiveness, the ability to invade and metastasize, and resistance to further therapeutic interventions. Chronic nitrosative stress may be contributing to this process by selecting for more virulent clones via inducing DNA mutational events and perpetuating these mutations through a relative inhibition of apoptosis. Moreover, as more investigations explore the potential use of NOX delivery as a possible cytotoxic chemotherapy, the issues of concentration, kinetics, and mechanism of delivery must be carefully considered in order to avoid untoward pro-carcinogenic side effects.
Conclusions
In conclusion, we demonstrate that tumor cells experience an increase in DNA strand breaks with increasing nitrosative stress relative to the concentration and kinetics of delivery. This nitrosative stress does not correlate with increased cell death or apoptosis in established tumor cell lines, which is in stark contrast to non-tumor immortalized cells. The lack of apoptosis associated with increased DNA fragmentation may in part be explained by the inhibition of caspase-3 and -9 activity by thiol-based delivery but not NONOate-based delivery of NOX. Taken together, these data support the role of NOX in nitrosative genomic instability as well as inhibiting apoptosis, implicating it in cancer promotion. The demonstration of these same findings in rodent cell lines would establish the foundation for animal models with which to fully elucidate the role of NOX in tumor development and growth.
Author's contributions
BB conceived of the study, participated in the design of the study, and performed the statistical analysis. GH interpreted the results of the FACS and immunoassays. NH carried out the majority of the COMET assays, as well as performing the caspase assay. JR participated in the study design and coordination. All authors read and approved the final manuscript.
Figure 6 Caspase 3 (DEVD-pNa) and Caspase 9 (LEDH-pNa) Activity as Measured by the in vitro Fluorogenic Caspase Assay in the Absence and Presence of High Concentrations of NOX Donors: Both endogenous and exogenous NOX appears to influence caspase activity in A549 cells. Caspase 3 activity appears to demonstrate endogenous NOX inhibition since the addition of 3 mM L-NMMA or 1 mM DTT significantly increased caspase 3 activity (p = 0.001, p = 0.003 respectively). No similar endogenous inhibition was seen with caspase 9. The addition of the thiol-based NOX donor, SNAP (600 μM), significantly inhibited both caspase 3 and caspase 9 (p < 0.001 for both) when compared with control cell caspase activity. This caspase activity was significantly reversed with the addition of DTT (p < 0.001 for both caspase 3 and 9). Treatment groups: 1) Control, 2) 3 mM L-NMMA, 3) 1 mM DTT, 4) 600 μM Spermine-NONOate, 5) 600 μM SNAP, 6) 600 μM SNAP + 1 mM DTT.
Acknowledgements
We would like to acknowledge the assistance Kim Elseth and Dave Burnett for their contribution of the collection of data, and the mentorship of Frank Fitzpatrick, Ph.D. in the preparation of this manuscript.
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| 15617570 | PMC544845 | CC BY | 2021-01-04 16:39:16 | no | J Carcinog. 2004 Dec 23; 3:16 | utf-8 | J Carcinog | 2,004 | 10.1186/1477-3163-3-16 | oa_comm |
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J CarcinogJournal of Carcinogenesis1477-3163BioMed Central London 1477-3163-3-161561757010.1186/1477-3163-3-16ResearchNitrosative stress induces DNA strand breaks but not caspase mediated apoptosis in a lung cancer cell line Bentz Brandon G [email protected] Neal D [email protected] James A [email protected] G Kenneth [email protected] Department of Surgery, Division of Otolaryngology-Head and Neck Surgery, University of Utah, 3362 Huntsman Cancer Institute, 2000 Circle of Hope, Salt Lake City, UT, 84112-5550, USA2 Center for Molecular Biology of Oral Disease (MC860), College of Dentistry, University of Illinois at Chicago, USA and the Jesse Brown VAMC, 801 South Paulina Street, Chicago, IL, 60612-7213, USA3 Department of Pathology, Northwestern University Medical Center, W127 Ward 6-223, 303 East Chicago, Ave., Chicago, IL, 60611, USA2004 23 12 2004 3 16 16 10 6 2004 23 12 2004 Copyright © 2004 Bentz et al; licensee BioMed Central Ltd.2004Bentz 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
Key steps crucial to the process of tumor progression are genomic instability and escape from apoptosis. Nitric oxide and its interrelated reactive intermediates (collectively denoted as NOX) have been implicated in DNA damage and mutational events leading to cancer development, while also being implicated in the inhibition of apoptosis through S-nitrosation of key apoptotic enzymes. The purpose of this study was to explore the interrelationship between NOX-mediated DNA strand breaks (DSBs) and apoptosis in cultured tumor cell lines.
Methods
Two well-characterized cell lines were exposed to increasing concentrations of exogenous NOX via donor compounds. Production of NOX was quantified by the Greiss reaction and spectrophotometery, and confirmed by nitrotyrosine immunostaining. DSBs were measured by the alkaline single-cell gel electrophoresis assay (the COMET assay), and correlated with cell viability by the MTT assay. Apoptosis was analyzed both by TUNEL staining and Annexin V/propidium iodine FACS. Finally, caspase enzymatic activity was measured using an in-vitro fluorogenic caspase assay.
Results
Increases in DNA strand breaks in our tumor cells, but not in control fibroblasts, correlated with the concentration as well as rate of release of exogenously administered NOX. This increase in DSBs did not correlate with an increase in cell death or apoptosis in our tumor cell line. Finally, this lack of apoptosis was found to correlate with inhibition of caspase activity upon exposure to thiol- but not NONOate-based NOX donor compounds.
Conclusions
Genotoxicity appears to be highly interrelated with both the concentration and kinetic delivery of NOX. Moreover, alterations in cell apoptosis can be seen as a consequence of the explicit mechanisms of NOX delivery. These findings lend credence to the hypothesis that NOX may play an important role in tumor progression, and underscores potential pitfalls which should be considered when developing NOX-based chemotherapeutic agents.
Nitric OxideDNA Strand Breaks
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Background
Nitric oxide (NO•) is a ubiquitous nitrogen radical species that has been found to exert protean influences on physiologic and pathophysiologic processes in a wide variety of organ systems. Vastly increasing the functional consequences of NO• production is its interrelationship between the nitroxyl anion (NO-) and the nitrosonium cation (NO+) depending upon the redox environment in which NO• is being produced. Each of these interrelated redox species demonstrates its own biological consequences. Collectively, nitric oxide biology attributable to the integrated actions of these three species (referred to as NOX) demonstrates broad reaching consequences.
It is generally thought that well regulated levels of NOX production is important to numerous physiologic processes, while NOX overproduction is increasingly being implicated in pathophysiologic processes via nitrosative stress. One molecular mechanism underlying these pathophysiologic processes is NOX-mediated genomic damage inducing apoptosis in susceptible cells [1]. This induction of apoptosis is thought to be dependent upon an intact p53-pathway in response to genotoxicity [2].
While the field of NOX in cancer biology is a new and developing area of research, the intricacies of NOX influence are beginning to be elucidated. We have previously demonstrated widespread increases in expression of nitric oxide synthase isoforms within human primary tumors when compared to surrounding normal tissues [3-6]. Both cancer promotive and cancer protective roles have been ascribed to NOX. Cancer promoting effects include: 1) the capacity to trigger mutagenesis, 2) enhance growth, invasion, angiogenesis and metastasis of tumors, 3) select for increasingly virulent tumor cell clones, and 4) suppress the host anti-tumor immune response [7,8]. Cancer protective actions are manifest in the ability of immune-mediated NOX production to attenuate tumor cell respiration and DNA synthesis as well as to trigger apoptosis. The mechanistic basis of these differences remains unknown, but may be explained by: 1) differences in the relevant levels of NOX-species, 2) the redox environment of NOX production, 3) the presence and proximity of downstream response elements within cells under study, 4) the susceptibility of these NOX targets, 5) as well as the rate of NOX detoxification.
If DNA repair mechanisms are overwhelmed or are incapable of adequate repair, endogenous cell death is triggered via apoptosis. This "programmed cell death" thus prevents damaged cells from undergoing replication perpetuating mutational events. Chronic nitrosative stress is implicated in inhibiting apoptosis. Potential mechanisms for this inhibition of apoptosis include the alteration of protein transcription and translation, or post-translational control of protein function [9]. NOX can alter protein function through post-translational modification of thiol groups via S-nitrosation, a key motif within many enzymes and structural proteins [10]. One target of this type of post-translational modulation of apoptotic enzymatic activity are the caspases [11].
Thus, nitrosative stress can on one hand induce DNA damage while inhibiting apoptosis, on the other. This lends mechanistic support to the hypothesis that chronic nitrosative stress may be cancer promotive in specific situations. Based on these data, the purpose of this study was to investigate whether cultured tumor cells exposed to exogenously administered nitrosative stress undergo NOX-mediated DNA damage. Furthermore, we investigate the relationship between this ongoing DNA damage and cell death via apoptosis within our cell lines of interest.
Methods
Cell Lines
The human lung adenocarcinoma cell line (A549) and the control SV40 transformed human fibroblast cell line (WI38) were obtained from the American Type Culture Collection (Manassas, VA). A549 was grown in RPMI media, while WI38 cells were grown in MEM (Gibco, Paisley, UK). All media was supplemented with 10% fetal calf serum, penicillin, streptomycin, L-glutamine, and fungizone. For various tests, cells were harvested after trypsin-EDTA treatment, washed with Dulbecco's PBS, and resuspended in serumless media.
Chemicals
Cells were incubated with one of four NOX-donor compounds that differed in their mode of donation of NO-equivalents and their half-lives. Two of these compounds were NONOate donor compounds [diethylenetriamine-NONOate (DETA-NONOate, Sigma Chemical Company, St. Louis, MO) and Spermine-NONOate (Oxis International, Portland, OR)] which donate NO• its pure form into the aqueous environment. The other two compounds were thiol-based nitric oxide donors [(±)-S-Nitroso-N-acetypenicillamine (SNAP) and N-(β-D-Glucopyranosyl)-N2-acetyl-S-nitroso-D,L-penicillamide) glyco-SNAP (Oxis International, Portland, OR)], which preferentially donate NO+-equivalents to other thiols. Additionally, these donors differed in their stability. Based upon our spectrophotometric analysis at 37°C and a pH of 7.4, Spermine-NONOate has t1/2 = 5 hours, DETA-NONOate a t1/2 ≈ 24 hours, SNAP a t1/2 = 10 hours, and glyco-SNAP a t1/2 = 28 hours [data not shown]. Furthermore, NONOate-based donors donate two molar equivalents of NOX per mole of donor compound, whereas the thiol donors deliver only one mole of NOX per mole of donor compound. These donor compounds were chosen to assess whether differences in the concentration, mode, or the rate of NOX delivery alter the genotoxicity of this radical species.
Nitrite Production in Media
Nitric oxide donors were added to media without cells or to cell supernatants at increasing concentrations from 75 to 600 μM. After 24 hours of incubation the amount of nitrite produced in the media was assayed by the Greiss reaction as previously described [12,13]. Briefly, 50 μl of media was added to 50 μl of 1% sulfanilamide in 2.5% H3PO4. Then 50 μl of 0.1% napthylethylenediamine dihydrochloride in 2.5% H3PO4 was added in a 96-well microtiter plate. After incubation at room temperature for 30 minutes, the absorbance was measured on a microplate reader (Molecular Devices, Sunnyvale, CA) at 540 nm. The concentration of nitrite in the media was quantified as derived from standard curves created by adding known concentrations of NaNO2 from 50 to 300 μM sodium nitrite. NOX delivery kinetics was confirmed by determining changes in λ maximum of each compound over time on a spectrophotometer (Beckman Coulter DU530, Fullerton, CA).
Single Cell Gel Electrophoresis (COMET assay)
The COMET assay was performed according to the procedure of Singh et al. with a few modifications [14]. Briefly, 120 μl of 0.5% normal melting point agarose in Ca+2 and Mg+2-free phosphate buffer at 56°C were quickly layered onto a fully frosted slide and immediately covered with a cover-slip. The slides were kept at 4°C to allow the agarose to solidify. After gently removing the cover-slip a 50 μl aliquot of cell suspension was mixed with an equal volume of 1% low melting point agarose (Sigma, St. Louis, MO) at 37°C and quickly pipetted onto the first agarose layer in the same manner. Finally, 70 μl of 0.5% LMP agarose was added to cover the cell layer. The slide sandwiches without cover-slips were immersed in freshly prepared, cold lysing buffer [2.5 mol/l NaCl, 100 mmol/l Na2EDTA, 10 mmol/l Tris, 1% N-Lauroyl sarcosine sodium salt, pH 10, with 1% Triton X-100 added just before use] and kept at 4°C for 45 min to 1 hour.
The slides were placed on a horizontal gel electrophoresis platform and covered with cold alkaline buffer [300 mmol/l NaOH, and 1 mmol/l Na2EDTA] for 8 to 20 minutes in the dark at 4°C to allow DNA unwinding and expression of the alkali-labile sites. The timing for lysis and unwinding was determined empirically for each cell line. Electrophoresis was conducted at 4°C in the dark for 20 min at 25 V and 300 mA. The slides were then rinsed gently twice with neutralizing buffer (0.4 mol/l Tris, pH 7.5). Each slide was stained with 120 μl of propidium iodine (Sigma) at a concentration of 5 μg/ml and covered with a cover-slip.
COMET tail lengths were quantified as the distance from the centrum of the cell nucleus to the tip of the tail in pixel units, with the mean tail length being determined as the mean length of twelve tails.
TUNEL Assay for Apoptosis
A terminal deoxynucleotidyl transferase (TdT)-mediated dUTP labeling (TUNEL) method was used for the detection of apoptotic cell death. For this purpose, the Apop Tag-Plus in-Situ Apoptosis Detection Kit (peroxidase) (Oncor, Gaithersburg, MD) was used. The staining was performed according to the manufacturer's recommended procedure. In brief, cell cultures of unexposed or exposed cells were initially treated with proteinase K (20 μg/ml) for 15 minutes at room temperature prior to using TdT to label the 3'-OH ends of DNA with digoxigenin-labeled nucleotides (1 hour incubation at 37°C). The slides were then treated with anti-digoxigenin antibody-peroxidase conjugate for 30 minutes at room temperature, stained with 3'-3 diaminobenzidine tetrahydrochloride (DAB) for 5 minutes to produce the characteristic brown color of positive cells, counterstained with hematoxylin, and mounted. Sections included in the kit were stained and served as positive controls. Consecutive oil immersion (100× objective) fields were counted on an Olympus BX40 microscope. A minimum of 1000 cells were counted and the apoptotic index was calculated as the percentage of staining cells. Cells were defined as apoptotic when the whole nuclear area was labeled or when occasional labeled globular bodies (apoptotic bodies) could be observed in the cytoplasm. Apoptosis found in the untreated cell cytospins at this time point were found to be 1%. Negative controls were cytospins of our cell lines in their logarithmic phase of growth supplemented with conditioned media and FCS. No apoptotic cells were demonstrated in these cytospins.
Annexin V-FITC/propidium iodine FACS
Flow cytometric determination of apoptosis was performed using a commercially available (R&D Systems, Minneapolis, MN) Annexin V-FITC/propidium iodine apoptosis detection kit. Untreated and treated cells were collected after 24 hours of incubation by trypsinization and centrifugation at 500 × g for 5–10 minutes at room temperature. Cells were washed and resuspended in ice cold PBS and pelleted by centrifugation. Cells were then resuspended in Annexin V incubation reagent at a concentration of 1 × 106 cells/100 μl, and incubated in the dark for 15 minutes at room temperature. Binding buffer was then added to each sample. Samples were then analyzed within one hour by flow cytometry, and evaluated based on the percentage of the population of cells staining low or high for Annexin V (apoptotic cells) and propidium iodide (necrotic cells).
Caspase Activity Assay
Activities of caspase-3 and -9 were determined using the corresponding caspase activity detection kits (R&D Systems, Minneapolis, MN) as described previously [15]. Briefly, 100 μg of total protein was added to 50 μl of reaction buffer, and 5 μl of substrates DEVD-pNA and LEDH-pNA were used to analyze the activity of caspase-3 and -9 respectively. Samples were incubated at 37°C for 3 hours and the enzyme-catalyzed release of pNA was quantified at 405 nm using a microtiter plate reader. The values of treated samples were normalized to corresponding untreated controls allowing determination of the fold increase or decrease in caspase activity. Alterations in enzymatic activity directly attributable to NOX were determined by the change in caspase activity in the presence or absence of 1 mM DTT.
Cell Viability Assay
Cell viability was determined via a modified MTT assay as previously described [16]. In brief, cells were incubated with the various NOX donors for 24 hours at 37°C prior to cell viability assay. The media was aspirated and replaced with 0.2 ml of MTT (1 mg/ml in PBS), followed by incubation at 37°C for 5 hours. MTT was then aspirated and the wells were air dried for 5 minutes. The crystals were dissolved with 200 μl of DMSO, until the solution turned purple and absorbance analyzed in an enzyme-linked immunosorbent assay (ELISA) plate reader at 540 nm.
Nitrotyrosine Staining
Pathologic nitric oxide exposure was confirmed by immunostaining using an anti-nitrotyrosine (Upstate Biotechnology, Lake Placid, NY) monoclonal antibody by immunoperoxidase as previously described [17]. The nitrotoyrosine Mab have previously demonstrated specific activity against human nitrotyrosine.
Statistical Analysis
Statistical evaluation was performed using Prism-3.00 (GraphPad Software Inc., San Diego, CA). A p-value of <0.05 was considered significant. Values listed represented mean ± the standard error of the mean unless otherwise indicated.
Results
Increasing Concentrations of NOX donors Increases Nitrosative Stress
Our first aim was to confirm that adding nitric oxide donors into aqueous media increased NOX production in a concentration dependent fashion. To investigate this, we determined the accumulation of NO2- in the supernatants based on the Greiss reaction. Figure 1 demonstrates the concentration of nitrite in media in the absence of and in the presence of our two cell lines. In the absence of cells, we demonstrated an increasing concentration of nitrite within the media after 24 hours of incubation of various concentrations of all four NOX donors. This increase in nitrite accumulation was the same when NOX donors were added to the media alone or to the supernatants of our two cell lines. These data proved that our NOX donors were liberating NOX in a concentration dependent fashion.
Figure 1 Nitrite Production in Media and Supernatants in the Presence of Increasing Concentrations of NOX Donors: A. Nitrite production in media without cells, B. Nitrite production in WI38 supernatants, C. Nitrite production in A549 Supernatants. Measurements of nitrite production in media alone or the supernatant of WI38 and A549 as measured by the Greiss reaction demonstrate increasing concentrations of nitrite production in the presence of increasing concentrations of NOX donors. This confirms that our NOX donors are liberating NOX in a concentration-dependent fashion. Interestingly, the curves for SNAP and glycol-SNAP were shifted toward the right in the supernatants of both WI38 and A549 possibly reflecting the possibility that these thiol-based NOX donors were preferentially donating their NOX to intracellular thiols.
We then confirmed that the published half-lives of our donor compounds were consistent with the kinetic release of NOX in our system. Using λ maximum specific for each donor compound, we found that the decay in absorbance to half-maximum roughly reflected the published t1/2 for each of our donor compounds [data not shown].
To confirm that our cell lines were indeed being exposed to increasing amounts of NOX resulting from the addition of our NOX donors, we examined immunostaining for nitrotyrosine [Table 1]. In the absence of NOX donors, both cell lines demonstrated low levels of nitrotyrosine staining which increased with the addition of our donor compounds. The addition of exogenous NOX increased the staining intensity for nitrotyrosine in both cell lines, which appeared to saturate at a moderate level of immunostaining for nitrotyrosine. This result suggests ongoing low level of autologous nitration, and confirms that cells are being exposed to increasing nitrosative stress with the addition of NOX donors.
Table 1 Cell Line Nitrotyrosine Staining (Intensity)
NO• Donor Concentration (μM) Control SNAP Glyco-SNAP DETA Spermine
A549 0 +
75 ++ + + ++
150 ++ ++ ++ ++
300 ++ ++ ++ ++
WI38 0 +
75 + ++ + ++
150 + ++ ++ ++
300 ++ ++ ++ ++
Mean nitrotyrosine staining intensity per 100 cells: 0+-no staining, 1+-mild staining, 2+-moderate staining, 3+-intense staining.
DNA strand-breaks after exposure to NOX
In order to quantify the ability of exogenous NOX to cause mutational events, we performed the in-vitro single-cell gel electrophoresis assay (the COMET assay). Increasing concentrations of these NOX donors were added to the supernatants of our cell lines and DNA strand-breaks were measured. As seen in Figure 2, distinctly different susceptibility to NOX exposure was seen in WI38 when compared to the tumor cell lines A549. Increasing concentrations of NOX donors increased DNA strand breaks in a concentration dependent fashion in the lung adenocarcinoma cell line, A549. Exposure of WI38 cells to increasing concentrations of NOX failed to cause an increase in DNA fragmentation that was detectable by the COMET assay. Examples of COMET tail moments seen in untreated A549 cells, in cells exposed to low concentrations, and high concentrations of NOX donors are seen in Figure 3.
Figure 2 COMET Tail Moment Lengths After 24 hour Exposure to Various NOX Donors: A. COMET tail moment of WI38 after 24 hours of exposure to increasing concentrations of NOX, B. COMET tail moment of A549 after 24 hours of exposure to increasing concentrations of NOX. Measurements of the COMET tail moments for WI38 and A549 in the presence of increasing concentrations of NOX donors demonstrate that A549 was found to have increasing DNA strand breaks as nitrosative stress increased which was not seen in the WI38 control cell line. As seen in the graph, NOX donors with a long half-life [i.e. glyco-SNAP with a t1/2 = 28 hours and DETA-NONOate with a t1/2 of 20 hours] demonstrated significant increases in COMET tail moments only at the higher concentrations of NO donor, whereas NO donors with a shorter half-life [i.e. SNAP with a t1/2 of 10 hours and Spermine-NONOate with a t1/2 of 39 minutes] demonstrated significant increases in DNA strand breaks at lower concentrations. This confirms that A549 demonstrates more genomic instability in the presence of increasing nitrosative stress, which is not seen in control cells.
Figure 3 Example COMET Tail Moments for A549: These are example COMET tail moments for A549 in the presence of A.) 0 μM, B.) 75 μM, and C.) 150 μM Spermine-NONOate. Note that a significant increase in tail moment can be visualized with increasing concentrations of this NOX donor.
Determination of Cell Viability as a result of NOX-dependent DNA Fragmentation
We then utilized the MTT assay to explore if this NOX-donor compound induced DNA fragmentation caused a decrease in cell viability. As demonstrated in Figure 4, both cell lines were found to have a decrease in cell viability after exposure to increasing concentrations of either thiol based or NONOate-based NOX donors. Our control fibroblast cell line appeared to be more susceptible to NOX exposure, in that WI38 demonstrated a significant decrement in cell viability between 75 and 300 μM. Interestingly, the NONOate-based NOX donors were able to significantly decrease the cell viability of WI38 cells at a lower concentration than the thiol-based NOX donors. This is contrasted with our tumor cell line, A549. The decrement in cell viability for A549 was not significant until 600 μM of these NOX donors. In fact, a significant increase in cell growth was seen at the lowest concentration (75 μM) of SNAP in our A549 cells. Therefore, within the range of concentrations of NOX donor compounds studied, we have found that NOX has the ability to induce DNA fragmentation in A549 but not in the WI38 cells, while significantly decrease cell viability for WI38 relative to A549 cells.
Figure 4 Cell Viability as Determined by the MTT Assay with Exposure to Increasing Concentrations of NOX Donors: A. Cell viability of WI38 as measured by the MTT assay after exposure to increasing concentrations of NOX, B. Cell viability of A549 as measured by the MTT assay after exposure to increasing concentrations of NOX. The cell viability was significantly reduced in WI38 the presence of increasing concentrations of NOX donors at all concentrations greater than 150 μM (p < 0.007 for all) for DETA- and spermine-NONOate, whereas the thiol-based glycol-SNAP and SNAP did not significantly decrease cell viability until 300 μM concentrations. These results are contrasted with A549 cells which did not demonstrate a significant decrease in cell viability (p < 0.03) until 600 μM concentrations of either thiol-based or NONOate-based NOX donors. Interestingly, the lowest concentration of SNAP (75 μM) demonstrated a significant growth advantage (p = 0.01).
Determination of apoptosis after exposure to NOX donors
We then sought to determine whether NOX-mediated increases in DNA strand breaks correlated with apoptosis via the TUNEL assay. Since a decrease in cell viability was not seen until A549 cells were exposed to 600 μM NOX-donor, we examined apoptosis after exposure to this concentration. Cultured cells were exposed to 600 μM of the thiol-based NOX-donor, SNAP, or the NONO-ate based NOX donor, Spermine. Either NOX donor compound exposure for 24 hours did not significantly increase the percentage of TUNEL positive cells when compared with untreated controls [data not shown].
Because TUNEL positivity represents a late event in the apoptotic cascade, we wanted to ensure that the assay time frame did not represent assessment prior to detectable apoptosis. Therefore, we confirmed our apoptosis results via Annexin-V/propidium iodide FACS sorting. After exposure of our cell lines to 600 μM of SNAP or Spermine-NONOate, we confirmed the absence of any significant increase in apoptosis (Figure 5).
Figure 5 Annexin-V/Propidium Iodine FACS in A549 Cells Unexposed and Exposed to High Concentrations of NOX Donors: A. Untreated control, B. 600 μM Spermine NONOate treated, C. 600 μM SNAP treated. Annexin-V/Propidium Iodine FACS confirms the TUNEL results that demonstrate that no significant increase in apoptosis is noted in A549 with or without exposure to high nitrosative stress.
Determination of caspase activity after NOX exposure or inhibition of NOX production
In an attempt to explain these seemingly contradictory data, we examined the role of NOX in the modulation of caspase enzyme function via an in vitro caspase activity assay (figure 6). Using the artificial substrates DEVD-pNA and LEDH-pNA for assaying caspase-3 and -9 activity respectively, we demonstrated a basal inhibition of caspase-3 in A549 cells as demonstrated by a significant increase in caspase substrate cleavage with the addition of the NOX-specific inhibitor, NΩ-monomethyl-L-arginine (L-NMMA), when compared to control cells. This inhibition appears to be attributable to basal S-nitrosation of caspase-3, since the addition of 1 mM DTT also significantly increased caspase-3 activity. With the addition of exogenous NOX via our thiol-based donor compound, SNAP, a significant decrease in the activity of both caspases was found. In contrast, our NONOate-based NOX donor, Spermine, was unable to decrease either caspase-3 or -9 activities. The inhibition of both caspase's activity by SNAP was reversed with exposure to 1 mM DTT, suggesting that transnitrosation of these caspase enzymes may contribute to the inhibition of enzymatic activity. These data suggest that thiol-based NOX-mediated inhibition of caspase activity in A549 cells may cause a relative tolerance to ongoing DNA damage by inhibiting apoptosis.
Discussion
We have demonstrated that NOX genotoxicity is highly dependent upon the concentration and kinetics of delivery of NOX. In addition, tumor cells appear to have increased genomic instability but increased resistance to ongoing NOX-mediated genotoxicity when compared to more normal transformed cells which demonstrate less genomic instability but increased susceptibility to ongoing DNA damage. Moreover, the mechanism of NOX delivery appears to have profound consequences upon the function of downstream effector molecules such as caspases.
Over the past 20–30 years key proteins have been increasingly implicated as targets for nitric oxide signaling. With respect to its role in cancer, both carcino-protective [18] and carcinogenic roles [19] have been attributed to NOX. The seemingly paradoxic nature of NOX in cancer has made investigations in this area quite enigmatic.
Increasing evidence supports NOX's role in the induction and promotion of cancer [8,20]. Chronic inflammatory nitrosative stress has been implicated in carcinogenesis in a number of other organ systems [21,22]. Within this lung adenocarcinoma model, it is easy to hypothesize that either endogenous chronic inflammatory nitrosative stress as seen with idiopathic pulmonary fibrosis, or exogenous NOX as a result of cigarette smoking may predispose individuals to the development of this type of cancer.
One mechanism by which NOX has been hypothesized to be carcinogenic is through oxidative/nitrosative DNA damage and genotoxicity [23-25]. NOX can induce mutational events via DNA oxidation, deamination, point-mutations, and strand-breaks, thus contributing to the multi-step process of carcinogenesis. A number of papers have documented that NOX can cause DNA strand breaks in areas of chronic nitrosative stress [26-28]. Our data demonstrates that in the human lung adenocarcinoma cell line, A549, increasing concentrations of NOX donors increases DNA strand-breaks in a concentration dependent fashion. Moreover, the kinetics of NOX exposure also affects its genotoxicity. It is hypothesized that this ongoing DNA damage can lead to cancer development. In support of this notion, in a mouse model of inflammatory mediated lung carcinogenesis, studies have demonstrated that genetic ablation of the inducible isoform of nitric oxide synthase decreased the lung tumor multiplicity after exposure to the carcinogen, urethane [29].
The data presented herein of NOX-dependent DNA strand breaks in A549 cells is in stark contrast to NOX's inability to induce DNA fragmentation in our control fibroblast cell line, WI38. Normally, DNA damage is repaired by a complex series of DNA repair mechanisms. Defects in repair mechanisms can predispose people to cancer development [30]. An increasing body of evidence supports epigenetic modulation of enzymatic function, such as S-nitrosation [31]. As a result of nitrosation of key thiol groups within DNA repair enzymes, DNA-damage repair can be inhibited potentially perpetuating mutational events. One possible explanation for these opposing sets of data is that A549 may be capable of expressing unstimulated NOS activity [32], and therefore may have saturated such intracellular thiols as glutathione. In support of this hypothesis, immunostaining for nitrotyrosine in both cell lines found low levels of nitrotyrosine staining in the absence of exogenous NOX. Research is ongoing to determine whether tumor elicited NOX production may contribute to tumor progression.
If DNA damage overwhelms repair mechanisms, normal cells are triggered to undergo apoptosis. Redundant mechanisms for the induction of apoptosis exist, but the most common pathway involves the induction of p53. With the induction of p53, apoptosis is triggered through the release of cytochrome c and caspase activation. Despite possessing wild-type p53, our data demonstrated that in A549 cells we were unable to detect a decrease in cell viability as measured by the MTT assay until very high concentrations of donors, or an increase in cell apoptosis via either the TUNEL assay or Annexin V/propidium iodide FACS. There are conflicting results in the literature regarding the effects of exogenous NOX on cell viability and apoptosis. Certain studies have shown that NOX by itself does not cause A549 cells to die, but the addition of hyperoxia induces rapid cell death [33], perhaps through the production of peroxynitrite. On the other hand, other studies have demonstrated that the addition of the NOX donor SNAP decreased cell viability via apoptosis in a concentration dependent fashion [34]. This study went on to analyze apoptosis in cells other than A549, thus precluding a direct correlation with A549 and apoptosis after SNAP exposure.
Further confusing the interrelationship between DNA fragmentation and apoptosis is the fact that the prevailing opinion in the literature is that there exists a direct correlation between the fragmentation seen in the COMET assay and the fragmentation seen in apoptosis. With triggering of apoptosis, DNA strands are cleaved or nicked by nucleases, exposing 3'-hydroxyl ends. Nick-end cleavage may not necessarily equate with strand breaks seen with various mutagens. A growing body of evidence suggests that the fragmentation found in the COMET assay is not necessarily related to apoptotic fragmentation [35,36]. Studies have demonstrated that COMET tail moments of cells undergoing apoptosis are highly fragmented to the point of loosing nuclear architecture [37]. Furthermore, nitroso-compound related DNA damage appears to be independent of more commonly accepted measures of apoptosis, such as the TUNEL assay [38]. The mechanistic basis for these disparate measures remains to be determined, but appears to represent fundamental differences in the characteristics of exposed 3' ends seen with oxidative DNA damage versus apoptosis related DNA cleavage. Therefore, the COMET assay may be a measure of genotoxicity, without necessarily detecting apoptosis.
Lending credence to NOX's possible carcinogenic role is the implication from several lines of study that NOX can influence enzymatic function of such enzymes as caspases [11]. In this way, NOX may increase the threshold by which cells that have undergone a level of genotoxicity would be triggered to undergo apoptosis. If any member of the apoptotic pathway is lost or inhibited, the threshold for apoptosis would theoretically be increased. NOX has demonstrated a capability of inhibiting caspase activity through S-nitrosation of key thiols [15,39]. Our data supports the NOX-mediated inhibition of both caspase-3 and -9 in A549 cells with ongoing DNA mutational events. In fact, prior data has demonstrated that basal inhibition of mitochondrial caspases by cysteine nitrosation must be removed in order to activate the caspase-mediated apoptotic pathways [40]. Our data supports this in A549 in that the addition of the antioxidant, DTT, increased caspase activity detectable by our in fluorogenic assay. One potential mechanism for this inhibition of casapase activity is the S-nitrosation of essential thiol groups [40]. The fact that our thiol-based NOX donor appeared to inhibit caspase activity more efficiently that the NONOate donor further supports this notion, since it is well known that nitroso-thiols will preferentially transnitrosate other thiols [41]. In light of the increasing interest in NOX-donor compounds as cytotoxic cancer therapy, a careful consideration of potential downstream effects of the mode of NOX delivery should be undertaken. Caspases can be inhibited not only by direct protein S-nitrosation, but also indirectly by a cGMP-mediated pathways [39]. The return of caspase activity with the addition of DTT would further suggest that caspase inactivation was mediated by S-nitrosation in our lung adenocarcinoma model.
The implications of these data can be applied both to the understanding of tumor progression as well as the design of NOX-based chemotherapeutic strategies. It is well established that tumors demonstrate a high rate of cellular turnover, with the vast majority of cells undergoing cell death resulting from ongoing genomic instability. Only those clones possessing a survival advantage will be able to repopulate the tumor and thus contribute to increasing aggressiveness, the ability to invade and metastasize, and resistance to further therapeutic interventions. Chronic nitrosative stress may be contributing to this process by selecting for more virulent clones via inducing DNA mutational events and perpetuating these mutations through a relative inhibition of apoptosis. Moreover, as more investigations explore the potential use of NOX delivery as a possible cytotoxic chemotherapy, the issues of concentration, kinetics, and mechanism of delivery must be carefully considered in order to avoid untoward pro-carcinogenic side effects.
Conclusions
In conclusion, we demonstrate that tumor cells experience an increase in DNA strand breaks with increasing nitrosative stress relative to the concentration and kinetics of delivery. This nitrosative stress does not correlate with increased cell death or apoptosis in established tumor cell lines, which is in stark contrast to non-tumor immortalized cells. The lack of apoptosis associated with increased DNA fragmentation may in part be explained by the inhibition of caspase-3 and -9 activity by thiol-based delivery but not NONOate-based delivery of NOX. Taken together, these data support the role of NOX in nitrosative genomic instability as well as inhibiting apoptosis, implicating it in cancer promotion. The demonstration of these same findings in rodent cell lines would establish the foundation for animal models with which to fully elucidate the role of NOX in tumor development and growth.
Author's contributions
BB conceived of the study, participated in the design of the study, and performed the statistical analysis. GH interpreted the results of the FACS and immunoassays. NH carried out the majority of the COMET assays, as well as performing the caspase assay. JR participated in the study design and coordination. All authors read and approved the final manuscript.
Figure 6 Caspase 3 (DEVD-pNa) and Caspase 9 (LEDH-pNa) Activity as Measured by the in vitro Fluorogenic Caspase Assay in the Absence and Presence of High Concentrations of NOX Donors: Both endogenous and exogenous NOX appears to influence caspase activity in A549 cells. Caspase 3 activity appears to demonstrate endogenous NOX inhibition since the addition of 3 mM L-NMMA or 1 mM DTT significantly increased caspase 3 activity (p = 0.001, p = 0.003 respectively). No similar endogenous inhibition was seen with caspase 9. The addition of the thiol-based NOX donor, SNAP (600 μM), significantly inhibited both caspase 3 and caspase 9 (p < 0.001 for both) when compared with control cell caspase activity. This caspase activity was significantly reversed with the addition of DTT (p < 0.001 for both caspase 3 and 9). Treatment groups: 1) Control, 2) 3 mM L-NMMA, 3) 1 mM DTT, 4) 600 μM Spermine-NONOate, 5) 600 μM SNAP, 6) 600 μM SNAP + 1 mM DTT.
Acknowledgements
We would like to acknowledge the assistance Kim Elseth and Dave Burnett for their contribution of the collection of data, and the mentorship of Frank Fitzpatrick, Ph.D. in the preparation of this manuscript.
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| 15631625 | PMC544846 | CC BY | 2021-01-04 16:39:05 | no | World J Surg Oncol. 2005 Jan 4; 3:1 | latin-1 | World J Surg Oncol | 2,005 | 10.1186/1477-7819-3-1 | oa_comm |
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Microb Cell FactMicrobial Cell Factories1475-2859BioMed Central London 1475-2859-3-161560691810.1186/1475-2859-3-16ResearchDirected evolution of single-chain Fv for cytoplasmic expression using the β-galactosidase complementation assay results in proteins highly susceptible to protease degradation and aggregation Philibert Pascal [email protected] Pierre [email protected] CNRS UMR 5160, Faculté de Pharmacie, 15, av. Charles Flahault, BP14491, 34093. Montpellier Cedex 5, France2004 17 12 2004 3 16 16 8 12 2004 17 12 2004 Copyright © 2004 Philibert and Martineau; 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
Antibody fragments are molecules widely used for diagnosis and therapy. A large amount of protein is frequently required for such applications. New approaches using folding reporter enzymes have recently been proposed to increase soluble expression of foreign proteins in Escherichia coli. To date, these methods have only been used to screen for proteins with better folding properties but have never been used to select from a large library of mutants. In this paper we apply one of these methods to select mutations that increase the soluble expression of two antibody fragments in the cytoplasm of E. coli.
Results
We used the β-galactosidase α-complementation system to monitor and evolve two antibody fragments for high expression levels in E. coli cytoplasm. After four rounds of mutagenesis and selection from large library repertoires (>107 clones), clones exhibiting high levels of β-galactosidase activity were isolated. These clones expressed a higher amount of soluble fusion protein than the wild type in the cytoplasm, particularly in a strain deficient in the cytoplasmic Lon protease. The increase in the soluble expression level of the unfused scFv was, however, much less pronounced, and the unfused proteins proved to be more aggregation prone than the wild type. In addition, the soluble expression levels were not correlated with the β-galactosidase activity present in the cells.
Conclusion
This is the first report of a selection for soluble protein expression using a fusion reporter method. Contrary to anticipated results, high enzymatic activity did not correlate with the soluble protein expression level. This was presumably due to free α-peptide released from the protein fusion by the host proteases. This means that the α-complementation assay does not sense the fusion expression level, as hypothesized, but rather the amount of free released α-peptide. Thus, the system does not select, in our case, for higher soluble protein expression level but rather for higher protease susceptibility of the fusion protein.
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Background
Because of their high affinity and specificity against their antigen, antibody molecules and their fragments have many applications in research, diagnosis and therapy [1]. E. coli is a widely used organism for the production of proteins, including antibody fragments such as Fab or single chain variable domains (scFv). Active scFv can be obtained by targeting the protein to E. coli periplasm, where the two disulfide bonds needed for protein folding and stability [2-4] can form. The amount of scFv produced is however usually low, in the range of 0.1–1 mg l-1 of culture at an optical density (OD600) of 1, even if expression levels of 10 mg l-1 have sometimes been reported [5].
An alternative strategy to produce antibody fragments in E. coli has been to maintain the scFv in the cytoplasm by removing its signal sequence. Under those conditions, scFv might be expressed at very high levels, albeit in an insoluble and inactive conformation. Even if highly efficient in vitro refolding procedures have been developed for scFv and Fab [6,7], it would be more suitable to directly recover soluble active protein from the cytoplasm of the cell. This has been partially accomplished by modifying the cytoplasmic oxido-redox environment by mutating components of the thioredoxin and glutaredoxin pathways [8], resulting in the accumulation of soluble intra-cytoplasmic oxidized antibody fragments [9-11]. The soluble expression levels reported, however, are not higher than those obtained in the periplasm of the cell. Another interest in expressing soluble active scFv in the reducing environment of the cytoplasm is that the expression of scFv molecules inside the cell can be used to block viral replication and to inhibit oncogene products [12-15]. The use of so-called intrabodies opens many interesting possibilities in gene therapy [16] and in the in vivo study of protein function [17].
Several methods have been proposed in recent years to increase the soluble expression levels of foreign proteins expressed in E. coli cytoplasm [18]. Most of these methods rely on fusion between the protein of interest and a reporter enzyme. If the protein folds into a soluble conformation, the fused enzyme will be active; but if the protein ends up as an inclusion body, the fused enzyme will be inactive, resulting in a null phenotype. Three reporter enzymes have been used to date, the green fluorescent protein (GFP [19]), the chloramphenicol acetyl transferase (CAT [20]) and the β-galactosidase (βgal [21]). In the case of βgal, the whole enzyme was not fused to the protein of interest but only a small N-terminal fragment of about 50 aminoacids called the α-fragment [22]. The inactive enzyme remainder was expressed in trans by the bacteria (β-galactosidase ΔM15 protein), resulting in in vivo complementation and a lactose+ (lac+) phenotype [23]. The role of the α-fragment is to promote the tetramerization of the inactive dimeric ΔM15 mutant [24], resulting in βgal activation.
Using the GFP fusion method, Waldo and collaborators [18,25,26] isolated several soluble variants of aggregating proteins. However, since the method is based on a phenotypic screen, it is limited to the exploration of libraries of about 105 clones and does not allow the isolation of mutants from very large libraries. This is not the case for the two other methods which should allow the selection of very rare events by selecting for chloramphenicol resistance or growth on lactose as the carbon source. To date, there is, however, no report of protein evolution and selection using these latter two methods even if the CAT system has been used to pre-select libraries of hybrid proteins to avoid stop codons and enrich in properly folded molecules [27].
We previously showed that it is possible to evolve an scFv molecule for very high expression levels in E. coli cytoplasm [4,28,29]. This evolved scFv is active in the cell and its expression level is as high as 100 mg l-1 at an OD600 of 1. This was, however, a very rare event, and the selection had to be conducted using large libraries of 107–108 mutants. The selection procedure used was restricted to the properties of a particular antibody molecule able to activate βgal mutants in vitro and in vivo. To extend this result to other scFv molecules, we constructed fusions between two scFv and the α-fragment of βgal to monitor the soluble expression level in vivo. The lactose phenotype of the strain was then used to select mutants with improved lactose utilization and thus presumably expressing scFv at a higher level in the bacterial cytoplasm.
Results
System design
In order to easily fuse scFv to the α-fragment of βgal, we constructed the plasmid pPM170, derived from pUC119 and which contains a lac promoter followed by a NcoI and a NotI site in which an scFv can be easily cloned in frame with the α-fragment present in the pUC119 plasmid. The α-fragment is separated from the protein by a linker consisting of two tags (Fig. 1).
To verify that the system was indeed able to discriminate soluble scFv expression levels in the cytoplasm, we used a set of mutant scFv derived from the human scFv13 and presenting a gradual increase in soluble expression [28]. The expression levels of these scFv have been previously studied in E. coli cytoplasm using the tryptophan promoter: The best mutant, 13R4, is expressed at about a 50 times higher level than the wild type scFv13 and the expression levels increased gradually from scFv13 to scFv13R4 (in the rank order 13 < 13R1 < 13R2 < 13R3 < 13R4). These scFv were cloned in pPM170 (plasmids pPM173 to pPM173R4). Fig. 2A shows the phenotype on MacConkey lactose plates of strain TG1 containing the plasmids. As expected, the phenotype gradually increased from lac- (white colonies) for pPM173 expressing the wild type scFv13 to a strong lac+ phenotype when the plasmid contained the best expressed mutant 173R4. The phenotype of this latter strain is comparable to the phenotype of the strain containing an unmodified pUC119 plasmid giving normal α-complementation (T+).
The level of α-complementation was further characterized by measuring the βgal activity present in the cell using the whole cell assay developed by Miller [30]. The result shown in Fig. 2B demonstrated that the βgal activity in TG1(pPM173) was close to background level and that the activity increased gradually to 100% of the activity of the positive α-complementation control. This clearly demonstrated the correlation between the soluble cytoplasmic expression levels of the scFv and the lactose phenotype of the strain, opening the way to direct selection of mutant proteins using this assay.
Spontaneous lactose plus mutants
When TG1(pPM173) was incubated 2 to 3 days at 37°C, red papillae appeared on the MacConkey plates. These clones were isolated and showed a stable lac+ phenotype, comparable to a wild type lac+ strain. One of the clones, 173S1, had even a higher βgal activity in its cytoplasm than the pUC119 rescued ΔM15 strain (Fig. 2B). The increase in βgal activity was shown to be linked to the plasmid by transforming TG1 with the plasmid extracted from TG1(pPM173S1). To determine if the mutation was located in the scFv, we excised the gene from the plasmid using the NcoI and NotI sites and cloned it back in pPM170. The resulting clone was lac- (data not shown), demonstrating that the mutation responsible for the lac+ phenotype was associated with the plasmid but not with the scFv.
To understand the origin of this phenotypic change, we sequenced the α-fragment of two of the clones, 173S1 and 173S2. Both contained a mutation in the α-fragment of the βgal, resulting in a stop codon (Fig. 1) at positions 49 and 50. It should be noted that the stop codon of 173S1 is an amber codon which is partially suppressed in TG1 and replaced by a Gln, resulting in this case in two different fusions, one stopping at position 49 of the βgal and the second with a complete α-fragment, showing that the mutant is dominant over the wild type.
Analysis of the sequence of the α-fragment present in pUC119 (and pPM170), showed that a 30 aminoacid long peptide of unknown origin was fused to the C-terminal extremity of the α-fragment (Fig. 1). This peptide (or part of it) was present in all the pUC derived plasmids. Sequence analysis showed that this peptide originated from a fusion between a DNA sequence present in pBR322 plasmid between the NdeI and EarI sites (nucleotides 2297 to 2351 of pBR322; LMR...HRI translated sequence) and the 3' end of M13 gene IV (nucleotides 5470 to 5511 of M13; RQS...AAH translated sequence). The pBR322 sequence, located between the rop gene and the origin of replication, is not normally translated, and the M13 gene IV sequence is read out of frame.
Two explanations could account for the stronger lactose phenotype of the 173S1 clone when the extra peptide was removed from the fusion. Either this peptide impaired the α-complementation, or the presence of this peptide changed the expression level of the scFv. To test these hypotheses, we expressed the scFv13 and scFv13R4 clones as fusion proteins either to the α-peptide followed by the 30 aminoacid long peptide (173 and 173R4) or to the shorter α-peptide of clone 173S1 (173S1 and 173S1-R4). As shown in Figure 3B, the expression level of the fusions was much higher with the shorter α-peptide. This demonstrated that the additional C-terminal peptide lowered the protein expression level, presumably by directing the scFv to the cell degradation machinery since both the insoluble and the soluble expression levels where decreased. In all cases, the fusion proteins were degraded, and a band migrating to about 30 kDa was visible. This band may correspond to an unfused scFv liberated by host proteases.
However, despite a strong increase in the expression level of 173S1-R4 versus 173S1, the lactose phenotypes of both clones expressed in strain TG1 were comparable (Fig. 3A). This showed that the link between the lactose phenotype and the intracellular expression levels of scFv13 shown in Figure 2 was due to the presence of the 30 aminoacid long peptide. Indeed, in the case of 173S1, the α-complementation detection system is saturated and the strong lactose phenotype of strain TG1(173S1) would not allow the selection of evolved mutants with a stronger phenotype. This prompted us to retain this foreign 30 aminoacid long peptide for selection and to adapt the procedure to avoid the selection of spontaneous stop codons.
Molecular evolution
The outline of the selection procedure is shown in Figure 4A. We first introduced random mutations at a low rate (0.2%) in the scFv gene using error-prone PCR. The mutagenized gene was cloned in the pPM170 vector and transformed in TG1 bacteria to give a library of at least 107 clones. Transformed bacteria were plated on selective medium containing lactose as the unique carbon source, allowing the selection of lac+ clones. After 1 to 3 days at 37°C, about 10 to 100 clones able to grow under these conditions were cultured in 96-well microtiter plates, then pooled before plasmid extraction. To avoid the isolation of spontaneous lac+ mutations in the α-fragment (see above), the NcoI-NotI fragment containing the scFv gene was excised from the pooled plasmids and cloned back in pPM170. After transformation in TG1, a small number of clones (typically 100 to 1000) was screened on MacConkey lactose agar plates and the deepest red colonies further analyzed. This last screen was done after a 24-h incubation at 37°C in order to avoid the selection of spontaneous red colonies that appeared after about 2 to 3 days.
Two scFv were chosen as models. We first used a humanized D1.3 anti-lysozyme antibody, HuLys11 [31]. This antibody has been extensively studied and its structure solved by X-ray crystallography. The second scFv was derived from the mouse monoclonal antibody 225.28S and recognizes the high molecular weight melanoma-associated antigen [32]. As such, this antibody might have potential applications in the treatment of that type of cancer.
Four rounds of mutagenesis and selection were done for both scFv. The size of the libraries and the number of clones isolated after each round are shown in Figure 4B. The phenotype of the clones increased during the selection from an almost lac- to a clearly lac+ phenotype. This is shown in Figure 5A for 225.28S scFv, where the best five isolated clones (R4.1 to R4.5) exhibited a deeper red color than the original clone (175), demonstrating a greater ability to use lactose as a carbon source. In addition, the phenotype was close to the phenotype obtained with the pUC119 plasmid, that is, normal α-complementation. The results obtained with HuLys11 were comparable, but the colonies showed a weaker lactose phenotype (data not shown).
All the isolated clones were different at the DNA and protein level. The mutants of scFv225.28S contained 8–14 nucleotide substitutions and 3–9 mutations in the protein (see additional files 1 and 2, 22528Sdna.txt and 22528Saa.txt). In the case of HuLys11, the three isolated mutants contained 4–5 nucleotide substitutions and 2–3 aminoacid mutations (see additional files 3 and 4, HuLys11dna.txt and HuLys11aa.txt). The mutations did not cluster together either in the DNA sequence nor in the protein or in the structure of the scFv. In the case of HuLys11, for which a high resolution structure is available, three of the four isolated mutations are located in β-strands and are solvent exposed (see additional file 4, HuLys11aa.txt and Fig. 6).
To confirm the phenotype shown on the plates, βgal activity was measured using the assay described by Miller [30]. The assay relies on cell lysis by chloroform/SDS and as such measures the amount of βgal activity present in the whole cell. As shown in Figure 5B, the βgal activity present in the mutants was about five to seven times higher than in the strain expressing the wild type 225.28S scFv fused to the α-fragment and five times lower than in the control cells expressing the unfused α-fragment from the pUC119 plasmid. In oder to confirm that this increase in βgal activity was localized in the soluble protein fraction, we measured the activity in soluble extracts of the cells prepared by lysozyme treatment and centrifugation. The results were comparable to those obtained with the whole cell assay (Fig. 5B), demonstrating that indeed the increase in βgal activity was due to an increase in α-complementation in the soluble protein fraction.
Fused scFv-α characterization
To determine whether the expression levels of the mutant scFv were higher than those of the wild type protein, soluble and insoluble extracts were prepared and analyzed (Fig. 7). No protein was detectable by western blot in the case of the wild type scFv225.28S or mutants R4.2 and R4.4, and only a faint band was visible in the soluble extracts of the other clones (R4.1, R4.3 and R4.5. Fig. 7C &7D). This showed that indeed, as expected from the βgal levels measured in Figure 5, some of the mutants were expressed at a higher level in the cytoplasm than the wild type but that most of the scFv was degraded by the host proteases.
To test this hypothesis, we expressed the clones in a TG1 strain deficient in the cytoplasmic Lon protease. This protease has been shown to be involved in the degradation of many recombinant proteins in E. coli [33]. When expressed in such a strain, the wild type scFv225.28S was detected in the soluble fraction, albeit at a very low level (Fig. 7C), and no scFv was present in the insoluble fraction. All the evolved scFv were expressed at a much higher level than the wild type, mainly as soluble proteins (Fig. 7C &7D), showing that the scFv225.28S proteins were predominantly degraded by the Lon protease in vivo. The same type of result was obtained with scFv HuLys11 since the wild type scFv was not detected in any of the extracts, and the evolved scFv were detected in the soluble fraction of the Lon deficient strain (Fig. 7F&7G). The Lon protease is not the only protease present in the E. coli cytoplasm [34] and in its absence some degradation of the fusion is still present as shown by the additional band at about 30 kDa in Fig. 7C &7D. This molecular weight is consistent with a degradation site located between the scFv and the α-peptide and could be one of the sources of the free α-peptide liberated in the cell cytoplasm (see below). It should be noted that in the reported cases of the successful isolation of soluble variants of aggregating proteins using the GFP system [25,26], the authors used an E. coli B strain, naturally deficient in the Lon protease [33,35].
The soluble extracts of scFv225.28S and its mutants were tested for the presence of βgal activity (Fig. 7E). The results obtained in strain TG1 were comparable, as expected, to those obtained previously (Fig. 5). There is however no clear correlation between the soluble expression level and the βgal activity since clone R4.1, which is expressed at the highest level, did not give a higher signal than the non-detected clones, R4.2 and R4.4. In the Lon-deficient strain, all the clones, including the wild type scFv225.28S, gave the same signal despite the fact that the wild type scFv was only barely detectable in the extract. These results showed that there was no correlation between the soluble expression level of the fusion protein and the βgal signal. The most likely explanation of this phenomenon is that the βgal signal is not due to the fusion protein detected by western blot but to some α-peptide released from the fusion by the host proteases. This free α-peptide is presumably also much more efficient for βgal complementation than the fusion protein since it may penetrate the tunnel present in M15 protein [36] at a much faster rate.
Unfused scFv characterization
The previous results were obtained with the scFv fused to the α-peptide. Since it has been shown in several systems that fusion to a partner may influence the fate of a protein [37-40], we next examined the expression level of scFv225.28S and its mutants as unfused proteins. After cloning in plasmid pPM210, the scFv were expressed in the Lon-deficient TG1 strain. The wild type scFv225.28S was only detected in the soluble fraction (Fig. 8) and the mutants were present in both the soluble and insoluble fractions. All the mutant were expressed at a higher level than the wild type scFv, but the increase in expression was less pronounced than when the proteins were fused to the α-peptide (Fig. 7). This was however mainly due to the fact that the wild type scFv225.28S was expressed at a higher level when not fused to the α-peptide because the fusion was degraded by the host degradation machinery as previously described for other βgal fusions [37].
We next compared the expression level of the best isolated clone (scFv225.28S R4.1) in the periplasm and in the cytoplasm. It must be noted that the two vectors used, pPM210 and pAB1, are derived from the same pUC119 plasmid and that in both cases the scFv gene is under the control of the lac promoter. The scFv expressed by the two plasmids are exactly the same except for the pelB leader peptide present at the N-terminal extremity of the scFv expressed in the periplasm. After cleavage of this signal sequence, the two scFv only differ by an additional N-terminal Met-Ala dipeptide present in the scFv produced in the cytoplasm.
As shown in Figure 9, the soluble expression level of mutant R4.1 is higher both in the periplasm and in the cytoplasm than that of the wild type scFv225.28S. In addition, the soluble cytoplasmic expression level of the evolved R4.1 clone is higher than the wild type periplasmic expression level. This suggests that evolving scFv for cytoplasmic expression is a valuable approach for increasing the production of scFv in E. coli even if in this case it is only a twofold increase much lower than in previously reported cases [28].
In an attempt to increase further the cytoplasmic expression, the genes were cloned under the control of the strong T7 promoter and expressed in strain BL21(DE3)pLysS [41], which is naturally deficient in the Lon protease [33,35]. As seen in Figure 10, the mutant proteins were expressed at much higher levels than the wild type. This is particularly the case for scFv225.28S (Fig. 10B). To further characterize the mutant proteins, soluble and insoluble protein fractions were prepared. As shown in Figure 10, the increase in expression was due to an increase in the insoluble fraction. In all cases, the amount of soluble protein produced by the mutants was comparable or even lower than that produced by the wild type. This was true both for HuLys11 and 225.28S.
Discussion
In this report, we used the α-complementation assay as a probe to detect soluble scFv expression in E. coli cytoplasm [21]. The system was used to evolve two scFv to increase their expression levels. After four rounds of mutation and selection, we were able to select for scFv fusions giving high βgal activity in vivo and in vitro.
Characterization of these mutant proteins showed that they were expressed at higher levels in the cytoplasm than the wild type scFv. The proteins were however quickly degraded in the cell cytoplasm, and only a faint band was detected in the soluble fraction and no protein at all in the insoluble fraction (Fig. 7). This is not surprising for scFv expressed in the reducing environment of the cytoplasm since the lack of the two disulfide bonds results in only marginally stable proteins quickly degraded by the host proteases [42]. Since it has been shown that the Lon protease is involved in the degradation of many recombinant proteins in E. coli [33], we expressed the scFv in an isogenic strain deficient in this protease. Under those conditions, the mutant scFv were expressed at a much higher level than the wild type scFv. This was true for both scFv, HuLys11 and 225.28S.
This increase in soluble cytoplamic production of the scFv-α fusion was however not correlated with the βgal activity (Fig. 7). For example, clone scFv225.28S R4.1 and R4.2, which were expressed at a very different soluble level (Fig. 7C), gave the same βgal signal (Fig. 5 &7E). This indicates that most, if not all, of the βgal activity present in the cell was not due to the scFv-α-peptide fusion but to some free α-peptide released from the fusion by the host proteases. This means that the α-complementation assay does not sense the scFv-α expression level, as hypothesized, but rather the amount of free α-peptide in the cell cytoplasm. This could also explain why the system was not able to detect differences between the clones in a lon background (Fig. 7E).
Recently, Betton and collaborators [43] presented a model of the possible in vivo competition between folding, aggregation and degradation. Although this model was presented in the case of periplasmic proteins it might also apply in the cytoplasm. The possible model and the results for in vivo βgal complementation are shown in Figure 11. The "classical" competition between aggregation and folding is represented by the green arrows at the top of Figure 11. The scFv, emerging from the ribosome, will fold into folding intermediates. A folding intermediate may fold into a soluble native conformation, or may misfold, leading to aggregation. Such a model has been extensively studied in vivo and in vitro [44-47]. In this model, an increase in α-complementation would result in an increase in soluble protein expression and is the basis of the tag-based systems to detect and evolve soluble proteins [18]. As proposed by Betton and collaborators [43], there is however a third pathway, leading to protein degradation, in kinetic competition with aggregation. When the proteins are expressed at a low level under the lac promoter, most of the protein ends up as degraded peptide fragments. However, since aggregation is a high order kinetics, this is favored over degradation under the high transcription rate due to the T7 promoter. This would not be the case if degradation had not originated from the same misfolded conformation as the aggregated protein. However, since the soluble expression levels of the scFv were higher in a lon strain, we must also admit that there is a supplementary pathway involving the Lon protease and leading from soluble fusion to degraded protein as suggested by Parsell and Sauer [42].
There is a striking difference between the properties of the fused and the unfused scFv. Indeed, in the structural context of the selection (as fusion to the α-peptide), all the scFv, albeit highly sensitive to degradation, were mainly expressed as soluble proteins. This is particularly the case of HuLys11 for which no insoluble protein was detected in Fig. 7G. As unfused scFv, all the clones were, however, mainly found in the insoluble fraction, particularly when expressed using the strong T7 promoter (Fig. 10) but also when we used the same promoter than during the selection process (Fig. 8). This shows that interactions could take place between the scFv and the fused α-peptide either during or after folding of the fusion protein. This may explain why all the mutations where localized on the surface of HuLys11 (Fig. 6) since these residues are more likely to take part in such an interaction. This is particularly the case of the H46 mutation, which is present in all three isolated mutants. In this case a hydrophilic and charged residue (Glu) is replaced by an hydrophobic Gly. As the α-peptide and the 30 aminoacid peptide fused to the scFv contain some highly hydrophobic patches of residues (data not shown), this increase in the hydrophobicity of the scFv surface may favor interaction between the two partners that could enhance the solubility of the fusion by a chaperone-like effect, as proposed for other fusions [48]. When the scFv is expressed alone, without the C-terminal α peptide, increasing the hydrophobicity of the surface residues could result in an increase in aggregation [49], as noted in our case (Fig. 8 &10).
Finally, another problem during the selection originated in the presence of the 30 aminoacid peptide at the end of the α-peptide. As we showed in the case of the model antibody scFv13, this peptide is needed to obtain good correlation between protein soluble expression levels and in vivo α-complementation since the introduction of a stop codon at the end of the α-peptide resulted in a very strong complementation with all the scFv, even the wild type (clone 173S1 in Fig. 2 &3). This may explain why the selection was biased towards degraded C-terminal α-peptide. Indeed, Figure 3 shows that the role of the additional 30 aminoacid long C-terminal peptide is to decrease the expression level of the fusion in order not to saturate the detection system with the wild type scFv. This decrease in the expression level is presumably accomplished through targeting of the fusion to the cell degradation systems, resulting in a rapid conversion of the fusion to free α-peptide and in a bias in the selection procedure towards degradation and thus aggregation. It must be noted that Schwalbach and collaborators [50] recently selected a mutant scFv using the GFP system, and that despite an increase in GFP activity, they did not notice any increase in soluble protein expression but rather an increase in protein aggregation. They also noticed that protease degradation of the fusion released free GFP in the cytoplasm, particularly when cells were induced for long periods, that is, the conditions used during the selection process. It should be noted that the authors used a lon+ strain for the selection instead of the E. coli B lon- strain used by other authors [25,26,35]. These failures may be due, in the specific case of cytoplasmic scFv expression, to the lack of disulfide bond formation, leading to marginally stable reduced scFv, quickly degraded by the cell proteases [42].
Despite the difficulties associated with cytoplasmic scFv expression, some scFv have been previously evolved for folding under reducing conditions [28,51]. The systems used relied on the binding activity of the scFv molecule, avoiding the problem of protein degradation since degraded antibody cannot bind to its antigen anymore. It would thus be more appropriate to use yeast or bacterial two-hybrid systems to evolve scFv [52,53]. The use of such systems could also avoid the selection of mutations that modify or abolish the antigen-binding properties of the scFv as is presumably the case in our selection since several of the mutations are located in the CDR loops of the antibody fragments (see additional files 4 and 2, HuLys11aa.txt and 22528Saa.txt). Another possibility could be to use E. coli protease-deficient strains to limit protein degradation during the selection, but the results shown in Fig. 7E demonstrate that even in such a strain there is no correlation between the soluble expression level and the βgal activity present in the cell.
Conclusions
In this report, we used the α-complementation assay as a probe to detect soluble scFv expression in E. coli cytoplasm. The system was used to evolve two scFv in order to increase their expression levels. After four rounds of mutation and selection, we were able to select for scFv fusion giving a high βgal activity in vivo and in vitro.
Characterization of these mutant proteins showed that their expression levels were much higher in the cytoplasm than those of the wild type scFv, particularly in a Lon-deficient strain. There was however no correlation between the βgal activity present in the cell and the soluble expression level of the scFv, showing that the βgal signal is presumably due to some free α-peptide released from the fusion protein by the host proteases and not to the non-degraded soluble scFv-α fusion.
Methods
Media, plasmids and bacterial strains
MacConkey agar, M9 and LB media were previously described [30]. Strain TG1 is E. coli K-12, [F' traD36 lacIq Δ(lacZ)M15 proA+B+] supE44 Δ(hsdM-mcrB)5 thi Δ(lac-proAB). CAG626F' is E. coli K-12, [F' lacIqΔ(lacZ)M15 zzf::mini-Tn10(KanR)proA+B+] lacZ(am) pho(am) lon trp(am) tyrT [supC(ts)] rpsL mal(am) [54].
TG1lon strain was constructed by P1 transduction. First, zaj-3054::Tn10, located at 9.95 minutes, was inserted near to the lon gene in strain CAG626F' by P1 transduction from CAG12017 [55]. In a second step, lon and zaj-3054::Tn10 were co-transduced in TG1 using P1 phage. The introduction of the lon mutation was verified by comparing the resistance to UV irradiation and to nitrofurantoin of TG1 and TG1lon.
Plasmid pPM170 for the cytoplasmic expression of scFv fused to the α-fragment of βgal was constructed as follows (Fig. 1). A fragment containing a Shine Dalgarno sequence followed by a NcoI site containing the ATG initiator was obtained by PCR amplification of pPM160 [28] with pTrpFOR (CGGGAATAAGCTTCAACGCCAG) and EcoAlpha.for (GTGAATGAATTCGAATGGTGATGATGG) primers. The underlined sequence corresponds to an EcoRI site designed in order to get in frame fusion of the scFv with the α-fragment of βgal present in pUC119 when the fragment was cloned in the EcoRI site of pUC119. The amplified fragment also contained an HpaI site located 34 bp upstream from the ATG initiator of the scFv gene. This PCR band was digested with HpaI and EcoRI enzymes and ligated with HincII-EcoRI-digested pUC119 plasmid, leading to the pPM170 plasmid.
Plasmid pAB1 for the periplasmic expression of scFv under the lac promoter has been described previously [28]. Plasmid pPM210 is identical to pPM170 except for the presence of a stop codon between the scFv and the α-fragment of βgal. It was obtained by cloning the NcoI-EcoRI fragment of pAB1 in the NcoI and EcoRI sites of pPM170. scFv cloned in pPM210 were transcribed from a lac promoter and tagged at their C-terminal extremity by both a c-myc and a polyhistidine flags.
Plasmid pET23NN was designed to easily clone NcoI-NotI fragment containing scFv from pPM170-derived plasmids under the T7 promoter with C-terminal c-myc and His6 tags. pET23d(+) obtained from Novagen was first digested with XhoI enzyme, filled-in with T4 DNA polymerase then digested with NcoI. This fragment was ligated with a NcoI-EcoRI(filled) fragment excised from the pAB1 plasmid [28]. The resulting plasmid contained a T7 promoter, followed by a NcoI site containing the ATG initiator, a NotI site followed by a c-myc and a His6 tag [same sequence as pPM170 (Fig. 1) but with a stop codon before the EcoRI site]. Due to the removal of a T, presumably during T4 DNA polymerase treatment, ligation between the filled EcoRI and XhoI sites reconstituted the EcoRI site (GAATTCGAG instead of GAATTTCGAG).
Error-prone mutagenesis
The conditions used are those described to obtain 0.2% mutations with p(AT->NN) = p(GC->NN) and p(AT->GC) = p(AT->TA) for ten duplications [28,56]. The amplified band was digested with NcoI and NotI and cloned in pPM170. The ligation was transformed in TG1 by electroporation. Bacteria were plated on MacConkey lactose and M9 lactose agar plates supplemented with 100 μg ml-1 ampicillin, 1 mM isopropyl-β-D-thiogalactopyranoside (IPTG) and 1 μg ml-1 of vitamin B1 in the case of the M9 plates.
Preparation of bacterial extracts
Expression under the T7 promoter was conducted essentially as described except that LB was used instead of M9ZB [41]. Freshly transformed cells were grown in LB supplemented with 100 μg ml-1 ampicillin and 20 μg ml-1 chloramphenicol until an OD600 of about 1 and kept at 4°C overnight. In the morning, the cells were diluted to an OD600 of 0.1 in the same medium and incubated with shaking for 2 hours at 37°C, then 0.4 mM IPTG was added and the culture continued for 3 hours at 30°C. The cultures were cooled down on ice for 5 to 30 minutes, then centrifuged and resuspended at a concentration of 40 OD600 in 50 mM Tris-HCl (pH 8.0), 2 mM EDTA. Cells were lysed by freezing/thawing followed by a brief sonication. The insoluble fraction was collected by centrifugation (30 minutes at 17 500 g), washed once, then resuspended in the same volume of buffer.
For expression under the lactose promoter (plasmid pPM170), an overnight culture in LB supplemented with 100 μg ml-1 ampicillin and 1% glucose was diluted to a final OD600 of 0.1 in LB supplemented with 100 μg ml-1 ampicillin and incubated with shaking for 2 hours at 37°C, then 1 mM IPTG was added and the culture continued for 3 hours at 30°C. Cytoplasmic soluble extracts were prepared as follows. 16 OD of culture were cooled down on ice for 5 to 30 minutes, then centrifuged and resuspended in 400 μl of 20 mM Tris-HCl (pH 8.0), 0.7 M sucrose. Hen egg-white lysozyme at a 0.1 mg ml-1 concentration was added and the extract incubated 2 minutes on ice. 800 μl of a cold solution of 1.5 mM EDTA were added slowly within ten minutes. Extract were kept 30 minutes on ice then sonicated 20 s. The insoluble fraction was collected by centrifugation (30 minutes at 17 500 g), washed once, then resuspended in the same volume of buffer than the soluble fraction (1.2 ml).
Measurement of βgal activity
Cells were grown under the same conditions as those described in "Preparation of bacterial extracts" for the lactose promoter. Five hundred μl of cells were lysed using SDS/chloroform and βgal activity determined and expressed as described in Miller [30]. For Figure 5 and 7, we also prepared soluble extracts using the protocol described in "Preparation of bacterial extracts" and βgal activity was determined as above.
Authors' contributions
PP carried out some of the mutant characterization and participated in manuscript preparation. PM conceived of the study, performed the mutagenesis and selection experiments, and participated in manuscript preparation. All authors read and approved the final manuscript.
Supplementary Material
Additional File 1
DNA sequences of 225.28S and mutants scFv. The position of the six CDRs are indicated as H1, H2 and H3 for the VH and L1, L2 and L3 for the VL. The scFv 225.28S translated sequence is shown above the DNA. Proteins are numbered according to Kabat [57]
Click here for file
Additional File 2
Aminoacid sequences of 225.28S and mutants scFv. The position of the six CDRs are indicated as H1, H2 and H3 for the VH and L1, L2 and L3 for the VL. Proteins are numbered according to Kabat [57]
Click here for file
Additional File 3
DNA sequences of HuLys11 and mutants scFv. The position of the six CDRs are indicated as H1, H2 and H3 for the VH and L1, L2 and L3 for the VL. The scFv HuLys11 translated sequence is shown above the DNA. Proteins are numbered according to Kabat[57]
Click here for file
Additional File 4
Aminoacid sequences of HuLys11 and mutants scFv. The position of the six CDRs are indicated as H1, H2 and H3 for the VH and L1, L2 and L3 for the VL. Proteins are numbered according to Kabat
[57]. The secondary structure of the protein, as indicated in the header of the PDB file 1BVK, is summarized above the sequence (H: helix; E: strand).
Click here for file
Acknowledgements
The authors thank Dr S. L. Salhi for help in preparing the manuscript. PM is grateful to Dr. G. Winter for his support and for critical reading of the manuscript. Clones HuLys11 and 225.28S were a kind gift from Dr. Greg Winter and Dr. Dario Neri. This work was supported by funds from the French "Centre National de la Recherche Scientifique".
Figures and Tables
Figure 1 Schematic view of plasmid pPM170 Plasmid pPM170 allows the cloning of scFv genes between the NcoI and NotI sites under the control of the lactose promoter. The translated protein is fused at its C-terminal extremity to the α-fragment of βgal. A) Plasmid map with the main restriction sites; B) DNA and aminoacid sequences fused to the 3' end and the C-terminus of the cloned scFv. The scFv sequence is followed by two tags (in blue), the α-fragment of βgal (in green, aminoacids 6 to 59 of βgal) and a 30 aminoacid long peptide (in orange) originating from pBR322 plasmid and M13 gene IV sequence (see text). The sequences of the two sequenced spontaneous lac+ mutants, 173S1 and 173S2 (see text), are also shown.
Figure 2 Lactose phenotype correlates with the soluble expression levels Phenotype of strain TG1 transformed with the pPM170 plasmid containing a series of scFv with different soluble expression levels in E. coli cytoplasm. The scFv13, 13R1, 13R2, 13R3 and 13R4 are described in [28]. T- and T+ are TG1 transformed with the pTrc99A and pUC119 plasmids, respectively. 173S1 is a spontaneous lac+ mutant isolated from TG1(pPM173) (see text and Fig. 1). A) Lactose phenotype on MacConkey lactose plates. The ability of the cell to use lactose is proportional to the depth of the red coloration. B) βgal activity present in the cytoplasm of the cells measured using Miller's whole cell assay [30].
Figure 3 Effect of the 30 aminoacid long C-terminal peptide on scFv expression level scFv13 and scFv13R4 were cloned either in pPM170 (173 and 173R4) or in pPM173S1 (173S1 and 173S1-R4), and expressed in strain TG1. A) Phenotype of the strains on MacConkey lactose plates after 24 h at 37°C. B) Soluble and insoluble extracts of the four strains were analyzed by Coomassie blue staining (top) or by western blot (bottom) using the 9E10 anti-c-myc monoclonal antibody followed by an anti-mouse HRP-conjugated antibody and detected with a commercial kit (Pierce Supersignal West Pico kit #14079). The arrows show the position of the fusions (38 kDa for 173 and 173R4; 34 kDa for 173S1 and 173S1-R4) and the unfused scFv (28 kDa).
Figure 4 Outline of the selection procedure A) Outline of the steps followed during the selection procedure; B) Size of the libraries generated and number of clones isolated after each round of selection and used as a pool for the next round.
Figure 5 Phenotype of the isolated mutants After four rounds of mutagenesis and selection, the five best clones of scFv 225.28S were analyzed in strain TG1. pPM175 contains the wild type scFv and pPM175R4.1 to pPM175R4.5 the five isolated mutants. T+ and T- are the same as in Fig. 2. A) Phenotype of the strains on MacConkey lactose plates; B) βgal activity measured as in Figure 1. In addition to the Miller's whole cell assay (blue), we analyzed in B the βgal activity present in soluble extracts (red).
Figure 6 Location of isolated mutations in HuLys11 scFv structure Schematic representation of the HuLys11 scFv (pdb 1BVK). The CDR loops are represented in blue. The side chain of three of the four isolated mutations (see additional file HuLys11aa.txt) are represented in red. The fourth mutation, located in the linker sequence, is not present in the structure.
Figure 7 Expression of scFv-α fusions in TG1 and TG1lon strains SDS-PAGE of soluble (5 μl : A, C & F) and insoluble extracts (5 μl ; B, D & G) of the five best clones of scFv 225.28S (A, B, C & D) and scFv HuLys11 (F & G) expressed in TG1 and TG1lon. Proteins were revealed either by Coomassie blue staining (A & B) or by western blot (C, D, F & G) using an HRP-coupled anti-polyHistidine monoclonal antibody (Sigma A7058) and detected with a commercial detection kit (Pierce Supersignal West Pico kit #14079). In panel E, the same soluble extracts than in C were tested for βgal activity.
Figure 8 Cytoplasmic expression of unfused scFv After cloning in plasmid pPM210, scFv225.28S and mutants were expressed in strain TG1lon. 5 μl of soluble and insoluble extracts were analyzed by Coomassie blue staining (A) and western blot (B) using the 9E10 anti-c-myc monoclonal antibody followed by an anti-mouse alkaline phosphatase-conjugated antibody and detected with the chromogenic substrate BCIP/NBT.
Figure 9 Comparison of cytoplasmic and periplasmic soluble expression Genes of scFv225.28S and its mutant R4.1 were cloned in plasmid pAB1 [28]. In this plasmid, scFv genes are expressed under the control of the lac promoter with the pelB signal sequence fused at their N-terminal extremity in order to target protein to the periplasm. Soluble and insoluble extracts were prepared from strain TG1 and were analyzed by Coomassie blue staining (A) and western blot (B) as in Figure 8. The two last extracts (Cyto) are those analyzed in lanes 1 and 2 of Figure 8 (soluble cytoplasmic extracts of scFv225.28S and R4.1, cloned in pPM210 and expressed in TG1lon).
Figure 10 Cytoplasmic expression under the T7 promoter SDS-PAGE of soluble and insoluble extracts (5 μl) of the best clones isolated after four rounds of mutagenesis, cloned in the pET23NN plasmid. Proteins were revealed either by Coomassie staining (top) or by western blot using the 9E10 anti-c-myc monoclonal antibody followed by an anti-mouse alkaline phosphatase-conjugated antibody and detected with the chromogenic substrate BCIP/NBT. A) HuLys11 scFv and mutants; B) 225.28S scFv and mutants.
Figure 11 Schematic representation of the possible in vivo fates of scFv-α protein fusion Schematic representation of folding, aggregation and degradation processes. In the scheme presented, the newly translated protein (left) is assumed to proceed through two alternative pathways: either it folds to give the folded protein (soluble native) or it evolves via a side reaction leading to a misfolded protein (misfolding). A second kinetic competition is thought to occur between degradation and aggregation of the misfolded protein. Green arrows represent the kinetic competition between folding and aggregation [44] on which the soluble reporter assays are based [18]. Red arrows represent an additional pathway leading to degradation [43] and release of free α-peptide in the cytoplasm.
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| 15606918 | PMC544847 | CC BY | 2021-01-04 16:05:46 | no | Microb Cell Fact. 2004 Dec 17; 3:16 | utf-8 | Microb Cell Fact | 2,004 | 10.1186/1475-2859-3-16 | oa_comm |
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Dyn MedDynamic medicine : DM1476-5918BioMed Central London 1476-5918-4-11563163310.1186/1476-5918-4-1ResearchAccuracy of body composition measurements by dual energy x-ray absorptiometry in underweight patients with chronic intestinal disease and in lean subjects Haderslev Kent Valentin [email protected] Pernille Heldager [email protected] Michael [email protected] Department of medical Gastroenterology, Copenhagen University Hospital, Rigshospitalet, Blegdamsvej 9, 2100 Copenhagen, Denmark2005 4 1 2005 4 1 1 10 11 2004 4 1 2005 Copyright © 2005 Haderslev et al; licensee BioMed Central Ltd.2005Haderslev 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
To assess the accuracy of Dual-energy X-ray absorptiometry (DXA) in underweight patients with chronic gastrointestinal disease, we investigated the ability of DXA to detect variations in body composition induced by infusion of parenteral nutrition (PN). Furthermore, the influence of a low body weight per se on the accuracy of DXA was studied by placing packets of lard on lean healthy subjects.
Methods
The hydration study included 11 patients with short bowel syndrome on long-term home parenteral nutrition (9 women and 2 men), and (mean ± SD) 49.5 ± 17.1 yr., 19.3 ± 3.1 kg/m2. The lard study, where packets of lard were placed either over the thighs or the trunk region, was performed in 8 healthy lean male volunteers, 26.4 ± 7.4 yr., and 21.0 + 0.9 kg/m2. Body composition, including measures of the total mass (TM), soft tissue mass (STM), lean tissue mass (LTM), fat mass (FM), and total body mineral content (TBBMC), was assessed by DXA. The fat fraction of the lard packets (3.49 kg), measured in triplicate by chemical fat extraction, was 52.2%.
Results
Hydration study; The increase in scale weight (BW) of approximately 0.90 kg due to infusion of PN correlated significantly to the increase in TM (R-square = 0.72, SEE 0.36 kg, p < 0.01), and the increase in STM (R-square = 0.69, SEE 0.38 kg, p < 0.01), however not with the increase LTM (R-square = 0.30, SEE 1.06 kg, p = 0.08). Mean changes in TM (0.88 kg), STM (0.88 kg), and LTM (0.81 kg) were not significantly different from changes in BW (p > 0.05). Lard study; Regardless of position, measurements of FM and LTM of the added lard were not significantly different from expected values. However, the composition of the lard packets into FM and LTM was more accurately detected when the packets were placed over the thighs than over the trunk region. The accuracy of DXA in individual subjects, expressed as the SD of the difference between expected and measured values, was 1.03 kg and 1.06 kg for the detection of changes in LTM and FM, respectively, and 0.18 kg for the detection of changes in STM and TM.
Conclusions
On a group level, DXA provided sufficient accuracy to detect small changes in body composition in underweight patients with chronic gastrointestinal disease. However, the accuracy errors were higher than reported in normal weight subjects. The accuracy was not influenced by a low body weight per se.
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Background
Malnutrition is commonly observed in patients with chronic gastrointestinal disease and nutritional support is therefore an integral part of the management of these patients. Accurate and precise methods that are sensitive enough to track small changes in body composition are important for assessing the effect of nutritional intervention. At present, there are available a variety of well-established techniques such as hydrodensitometry, isotope dilution, air-displacement plethysmography [1], and potassium spectroscopy, methods which are based on the 2-compartment model separating the body into fat tissue mass (FM) and fat-free tissue mass (FFM). However, these methods rely on assumptions that are often not met in malnourished patients, e.g. a fixed hydration and density of the FFM, which may negatively influence the accuracy of measurements. Therefore, such methods may not be sufficiently accurate to detect small changes (<1.5 kg) in FM or FFM, changes that would be considered clinically significant when making decisions about treatment of underweight patients with chronic gastrointestinal disease.
Dual-energy X-ray absorptiometry (DXA) is a precise, accurate, non-invasive, safe, and convenient technique, founded on a three compartment model separating the body into total body mineral mass (TBBMC), FM, and lean tissue mass (LTM), the latter being the remaining bone-free fat-free tissue mass [2,3]. In theory, DXA has the distinct advantage over most other body composition methods of not requiring any assumptions about the chemical constancy of the LTM, although it does assume constant attenuation of the lean and fat tissues. Thus, DXA may be an appealing option for body composition analysis in underweight patients with chronic gastrointestinal disease. However, the accuracy of DXA measurements in this particular group has only been sparsely documented [4-7]. Several factors may hypothetically affect the accuracy of DXA measurements in these patients, such as a low body weight per se, and an abnormal hydration status. In addition, it is unknown whether the constants and standard mathematical algorithms used for soft tissue determination are appropriate in this subgroup of patients.
The purpose of this study was twofold. Primarily, in underweight patients with short bowel syndrome dependent on long term home parenteral nutrition we evaluated whether DXA could accurately detect small changes in body composition induced by intravenous infusion of parenteral nutrition (PN). Secondly, to explore the influence of a low body weight per se on the accuracy of DXA, we investigated the ability of DXA to detect changes in body composition by placing packets of lard on the trunk and thighs of lean healthy volunteers.
Methods
Two separate experiments were undertaken. In the hydration study, changes in body composition were induced by intravenous infusion of PN in underweight patients with short bowel syndrome. In the lard study, changes were induced by placing packets of lard on lean healthy volunteers.
Anthropometry
All participants were weighed (after voiding) on a calibrated digital scale accurate within 0.1 kg (subjects wearing light clothes). The heights were measured after a maximal inhalation to the nearest 0.1 cm by using a wall-mounted stadiometer. The averages of two measurements for both height and weight were used as the criterion measurement. The body mass index was calculated as weight divided by height squared (kg/m2).
Dual energy X-ray absorptiometry
Measurements of body composition were performed with the Norland XR-36 DXA densitometer (Norland Corporation, Fort Atkinson, WIS, U.S.A.) with the subject supine. The host software was rev. 2.5.2. and the scanner software rev. 2.0.0. The theory and methodology for body composition by DXA has previously been described [2]. Briefly, while the patient lightly dressed lay on a scan table for about 20 min., transverse scans approximately 1 cm apart were performed from top to heel. The instrument uses X-rays of two distinct energy levels that are attenuated by fat, bone and lean mass to different extent. By computerization of data inputs from approximately 11000 pixels DXA estimates body composition based on a three-compartment model, measuring TBBMC, FM, and LTM. The total mass (TM) by DXA is the sum of all three compartments, whereas the soft tissue mass (STM) by DXA includes only the LTM and FM. The fat free tissue mass (FFM) includes both the TBBMC and LTM. Among others, Hendel et al. [8] have reported precision errors of body composition of the Norland XR-36 densitometer. They were 2.2% for TBBMC, 2.7% for FFM, and 2.6% for FM%. In our hands the between-measurements CV%'s of TBBMC, LTM and FM, were 1.5%, 1.6%, and 3.9%, respectively [5].
Hydration study
This study comprised 11 patients (9 women and 2 men) with short bowel syndrome treated with daily supplements of PN. The participants were selected for low body weight (BMI < 22 kg/m2). The diagnoses were: Crohn's disease (n = 6), ischaemic infarction (n = 2) and other (n = 3). Patients were on average (mean ± SD) 49.5 ± 17.1 yr., 1.58 ± 0.07 m, 48.5 ± 9.5 kg, and 19.3 ± 3.1 kg/m2. The PN was in all patients provided as a 3 L 'all in one' plastic bag containing a fixed composition of protein, glucose, and electrolytes, and four patients had additional supplements of 1–2 L of saline. The infusion was given continuously over a period of 8–10 hours during the night. Before starting the infusion all patients were weighed on a scale and scanned as described below. Immediately after completing the infusion the patients were reweighed and rescanned. Due to the large volume of intravenous fluid provided with the parenteral nutrition, patients were allowed to void during the study.
The theoretical soft-tissue attenuation (RST) of an 'all in one' 3 L bag of PN was calculated using the equation RST = Σ (-fi × (μmi)L) / Σ (-fi × (μmi)H), where (fi) is the mass fraction, (μmi) is the mass attenuation coefficient of the i'th component at high (H) and low (L) photon energy levels [9]. The theoretical RST-value for PN was calculated to 1.365, which is very close to that of normal saline (1.377). Given the calculated RST-value PN should theoretically be scanned by DXA as consisting of approximately 2% FM and 98% LTM. Such values were confirmed in vivo by scanning one subject three times before and after placing 2 bags (6.96 kg) of PN on the subject's legs. By DXA, the equivalent 6.99 kg increase in TM (TM) resulted from a 7.31 kg gain of LTM (104.5%) and a 0.32 kg loss of FM (- 4.6%), whereas the TBBMC changed only 6 g (0.1%).
Lard study
For this experiment two packets of porcine lard (with a small amount of muscle-tissue attached) were constructed and enclosed in plastic wrap. The lard packet dimensions were 19.8 cm × 38.8 cm × 2.6 cm, and weighed, using a beam scale, 3.49 kg. The total fat fraction of the lard, measured in triplicate by chemical fat extraction according to the method of Folch et al. [10], was 52.2% (CV% = 6.4%). The participants for this study were selected for low body weight (BMI < 22 kg/m2). Eight healthy lean male volunteers, who were on average (mean ± SD) 26.4 ± 7.4 years of age, agreed to participate. Anthropometric measures of the participants were taken immediately before the study and averaged 1.81 ± 0.07 m in height, 69.0 ± 7.7 kg in weight, and 21.0 ± 0.9 kg/m2 in BMI. Without reposition, four consecutive total body DXA scans were performed on each participant. Two scans without added lard served as baseline measurements (the average values were used as the criterion measurements), and two scans were performed with the lard packets placed alternately on the abdomen centred over the lumbar vertebrae, and on the thighs at midpoint of the femur. The placements of the lard over the thighs and trunk were chosen to represent regions where the ability of DXA to correctly measure soft tissue composition is known to be good and poor, respectively.
Ethics
The Ethics Committee for Medical Research in Copenhagen, Denmark, approved the study protocol and the study was conducted in accordance with the Declaration of Helsinki of 1975, as revised in 1983. Written and oral informed consent was obtained from all patients prior to inclusion.
Statistics
All results are expressed as means ± standard deviation (SD) unless otherwise indicated. A paired Students t-test was used to compare paired variables. Association between variables was established by Pearson's correlation coefficients and linear regression. The CV%'s for the measurements of TBBMC or FM were calculated from the within-subjects SD's divided by their respective grand means. All statistical tests were two-tailed, and a p value of less than 0.05 was considered statistically significant. The SPSS statistical program version 10.0 (SPSS Inc., Chicago, USA) was used for all analyses.
Results
Hydration study
The descriptive statistics for body weight (BW) and body composition variables by DXA before and after intravenous infusion of PN are given in Table 1. An average increase of 0.90 ± 0.45 kg of BW was achieved by infusion of PN. This corresponded to an increase in TM of 0.88 ± 0.63 kg, STM of 0.88 ± 0.64 kg, and LTM of 0.81 ± 1.20 kg which were significantly higher than the baseline values. No significant differences were found between baseline and post infusion estimates of FM and TBBMC. The average within subject CV%'s for TBBMC and FM were 2.0% and 3.5%, respectively (CV% for FM was expressed as the geometric mean, due to violation of the normality assumption). The correlations between changes in BW and changes in mass and composition by DXA are summarised in Table 2. The increase in BW correlated significantly to the increase in TM (R-square = 0.72, SEE 0.36 kg, p < 0.01), and the increase in STM (R-square = 0.69, SEE 0.38 kg, p < 0.01), however not with the increase LTM (R-square = 0.30, SEE 1.06 kg, p = 0.08). For all three regression lines (diff. BW vs. diff. TM, STM, and LTM) the intercepts were not significantly different from zero and the regression slopes were not significantly different from 1.00. Fig. 1 displays the limits of agreement plots of the comparison of change in BW and changes in TM, STM and LTM by DXA. The accuracy of DXA in the individual subject, expressed as the 95% confidence intervals for the difference between changes in BW and changes in DXA variables, was ± 2.06 kg for the detection of changes in LTM, and ± 0.71 kg for the detection of changes in STM and TM.
Table 1 Changes in body weight and body composition variables by DXA after infusion of parenteral nutrition in 11 patients on permanent home parenteral nutrition.
Baseline Post infusion Change Range
Body weight (kg) 48.39 ± 9.42 49.43 ± 9.20* 0.90 ± 0.45 0.30 ; 1.70
Total mass (kg) 47.91 ± 9.46 48.78 ± 9.06* 0.87 ± 0.65 -0.05 ; 1.72
Soft-tissue mass (kg) 45.88 ± 9.15 46.75 ± 8.75* 0.88 ± 0.65 -0.14 ; 1.74
Lean-tissue mass (kg) 32.51 ± 5.92 33.04 ± 5.33* 0.53 ± 1.36 -1.53 ; 3.54
Fat mass (kg) 13.37 ± 7.46 13.72 ± 7.82 0.35 ± 1.00 -1.93 ; 1.88
Total body bone mineral mass (kg) 2.03 ± 0.38 2.03 ± 0.39 0.00 ± 0.07 -0.10 ; 0.11
Values are mean ± SD. *Significantly different from baseline values (Students paired t-test)
Table 2 Intercorrelation of change in body weight and change in total mass, soft tissue mass, and lean tissue mass by DXA after infusion of parenteral supplements in 11 patients on permanent home parenteral nutrition.
Body weight Total mass Soft-tissue mass
Total mass 0.846 * - -
Soft-tissue mass 0.832 * 0.994 * -
Lean-tissue mass 0.550 0.497 0.471
* Correlations are significant (p < 0.01)
Figure 1 The figure displays the limits of agreement plots of the comparison of change in BW and changes in TM, STM and LTM by DXA following intravenous infusion of parenteral nutrition in 11 patients with short bowel syndrome.
Lard study
The placement of 3.49 kg of exogenous lard (with a composition of 52.2% FM and 47.8% LTM) over central and peripheral regions of the body of eight lean healthy volunteers had no effect on the estimate of TBBMC (p > 0.05, CV% = 1.4%). The descriptive statistics for the corresponding mean changes in TM, STM, LTM, and FM are given in Table 3. Except from a minor overestimation (0.23 kg) of TM and STM when the lard packets were placed over the trunk region, the measured changes in DXA variables were not significantly different from the expected values (p > 0.05, for all comparisons). However, DXA appeared slightly more accurate in detecting both the mass and composition of the added lard when the packets were placed over the thighs Table 3. Thus, whereas the composition into FM and LTM of the added lard placed over the thighs corresponded closely to the actual values, the FM was slightly underestimated and LTM correspondingly overestimated when the lard was placed over the trunk region. Fig. 2 shows the individual differences between the expected value of LTM, FM, TM and STM and the measured changes in the respective DXA variables. The accuracy of DXA in the individual subjects, expressed as the SD of the difference between expected and measured values, was 1.03 kg and 1.06 kg for the detection of changes in LTM and FM, respectively, and 0.18 kg for the detection of changes in STM and TM.
Table 3 The actual weight and chemical composition of lard packets placed on the thighs and abdomen of 8 healthy lean male volunteers and the composition measured by DXA.
Thighs Trunk
Actual Mean ± SD 95% CI Percentage Mass Detected Mean ± SD Mean ± SD 95% CI Percentage Mass Detected Mean ± SD
Total mass (kg) 3.49 3.48 ± 0.17 3.34 ; 3.62 99.7 ± 4.9 3.72 ± 0.18 3.57 ; 3.87 106.6 ± 5.1
Soft-tissue mass (kg) 3.49 3.48 ± 0.18 3.33 ; 3.63 99.6 ± 5.2 3.66 ± 0.19 3.50 ; 3.82 104.9 ± 5.5
Fat mass (kg) 1.82 1.92 ± 1.13 0.97 ; 2.86 105.2 ± 62.3 1.42 ± 0.92 0.65 ; 2.19 77.9 ± 50.8
Lean-tissue mass (kg) 1.67 1.56 ± 1.16 0.59 ; 2.53 93.5 ± 69.5 2.24 ± 0.96 1.44 ; 3.04 134.3 ± 57.3
Figure 2 The figure shows the differences between the actual weight and composition of added lard and the measured values by DXA, in eight lean healthy volunteers. In each subject DXA measurements were performed with lard placement both on the trunk and on the thighs. Each symbol indicates measurements in one subject. Error bars are the 95% confidence intervals of the difference.
Discussion
Compared to several other body composition techniques DXA has a very high precision [8,11], which on paper should make DXA able to detect small changes in body composition. The precision errors, generally reported as the coefficient of variation (CV%) of repeated measurements, are about 2–3% for TBBMC, FM, and FFM in healthy subjects, and values of a quite similar proportion have been documented in underweight patients with chronic intestinal disease [5].
In addition to a high precision, DXA has proven an accurate method for body composition analysis in normal weight healthy subjects [12-17]. However, the accuracy in underweight patients with chronic gastrointestinal disease or in very lean subjects has only been sparsely elucidated [4,5,18], but may theoretically be lower due to factors inherent in the DXA methodology. DXA operates on a scanning principle separating the body into approximately 11 000 pixels of each 6.5 × 13.0 square mm, of which about 6000 pixels only contain soft tissue and about 5000 pixels contain both soft tissue and bone. Determination of total body composition of bone, fat, and lean tissue masses is based on computerised analysis of the soft tissue composition of each pixel separately. One important limitation of the DXA methodology is that direct estimation of soft tissue composition is possible only in pixels with no bone present. Evaluation of soft tissue composition in pixels with bone mineral as well as soft tissue is performed by extrapolating calculated values for soft tissue composition in adjacent bone-free pixels to the pixels with bone. In underweight patients with chronic gastrointestinal disease or other lean subjects, the number of none-bone containing pixels available is obviously reduced compared to normal weight subjects, and in theory, this may lower the accuracy of DXA. Additionally, in malnourished underweight patients, deviations in the state of hydration frequently occur, which might influence the accuracy of soft tissue determination by DXA. Thus, Pietrobelli et al. [19] demonstrated that fluctuations in the hydration affected soft tissue attenuation and gave rise to systematic and predictable errors in the determination of LTM and FM, although these errors were quite small with changes in hydration within normal physiological limits. Thus, simulated experiments showed that errors in the detection of FM% is <1% with hydration changes of 1–5% [19].
We studied underweight patients with gut failure due to short bowel syndrome, who were dependent on long-term home parenteral nutrition. The changes in TM, STM, and LTM of 0.90 kg induced by infusion of PN were accurately detected on a group level. Furthermore, measurements of TBBMC and FM, body constituents that should not be affected by changes in hydration, remained unchanged during the experiment, and the respective CV%'s were very close to normal precision errors for repeated measurements [5,8,11]. Our data agree with results of comparable experiments were changes in the hydration status of healthy volunteers were induced by intravenous saline infusion [15,16]. However, the accuracy errors (SEE's of the regression lines) of DXA in the individual patient were about 35% higher for the detection of TM and STM, and nearly 100% higher for the detection of LTM in our study compared to results in healthy subjects reported by Going et al. [16]. Yet, in the present study BW increased by only 0.90 kg compared to an increase of 1.21 kg in the study by Going et al. [16], a difference that may have affected the results.
To explore the influence of a low body weight per se on the accuracy of DXA, we investigated otherwise healthy lean young men (BMI = 21 kg/m2) with packets of lard placed over the trunk and thigh region. In agreement with previous studies in normal weight healthy volunteers [3,12-14,17] we found that the TM and STM of packets of lard were very accurately assessed by DXA regardless of position. In addition, the measured composition of the lard packets into FM and LTM was not significantly different from expected values with the packets overlying either the thigh or the trunk region. The accuracy error of DXA in lean subjects, expressed as the SD of the difference between expected and measured values, was about 1.04 kg for the detection of changes in LTM and FM, and 0.18 kg for the detection of changes in STM and TM. These values agreed closely with the reported accuracy errors for the Norland XR-36 scanner in normal weight healthy volunteers [17]. This indicates that a low body weight (BMI = 21 kg/m2) per se does not affect the accuracy of DXA. DXA appeared somewhat more accurate in detecting both the mass and composition of the added lard when the packets were placed over the thighs. Thus, the FM was slightly underestimated and the LTM correspondingly overestimated when the lard was placed over the trunk. Limitations in the ability of DXA to accurately assess the composition of soft tissue in the trunk region have been reported earlier. Thus, in common with our results Snead et al. [12] and Milliken et al. [13] reported that DXA underestimated FM of added lard placed on the trunk of healthy volunteers. This may be related to the fact that the trunk region contains a high degree of pixels with bone present because of the complex bone geometry in the trunk, which leaves relatively fewer bone-free pixels for the calculation of soft tissue composition in this region. Therefore, measurement of fat and lean may be less accurate in the trunk region compared to the extremities, which have more simple bone geometry and a relatively higher number of bone-free pixels.
We evaluated the performance of the Norland XR-36 (software version 2.5.2) densitometer to measure small changes in soft tissue composition in underweight patients with chronic gastrointestinal disease. DXA accurately detected changes in TM, STM, and LTM induced by infusion of PN on a group level, however the accuracy errors were up to 100% higher in this group compared to normal weight healthy subjects. Also, DXA performed well in detecting the composition of added lard placed on lean healthy subjects with accuracy errors similar to those reported in normal weight subjects, supporting that a low body weight per se does not affect the accuracy of DXA.
Conclusions
We conclude that DXA is an accurate method for body composition analysis in underweight patients with chronic gastrointestinal disease. The individual accuracy errors however were higher than in normal weight subjects and this should be taken into account when evaluating the changes in body composition in the individual patient.
Authors' contributions
KH and MS were responsible for conception and design of the study. KH, PHH and MS were responsible for data interpretation, and manuscript preparation. None of the authors have personal or financial interests in any organization sponsoring the research.
Acknowledgements
The technical assistance of Jette Christiansen, Dorte Christensen and Bodil Petersen is greatly appreciated.
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| 15631633 | PMC544848 | CC BY | 2021-01-04 16:37:29 | no | Dyn Med. 2005 Jan 4; 4:1 | utf-8 | Dyn Med | 2,005 | 10.1186/1476-5918-4-1 | oa_comm |
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J Transl MedJournal of Translational Medicine1479-5876BioMed Central London 1479-5876-2-461561323010.1186/1479-5876-2-46ResearchClinical significance of BRAF mutations in metastatic melanoma Chang David Z [email protected] Katherine S [email protected] Iman [email protected] David [email protected] Klaus [email protected] Paul B [email protected] Department of Medicine, Memorial Sloan-Kettering Cancer Center, New York, New York, USA2 Department of Epidemiology and Biostatistics, Memorial Sloan-Kettering Cancer Center, New York, New York, USA3 Ronald O. Perelman Department of Dermatology, New York University School of Medicine, New York, New York, USA4 Department of Pathology, Memorial Sloan-Kettering Cancer Center, New York, New York, USA2004 21 12 2004 2 46 46 7 12 2004 21 12 2004 Copyright © 2004 Chang et al; licensee BioMed Central Ltd.2004Chang 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.
Forty to eighty percent of melanoma tumors have activating mutations in BRAF although the clinical importance of these mutations is not clear. We previously reported an analysis of BRAF mutations in metastatic melanoma samples from 68 patients. In this study, we correlated patient baseline characteristics, prognostic factors, and/or clinical outcomes with the presence of BRAF mutations. No significant differences were observed in age, gender, location of primary melanoma, stage at the diagnosis, and depth of primary tumor between patients with and without BRAF mutations. Melanomas harboring BRAF mutations were more likely to metastasize to liver (P = 0.02) and to metastasize to multiple organs (P = 0.048). Neither time to progression to stage IV nor overall survival were associated with BRAF mutations. In conclusion, we observed no significant differences in clinical characteristics or outcomes between melanomas with or without BRAF mutations. Although there was an increased frequency of liver metastasis and tendency to metastasize to multiple organs in tumors with BRAF mutations, there was no detectable effect on survival. Future prospective studies should include analysis of whether BRAF mutations in melanoma tumors correlate with an increased tendency to metastasize to liver or to multiple organs.
Metastatic melanomaBRAF mutationClinical significance
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Introduction
The mitogen-activated protein kinase (MAPK) pathway mediates cellular responses to growth signals and activation of this pathway has been shown to be critical in tumor formation, particularly in melanoma [1-3]. Recently, activating BRAF mutations were found with high frequency in malignant melanomas, including primary tumors and cell lines [4,5]. Suppression of activating BRAF mutations in cultured human melanoma cells inhibited the MAPK cascade causing growth arrest and promoting apoptosis [6], further suggesting the potential critical role of activating BRAF mutations in malignant transformation in melanoma.
We have reported the analysis of BRAF mutations in a cohort of metastatic melanoma patients [7] and noted a mutation proportion of 44%. As expected from previous reports, the most frequent mutation was BRAFV599E, which was found in 40% of samples. Since little is known about the clinical implications of activating BRAF mutations in melanoma tumors, we examined whether the melanoma tumors harboring BRAF mutations in this cohort showed different clinical or biological features compared to the melanoma tumors without mutations.
Materials and Methods
Retrieval of Tumor Specimens and Patient Information
Cryopreserved metastatic melanoma samples from 68 patients were selected from the Memorial Sloan-Kettering Cancer Center Tumor Bank. Patient demographic data were collected on the 68 patients whose tumors we had previously analyzed for BRAF mutations [7]. Data collected included: location of primary tumor, thickness, ulceration, stage of disease (according to American Joint Committee on Cancer Staging System), sites of metastasis, site of tumor biopsy, and history of and responsiveness to chemotherapy. This retrospective analysis was performed with IRB approval which determined that this was exempt research under 45 CFR 46.101.b(4).
BRAF Mutations Detection
BRAF (exons 11 and 15) was sequenced as previously reported [7]. For 65/68 patients, a single metastatic site was sequenced for BRAF. In three patients, two to four metastatic sites were available for sequencing. For patients with multiple specimens, we considered only the first acquisition of tissue in assigning patients to mutant or wild type categories.
Clinical Correlation and Statistical Analysis
The patients were first seen at MSKCC between June 1993 and April 2000. Clinical follow up was available through April, 2003. Comparisons between mutated and wild type were made using either the χ2 test, t-test or Cochran-Armitage test to trend. Survival distributions were estimated using the Kaplan-Meier method and compared using the log-rank test. Stage IV patients were stratified into two categories: those with stage M1a or M1b (lymph nodes, soft tissues and/or lung metastasis) and those with stage M1c (all other sites).
Results
We studied 74 cryopreserved metastatic melanoma samples from 68 patients: 42 men and 26 women (Table 1). Thirty-five patients had stage III, 33 were stage IV at the time the biopsies were obtained. These samples were melanoma metastasis from the following sites: lung (9), liver (3), gastrointestinal mucosa (9), soft tissues (20), lymph nodes (31), fallopian tube and ovarian (1), and uterus (1). Of the 68 patients analyzed, 30 had mutations in BRAF, including one with mutations in both BRAF and NRAS, and 38 patients were wild type. Overall, mutations in BRAF exons 11 and 15 were detected in 30 of 68 (44%) patients.
Table 1 BRAF mutations and clinical characteristics
Clinical Features BRAF Status P value
Mutation N = 30 (44.1%) Wild Type N = 38 (55.9%)
Gender
Female 11 15 0.81
Male 19 23
Age1
Mean 63.3 57.3 0.12
Median (range) 56.5 (29–91) 65.0 (42–97)
Stage at Diagnosis
I 5 3 0.92
II 13 19
III 7 10
IV 4 2
Unknown 1 4
Thickness (Number available) (N = 18) (N = 22)
Mean 2.98 4.83 0.29
Median (range) 1.75 (0.2, 20) 2.80 (0.4, 35)
Primary Site
Head/Neck 1 6
Trunk 10 11
Extremities 10 14
Ocular 1 0
Mucosal 1 0
Unknown 7 7
Response2
CR 2 3
PR 0 2
NR 16 10
Response Rate 11% 33% 0.12
1 Age at time of biopsy used to assess BRAF sequence.
2 Response data is based on the 33 patients who received systemic therapy.
Patients' age ranged from 29 to 97 years; there was no statistically significant difference in patients' age with regards to BRAF mutations (p = 0.12). Similarly, there was no difference in the distribution of primary sites and stages at diagnosis between patients with and without BRAF mutations. We noted that among the 7 melanomas arising from the head and neck region, only 1 harbored a BRAF mutation. Although there were too few of these patients for a meaningful statistical analysis, this observation is consistent with a recent report indicating that mucosal melanomas did not harbor BRAF mutations [8,9]. The mean thickness of primary tumor was 2.98 mm (range: 0.2, 20 mm) for patients with BRAF mutations, and 4.83 mm (range: 0.4, 35 mm) for patients without (p = 0.29). The effect of BRAF mutation on other known prognostic features of primary tumor such as the presence or absence of ulceration, regression, tumor-infiltrating lymphocytes, lymph-vascular invasion, and mitotic index could not be assessed because this information was available for only a small proportion of patients.
Patients with tumors harboring BRAF mutations were more likely to have metastasis to liver compared to those without the mutations (41% and 13%, respectively; p = 0.02) (Table 2). Tumors with BRAF mutations were also more likely to metastasize to multiple organs (p = 0.048) (Table 3). Among the 51 patients who developed stage IV disease (either at the time of the biopsy or during subsequent follow up), 19 out of the 27 patients (70.4%) with BRAF mutations in their melanomas were found to have more than one metastatic site compared to only 11 of the 24 patients (37.5%) with wild type BRAF.
Table 2 Correlation between BRAF mutations and number of metastasis among patients with stage IV melanoma
Sites of Metastasis BRAF Status P value
Mutation N = 27 (%) Wild Type N = 24 (%)
Soft Tissue/Lymph Nodes/Lung only 8 (30%) 12 (50%) 0.16
Non-soft tissue site 19 (70%) 12 (50%) 0.14
Liver 11 (41%) 3 (13%) 0.02
Table 3 Association of BRAF mutations with the number of metastatic sites in patients with stage IV melanoma
Number of Sites Per Patients BRAF Status P value*
Mutation N = 27 (%) Wild Type N = 24 (%)
5 4 (14.8%) 0 p = 0.048
4 4 (14.8%) 3 (12.5%)
3 6 (22.2%) 5 (20.8%)
2 5 (18.5%) 3 (12.5%)
1 8 (29.6%) 13 (54.2%)
* Cochran-Armitage test for trend
We examined the response to systemic therapy (chemotherapy or biochemotherapy) for the 33 patients who received such treatments. For patients with BRAF mutations, 18 patients received systemic therapy of who two patients achieved complete remission (response rate 11.1%). Fifteen patients with wild-type BRAF received systemic therapy of whom three patients achieved complete remission and two achieved partial remission (response rate 33.3%) (p = 0.12).
There was no statistically significant difference between time to progression to stage IV disease either from the time of diagnosis or from stage III in patients with or without BRAF mutations (data not shown). As this is a retrospective study, we cannot rule out the possibility that differences in interval assessments affected our ability to detect a difference in time to progression. On the other hand, date of death is an endpoint not affected by interval assessment times. There was no statistically significant difference between patients with BRAF mutations and those without BRAF mutations.
Discussions
High frequency of BRAF mutations has been reported in malignant melanoma [4,5,7], however, there has been little clinical correlation data elucidating the biological effects of these mutations in patients. We initiated this study in an attempt to address this question.
The observation that BRAF mutations are common in melanocytic nevi [10] has led to the assumption that mutations in BRAF occur early in melanocytic transformation and play an important role in the initiation of malignant transformation. Recently, an alternative view has been suggested by Dong et al who confirmed the high frequency of BRAF mutations present both in nevi and later stage melanomas but found few BRAF mutations in early stage radial growth phase melanomas [11]. They interpret these findings to mean that BRAF mutations are not involved in the initiation of the majority of melanoma, but rather play a role later in progression.
Since little information was available on the biological effects of activating BRAF mutations in melanoma, we analyzed the clinical characteristics of 68 melanoma patients whose tumors we had previously analyzed for BRAF [7]. We found that patients with tumors harboring a BRAF mutation were more likely to have metastasis to the liver and tended to have more organs involved with melanoma than patients without mutations. This is consistent with the idea that activating BRAF mutations affect the pattern of metastatic spread in melanoma, although we await confirmation of these findings in a prospective study.
In our cohort of subjects, there were 33 patients who received systemic therapy (18 patients with BRAF mutations, 15 patients without detectable mutations). There was a trend towards lower response rates among patients with mutations, although this trend was not statistically significant and is confounded by the small number of patients, the heterogeneity of treatments these patients received, and the retrospective nature of these analyses. This is a question that deserves to be revisited in a prospective manner.
Kumar and colleagues found that melanoma patients with BRAF mutations showed a statistically significant diminished duration of response to treatment compared to those without the mutations [12,13]. Their retrospective analysis consisted of 38 patients with metastatic melanoma (stage III or IV) who had been treated with chemoimmunotherapy (dacarbazine, vincristine, bleomycin, lomustine, and human leukocyte interferon). This cohort of patients had a surprisingly high response rate of 55%. Although the likelihood of response did not correlate with the presence of a BRAF mutation, multivariate analysis revealed that among patients who had responded, patients with BRAF mutations had a shorter duration of response compared to patients without any BRAF mutations (median 3.4 versus 9.8 months). They did not analyze the effect of BRAF mutations on the site of metastatic spread or other biological characteristics of the tumor.
Houben et al. reported that the presence of BRAF mutation in a metastatic melanoma lesion was associated with a poor prognosis as measured by shortened survival [14]. In our study, we did not detect any impact on either progression free or overall survival by the presence of BRAF mutation. The patient characteristics were not reported by Houben and colleagues but they indicate that most patients had soft-tissue metastases (M1a or M1b). In contrast, most of our patients had M1c melanoma and this could account for the different findings.
In three patients, multiple metastatic samples were available for analysis; in 2 of these patients, there was discordance in the presence of detectable BRAF mutations. In one patient in whom 2 lung metastasis collected over a period of one month were analyzed, one metastasis contained a BRAFV599E mutation; the other metastasis was wild-type for BRAF. In another patient, metastasis from lung, gastrointestinal (GI) tract, lymph node, and soft tissue were collected of a period of 34 months. All tumors harbored the BRAFV599E mutation except for the GI metastasis which was wild-type. It is possible that this discordance represents a problem with assay sensitivity, but we cannot rule out the possibility that there is true heterogenicity among metastasis with regard to BRAF mutations. Although this discordance among metastasis seems to contradict the observation that BRAF mutations are an early event in melanocytic nevi transformation, one possibility is that in melanomas arising from non-nevus melanocytes, BRAF mutation is a late event occurring in individual metastasis. Consistent with this, Shinozaki et al. recently reported that the incidence of BRAF mutation of primary melanoma did not correlate with Breslow thickness, and there was significantly higher frequency of BRAF mutation in metastasis than in primary melanoma, arguing that BRAF mutation maybe acquired during development of metastasis [15]. Houben also reported that in 3/22 cases, the BRAF mutational status of the primary and metastasis did not correlate [14]. This issue merits further investigation.
In summary, this analysis represents the largest study to date correlating BRAF mutations and clinical outcomes in metastatic melanoma. Although we observed a statistically significant higher frequency of liver metastasis and tendency to metastasize to multiple organs in patients with BRAF mutations, there was no significant effect on survival or response to systemic therapy detected by this study. Although this analysis is limited by its retrospective nature and the relatively small number of patients, it appears unlikely from these observations that there will be a major qualitative difference in the biological behavior between melanomas with and without BRAF mutations. Larger prospective studies are required to verify these observations and to clarify other biological consequences of BRAF mutations in melanoma.
Acknowledgments
David Z. Chang is supported by the AACR-Bristol-Myers Squibb Oncology Fellowship in Clinical Research, ASCO Young Investigator Award sponsored by Roche Laboratories, CALGB Clinical Research Fellowship sponsored by Aventis Oncology, and Ladies Auxiliary Veteran of Foreign Wars Cancer Research Grant.
Paul B. Chapman is supported by NCI grant K24 CA81293 and the Swim Across America Foundation.
The authors wish to thank Jennifer Guido, Susan Clinco and Ami Patel for their help with the database.
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| 15613230 | PMC544849 | CC BY | 2021-01-04 16:39:24 | no | J Transl Med. 2004 Dec 21; 2:46 | utf-8 | J Transl Med | 2,004 | 10.1186/1479-5876-2-46 | oa_comm |
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Med ImmunolMedical Immunology1476-9433BioMed Central London 1476-9433-3-31560691710.1186/1476-9433-3-3ReviewThe quantal theory of how the immune system discriminates between "self and non-self" Smith Kendall A [email protected] The Division of Immunology, Department of Medicine, Weill Medical College, Cornell University, New York, New York, United States of America2004 17 12 2004 3 3 3 12 12 2004 17 12 2004 Copyright © 2004 Smith; licensee BioMed Central Ltd.2004Smith; 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 the past 50 years, immunologists have accumulated an amazing amount of information as to how the immune system functions. However, one of the most fundamental aspects of immunity, how the immune system discriminates between self vs. non-self, still remains an enigma. Any attempt to explain this most intriguing and fundamental characteristic must account for this decision at the level of the whole immune system, but as well, at the level of the individual cells making up the immune system. Moreover, it must provide for a molecular explanation as to how and why the cells behave as they do. The "Quantal Theory", proposed herein, is based upon the "Clonal Selection Theory", first proposed by Sir McFarland Burnet in 1955, in which he explained the remarkable specificity as well as diversity of recognition of everything foreign in the environment. The "Quantal Theory" is built upon Burnet's premise that after antigen selection of cell clones, a proliferative expansion of the selected cells ensues. Furthermore, it is derived from experiments which indicate that the proliferation of antigen-selected cell clones is determined by a quantal, "all-or-none", decision promulgated by a critical number of cellular receptors triggered by the T Cell Growth Factor (TCGF), interleukin 2 (IL2). An extraordinary number of experiments reported especially in the past 20 years, and detailed herein, indicate that the T cell Antigen Receptor (TCR) behaves similarly, and also that there are several critical numbers of triggered TCRs that determine different fates of the T cells. Moreover, the fates of the cells appear ultimately to be determined by the TCR triggering of the IL2 and IL2 receptor (IL2R) genes, which are also expressed in a very quantal fashion. The "Quantal Theory" states that the fundamental decisions of the T cell immune system are dependent upon the cells receiving a critical number of triggered TCRs and IL2Rs and that the cells respond in an all-or-none fashion. The "Quantal Theory" accounts fully for the development of T cells in the thymus, and such fundamental cellular fates as both "positive" and "negative" selection, as well as the decision to differentiate into a "Regulatory T cell" (T-Reg). In the periphery, the "Quantal Theory" accounts for the decision to proliferate or not in response to the presence of an antigen, either non-self or self, or to differentiate into a T-Reg. Since the immune system discriminates between self and non-self antigens by the accumulated number of triggered TCRs and IL2Rs, therapeutic manipulation of the determinants of these quantal decisions should permit new approaches to either enhance or dampen antigen-specific immune responses.
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Introduction
Perhaps one of the most unique and fundamental aspects of the immune system is the clonal nature of the response to the introduction of antigen. The "Clonal Selection Theory" as originally formulated by Burnet stated that the immune system is made up of cells, each of which are only capable of reacting with a single antigenic molecule[1]. Thus, Burnet improved upon the "Natural-selection theory of antibody formation" proposed by Neils Jerne[2], by giving the immune response a cellular basis. Also, Burnet introduced the notion that after antigen selection, the reactive cell clones proliferate, and the resulting expanded population of cells would only then be capable of removing the offending foreign antigen. Consequently, one of the most crucial decisions required of an antigen-selected cell is whether to undergo cell cycle progression. Ultimately this decision determines one of the other most fundamental characteristics of the immune system, the ability to discriminate between self vs. non-self.
At the time that Burnet formulated the concept of clonal selection in the mid 1950s, the identity of the cells comprising the immune system was unknown. Plasma cells had been found to be the source of antibodies[3], but lymphocytes had not yet been identified as the precursors of plasma cells. Moreover, thymic-derived lymphocytes (T cells) and bone marrow-derived lymphocytes (B cells) were not to be discovered for almost two more decades [4-6].
In the intervening 50 years since Burnet formulated his theory, much has been discovered and even more has been proposed to explain how the immune system functions, especially how the discrimination between self vs. non-self is made. Since 1959, several modifications of Burnet's original model have been offered, each of which introduced additional cells in an attempt to explain how the entire system could react with absolutely everything in the environment, but not react with any molecules comprising self [7-15]. However, none of the models proposed thus far have focused on one of the most fundamental aspects of the Clonal Selection Theory as originally formulated by Burnet, i.e. the molecular forces driving the proliferative expansion of the antigen-selected cell clones.
We now know that lymphocytes make up the immune system, and we know the molecular structures of the antigen receptors expressed by both T cells and B cells. Consequently we know that Burnet was largely correct. Each cell expresses a unique antigen receptor that has the capacity to bind only a single antigenic epitope. The exception to this rule is the "allelic inclusion" of two T Cell Receptor (TCR) α-chains, resulting in approximately 30% of α/β-chain bearing T cells as having dual antigenic specificity. As well, it is known that T cells recognize epitopes comprised of short peptides of only a few amino acid residues bound to molecules encoded by the Major Histocompatibility gene Complex (MHC), while B cells recognize both the tertiary surface structure of larger molecules, as well as linear determinants of molecules. Despite this information, it still remains unknown how the immune system discriminates between non-self vs. self-molecules, in that the molecular nature of the T cell Antigen Receptors (TCR) and the B cell Antigen Receptors (BCR) that recognize both self- and non-self- epitopes are identical. Moreover, the molecular natures of self-epitopes and non-self -epitopes are identical. Accordingly, it is even more perplexing as to how the immune system manages this discrimination.
One key to understanding the way in which the immune system operates is the observation that the capacity to recognize and respond to antigen is dependent on the dose of antigen introduced[16]. Thus, there appears to be several outcomes possible, such that at low antigen doses there is no detectable response, and with increasing doses of antigen there may be the induction of tolerance, while even higher doses are necessary to trigger an immune response, which involves more and more antigen-reactive cells as the antigen dose increases. At very high antigen doses there may even be a "paralysis" induced.
Another key to understanding the immune response resides in the realization that the absolute frequency of potential antigen-reactive lymphocytes is very low before the introduction of antigen, on the order of 1 in a million cells up to 1 in 10,000 cells (i.e. 10-6 to 10-4). However, after antigen selection, proliferative clonal expansion increases the frequency of antigen-reactive cells to as high as 1 in 10 cells, an astonishing 1–100,000-fold increase [17-20]. Thus, the decision by individual cells to proliferate in response to recognition of an antigen is the critical decision at the cellular level that controls the ability of the whole immune system to discriminate between self vs. non-self.
Until the discovery of mitogenic cytokines, it was assumed that antigens are solely responsible for stimulating proliferation. It is now known that there are antigen-activated cytokines with T cell Growth Factor (TCGF) activity that provide molecular signals that markedly stimulate cell cycle progression. Since the principle cytokine with TCGF activity driving T cell proliferation is the interleukin-2 (IL2) molecule[21,22], the molecular mechanism whereby IL2 promotes cell cycle progression is of utmost importance.
Accordingly, to understand how the immune system discriminates between self vs. non-self antigens, the IL2 molecule is first examined, particularly how IL2 promotes the proliferation of antigen-selected cells. Then, the cellular and molecular determinants of IL2 production and IL2R expression are traced. Ultimately, the maturation of T cells in the thymus must be considered, to understand how cells determine whether to produce IL2 or not, and whether to respond to it or not. Most of the discussion that follows is focused on T cells, but the general principles developed apply to B cells as well.
The Quantal Nature of IL2-Promoted T Cell Proliferation
At the level of the individual cell, the proliferative response to IL2 is quantal, i.e. it is "all-or-none"[23,24]. However, to proceed beyond simply a description of this phenomenon, one must understand the molecular basis for the response of individual cells to IL2, so as to predict the behavior of the immune system as a whole.
Soon after the development of the radiolabeled-IL2 binding assay[25], which permitted IL2 receptors to be quantified and defined for the first time, experiments could be performed to ascertain how the concentrations of IL2 that bind to IL2Rs compare with the IL2 concentrations that promote T cell proliferation. It was revealed that the binding and biological response curves are coincident, as shown in Figure 1[25]. The IL2 concentration-dependent response ranges from 1–100 pM, and the 50% effective concentration (EC50) equals the equilibrium dissociation constant (Kd) of the IL2-IL2R interaction, both of which are ~ 10 pM. Although this is a very high affinity for a ligand-receptor interaction (e.g. most TCR-peptide-MHC affinities are ~ a million-fold lower), it is noteworthy that in molecular terms, the EC50/Kd = 6 billion molecules/mL.
Figure 1 The IL2 binding and biological response curves are coincident. Radiolabeled IL2 and purified homogeneous IL2 were used in parallel experiments with the same IL2R+ T cell population to determine the relationship between IL2 binding and IL2-promoted T cell proliferation as monitored by 3H-TdR incorporation. From reference 25.
Of course, these experiments were performed using cell populations, and symmetrically sigmoid log-dose response curves are quite familiar in these circumstances. However, from these experiments it was not possible to discern whether the low thymidine incorporation found at low IL2 concentrations was due to all of the cells in population incorporating only a little thymidine, or whether only a few of the cells could respond at low IL2 concentrations. Therefore, it was not until the IL2 dose-response relationship could be examined at the single cell level did it become apparent that the cell populations are comprised of individual cells that differ markedly as to their responsiveness to the mitogenic ligand. Thus, as shown in Figure 2 using propidium iodide staining of DNA and the flow cytometer to compare with thymidine incorporation, it was found that some cells of an asynchronously proliferating population respond by proliferating to very low IL2 concentrations, e.g. only 1 pM, while others need 100-fold higher IL2 concentrations. Moreover, the marked heterogeneity in IL2 responsiveness could not be explained on a genetic basis, since even cloned cell populations behaved in an identical fashion.
Figure 2 The IL2 biological dose-response relationship is determined at the single cell level by a marked heterogeneity of responsiveness. Asynchronously proliferating IL2R+ cells were exposed to varying concentrations of IL2 for 18 hours. Subsequently, cell aliquots were either pulsed for 4 hours with 3H-TdR, or stained with propidium iodide prior to single cell analysis by flow cytometry. The cell population incorporates 3H-TdR in an IL2 concentration dependent manner, and the amount of 3H-TdR incorporation at each IL2 concentration is determined by the absolute number of cells that have entered S-phase, as indicated by the single cell analysis by propidium iodide staining. From reference 26.
Once it is realized that the effective IL2 concentrations span 2 orders of magnitude, the question becomes why some cells are capable of responding at only 1 pM, while others require IL2 concentrations that are 100-fold higher. The only logical answer to this question is that there must be intrinsic cellular differences, and that these differences are manifest in molecules that are critical for signaling cell cycle progression. Therefore, in experiments focused on understanding how IL2 promotes T cell proliferation after antigen activation, we found that in addition to the affinity of the IL2/IL2R interaction, and the concentration of IL2, the other variables involved are the IL2 receptor (IL2R) density, and the duration that the IL2 and the IL2R molecules interact[26].
In order to design experiments to approach these variables, it was necessary to use cell populations that were synchronized in early G1, so as to follow the rate at which individual cells progressed through G1 into S-phase. Thus, if G0/1 synchronized IL2R+ cells are exposed to two different IL2 concentrations, one receptor saturating and another only half-saturating; the receptor-saturating concentration promotes cell cycle progression twice as rapidly as the half-saturating IL2 concentration. However, as the ligand concentration dependency of cell cycle progression of cell populations was well known, these results were not surprising.
However, it was surprising to find that if one exposes two cell populations, one with a higher IL2R density than another, to the same receptor saturating IL2 concentration, the cell population with the higher IL2R density traverses G1 and enters S-phase more rapidly than the population with the lower IL2R density (Figure 3).
Figure 3 The effect of IL2R density on the rate of T cell cycle progression. Two G0/1 synchronized T cell populations that differed 3-fold in mean IL2R density were exposed to an IL2R saturating concentration (250 pM) for 48 hours and 3H-TdR incorporation was monitored as indicated at 1 hour intervals. The cells with the higher IL2R density (solid circles) entered S-phase before the cell population with the lower IL2R density (solid triangles). From reference 26.
These observations predict that the basis for the characteristic sigmoid IL2 log-dose response curve (Figure 1) is only explicable because of heterogeneity of IL2R density within a given population of T cells. The heterogeneity of IL2R density/cell is readily appreciated from examination of the flow cytometry plot of the log-normal distribution of IL2Rs as shown in Figure 4. Thus, at low IL2 concentrations, i.e. ~ 1 pM, only cells with the highest IL2R density are capable of responding. As the cell population is exposed to increasingly higher IL2 concentrations, cells with lower IL2R densities will meet the quantal requirement to enter the cell cycle. Finally, as the IL2 concentration reaches levels that saturate all IL2Rs, even cells with the lowest IL2R density can reach the critical number.
Figure 4 IL2R density determined at the single cell level by flow cytometry. IL2R+ T cells were labeled with anti-Tac (CD25) monoclonal antibody and analyzed by single cell flow cytometry. The IL2R density varies among cells within he population over 3 orders of magnitude. From reference 26.
These findings indicate that cells reach a decision to undergo cell cycle progression based on some critical number of IL2-IL2R intermolecular reactions at the cell surface. Also, they indicate that the duration of the IL2-IL2R interaction plays a role, such that it appears that if a cell has a low density of IL2Rs, the critical number of IL2-IL2R interactions can still be reached, but a longer time interval is necessary. Also, the data indicate that if one interrupts the IL2-IL2R interaction before the critical number of interactions is attained, then cell cycle progression will not occur, as shown for a 3-hour exposure in Figure 5. In other words, the cell "counts" the number of triggered receptors and waits until the requisite number has accumulated before proceeding beyond G1 to S-phase[24,27].
Figure 5 The effect of varying the IL2 exposure period on the proliferative response of G0/1 synchronized IL2R+ T cells. Aliquots of synchronized cells were exposed to IL2 for varying intervals (3, 6, 11, 26 hours) then washed and placed into culture without IL2 and pulsed with 3H-TdR. Symbols: IL2 exposure, 3 hr (solid circles), 6 hr (open circles), 11 hr (solid triangles), and 26 hr (open triangles). Inset, shows the 3H-TdR incorporation of each cell population in response to an IL2R saturating IL2 concentration (250 pM) monitored for 1 hr at the times indicated. From reference 26.
IL2/IL2R binding is rapid and comes to steady state within 10 minutes[25,28], yet several hours of IL2 exposure are necessary to reach the critical number of IL2/IL2R interactions required to trigger cell cycle progression. Therefore, it must be concluded that the requisite number of IL2Rs is not present on any cells before the addition of IL2. Instead, new receptors must be continuously synthesized, expressed on the cell surface and serially engaged over several hours to finally reach the quantal number. Accordingly, the decision to divide is regulated with exquisite high fidelity, and the decision must be quantal. Otherwise a cell might only partially replicate its DNA before undergoing cytokinesis, a situation clearly incompatible with life.
An estimate of this critical number of IL2/IL2R interactions required can be calculated[24], knowing the initial mean number of receptors (~ 750 Rs/cell), and the rate of internalization and degradation of IL2 bound IL2Rs from the cell surface at steady state (t1/2 = 15 minutes). Thus, the rate constant, κ = ln2/15 min = 4.67 × 10-2 min-1, governs the rate of new receptor synthesis necessary to maintain the surface expression at steady state. Moreover, 11 hours (660 minutes) of IL2 exposure is necessary to trigger 50% of the cells within the population to undergo cell cycle progression (Figure 5). Thus, the mean number of triggered IL2Rs necessary is:
R# (R/cell) = κ × R# @ steady state
= 4.67 × 10-2 min-1 × 750 R/cell
= 35 R/cell/min × 660 min
= 23,100 triggered R/cell
It follows that if the mean number of IL2Rs at steady state is lower, e.g. only 375 Rs/cell, 22 hours would be required to reach the quantal number and if the rate of new receptor synthesis is doubled, maintaining twice as many, 1,500 sites/cell, then only 5.5 hours would be required to trigger the cells.
Intracellular Sensors of the Extracellular Signals
There are 3 chains that make up the trimeric IL2R that has an extremely high affinity for IL2, termed α (CD25)[29], β (CD122)[30] and γ (CD132)[31]. Careful kinetic and equilibrium binding experiments using radiolabeled IL2 revealed that the α chain contributes a very rapid association rate (κ = 107 M-1sec-1), while the β chain contributes a slow dissociation rate (κ' = 10-4 sec-1), which together yield the high affinity (Kd = κ'/κ = 10-11 M) for the ligand observed at steady state[28].
Recent experiments focused on the energetics of assembly of IL2/IL2R signaling complexes have revealed that in solution, the IL2R α and β chains bind to one another, whereas the α chain does not bind to the γ chain[32]. Moreover, the γ chain can only bind to α,β dimers or isolated β chains if IL2 has already bound to these receptor chains. These data support the interpretation that the α,β dimer probably is formed on the surface of antigen-activated T cells, and serves as the initial receptor complex capable of binding IL2. Moreover, the 10–20-fold excess expression of the α chain vs. the β chain favors the formation of the α,β heterodimer by the law of mass action. Subsequently, the IL2, α,β trimeric complex then can attract and bind the γ chain, forming a quaternary complex that is capable of signaling the cell interior. These energetic experiments provide an explanation for the IL2-dependency of signaling, since signaling only occurs when the γ-chain and the β chain are brought into close proximity, thereby activating the tyrosine-specific kinases, JAK 1 and 3, which are already bound to the β and γ chains respectively[33]. Accordingly, the assembly and maintenance of this energetically stable multicomponent macromolecular signaling complex is a fundamental requirement for the cell ultimately to make the quantal decision to divide.
The important downstream events in IL2-promoted T cell cycle progression are the JAK-dependent activation of at least two distinct proliferative signaling pathways. One is mediated by the transcription factor STAT5, while the other is mediated by the adapter molecule Shc, which activates phosphatidylinositol 3-kinase (PI3K). Both cyclin D2 and D3 are expressed in response to IL2R triggering[34], and recent studies have shown that the STAT5 and PI3K pathways play distinct, but coordinated roles in the quantal IL2R induction of progression through the Restriction Point (R-point) in the G1 phase of the cell cycle [35-37]. In addition to Shc, STAT5 also facilitates the activation of the PI3K pathway by a delayed mechanism that requires protein synthesis, and PI3K activity is essential for the induction of cyclin D2 expression by STAT5. PI3K activity is required for the optimal binding of RNA polymerase II to the promoters of cyclin D2 as well as other IL2/STAT5-induced genes.
Because of these findings, it has been proposed that the D cyclins serve as intracellular sensors of the extracellular signals [38] generated at the cell surface by the formation of the stable quaternary IL2/IL2R signaling complex[39]. Thus, cyclin D2 and D3 complex with the cyclin-dependent kinases (cdk) 4 & 6 and the p27 protein, thereby forming active kinases that initiate phosphorylation of the Rb proteins that repress the E2F transcription factors. The E2Fs are already bound to response elements that regulate the expression of multiple genes, the expression of each of which is critical for both nucleotide synthesis and DNA replication. Also, the E2F transcription factors regulate the expression of multiple genes required for formation of the Pre-replication Complexes (Pre-RCs), which must first assemble and then disassemble at sites on DNA termed Origins of Replication (Ori) before the DNA strands can separate, thereby allowing DNA duplication. Accordingly, the cyclin D/cdk/p27-dependent phosphorylation of Rb has been proposed to initiate the passage through the R-point, and has been described as a quantal molecular "binary switch"[39,40].
However, recent experiments with mice that have had all 3 of the cyclin D genes deleted have revealed that although hematopoiesis is dependent on the coordinated expression of the cyclin D genes, nonhematopoietic cells can proliferate in the absence of the D-type cyclins and their cyclin-dependent kinases 4 and 6 (cdk4/6)[41,42]. Even so, mouse embryo fibroblasts lacking type D cyclins proliferate more slowly to stimulation by serum by comparison to their wild type counterparts. Therefore, it appears that there are at least 2 distinct pathways whereby extracellular signals can trigger G1 progression, one involving cyclin D and another that is cyclin D independent.
At this time, it remains unknown as to whether T cells can be stimulated to proliferate by both cyclin D-dependent and independent pathways, or only by cyclin D-dependent (and therefore IL2R/STAT5-dependant) pathways. However, it is clear that T cells are in G0 until activated by initial signals received via the TCR, so that it still remains possible that T cells may be capable of proliferating in response to both TCR- and IL2R-derived signals[33,43,44]. Alternatively, the TCR may be responsible for the G0 to G1 transition, while the IL2R is responsible for G1 progression to the R-point and S-phase transition. However, another possibility remains that it still may well be that TCR-derived signals can initiate early rounds of cell division, but that for a fully developed clonal expansion, IL2/STAT5-dependent signals are necessary. If so, one would predict that TCR-mediated cell cycle progression is STAT5- and cyclin D-independent, so that the role of IL2 is to markedly accelerate and extend cell cycle progression initiated by the TCR.
The Quantal Regulation of IL2 Gene Expression
Once one realizes that the quantal decision of T cells to proliferate is based upon a critical number of IL2-IL2R interactions, it becomes immediately obvious that the availability of IL2, together with the extent of IL2R expression, ultimately determines whether a immune response occurs that will be detectable at the systemic level. Since the discovery [45-51] and elucidation of the nature of the T Cell antigen Receptor (TCR) over the past 20 years [52-56], data have accumulated indicating that the regulation of IL2 gene expression and IL2R gene expression is under a tight and complex cell surface signaling mechanism that involves not only the TCR, but other surface molecules as well.
Before the elucidation of the structure and function of the receptors involved in antigen recognition, it was difficult to envision how the IL2/IL2R system is regulated. However, the principles from the quantal IL2-IL2R signaling of cell cycle progression can now be extrapolated to this more complex signaling system, and herein resides the ultimate control of "self-non-self" recognition and response. Like the IL2R[24,27], the TCR also is capable of counting the number of antigen interactions so as to acquire the critical number of triggered receptors necessary for IL2 gene expression.
Since the cloning and sequencing of the IL2 cDNA[57] and gene[58], detailed studies have revealed the nature of the molecules controlling IL2 expression. There are 3 distinct response elements in the promoter region of the IL2 gene that bind members of distinct families of transcriptional activating factors [59-61]. These factors include Activating Protein-1 (AP-1), Nuclear Factor of Activated T cells (NF-AT), and the Nuclear Factor kappa B/Rel (NF-κB).
Individual IL2 transcription factors from these three families cannot bind stably to their target DNA response elements in vivo without coengagement of each of the distinct factors that bind at neighboring sites[62]. Also, if the members of any one of these factors is prevented from binding to the IL2 promoter region, there is a marked attenuation of IL2 gene transcription[63]. Moreover, even after the factors have bound, inactivation of any of the three transcription factors pharmacologically extinguishes the binding of all three factors, thereby aborting transcription[64]. Therefore it has been proposed that there is a nonhierarchical, cooperative enhancement of binding at the IL2 gene locus, and that this binding and transcriptional activation of IL2 gene expression is consequently, quantal.
The quantal binding of IL2 transcription factors to the IL2 enhancer and promotion of IL2 gene expression has been extended by studies on the allelic expression of the IL2 genes. Under optimal TCR stimulating conditions, where antigen is in excess, and Antigen Presenting Cells (APCs) are not limiting, there is biallelic expression of the IL2 genes[65]. By comparison, when TCR stimulatory conditions are suboptimal, then expression can be monoallelic, and as well, fewer cells will be activated to express IL2. However, once engaged, the rate of IL2 production per cell remains constant. In this respect, IL2 gene expression per cell is always quantal.
The activation of NF-AT, AP-1 and NF-κB/Rel family members is controlled by signaling pathways triggered by the TCR, as well as costimulatory molecules and coinhibitory molecules. Thus, TCR triggering promotes the rapid influx of calcium into the cell, which activates the phosphatase calcineurin, thereby dephosphorylating and activating NF-AT[66], while other TCR-triggered pathways activate the kinases p56lck, ZAP-70, PLCγ, and Protein Kinase C-θ (PKC-θ), which simultaneously promote the activation and translocation of AP-1 to the nucleus, and the activation of NF-κB/Rel family members that are already present in the cytoplasm bound to the Inhibitors of κB (IκB)[67]. In addition, stimulation of co-stimulatory receptors, particularly CD28, by the B7 ligands expressed by APCs activates PI3K, thereby further activating both AP-1 and NF-κB/Rel members [68-70].
The Phenomenon of Anergy ("Abnormal Inactivity")
Soon after the derivation of the first T cell clones[71], experiments with cloned helper T cells revealed that high concentrations of specific antigenic peptide (i.e. 1–100 μM) would lead to unresponsiveness, i.e the incapacity to produce IL2 or to proliferate when subsequently exposed to a stimulatory concentration of antigenic peptide (i.e.1 nM-1 μM)[72]. The suppressive effect was peptide and clone specific, took several hours to develop, and was long lasting, up to 7 days in vitro. It could not be ascribed to nonspecific toxicity and cell death, in that the cells were still capable of proliferating in response to IL2 added exogenously.
Subsequently, anergy (defined as a state of proliferative unresponsiveness to normal mitogenic activation) was shown to be produced by delivery of "Signal-1" (i.e. TCR), without "Signal-2" (i.e. CD28)[11]. More recently, it has been shown that it is possible to induce anergy pharmacologically by stimulating calcium flux using calcium ionophores without activating the TCR or CD28[66,73,74]. In this instance, NF-AT is activated and translocates to the nucleus in the absence of activation of AP-1 and NF-κB/Rel. Activation of NF-AT without the participation of AP-1 and NF-κB/Rel results in the proteolytic degradation of PLCγ and PKC-θ [74], as well as the transcriptional activation of a unique set of genes that collectively suppress the capacity of the cell to respond to subsequent full TCR/costimulatory activation[73]. This induced "anergic state" is stable and is manifest by the inability to maintain a stable immunologic synapse, which precludes expression of the IL2 gene, so that anergic cells will not proliferate in response to a subsequent full TCR/CD28 stimulation. However, like the high antigen dose anergy, if IL2 is supplied exogenously, the proliferative block is bypassed, so that the anergized cells are still capable of proliferating in response to the IL2 signals.
These findings are reinforced by other experiments, which reveal that inhibitors of calcineurin, such as cyclosporine-A and tacrolimus (FK506), which prevent the activation of NF-AT, also prevent the induction of anergy[75]. As well, T cells from NFAT-1 (-/-) mice are resistant to anergy induction by calcium ionophores[76,77]. Moreover, NF-AT-1 (-/-) mice exhibit a syndrome characterized by the accumulation of hyperactivated T cells[76,77]. Thus, in situations where calcium-mediated activation of calcineurin and NF-AT predominates, and pairing with AP-1 and NF-κB/Rel transcription factors does not occur, such as in situations with little of no costimulatory activation, a biochemical milieu exists that favors the creation of an anergic state.
By comparison, NF-κB/Rel appears to be the most critical of the three families of transcription factors involved in the activation of IL2 gene expression[78]. Moreover, of the 5 members of the NF-κB/Rel family, c-Rel is the most important, and also the critical transcription factor activated by costimulatory signals[79]. C-Rel expression is restricted to cells of the lymphoid and myeloid lineages, whereas the other NF-κB family members are expressed ubiquitously in almost all tissues. T cells from c-Rel (-/-) mice cannot express the IL2 gene or proliferate in response to activation via full TCR/co-stimulation. However, they can proliferate normally if IL2 is supplied exogenously[79]. As well, c-Rel binds to the costimulatory response element IL2 CD28RE with a high affinity (Kd = 25 nM), while NF-κB/Rel p50/p65 heterodimers bind to this response element with a 10-fold lower affinity[80].
Accordingly, for productive CD4+ T cell activation manifest by IL2 gene expression and proliferative clonal expansion, the minimum requirements are optimal activation of the TCR via peptide-MHC complexes, and costimulation via activation of CD28 by the APC B7 ligands. In the case of a low affinity TCR-MHC-peptide interaction, or in the absence of costimulation, NF-AT activation may predominate and anergy can result[81]. The actual critical number of TCR/CD28 activating signals that result in the quantal expression of the IL2 gene have not been determined, but by extrapolation from the parameters regulating IL2-IL2R activation, it is logical that peptide-MHC ligand concentration, TCR receptor density and affinity, as well as duration of the ligand-receptor interaction, will dictate the number of triggered TCR/CD28 interactions, which determine whether the cell becomes anergized vs. activated to produce IL2, to express IL2Rs and to proliferate.
The Regulation of IL2R Gene Expression
Resting T cells that have not been recently activated via TCR/CD28 do not express detectable high affinity IL2Rs[25]. A recent study carefully examined resting T cells isolated from human peripheral blood for expression of the 3 chains of the IL2R by flow cytometry[82]. To ensure that the T cells represented only unactivated, truly "resting" T cells, any in vivo activated cells were removed using monoclonal antibodies reactive with the transferrin receptor (CD71), known to be an early TCR/CD28 activation molecule. Both CD4+ and CD8+ resting T cells have undetectable surface or cytoplasmic IL2R α and β chains, as monitored using very sensitive flow cytometry methods. By comparison, IL2R γ chains are detectable in the cytoplasm, but undetectable on the cell surface[83]. Upon activation via the TCR/CD28, the expression of the genes encoding both the IL2R α and β chains occurs, and γ chains are rapidly mobilized to the cell surface, so that high affinity trimeric IL2Rs are expressed, and the cells are competent to respond to IL2 by proliferating.
The regulation of α chain (CD25) gene expression is under the control of the TCR/CD28 via activation of NF-κB/Rel, AP-1 and NFAT, which interact with 2 distinct REs [84]. Therefore, it appears that the IL2Rα chain gene is coordinately regulated along with the IL2 gene by the same signaling pathways emanating from the TCR/CD28 receptors that activate the same families of transcription factors regulating the IL2 gene. However, it has not been determined whether the critical number of TCR/CD28 receptors necessary to trigger the IL2 gene and the IL2Rα chain gene are similar. Most experience suggests that there is a much lower number of triggered TCR/CD28 receptors regulating IL2Rα gene expression as compared with IL2 gene expression, but this question needs to be examined directly.
In addition to the TCR, IL2 enhances IL2Rα chain gene expression as much as 10–20-fold [85-87]. This IL2 effect on α-chain expression is readily appreciated by flow cytometry, in that IL2 shifts the mean fluorescence intensity more than an order of magnitude. As well, it is noteworthy that the IL2Rα chain expression is ~ 10-fold higher than expression of either the IL2Rβ or the IL2Rγ chains, so that when using flow cytometry to detect each of the chains, the IL2Rα chain (CD25) is always predominant. As well, the number of functional high affinity trimeric IL2Rs is determined by the number of β and γc chains, which are limiting. The energetics of IL2 chain assembly has now provided an explanation for the excess α chains, in that the law of mass action favors the formation of α,β heterodimers, to which IL2 binds and then recruits the γ chain, thereby forming the stable quaternary signaling complex.
The In Vivo Functions of IL2
Before the identification of the IL2 molecule in 1983[57,88], it was assumed that antigens stimulate T cell proliferation, and that mitogenic cytokines, which were first described almost 20 years earlier[89,90], functioned simply to amplify the signals already initiated by antigen[22]. Thus, when purified IL2 became available[88] and the IL2 receptor (IL2R) had been discovered[25], experiments became possible for the first time to determine the molecular mechanism whereby antigen activated T cells are stimulated to proliferate. Initial studies revealed that although antigen is necessary to activate T cells to leave G0 and to enter early G1, cell cycle progression through G1 to S-phase and mitosis appeared to be mediated by IL2 upon binding to the IL2R[26,91,92]. Resting T cells were found to be IL2R negative and IL2 unresponsive[25], while purified, homogeneous IL2 was capable of promoting long-term T cell proliferation of mitogen- or antigen-activated T cells. By comparison, mitogen or antigen alone could not sustain long-term T cell growth[26].
Moreover, immunosuppressive pharmacological agents such as glucocorticoids[93] were found to inhibit T cell proliferation by preventing IL2 production but not IL2 responsiveness. As well, experiments with monoclonal antibodies that block either IL2[88] or the IL2R[29] were found to inhibit T cell cycle progression after mitogen or antigen activation. These experiments all suggested that antigen per se could not promote T cell proliferation, and suggested that IL2 drives T cell proliferation after the initial antigen activation. Even so, the monoclonal IL2- or IL2R-reactive antibodies suppressed proliferation by > 90–95%, but never completely abrogated proliferation, leaving open the possibility that either the TCR itself or other mitogenic cytokines might also be operative.
In 1991, with the advent of IL2 gene deletion through genetic recombination, it became possible to test definitively the functional importance of IL2, both in vitro and in vivo, at least in mice. Initial in vitro experiments testing cells from IL2 (-/-) mice for proliferation in response to activation by the mitogenic lectin Concanavalin-A (Con-A) revealed a 70–75% diminution of tritiated thymidine incorporation, but not a complete abrogation[94]. Therefore, these experiments suggested that perhaps the TCR really was capable of promoting proliferation, and that IL2 functioned to merely amplify the response, as assumed originally. Alternatively, it was also considered possible that perhaps IL2 was not the only cytokine with TCGF activity operative to promote T cell cycle progression, and additional cytokines such as IL4[95] or IL7[96], which were thought originally to be primarily B cell stimulators, might also be playing roles.
Even so, it was still a great surprise when initial in vivo experiments with IL2 (-/-) mice infected with Vaccinia Virus (VV) and Lymphocytic Choriomeningitis Virus (LCMV), revealed that the generation of antigen-specific effector cytolytic T cell activity was reduced by only ~ 30% as monitored by Cytolytic T Lymphocyte (CTL) 51Cr-release assays[97]. Moreover, neutralizing IgG antibody responses to Vesicular Stomatitis Virus (VSV) infection, a T-helper-dependent function, were delayed but not reduced. Other in vivo experiments with staphylococcal super antigens indicated that CD4+ T cells doubled normally, while CD8+ T cells from IL2 (-/-) mice were only ~ 50% of wild type[98]. These findings led to the interpretation that in vivo IL2 is redundant for the generation of immune responses, and that the TCR or other cytokines with TCGF activity could substitute for IL2.
However, upon subsequent and more extensive testing of IL2 (-/-) mice, it was found that in the absence of IL2, the marked proliferative expansion of LCMV-induced CD8+ T cells was virtually eliminated, the total cytolytic effector capacity was reduced by > 90%, and IFN-γ production resulting from T cell activation was dramatically inhibited[99,100]. Moreover, IL2 (-/-) mice permitted prolonged viral replication compared with (+/+) and (+/-) controls, which could clear the virus within a few days.
Therefore, all of these data indicated that IL2 may not be the sole cytokine with TCGF activity, but it is one of the principle TCGFs responsible for the maximal proliferation of antigen selected mature peripheral T cells, as well as their differentiation to effector cells, both in vitro and in vivo. Moreover, IL2 (-/-) mice are immunocompromised without IL2; thereby indicating that the TCR or other yet undiscovered cytokines cannot fully substitute for IL2 in vivo.
The IL2 Deficiency Autoimmune Syndrome
Since IL2 (-/-) mice are immunocompromised, it was entirely unexpected to find that a syndrome of lymphocyte hyperactivity and apparent autoimmunity appears as the mice mature beyond puberty [101]. Thus, although lymphocyte development during embryogenesis is grossly unperturbed by the absence of IL2, generalized lymphoid hyperplasia ensues after the first several weeks and months of life, and T cells that express activation markers accumulate in the secondary lymphoid tissues. As well, autoimmune antibody-mediated hemolytic anemia appears, in addition to antibodies reactive to self-molecules, such as DNA and other nuclear antigens. Similar findings with mice deleted of the IL2R α chain (CD25)[102], β chain (CD122)[103], the JAK3 protein kinase[104,105], and the transcription factors STAT5a/b[106] all supported the idea that IL2 or one of the other interleukins that signal via the IL2R γc chain [107] somehow determines the selection of mature cells in the thymus, and in the absence of postnatal IL2 expression, the immune system begins to react to self as if it is nonself.
The accumulation of T cells with an activated phenotype in the setting of an initial lymphopenia and immunodeficiency, has recently been attributed to compensatory over stimulation by the cytokines responsible for homeostatic proliferation, e.g. IL7, IL15 and IL21, all of which signal via the γc chain[108]. Of these cytokines, IL7 and IL15 signal via STAT5, whereas IL21 signals via STAT1 and STAT3. Accordingly, IL21 stimulation via STAT1&3 may well be responsible for the hypersensitivity lymphoproliferative syndrome that is common to the IL2, IL2R and signaling (-/-) phenotype.
These observations in mice were reinforced by a report of a human homozygous mutation of CD25[109]. A male child of first cousin parentage presented at age 6-months with increased susceptibility to viral, bacterial, and fungal infections, suffering from cytomegalovirus pneumonitis, persistent oral thrush, candida esophagitis, and adenovirus gastroenteritis, chronic diarrhea, and failure to thrive. From the age of 8-months, lymphadenopathy and hepatosplenomegally became apparent. In vitro assays demonstrated a reduced responsiveness to stimulation by anti-CD3 (11% of control) and phytohemagglutinin (20% of control). Severe immunodeficiency was proven by the patient's inability to reject an allogeneic skin graft. Paradoxically, despite the obvious immunodeficiency, there was a normal sized thymus and lymphocytic infiltration of multiple tissues, including lung, liver, gut, soft tissue and bone. These findings were interpreted as possibly a result of a failure of negative selection of potential autoreactive cells in the thymus, as well as an inability to control autoreactive cells in the periphery, perhaps due to the absence of CD4+CD25+ Regulatory T cells.
Regulatory T Cells (T-Regs)
Soon after it was demonstrated that IL2 (-/-) and IL2Rα or β chain (-/-) mice develop an autoimmune syndrome, it was reported that immunocompromised (nu/nu) mice would also develop a wide spectrum of both organ-specific and systemic autoimmune diseases if they received normal cell populations from which CD4+CD25+ T cells were eliminated[110]. Furthermore, reconstitution of CD4+CD25+ T cells in the transferred cell populations prevented the development of autoimmunity. Subsequently, we found that IL2 treatment of IL2 (-/-) mice before day 10 after birth prevents the onset of the syndrome of lymphocyte activation and autoimmunity[111]. As well, thymocytes and spleen cells from IL2-treated IL2 (-/-) mice transferred to IL2 (-/-) recipients delayed the development of the autoimmune syndrome. These data suggested that IL2 treatment induced some normal cellular maturation/differentiation step in the transferred IL2 (-/-) cells that subsequently prevented the cells in the IL2 (-/-) mice from responding to self-antigens[111].
In other reports from as early as the 1960s[112], it was established that neonatal thymectomy in the first few days after birth can lead to subsequent widespread autoimmune phenomena, such as hemolytic anemia, thyroiditis, gastritis, oophoritis, or orchitis [113-115]. These two observations, i.e. autoimmune phenomena arising in both IL2 (-/-) mice and neonatal thymectomized mice, were connected when it was demonstrated that T cells expressing the IL2Rα chain (CD25) ontogenically begin to appear in the normal periphery immediately after day 3 of life, rapidly increasing within 2 weeks to adult levels, which comprise ~ 10% of CD3+ cells [116]. As well, neonatal thymectomy on day 3 eliminates CD25+ T cells from the periphery, and injection of CD25+ T cells from normal adult donors into day-3 neonatally thymectomized mice prevents the development of autoimmunity, while injection of CD25- T cells does not.
These observations were interpreted as consistent with the notion that neonatal thymectomy on day 3 can eliminate or reduce the autoimmune preventative CD25+ T cells, thereby leading to unchecked activation of the self-reactive T cells produced before neonatal thymectomy. Together with the observations on the IL2 treatment of IL2 (-/-) mice[111], these experiments fix the source of these CD25+ cells within the thymus, and also imply that IL2 is a necessary component in their development.
CD4+CD25+ "suppressor" regulatory T cells (T-Regs) could also be demonstrated in the secondary lymphoid organs of normal adult mice, and an in vitro assay was devised to test their suppressive activity[117]. CD4+CD25+ cells typically represent ~ 10%–15% of CD4+ T cells in lymph nodes from 8–10 week old mice. Paradoxically in view of their expression of CD25, these cells appear to be resting as well as anergic, in that they cannot proliferate in response to soluble and solid-phase anti-CD3 or Con-A. As well, even though the cells express the IL2R α chain, they cannot proliferate in response to exogenous IL2. However, these cells can be activated and proliferate in response to a combination of anti-CD3 + IL2. These data suggest that resting, anergic CD4+CD25+ T cells do not express the IL2R β and γ chains, but they can be induced to express them after activation of the TCR.
When co-cultured with CD4+CD25- cells, these CD4+CD25+ cells were found to markedly suppress the proliferative response of the CD25- cells, provided they are stimulated with low concentrations of soluble, but not solid-phase anti-CD3. This inhibition was dependent on cell-cell contact, not soluble factors, and dependent on the suppression of IL2 production by the CD25- responding cells. The inhibition could be bypassed by the addition of IL2, or costimulation with anti-CD28. These data were interpreted as consistent with the idea that CD4+CD25+ cells in normal unimmunized animals represent a distinct lineage of "professional" suppressor cells that have matured in the thymus. These T-Reg cells have been termed "naturally occurring" (nT-Reg).
It has also been reported that CD4+ T cells with regulatory function can be generated in vitro by the activation of mature peripheral CD4+CD25- T cells[118]. Thus, both regulatory and effector cells can, in principle, be generated from the same mature CD4+ T cell precursors. It has been postulated that these "inducible" cells (iT-Regs) might be triggered by "low-affinity or altered TCR signal transduction"[118], in that the conditions that favor the generation of T-Regs ex vivo from mature CD4+CD25- T cells, include antigen in the presence of immunosuppressive cytokines such as IL10 and TGFβ, immunosuppressive agents such as vitamin D3 and dexamethasone, CD40-CD40L blockade or immature DC populations[119,120].
Furthermore, it was reported recently that subcutaneous infusion of low doses of antigenic peptide by means of osmotic pumps over 14 days transforms mature peripheral T cells into CD4+CD25+ "suppressor cells" that can persist for long periods of time (i.e. several months) in the absence of antigen and confer specific immunological tolerance upon challenge with immunogenic doses of antigen[121]. Therefore, it appears that both in vitro and in vivo, low antigen concentrations can promote the differentiation of mature peripheral CD4+ T cells to express CD25, and to become "suppressor" cells rather than effector cells.
The dependency on the thymus for maturation of CD4+CD25+ T-Regs, as well as the dependency upon IL2, has been underscored by experiments with IL2β chain (-/-) mice made transgenic for the IL2Rβ chain under the influence of the proximal lck promoter, so that mature trimeric IL2Rs capable of signaling are only expressed in the thymus[122,123]. Transgenic expression of the IL2Rβ chain in IL2Rβ chain (-/-) thymocytes corrects the lack of CD4+CD25+ peripheral T cells in IL2Rβ chain (-/-) mice and prevents lethal autoimmunity. These experiments further emphasize the unique contribution of IL2 to the development of CD4+CD25+ T-Regs, in that the other γc chain cytokines, e.g. IL4 and IL7, IL9, and IL21 do not compensate for the lack of T-Regs in IL2Rβ chain (-/-) mice.
Suboptimal antigen concentrations and IL2 are both required for the generation and activity of T-Regs, and both in the thymus and in the periphery. However, the forces governing the simple generation of anergic and nonfunctional CD4+CD25+ T cells, as compared with the determinants of the generation of activated proliferating, suppressive CD4+CD25+ "Regulatory T cells", vs. CD4+CD25+ activated proliferating functional effector T cells remain to be defined.
Because each of these cell fates is functionally distinct, it is reasonable to hypothesize that these distinct cell fates are determined by different numbers of triggered TCRs and IL2Rs.
The Transient Nature of the In Vitro T Cell Proliferative Response: Feedback Inhibition of IL2 Gene Expression
Upon productive activation of T cells in vitro via TCR/CD28, there is a characteristic transient expression of the IL2 gene, such that IL2 mRNA first becomes detectable within 6 hours, and then peak levels occur after 12 hours, with a subsequent decline to undetectable levels by 24 hours[21]. Detectable IL2 protein in the culture media follows a similar, but delayed course with peak concentrations found after 24 hours, and by 48 hours barely detectable levels remain. Expression of high affinity trimeric IL2Rs follow a similar, but delayed transient expression course, with peak levels expressed at 24–48 hours, followed by a slow decline over several days[91]. By comparison, expression of the IFN-γ gene follows a much more protracted course, with detectable expression still evident after several days[124].
The mechanisms accounting for the transient expression of the IL2 gene and the genes encoding the IL2R chains have not been apparent, until recently. It is now realized that surface molecules of the coinhibitory CTLA-4 family[125] appear later after TCR/CD28 activation, first evident after ~ 24 hours with peak levels at 48–96 hours. In addition to CTLA-4, which has a 10-fold higher affinity for the B7 molecules expressed by APCs, the coinhibitory receptor PD-1[126], as well as the ligands reactive with this receptor, PDL-1[126] and PDL-2[127], appear on activated T cells. Still a third, later appearing coinhibitory receptor, BTLA, has also recently been described as expressed on both antigen-activated T cells and B cells[128].
This coinhibitory ligand-receptor family of molecules belongs to the Ig superfamily and the B7/CD28 costimulatory ligand/receptor family[129,130]. However, the CTLA-4 family of receptors does not function to deliver a costimulatory signal, as does CD28. Instead, members of the CTLA-4 family have inhibitory signaling motifs in their cytoplasmic domains and they have been shown to localize in close proximity to CD28, where they compete with activating signals from CD28. For example, by binding the phosphatase SHP-2, the CTLA-4 family of molecules inhibits the positive signals emanating from the TCR/CD28 at a very proximal position within the signaling cascade, thereby extinguishing the expression of the IL2 gene. Once IL2 production is shut down, because IL2 is internalized and degraded with a half-time (t1/2) of ~ 2 hours, IL2 is consumed very rapidly, resulting in cessation of IL2-promoted cell cycle progression and eventually apoptosis due to the lack of IL2-promoted anti-apoptosis gene expression, such as Bcl-XL.
The coinhibitory ligand/receptor pairs identified thus far can be shown to exert their negative effects by attenuating IL2 gene expression, but the administration of IL2 can bypass this block, in that the cells are still IL2-responsive. A similar phenomenon has been found with regard to the effects of T-Regs. T-Regs shut down IL2 gene expression, via a cell-cell contact mechanism that has not yet been delineated. However, IL2 supplementation will bypass the suppressive effects of T-Regs, thereby allowing for T cell proliferation. Also, in both instances, activation of CD28 via monoclonal antibodies serves to counteract the blockades, and permits the suppressed cells to both express the IL2 gene and to proliferate.
Accordingly, it would seem a plausible hypothesis that the cell-cell contact mechanisms employed by T-Regs to inhibit IL2 gene expression are mediated by members of the CTLA-4 family of coinhibitory ligands/receptors, either those already identified, or others yet to be identified. This is a particularly attractive hypothesis, in that the ligands that trigger the coinhibitory PD-1 receptor are also expressed by TCR/CD28-activated T cells[131].
The Transient Nature of the In Vivo T Cell Proliferative Response And "Adaptive Tolerance"
With the advent of the ability to label cells with 5- and 6-carboxyfluorescein diacetate succinimidyl ester (CFSE), combined with the use of gene deleted mice, it has been possible to follow the proliferation of T cells in vivo, and to test which proliferative signals are operative. In this regard, T cells from IL2Rβ (-/-) mice that have had the IL2Rβ chain expressed only during thymopoiesis do not express detectable IL2Rβ chains in the periphery, and therefore are incapable of delivering a proliferative signal. However, these cells are capable of undergoing 1–2 divisions upon stimulation with anti-CD3 + anti-CD28[132]. Similar findings have been reported in systems using TCR transgenic mice and adoptive transfer experiments when either IL2 or CD25 have been deleted[133,134]. One interpretation is that TCR/CD28 activation may be capable of initiating 1–2 rounds of cell division, but IL2 appears necessary for maximal and sustained proliferation. Alternatively, other, yet undiscovered cytokines with TCGF activity may be responsible for activating the initial proliferative responses to antigen activation.
After the initial proliferative clonal expansion of antigen-selected cells, the fate of the expanded effector cells has been found to depend greatly upon whether the antigen is cleared or whether it persists. In experimental viral infections where the virus is cleared rapidly within the first week after infection, expanded CD4+ and CD8+ effector cell populations undergo a contraction, with the loss of as much as 90% of the expanded effector cells[18]. We have found that this contractive phase is attributable to cytokine withdrawal apoptosis, in that the administration of IL2 during this phase prevents the contraction[135]. Others have shown a similar protective effect of IL2 after both CD4+ and CD8+ cells are expanded in response to activation by staphylococcal superantigen[136]. Subsequent studies have revealed that the residual populations of expanded effector cells eventually differentiate to "central" memory cells, which have the capacity for maintenance of the population size via slow proliferative renewal and as well, the capacity to respond to the reintroduction of antigen by rapidly producing IL2, proliferating and differentiating to effector cells[137].
With persistence of antigen, the fate of the expanded effector T cell populations changes dramatically. Instead of differentiating into responsive memory cells, the cells revert to a state of unresponsiveness, which has been termed "exhaustion" by those studying experimental persistent viral infections [138,139]. This exhausted state is manifested by an early loss of the capacity to produce IL2 and to proliferate. As antigen persists, the cells gradually lose their capacity to lyse target cells and to secrete antiviral effector cytokines such as TNF-α and IFN-γ. Eventually, clones of virus-specific cells can undergo apoptosis, which may be attributed to Activation-Induced Cell Death (AICD), leading to clonal disappearance[140].
A similar phenomenon has been described in experiments employing a paired transgenic model (TCR and Ag)[141]. In this model, CD4+ TCR-Tg T cells from antigen-naïve animals are labeled in vitro with CFSE, then transferred into recipient mice expressing low levels (~ 100 pM) of antigenic pigeon cytochrome c peptide. The cells become activated, express the IL2R α-chain (CD25) and CD69, and proliferate for the first 4 days, eventually expanding ~ 100-fold. However, over the subsequent 10–14 days, the cells lose expression of CD25 and cell recovery declines by ~ 50%. Thereafter, the cells are said to be in the "adaptive phase", which is characterized by a hyporesponsiveness to antigen in vitro, and is manifest by a decreased capacity to produce IL2 and other cytokines, and to proliferate.
This adaptive state persists in the host with chronic expression of the antigen, and in contrast to a similar paired transgenic model in a B cell system[142], is not associated with a decrease in the level of expression of the TCR[143]. However, the adaptive state is dependent upon continuous exposure to antigen; upon transfer to an antigen-negative host, the hyporesponsive state reverts. Moreover, the adaptive state is similar to a desensitization phenomenon akin to tachyphylaxis, and studies have revealed a down regulation of the TCR signaling molecules, involving an early block in tyrosine kinase activation, which primarily inhibits calcium mobilization, thereby suggesting that the desensitization involves the adaptation of the TCR signaling apparatus to the chronic persistence of low levels of antigen[143,144].
It is important to emphasize that the adaptive state is only a relative hyporesponsiveness, as compared to either the naïve situation or the host with central memory cells[141,143]. If cells are exposed to higher concentrations of peptide in vitro, i.e. between 1 nM and 1 μM, a response can be detected, but the antigen dose-response curve is shifted 100–300-fold to the right. In addition, the adaptive phenomenon cannot be ascribed to active suppression by a T-Reg differentiative process, in that the adapted cells do not express CD25, and in vivo experiments have excluded a suppressive mechanism.
What Determines the "Strength" of the Signal?
From the discussion thus far, it must be apparent that the strength of the signals delivered to T cells ultimately determines the outcome, i.e. either anergy, or activation of the IL2 gene, and if activation occurs, the duration that it persists. Thus, considerations of the ligand concentrations available, the receptor affinities and numbers expressed, and the duration of the ligand-receptor interactions again become important.
The duration of signaling via TCR/CD28 is known to be a major determinant of the magnitude of IL2 production and thus the extent of T cell proliferation. Even in the 1970s the magnitude of the proliferative response after mitogenic lectin administration was found to be directly related to the duration of lectin stimulation[145]. Thus, removal of the mitogenic lectin within the first 24 hours of stimulation attenuates the proliferative response markedly.
With the discovery that T cell proliferation after mitogen or antigen activation is principally mediated by IL2[22], it seemed obvious that a certain time interval was necessary after TCR/CD28 triggering to provide for maximal expression of the IL2 and IL2R genes. Indeed, experiments proved this to be the case[146]. Even so, several hours seemed somewhat prolonged, given that early biochemical events such as calcium flux and kinase activation were detectable within minutes after TCR engagement. Moreover, IL2 gene transcription could be detected within 45 minutes, using sensitive techniques[147].
A series of experiments reported in the mid 1990s began to provide an explanation for this perplexing problem. By equating "triggered" TCRs with their internalization and disappearance from the cell surface after ligand activation, it was shown that a single peptide-MHC complex is capable of serially triggering up to ~ 200 TCRs[148,149]. Furthermore, like the IL2Rs, T cells appeared to be able to "count" the number of triggered TCRs, and responded by proliferating when ~ 8,000 TCRs were triggered[149,150]. Other experiments showed that the duration of antigenic stimulation was one of the most critical parameters determining the fate of naïve and effector T cells, i.e. whether they would be activated or deleted[151]. However, these observations were not linked to the IL2/IL2R system.
With the discovery of Supramolecular Activation Clusters (SMACs), an additional level of complexity was added[152]. Also termed the "Immunological Synapse", the specialized junction between a T cell and an APC consists of a central cluster of T cell receptors together with costimulatory and coinhibitory receptors, surrounded by a ring of adhesion molecules[153]. Recent experiments, which employed peptide antigen-specific TCR αβ transgenic T cell blasts labeled with CFSE, the relationship between the duration of the TCR signal and the extent of the proliferative response could be examined further at the single cell level. Between 10–24 hours of continuous TCR stimulation by MHC-peptide in a stable SMAC is necessary to promote maximal IL2 production and proliferation[154].
Regarding the density of the TCR on responding T cells, it is important to restate and emphasize that the TCR density follows the same log-normal distribution as detailed for the log-normal distribution of IL2Rs, even on cloned T cells. Thus, there is at least a 2-log10 difference in TCR density among potential responding T cells, and consequently, those cells with the highest density of TCRs, will be capable of responding to lower concentrations of MHC-peptide epitopes, and also capable of responding more rapidly than cells with lower TCR densities to an optimal pMHC concentration. Detailed studies examining the cytokine response of murine T cell clones to graded peptide antigen concentrations have revealed a hierarchical organization of TCR signal-dependent response thresholds for elicitation of different cytokines in individual cells[155]. IL2 production was found to remain constant per cell as the ligand concentration was increased, with the primary change being in the number of cells making IL2 at a fixed level. Therefore, the decision to produce IL2 is a quantal decision on the part of each individual cell within the cloned population.
Exactly what leads to the heterogeneity of the quantal response of IL2 gene expression on the part of the cells within the cloned cell population has not been determined, but very similar findings have been reported for IFN-γ production by a human T cell clone in response to graded doses of antigenic peptide[156]. The peptide dose that stimulated 5% vs. 95% of the T cells was found to span over a log, and the response on the part of the cells comprising the population was quantal; i.e. at low antigen concentrations fewer cells expressed IFN-γ and as the antigen concentration was increased, an increasing number of cells expressed IFN-γ. To explain these results it was postulated that the intraclonal heterogeneity in antigen responsiveness could result from the different numbers of TCRs expressed by individual cells. However, this conjecture has not been examined directly.
Exactly the same considerations hold for the distribution of costimulatory and coinhibitory molecules. Thus, there is interplay between all of these receptors, which ultimately impacts the "strength" of the signal[157] and the duration that the signal must be applied to productively signal the IL2 gene response elements, and eventually lead to an "activated" T cell. Obviously, if any of these parameters are limiting, the activation events may favor the delivery of abortive transcriptional activating signals, which may favor anergy or differentiation to T-Regs, rather than activation[141].
As already detailed, if CD4+ T cells are activated via the TCR without adequate stimulation via CD28, such as may occur if self peptide is presented on immature dendritic cells as APCs, the conditions would favor the predominant activation of NF-AT yet inadequate AP-1 and NF-κB/Rel activation, which would promote anergy and perhaps even the irreversible differentiation of most of the cells to T-Regs. Thus, the concentration of antigen, the availability of adequate costimulatory molecule function, the affinity and density of the TCR/cell, as well as the duration that the TCR is triggered all influence signal "strength".
With regard to the affinity of the TCRs for the peptide-MHC complex, it is important to note that the TCR does not undergo somatic hypermutation and "affinity maturation" as does the BCR. Accordingly, the Kd of the TCR is fixed after recombination and rearrangement in the thymus, and is relatively low by comparison with the Kd of antibody molecules, which have ~ 1000-fold higher affinity for binding antigen. Measurements of the equilibrium dissociation constants of isolated agonist pMHC binding to TCR molecules by surface plasmon resonance have revealed Kds in the range of 1–100 μM, with koff rate constants of ~ 0.01–0.1 s-1, yielding t1/2 ~ 7–70 secs[158]. In contrast, antagonistic MHC-peptide-TCR interactions off-rates are ~ 10-fold faster than agonistic interactions. Thus, off-rate constants of ~ 5 sec-1 have been found, which yield t1/2 of only ~ 0.15 seconds.
Of course, the TCR does not bind MHC-peptide complexes in isolation or in solution. The formation of the immunological synapse greatly alters the way in which TCRs engage antigens and the way in which they are triggered[55]. Thus, T cell clones that have TCRs with a Kd = 1 μM binding to MHC-peptide in isolation can be triggered at peptide concentrations ranging as much as 100-fold lower in vivo. Furthermore, new studies have made it possible to "count" the exact number of ligands that a T cell encounters on another cell, and then monitor the consequences of that interaction with respect to the increase of intracellular calcium concentration[55]. It has been found that only 10 MHC-peptide ligands are sufficient to provide for the formation of a stable immunological synapse and sustained calcium flux for several hours. However, below this critical number of MHC-peptide ligands, only transient calcium increases occur, and a stable synapse does not form. Thus, an abortive signaling process appears to ensue, which could very well lead to the activation of NF-AT without adequate levels of AP-1 or NF-κB/Rel, which would be insufficient for IL2 and IL2Rα chain gene expression, thereby promoting anergy/T-Reg differentiation.
Recent experiments focused on how the TCR can respond to such low concentrations of agonist peptides indicate that the slower off rate of the agonist pMHC/TCR interaction allows the juxtaposition of CD4 with bound lck to the agonist pMHC/TCR, so that endogenous pMHC/TCR, which are in a large excess, can form a dimeric signaling complex comprised of agonist pMHC and endogenous pMHC[159]. Then, the endogenous pMHC with its fast off rate can trigger many TCRs serially and greatly amplify the TCR signals.
Given the long duration necessary to trigger a response, a kinetic model has been proposed to account for how serially triggered TCRs that interact very briefly with peptide-MHC complexes, then are rapidly internalized and degraded can be counted by the T cell, and how transient signaling events can be accumulated over time and integrated into a quantal response[160]. The model is based on a process first described in neuronal cell activation termed 'temporal summation'. The signaling events originating from successively triggered TCRs build up, with each adding to the falling phase of the one before. In this way, small and short signals that alone are unable to trigger a response can be summed up over time eventually to reach the level sufficient to trigger the quantal response, in this instance IL2/IL2R gene expression.
All of these considerations lead one to the conclusion that like the IL2/IL2R-determined quantal decision to undergo cell cycle progression, there appear to be quantal decisions operative at the level of the immunological synapse that lead to distinct cell fates, which in turn are ultimately determined by IL2 and IL2R gene expression. Thus, if the agonist pMHC ligand concentration is low, only T cells with a high density of TCRs will form stable synapses that will result in sustained activation of the IL2 gene and thereby cell cycle progression. It follows that at the same limiting agonist pMHC concentrations, cells with lower TCR densities may have abortive expression of the IL2 gene, which would favor differentiation to T-Regs, while cells with still lower TCR densities would not successfully trigger expression of the IL2 gene, thereby favoring the triggering of differentiation to an anergic state and unresponsiveness. Thus, there are at least 3 distinct cell fates that are determined by the accumulated number of triggered TCRs, which is determined by the agonist pMHC concentration, TCR density and the duration of the pMHC/TCR interaction.
The Quantal Numbers of Triggered Receptors are Specified in the Thymus
The structure and function of the immunological synapse essentially determines the fate of T cells as they mature in the thymus. Again, this cell fate determination is linked to IL2 and IL2R gene expression. From the above discussion, it is now clear that like the quaternary IL2/IL2R complex, the immunological synapse is a dynamic multicomponent molecular complex, the stability of which requires ongoing signaling through the TCR for stable calcium mobilization and kinase activation occurring over several hours. As well, the immunological synapse modulates the overall level of mature T cell activation by integrating positive (costimulatory) signals and negative (coinhibitory) signals from a variety of surface receptors. In this regard, it is noteworthy that the synapse that forms between thymocytes and thymic stromal cells differs qualitatively from that observed between mature peripheral T cells and peripheral APCs[161]. One reason that this may occur relates to the lack of expression of the costimulatory B7 molecules on thymic stromal cells[162]. In this regard, on would predict that thymocytes would not be activated to produce IL2 very readily, due to the lack of CD28/B7 costimulation.
From detailed experiments performed primarily with mice made transgenic for the TCR, it is now clear that the interaction of the TCR expressed on developing immature thymocytes with self peptide-MHC molecules expressed on thymic stromal cells is essential for the selection of those cells that ultimately are destined to leave the thymus and populate the periphery [163-166]. Thus, after productive rearrangement and expression of the αβ chains of the TCR, four fates are possible. If there is little or no affinity of the TCR for self peptide-MHC molecules, the T cells undergo apoptosis as a result of a lack of signal generation. However, if there is a "weak interaction" between the TCR and nonagonist or antagonist self peptide-MHC molecules (i.e. those molecules that have a rapid off-rate from binding to the TCR), the maturing T cells are "positively selected" to survive. Although it still remains controversial, most data are consistent with the notion that an intermediate strength of signal leads to the differentiation of T-Regs. By comparison, if the αβ TCR encounters an agonistic self peptide-MHC interaction, i.e. one that has a slower off-rate and a higher affinity, "negative selection" occurs and these T cells are induced to undergo apoptosis. Consequently, only those T cells that have "nonagonistic" reactivity with self peptide-MHC molecules make up the T cell repertoire.
With regard to the generation of quantal cellular responses, it is noteworthy that IL2 has now been implicated to be involved in both positive and negative selection, as well as T-Reg differentiation.
Recent experiments focused on the signals generated in thymocytes leading to positive selection have revealed that signaling via calcium and calcineurin is necessary for positive selection but dispensable for negative selection[167]. For example, deletion of the regulatory subunit-B1 of calineurin in thymocytes leads to loss of activation of NF-ATc proteins and also inefficient ERK activation, but normal activation of NF-κB/Rel. Of interest, positive selection was found to be markedly deficient in these animals, but negative selection remained intact.
As already discussed, experiments performed with IL2, IL2R, JAK3, and STAT5 (-/-) mice have all now demonstrated that the IL2/IL2R interaction is unnecessary for positive selection. In the absence of signals generated via the IL2/IL2R interaction, positive selection proceeds unimpeded, so that during development and after birth, a normal number and composition of cells mature in the thymus and populate the peripheral lymphoid tissues. Thus, if the "weak" signals between the TCR and self peptide-MHC are not strong enough to trigger expression of the IL2 and IL2R genes, the cells are permitted to survive, and leave the thymus to populate the secondary lymphoid tissues. Should agonistic nonself-peptide-MHC complexes be introduced that are capable of binding to the TCR with high affinity, these T cells, which are non-reactive with "nonagonistic" self peptide-MHC complexes, are not anergic. Rather, if stimulated by non-self agonistic peptide, they are fully capable of expressing IL2 and IL2R genes, and of undergoing IL2-dependent proliferative expansion and differentiation to effector cells in the periphery.
However, the development of T-Regs (i.e. CD4+CD25+ cells) is dependent on the IL2/IL2R interaction. In the absence of IL2 or functional IL2Rs, T-Regs are low or absent, both in the thymus and in the periphery. Moreover, if the IL2/IL2R interaction is restored, either genetically or pharmacologically, then T-Regs are reconstituted and the autoimmune phenomena are delayed or prevented altogether[106,111,168-170]. As well, the function of T-Regs in the periphery is also totally dependent on IL2/IL2R signaling, so that if potential positively selected autoreactive T cells are not continuously suppressed by T-Regs, the IL2 (-/-) syndrome of lymphoid hyperplasia and autoimmunity will occur.
At this juncture, it is logical to propose that the number of triggered TCRs and IL2Rs receptors necessary to generate T-Regs must be higher than the number required to generate simple "positive selection, in that IL2/IL2R gene expression must be triggered[73,171]. In this regard, it has been reported that α chain allelic "inclusion" results in a lower density of TCRs/cell, in that the β chains are paired with 2 distinct α chains in ~ 30% of αβ TCRs[172]. Upon introduction of antigenic peptide, these TCR bi-allelic cells can escape negative selection, presumably because their epitope-specific TCR density is only half normal, and accordingly they would not accumulate the same number of triggered TCRs as a mono-allelic cell. This difference could be responsible for the generation of T-Regs.
It also appears that differentiative signals triggered by the IL2/IL2R interaction are necessary to promote the differentiation to an anergic and suppressive T-Reg cell[81]. Furthermore, the number of triggered TCR receptors must be lower than those necessary to trigger "negative selection". These findings have led some investigators to propose that the main "nonredundant" function of IL2 is to promote the development and function of T-Regs[173,174].
"Negative selection" of potential self-reactive T cells has been proposed to occur via a TCR-triggered apoptosis. The exact molecular mechanisms responsible for this effect still remain obscure, but apoptosis appears to occur when there is a strong agonistic TCR-self-peptide-MHC interaction, which should trigger maximal IL2 and IL2R gene expression. However, data have been reported indicating that negative selection of CD8+ T cells proceeds normally without IL2[175]. By comparison, it remains controversial whether the IL2/IL2R interaction is necessary for negative selection of CD4+ T cells[176].
Quite convincing data have revealed a role for IL2 in CD4+ T cell negative selection [177] using nontransgenic and transgenic IL2-sufficient and deficient animal model systems. It could be shown that during TCR-mediated thymocyte apoptosis, IL2 protein is expressed and detectable in situ in the thymus, and apoptotic thymocytes up-regulate the expression of IL2Rs. Furthermore, IL2R+ CD4CD8 double-positive and CD4 single-positive thymocytes undergoing apoptosis bind and internalize IL2. As well, IL2-deficient thymocytes are resistant to TCR/CD3-mediated apoptotic death, which is overcome by providing exogenous IL2 to IL2 (-/-) mice. Finally, disruption or blockade of IL2/IL2R interactions in vivo during antigen-mediated negative selection rescues MHC class II restricted thymocytes from apoptosis. Thus, all of these findings provide evidence for the direct involvement of the IL2/IL2R signaling pathway in the deletion of self-reactive double-positive and CD4 single-positive T cells[177].
Accordingly, these data are all entirely consistent with the notion that the CD4+ T cell hyperplasia and autoimmunity observed in IL2 (-/-), IL2R (-/-), and IL2 signaling (-/-) mice are attributable, at least in part, to inefficient deletion of strongly agonistic self-reactive CD4+ T cells, as well as deficient maturation of T-Regs.
How Can the Immune System Ever Discriminate Between "Self & Non-Self" Peptides?
The foregoing considerations lead one to the realization that there are no known molecular mechanisms that can explain how the TCR can discriminate qualitatively between peptides of self-origin vs. peptides of nonself-origin. Both of these ligands are identical in structure, i.e. they are both peptides. Moreover, the αβ TCRs are also identical structures, whether they recognize self or non-self peptides bound to MHC. It follows that all of the data and logic support a quantitative mechanism of discrimination based upon the accumulated number of triggered TCRs and IL2Rs, as shown in Figure 6. Moreover, each triggered cellular differentiative fate of survival, death, anergy, or proliferative expansion, is quantal.
Figure 6 The number of triggered TCRs and IL2Rs determine quantal T cell fates in both the thymus and the periphery. On each plot, the number of triggered TCRs and IL2Rs increase from bottom to top. The different quantal cell fates are dictated by a definite number of triggered Rs as depicted.
Both in the thymus and in the periphery, there are 3 cellular fates specified by an increasing number of triggered TCRs, which dictates whether IL2 is produced and how much IL2 is produced. Thus, ultimately, the number of IL2-triggered IL2Rs determines the critical quantal fate decisions. A similar conclusion was introduced recently, with the difference that TCR avidity (i.e TCR affinity × density) was postulated to dictate the cell fates, but no role was postulated for IL2[178]. Since ultimately, self-nonself discrimination of the immune system depends on proliferative expansion of antigen-selected clones, the connection between the number of triggered TCRs and IL2Rs offers a molecular explanation for the quantal cellular response.
If T cells that have potential reactivity with self peptide-MHC ligands exit the thymus having escaped negative selection, these T cells will populate the secondary lymphoid tissues in the periphery at very low frequencies, ~ 1 in a million lymphocytes. Thus, these cells make up the TCR repertoire in the periphery, and as long as the distinct self-pMHC complexes remain below the critical number necessary for the formation of a stable synapse, and the TCR-pMHC off-rate is rapid, there is no necessity to introduce any additional mechanism to allow the immune system to ignore self. Instead, in the periphery the immune system only recognizes peptides, whether self or non-self, which are present at a high enough concentration to attain the critical density of 10 peptide-MHC molecules/synapse and as well, that generate a slow enough off-rate from the TCR to form a stable synapse, so that the critical number of triggered TCR/CD28 receptors for activation is reached. The only caveat beyond these considerations is that if an abortive pMHC/TCR synapse forms, the cell receives signals that it interprets as instructions to become anergic, or possibly to differentiate to become suppressive cells (T-Regs), thereby solidifying the non-reactivity on the part of the host to these peptides.
Conclusions
The "Quantal Theory" states that the fundamental decisions of the T cell immune system are dependent upon the cells receiving a critical number of triggered TCRs and IL2Rs and that the cells respond in an all-or-none fashion. Any reductionist approach to understand how the immune system discriminates self from non-self must begin with a systemic immunological response that most closely correlates with immunity. Thus, the "Quantal Theory" is based on Burnet's axiom that the proliferative expansion of antigen-selected clones is central to the generation of a protective immune response[1]. Secondly, a successful reductionist theory must explain how individual cells of the immune system make the decision to proliferate or not. As each decision of the individual cell is quantal, one must explain the molecular basis of the quantal cellular decision. At each decision point, the assembly of essentially irreversible multicomponent macromolecular complexes underlies the quantal cellular responses. Given the data that the simple IL2/IL2R interaction promotes the quantal decision to undergo cell cycle progression by reaching a critical number of triggered IL2Rs[26], it follows that the quantal decision to express the IL2 gene and the IL2R genes is similarly regulated by a critical number of triggered TCR/CD28 molecules. Thus, the TCR/CD28-triggered expression of the IL2 and IL2R genes is pivotal for the quantal cellular decisions in the thymus that determine distinct fates such as positive selection (survival), the differentiation to T-Regs (anergy), and negative selection (apoptosis), while it is also pivotal for the quantal cellular decisions in the periphery that determine whether to remain unresponsive (survival), to differentiate to T-Regs (anergy), or to begin proliferating (immunity). Accordingly, the "Quantal Theory" offers a unifying explanation at the molecular level that provides the cellular mechanisms for the immune system as a whole to make the quantal discrimination between self antigens and nonself antigens.
These considerations lead to the speculation that non-self peptides that are introduced in low enough concentrations may well be perceived by the immune system as "self" and will generate tolerance. Thus, we now have a molecular, cellular and immunological explanation for the phenomenon of "Low Zone Tolerance", first demonstrated by Mitchison 40 years ago[16]. Accordingly, one might have a means to tolerize individuals to specific defined peptides that may be useful in the treatment of allergy, autoimmunity, and allograft rejection. In contrast, a similar situation could be operative in the tumor-bearing host, and in the host infected chronically with viruses such as the Human Immunodeficiency Virus, and Hepatitis C Virus. In these instances, low persistent antigen levels may well serve to maintain a state of "low zone" tolerance. Accordingly, the question arises as how to break this tolerant state?
Competing interests
The author declares that he has no competing interests.
Acknowledgements
The author wishes to thank Drs. Marc Feldmann, Ellis Reinherz, Sofija Andjelic, Hsiou-Chi Liou, Zoran Popmihajlov and Anjana Rao for reading the manuscript and for their helpful comments and suggestions. The author is also thankful for financial support from the NIAID, NIH (Grants R01-AI 44207, U01-48224, R01-51181) the Weill Cornell General Clinical Research Center (Grant M01-00047) and The Doris Duke Charitable Foundation.
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| 15606917 | PMC544850 | CC BY | 2021-01-04 16:39:07 | no | Med Immunol. 2004 Dec 17; 3:3 | utf-8 | Med Immunol | 2,004 | 10.1186/1476-9433-3-3 | oa_comm |
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BMC BioinformaticsBMC Bioinformatics1471-2105BioMed Central London 1471-2105-5-2061561324210.1186/1471-2105-5-206Research ArticleAn empirical analysis of training protocols for probabilistic gene finders Majoros William H [email protected] Steven L [email protected] The Institute for Genomic Research, 9712 Medical Center Drive, Rockville, MD 20850, USA2004 21 12 2004 5 206 206 5 11 2004 21 12 2004 Copyright © 2004 Majoros and Salzberg; licensee BioMed Central Ltd.2004Majoros and Salzberg; 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
Generalized hidden Markov models (GHMMs) appear to be approaching acceptance as a de facto standard for state-of-the-art ab initio gene finding, as evidenced by the recent proliferation of GHMM implementations. While prevailing methods for modeling and parsing genes using GHMMs have been described in the literature, little attention has been paid as of yet to their proper training. The few hints available in the literature together with anecdotal observations suggest that most practitioners perform maximum likelihood parameter estimation only at the local submodel level, and then attend to the optimization of global parameter structure using some form of ad hoc manual tuning of individual parameters.
Results
We decided to investigate the utility of applying a more systematic optimization approach to the tuning of global parameter structure by implementing a global discriminative training procedure for our GHMM-based gene finder. Our results show that significant improvement in prediction accuracy can be achieved by this method.
Conclusions
We conclude that training of GHMM-based gene finders is best performed using some form of discriminative training rather than simple maximum likelihood estimation at the submodel level, and that generalized gradient ascent methods are suitable for this task. We also conclude that partitioning of training data for the twin purposes of maximum likelihood initialization and gradient ascent optimization appears to be unnecessary, but that strict segregation of test data must be enforced during final gene finder evaluation to avoid artificially inflated accuracy measurements.
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Background
The number of generalized hidden Markov model (GHMM) gene finders reported in the literature has increased fairly dramatically of late [1-8], and the community is now contemplating various ways to extend this attractive framework in order to incorporate homology information, with a handful of such systems having already been built (e.g., [9-12]). GHMMs offer a number of clear advantages which would seem to explain this growth in popularity. Chief among these is the fact that the GHMM framework, being (in theory) purely probabilistic, allows for principled approaches to constructing, utilizing, and extending models for accurate prediction of gene structures.
While the decoding problem for GHMM gene finders is arguably well understood, being a relatively straightforward extension of the same problem for traditional HMMs and amenable to a Viterbi-like solution (albeit a more complex one), methods for optimally training a GHMM gene finder have received scant attention in the gene-finding literature to date. What information is available (e.g., [2,4]) seems to indicate that the common practice is to optimize the submodels of the GHMM independently, without regard for the optimality of the composite model.
The training of HMMs and GHMMs has traditionally been carried out using some form of maximum likelihood estimation (MLE). Baum-Welch training [13], which is an instance of the well-known expectation maximization (EM) procedure, is itself a form of MLE [14]. In the case of GHMM gene finders, one typically applies some form of MLE to each of the submodels (states) in the GHMM so as to render training features of each type (e.g., exon, intron, donor site) maximally likely under the induced (sub)model; i.e., maximizing:
for state q and for Si a feature of length di from the state-q-specific training set T. The submodels are then merged into a composite model (i.e., the full GHMM) by observing transition probabilities between features in the training data corresponding to each of the GHMM states.
For example, an exon state in a GHMM can be trained by collecting n-gram statistics (i.e., counts of n-letter substrings) from known exon sequences and normalizing these into transition probabilities for an (n-1)th-order Markov chain [15]. Similarly, intron, intergenic, and untranslated region (UTR) states can be modeled by collecting appropriate statistics from corresponding sample features and using these to train individual content-scoring models, such as Markov chains, neural networks, decision trees, etc. Signal sensors for donor and acceptor splice sites and start and stop codons can be trained by aligning known signals of the appropriate type and counting nucleotide frequencies at each position within a fixed window around the signal; converting these counts to relative frequencies produces probability estimates for use in a weight matrix or similar type of model. Transition and duration probabilities can likewise be estimated by observing appropriate frequencies in training data. All of these estimation activities can be performed independently, resulting in a GHMM consisting of distinct subsets of maximum likelihood parameters.
Such an approach does not, however, attend to the global optimality of the GHMM as a whole. Ideally, one would like to maximize the expected accuracy of the gene finder on unseen data. A reasonable approximation to this ideal would be to maximize the average probability of the gene parses in the training set:
where the collection of model parameters making up the GHMM is denoted θ and the elements (S, φ) of the training set T comprise pairs of sequences S and their known parses φ. This argmax gives us the parameterization under which the full gene parses (rather than the sequences) in the training set will be maximally likely (on average). Decomposing each parse φ into a series of (qi, di) pairs, for state qi and state duration (i.e., feature length) di, we get:
where Pe, Pt, and Pd represent the emission, transition, and duration probabilities of the GHMM, respectively. Whereas the common MLE training procedure for GHMMs as described above optimizes the individual terms in the numerator of Equation 3 independently, the argmax above calls instead for these terms to be jointly tuned so as to optimize the entire ratio in parentheses. Intuitively, one can think of this alternate formulation as attempting to account for the process in the Viterbi algorithm (during later decoding) whereby the individual submodels "compete" for nucleotides (in the sense that each nucleotide can be emitted by only one submodel in any given parse, and the Viterbi algorithm chooses the final, predicted parse based on the values of the model parameters). Our hope is that by addressing the issue of submodel competition explicitly during parameter estimation, we will thereby empower the gene finder to better discriminate at a global sequence level between the features modeled by individual submodels in the GHMM, thereby producing more accurate gene predictions.
A similar optimization problem occurs in the field of speech recognition, in which systems of interacting acoustic models and language models are employed to optimally parse an audio stream into a series of discrete words. Interestingly, the trend in that field, starting with Bahl et al. in 1986 [16], has increasingly been away from the sole use of MLE and toward an alternative approach very similar to that prescribed by Equation 2 known as global discriminative training [17-19] or conditional maximum likelihood [20]. The problem also appears in a slightly different form in the related field of statistical natural language parsing, in which it has been suggested that global methods for optimizing competing stochastic grammar models may improve the accuracy of systems at the level of whole-sentence parses [21]. Maximum discrimination HMMs have already been applied successfully to problems in the realm of biological sequence analysis [22], though their use in gene finding has apparently not yet seen widespread adoption. To our knowledge, the only gene finder reported to use discriminative training is HMMgene [23], a gene finder based on a non-generalized HMM.
In light of these considerations, it is worth contemplating the possible gains in gene finder accuracy that might be obtained through the use of some form of discriminative training applied to a GHMM – that is, training aimed more directly at optimizing the ability of the gene finder to discriminate between exons and non-exons, thereby improving the expected accuracy of the gene finder's predictions. Anecdotal evidence already suggests that investigation of such methods may indeed be fruitful, as the process of manual tuning of GHMM parameters (i.e., "tweaking") after MLE training is commonly acknowledged by those with experience training GHMM-based gene finders (including our own systems). The practice of performing such tuning on the training set, especially when done iteratively, can be viewed as a manual form of gradient ascent optimization using the percentages of correctly predicted nucleotides, exons, and whole genes as surrogates for the Σ(S,φ)∈T P(φ|S,θ) term in Equation 2.
We therefore decided to investigate the use of a simple form of global discriminative training for gene-finding. We did this by building a rudimentary gradient ascent optimizer and applying it to a subset of the model parameters for our GHMM-based gene finder, TigrScan, as described in the Methods.
Results
Maximum likelihood versus discriminative training
Results for Arabidopsis thaliana are shown in Table 1 and those for Aspergillus fumigatus are shown in Table 2. The two methods being compared are maximum likelihood estimation (MLE) versus maximum likelihood followed by gradient ascent parameter estimation (GRAPE).
Table 1 Results on Arabidopsis thaliana
method train test nucAcc exonF geneSn
GRAPE CV CV 95 ± 1% 82 ± 2% 49 ± 3%
GRAPE CV H 93 ± 1% 80 ± 2% 44 ± 3%
GRAPE T T 95% 86% 57%
GRAPE T H 94% 81% 48%
MLE CV CV 90 ± 1% 72 ± 2% 33 ± 4%
MLE T T 91% 75% 36%
MLE T H 90% 71% 33%
GRAPE = GRadient Ascent Parameter Estimation, MLE = Maximum Likelihood Estimation only. CV=cross validation, T = training set, H = 1000-gene hold-out ("test") set. CV in the train column means training on 800 genes from T. CV in test column means testing on 200 genes from T. In rows with a CV in either column, numbers are averages from 5 runs. nucAcc = nucleotide accuracy, exonF = exon F score, geneSn = gene sensitivity. F = 2SnSp/(Sn+Sp) for Sn = sensitivity and Sp = specificity. CV averages are reported ± SD.
Table 2 Results on Aspergillus fumigatus
method train test nucAcc exonF geneSn
GRAPE CV CV 88 ± 1% 54 ± 4% 35 ± 4%
GRAPE CV H 88 ± 1% 51 ± 2% 29 ± 1%
GRAPE T T 92% 65% 48%
GRAPE T H 87% 51% 31%
MLE CV CV 81 ± 3% 27 ± 8% 16 ± 5%
MLE T T 88% 42% 28%
MLE T H 83% 30% 18%
See Table 1 for legend.
The train column indicates whether training (i.e., parameter estimation) was performed on the entire training set (T) or on separate 800-gene cross-validation partitions (CV). The test column indicates whether accuracy was measured on the full training set (T), on one-fifth of the training set (CV), or on the unseen data (H). We will consider the evaluation on H to be the most reliable measure of gene finder accuracy. For any row containing a CV, we report the average of five runs, where each run used a different 800-gene subset of the training data for parameter estimation.
Both tables give compelling evidence for the value of gradient ascent training, as shown in Figure 1. In Arabidopsis, gradient ascent applied to the full training set improved over the MLE method from 71% to 81% at the level of exons and 33% to 48% at the level of whole genes. In Aspergillus the improvement was even more dramatic: 30% to 51% at the exon level and 18% to 31% for whole genes. A gain of 4% nucleotide accuracy was measured for both organisms.
Figure 1 Maximum likelihood versus gradient ascent Gradient ascent parameter estimation (GRAPE) improves accuracy over MLE at the nucleotide, exon, and whole gene levels. arab = Arabidopsis thaliana, asp = Aspergillus fumigatus.
Data partitioning and cross validation
A tangible improvement was still seen when a cross-validation design was used to split the training set so as to separate the data used for maximum likelihood estimation (800 genes) and subsequent gradient ascent (200 genes). However, results from both organisms suggest that this separation did not improve the accuracy of the gene finder, as shown in Figure 2. Indeed, on Arabidopsis, gradient ascent training produced greater gains in accuracy when performed on the entire training set rather than using the cross-validation structure, while on Aspergillus the improvement due to using a cross-validation structure was either small (nucleotide level: 1%), zero (exon level), or negative (gene level: -2%). Thus, the recommended training protocol would be to apply MLE to the entire training set followed by gradient ascent on the full training set as well.
Figure 2 Data partitioning for gradient ascent Separating the training set into an 800-gene MLE set and a 200-gene gradient ascent set provides no improvement over simply performing MLE and GRAPE on the full training set.
Although use of a cross-validation structure to split the training set for the twin purposes of maximum likelihood estimation of ~90,000 parameters and gradient ascent refinement of 29 parameters is therefore not justified (according to the above results), cross-validation does seem to have some value in terms of predicting how well the gene finder will perform on unseen data, as suggested by Figure 3.
Figure 3 Cross-validation versus testing on unseen data Cross-validation scores provide a reasonably accurate prediction of performance on unseen data. Results shown for A. thaliana only; results for A. fumigatus are given in Table 2.
On both genomes and at all levels (nucleotide, exon, gene), accuracy measurements obtained through cross-validation were closer to the accuracy measured on unseen data than were the measurements taken from the full training set, as we expected. This was true both with and without gradient ascent, though when gradient ascent was applied, even the cross-validation results were slightly inflated. The latter observation is presumably attributable to the "peeking" that was permitted (see Methods), whereby the gradient ascent procedure received feedback from the 200 evaluation genes held out from the training set, T. This suggests that estimating even small numbers of parameters (in this case 29) from the test set can artificially inflate accuracy measurements on that set.
Figure 4 illustrates the effects of testing the gene finder on the training set. As can be seen from the figure, the accuracy measurements taken from the training set can be substantially inflated relative to the more objective measurements taken from the hold-out set, thereby promoting overly optimistic expectations for how the gene finder will perform on unseen data.
Figure 4 Evaluation on the training set Accuracy measurements taken from the training set were artificially inflated, as expected. Results are shown only for A. thaliana; results for A. fumigatus were even more extreme.
Discussion
The results presented above provide a clear demonstration that independent maximum likelihood estimation of submodel parameters is sufficiently neglectful of global GHMM behavior as to compromise gene finder accuracy. Even such a crude method as our 29-parameter gradient ascent procedure proved to be effective at significantly improving accuracy over that achievable by simple MLE training. The potential for more sophisticated global discriminative training methods to produce even greater improvements is surely worthy of investigation.
It is interesting to observe that the natural language processing and speech recognition communities, from whom HMM-based methods were originally borrowed for use in bioinformatics, have been moving toward global discriminative training methods for some time. The two most popular forms of discriminative training for speech recognition are Maximum Mutual Information (MMI) and Minimum Classification Error (MCE). Both methods can be implemented using an iterative gradient ascent/descent algorithm. Our approach is most similar in spirit to that of MCE.
In the case of "pure" (i.e., non-generalized) HMMs, expectation-maximization (EM) update formulas have been derived for both MMI and MCE. These formulas allow model parameters to be updated in an axis-oblique (rather than axis-parallel) manner; i.e., multiple parameters can be adjusted simultaneously, so that the optimizer is less constrained in following the direction of steepest gradient in parameter space. This may reduce the number of steps required for convergence. Indeed, more rapid convergence (in terms of numbers of re-evaluation steps) has been cited as a concrete advantage of these EM-style formulations over more generalized gradient ascent methods [23]. However, EM-style approaches to the discriminative training problem for HMMs have typically involved a number of simplifying assumptions and/or heuristics, thereby voiding formal assurances of optimality (e.g., [17,24,18,26]). Furthermore, as with more generalized gradient ascent procedures, EM often tends to find only a local optimum rather than a global one [13].
In the case of GHMM-based gene finders, the advantages of EM over a generalized gradient ascent procedure may indeed be rather slim. The very flexibility which we find attractive in GHMMs can be expected to complicate the derivation of such EM-like update formulas for arbitrary GHMM-based gene finders, likely requiring additional assumptions and approximations that would further compromise the optimality of the EM procedure. It was for this reason that we decided to employ a more generalized gradient ascent method for the present study. A rudimentary gradient ascent optimizer is simple to implement, and the use of prediction accuracy as an objective function affords great convenience in approximating Σ(S,φ)∈TP(φ|S,θ). Although P(φ|S,θ) can be more directly computed using a modified Forward algorithm [23], to do so would in theory be no more efficient than running the full gene finder, since the asymptotic run times of the Forward and Viterbi algorithms for GHMMs are equivalent. Nevertheless, inasmuch as the Forward algorithm provides a more direct approximation of P(φ|S,θ), its use for this purpose is worthy of investigation.
There are a number of other variations and enhancements which we are at present contemplating for our discriminative trainer. One of these involves the joint training of pairs of submodels in the GHMM using a maximum discrimination criterion rather than the usual one based on maximum likelihood. Although such an approach would not in itself directly attend to the global optimality of the GHMM (indeed, we already apply such an approach to our signal sensors during our so-called "MLE" training regime, as remarked earlier), it would at least seem to offer a promising direction for improving our existing optimizer and may be feasible without increasing the computational cost beyond what is practical.
For the present, we feel confident in making the recommendation that others tasked with the training of GHMM gene finders consider applying an automated gradient ascent procedure like that described here as a more systematic alternative to manual tuning of parameters following maximum likelihood training of individual submodels. Beyond the obvious advantage of likely improving gene finder accuracy, such an automated method may offer some degree of reproducibility (notwithstanding the typically stochastic nature of such methods) and uniformity for the purposes of comparing gene finders and gene finding algorithms. In addition, we urge those practicing manual tuning on their final "test" set to consider that their reported accuracy results may well be inflated as a result of "peeking" at the test set before the final evaluation – a practice that has been criticized in the field of machine learning (eg., [27]). That significant inflation was seen in our studies as a result of tuning only 29 of the ~90,000 GHMM parameters on the 200-gene "test" set suggests that the phenomenon may conceivably occur to some degree even when an automated procedure is not employed.
Finally, we would like to make note of an unfortunate consequence of discriminative training of HMMs for biological sequence analysis, namely, that while the resulting models may possess improved ability for discrimination and therefore greater utility for specific tasks such as gene prediction, their suitability as representative models of biological knowledge (especially probabilistic knowledge) may well be reduced relative to models induced with simple MLE techniques. Indeed, some authors in the field of speech recognition (e.g., [20]) have noted that more accurate discrimination can sometimes be obtained by relaxing sum-to-one constraints for probability distributions, thereby permitting the gradient ascent procedure to automatically discover appropriate weightings between states or inputs. This is reminiscent of the exon "optimism" parameter which we employ and which seems to have no principled justification (and indeed, we might speculate that this extraneous parameter proved useful precisely because it enabled a primitive form of discriminative training by providing an explicit "correction factor" or weighting between submodels). Thus, despite the apparent value of discriminative training in improving gene finder accuracy, our ability to extract biological knowledge by inspecting the parameters of a gene finder trained in this way may be somewhat hindered. For the present, this does not seem to be of great practical significance, but it is a consideration worthy at least of mention.
Conclusions
We have shown that discriminative training for GHMM-based gene finders is feasible using a rudimentary gradient ascent approach, and have briefly explored the relation between this method and the EM-like techniques which have been proposed in the field of speech recognition. Our experiments show that the gradient ascent method can result in a gene finder with substantially greater prediction accuracy. It is our hope that even greater gains in accuracy will result from extension and refinement of discriminative training techniques applied to GHMM-based gene finders.
Methods
Description of the GHMM
The gene finder TigrScan [8] is a GHMM-based program similar to Genie [1] and Genscan [2,28]. The forward-strand model contains six signal states (donor and acceptor sites; start and stop codons; promoter; poly-A signal) and eight content states (intron; intergenic; 5' and 3' UTR; initial, internal, final, and single exons). The reverse-strand model mirrors that of the forward strand. Four relative frequency histograms are used to estimate the duration probabilities of the four exon types; the four noncoding states are assumed to have geometric duration distributions and are therefore each parameterized by a single value representing the mean duration. Each content state is scored using a separate fifth-order Interpolated Markov Model (IMM) [29]. TigrScan offers a number of signal sensors, including WMMs, WAMs, WWAMs, and MDD trees [28] having any of the foregoing signal sensors as leaf models; for this study we used only (non-MDD) WAMs, though the order of the Markov chains within the WAMs was allowed to vary. Putative signals scoring below a given signal threshold are ignored by TigrScan. This threshold is chosen separately for each signal sensor so as to achieve a desired sensitivity Sn (Sn = TP/(TP+FN), TP = true positive count, FN = false negative count) on a training set of true and "decoy" signals. "Boosting" of signal sensors was performed by iteratively retraining each signal sensor on sets of training features in which the lowest scoring features were duplicated so as to focus the training procedure on the most difficult examples. Boosting has been found to improve signal detection in other application areas [30]. Most transitions in the GHMM are obligatory (such as "donor site → acceptor site"); of the non-obligatory transitions, sum-to-one constraints and the forward/reverse strand equivalence reduce the number which can be independently varied to just four. Transitions into exon states are modified by an exon "optimism" multiplier (similar to that described in [6]) which has been seen anecdotally to be useful in improving prediction accuracy (unpublished data).
Parameters to be optimized
The total number of parameters which need to be estimated when training TigrScan is roughly 90,000; the large bulk of these are the n-gram statistics comprising the IMMs used for the content sensors. As an initial attempt at applying discriminative training to TigrScan, we selected 29 of these ~90,000 parameters to subject to gradient ascent optimization. Although this is a miniscule proportion of the available parameters, our previous experiences with hand-tuning our GHMM on other data sets suggested that these 29 parameters exert a disproportionately large influence on the accuracy of the gene predictions. By limiting the number of parameters to be optimized we hoped to both accelerate the training procedure and also reduce the risk of overtraining. The selected parameters were:
• mean intron, intergenic, and UTR lengths (3)
• transition probabilities (4)
• exon optimism (1)
• WAM size and relative positioning (8)
• WAM order (4)
• signal sensitivity (1)
• number of signal boosting iterations (8)
• skew and kurtosis of exon length distributions
Modifications to skew and kurtosis of exon length distributions were found during early exploration to produce no improvements; these parameters were therefore left unchanged in all further experiments. All remaining parameters were estimated using standard MLE techniques.
For those runs in which gradient ascent was disabled (see below), the following methods were used to estimate the above 29 parameters: mean intron and UTR lengths as well as transition probabilities were estimated using MLE from training data; mean intergenic length was set to a fixed value based on the known intergenic lengths in the test set; exon optimism was set to zero; remaining parameters were selected so as to minimize the misclassification rate on a set of true and "decoy" signals selected from the training set.
Objective function and optimization procedure
As an objective function for use by the gradient ascent procedure, we decided to measure the accuracy of the current parameterization by running the gene finder on a subset of the training genes. Our hope was that this accuracy measure would provide a reasonable approximation of Σ(S,φ)∈T P(φ|S,θ) by indicating roughly how often the current model θ would cause the correct parse φ to be predicted for training sequence S. We defined the nucleotide accuracy Anuc as the percentage of nucleotides correctly classified as coding vs. noncoding; Aexon was defined as an average of exon sensitivity and specificity (where a predicted exon is considered correct only if both boundary coordinates were predicted correctly); and Agene was defined as the percentage of training genes which were predicted exactly correctly. These were all rounded to integral percentages between 0 and 100%. The objective function was then defined as:
f(θ) = 100Anuc+Aexon+Agene. (4)
The Anuc and Aexon terms were included in an effort to smooth the function, which would otherwise have been insensitive to changes not reflected in the number of genes predicted exactly correctly – i.e., a step function. Though the Anuc term was given much greater weight for this study, additional work needs to be undertaken to determine the most suitable set of weights for our objective function.
Parameters were optimized using an iterative gradient ascent procedure operating in the selected 29-dimensional parameter space, as illustrated schematically in Figure 5. Steps were taken in an axis-parallel manner (one step per axis per iteration), with the step size for each axis decreasing by half whenever a local maximum was reached on that axis.
Figure 5 Gradient ascent training Schematic diagram of gradient ascent training procedure. Of 29 parameters modified by gradient ascent, some (e.g., WAM size) were used to control the MLE estimation procedure, while others (e.g., mean intron length) were used directly as parameters to the GHMM. Testing of the gradient direction was performed on the 200-gene cross-validation set, which was part of the 1000-gene training set, T.
Data and experimental design
The quality of a given parameterization θ was measured by evaluating the objective function f(θ) on a held-out subset of the training set. The training set was limited to 1000 genes, and all experiments were repeated separately on two highly divergent species, the model plant Arabidopsis thaliana and the pathogenic fungus Aspergillus fumigatus. Five-fold cross-validation was employed, so that the entire optimization procedure was carried out five times on four-fifths of the data (800 genes) and each time evaluated on the remaining one-fifth (200 genes); accuracy results reported here were obtained by averaging the five sets of accuracy numbers obtained from the cross-validation.
The held-out one-fifth was also used by the gradient ascent procedure to tune the selected 29 parameters. The practice of using a held-out set for smoothing or to estimate a small number of additional parameters is common in the natural language processing field [31], where it is recognized that such "peeking" at the test set (by which we mean iterative re-estimation of model parameters from the training set after receiving accuracy feedback on the test set) by the training procedure can (unfortunately) artificially inflate reported accuracy numbers. For this reason, an additional 1000 genes were used for testing the gene finder after each cross-validation run. The results of this final testing were not made available to the optimizer, but are instead reported here as a more objective assessment of final model accuracy. We will refer to the training set as T and the additional 1000 genes for testing as H. BLAST [32] was used to ensure that no two genes in T∪H were more than 80% similar over 80% of their lengths at the nucleotide level. This training protocol is illustrated in Figure 6.
Figure 6 Cross-validation experiments Five-fold cross-validation was used both in the gradient ascent and in the MLE-only experiments. For gradient ascent training, MLE was performed on four-fifths of the training set (T) and then gradient ascent was performed on the other one-fifth. A separate hold-out set (H) of 1000 genes was used to obtain an unbiased evaluation of all final models.
Several variations of this experiment were also performed. To evaluate the utility of splitting the training set and performing MLE and gradient ascent parameter estimation on separate subsets (as described above), we also performed MLE followed by gradient ascent training on the full training set T and again evaluated the induced models on H. To assess whether gradient ascent provided any improvement in accuracy we also trained a model on T using only MLE and evaluated that model on H. Although the virtues of cross-validation have been well explored in the context of many other applications, we decided to use the above experimental design as a convenient opportunity to verify our expectation that it would also prove useful for objective analysis of gene finder accuracy.
Authors' contributions
Software implementation and computational experiments were performed by WHM. The manuscript was written by WHM with assistance from SLS.
Acknowledgements
This work was supported in part by NIH grants R01-LM06845 and R01-LM007938. Thanks to I. Korf, D. Kulp, M. Brent, J. Allen, M. Pertea, M. Pop, A. Delcher, and L. Pachter for useful discussions. I. Korf provided valuable comments on a previous version of the manuscript and suggested the use of boosting in the training of signal sensors.
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| 15613242 | PMC544851 | CC BY | 2021-01-04 16:36:38 | no | BMC Bioinformatics. 2004 Dec 21; 5:206 | utf-8 | BMC Bioinformatics | 2,004 | 10.1186/1471-2105-5-206 | oa_comm |
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Health Qual Life OutcomesHealth and Quality of Life Outcomes1477-7525BioMed Central London 1477-7525-2-691557920910.1186/1477-7525-2-69ResearchThe value of the qualitative method for adaptation of a disease-specific quality of life assessment instrument: the case of the Rheumatoid Arthritis Quality of Life Scale (RAQoL) in Estonia Tammaru Marika [email protected]ömpl Judit [email protected] Kadri [email protected] Ele [email protected] Department of Internal Medicine, Faculty of Medicine, University of Tartu, Puusepa 6, Tartu, 51014, Estonia2 Department of Social Policy and Social Work, Faculty of Social Sciences, University of Tartu, Estonia2004 4 12 2004 2 69 69 18 8 2004 4 12 2004 Copyright © 2004 Tammaru et al; licensee BioMed Central Ltd.2004Tammaru 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
Due to differences in current socio-economical situation and historically shaped values, different societies have their own concepts of high-quality life. This diversity of concepts interferes with quality of life (Qol) research in health sciences. Before deciding to apply a Qol assessment tool designed in and for another society, a researcher should answer the question: how will this instrument work under the specific circumstances of my research. Our study represents an example of the utilization of qualitative research methods to investigate the appropriateness of the Rheumatoid Arthritis Quality of Life Scale (RAQol) for the assessment of Qol in Estonian patients.
Methods
Semi-structured interviews were conducted with the rheumatoid arthritis (RA) patients of Tartu University Hospital and these were analyzed using the principles of the grounded theory.
Results
We described the significance of the questionnaire's items for our patients and also identified topics that were important for the Qol of Estonian RA patients, but that were not assessed by the RAQol. We concluded that the RAQol can be successfully adapted for Estonia; the aspects of Qol not captured by the questionnaire but revealed during our study should be taken into account in future research.
Conclusions
Our results show that qualitative research can successfully be used for pre-adaptation assessment of a Qol instrument's appropriateness.
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Background
With this article we are introducing our experience of how qualitative research can be utilized in the process of adapting of a quality of life (Qol) assessment instrument. We will argue the unique benefits of the qualitative method for assuring the validity of an adapted measure.
Qol cannot be treated as something uniform and stable. One reason for its variability in time and space is the social nature of the "quality". Different societies with their current socio-economic situation, values and traditions, carry their own, to certain degree dissimilar understandings of high-quality life. Therefore Qol can be seen as a social construct. For further discussion on Qol domains see Schalock, 2004 [1].
Inclusion of Qol assessment into a set of health outcome measures is, regarding the achievements in improving the survival of chronically ill patients, well justified. But the social complexity of the construct means that its evaluation is also complex compared to traditional outcome measures e.g. symptoms or results of lab tests. Restricting Qol assessment to condition-bound groups (disease-specific Qol) permits potential influences on everyday life to be homogenized; problems with the various individual significances of these condition-characteristic impacts remain. Different Qol concepts in health sciences try to improve assessment's generalizability. Functionalistically oriented health-related Qol concentrates on restrictions in performance of everyday activities [2]. Although this approach allows presumably quite characteristic impacts of health condition or disease to be described and compared, it does omit several, mostly social and cultural, factors. Without considering these influences the detailed specification of Qol remains incomplete. The needs-based Qol model proceeds from the motivationalists' idea about universal human needs and defines Qol as a level of satisfaction of these needs [3]. Although universal in theory, this approach still has its imperfections regarding practical assessment: ways of fulfillment of even universal needs depend on the possibilities offered by society; measurement of these needs' satisfaction is accessible through rating their fulfillment acts; thereby, assessment of Qol cannot be liberated from time and space dimensions.
Before deciding to use an existing instrument for Qol assessment, a researcher should answer the question: how will this questionnaire work in the particular circumstances of the research? Even an instrument that has performed excellently in the country of origin can loose some validity when applied in a different social context. There are three categories of topics that should be recognized for every Qol instrument: first, those important for the patients being investigated, and already assessed by the instrument ; second, those unimportant (or not so important) for the patients being investigated, but still assessed by the instrument; and third, those important the for the patients being investigated, but not assessed by the instrument. It is evident that an appropriate questionnaire consists mostly of the first category items, the content of the second category items is minimized and all topics important for patients are included.
The items belonging to the two first categories can be determined during a standard adaptation process. The drawback of this approach is the amount of adaptation work that usually has to be done before receiving evidence about the merits of the questionnaire. Delimiting of the third category requires a deeper insight into the society-specific determinants of Qol.
Because of the smallness of Estonia (population about 1.4 million) studies involving only our patients may demonstrate a lack of statistical power; also some restrictions concerning scientific potential and funding should be admitted. Therefore a promising choice for Estonian clinical and health sciences can be seen in cooperative research projects. Reasoned selection, judicious adaptation and application of internationally approved assessment instruments could be crucial for success. As a part of the Soviet Union, Estonia stayed isolated from Europe for years and quite strong ideological pressure was brought to bear on the citizens. After establishing independence in the 1990s, Estonian society has undergone abrupt ideological and economic changes. A researcher working in the Qol assessment field should take into account the suspected impact of these factors on understanding life quality among the local population.
Our work offers an example of the application of qualitative interviews for exploring the essence of quality of everyday life for Estonian rheumatoid arthritis (RA) patients. We will illustrate the evaluation of the three topic categories using an example of RA specific Qol assessment scale.
The questionnaire of interest
Rheumatoid Arthritis Quality of Life Scale (RAQol) was developed in the 1990s as an outcome measure to assess the impact of RA and its treatment on QoL. The content was derived from interviews with 50 RA patients conducted simultaneously in Great Britain and the Netherlands [4,5]. The theoretical basis for the RAQoL is the needs-based model of QoL. The basic list of needs the model considers to be crucial to Qol had been published earlier [3].
List of needs
• food, drink, sleep, activity, sex, pain avoidance
• warmth, shelter, security, safety, freedom from fear, stability
• affection, love, physical contact, intimacy, attachment, communication, sharing experiences, sharing goals, affiliation
• curiosity, exploration, play, stimulation, enjoyment, creativity, meaningfulness
• identity, status, recognition, approval, appreciation, usefulness to others, respect, competence, self esteem, mastery, achievement, power, independence, freedom
• time structure
• self actualization
As a RA specific instrument, the RAQol was designed to assess the fulfillment of only those needs whose importance for RA patients emerged during the interviews. The original wording from the interviews was retained as much as possible. The final RAQoL is a 30-item measure where each item is in the form of a simple statement to which patients indicate whether or not it is true for them at that moment. The following example of the RAQol items demonstrates the wide range of everyday areas covered by the instrument: self-care, different indoor and outdoor activities, emotions and conditions, and interpersonal relations.
An example of the RAQol items
• Item 18. I have problems taking a bath/shower
• Item 7. Jobs about the house take me a long time
• Item 6. I find it difficult to walk to the shops
• Item 16. I often get depressed
• Item 12. I find it hard to concentrate
• Item 13. Sometimes I just want to be left alone
• Item 29. I avoid physical contact
An affirmed item indicates an adverse quality of life. Each item on the RAQoL is scored '1' for the affirmed statement or '0' for the disaffirm statement. All item scores were summed to form a total score ranging from 0 (good QoL) to 30 (poor QoL). The original RAQol exists in two versions – UK English and Dutch; currently seven language adaptations of the RAQoL are available for use. Excellent test-retest reliability, internal consistency and construct validity of the original instrument and its versions has been demonstrated [6-10]. The RAQoL provides a valuable tool for assessing the impact of RA on QoL in international clinical trials and other research studies. For a full list of the RAQol items see de Jong et al. 1997 [5].
Goal and research questions
The lack of an instrument for systematic assessment of the Qol of RA patients in Estonia raised the question of the feasibility of adapting the RAQol. Our task was to assess the value of application of the qualitative method for describing the appropriateness of the questionnaire before the adaptation. For studying the three above-described topic categories in connection with the RAQol, we formulated the following two research questions:
• Are the RAQol's topics important for our patients?
• What else do our patients find to be significant in connection with their everyday life quality?
Method
The choice of method and structure of interview
We decided to apply thematic analysis following the principles of the grounded theory. Our choice was determined by the second research question. To answer it, the analysis had to be guided by the data itself, to enable new motifs and theories to spring up. The grounded theory, evolved by Barney G Glaser and Anselm L Strauss in the 1960s [11] and developed later by them and other scholars [12,13], offers a systematic approach for analyzing qualitative data using both inductive (open and axial coding, generation of core categories) and deductive (selective coding and theoretical/selective sampling) approaches in data processing [12]. The prerequisite for this inductive-deductive data handling is the simultaneous running of the processes of data collecting, coding and analysis [13].
For data collection we decided to apply individual interviews, which we considered to be the method of choice for investigating RA patients' perceptions of quality of their everyday lives. An alternative focus-group method was rejected because of the possible intimacy of some topics, which might have been difficult to discuss openly in a group setting.
Considering the formulated research questions we agreed that the semi structured interview format should be preferred.
We decided not to acquaint our research subjects with the original version of the questionnaire. We thought that familiarization with the instrument might restrain the respondents from disclosing their everyday life problems in full, especially those problems not captured by the RAQol.
The RAQol items were arranged into groups according to the dimensions of everyday life they reflect. Four groups emerged (note some overlapping): self-care and indoor activities (items 1, 3, 5, 7, 8, 10, 11, 18, 21, 26, 30), outdoor activities (items 2, 4, 6, 10, 14, 17, 20, 21, 25), emotions and conditions (items 9, 12, 13, 16, 19, 21, 22, 23, 24, 28) and relations (items 2, 4, 13, 15, 17, 20, 22, 25, 27, 29). We formed four open-ended interview questions to cover these dimensions. The questions were intended to introduce informal conversation during which different Qol aspects connected with the everyday life could be revealed.
1. How does your disease influence your coping with everyday indoor activities including self-care?
2. How does your disease influence your coping with outdoor activities?
3. What emotions can you describe in connection with your disease?
4. How has your disease influenced your relations with other people?
We prepared three to four additional secondary questions for each of the four interview questions for cases when some guidance of the interviewee is necessary.
The fifth interview question was added in order to give the interviewees a possibility to speak freely about their everyday life problems not assessed by the RAQol. The sixth question was intended to elucidate the hierarchical importance of everyday problems/restrictions for our patients.
5. What other impacts does your disease have on your life?
6. Which disease impacts on your life do you consider to be the most important?
We assumed the structure of the interview – from more detailed to general – to be appropriate for our patients; for most of them it would be the first time to be interviewed.
The respondents
We were determined to include patients who met ARA 1987 diagnostic criteria for RA [14]. We decided to exclude patients with a concurrent disease or health condition, which, according to available medical documentation and the opinion of their physician, can be considered to have a significant impact on Qol.
By choosing interviewees from among the inpatients of the Rheumatology department of Tartu University Hospital, one of two specialized rheumatology centers in Estonia, we had access to sufficient medical data to follow the established inclusion and exclusion criteria. In Estonian rheumatology inpatient care is the dominating approach and it often also comprises some traditionally outpatient procedures. Recruiting inpatients gave us the opportunity to sample the whole spectrum of RA patients in circumstances where participation in the research would not interfere with their everyday routine. Also, conducting interviews under hospital conditions allowed us to create similar settings, free of major distractors, for each conversation.
Our sampling strategy derived from the wish to collect as multifaceted data as possible. We applied principles of theoretical sampling where information gathered during previous interviews determined the selection of subsequent respondents in order to get views from different positions. The final list of characteristics, which we considered to be important for guaranteeing versatility of the sample, included: age, gender, duration of RA, severity of RA (assessed by functional class and radiological stage), education, working status, marital status, members of family unit, and living conditions. The characteristics of the patients are presented in Table 1.
Table 1 Patients' characteristics. For radiographic stage and functional class estimation, data were collected from medical records; the Larsen-Dale [28] and Steinbrocker [29] classifications were used respectively.
ID Gender Age Duration of RA, years Radiographic stage Functional class Education Working status Marriage status Living with Living conditions
1. Male 30 1 II II basic not working because of the condition separated parents flat in village
2. Male 58 12 III II vocational not working because of the condition married wife flat in town
3. Female 38 15 IV III vocational not working because of the condition married husband and daughter (15 years old) house in village
4. Female 54 20 III II vocational working married husband house in village
5. Female 66 10 V II secondary retired married husband, grown-up daughter, grandson (11) flat in town
6. Female 49 4 IV III higher not working because of the condition single female flat mate flat in town
7. Female 47 13 III II higher half-time working separated son (13) flat in town
8. Male 54 10 IV III secondary working married wife farm
9. Female 64 20 IV II vocational not working because of the condition widow grown-up daughter flat in town
10. Female 69 50 V III higher retired separated alone flat in town
The process of data collection
The interviewing took place from February to June 2002.
We consulted medical records to determine inclusion and exclusion criteria, patients' demographics and disease characteristics. No patients were contacted earlier than on the third day in hospital in order to allow them some time for adjustment. A day before the planned interview the goal and expected course of the interview were explained to the patient, the patient's were asked for their agreement to be interviewed. One of the patients we contacted refused to participate on account of being due to leave hospital the next afternoon. All recruited patients gave their informed consent.
Interviewing was conducted in a private room in the rheumatology department. All interviews were conducted by one researcher (MT) and were audio taped. Eight interviews were carried out in Estonian; two respondents (3 and 5) were interviewed in Russian, the respondents' native language. Interviews lasted from one and a half hours to three hours.
For most of our respondents it was their first chance to discuss their everyday life problems with somebody outside the family. But all of the interviewees were very willing to share their experiences after overcoming some diffidence at the beginning of the interview, and talked openly about their lives with the disease. For the interviewer, growing knowledge with every subsequent interview allowed her to move away from strict adherence to the interview questions, towards more informal conversation and, if necessary, the examination of some topics in depth.
Every interview was transcribed word-for-word and discussed before recruiting the next participant. Those interviews conducted in Russian were translated from the tape by the interviewer and transcribed in Estonian.
The tenth interview was exceptional. This patient was not hospitalized in the rheumatology department at that time, but we decided to invite her to our study due to the exceptionally long duration of RA recorded in her medical documentation – 50 years. The patient was contacted by phone and her agreement to participate was reached. We met at the patient's home and she was asked to tell her life story with stress on everyday problems. Thus we got an exiting narrative history of Estonian rheumatology from a patient's perspective and collected valuable information for our current research.
No new topics relevant to our research questions came forth after the seventh interview. Therefore we decided to stop at the tenth. This decision agrees with the theoretical sampling idea that one should stop recruitment of respondents when the researchers decide that the study has reached its saturation [15,16]. The intensive discussion we carried out simultaneously with the data collection gave us the opportunity to determine the stage when the inflow of new data no longer added any essential information to our study.
Coding and analyzing the data
As the first step we read and discussed the interviews' transcriptions extensively. Tentative code families were chalked out as a notional framework for subsequent coding.
Open coding of the transcripts was performed independently by the two of us, MT and JS. The codes adhering to the previously identified code families were ascribed to expressions composed mostly of one or two sentences in order to distinguish their leading ideas; to some expressions several codes were attached Due to the different structure and extent of the tenth interview, selective coding was applied and only those parts relevant for our research questions were coded.
There were no disagreements between researchers at the level of code families; some inter-coder discrepancy appeared in appointing particular codes. Still, full consensus on open codes was reached in discussion. We analyzed the differences in coding and concluded that they could be ascribed to the coders' different – medical and sociological – backgrounds. We illustrate our conclusion with the example of coding a patient's expression: can't even go help my sister in the country with potato planting, a bit sad (2). It was coded as 'inability to offer physical help' by MT and 'alterations in traditional family relations' by JS; coders agreed on the 'relations with close ones' code family. During discussion the consensus code 'inability to perform in family roles' was established, which introduced a deeper exploration of the topic of role performance.
We agreed on the coding discrepancies being a benefit rather than a drawback of our research process. They allowed us to highlight and subsequently integrate different aspects of applied codes on the boundaries of marked tentative code families. Hence we decided not to perform formal inter-coder reliability analysis due to its diminished informative potential for this particular research.
Axial codes were created through grouping and condensing of open codes by MT. Axial coding was discussed and approved by all researchers. Side by side examination of affined axial codes of different interviews formed the basis for creating core categories – composition of a syllabus of motifs that emerged from interviews. Core categories were formed together by MT and JS, and were discussed and acknowledged by the whole research group.
In analysis we used the selective sampling of core categories to meet our two research objectives. First, to assess the importance of the RAQol topics for our patients, we compared every single item of the RAQol with the coded data of our interviews. Our assessment of importance was born in discussion and took account of the closeness of the meanings of the item and the relevant expressions of the interviewee, the frequency of their occurrence, and the significance for the respondents. Second, to describe the Qol topics that were significant for our patients but were not evaluated by the questionnaire, we compared the coded data without a counterpart among the items with the list of needs offered by the needs-based Qol model.
Results
We will present our results as answers to the research questions. The descriptions of two of the three topic categories are included in the first, and the description of the third category in the second answer.
The importance of the RAQol topics for our patients
Three groups of items can be highlighted: items whose importance was demonstrated by the data; items that could conditionally be considered to be important; items whose significance could not be shown on the basis of the interview data.
Important items
Most topics assessed by the instrument were essential for our interviewed patients. For 22 of 30 items, the interview data provided sufficient evidence to consider them applicable for the evaluation of the Estonian RA patients' Qol. Examples of patients' utterances supporting the significance of the content and also the appropriateness of the format of each of these items can be given from two or more interviews.
In some cases a remarkable diversity of utterances connected with one particular item was noted. We will illustrate this finding using item 17 as an example: I'm unable to join in activities with my family or friends. The following responses represent six different reasons to agree with the proposition included in the item. We have given word for word translations of the quotes into English, the IDs of patients are given in brackets.
Walking difficulties
• physically difficult to walk anywhere (1)
• a whole fuss with moving, don't want to torture myself (2)
Financial restrictions
• I used to ride the bus a lot before, now it's so expensive, I can't afford to visit anyone very often (9)
Difficulties connected with forced immobility
• Whenever you're sitting somewhere, are somewhere, it's hard to stay in one position all of the time, you have to make yourself move, go somewhere, or whatever (1)
• when your feet are ill and you sit for a very long time, then you can't even get up and move (4)
Changed quality of participation
• what's the use of going if you're no good anyway (2)
• of course I didn't go anywhere, only peeped from the car window (5)
• not going to the pub, don't know what to do there, can't handle dancing, and drinking doesn't work out either, no point in just sitting there the whole night (8)
Being ashamed of themselves
• when my joints were so tender and painful that I had to talk about my disease all the time then I definitely didn't look for company and I couldn't eat anyhow and I used a spoon for eating food that you ought to eat with a fork (9)
Unwillingness to create problems
• as I cause such a situation that my hosts have to help and watch me all of the time, I'd rather not go (6)
Worries about coping
• conditions of a home I go to, how cold it might be, if there's only cold water, is the toilet outside (6)
Difficulties related to bathing were mentioned in eight interviews and therefore item number 18: I have problems taking a bath/shower, was included in the group of the appropriate items. Still, the interviews also offered some hints that the value of the item as a measure of ability to carry out body care could be diminished by Estonian traditional sauna culture, especially popular in rural areas (more than one third of the Estonian population is rural.)
I don't care much for the bath, mainly I have let my kids take me to the country and have gone to the sauna, that's much more like it (2)
• sometimes I go to the sauna, we have a sauna in the cottage in the countryside, its no big deal to wash myself there, in the summer at least; a sauna might really be more convenient than this bath; there's just no hassle with getting up (7)
• I can manage washing myself in the sauna but I can't get up from the bath on my own (8)
Items that could conditionally be considered to be important
Motifs related to four of the remaining eight items emerged a number of times from the interviews. However, before adding these items to the list of appropriate ones, certain aspects should be taken into consideration.
Two respondents (2 and 5) burst into tears during the interview when looking back on their lives with the disease, which verified the significance of item number 19: I sometimes have a good cry because of my condition. The third interviewee spontaneously talked about crying, describing it as something bright and relieving. It can be presumed that in some cases crying may be interpreted as a way of coping, which is not unambiguously related to the level of Qol.
• if you cry, you should cry thoroughly and then you'll feel better, but if you keep something inside you it'll start eating at you, if you get a chance to have a good cry it'll make you feel good /.../ crying is self-purification, later you feel light; that's a feeling a healthy person doesn't get (9)
A single word or expression matching English 'frustration' cannot easily be found in Estonian. The foreign word 'frustratsioon' has been introduced into Estonian quite lately but remains unknown to the majority of lay people. Although there are a number of expressions in the interview data corresponding to item number 9: I often get frustrated, formulation of an Estonian version of the item suitable for embracing them all will be complicated.
• this is the feeling that you just are, the disease has already got a hold, the sequence of events just keeps going on and on (2)
• feelings of injustice that someone else is well but I'm not (5)
• it depresses me, and what the mind has built up is ruined by some moment and then the emotional breakdown comes again (6)
• often my thoughts run ahead of me and I'm feeling rested and would like to do something, but when I get down to it, then that's it, I end up hindering the work and not doing anything myself (8)
• I can't forgive my disease for ruining my life structure that had been carefully and arduously built over a long time (6)
In four interviews the insecurity connected with the disease progression and its unpredictability was one of the main topics. In Estonia's rapidly changing society, securing ones future cannot be an easy task and disabled people do not have much of a chance for success. Therefore the likening of capability to determine their future to ability to control their disease by patients is expected, and item number 28: I feel that I'm unable to control my condition, can be considered to be important. But again, difficulties in the formulation of the Estonian wording of the item will arise. Though quite common in everyday spoken Estonian the equivalent to the expression 'to control a condition' was never used by the interviewees when talking about their concerns.
• afraid of planning, you never know when it strikes back again (1)
• not yet [unable to cope] but it might come in the future; I don't know what will happen next year /.../ you're afraid that if you really get so poorly that you can't take care of yourself who will take care of you (4)
• I still try to be like a human being but don't know how long I can manage (5)
• there's really nothing for granted in this world of course, but I am preparing a back-up solution in case things get worse (8)
No patients reported that they were disturbed by continuously thinking about the disease. Three interviewees with a disease duration of over 10 years talked about not thinking of their condition as a positive phenomenon connected with adaptation to the disease. We concluded that item number 23: My condition is always on my mind, can be treated as significant, although our interviews revealed no evidence that the problem is recognized when present.
• I don't think that I'm an ill person at the moment, just when these hands are painful or I just can't cope with everything; I don't think that I'm ill, that I'm so miserable, I don't think about it (4)
• I have to change and re-adjust my basic values, but I don't think about it all of the time, maybe I have adjusted them subconsciously /.../ I then, subconsciously, not thinking about it, eat something softer or don't go biting on a big apple if my jaw joint is painful (7)
• I don't' even think about my disease at this point, it's like a husband now, day and night, its there in everything I do and I know that as long as I live I will have it and I cant get rid of it and I don't make it a problem anymore (9)
Items, whose significance could not be shown
To this group we included four items.
In one interview, the impact of pain on attention was described. We did not find this evidence sufficient for designating item number 12: I find it hard to concentrate, as significant. In our opinion, these expressions describe switching of attention to another stimulus and do not refer to concentration difficulties. One reason why the interview data did not support the importance of this item can be the lack of currently active intellectual workers in our sample.
• You can't think about anything but pain; crossing the road you might get hit because you're only thinking about the pain and forget that you have to keep an eye on the road (6)
No data corresponding to item number 1: I have to go to bed earlier than I would like, emerged from the interviews. Because tiredness is a characteristic feature of RA, one explanation for the inability of our data to show this item's importance is associated with the paucity of absorbing nighttime activities, especially in Estonian rural areas.
Item number 24: I often get angry with myself, had no matches in our interviews. It is difficult to find any obvious explanations for this particularity but we can suggest that in some cases anger was rechanneled against the medical system – a phenomenon that we will discuss later.
Our data failed to demonstrate the importance of item number 28: I avoid physical contact. We believe that intimacy connected with this topic could be the reason our respondents avoided openly discussing it. If so, due to the greater impersonality of a questionnaire format, the item can retain its significance as a part of the instrument.
What else our patients found to be significant in connection with their everyday life quality
As a result of the analysis, three groups of topics connected with satisfaction of the needs included in the list of those crucial for Qol and important for our patients, but not assessed by the questionnaire, were described.
Next we will name these needs and present the evidence of the limitations in their fulfillment. Our deeper inquiry into the reasons for these peculiarities will be presented in the discussion section.
Identity, status, appreciation, respect, usefulness to others, and self esteem
Adaptation to a disease is a difficult process comparable to passing through phases of grief. An inevitable and hurtful part of it is abandoning of old roles and the recognition of new ones.
Changing role functioning was a common motif in the interviews. Regret and anxiety due to inability to perform in the roles of a healthy person were expressed by the majority of respondents.
Gender role
• What man isn't disturbed by the inability to take care of himself, then you're like a kid not a man (8)
• My appearance isn't as attractive [as a woman] anymore as it could have been without the disease (4)
Role in family
• I can't even go help my sister in the country with potato planting, a bit sad (2)
• just sad because of him [the son], he asks me why I can't come outside to play soccer with him, well, I really can't (7)
Age role
• totally like a small kid, someone else has to help you all the time, whatever it is, meals or something else (1)
• I walked with a stick, it was a catastrophe, such an old granny (3)
Work related role
• I want to do something, just to make something and do a job, my hands want to work; people drop by and try to rope you in – I can't, there's no doer, all the time "I can't", other days I can't at all (2)
• I am afraid of going to work soon; this hand is so ugly; what a hairdresser with such a horrible hand! (4)
The new role of a diseased person was generally interpreted as something deprecatory; being ill was considered as opposite to being normal.
• I can't move; my movements aren't like they should be, not like healthy and normal people have (1)
• before I used to wear high heel shoes like normal people (3)
• you're like some prehistoric creature, everybody goes by modern means but you like going back to the stone age (6)
Feeling ashamed of the disparity led to preoccupation with concealing the condition. Surrounding people were often seen as appraisers; their opinions were valued more highly than success in coping was. As a result, even the simplest aids that could be noticed by others were rejected. In some cases the fear of being labeled as different elicited the preference of social isolation.
Concealing the condition
• I don't want to see things that point to my condition, don't want these to be seen, and of course, don't want to have to always hide everything (6)
• I control myself so that others won't notice the way I am – I have so many acquaintances that for a long time didn't know I was ill (9)
• I'm even afraid to tell anyone that this hand is so ill, I'm so quiet (4)
Rejection of aids
• [a special cup with two ears] I have it at home, but I don't use it; at this point I still try to be humanlike (5)
• you don't go shopping with a crutch, no way (8)
• I feel very uneasy eating in company; I still want to eat like people do, with regular tableware (9)
Preference of social isolation
• if I still find it impossible to eat or drink in company, then I don't; I won't go into company [to eat] like this (6)
Safety, freedom from fear, and stability
A well functioning medical system should strengthen the feelings of security and stability of its clients. In the words of our respondents the medical system constituted an enemy. The system was something to blame, to vent anger on; at the same time it was described as an inevitability that had to be obeyed and against which protection was required.
Blaming
• the system is wrong, the sick funds and all, why can't I get procedures done that are necessary for me (3)
• you sign up there [for a rheumatologist], you wait and wait, a lot changes in that time; you wait a month and a half; you get there; by that time the drugs have run out, later you're at fault for not having taken them (2)
• I went to a private clinic, almost like crying at the door, there are no vacancies, there's no one I could talk to, and I didn't. I went away to the country/.../now I'm here and now I'm told that this Achilles' has been broken for at least a month (10)
Obeying and need for protection
• [left alone with the disease] just can't be such a thing, not alone, but life is like this, don't know what to wish (2)
• helplessness, no person to protect me [against the system] (3)
Physicians, some of them named as saviors and supporters, were still more often seen as being an impersonal part of the system. Professional incompetence and superficiality were connected with non grata turns in the course of the disease. One of the respondents regarded physicians as co-victims of the system, incapable of defending their patients.
Part of blamed system
• if I had been sent to the rheumatologist right away then maybe things wouldn't have gotten this bad; thus, when my neck was stiff at first, it was thought I had caught a cold and it would pass (1)
• the troubles started when my leg trauma was labelled as radiculitis (2)
• initially no action was taken and consequently the general attitude [of the doctors] became such that nobody had the guts to do anything with me, and because it was such a awkward situation because they didn't have the right doctor who would have done something, I was left a bit high and dry (10)
Co-victims
• the kidney doctor said: not to come to me, I am out of money, go to the GP /.../you go to the GP, she can't help you, prescribes what she can, she can't prescribe rheuma-drugs (2)
Procuring food, drink and other necessities of life
The ability to do necessary shopping is directly assessed by item number 6: I find it difficult to walk to the shops. Difficulties with walking to the shop were described by our respondents and the item was considered to be important. But other problems connected with shopping emerged even more often in the interviews – managing in the shop, checking out, and carrying the purchases.
Managing in a shop
• I don't want to stand in a line, my feet start to ache (7)
• [shopping trolley] is very big, you have to push it hard and it's narrow there [in the shop] (9)
Checking out
• checking out at the counter you're in a hurry and then comes this psychological moment that you get nervous and can't handle it (7)
• I open my wallet, they take what they need (5)
Carrying the purchases
• when the bag is too heavy then my feet can't stand it; you have calculate how much you buy (6)
• sometimes you would like to buy more but you can't carry it; I just put stuff that I really need in the cart (9)
For our interviewees, a topic closely related to shopping was the capability to use public transport. The obstacles encountered when entering, leaving and riding a bus were described.
Getting on
• I can't get onto the bus, even worse with the tram; the steps are very steep, I can't manage however I try (3)
Getting off
• I can't get off, I ride to the next stop; the bus doesn't pull over to the sidewalk but stops further away (6)
Riding
• very bad to ride, I just stagger, lose my balance, sometimes you can fall quite badly; the worst is when you have to stand (9)
• sitting down and getting up [on bus] is difficult for me, it's better I hold on to something for some time (5)
The lack of money was mentioned by all of our respondents. Its interference with the satisfaction of the majority of needs was described.
• I wouldn't say my spending has increased a lot, but my income is, yes, notably lower; before I even paid more income tax than I get paid now; the habits I had before have become impossible for me, financially (1)
• a handicapped person could also make his/her life comfortable and tolerate everything if it was possible financially (6)
• the pension is so tiny, I can't do anything with this; I can't manage a family with this (7)
Discussion
Our results showed the high significance of the majority of the RAQol items for the interviewees. This allows us to state that the RAQol can successfully be adapted into Estonian for usage in international research projects. Our results also highlight the difficulties in translating some specific items (number 9 and 28), which should be reckoned with during the adaptation process.
Three Qol aspects that were important for Estonian RA patients but were not evaluated by the RAQol – the issues concerning changes in role performance, safety and stability of communication with the medical system, as well as some issues of procuring the necessities of life-were revealed by the analysis of interviews.
Next we would like to discuss the reasons for these peculiarities and suggest some additional resources for in-depth reading.
Only 13 years have elapsed since the end of Soviet rule in Estonia. Our respondents spent a considerable part of their lifetimes in the Soviet ideological environment and this has undoubtedly influenced their values and beliefs. An obligatory component of the homo sovieticus mentality was the placing of collective interests above personal ones; individuals were appreciated by their contribution to the common good. Successful performance in social roles approved by the regime was honored; inability to meet validated ideals was considered shameful. Although we can talk of a dramatic change of approbated values and ideals after the end of the Soviet era – an independent, successful, competitive young individual is idealized now –, but the tendency to disapprove of the inability to fit expectations has remained.
We believe that this potpourri of old and new values and attitudes could explain the high significance of themes connected with roles and role functioning in our respondents' conversations. A person disabled because of disease could not perform successfully in acknowledged Soviet-time roles (the existence of people with special needs was simply hushed up by the Soviet media); the same is true for present-day Estonia. Being categorized as "socially uncompetitive" would alter the identity and self-esteem of the disabled individuals and concealment of the condition could be seen as defense reaction. In the light of this, topics connected with role functioning and social acceptability should be included among those observed during a Qol investigation. See also [17-21].
The transition from one economic and ideological system to another in Estonia has caused social instability, which is reflected in an increase in people's subjective sense of insecurity and fear. The most insecure time for the Estonian population was in the early 90ties; since that time, due to stabilization of the economy and economic development, a sense of security is returning. Still, there is remarkable disaffection with different spheres of public administration; future-oriented reorganizations that do not provide immediate benefits are treated with caution. The health care system, which has undergone fundamental reforms in the post-soviet period, is the favored object of criticism for the Estonian media. In the Soviet era health care was funded from the state budget and all citizens had free access to health services. Today's health care delivery system in Estonia is financed through health insurance; the private sector is growing. The introduction of family practitioners, with novel financing principles and responsibilities, in the mid 1990ties has changed previously existing relations between patients and medical specialists; access to consultants has lost its immediacy. Bureaucracy, long waiting lists, visit charges (something unthinkable for Soviet medicine) and open discussion of the financial difficulties of the health service in the Estonian media have all made their contributions to lowering confidence in the health care system in the eyes of our patients. Communication with health care deliverers constitutes a significant part of everyday life for RA patients. Therefore the impact of this communication on the everyday life of patients cannot not be ignored. In the case of a transitional society like Estonia's, the effect of health care delivery on patients' needs for stability and safety should be considered. See also [22-26].
In health care planning, the strategic military interests of the Soviet Union were given priority; this resulted in the development of an excessive large hospital network. Habilitation and rehabilitation were upstaged and drastically under-funded. In the Soviet era the people with special needs were partly institutionalized as disabled; their problems and even their existence (with the exception of war veterans) were simply hushed up by the authorities and media. There were no adaptations for people with physical special needs in public places and in the physical environment of towns in Soviet Estonia; elementary facilities were not accessible. People in wheelchairs began to be seen on our street with arrival of western tourists in early 90ties; they were joined shortly afterwards by our own special needs persons. During the following years adaptations considering the requirements of people with special needs have little by little been introduced into the physical environment. However, due to limited resources the improvements are coming slowly and conditions have not reached the level where the comfort of special needs persons can be guaranteed. Coping with errands and chores should be taken into account as a distinct Qol topic in Estonia today. See also [27].
According to the Statistical Office of Estonia the average monthly pension for the disabled in Estonia in 2003 was 1110 Estonian kroons (approximately 71 EUR). This constituted one sixth of average monthly gross wages and salaries, and was 301 kroons (19 EUR) lower than estimated minimum means of subsistence for 30 days in the same period. From these figures we can see that financial troubles can overshadow every aspect of the everyday life of our patients.
In our opinion adding the assessment of these three aspects – changes in role performance, safety and stability of communication with the medical system, and some aspects of procuring the necessities of to life – should be considered by Estonian researchers, especially when carrying out disease-specific Qol studies at the national level.
Conclusions
In our research we used qualitative interviews for assessing the appropriateness of a RA-specific Qol instrument, the RAQol, for adaptation for use in Estonia. We described the importance of the items to our patients and identified Qol topics that were significant for our respondents but that were not assessed by the questionnaire. We also discussed the nature of the discrepancies in significance of Qol topics for Estonian patients.
Our results show that the utilization of a qualitative study as an introductory part of Qol assessment instrument adaptation provides possibilities for more thoroughly considered Qol research. By evaluating the significance of items in the particular context, it allows the mechanical acceptance of instruments just because they have performed well in other societies and cultures to be avoided. Moreover, it offers an opportunity to identify topics that are not included in the instrument but are important for local interpretation of Qol, which are otherwise often overlooked. For researchers, qualitative studies offer a deeper understanding of the instrument in question and of the research topic – Qol of the patients.
The data collected during this qualitative research process also has the potential to be used for a wider analysis of the Qol of Estonian RA patients. Still, our current interests were centered on the appropriateness of the adaptation of a particular Qol instrument, and therefore the use of the gathered data was quite limited. But we believe that the knowledge gained will be beneficial for our forthcoming research projects.
Authors' contributions
MT: the idea and conceptual construction of the research, recruitment of the participants and conduction of the interviews, coordination of the coding and analysis processes
JS: methodological construction and supervision of the research, active participation in the coding and analysis processes
KM and EH: participation in discussions and decision-making throughout the whole course of the research
All authors have read and approved the final manuscript.
Acknowledgements
We wish to thank Mrs. Eve Möls for technical assistance with transcribing and Mr. Silver Pukk and Mr. Ilmar Part for linguistic consultation. We are also very thankful to the anonymous reviewers for the valuable thoughts and good advices. The study was supported by the Estonian Science Foundation grant number 5239.
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| 15579209 | PMC544852 | CC BY | 2021-01-04 16:38:12 | no | Health Qual Life Outcomes. 2004 Dec 4; 2:69 | utf-8 | Health Qual Life Outcomes | 2,004 | 10.1186/1477-7525-2-69 | oa_comm |
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BMC BiotechnolBMC Biotechnology1472-6750BioMed Central London 1472-6750-4-321559835010.1186/1472-6750-4-32Research ArticleProduction of soluble mammalian proteins in Escherichia coli: identification of protein features that correlate with successful expression Dyson Michael R [email protected] S Paul [email protected] Karen J [email protected] Rajika L [email protected] John [email protected] The Atlas of Gene Expression Project, The Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge, CB10 1SA, UK2004 14 12 2004 4 32 32 26 10 2004 14 12 2004 Copyright © 2004 Dyson 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 search for generic expression strategies for mammalian protein families several bacterial expression vectors were examined for their ability to promote high yields of soluble protein. Proteins studied included cell surface receptors (Ephrins and Eph receptors, CD44), kinases (EGFR-cytoplasmic domain, CDK2 and 4), proteases (MMP1, CASP2), signal transduction proteins (GRB2, RAF1, HRAS) and transcription factors (GATA2, Fli1, Trp53, Mdm2, JUN, FOS, MAD, MAX). Over 400 experiments were performed where expression of 30 full-length proteins and protein domains were evaluated with 6 different N-terminal and 8 C-terminal fusion partners. Expression of an additional set of 95 mammalian proteins was also performed to test the conclusions of this study.
Results
Several protein features correlated with soluble protein expression yield including molecular weight and the number of contiguous hydrophobic residues and low complexity regions. There was no relationship between successful expression and protein pI, grand average of hydropathicity (GRAVY), or sub-cellular location. Only small globular cytoplasmic proteins with an average molecular weight of 23 kDa did not require a solubility enhancing tag for high level soluble expression. Thioredoxin (Trx) and maltose binding protein (MBP) were the best N-terminal protein fusions to promote soluble expression, but MBP was most effective as a C-terminal fusion. 63 of 95 mammalian proteins expressed at soluble levels of greater than 1 mg/l as N-terminal H10-MBP fusions and those that failed possessed, on average, a higher molecular weight and greater number of contiguous hydrophobic amino acids and low complexity regions.
Conclusions
By analysis of the protein features identified here, this study will help predict which mammalian proteins and domains can be successfully expressed in E. coli as soluble product and also which are best targeted for a eukaryotic expression system. In some cases proteins may be truncated to minimise molecular weight and the numbers of contiguous hydrophobic amino acids and low complexity regions to aid soluble expression in E. coli.
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Background
The production of purified proteins is important for several experimental approaches aimed to assign gene function including antibody generation for immunocytochemistry and immunoprecipitation studies [1-3], in vitro mapping of protein – protein, protein – DNA or protein – RNA interactions [4,5] and structure determination [6]. The availability of proteins is also important for biomedical applications such as small molecule drug discovery and the production of therapeutic proteins and vaccines. In these situations it is essential to be able to reliably express the proteins in a heterologous system and purify them so that they possess the same folds and structure as they would in a natural in vivo state. To achieve this on a whole proteome scale a generic approach must be taken to the expression of protein families, unlike the traditional approach of protein chemistry in optimising the isolation of individual proteins on a case by case basis. E. coli has been the expression system of choice for the majority of laboratories engaged in high-throughput, multi-plexed cloning, expression and purification of proteins for structural genomics [7]. The advantages of E. coli as an expression host include well studied physiology, genetics and availability of advanced genetic tools [8-10], rapid growth, high-level protein production rates achieving up to 10–30% of total cellular protein, ease of handling in a standard molecular biology laboratory, low cost and the ability to multiplex both expression screening [11] and protein production [12]. There are however several disadvantages, particularly for eukaryotic proteins, of expression in a prokaryotic system. The lack of eukaryotic chaperones, specialised post-translational modifications, ability to be targeted to sub-cellular locations or to form complexes with stabilising binding partners can result in protein mis-folding and aggregation. For example, when 2078 randomly selected C. elegans full-length genes were cloned and expressed in E. coli only 11 % yielded soluble protein [13]. Similarly for 44 cloned human proteins, 12 were expressed solubly and 4 purified to homogeneity [14]. With the exception of full-length membrane proteins, the property of protein solubility has been shown to be a good indicator of correct folding as determined by functional binding [15,16] or enzymatic [17] assays. Purification of inclusion bodies and in vitro refolding has been used in a number of cases, but refolding conditions are highly protein specific and so unlikely to be useful for high-throughput protein expression.
There are several fall-back strategies for expression of correctly folded eukaryotic proteins in E. coli one of which is to truncate long multi-domain proteins into separate domains, as has been performed for the Ephb2 receptor [15,18,19]. Reducing translation rates so that proteins have an increased chance of folding into a native state prior to aggregating with folding intermediates, can be successful by lowering the temperature after induction [20] or inducing with lower concentrations of IPTG [21]. Alternate approaches include: co-expressing stabilising binding partners (see review [7]) or chaperones [22]; the induction of chaperones by heat shock [23] or chemical treatment [24]; or the use of genetically modified host-strains that can conduct oxidative protein folding in the cytoplasm [25,26], over-express rare tRNAs [27] or lipid rafts [28]. Perhaps one of the most successful generic strategies to enhance the expression of soluble proteins is the fusion with solubility enhancing tags, such as maltose binding protein (MBP), thioredoxin (Trx) and glutathione-S-transferase (GST) [29-31].
The aim of this work was to ask if it is possible to derive some general conclusions regarding which expression strategy would most likely result in the expression of soluble, functionally active mammalian protein on a family-by-family or domain-by-domain basis. A deep-mining approach was taken to maximise the chances of successful expression by examining the soluble expression of 30 different proteins using 14 different expression vectors. This study allowed us to make several conclusions regarding the best strategies to adopt for the soluble expression of different mammalian proteins in bacteria. The conclusions were tested by the expression of an additional 95 mammalian proteins.
Results
Expression clone construction
The 30 proteins chosen for this expression study are listed in Table 1. With the exception of GFP, they are all human or mouse proteins, and represent several diverse protein families with extra-cellular, cytoplasmic and nuclear cell locations. The list includes a mixture of full-length and truncated proteins expected to be easy or more challenging to express in a bacterial system. Protein truncations were designed to express individual domains annotated from the SwissProt [32] or Pfam [33] databases or following previous examples of successful expression [15]. The genes were isolated from cDNA using a nested PCR strategy [34] or provided by the FlexGene Consortium and sequence confirmed. A recombinational cloning strategy was employed termed "GATEWAY" cloning [35,36] based on a modification of the phage lambda site-specific recombination system [37]. Primers were designed using the nearest neighbour algorithm [38] and open reading frames (ORFs) were PCR amplified from first strand cDNA with 5' attB1 and 3' attB2 linkers and then recombined with pDONR221 (Invitrogen) to give a set of entry clones which were sequence confirmed and then recombined with various destination vectors to give the expression constructs. Two sets of clones for each ORF were generated with and without stop codons for expression with N or C-terminal tags respectively. Recombinational cloning was useful in this study where the same set of ORFs could be cloned into a large set of different expression vectors without the requirement to check for compatible restriction sites in each vector or their absence within the ORFs.
For this study a set of destination vectors were constructed by modifying pET-DEST42 (see Materials and Methods). The T7 promoter was chosen over other promoters commonly used for bacterial expression because of the high specificity and processivity of T7 RNA polymerase and the wide choice of expression strains currently available. Briefly, multicloning sites were created either 5' of the attR1 or 3' of the attR2 recombination sites for insertion of DNA inserts encoding N or C-terminal tags respectively. The expression vectors contained a T7lac promoter [39] for improved control of basal expression. The N-terminal tag expression vectors contained a sequence at the translational start site to provide a partial match with the down-stream box (ATG AAT CAC CAT), shown to provide enhancement of translation [40] and a decahistidine (H10) tag for enhanced affinity for Nickel resins compared with hexahistidine (H6) tags (data not shown). A fusion partner was inserted between the H10 tag and recombination sites to examine the effect on soluble protein expression. Unlike previous tag comparisons [29-31] here the same promoter and 5'-UTR sequence was employed so that any expression differences observed would be purely due to the presence the fusion partner. A vector was also included in this study (pDEST17) with a T7 promoter and no downstream lac operator, which would add a H6 tag at the N-terminus (Figure 1).
Effect of different N-terminal fusions on expression
Expression plasmids generated by recombination reactions were used to transform E. coli BL21(DE3), an expression strain containing chromosomally integrated T7 RNA polymerase gene (λDE3 lysogen) under the control of the lacUV5 promoter. To handle a large number of expression experiments (420 total) and associated manipulations to screen for total and soluble expression in E. coli, the recombinational cloning, transformation, growth of expression cultures and cell lysis and filtration separation of insoluble protein were performed in 96-well plate format. Figure 2 shows Western blots for total and soluble protein expression 2 hours after induction with 1 mM IPTG as described in Materials and Methods. The method for separating total from soluble proteins was based on that of Knaust and Nordlund [11] and consisted of detergent lysis of harvested cells followed by filtration through a 0.65 μm 96-well filter plate, which separates larger inclusion bodies from the soluble fraction. The filtration method agrees well with traditional centrifugation methods to separate soluble from insoluble protein [11,41] and has the advantage that multiple samples can be processed in parallel. Quantitation was achieved by separating the proteins by SDS-PAGE, electro-blotting onto PVDF membranes and detecting His tagged proteins with an anti-His5 monoclonal antibody followed by probing with an anti-mouse Cy-5 labelled antibody. The advantage of expression analysis by Western blot, compared to dot-blots, is that this allows one to quantitate the expression levels of full-length constructs and eliminate the contribution from cleaved protein tag. It was found that Western blots based on fluorescence detection [42] gave a greater dynamic range of detection compared with detection based on enzymatic amplification such as horse radish peroxidase (data not shown). A His-tagged protein molecular weight ladder was used for normalisation to eliminate any blot to blot variation. Table 2 shows the results of this analysis, quantitating expression yields in terms of mg expressed protein per litre of induction media for total and soluble expression. Expression yields greater than 2 mg/l are highlighted in bold.
Looking first at the results for total (soluble and insoluble) expression, no clear patterns emerge for the various expression vectors used. With the exception of CASP2, CDKN2A, Trp53, EGFR(TK), FOS and CD44 most proteins expressed well across all expression vectors. Interesting differences are apparent however when one looks at the production of soluble protein. Using decahistidine green fluorescent protein (H10-GFP) or decahistidine glutathione-S-transferase (H10-GST) as fusion partners at the N-terminus gave poor yields of soluble intact product. This may not be because they were poor at promoting soluble expression but because they were prone to proteolysis during cell lysis reducing the yield of full-length soluble protein. A set of proteins (GFP, RAF1(Ras-bd), HRAS, mdm2(p53-bd), Ephb2(TK) and CCND2) gave high soluble expression levels in the baseline N-terminal decahistidine vector, which was not improved when expressed as decahistidine thioredoxin (H10-Trx) or decahistidine maltose binding protein (H10-MBP) fusions. The molecular weight of these proteins ranged from 9 – 35 Kda and averaged 22.8 Kda. These proteins are all expressed in the cytoplasm, have an average of 1 low-complexity region, 3.8 contiguous hydrophobic amino acids (hp_aa), pI of 6.6, grand average of hydropathicity index (termed GRAVY[43] where increased positive number indicates increased hydrophobicity) of -0.32, 2.6% cysteine residues and no coiled-coil structures. A second grouping of proteins was observed where soluble expression was improved when expressed as H10-Trx or H10-MBP fusions compared with the H10 tag alone. This grouping included GRB2, Efnb2(EC1 or 2), MAD, MAX, Efna1 (FL and EC). The molecular weight of these proteins ranged from 16 – 25 Kda and averaged 20.5 Kda. These proteins were a mixture of those expressed in the cytoplasm, nucleus and extra-cellular, have an average of 0.71 low-complexity regions, 3.6 contiguous hydrophobic amino acids (hp_aa), pI of 6.8, GRAVY score of -0.79 and 1.7% cysteines. A third set of proteins resulted in almost undetectable soluble expression with a H10 tag but good expression with H10-Trx or H10-MBP fusions. These included CDK2, FLI1, CDKN-1B, mdm2, GATA2, Ephb2(LB) and CASP2 with molecular weights ranging from 22.5 – 54.5 Kda, with an average molecular weight of 40.4 Kda. These proteins were also a mixture cytoplasmic, nuclear and extra-cellular proteins, have an average of 2 low-complexity regions, 5 contiguous hydrophobic amino acids (hp_aa), pI of 6.9, GRAVY score of -0.55 and 2.3% cysteines. Finally a set of proteins was grouped (MMP1, FOS, EGFR(TK), Trp53, CD44) where very low (< 1 mg/l) soluble full-length expression was observed, even when expressed as MBP or Trx fusions. Here the molecular weight ranged from 40.7 – 81.6 Kda and averaged 51.4 kDa. These proteins were a mixture of those expressed in the cytoplasm, nucleus and extra-cellular, have an average of 3 low-complexity regions, 5.6 contiguous hydrophobic amino acids (hp_aa), pI of 5.7, GRAVY score of -0.50 and 1.8% cysteine content.
Comparing the 20 mammalian proteins where there are examples in all 6 expression vectors the average yields of soluble protein for the H10, H10-GFP, H10-GST, H10-Trx and H10-MBP tags are 3.3, 1.0, 1.4, 6.0 and 5.8 mg per litre of culture. This ranks the ability of the tag fusions to produce full-length soluble protein as H10-Trx ~ H10-MBP > H10 > H10-GST > H10-GFP. The pDEST17 vector (which encodes a H6 tag) was dramatically poorer at expressing soluble protein compared with the vector pN110 (which encodes a H10 tag), with average soluble expression yields of 0.8 and 3.3 mg per litre of culture respectively. Both vectors contain T7 RNA polymerase promoters, but pN110 also contains a lac operator (lacO) downstream of the promoter and the gene encoding the lac repressor (lacI) for tighter control of gene expression. This may result in a faster rate of transcript synthesis, after induction with IPTG, and hence translation rates (due to an increased concentration of mRNA) for pDEST17 compared with pN110. If translation rate exceeds the rate of protein folding, then increased production of insoluble protein would occur.
Effect of different C-terminal fusions on expression
A similar study was performed where the 30 ORFs were cloned into 8 different C-terminal tag expression vectors shown in Figure 1. C-terminal fusions studied here included V5-H6 or H10 or protein fusions MBP, GST, Trx, murine or human dihydrofolate reductase (Dhfr or DHFR respectively), all with H10 at the C-terminus. The expression screen and quantitation of total and soluble protein expression was performed as for the N-terminal tag study. Figure 3 shows the fluorescence western blots for this C-terminal tag study. Here a greater number of constructs were observed with either undetectable or low levels of expression compared with the N-terminal tag study. Table 3 quantitates the Western blot data for the intact fusion products, with expression yields greater than 2 mg/l in bold. The last row of the table describes the average expression yield for each C-terminal fusion partner. For total protein expression levels there are large expression level differences observed between the various C-terminal tags. The C-terminal decahistidine tag was particularly poor here with an average total expression yield of only 0.7 mg/l compared with 7.3 mg/l when this tag was fused to the N-terminus. In contrast the C-terminal MBP-H10 tag resulted in an average total expression yield of 20.2 mg/l. The ranking of the C-terminal fusion partners in promoting total expression was MBP-H10 > GST-H10 > V5-H6 > Trx-H10 > Dhfr-H10 > DHFR-H10 > GFP-H10 > H10.
MBP-H10 was the most effective tag at the C-terminus to promote protein solubility with an average construct full-length soluble yield of 5.0 mg/l, which compares well with an average of 5.8 mg/l when this tag is fused at the N-terminus. The order of C-terminal tags to promote soluble expression was similar for total expression: MBP-H10 > GST-H10 > V5-H6 > Dhfr-H10 ~ GFP-H10 ~ Trx-H10 > H10 ~ DHFR-H10. Thioredoxin was not as effective a solubility enhancing tag when fused at the C-terminus with an average soluble yield of only 0.7 mg/l compared with 6.0 mg/l when fused to the N-terminus.
Several correlations with protein features are seen when one groups the MPB fusions according to soluble protein expression levels. For the first group, where soluble expression levels were in the range of 5 – 50 mg/l, the average molecular weight, pI and GRAVY score were 20.6 KDa, 5.9 and -0.58 respectively. The average numbers of contiguous hydrophobic amino acids, low complexity and coiled-coil regions were 3.1, 0.56 and 0.22 respectively. The second group displayed soluble expression levels between 1 – 5 mg/l. Here, the average molecular weight, pI and GRAVY score were 25.1 KDa, 7.9 and -0.39 respectively and the average numbers of contiguous hydrophobic amino acids, low complexity and coiled-coil regions were 4.3, 0.71 and 0 respectively. The last group displayed soluble expression levels between 0 – 1 mg / l. Here the average molecular weight, pI and GRAVY score were 41.1 KDa, 6.2 and -0.51 respectively and the average numbers of contiguous hydrophobic amino acids, low complexity and coiled-coil regions were 5, 2.43 and 0.21 respectively. There were representatives of nuclear, cytoplasmic and extra-cellular proteins in all three groupings.
Expression of a test set of 95 mammalian proteins
A diverse set of proteins were chosen to test the conclusions of this study (Table 4). They range from proteins that are well annotated, some of which have been expressed in E. coli previously (Nfkb1), to those that contain no PfamA domains and have not been expressed in E. coli previously (Maat1, BC031407, Ttyhl, 1500001H12RIKEXT2, Ext2, KIAA1136, G2 and KIAA1549). They included 24 proteins not annotated as PfamA domains, with unknown function. All cDNAs were amplified from a primary cDNA library, cloned into pDONR221 and sequence confirmed prior to transfer to pDEST-N112-MBP (Figure 1) for expression as N-terminal H10-MBP fusions. In some cases primers were designed to clone protein fragments to express particular PfamA domains or minimise the molecular weight or numbers of low complexity (LC) regions or contiguous hydrophobic amino acids (hp_aa). For proteins with no PfamA annotations, such as BC031407, SMART sequence analysis [44] was performed to identify the low complexity regions of the protein and truncations performed accordingly. Protein expression and quantitation of intact soluble fusion protein product was performed as for the N- and C-terminal tag comparison study. The total and soluble expression levels (mg of protein per litre culture) are listed in the last column of Table 4 together with selected protein features. 63 of the 95 proteins yielded soluble expression levels of greater than 1 mg/l and the average molecular weight, number of LC regions and hp_aa for these proteins was 24.4 kDa, 0.9 and 3.7 respectively. For the 32 proteins that failed to give soluble product of the correct size, the average molecular weight, number of LC regions and hp_aa was 37.1 kDa, 1.8 and 4.5 respectively.
Discussion
Correlation between protein properties and solubility
To guide future expression strategies for new proteins, particularly regarding the choice of expressing a full-length protein in a bacterial or eukaryotic system and also where to truncate multi-domain containing proteins, it is interesting to investigate if the proteins expressed in a soluble form in this study share any common properties. Recently Goh et al. [45] used data generated by a structural genomics consortium to examine the ability of proteins to progress from cloning to expression and purification to crystallisation. The data used was very large, consisting of 27,000 targets from over 120 organisms and a number of important features were inferred that correlated with success including percentage composition of charged residues, occurrence of hydrophobic patches and length. Although a large study, there was a problem with interpretation of all the data-sets as it was unclear whether targets were simply waiting in the pipeline or had failed. Also structural genomics targets are often initially biased in favour of easy to express proteins, not representative of the whole proteomes of these organisms.
The present study, focused on mammalian proteins from several diverse families, examined the relationship between successful soluble expression with various protein properties. Several protein features were identified in this study to correlate with soluble expression, which had not previously been shown experimentally. For both the N and C-terminal tag expression studies it was observed that the presence of several features did not correlate with successful expression including protein pI, grand average of hydropathicity index (GRAVY) [43], sub-cellular location, the cysteine content as a percentage of the total number of amino acids and the number of coiled-coils. Protein pI has been linked to sub-cellular location [46] with a bimodal distribution observed in bacterial and archaeal genomes and trimodal pattern in eukaryotes. Proteins are thought to be less soluble at a pH environment near their pI. GRAVY simply calculates overall hydrophobicity of the linear polypeptide sequence with increasing positive score indicating greater hydrophobicity, but no account is taken of the way the protein folds in three dimensions or the percentage of residues buried in the hydrophobic core of the protein. In a recent study Luan et al. [47] tested the soluble expression of 10,167 full-length C. elegans ORFs and found that protein hydrophobicity was an important factor for an ORF to yield a soluble expression product. This different result may be attributable to the fact that the C. elegans study included a greater proportion of membrane proteins. Therefore the lack of correlation between GRAVY score and soluble expression we observed may be true for non-membrane proteins or for proteins where the trans-membrane domain has been deleted.
There was a strong correlation between successful soluble expression and molecular weight of the protein. Small proteins with an average molecular weight of 22.8 KDa did not require to be fused with solubility enhancing proteins for soluble expression whereas proteins that required to be fused with N-terminal MBP or Trx for soluble expression had an average molecular weight of 40.4 KDa and those where the addition of a N-terminal fusion could not rescue soluble expression had an average size of 51.4 KDa. The same pattern also emerged in the C-terminal fusion study. The decreasing probability of successful soluble expression of mammalian proteins with increasing molecular weight is likely due to increasing protein complexity, perhaps requiring specialised eukaryotic chaperones for folding or stabilising binding partners. The majority of proteins solubly expressed in this study contained single domains and as fusion proteins were either capable of self-folding or were folded with the aid of prokaryotic chaperones. Braun et al. found a similar relationship with their set of 32 human proteins with 4 different N-terminal fusions [30].
A correlation in this study was observed between increasing numbers of contiguous hydrophobic amino (hp_aa) acids (AILFWV) and soluble expression. This ranged from an average of 3.8 hp_aa for those proteins not requiring a N-terminal fusion for high level soluble expression to 5 hp_aa for proteins requiring a N-terminal fusion for successful expression and 5.6 hp_aa where expression failed under the conditions described here. This pattern was also repeated in the C-terminal fusion study where good expression proteins had an average of 3.1 hp_aa whereas poor expression proteins had an average of 5 hp_aa. In a study of the sequences of 2753 non-membrane proteins it was found that the sequences of three or more consecutive hydrophobic residues are suppressed in globular proteins [48]. Low complexity regions of proteins are regions of a protein of biased composition containing a small number of amino acids [33] and can have a disordered structure important for protein function [49]. Here we found that the greater the number of low complexity regions contained within the target protein, the less likely soluble expression would be achieved. This was true for both the N- and C-terminal fusion protein studies with 0.6 – 1 low complexity regions for proteins easy to express in a soluble form to 2.4 – 3 low complexity regions for proteins difficult to express. Low complexity regions are less common in bacterial proteins and these may be targets for proteolytic degradation in vivo.
Some interesting conclusions were drawn when soluble expression was measured for an additional set of 95 mammalian proteins expressed as H10-MBP fusions (Table 4). In several cases (ELF1, Fli1, Ldb1, BC031407, Nfkb1 and RelA-p65) truncating the proteins to minimise the molecular weight and the numbers of low complexity regions and contiguous hydrophobic amino acids made the difference between failed expression and good soluble protein expression. For proteins such as BC031407, with no annotated PfamA domains, it was found that truncating at low complexity regions was a good method to identify a fragment that could express in a soluble form of the correct size (protein 81). Although we found that successful soluble expression of the 95 protein set correlated with lower molecular weight, number of low complexity regions and contiguous hydrophobic amino acids compared with proteins that failed to express solubly with the correct size, validating our earlier conclusions, there were some exceptions. For example Elf1 and Gata1 both expressed well despite having 4 and 6 low complexity regions respectively and molecular weights of 66 and 42.5 kDa, whereas some smaller proteins such as the PDZ domains of Dlgh3 and Grip1 failed to express. It may be that there are additional protein features, such as the ability to form a stabilising interaction with a binding partner, that are also important for soluble expression. Also ensuring correct protein domain boundaries may be important since the annotated Pfam domain boundaries, based on sequence alignment, do not always match the structural or folding domain boundaries.
Protein fusions that enhance protein solubility
There have been three comparative studies recently where sets of proteins were cloned into several expression vectors and the effects of the fusion partner on total and soluble expression yield were examined. Hammarstrom et al. [29] cloned 27 human proteins (MW < 20 Kda) into various expression vectors and ranked the tags ability to promote soluble expression as Trx ~ MBP ~ Gb1 > ZZ > NusA > GST > His6. Another study ranked tags in terms of increased expression and yield after purification as GST ~ MBP > CBP > His6 when comparing the expression of 32 human proteins where the molecular weight varied from 17 – 110 kDa.[30] Here GST was preferred because of the weak affinity between MBP and amylose resin. In a third study of 40 different proteins (10 mammalian, 3 plant and 2 insect) with 8 different tags MBP gave the best overall results in terms of total and soluble expression [31]. However, these studies used different combinations of promoter and fusion partner, so it was unclear whether the observed effect was purely due to expression with the fusion partner or variable rates of transcript synthesis that would also affect translation rates.
In this study it was found that, on average, N-terminal fusion partners are preferable for optimal protein expression. When proteins are expressed with their native N-terminus, as in our C-terminal fusion proteins, total expression levels can be more variable than when expressed with a constant N-terminal tag. This may be because of variable RNA secondary structures in the region around the start codon which could interfere with ribosome binding. An additional explanation is that during translation the expressed protein emerges from the ribosome first and initiates an incorrect, irreversible, folding pathway before the soluble fusion partner has been translated and folded. The mis-folded protein would be ubiquitin labelled and targeted to the proteasome for degradation resulting in lower total expression levels. This scenario is more likely when expressing mammalian proteins in a bacterial system which lacks specific eukaryotic chaperone proteins. It has been shown previously that proteins prone to mis-folding and aggregation can arrest GFP folding when fused at the C-terminus [17]. However, when the soluble protein is fused at the N-terminus, this would be translated first and perhaps increase the solubility of the downstream protein domain folding intermediates, increasing their half lives prior to irreversible aggregation. This would allow greater reversibility in the individual steps along the folding pathway and increase the probability that the protein would eventually reach the lowest free energy native conformation.
It was found that Trx and MBP were the best N-terminal protein fusions to promote protein solubility. The best C-terminal fusion to promote protein solubility was MBP and this may be acting as a true intra-molecular chaperone [50], able to promote folding of the N-terminal protein fusion. The mechanism could be due to direct binding to folding intermediates [51], allowing stabilisation prior to correct folding and inhibition of aggregate formation. The observation that MBP was effective at enhancing soluble expression when fused at the C-terminus, in contrast to thioredoxin, suggests that MBP can actually reverse the process of incorrect folding that would have started prior to the translation of the downstream MBP. This property was not observed for thioredoxin when fused to the C-terminus suggesting either that, in three-dimensions, different proximal faces of the fusion partners have different solubility enhancing properties or that thioredoxin does not posses any chaperone properties and acts only as a solubility enhancer. Alternatively, the folding of thioredoxin may be more prone to inhibition than MBP. Also there are examples where MBP fusions can form soluble inclusion bodies [52,53], and this cannot be ruled out as a possibility here, although there are also several examples where MBP fusion proteins are fully functionally active [50,52,54,55].
It must be stressed here that although protein solubility is a useful indicator of correct folding, additional measurements need to be performed to give supporting evidence for correct folding. These may include removing the protein fusion with a protease and analysis of the cleaved protein of interest by a variety of biophysical and functional assays such as analysis of monodispersity by light scattering [52], NMR [56,57], CD spectropolarimetry, bis-ANS binding [53], ligand binding or enzymatic activity. In this study a protease cleavage site was not included in the vector constructs because the main use of the proteins generated in our laboratory will be in high-throughput antibody production where the cleavage of the fusion partner is unnecessary.
GFP did not significantly enhance soluble protein expression when fused to the C-terminus of the proteins in this study, supporting the use of this tag as an indicator of soluble protein expression of fused ORFs.[17,41] The observation that the V5-His6 tag resulted in a higher average soluble expression level than the His10 tag (1.7 compared with 0.3 mg/l) indicates that the identity of the peptide tag can also affect overall solubility of expressed proteins.
Conclusions
What guidelines have emerged from this study in developing a strategy for the production of soluble mammalian proteins in E. coli? If the protein has a molecular weight of less than 30 KDa and contains 1 or less low complexity regions and less than 4 contiguous hydrophobic amino acids expression of the full-length protein in E. coli should give good levels of soluble protein. As a generic strategy we would recommend expressing the protein with a fusion partner and found MBP and Trx to be the best fusions to enhance protein solubility as N-terminal tags with MBP being superior as a C-terminal fusion. C-terminal fusions are desirable for proteins such as the P450s where N-terminal tags can inhibit functional activity. When fused to an optimal fusion partner, nuclear, cytoplasmic and extra-cellular domains were equally likely to be expressed solubly. For larger proteins over 50 KDa, truncations should be considered to express specific protein domains and to minimise the molecular weight, number of low complexity regions and contiguous hydrophobic amino acids. In conclusion, this study will help enable a systematic expansion in the number mammalian proteins and domains that can be successfully expressed in E. coli as soluble product, and also predict which are best targeted for a eukaryotic expression system.
Methods
Materials
Oligonucleotides were synthesised by Qiagen-Operon (Cologne, Germany) or Sigma-Genosys (Haverhill, UK). All restriction enzymes were from New England Biolabs (Hitchin, UK). The vectors pET-DEST42, pDEST17 and pDONR201 and E. coli DB3.1 and BL21(DE3)Star pLysS, Gateway BP and LR clonase enzyme mix, pre-cast 4–12 % NuPAGE Bis-Tris gels and PVDF membranes (0.45 μm pore size) were all from Invitrogen (Paisley, UK). Entry plasmids in both open (minus stop codon) or closed format (plus stop codon) containing the full-length genes for GRB2, HRAS, JUN, FOS, MAD, MAX, CDK2, CDK4, CDKN1B, CASP2, MMP1, CDKN2A and CD44 were provided by Pascal Braun and Josh LaBaer (Harvard Institute of Proteomics, Cambridge, USA). A full length clone containing the full-length human EGFR ORF was provided by the RIKEN BioResource Center (Tsukuba, Japan) and Efna1 from the Mammalian Gene Collection (MGC) archived at the Wellcome Trust Sanger Institute (Hinxton, UK). First strand synthesis human and mouse cDNA was from BD Biosciences (Oxford, UK). Plasmid, gel extraction and PCR purification kits and 6xHis protein ladder were purchased from Qiagen (Crawley, UK). The expression strain BL21(DE3), BugBuster protein extraction reagent and His tag monoclonal antibody was from Merck Biosciences (Nottingham, UK). The 96-well multiscreen-DV durapore filter plate with 0.65 μm pore size was from Millipore (Watford, UK) and Cy5-labelled goat anti-mouse IgG from Amersham Biosciences (Little Chalfont, UK). Europium labelled antibodies and DELFIA reagents were from Perkin Elmer (Beaconsfield, UK) and all other chemicals unless otherwise stated were from Sigma-Aldrich (Gillingham, UK).
N-Terminal fusion GATEWAY destination vector construction
To prepare pET-DEST42-MCS, a multi-cloning site was inserted into pET-DEST42 (Invitrogen) at nt396, between the shine-dalgarno sequence and the attR1 recombination site, encoding the recognition sequences for NdeI, KpnI, DraIII and BfrBI. Inverse or whole plasmid PCR was performed on pET-DEST42 with 5'-phosphorylated PAGE purified primer pairs 20 (5' TACCCACGAAGTGATGCATACAAGTTTGTACAAAAAAGCTGAACG 3') and 21 (5' CCCATATGTATATCTCCTTCTTAAAGTTAAACAAAATTATTTCTAGAG 3') in a 20 μl reaction containing 10 ng pET-DEST42, 0.3 μM primers 20 and 21, 20 mM Tris-HCl (pH 7.5), 0.5 mM DTT, 200 μM each of dATP, dCTP, dGTP and dTTP, 1 mM MgSO4, and 0.5 unit KOD hot start DNA polymerase (Novagen). PCR cycling conditions were: 94°C – 2 mins followed by 15 cycles of 94°C – 15 s, 59°C – 30 s, 68°C – 9 mins. The 7468 bp PCR product was purified using a PCR purification spin column (Qiagen) and eluted with 30 μl of 10 mM Tris-HCl (pH8.5), digested with 20 units of DpnI enzyme at 37°C for 4 hrs, to remove methylated plasmid DNA, purified by spin column and an intramolecular ligation reaction performed using 16 ng of linear PCR product and 5 units T4 DNA ligase and the buffers from the rapid ligation kit (Roche). The ligated PCR product was used to transform E. coli DB3.1 and the resultant pET-DEST42-MCS plasmid DNA prepared and sequence confirmed. Insert 1, encoding a decahistidine tag with a 5'-NdeI site and blunt 3' end, was prepared by PCR with primer pairs 22 (5' GGAATTCCATATGAAUCAC 3') and 24 (5' pGTGATGGTGATGGTGATGGTGATGGTGATTCATATGGAATTCC) and insert 2 encoding a decahistidine tag flanked by a 5'-NdeI site and 3'-KpnI site was prepared with primer pairs 22 and 26 (5' CGGGGTACCATGGTGATGGTGATGGTGATGGTGATGGTGATTCATATGGAATTCC 3'). PCR reactions were as above except the annealing temperature dropped to 44°C, extension time to 10 s and 12 cycles employed. Insert size was checked by 10 % TBE-PAGE and purified by a nucleotide removal kit (Qiagen). Expression vectors (b) pDEST-N110 and pDEST-N112 (Figure 1) were prepared by digestion of inserts 1 and 2 with NdeI only or NdeI and KpnI combined respectively, purified by spin column and ligated in a 1:1 ratio to NdeI, BfrBI or NdeI, KpnI digested pET-DEST42-MCS respectively prior to transformation of E. coli DB3.1. Inserts encoding MBP, GFP, GST or Trx flanked by a 5' DraIII site and a 3' blunt end were generated by PCR amplification from the plasmids pMALc2 (New England Biolabs), pET41a or pET32 (Novagen) respectively The primer pairs for MBP were 78 (5' TTATTACACGAAGTGAAAATCGAAGAAGGTAAACTGGTAATC 3') and 79 (5' pGTTCGAGCTCGAATTAGTCTGCGCGTCTTTC), for GFP 84 (5' TTATTACACGAAGTGGCTAGCAAAGGAGAAGAACTTTTCACTGGAG 3') and 85 (5' pTTTGTAGAGCTCATCCATGCCATGTGTAATC 3'), for GST 86 (5' TTATTACACGAAGTGTCCCCTATACTAGGTTATTGGAAAATTAAGGG 3') and 87 (5' pATCCGATTTTGGAGGATGGTCGCCACC 3') and for Trx 88 (5' TTATTACACGAAGTGAGCGATAAAATTATTCACCTGACTGAC 3') and 89 (5' p CAGGTTAGCGTCGAGGAACTCTTTC 3'). The inserts were digested with DraIII and ligated with DraIII, BfrBI cut pDEST-N112 vector to create the GATEWAY destination vectors pDEST-112-MBP, pDEST-112-GFP, pDEST-112-GST, pDEST-112-Trx.
C-Terminal fusion GATEWAY destination vector construction
pDEST-C101 was designed to insert a decahistidine encoded sequence between the attR2 recombination site and T7 transcription termination region. pDEST-C102 is as C101 except a DraIII, BfrBI site was inserted downstream of the attR2 recombination site. Inverse PCR was performed as described above with primer pairs 1 (5' pCACCATCACCATCATCACCATCACCATTGAGTTTGATCCGGC) and 2 (5' pATGCACCACTTTGTACAAGAAAGCTGAAC) to generate pDEST-C101 and primer pairs 1 and 3 (5' pATGCATACCACTCACTTCGTGCACCACTTTGTACAAGAAAGCTGAAC) to prepare pDEST-C102. Murine and human dihydrofolate reductase (Dhfr and DHFR respectively) inserts flanked by a 5' DraIII site and blunt end at the 3' were amplified from MGC clones using the primer pairs 82 (5' TTATTACACGAAGTGCGACCATTGAACTGCATCGTCGCCGTG) and 83 (5' pGTCTTTCTTCTCGTAGACTTCAAACTTATAC 3') for Dhfr and 80 (5' TTATTACACGAAGTGGGTTCGCTAAACTGCATCGTCGCTGTG) and 81 (5' pATCATTCTTCTCATATACTTCAAATTTG) for DHFR. The DraIII digested inserts were ligated with DraIII, BfrBI digested pDEST-C102 vector to create pDEST-C102-MBP, GFP, GST, Trx, Dhfr and DHFR as shown in Figure 1.
cDNA isolation and expression clone generation
A nested PCR strategy was used to isolate protein encoding ORFs directly from cDNA adapted for GATEWAY cloning from the method described by J. E. Collins et al. [34]. Briefly 2 sets of primer pairs were designed, the first pair of optimised primers binding 1 – 200 bp 5' and 3' of the ORF using DS-Gene software (Accelerys) and a second set of primers targeted to the beginning and end of the ORF. All primers were designed with melting temperatures around 60°C. PCR 1 contained 50 pg of either human universal QUICK-clone II cDNA (Clontech) or 50 pg of a mixture of mouse brain, heart, kidney, liver, smooth muscle, spleen, testis and 7, 11, 15 and 17-day embryo QUICK-clone cDNA (Clontech), 0.25 μM primers, 20 mM Tris-HCl (pH 7.5), 0.5 mM DTT, 200 μM each of dATP, dCTP, dGTP and dTTP, 1 mM MgSO4, and 0.5 unit KOD hot start DNA polymerase (Novagen) in a total volume of 20 μl. The PCR reaction consisted of 94°C – 2 mins, and 30 cycles of 94°C – 15 s, 55°C – 30 s, 68°C – 2.5 mins followed by 68°C – 5 mins. A 50-fold dilution of the PCR 1 reaction was made for the second 30 cycle PCR containing the ORF specific primers. Linkers were added to these primers encoding half the attB1 and attB2 sites for forward and reverse primers respectively. For entry clone generation to be transferred to N-terminal tag expression vectors the 5'-linkers for the forward and reverse primers were 5' AAAAAGCAGGCTCT 3' and 5' AGAAAGCTGGGTTCTA 3' respectively with the reverse primer adding a stop codon. For inserts destined to the C-terminal tag expression vectors the forward and reverse primers were 5' AAAAAGCAGGCTTCGAAGGAGATAGAACCATGG 3' and 5' AGAAAGCTGGGTT 3' respectively with the forward primer encoding the shine-dalgarno and kozak sequences and start codon. PCR 2 products were analysed by 1 % TBE-agarose electrophoresis[58] and correct size fragments were then subjected to an adapter PCR step to complete the flanking attB1 and attB2 sites. This consisted of a PCR reaction as described above using 1 μl of a 50-fold dilution of the PCR 2 reaction in a total volume of 20 μl and primer pair 113 (5' GGGGACAAGTTTGTACAAAAAAGCAGGCT 3') and 114 (5' GGGGACCACTTTGTACAAGAAAGCTGGGT 3') except that the annealing temperature was 45°C, only 12 cycles were used and extension time was 2 mins. The products of the adapter PCR were purified by a 96-well PCR clean-up kit (Qiagen), eluted in 100 μl 10 mM Tris-HCl (pH8.5) and had an average concentration of 40 ng /μl. Recombinational cloning of attB flanked PCR products with an attP containing pDONR vector to generate a set of entry plasmids was as described previously [35] except that pDONR221 (Invitrogen) was used. The ORFs within sequence confirmed attL containing entry plasmids were then recombined the various attR destination vectors described above to generate sets of expression plasmids. The LR recombination reactions [35] were used to transform E. coli DH5α cells, miniprep plasmid DNA prepared and this used to transform the various BL21(DE3) expression strains used in this study.
Expression screening and quantitation
All BL21(DE3) transformants were selected and propagated in the presence of 100 μg/ml ampicillin. A single antibiotic resistant colony was used to inoculate 0.5 ml 2xYT media in a 96-deep well block containing the appropriate antibiotics and shaken at 210 rpm at 37°C. When the average OD600 had reached 1 (3 hrs for BL21(DE3)), 60 μl was transferred to 1.2 ml 2xYT media in a 96-deep-well block containing the appropriate antibiotics, placed on a shaking incubator at 37°C and when the OD600 reached 0.5 (2 hrs for BL21(DE3)) IPTG added to a final concentration of 1 mM and shaking continued at 25°C for 12 hours. Total protein was analysed by transferring a 20 μl aliquot of the induced culture to a 96-well PCR plate containing 20 μl of 2 × NuPage LDS loading buffer (Invitrogen), 0.1 M DTT, heated to 95°C for 10 mins and cooled on ice prior to loading 10 μl on a 17-well 4–12 % NuPAGE Bis-Tris gels with a multi-channel gel loading syringe (Hamilton). Soluble protein was extracted by transferring 290 μl of induced culture to a shallow well plate, centrifugation at 3000 g for 5 mins, supernatant removed and cells were resuspended in 58 μl BugBuster containing 1.4 units of benzonase and 58 units of recombinant lysozyme (Novagen). For the C-terminal tag and expression strain comparison this buffer was also supplemented with 0.58 μl protease inhibitor cocktail set III 10-fold diluted in DMSO (Novagen). The cell-pellets were resuspended with a multi-channel pipette and incubated with slow shaking for 20 mins at room temperature prior to transfer to 96-well multiscreen-DV durapore filter plates with 0.65 μm pore size (Millipore). The filter plate was placed on top of a shallow 96-well plate and centrifuged at 1000 g for 2 mins. 4 μl of the filtrate was then added to a 96-well plate containing 5 μl of 4 × NuPage LDS loading buffer (Invitrogen), 11 μl of 182 mM DTT, the plate heated at 95°C for 5 mins and loaded onto a 17-well 4–12 % NuPAGE Bis-Tris gel. A His-tagged molecular weight ladder (Qiagen) was also loaded onto each gel. Gel electrophoresis and electro-transfer to PVDF membrane was as described.[58] Blots were blocked with 3 % Marvel milk powder in PBS-Tween (PBS with 0.1% Tween) either 1 hour at room temperature or over-night at room-temperature, washed with PBS-Tween and incubated with 40 ng/ml anti-His5 tag monoclonal antibody (Novagen), 3 % Marvel, PBS-Tween for 1 hr, washed 3 × PBS-Tween, incubated with 1 μg/ml Cy5 labelled goat anti-mouse in 3% Marvel, PBS-Tween for 1 hr, washed 3 × PBS-Tween and 2 × PBS and blots dried at 37°C for 10 mins between blotting paper. The blots were scanned on a Typhoon 8600 variable mode imager (Amersham) with fluorescence scan mode, 633 nm excitation laser, 670 nm emission filter, 600 V PMT and 200 μm / pixel scan resolution. The integrated fluorescence intensity volumes of bands on the gel were quantitated using ImageQuant TL software (Amersham). Conversions to protein yield were made by using a calibration curve of purified His-tagged single chain antibody (scFv). Differences between the molecular weight (MW) of the scFv (31 KDa) and each expressed fusion protein were taken into account by multiplying each protein quantitation by the ratio MW construct (KDa) / 31. The numbers were normalised to eliminate blot to blot variation using a His-tagged molecular weight ladder (Qiagen).
Authors' contributions
MRD performed the molecular biology, participated in the bioinformatics, expression screening, quantitation, experimental design and drafted the manuscript. SPS and RLP participated in the expression screening and quantitation. KJV helped with the bioinformatics (database searching, protein domain annotation and primer design). JM participated in the experimental design, coordination and helped to draft the manuscript. All authors approved the final manuscript.
Acknowledgements
We thank Pascal Braun and Josh LaBaer (Harvard Institute of Proteomics, Cambridge, USA) for providing some entry clones containing full length human open reading frames used in this study, Geoff Waldo (Los Alamos National Laboratory, USA) for providing a plasmid containing cycle 3 mutated GFP and John Collins and Ian Dunham (The Wellcome Trust Sanger Institute, UK) for sharing their cDNA isolation protocol. This work was supported by The Wellcome Trust.
Figures and Tables
Figure 1 Expression vector constructs after recombination between the destination and entry plasmids. (A) Schematic representation where shaded and clear boxes indicate translated and untranslated regions respectively. T7 = T7 RNA polymerase promoter, lacO = lac operator, SD = shine dalgarno, H6 or H10 = hexahistidine or decahistidine, attB1 or attB2 = attB recombination sites, ORF = open reading frame, stop = stop codon, fusion = protein fusion (MBP, GFP, GST, Trx, DHFR or Dhfr), V5 = V5 epitope. (B) and (C) DNA sequences of pDEST-N112 and pDESTC102 respectively from T7 RNA polymerase promoter to stop codon.
Figure 2 Effect of N-terminal fusion on protein expression Total (A) and soluble (B) expression for protein 1 – 30 (Table 1) with various N-terminal fusion partners analysed by SDS-PAGE fluorescence western blots as described in Materials and Methods. Expression plasmids employed were (a) pDEST17, (b) pDEST-N110 or pDEST-N112 with either (c) MBP, (d) GFP, (e) GST or (f) Trx inserted between the DraIII and BfrBI sites as shown in Figure 1.
Figure 3 Effect of C-terminal fusion on protein expression Total (T) and soluble (S) expression for protein 1 – 30 (Table 1) with different C-terminal fusion partners analysed by SDS-PAGE fluorescence western blots as Figure 2. Expression plasmids employed were (g) pET-DEST42, (h) pDEST-C101 or pDEST-C102 with either (i) MBP, (j) GST, (k) GFP (l) Trx (m) Dhfr or (n) DHFR inserted between the DraIII and BfrBI sites as shown in Figure 1.
Table 1 Proteins for expression study with selected features
No Proteina Domainb Constructc Organismd Protein Familye MW (Kda) pI Cys % GRAVYf hp_aag Sub-cellular Location LCh CCi
26 CASP2 FL 1–435/435 Hs CARD, Peptidase_C14 48.9 6.3 4.1 -0.30 5 Cytoplasm 1 0
24 CCND2 FL 1–289/289 Hs cyclin, cyclin_C 33.1 4.9 4.1 -0.21 4 Cytoplasm 2 0
29 CD44 FL 1–742/742 Hs Xlink, Pfam-B × 9 81.6 5.0 1.2 -0.77 10 Extra-cellular 9 0
22 CDK2 FL 1–298/298 Hs pkinase 33.9 8.9 1 -0.08 4 Cytoplasm 0 0
23 CDK4 FL 1–303/303 Hs pkinase 33.7 6.6 1.3 -0.17 4 Cytoplasm 0 0
25 CDKN1B FL 1–198/198 Hs CDI, Pfam-B × 2 22.1 6.6 2 -1.26 2 Cytoplasm 0 1
28 CDKN2A FL 1–156/156 Hs ank 16.5 5.4 0.6 -0.23 4 Cytoplasm 0 0
6 Efna1 FL 18–205/205 Mm Ephrin 21.9 6.4 2.1 -0.59 8 Extra-cellular 1 0
7 Efna1 EC 18–154/205 Mm Ephrin 16.2 6.5 2.9 -0.86 2 Extra-cellular 0 0
5 Efnb2 EC1 29–176/336 Mm Ephrin 16.6 5.3 2.7 -0.47 3 Extra-cellular 0 0
4 Efnb2 EC2 29–210/336 Mm Ephrin 20.1 8.6 2.2 -0.64 3 Extra-cellular 0 0
15 EGFR TK 694–1022/1210 Hs Pkinase, Pfam-B 37.3 5.5 1.8 -0.22 3 Cytoplasm 1 0
8 Epha2 LB 24–206/977 Mm EPH_lbd 21.1 4.7 2.7 -0.30 4 Extra-cellular 0 0
1 Ephb2 LB 28–210/994 Mm EPH_lbd 22.5 5.8 2.2 -0.14 4 Extra-cellular 0 0
3 Ephb2 SAM 922–994/994 Mm SAM_1 8.3 4.9 0 -0.03 2 Cytoplasm 0 0
2 Ephb2 TK 595–906/994 Mm Pkinase 35.3 5.6 1.6 -0.27 5 Cytoplasm 0 0
10 Fli1 FL 1–452/452 Mm Ets, SAM_PNT, Pfam-B × 5 51.0 6.6 0.9 -0.79 3 Nuclear 1 0
19 FOS FL 1–380/380 Hs bZIP, Pfam-B × 4 40.7 4.6 2.1 -0.37 5 Nuclear 5 1
9 GATA2 FL 1–480/480 Hs GATA 50.3 9.7 2.7 -0.51 13 Nuclear 7 0
30 GFP FL 1–238/238 Av GFP 26.9 5.6 0.8 -0.52 3 Cytoplasm 0 0
14 GRB2 FL 1–217/217 Hs SH2, SH3 25.2 5.9 0.9 -0.67 5 Cytoplasm 0 0
17 HRAS FL 1–189/189 Hs ras 21.3 5.0 3.2 -0.42 4 Cytoplasm 1 0
18 JUN FL 1–331/331 Hs bZIP, Jun 35.7 9.0 0.9 -0.47 3 Nuclear 3 1
20 MAD FL 1–221/221 Hs HLH, Pfam-B × 2 25.3 8.9 1.4 -0.97 2 Nuclear 3 1
21 MAX FL 1–160/160 Hs HLH, Pfam-B × 2 18.3 5.9 0 -1.32 2 Nuclear 1 1
12 Mdm2 FL 1–489/489 Mm SWIB, zf-RanBP, Pfam-B × 8 54.5 4.5 3.5 -0.83 4 Nuclear / Cytoplasm 5 0
13 Mdm2 p53-bd 19–230/489 Mm SWIB, Pfam-B × 2 11.7 8.8 0.5 -0.25 4 Nuclear / Cytoplasm 3 0
27 MMP1 FL 1–469/469 Hs Peptidase_M10_N, Peptidase_M10, Hemopexin 54.0 6.5 0.6 -0.57 7 Extra-cellular 0 0
16 RAF1 Ras-bd 51–131/648 Hs RBD 9.2 9.9 3.8 -0.30 3 Cytoplasm 0 0
11 Trp53 FL 1–390/390 Mm P53 43.5 7.0 3.1 -0.59 3 Nuclear / Cytoplasm 1 0
aLocusLink symbol. bDomain: LB, ligand binding; TK, tyrosine kinase; SAM, sterile alpha motif; EC, extra-cellular; FL, full-length; bd, binding domain. cConstruct expressed numbered by amino acid position (start – finish / total). dOrganism: Mm, Mus musculus; Hs, Homo sapiens; Av, Aequoria Victoria. eProtein family nomenclature according to the Pfam database . fGRAVY, grand average of hydropathicity index. gHighest number of contiguous hydrophobic amino acids (A, V, I, L, W or F). hLC and iCC, number of low complexity and coiled coil regions according to Pfam database.
Table 2 N-Terminal fusion expression comparison
N-TERMINAL FUSION
Protein (domain) H6 H10 H10-GFP H10-GST H10-Trx H10-MBP
T S T S T S T S T S T S
CASP2 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.20 0.20 2.50 2.11
CCND2 21.85 0.00 12.36 5.81 6.14 0.02 1.20 0.00 12.50 4.35 8.12 3.06
CD44 0.00 0.00 0.00 0.00 0.00 0.00 nc nc 0.00 0.00 0.00 0.00
CDK2 14.81 1.84 1.03 0.07 nc nc 4.88 2.17 2.00 1.54 25.00 25.00
CDK4 8.78 0.71 1.47 1.37 nc nc 1.32 0.00 nc nc 2.79 0.00
CDKN1B 7.44 0.17 0.85 0.31 1.63 0.57 12.00 4.30 4.00 1.69 8.00 5.19
CDKN2A 0.00 0.00 0.00 0.00 0.00 0.00 0.40 0.32 nc nc 3.65 0.00
Efna1 0.00 0.03 1.50 1.33 7.47 0.22 2.29 0.06 nc nc 5.73 3.02
Efna1 (EC) 24.74 0.05 5.00 4.81 50.00 1.71 28.00 0.07 60.00 4.10 11.93 4.93
Efnb2 (EC1) 3.58 0.00 11.53 1.18 10.38 0.86 6.43 1.30 44.70 6.82 22.67 20.00
Efnb2 (EC2) 2.07 0.04 3.00 2.79 50.00 6.50 55.00 0.00 nc nc 17.00 16.00
EGFR (TK) 0.00 0.00 0.21 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
Epha2 (LB) nc nc nc nc 50.00 0.43 2.83 0.05 4.92 1.99 14.57 3.78
Ephb2 (LB) 40.00 0.00 0.66 0.08 4.93 0.14 10.00 0.03 29.39 2.53 20.00 2.11
Ephb2 (SAM) 0.00 0.00 nc nc 50.00 5.63 35.00 11.47 10.06 1.34 0.00 0.00
Ephb2 (TK) 0.00 0.00 65.84 15.00 20.00 0.35 15.00 0.14 50.00 14.23 20.00 4.15
Fli1 3.82 0.05 0.89 0.08 2.00 0.00 1.50 0.00 12.00 5.14 58.00 6.50
FOS 0.25 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.49 0.08 0.27 0.00
GATA2 0.33 0.00 0.19 0.07 2.50 0.04 0.00 0.00 5.00 3.47 0.00 0.00
GFP 60.00 60.00 25.00 25.00 25.00 1.33 22.08 20.00 25.00 25.00 10.00 9.83
GRB2 1.75 0.04 6.00 3.88 25.00 12.82 2.77 0.84 13.00 11.96 18.00 16.00
HRAS 30.10 0.34 6.40 5.59 6.16 0.17 7.37 0.54 26.96 25.00 8.40 7.69
JUN 40.00 0.00 5.84 1.09 2.08 0.00 1.50 0.00 5.70 0.41 30.00 0.22
MAD 32.77 0.15 3.50 1.78 20.66 0.37 15.04 0.13 9.21 4.74 4.00 4.10
MAX nc nc 9.43 1.09 4.44 1.18 0.00 0.03 2.05 2.01 3.00 2.71
Mdm2 0.00 0.00 1.20 0.91 0.00 0.00 0.00 0.00 10.00 5.57 3.60 2.65
Mdm2 (p53-bd) 1.62 0.36 4.75 4.70 20.84 3.20 20.00 0.22 9.54 4.72 12.00 12.00
MMP1 2.56 0.00 0.36 0.10 11.44 0.00 39.63 0.04 0.32 0.32 30.00 0.48
RAF1 (Ras-bd) 15.48 15.00 20.00 20.00 26.92 0.00 20.00 19.82 25.00 25.00 40.00 25.00
Trp53 0.85 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
AVERAGE 11.36 0.81 7.28 3.27 10.53 1.01 9.02 1.37 15.90 6.02 14.87 5.81
Numbers correspond to total (T) or soluble (S) expression yield (mg/l). Yields greater than 2 mg/l are in bold, nc-not cloned.
Table 3 C-Terminal fusion expression comparison
C-TERMINAL FUSION
Protein (domain) V5-H6 H10 GFP-H10 GST-H10 Trx-H10 MBP-H10 Dhfr-H10 DHFR-H10
T S T S T S T S T S T S T S T S
CASP2 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
CCND2 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
CD44 1.37 0.72 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
CDK2 23.47 12.30 1.88 1.30 0.70 0.19 0.53 0.08 0.86 0.16 7.52 3.48 5.82 0.51 0.00 0.00
CDK4 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.13 0.00 0.00 0.00 2.07 0.44 0.00 0.00
CDKN1B 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 1.59 0.61 0.00 0.00 5.76 0.00
CDKN2A 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 2.59 0.56 0.00 0.00 0.48 0.22 0.00 0.00
Efna1 10.64 8.29 0.00 0.00 0.00 0.00 0.00 0.00 0.24 0.03 2.30 1.23 0.00 0.00 5.24 0.56
Efna1 (EC) 1.71 0.46 2.76 0.58 0.25 0.00 19.99 0.24 3.71 0.00 31.94 15.60 14.01 0.00 1.10 1.02
Efnb2 (EC1) 7.91 0.37 0.43 0.04 0.32 0.04 0.56 0.00 2.53 0.00 6.30 0.80 0.00 0.24 5.84 0.00
Efnb2 (EC2) 3.57 0.38 0.52 0.11 0.54 0.06 0.00 0.00 2.08 0.00 3.53 1.72 2.59 0.00 0.00 0.00
EGFR (TK) 1.14 0.00 0.00 0.00 0.00 0.00 0.28 0.00 12.76 1.11 3.03 0.25 2.07 0.00 0.00 0.00
Epha2 (LB) 3.97 0.27 0.00 0.00 0.15 0.00 2.37 0.00 4.33 0.42 19.33 8.60 7.42 0.00 0.00 0.00
Ephb2 (LB) 15.17 7.46 0.00 0.00 0.00 0.00 0.64 0.00 2.34 0.94 11.93 7.93 2.48 0.37 12.65 0.00
Ephb2 (SAM) 0.27 0.03 0.00 0.00 0.29 0.08 43.05 0.00 0.98 0.10 156.52 20.33 33.57 7.37 2.20 0.00
Ephb2 (TK) 43.00 1.89 8.85 0.12 0.00 0.00 205.64 2.13 24.04 0.00 49.99 3.01 27.02 1.06 0.00 0.00
Fli1 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 3.11 0.00 1.10 0.00 0.00 0.00 7.14 0.00
FOS 4.72 0.27 0.22 0.00 0.92 0.49 0.00 0.00 0.11 0.00 1.16 0.62 0.00 0.00 4.35 0.00
GATA2 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
GFP 8.14 1.38 1.11 0.67 12.94 9.86 2.03 2.01 21.50 4.78 118.38 40.52 4.66 2.93 9.56 2.74
GRB2 15.66 5.41 0.71 0.17 3.55 3.02 2.43 1.70 5.78 3.48 110.84 14.48 4.83 3.04 1.30 0.00
HRAS 19.21 6.44 0.29 0.25 11.49 6.46 0.56 0.39 1.06 0.37 12.32 10.07 3.21 3.01 0.75 0.57
JUN 6.75 0.00 0.31 0.00 0.00 0.00 0.48 0.00 1.41 0.00 8.81 0.35 0.00 0.00 7.79 0.00
MAD 9.76 2.62 3.94 5.05 0.49 0.13 1.37 0.00 5.91 3.34 19.02 6.94 1.86 0.13 0.88 0.00
MAX 0.00 0.00 0.00 0.00 0.71 0.53 101.26 80.86 1.59 0.62 9.70 5.17 5.82 3.48 0.94 0.00
Mdm2 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
Mdm2 (p53-bd) 1.10 0.25 0.60 0.14 3.94 0.33 2.50 0.47 69.23 3.03 19.57 3.82 5.46 0.83 0.00 0.00
MMP1 12.51 0.85 0.00 0.00 0.00 0.00 3.27 0.00 0.00 0.00 0.00 0.00 4.29 0.00 0.52 0.00
RAF1 (Ras-bd) 0.48 0.06 0.00 0.00 0.45 0.16 0.96 0.19 2.66 0.71 5.83 1.63 2.08 1.07 0.00 0.00
Trp53 0.52 0.07 0.00 0.00 0.00 0.00 0.23 0.00 3.59 0.91 5.66 2.44 1.40 0.79 2.76 0.00
AVERAGE 6.37 1.65 0.72 0.28 1.23 0.71 12.94 2.94 5.75 0.69 20.21 4.99 4.37 0.85 2.29 0.16
Numbers correspond to total (T) or soluble (S) expression yield (mg/l). Yields greater than 2 mg/l are in bold.
Table 4 Expression levels of mammalian proteins expressed with N-terminal H10-MBP fusions, with selected protein features
No. Protein Accession No. PfamA Domaina Construct MW (kDa) hp_aa LC Total Expression (mg / l) Soluble Expression (mg / l)
31 TAL1 P17542 HLH 179–331/331 16.5 3.0 1.0 13.2 8.3
32 ELF1 P32519 Ets 2–619/619 67.4 6.0 5.0 0.0 0.0
33 ELF1 P32519 nab 2–167/619 18.0 4.0 2.0 14.6 14.6
34 ELF1 P32519 Ets 204–619/619 45.4 6.0 2.0 0.0 0.0
35 ELF1 P32519 na 316–619/619 32.2 3.0 1.0 0.0 0.0
36 ELF1 P32519 Ets 204–306/619 12.1 6.0 0.0 65.0 19.3
37 Elf1 Q60775 Ets 2–612/612 66.1 6.0 4.0 24.0 23.5
38 Elf1 Q60775 Ets 2–306/612 34.0 6.0 3.0 60.6 27.2
39 Elf1 Q60775 na 2–167/612 17.8 4.0 2.0 8.6 8.4
40 Elf1 Q60775 Ets 204–612/612 44.2 6.0 1.0 15.5 10.4
41 Elf1 Q60775 Ets 204–306/612 12.1 6.0 0.0 18.5 12.1
42 Elf1 Q60775 na 316–612/612 30.7 3.0 0.0 0.0 0.0
43 Gata1 P17679 GATA × 2 2–413/413 42.5 3.0 6.0 16.0 14.3
44 Gata1 P17679 GATA × 2 2–319/413 33.8 3.0 5.0 67.0 15.8
45 Gata1 P17679 na 2–182/413 18.6 3.0 3.0 9.7 7.2
46 Gata1 P17679 GATA × 2 191–413/413 23.1 3.0 5.0 19.7 9.6
47 Gata1 P17679 GATA × 2 191–319/413 14.3 3.0 0.0 0.0 0.0
48 Gata2 O09100 GATA × 2 2–480/480 50.3 5.0 5.0 0.0 0.0
49 Gata2 O09100 na 2–189/480 19.4 4.0 2.0 0.0 0.0
50 Gata2 O09100 GATA × 2 275–480/480 22.4 5.0 2.0 0.0 0.0
51 Gata2 O09100 GATA × 2 275–402/480 14.2 1.0 1.0 0.0 0.0
52 Fli1 P26323 SAM_PNT, Ets 2–452/452 50.9 3.0 1.0 0.0 0.0
53 Fli1 P26323 SAM_PNT, Ets 2–363/452 41.7 3.0 0.0 134.5 61.0
54 Fli1 P26323 SAM_PNT 2–198/452 22.2 3.0 0.0 121.2 86.8
55 Fli1 P26323 SAM_PNT, Ets 114–452/452 38.6 3.0 1.0 61.0 38.2
56 Fli1 P26323 SAM+ETS 114–363/452 29.4 3.0 0.0 96.5 73.1
57 Fli1 P26323 SAM_PNT 114–196/452 10.0 3.0 0.0 71.8 51.7
58 Fli1 P26323 Ets 280–452/452 20.1 3.0 1.0 28.6 16.3
59 Fli1 P26323 Ets 280–363/452 10.9 3.0 0.0 23.4 23.0
60 Lmo2 P25801 LIM × 2 2–158/158 18.2 3.0 0.0 106.7 23.8
61 Ldb1 P70662 LIM_bind 2–375/375 42.6 3.0 2.0 0.0 0.0
62 Ldb1 P70662 LIM_bind 2–273/375 31.9 3.0 0.0 133.8 62.0
63 Ldb1 P70662 LIM_bind 275–375/375 10.5 2.0 1.0 2.0 1.7
64 Lyl1 P27792 HLH 40–278/278 26.2 3.0 0.0 3.8 2.6
65 Lyl1 P27792 HLH 40–215/278 19.6 3.0 0.0 4.2 2.5
66 Lyl1 P27792 na 40–135/278 10.3 3.0 0.0 3.6 1.7
67 Lyl1 P27792 HLH 150–278/278 14.8 3.0 0.0 40.1 32.2
68 Lyl1 P27792 HLH 150–215/278 8.2 3.0 0.0 60.3 20.8
69 Ttr P07309 transthyretin 20–147/147 13.6 5.0 0.0 59.2 49.7
70 Pin1 Q9QUR7 WW, Rotamase 2–163/163 18.2 2.0 0.0 36.9 19.4
71 Whsc1 Q7TSF5 PHD × 2, PWWP, SET 2–558/558 63.8 4.0 2.0 9.4 1.3
72 Whsc1 Q7TSF5 PHD, PWWP, SET 2–373/558 43.0 4.0 0.0 21.6 12.4
73 Whsc1 Q7TSF5 PHD, PWWP 2–149/558 17.2 4.0 0.0 0.0 0.0
74 Whsc1 Q7TSF5 PWWP, SET, PHD 70–558/558 56.1 4.0 2.0 5.1 2.1
75 Whsc1 Q7TSF5 PWWP, SET 70–373/558 35.3 4.0 0.0 18.2 17.9
76 Whsc1 Q7TSF5 PWWP 70–149/558 9.5 4.0 0.0 56.8 14.6
77 Whsc1 Q7TSF5 SET 249–373/558 14.3 4.0 0.0 34.7 22.6
78 Maat1 NM_024227 na 2–257/257 30.0 3.0 0.0 2.7 2.4
79 BC031407 NM_145596 na 2–630/630 67.3 6.0 6.0 0.0 0.0
80 BC031407 NM_145596 na 2–455/630 48.5 6.0 5.0 0.0 0.0
81 BC031407 NM_145596 na 2–179/630 19.4 4.0 1.0 9.6 8.7
82 BC031407 NM_145596 na 178–630/630 48.0 6.0 5.0 0.0 0.0
83 BC031407 NM_145596 na 178–455/630 29.1 6.0 4.0 0.0 0.0
84 BC031407 NM_145596 na 413–630/630 23.7 4.0 1.0 0.0 0.0
85 Bzrp2 P50637 TspO_MBR 2–169/169 18.7 4.0 0.0 0.0 0.0
86 MGC19339 NM_145954 Aldedh 40–486/803 47.0 5.0 2.0 37.2 18.7
87 Bsg NM_009768 Ig × 2, V-set 28–323/389 32.4 4.0 0.0 61.2 36.6
88 Snx15 NM_026912 PX, MIT 2–337/337 37.6 4.0 1.0 32.4 32.0
89 Snx15 NM_026912 PX 2–226/337 25.6 4.0 1.0 20.0 18.8
90 Atp2b2 Q9R0K7 Cation_ATPase_N 2–94/1198 10.4 2.0 0.0 42.1 24.7
91 Atp2b2 Q9R0K7 Cation_ATPase_N 1039–1198/1198 17.9 2.0 3.0 6.1 5.0
92 cdh23 Q99PF4 Cadherin 33–132/3354 11.1 4.0 0.0 174.7 38.8
93 Myo15 Q9QZZ4 SH3_2 2847–2937/3511 9.8 4.0 0.0 8.6 0.0
94 Myo7a P97479 SH3_1 1602–1672/2215 7.8 4.0 0.0 76.0 35.4
95 tmc1 Q8R4P5 na 2–193/757 22.9 3.0 3.0 0.0 0.0
96 Trvp4 NM_022017 na 500–718/871 24.6 9.0 0.0 0.0 0.0
97 Whrn XM_196324 PDZ 811–908/908 11.0 3.0 0.0 34.1 31.1
98 Espn NM_019585 WH2 2–253/253 28.0 3.0 2.0 5.5 3.8
99 Map2 P20357 Tubulin-binding 1657–1755/1828 10.6 2.0 0.0 47.1 46.8
100 Prom O54990 Prominin 124–162/867 4.3 2.0 1.0 16.3 8.5
101 GluR1 P23818 ANF_receptor 19–538/907 59.0 4.0 0.0 0.0 0.0
102 GluR2 P23819 ANF_receptor 22–545/883 58.6 4.0 0.0 0.0 0.0
103 Grin1 P35438 na 834–938/938 12.0 5.0 0.0 13.8 13.8
104 Grin2a P35436 na 23–555/1464 59.9 6.0 0.0 58.5 8.2
105 Grin2b Q01097 Lig_chan 656–817/1482 18.1 4.0 0.0 0.0 0.0
106 Dlgh2 NM_011807 PDZ 419–530/852 11.7 4.0 0.0 13.8 13.7
107 Dlgh4 Q62108 PDZ 311–394/724 8.7 4.0 0.0 37.4 29.2
108 Dlgh3 P70175 PDZ 402–509/849 11.7 5.0 0.0 0.0 0.0
109 Dlgh1 U93309 PDZ 432–572/927 15.1 5.0 0.0 26.7 23.4
110 Syngap1 XM_139847 RasGAP 405–615/1318 23.9 3.0 0.0 0.0 0.0
111 Grip1 Q925T5 PDZ 1–112/1034 9.6 3.0 0.0 0.0 0.0
112 Homer1 Q9Z2Y3 WH1 2–107/354 12.1 3.0 0.0 17.9 17.6
113 Homer3 Q99JP6 WH1 2–110/356 39.3 4.0 0.0 0.0 0.0
114 TtyhI Q9EQN7 na 263–450/450 20.6 5.0 0.0 35.6 28.3
115 1500001H12RIKEXT2 NM_021316 na 2–149/149 14.8 5.0 3.0 66.1 66.1
116 Ext2 NM_010163 na 99–392/718 33.0 4.0 0.0 18.5 3.5
117 KIAA1136 Q9ULT3 na 45–214/597 19.2 2.0 0.0 26.8 10.4
118 G2 Q12914 na 1046–1692/1692 71.3 5.0 3.0 0.0 0.0
119 KIAA1549 Q9HCM3 na 184–464/1865 29.5 5.0 4.0 3.5 0.0
120 Nfkb1 P25799 RHD 39–365/971 36.7 5.0 0.0 27.1 22.7
121 Nfkb1 P25799 RHD, TIG, Ank × 6, Death 2–971/971 105.5 7.0 3.0 0.0 0.0
122 RelA-p65 Q04207 RHD, TIG 18–306/549 32.9 4.0 0.0 24.1 18.2
123 RelA-p65 Q04207 RHD, TIG 2–549/549 60.0 5.0 2.0 0.0 0.0
124 RelB Q04863 RHD, TIG 102–418/558 35.8 4.0 0.0 46.0 25.9
125 myog P12979 HLH, Basic 2–224/224 25.1 3.0 1.0 25.8 12.4
Features listed as Table 1 except: aPfamA domains contained within expressed protein and bna – no PfamA domains annotated.
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| 15598350 | PMC544853 | CC BY | 2021-01-04 16:02:57 | no | BMC Biotechnol. 2004 Dec 14; 4:32 | utf-8 | BMC Biotechnol | 2,004 | 10.1186/1472-6750-4-32 | oa_comm |
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Nutr Metab (Lond)Nutrition & Metabolism1743-7075BioMed Central London 1743-7075-2-11563435510.1186/1743-7075-2-1ReviewTranscriptional regulation of lipid metabolism by fatty acids: a key determinant of pancreatic β-cell function Fatehi-Hassanabad Zahra [email protected] Catherine B [email protected] Department of Biomedical Sciences, University of Prince Edward Island, 550 University Avenue, Charlottetown, PE C1A 4P3 Canada2005 5 1 2005 2 1 1 20 10 2004 5 1 2005 Copyright © 2005 Fatehi-Hassanabad and Chan; licensee BioMed Central Ltd.2005Fatehi-Hassanabad and Chan; 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
Optimal pancreatic β-cell function is essential for the regulation of glucose homeostasis in both humans and animals and its impairment leads to the development of diabetes. Type 2 diabetes is a polygenic disease aggravated by environmental factors such as low physical activity or a hypercaloric high-fat diet.
Results
Free fatty acids represent an important factor linking excess fat mass to type 2 diabetes. Several studies have shown that chronically elevated free fatty acids have a negative effect on β-cell function leading to elevated insulin secretion basally but with an impaired response to glucose. The transcription factors PPARα, PPARγ and SREBP-1c respond to changing fat concentrations in tissues, thereby coordinating the genomic response to altered metabolic conditions to promote either fat storage or catabolism. These transcription factors have been identified in β-cells and it appears that each may exert influence on β-cell function in health and disease.
Conclusion
The role of the PPARs and SREBP-1c as potential mediators of lipotoxicity is an emerging area of interest.
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Introduction
Fatty acids are physiologically important both structurally, as components of phospholipids and glycolipids, as well as functionally, as fuel molecules. Metabolites of fatty acids, such as leukotrienes or prostaglandins, act as potent mediators in many biological processes. Fatty acids provide energy [1,2], particularly in the fasted state (Figure 1), but abnormalities in the metabolism of fatty acids can contribute to the pathogenesis of obesity and type 2 diabetes.
Figure 1 Schematic diagram of fatty acid metabolism in the fasted state. Counter-regulatory hormones such as catecholamines act on adipocytes to increase lipolysis via hormone-sensitive lipase (HSL). Circulating FFA enter the cell and are converted to acyl CoAs, catalyzed by acyl CoA synthase (ACS). Acyl CoA enter the mitochondria via carnitine palmitoyl transferase-I (solid square) and enter the β-oxidation cycle (stippled circle) to produce acetyl CoA that is then available for further metabolism in the TCA cycle, leading to increased ATP and substrates for anaplerosis. In the β-cell, acyl CoA also participate as signalling molecules to promote insulin secretion (see text).
Type 2 diabetes and free fatty acids
Diabetes affects 6 % of the adult population and, with a growth rate of 6% per year, it is estimated that 200 to 300 million people worldwide will be afflicted by the end of this decade [3]. Type 1 diabetes, which accounts for < 10 % of all cases of diabetes [4], results from autoimmune-mediated destruction of pancreatic β-cells. The destruction may occur over months to years and can result in complete loss of the endogenous insulin supply and therefore results in exogenous insulin dependency.
Type 2 diabetes, which accounts for 90 to 95 % of diabetes cases worldwide, is a heterogeneous disorder and its prevalence is rising. Type 2 diabetes is accompanied by chronic insulin resistance and a progressive decline in β-cell function [5]. Obesity is a major risk factor for the development of type 2 diabetes [6] and is believed to confer increased risk through obesity-associated insulin resistance [7]. Type 2 diabetes is often associated with hypertriglyceridemia or increased circulating concentrations of free fatty acids (FFA) [8]. Therefore, type 2 diabetes can be considered a lipid disorder as well as a disease of glucose intolerance [9].
Metabolism of fatty acids in the beta cell and insulin secretion
Fatty acids, not glucose, are the major endogenous energy source for unstimulated islets [10]. This is consistent with the observation that although islets contain little glycogen, they maintain high rates of oxygen consumption in the absence of glucose [11]. Stimulation of islets by glucose diminishes fatty acid oxidation and increases total respiration [12]. Thus, rising post-prandial plasma glucose shifts the β-cells from fatty acids to glucose as an oxidative fuel. However, plasma concentrations of other nutrients such as FFA and amino acids can modulate the process of glucose-induced insulin secretion [9]. The plasma levels of nutrient metabolites vary with dietary composition. Thus, feeding behavior plays an important role in the control of islet β-cell function [13,14].
Short-term (2–6 hours) elevation of the plasma FFA concentration in human subjects [15] and animals [16,17] enhances while an acute decrease inhibits glucose-stimulated insulin secretion [15,18]. Following lipid infusion or ingestion of a mixed meal, the plasma FFA concentration rises and FFA diffuse into the β-cells [19]. Within the cytosol, fatty acids are converted to their fatty acyl CoA derivatives (Figure 1), which in turn augment insulin secretion via different signalling mechanisms: increased formation of phosphatidic acid and diacylglycerol, which directly and indirectly (through activation of protein kinase C) enhance exocytosis of insulin stored within secretory granules; stimulation of endoplasmic reticulum Ca2+-adenosine triphosphatase, leading to an increase in intracellular calcium concentration and augmentation of insulin secretion; and closure of the K+- ATP channel with resultant depolarization of the β-cell membrane, which causes an increase in intracellular Ca2+ and stimulation of exocytosis of insulin-containing granules [21]. In addition to being oxidized, glucose can be metabolized through anaplerotic processes to increase malonyl CoA concentrations in the β-cell. Malonyl CoA inhibits CPT-I, thus impairing the transport of fatty acyl CoAs into the mitochondria where they would be oxidized [20,21]. The fact that de novo fatty acid synthesis in the β-cell is very low [22] indicates that malonyl-CoA is used as a switch compound, not as a precursor or effector molecule like long chain fatty acyl-CoA. The cytosolic concentration of long chain fatty acyl-CoA is controlled by feedback inhibition of acyl-CoA synthetase, and is buffered by fatty acid and long chain fatty acyl-CoA binding proteins [23]. The total concentration of long chain fatty acyl-CoA in livers of fed and fasted rats, is about 95 and 220 nmol/g dry weight, respectively [24], however quantification of cytosolic long chain fatty acyl-CoA in other tissues has yet to be done.
In contrast to the acute effect of elevated plasma FFA to enhance insulin secretion, longer-term (> 48 h) exposure results in an impaired β-cell response to glucose both in vitro and in vivo in animals [25,26] and humans [27-31]. The inhibitory effect of chronically elevated plasma FFA is more prominent in individuals with a genetic predisposition to develop type 2 diabetes [32], thus a reduction in the plasma FFA concentration in type 2 diabetes improves insulin secretion [32,33]. The term lipotoxicity describes the deleterious effect of chronic FFA elevation on insulin secretion from the pancreatic β-cell [34]. In the Zucker diabetic fatty rat, chronically increased plasma FFA levels lead initially to a physiological impairment in insulin secretion. With time, β-cell mass is reduced by more than 50 % [26]. Within the β-cell, elevated fatty acyl CoAs increase the formation of ceramide, a sphingolipid. Ceramide, in turn, augments the formation of the inducible isoform of nitric oxide, which is toxic to the β-cell [35]. Incubation of human islets with FFA or ceramide has been shown to cause β-cell apoptosis [36].
Transcriptional regulation of free fatty acid metabolism
Free fatty acid metabolism responds to varying metabolic states partially by induction of enzymes that promote either catabolic or anabolic processes. There are two major classes of transcriptional regulators of enzymes involved in fatty acid metabolism, the peroxisome proliferator-activated receptors (PPARs) and the sterol regulatory element binding proteins (SREBPs), which both exist in several isoforms. In general, PPARγ and SREBP-1c regulate processes involved in lipogenesis whereas lipolytic enzymes are induced by PPARα [37].
Peroxisome proliferator-activated receptors
The PPARs form a subfamily in the nuclear receptor superfamily. PPARs, like other nuclear receptors, regulate gene expression in response to specific ligands through their actions as transcription factors. Peroxisomes contain PPAR-regulated enzymes involved in fatty acid β-oxidation [38]. Genetic deficiencies in peroxisome biogenesis in the human cause an accumulation of long chain fatty acids in cells [39]. So far, three isoforms encoded by separate genes and designated PPARα, PPARδ and PPARγ have been identified [40].
PPARα
PPARα was the first member of this nuclear receptor subclass to be described. PPARα is expressed in numerous metabolically active tissues including liver, kidney, heart, skeletal muscle, brown fat [41-43], and also in monocytes [44], vascular endothelium [45] and vascular smooth muscle cells [46].
PPARα plays an important role in the regulation of cellular uptake, activation and β-oxidation of fatty acids. The natural, preferentially-binding ligands of PPARα are long chain unsaturated fatty acids including arachidonic acid, linoleic acid, and oleic acid but saturated fatty acids like palmitic acid can also act as ligands [47]. In hepatocytes and other tissues where it has been studied, ligand-activated PPARα binds to peroxisome proliferator response elements (PPRE) of DNA (Figure 2) and up-regulates transcription of genes involved in lipid catabolism and lipoprotein metabolism (Table 1) [48,49]. Consequently PPARα serves as a long chain fatty acid sensor that leads to autoregulation of long chain fatty acid metabolism mainly in the liver and heart and to a lesser extent in muscle, thus decreasing tissue content of lipids and minimizing lipotoxicity as circulating levels fluctuate [50]. Activation of PPARα also induces hepatic proliferation, hepatomegaly and hepatocarcinogensis in animal [51] but not human liver [52]. Obesity is a major risk factor in the development of type 2 diabetes and PPARα may affect body weight through regulation of fatty acid catabolism or expending energy [53]. PPARα ligands (such as fibrate drugs) could therefore improve insulin sensitivity by reducing lipid accumulation in tissues [54].
Figure 2 Overview of PPAR activation and effects. FFA (eg. oleic acid) interact with PPAR, which dimerize with retinoid X receptor (RXR) and translocate to the nucleus where the complex interacts with PPRE to activate gene transcription. The general effects of transcriptional activation of PPARα, PPARδ and PPARγ are shown on the right of the figure.
Table 1 Selected hepatic PPARα regulated genes with at least one functional peroxisome proliferator receptor element (PPRE) identified within the promoter sequence
Gene Function Species References
Acyl CoA binding protein fatty acyl-CoA ester transport rat 127
Acyl CoA oxidase peroxisomal β-oxidation rat, human 128-130
Apolipoprotein-AI and AII plasma HDL metabolism human, mouse, rat 131-134
Apolipoprotein-AV plasma triglyceride metabolism human 134
Apolipoprotein-CIII plasma HDL metabolism rat 135
Bifunctional enzyme peroxisomal β-oxidation rat 136
Carnitine palmitoyl transferase-I and -II mitochondrial β-oxidation human, mouse, rat, hamster 132, 137-139
Cytochrome P450 enzymes fatty acid and cholesterol metabolism rat, mouse, human 130, 141-145
Δ6- and Δ5-desaturase desaturation of fatty acyl-CoA mouse 146
Fatty acid binding protein fatty acid binding/transport mouse 147
Fatty acid transport protein and translocase fatty acid transport mouse 148, 149
Lipoprotein lipase triglyceride clearance mouse 148, 149
Liver X receptor α cholesterol metabolism mouse 150, 151
Long-chain acyl-CoA synthetase fatty acid activation human, mouse 139, 152
Malic enzyme fatty acid synthesis mouse, rat 153, 154
Mitochondrial HMG-CoA synthase ketogenesis rat, human 152, 155
Medium-chain acyl-CoA dehydrogenase mitochondrial β-oxidation mouse 138, 139
Phospholipid transfer protein HDL metabolism human 156
Stearoyl-CoA desaturase-1 desaturation of fatty acyl CoA mouse 157
Superoxide dismutase free radical metabolism rat 158
Thiolase B mitochondrial β-oxidation rat 159
Transferrin iron transport human 160
Very long- and long-chain acyl CoA dehydrogenases mitochondrial β-oxidation mouse 139
Abbreviations: HDL, high density lipoprotein; HMG-CoA, hydroxymethylglutaryl-Coenzyme A
PPARδ
PPARδ was initially reported as PPARβ in Xenopus laevis [49]. Subsequently, the receptor was cloned in the human [55] as well as in rodents [56] and was named PPARδ. PPARδ is expressed in a wide range of tissues and cells with the highest levels of expression found in digestive tract, heart, kidney, liver, adipose and brain [57]. Saturated and unsaturated fatty acids are natural ligands for PPARδ [58,59]. PPARδ is implicated in adipocyte differentiation, which is induced by long-chain fatty acids [60]. In skeletal muscle, activation of PPARδ results in induction of proteins involved in lipid catabolism, cholesterol efflux and respiratory coupling in skeletal muscle independent from the effects of PPARα and PPARγ agonists [61].
PPARγ
PPARγ stimulates fatty acid storage in adipose tissue by increasing both the storage capacity and the fatty acid flux into adipocytes. PPARγ is expressed in many cell types, including epithelial cells, B and T cells, macrophages, endothelial cells, smooth muscle cells [62,63] and predominantly in adipose tissue where it is necessary for the differentiation of adipocytes [64]. There are 2 splice variant of the isoform called PPARγ1 and γ2; the expression distribution of PPARγ2 is more limited than that of PPARγ1 [65].
The natural ligands of PPARγ are several unsaturated fatty acids such as oleate, linoleate, eicosapentaenoic and arachidonic acids [53]. Members of the thiazolidinedione (TZD) family, which are known as antidiabetic compounds, are synthetic ligands of PPARγ [54]. In adipocytes, PPARγ increases the expression of numerous genes involved in lipid metabolism and uptake [66,67]. Activation of PPARγ also induces adipocyte apoptosis, which is restricted primarily to large fully differentiated adipocytes [68]. This pro-apoptotic effect of PPARγ activation on large adipocytes, coupled with its capacity to enhance differentiation of adipocytes de novo, favours the formation of small adipocytes that tend to replace the large adipocytes normally constituting white adipose tissue [68].
PPARγ also negatively regulates transcription of several genes that impair insulin action, including tumor necrosis factor-α (TNFα) and leptin, proinflammatory cytokines produced by adipocytes and associated with insulin resistance [69-72]. Thus, the TZD drugs lower hyperglycemia, hyperinsulinemia and hypertriglyceridemia by indirectly enhancing the sensitivity of tissues to insulin, especially in skeletal muscle. However, the function of PPARγ is not restricted to adipogenesis and insulin sensitization. In peripheral monocytes and macrophages, PPARγ agonists inhibit the production of inflammatory cytokines [73] and induce differentiation and apoptosis in various cancer cells [74,75].
Peroxisome proliferator-activated receptors and β-cell function
Both PPARα and PPARγ have been detected in pancreatic β-cells [76,77]. One caveat that complicates interpretation of some of the work described below is that PPARα and PPARγ agonists have effects on β-cell function independent of their interaction with the transcription factors. Thus, both fibrates and TZD can alter ATP-dependent K channel activity and rapidly (within 10 minutes) increase insulin secretion [78]. In addition, the generalized metabolic effects of these compounds may mean that effects observed in vitro on isolated islets may not apply to the in vivo situation. Therefore, the mode of delivery of the agents (directly onto islets versus in diets) and the time frame of study are important considerations.
In pancreatic islets, exposure to long chain fatty acids (mixed unsaturated and saturated) induces PPARα expression [76] whereas high glucose in vitro or hyperglycemia in vivo suppresses expression [79,80]. Artificial ligands of PPARα such as WY14643 and clofibrate also induce PPARα expression in rat islets [76,81,82]. Similar to hepatocytes, this leads to up-regulation of enzymes favouring lipolysis, including acyl-CoA oxidase [76,81], pyruvate dehydrogenase-4 [82] and CPT-I [76,81].
The question arises as to the role of PPARα in the physiological regulation of insulin secretion. Its induction by long chain fatty acids and its ability to augment the insulin response to low glucose [81] suggests that it may play a role in sustaining β-cell secretory capacity during normal, cyclical periods of fasting. Thus, when glucose is low, PPARα will be induced, favouring β-oxidation of lipids to maintain β-cell ATP at a maintenance level. Moreover, the ability of β-cells to oxidize lipids is a critical for resumption of glucose-stimulated insulin secretion at the end of the fasting period [17]. However, when glucose is elevated above basal, PPARα will be reduced, allowing efficient glucose metabolism-dependent insulin secretion while inhibiting fatty acid oxidation. Overall, the effect of oscillating PPARα activity inversely with glucose concentration may help to maintain glucose responsiveness of the β-cell [83]. Four-to-six-hour fasted PPARα KO mice had normal circulating insulin [84,85] and their islets had normal glucose sensitivity [84] whereas 24 hour fasted mice had a 3-fold increase in circulating insulin [85]. The longer-term fast would allow for greater adaptation to occur; higher fasting insulin may reflect hepatic insulin resistance rather than altered β-cell function.
In addition to these postulated mechanisms of PPARα control over β-cell glucose and lipid metabolism, it has also been proposed that amino acid metabolism might be affected. In the liver, an increase in PPARα is associated with a decrease in amino acid catabolism [86]. Because glutamine metabolites are potential signaling molecules in the β-cell [87], PPARα induction under conditions of low glucose could impair glucose-stimulated insulin secretion via its effects on glutamine catabolism [83]. This hypothesis has yet to be proven.
In pathophysiological conditions involving deranged glucose and lipid metabolism, altered expression of PPARα may be important in the β-cell's lack of glucose responsiveness. In Zucker diabetic fatty rat islets, despite chronic hyperlipidemia, expression of PPARα, acyl-CoA oxidase and CPT-I mRNA is reduced [88]. It has thus been proposed that glucose is the dominant regulator of PPARα in the β-cell and that its suppression is a component of glucolipoxicity [89].
Glucolipotoxicity is a state in which β-cells are exposed to elevated plasma concentrations of both glucose and FFA, as is the case in insulin resistance. Several signalling pathways of the β-cell may be affected by altered PPARα expression and the overall outcome is predicted to depend upon whether fat or glucose has the dominant effect. In cases where glucose is elevated relative to lipid, a chronic reduction in PPARα would be expected to decrease the lipid oxidizing capacity of the β-cell [88,89], eliminating a detoxification route for fat metabolites [89]. Accumulation of lipids, for example as triglyceride within the β-cell, is associated with impaired glucose-stimulated insulin secretion, increased ceramide formation and apoptosis [88]. When lipid is chronically elevated relative to carbohydrate, induction of PPARα presumably would cause strong up-regulation of fat oxidizing genes but also UCP2 (see below), which would suppress glucose-stimulated insulin secretion. The implication of these hypotheses is that either too much or too little PPARα would impair β-cell function. Evidence in the literature supports this contention when in vitro models are employed. Notably, culture of islets or INS-1 cells with high glucose (6–20 mM) for 48 hours strongly suppresses PPARα protein expression by 80%. As predicted, fatty acid oxidation and glucose-stimulated insulin secretion are attenuated, while islet triglyceride and lipid esterification are increased [79]. Conversely, induction of endogenous β-cell PPARα (with clofibrate) leads to an increase in CPT-I expression and fatty acid oxidation, resulting in blunted basal and glucose-stimulated insulin secretion [90]. However, the situation is less clear when experiments are performed in vivo, leading to the conclusion that activation of PPARα in tissues other than β-cells causes indirect effects on insulin secretion secondary to changes in peripheral insulin sensitivity [83]. Thus, type 2 diabetic mice given dietary WY14,643, a PPARα agonist, have normalized serum lipids, glucose and insulin. PPARα activation also improves glucose-stimulated insulin secretion, reduces β-cell proliferation and β-cell mass compared with untreated controls [91]. Similarly, fenofibrate-treated obese diabetes-prone OLETF rats retain β-cell mass and have lower islet triglyceride content and fatty oxidation than untreated animals [92]. In both cases, the effects on β-cells are likely secondary to the observed weight loss and increase in insulin sensitivity of peripheral tissues.
Chronic induction of PPARα may influence also insulin secretion indirectly because PPRE have been found in the promoter region of uncoupling protein-2 (UCP2) [93]. In general, uncoupling proteins (numbered 1–3 in order of their discovery) decrease metabolic efficiency by dissociating ATP synthesis from substrate oxidation in the mitochondrion by promoting translocation of protons from the inter-membrane space, across the inner mitochondrial membrane to the matrix [94]. Therefore, circumstances that limit mitochondrial proton gradient formation, such as up-regulation of UCP2 expression and activity, are predicted to limit insulin secretion. A study specifically examining the role of PPARα by use of the ligand clofibrate demonstrated induction UCP2 in islets [90]. In liver, stimulation of PPARα (or PPARδ when PPARα was absent) caused induction of UCP2 [95]. UCP2 expression inversely correlates with β-cell ATP and glucose-stimulated insulin secretion [96-99]. The significance of these findings is that up-regulation of UCP2 expression suppresses glucose-stimulated insulin secretion and is implicated as a potential contributor to lipotoxic effects mediated by PPARα in β-cells.
PPARγ may also be an important transcriptional regulator of both normal and abnormal metabolism in pancreatic β-cells. In hyperglycemic, pancreatectomized rats the expression of PPARγ mRNA is increased [80] but others found that high fat but not high glucose up-regulates PPARγ protein expression in vitro [100]. In adipocytes, PPARγ alters the expression of fat metabolizing enzymes to increase FFA uptake into storage while simultaneously preventing the release of FFA [66,67]. However, in the β-cell some actions of PPARγ seem to mimic those of PPARα. Thus, one of the earliest demonstrations in islets of direct activation of PPARγ showed that TZD caused mobilization of triglyceride and increased FFA oxidation in Zucker diabetic fatty rats [101], resulting in improved insulin secretion [101,102]. This observation has been reinforced in more recent work. Induction of PPARγ by three different methods enhances expression of genes that participate in fatty acid oxidation [103]. Glucose-stimulated insulin secretion is enhanced by both PPARα and -γ agonists in db/db mice [104]. Consistent with this, mice with a partial global knockdown of PPARγ (PPARγ+/-) on a high fat diet had blunted glucose-stimulated insulin secretion in isolated islets that was associated with an islet-specific accumulation of triglyceride [105] even though insulin resistance was partially prevented [106].
The TZD increase glucokinase and GLUT2 expression and activity via interaction with PPRE in the respective gene promoters [107,108]. A PPARα-agonist also induced GLUT2 expression in islets but the effect on glucokinase was not documented [109]. Improved glucose metabolism, however, has not been a consistent outcome of PPARγ induction [103]. Nonetheless, overexpression of PPARγ in a β-cell line is detrimental to glucose-stimulated insulin secretion and proinsulin synthesis, with PPARγ agonists causing a further negative effect [110]. Since PPARγ was not detected in control cells, it is unclear whether these results are physiologically relevant to primary β-cell function. Interestingly, in rodent islets PPARα is expressed at higher levels than PPARγ [76], while in human islets the situation is reversed [111]; the functions regulated by PPARα in rodents may be more pertinent to PPARγ in human β-cells.
PPARγ activation also regulates some β-cell functions that have not been ascribed to PPARα. PPARγ activation by TZD may relieve oxidative stress in β-cells of diabetic animals [112], leading to preservation of β-cell mass [104,112-114] and partial improvement in glucose-stimulated insulin secretion from isolated islets [112]. The potential anti-oxidative or anti-inflammatory effects of TZD in islets of type 2 diabetes models are interesting in light of reports that TZD reduce diabetes incidence in non-obese diabetic (NOD) mice [115] and more generalized inflammatory/immune responses in a variety of tissues [116]. Moreover, PPARγ appears to be a critical determinant of β-cell expansion in response to a high fat diet [117]. However, despite these studies showing that PPARγ exists in β-cells and that its activation can regulate gene expression and cell function, Rosen et al. [117] recently showed that the TZD's antidiabetic effects are still fully present in mice in which PPARγ has been specifically eliminated only in β-cells. Thus, the dominant effects of dietary TZD on insulin secretion are likely indirect, a consequence of improved lipemia and glycemia.
Sterol regulatory element binding protein
The family of SREBPs governs transcriptional activation of a large number of genes involved in regulation of lipid metabolism, including lipogenesis, cholesterol transport and synthesis [118]. Of interest is the high expression of SREBP-1c in liver and adipose tissue [119], and its detection in pancreatic β-cells [120]. The primary function of SREBP-1c is to regulate transcription of genes involved in lipogenesis, such as acetyl-CoA carboxylase, fatty acid synthase and steroyl-CoA desaturase [119] and enzymes of glycolysis [118,119]. In the liver SREBP-1c appears to mediate the transcription of most insulin-responsive genes and in turn its expression, and possibly its activation, are induced by insulin [119]. Thus, SREBP-1c activity is enhanced during periods of dietary plenty; when glucose is abundant and insulin is stimulated. The outcome of SREBP-1c activation is to promote fat-sparing, leading to an increased synthesis of saturated and monounsaturated fatty acids, triglycerides and phospholipids, as well as enhanced glucose utilization via the glycolytic pathway [119]. Elevation of SREBP-1c in obesity characterized by hyperinsulinemia may therefore explain the onset of fatty liver.
SREBP-1c appears to have a similar function in lipogenesis in pancreatic β-cells as in hepatocytes, but the effects on glycolytic enzymes have received little attention. Notably, blockade of SREBP-1c expression attenuates the glucose-induced increase in acetyl-CoA carboxylase activity seen in control β-cells [121] whereas an increase in SREBP-1c induces lipogenic enzymes, triglyceride accumulation and UCP2 expression [105,122-124]. The outcome of elevated SREBP-1c is a decrease in glucose metabolism and glucose-stimulated insulin secretion in all cases. Consistent with these studies utilizing molecular manipulation of SREBP-1c expression, studies of Zucker diabetic fatty rats demonstrate increased SREBP-1c levels in islets [120]. SREBP-1c has also been implicated as a regulator of apoptosis in β-cells [122]; thus the loss of β-cell mass seen in obese-diabetic models might be related to events triggered by this transcription factor. Indeed, β-cell apoptosis might be under control of both PPARγ and SREBP-1c because TZD has been reported to block the increase in SREBP-1c in diabetic fatty rats [120]; this implies that PPARγ regulates SREBP-1c. Conversely, other groups have evidence that SREBP-1c can up-regulate PPARγ mRNA expression [103,123] ; thus, the relationship between these two factors is not yet clear. The UCP2 promoter has a sterol response element [124] so the negative effects of SREBP-1c on insulin secretion might be caused by its induction of UCP2. However, reducing UCP2 expression by means of a small interfering RNA only partially restored glucose-stimulated insulin secretion in SREBP-1c-overexpressing cells. Likewise, activation of the AMP-activated kinase partially rescued the phenotype of the cells with SREBP-1c induction [125]. Certainly, SREBP-1c is implicated as a key contributor to lipotoxicity, as proposed elsewhere [89,126] but further research is required to fully understand its role in regulating insulin secretion in health and diabetes.
Conclusions
FFA exert dual effects on insulin secretion, dependent on the duration of exposure. Acute exposure to FFA increase glucose-stimulated insulin secretion whereas chronic exposure attenuate glucose sensitivity of pancreatic β-cells. The coordinated control of these processes by lipid-sensing transcription factors and its relevance to β-cell dysfunction in type 2 diabetes mellitus is increasingly a subject of investigation.
PPARs (especially PPARα and PPARγ) are involved in the long-term regulation of lipid metabolism and their activity is modulated by endogenous lipid-derived ligands. PPAR agonists have positive effects on glucose homeostasis and lipid metabolism and can reduce cardiovascular events in obese-diabetic patients. PPARα is a fasting lipid oxidation-glucose sparing regulator whereas PPARγ is post-prandial lipid storing-glucose utilizing regulator. In islets, however, both PPARα and -γ appear to have some functions more consistent with PPARα, particularly induction of lipid oxidizing enzymes, which is potentially particularly important for maintaining basal insulin secretion. Growing evidence suggests that PPARγ is a regulator of β-cell proliferation and that PPARγ agonist-mediated anti-oxidative effects may also contribute to anti-diabetic activity.
SREBP-1c up-regulates lipogenic enzymes in β-cells as it does in liver. Its chronic induction in islets of obese-diabetic rodents may therefore contribute to lipotoxicity by promoting triglyceride accumulation and removing fatty-acid derived signalling factors from the cellular pool. SREBP-1c and PPAR functions appear to be closely linked through cross-talk between the pathways that control their own expression, and may function in concert to affect not only fatty acid metabolism but also glucose metabolism, β-cell proliferation and apoptosis.
Drugs given orally to activate PPARs can improve insulin sensitivity of peripheral tissues and generally appear to enhance β-cell function secondary to their insulin-sensitizing effects. However, it remains possible that specific effects on β-cells are also important contributors to the positive metabolic effects of PPAR agonists in type 2 diabetes treatment.
Declaration of Competing Interests
The author(s) declare that they have no competing interests.
Authors Contributions
ZF-H and CBC contributed equally to the writing of this review.
Acknowledgments
Research by the authors' group is supported by the Canadian Institutes for Health Research and the Canadian Diabetes Association. CBC holds a Levesque Research Chair in Nutrisciences and Health at the University of Prince Edward Island. The authors thank MB Wheeler and MC Saleh for reading the manuscript and for their helpful comments.
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| 15634355 | PMC544854 | CC BY | 2021-01-04 16:37:46 | no | Nutr Metab (Lond). 2005 Jan 5; 2:1 | utf-8 | Nutr Metab (Lond) | 2,005 | 10.1186/1743-7075-2-1 | oa_comm |
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BMC BioinformaticsBMC Bioinformatics1471-2105BioMed Central London 1471-2105-5-2051560691910.1186/1471-2105-5-205Methodology ArticlePredicting binding sites of hydrolase-inhibitor complexes by combining several methods Sen Taner Z [email protected] Andrzej [email protected] Robert L [email protected] Changhui [email protected] Vasant [email protected] Kai-Ming [email protected] Cai-Zhuang [email protected] Yungok [email protected] Haibo [email protected] Xun [email protected] Drena [email protected] L.H. Baker Center for Bioinformatics and Biological Statistics, Iowa State University, Ames, IA 50011, USA2 Department of Biochemistry, Biophysics, and Molecular Biology, Iowa State University, Ames, IA 50011, USA3 Department of Computer Science, Iowa State University, Ames, IA 50011, USA4 Bioinformatics and Computational Biology Program, Iowa State University, Ames, IA 50011, USA5 Department of Physics and Astronomy, Iowa State University, Ames, IA 50011, USA6 Department of Genetics, Development and Cell Biology, Iowa State University, Ames, IA 50011, USA2004 17 12 2004 5 205 205 23 9 2004 17 12 2004 Copyright © 2004 Sen et al; licensee BioMed Central Ltd.2004Sen 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
Protein-protein interactions play a critical role in protein function. Completion of many genomes is being followed rapidly by major efforts to identify interacting protein pairs experimentally in order to decipher the networks of interacting, coordinated-in-action proteins. Identification of protein-protein interaction sites and detection of specific amino acids that contribute to the specificity and the strength of protein interactions is an important problem with broad applications ranging from rational drug design to the analysis of metabolic and signal transduction networks.
Results
In order to increase the power of predictive methods for protein-protein interaction sites, we have developed a consensus methodology for combining four different methods. These approaches include: data mining using Support Vector Machines, threading through protein structures, prediction of conserved residues on the protein surface by analysis of phylogenetic trees, and the Conservatism of Conservatism method of Mirny and Shakhnovich. Results obtained on a dataset of hydrolase-inhibitor complexes demonstrate that the combination of all four methods yield improved predictions over the individual methods.
Conclusions
We developed a consensus method for predicting protein-protein interface residues by combining sequence and structure-based methods. The success of our consensus approach suggests that similar methodologies can be developed to improve prediction accuracies for other bioinformatic problems.
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Background
Protein-protein interactions play a critical role in protein function. Completion of many genomes is being followed rapidly by major efforts to identify experimentally interacting protein pairs in order to decipher the networks of interacting, coordinated-in-action proteins. Identification of protein-protein interaction sites and detection of specific residues that contribute to the specificity and strength of protein interactions is an important problem [1-3] with broad applications ranging from rational drug design to the analysis of metabolic and signal transduction networks. Experimental detection of residues on protein-protein interaction surfaces can come either from determination of the structure of protein-protein complexes or from various functional assays. The ability to predict interface residues at protein binding sites using computational methods can be used to guide the design of such functional experiments and to enhance gene annotations by identifying specific protein interaction domains within genes at a finer level of detail than is currently possible.
Computational efforts to identify protein interaction surfaces [4-6] have been limited to date, and are needed because experimental determinations of protein structures and protein-protein complexes, lag behind the numbers of protein sequences. In particular, computational methods for identifying residues that participate in protein-protein interactions can be expected to assume an increasingly important role [4,5]. Based on the different characteristics of known protein-protein interaction sites [7], several methods have been proposed for predicting interface residues using a combination of sequence and structural information. These include methods based on the presence of "proline brackets"[8], patch analysis using a 6-parameter scoring function [9,10], analysis of the hydrophobicity distribution around a target residue [7,11], multiple sequence alignments [12-14], structure-based multimeric threading [15], and analysis of amino acid characteristics of spatial neighbors to a target residue using neural networks [16,17]. Our recent work has focused on prediction of interface residues by utilizing analyses of sequence neighbors to a target residue using SVM and Bayesian classifiers [2,3].
There is an acute need for multi-faceted approaches that utilize available databases of protein sequences, structures, protein complexes, phylogenies, as well as other sources of information for the data-driven discovery of sequence and structural correlates of protein-protein interactions [4,5]. By exploiting available databases of protein complexes, the data-driven discovery of sequence and structural correlates for protein-protein interactions offers a potentially powerful approach.
Results and discussion
Here we are using a dataset of 7 hydrolase complexes from the PDB, together with their sequence homologs. The application of our consensus method to other types of complexes, e.g. antibody-antigen complexes is currently under study and will be published later. It should be noted, however, that prediction of binding sites for other types of protein complexes, especially those involved in cell signaling, is likely to be more difficult than for the hydrolase-inhibitor complexes.
Figure 1 shows an example of the consensus method prediction mapped on the structure of proteinase B from S. griseus in a complex with turkey ovomucoid inhibitor (PDB 3sgb [18]). The inhibitor (3sgb_I) is shown at the top in wire frame and the proteinase B chain (3sgb_E), is shown at bottom. Actual interface residues in the proteinase B chain, i.e., amino acids that form the binding site between proteinase B and the inhibitor, were extracted from the PDB structure (see Materials and Methods). Predicted interface and non-interface residues, identified by the consensus method, are shown as color coded atoms as follows: Red spheres = true positives (TP), actual interface residues that are predicted as such; Gray strands = true negatives (TN), non-interface residues that are predicted as such; Yellow spheres = false negatives (FN), interface residues that are misclassified as non-interface residues; Blue spheres = false positives (FP), non-interface residues that are misclassified as interface residues. Note that the binding site in proteinase B is strongly indicated, with 14 out of 15 interface residues correctly classified, along with 2 false positives.
The primary amino acid sequence for proteinase B chain and the interface residue prediction results for the four individual methods and the consensus method are shown in Figure 2. Actual interface residues are identified highlighted in red. The five lines below the amino acid sequence show the locations of interface residues predicted by the different methods (described in detail below): P = Phylogeny; C = Conservatism of Conservatism (CoC); S = Data mining by SVM; T = Threading; E = Consensus. Similar Figures for each protein studied in this work are provided in Supplementary Materials [see Additional files 1, 2, 3, 4, 5, and 6].
Figure 1 Interface residues predictions mapped on the three dimensional structure of Proteinase B from Streptomyces griseus (3sgb). The target protein is shown in ribbons and atomic spheres; the inhibitor partner is shown at the top in faint wire frame. The residues are color coded as: red = true positives (TP), gray = true negatives (TN), yellow = false negatives (FN), and blue = false positives (FP). Red, yellow, and blue residues are shown in spacefill representation. Note that the actual interface residues extracted from the PDB structure include the red (TP) and yellow (FN) residues. Red and gray residues represent correct predictions of interface and non-interface residues (14 TP+ 210 TN = 224 correct predictions); yellow and blue residues represent incorrect predictions (1 FN + 2 FP= 3)
Figure 2 Comparison of individual methods for interface residue prediction with the consensus method. Results are shown for Proteinase B from Streptomyces Griseus (3sgb_E), the same protein shown in Figure 1. Actual interfaces are highlighted in red. Interface residues predicted by each of five different methods are indicated as follows: P = Phylogeny (none predicted for this protein), C = Conservatism of Conservatism; S = Support Vector Machine; T = Threading; and E = Consensus. Amino acid residues present in the protein sequence, but not included in the PDB structure file, are indicated by "X"s in the sequence.
The prediction results for all methods are shown in Table 1 and Table 2. Table 1 shows a complete summary of the classification performance on the proteinase B chain for all 5 methods including the overall Sensitivity (Sen) and Specificity (Spec); Sensitivity (Sen+) and Specificity (Spec+) for interface residues (the "positive" class); and Correlation Coefficient (see Materials and Methods for definitions of these performance parameters). Table 2 shows the overall average performance results for all seven protein complexes studied in this work. Two kinds of averages are considered: the numerical average over each of 7 proteins in the dataset, i.e., the average on a "per protein" basis (<...>p); and the average over the total number of residues, i.e., the average on a "per residue" basis (<...>r).
Table 1 Classification results for Proteinase B from S. griseus (3sgb_E). TP is the number of true positive; TN is the number of true negatives; FP is the number of false positives, and FN is the number of false negatives. Overall sensitivity, overall specificity, sensitivity+, specificity+, and correlation coefficient are defined in the text.
3SGBE TP # TN # FP # FN # Overall Sen Overall Spe Sen+ Spe+ CC
Phylog. 0 212 0 15 0.94 0.91* 0 - 0*
COC 15 194 18 0 0.92 0.96 1 0.45 0.64
SVM 3 205 7 12 0.92 0.90 0.20 0.30 0.20
Thread. 14 201 11 1 0.95 0.97 0.93 0.56 0.70
Cons. 14 210 2 1 0.99 0.99 0.94 0.88 0.90
Table 2 Overall Classification Performance Results Averaged over 7 Proteins. Average results for Sensitivity+, Specificity+, overall Sensitivity, overall Specificity, and Correlation Coefficient averaged over the 7 proteins in the dataset. <>pdenotes averaging over the total number of proteins, <>rdenotes averaging over the total number of residues.
Method <Sen+>p <Spe+>p <Spe>p <Spe>r <Sen>p <Sen>r <Cor>p <Cor>r
Phylog. 0.39 0.71 0.90 0.89 0.91 0.89 0.43 0.37
COC 0.71 0.31 0.89 0.88 0.81 0.80 0.38 0.37
SVM 0.51 0.41 0.89 0.88 0.88 0.88 0.39 0.37
Thread. 0.59 0.57 0.91 0.89 0.92 0.91 0.53 0.48
Cons. 0.70 0.56 0.92 0.91 0.90 0.89 0.56 0.55
Sequence and structure conservation
Amino acid sequences are conserved for many different reasons related to the structure and function of proteins: for stability [19,20], enzyme active sites, subunit interfaces, facilitation of an essential motion (hinges), and binding sites. Developing methods to identify the reason for conservation of individual highly conserved residues is a difficult problem. This is one of the reasons that a combination of approaches may be more likely to permit identification of residues that participate in protein-protein interactions. Even identifying the conserved residues themselves is not completely straightforward, and as will be seen, different approaches will indicate the same residue being conserved to different extents. In this study, we take advantage of this by using several methods to identify sequence and structure conservation. Here we use two principal methods for this purpose, one based on phylogeny to identify sequence conservation and one based on Conservatism of Conservatism [21] to identify structure conservation. These two methods often identify different residues as being conserved.
Phylogeny
To identify protein residues that are conserved – perhaps due to their functional role in forming specific protein-protein interactions – we use ClustalX [22] multiple sequence alignments of protein sequences to generate phylogenetic trees (see Materials and Methods). Conserved residues are defined as those that are identical at a given position in more than 85% alignments, i.e., only 15% substitutions or gaps were allowed. This 85% cutoff value is found to give optimal results (data not shown). Because phylogenetic trees of closely related sequences result in many residues that satisfy this condition (due to the high conservation of sequences, apparently important for protein folding, located in the protein core) we filter the results to focus on surface residues by removing conserved residues residing inside the protein core, i.e., having low solvent accessibility (see Materials and Methods).
As shown in Figure 2, the phylogenetic method does not classify any of the amino acids in proteinase B chain (3sgb_E) as interface residues, i.e., TP = 0 and FP = 0. Thus, for the phylogenetic method prediction, the correlation coefficient (CC), which can range from -1 to +1, converges to zero, whereas overall specificity converges to 0.905. The latter misleading statistic is due to the large number of negative examples (non-interface residues), which are correctly classified. In cases such as this (with unbalanced numbers of positive and negative examples), sensitivity+ and specificity+ measures are especially useful because they more clearly reflect the ability of a method to detect "positive" interface residues. (See the Methods section for definition and further discussion of performance measures). Note that even though Figure 2 shows that the phylogenetic method does not identify any interface residues in this particular example, the results summarized in Table 1 for all seven proteins demonstrate that the ability of the phylogenetic method to correctly predict non-interface residues (reflected in the high overall sensitivity and specificity values), and in combination with other methods, to lead to significantly improved predictions.
Conservatism of conservatism
To detect structurally conserved residues that are possible binding sites we have used the Conservatism of Conservatism method (CoC) developed by Mirny and Shakhnovich[21] We use structural alignments generated by FSSP (fold classification based on structure-structure alignment of proteins) developed by Holm and Sander [23]) to identify protein families with folds similar to that of the each of the 7 proteins. For each family, HSSP [24] (homology-derived secondary structure of proteins) alignments are used to calculate the sequence entropy at each position of the alignment. The HSSP profile is based on the multiple alignment of a sequence and its potential structural homologues [25]. The structural alignment generated by FSSP is used to calculate the value of CoC (see Materials and Methods). Each residue in the protein chain was ranked according to its CoC value at a given position in the sequence. The top 75% of total residues ranked according to their CoC values are defined as conserved. We filter the results of the CoC ranking by removing all structurally conserved residues located inside the protein core by only choosing the residues that have a relative accessibility of at least 25 as calculated by DSSP [26] (dictionary of protein secondary structure). Interface residues in proteinase B predicted by this method are indicated by a "C" in Figure 2. The overall performance of the CoC method is summarized in the second row of Tables 1 and 2. Although the correlation coefficient of the COC method is in the same range of those obtained by phylogeny and support vector machines, 0.37, the sensitivity+ value, 0.71, is surpassed only by the consensus value. Therefore, a larger fraction of interface residues is predicted by CoC than the other three methods. However, the CoC method alone is not sufficient to successfully predict binding sites, and combining this method with other prediction techniques in the consensus method gives improved results (Tables 1 and 2).
Data mining for binding residues
We have generated a support vector machine (SVM) classifier to determine whether or not a surface residue is located in the interaction site using information about the sequence neighbors of a target residue. An 11-residue window consisting of the residue and its 10 sequence neighbors (5 on each side) is chosen empirically. Each amino acid in the 11 residue window is represented using 20 values obtained from the HSSP profile of the sequence. Each target residue is therefore associated with a 220 (11 × 20) element vector. The SVM learning algorithm is given a set of labeled examples of the form (X, Y) where X is the 220 element vector representing a target residue and Y is its corresponding class label, either interface or non-interface residue. The SVM algorithm generates a classifier which takes as input a 220 element vector that encodes a target residue to be classified and outputs a class label. Our previous study [2] reported results for classifiers constructed using a combined set of 115 proteins belonging to six different categories of complexes: antibody-antigen, protease-inhibitor, enzyme complexes, large protease complexes, G-proteins, cell cycle signaling proteins, signal transduction, and miscellaneous. In another study [3], we trained separate classifiers for each major category of complexes (e.g., protease-inhibitor complexes). In the case of protease-inhibitor complexes, leave-one-out experiments were performed on a set of 19 proteins. In each experiment, an SVM classifier was trained using a set of surface residues, labeled as interface or non-interface, from 18 of the 19 proteins. The resulting classifier was used to classify the surface residues of the remaining target protein into interface residue and non-interface residue categories. The interface residues obtained for 3sgb_E are reproduced in Figure 2 and marked by "S". The performance of the SVM classifier for the current test set of complexes is summarized in Tables 1 and 2. The results show that SVM yields relatively high sensitivity+ (0.51) and specificity+ (0.41).
Threading of sequences through structures of interface surfaces
Structural threading was performed for the set of 7 protein complexes using a recently developed threading algorithm [27], which was first used in the CASP5 [28] competition. For each complex structure, we first extract the interfacial region, essentially as described earlier. Residue-residue contacts in the interfacial region are described with contact matrices. The total energy in this threading method is the sum of all pair-wise contact energies for the conformation. Detailed residue-level contact potentials were obtained from the Li, Tang and Wingreen [29] parameterization of the Miyazawa and Jernigan [30] matrix. We represent a protein sequence vector s by the hydrophobicity values of its amino acids hi obtained in this factorization and protein structure by the contact matrix Γ. The problem of finding the best alignment of a query sequence s with a structure having contact matrix Γ is to find the transformation from s to s' that optimizes the energy function. The optimum s' is the dominant eigenvector v0 of the contact matrix Γ. There is a strong correlation between a protein sequence and the dominant eigenvector of its native structure's contact matrix. Here the transformation we seek is obtained by maximizing the correlation between s' and v0. This is an alignment problem, and a dynamic programming method from sequence alignment has been adapted to solve this problem [27].
For each sequence, threading is performed against structures in our template database and alignment results used only when the score exceeds a length-dependent threshold. From the alignments, residues involved in contacts at the interface are identified using a scale based on the number of times a particular residue is indicated and the strength of the threading score. The predicted binding sites for 3sgb_E by the threading method are marked in Figure 1 by "T" and the prediction results are summarized in Tables 1 and 2. The threading-based approach is somewhat more successful than other methods based on its sensitivity+, selectivity+, and correlation coefficient values, but still not as good as the performance obtained by combining it with methods in the consensus approach.
Consensus method for predicting protein binding sites
Based on the results from the predictions with the four independent methods, we have developed a simple consensus method to obtain a better prediction. In the consensus method results presented here, an amino acid is considered to be an interface residue if any of the following conditions are met:
i) at least three independent methods classify it as an "interface residue"
ii) any two methods (except the Phylogeny-Threading pair) predict it
For this set of proteins, the parameters for combining results in the consensus method have been empirically determined without a systematic comparison of the strengths and weaknesses of each method. We employ this simple approach because it provides demonstrable improvement in prediction performance over the individual methods. The consensus interface residue predictions are indicated by an "E" in Figure 1, and performance results are summarized in the last rows of Tables 1 and 2. The consensus method generally results in an enhanced correlation coefficient and sensitivity+, demonstrating the superior performance of the consensus method for identifying interface residues in this protein set. Predictions for each protein, provided in Supplementary Materials [see Additional files 1, 2, 3, 4, 5, and 6], illustrate that the improvements can be even more pronounced when the individual predictions of all four methods are relatively weak. This suggests that combining diverse prediction methods may be an excellent approach for the prediction of the binding sites in protein complexes.
Conclusions
Each of the four prediction methods presented in this paper sheds a different light on the conservation and prediction of protein interaction sites, but none of the methods taken separately is as powerful as the combination of all four methods. The simple consensus approach presented here could perhaps be improved by generating an ensemble predictor with more detailed probabilities. Our current work is directed at this approach. It is clear that the present subject is an active field of research [31-38].
Methods
Dataset of hydrolase-inhibitor complexes
The dataset of 7 hydrolase-inhibitor complexes used in this work has been derived from a larger dataset of 70 protein heterocomplexes extracted from PDB by Chakrabarti and Janin [39] and used in our previous studies [2,3]. All are proteins from hydrolase-inhibitor complexes, with six being proteinases: 1acb_E [40] (chain E of PDB structure 1acb), 1fle_E[41], 1hia_A[42], 1avw_A[43], 2sic_E[44], 3sgb_E [18]; and one being a carboxypeptidase: 4cpa [45].
Definition of surface and interface residues
Surface and interface residues for the proteins were identified based on information in the PDB coordinate files as previously described [2,3]. Briefly, solvent accessible surface areas (ASA) for each residue in the unbound protein and in the complex are calculated using DSSP [26]. A surface residue is defined as an interface residue if its calculated ASA in the complex is less than that in the monomer by at least 1 Å2 [46]. In the extraction of interfacial region for threading, however, a distance-based definition of surface is used: a surface residue is defined as an interface residue if its side-chain center is within 6.5Å of the side-chain center of a residue belonging to another chain in the complex.
Based on the ASA definitions, 41% of the residues in the set of 7 proteins were surface residues, corresponding to a total of 631 surface residues. Among these surface residues, 166 were defined as interface residues and 465 as non-interface residues (i.e. surface residues that are not in the interaction sites). Thus, on average, interface residues represent 26% of surface residues, or 11% of total residues for proteins in our dataset.
Using phylogeny to identify conserved residues
Many computational tools have been developed for identifying amino acids that are important for protein function/structure, but there is no consensus regarding the best measure for evolutionary conservation [47]. Evolutionary conservation can be decomposed into three components: i) the overall selective constraints – the number of changes observed at a site; ii) the pattern of amino acid substitutions – the number of amino acid types observed at a site; and iii) the effect of amino acid usage. We have established a reliable relationship between each measure and various aspects of structure. To explore the connection between sequence conservation and functional-structural importance, we proposed a new measure that can decompose the conservation into these three components [47]. This measure is based on phylogenetic analysis. The evolutionary rate at site k during lineage l from amino acids i to j (i,j = 1,...20) can be expressed as λkl (i,j) = ck × alk × Q(i,j|k), where ck accounts for the rate variation among sites, alk for site-specific lineage (or subtree) effect caused by functional divergence [48], and the 20 × 20 matrix Q(i,j|k) is the (site-specific) model for amino acid substitutions. The likelihood function for a given tree can be determined according to a Markov chain model [49]. We have developed an integrated computer program (DIVERGE [50]) that can map these predicted sites onto the protein surface to examine these relationships. We use the solvent accessibility data from DSSP [26] to restrict predicted conserved residues to those located on the protein surface.
Conservatism of conservatism
The phylogeny-based conservation of residues relies on sequence homology. It is well known, however, that many non-homologous proteins share similar folds [51]. It is therefore highly desirable to study the conservation of residues in proteins based on the structural superimposition of non-homologous proteins. In order to obtain insight into the evolutionary conservation of residues in proteins, we use the Conservatism of Conservatism method (CoC). The CoC method was developed by Mirny and Shakhnovich [21] for studying evolutionary conservation of residues in proteins with specific folds from the FSSP database [23]. With the FSSP database, Mirny and Shakhnovich performed an analysis of conserved residues in several common folds. The 20 naturally occurring amino acids were subdivided into 6 different classes, based on their physicochemical characteristics and frequencies of occurrence at different positions in multiple sequence alignments. The evolutionary conservatism within families of homologous proteins was measured through sequence entropy. Structural superimposition of different families of proteins with similar folds was used to calculate CoC for all positions of residues within a fold. Here we have applied a similar approach to identify structurally conserved residues involved in protein interactions.
For each protein, we first calculate the sequence entropy at each position within a family of related sequences from the HSSP database [25]
where is the frequency of the class i of residues (for each of the six classes) at position l in sequence in the multiple sequence alignment. Then we use the FSSP database to obtain the structural alignment. The structural superimposition of different families was used to calculate the conservatism of conservatism (CoC)
where sm(l) is the intrafamily conservatism within the family m at position l, and M is the number of families. The CoC is the measure of the evolutionary conservation of the specific sites within the protein fold. Because the CoC method does not distinguish between residues at the protein surface evolutionarily conserved for functional reasons and residues inside the protein core that are conserved because of their importance to the folding process, we use solvent accessibility data for the unbound molecules to eliminate those conserved residues located inside the protein core.
Data mining approaches to binding site identification
Recent advances in machine learning [52] or data mining [53] offer a valuable approach to the data-driven discovery of complex relationships in computational biology [54,55]. In essence, a data mining approach uses a representative data training set to extract complex a priori unknown relationships, e.g., sequence correlates of protein-protein interactions. Examination of the resulting classifiers can help generate specific hypotheses that can be pursued using molecular and biophysical methods. For example, a classifier that is able to identify protein-protein interface residues on the basis of sequence or structural features can provide insights about sequence characteristics that contribute to important differences in function. The data mining approach for binding site identification consists of the following steps:
• Identify the surface residues in each protein.
• Label each residue in each protein as either an interface residue or a non-interface residue based on appropriate criteria for defining residues in interaction sites.
• Use a machine learning algorithm to train and evaluate a classifier to categorize a target amino acid as either an interface or a non-interface residue. Different types of information about the target residue (e.g., the identity and physicochemical properties of its sequence neighbors, whether or not the target residue is a surface residue) can be supplied as input to the classifier. A variety of machine learning algorithms [52,54] can be used for this purpose.
• Evaluate the classifier (typically using cross-validation or leave-one-out experiments) on independent test data (not used to train the classifier).
• Apply the classifier to identify putative interface residues in a protein, given its sequence (and possibly its structure), but not the sequence or structure of its interaction partner.
Here we have used a support vector machine (SVM) learning algorithm because SVMs are well-suited for the data-driven construction of high-dimensional patterns and are especially useful when the input is a real-valued pattern [56]. In addition, algorithms for constructing SVM classifiers effectively incorporate methods to avoid over-fitting the training data, thereby improving its generality, i.e., the performance of the resulting classifiers on test data. Support vector machine algorithms have proven effective in many applications, including text classification [57], gene expression analysis using microarray data [58], and predicting whether or not a pair of proteins is likely to interact [59].
Threading of sequences through structures of protein-protein interface surfaces
In phylogenetic and data mining approaches, the properties of the protein-protein interface are deduced by concentrating on the sequence information contained in the protein pair under investigation. However, it is well accepted that the physical origin of the specificity of protein-protein interactions comes predominantly from their structures. Thus, in any thorough investigation of protein-protein interactions, it is essential to include information from structural studies. Here we have adapted methods employed in protein structure recognition [60-63] to the problem of predicting protein-protein interface residues. In the first stage, structural models for identifying protein-protein interfaces are generated from existing protein databank (PDB) structures by extracting portions of contacts between different protein chains. We found that if we define the interaction region by the criterion that backbone Cα atoms on the two interacting chains are less than 15 Å apart, reasonably well connected fragments suitable for threading studies are obtained. In the second stage, after identifying a set of candidate template structures, threading is performed to examine the probability that a given model resembles the real interface. The threading algorithm is described in Cao et al. [27]. The threading alignments and scores obtained allowed us to predict which parts of each protein are in the interfacial region in the hydrolase-inhibitor complexes and to predict the most probable residue-residue contacts between the two proteins.
Ensemble predictions for combining results from multiple methods
Different approaches for identifying binding sites from amino acid sequence information yield different (sometimes contradictory, sometimes complementary) results. In such cases, approaches for combining results from multiple predictors have a potential importance. The key idea is that results obtained by using different approaches, which we will call classifiers henceforth, may be correlated (or, more generally, statistically dependent) due to a variety of reasons including the use of a common dataset for constructing or tuning classifiers, use of intermediate variables for encoding input to the classifiers, and similarities between methods (e.g., SVM, neural networks). Regardless of the source of statistical dependency, the goal is to develop methods for weighting the output of each classifier appropriately for the purpose of producing more accurate predictions. Our method takes as input the binary (True/False) output of each classifier (e.g., SVM, CoC) and produces as output a probability that the residue under consideration is an interface residue, using the outputs produced by each of the classifiers. Algorithms for learning Bayesian (or Markov networks) can be then used to learn the network of dependences and the relevant conditional probabilities.
General evaluation measures for assessing the performance of classifiers
Let TP denote the number of true positives – residues predicted to be interface residues that are actually interface residues; TN the number of true negatives – residues predicted not to be interface residues that are in fact not interface residues; FP the number false positives – residues predicted to be interface residues that are not interface residues;FN the number of false negatives – residues predicted not to be interface residues that actually are interface residues. Let N = TP+TN+FP+FN. Sensitivity (recall) and Specificity (precision) are defined for the positive (+) class as well as the negative (-) class. Sensitivity+ = TP/(TP+FN), Sensitivity-= TN/(TN+FP), Specificity+ = TP/(TP+FP), Specificity- =TN/(TN+FN). Overall sensitivity and overall specificity correspond to expected values of the corresponding measures averaged over both classes. The performance of the classifier is summarized by the correlation coefficient, which is given by
The correlation coefficient ranges from -1 to 1 and is a measure of how predictions correlate with the actual data [64]. It is important to note, that when the number of negative instances is much larger than the number of positive instances – as is the case for prediction of interface residues – the Sensitivity+ and Specificity+ measures are more appropriate for assessing prediction performance than the overall Sensitivity and Specificity measures [64]. In the extreme case when a classifier predicts every example to be negative (due to a preponderance of negative training instances) these overall performance measures would still show a high success rate despite the obvious failure of the prediction method. In such cases, the Correlation Coefficient, as well as the Sensitivity+, which is a measure of the fraction of positive instances that are correctly predicted, and Specificity+, which is a measure of the fraction of the positive predictions that are actually positive instances, may provide better performance assessment. Of course, a meaningful comparison of the performance of different classification methods depends critically on the specific application and goal.
Author's contributions
CY, VH and DD performed data mining calculations. XG performed phylogenetic calculations. KMH, CZW, YI, DD, and HC worked on threading. TZS, AK, and RLJ worked on the implementation of CoC and the development of consensus methodology. Every author contributed to the final draft of the paper.
Supplementary Material
Additional File 1
Comparison of individual methods for interface residue prediction for bovine α-chymotrypsin (1acbe).
Click here for file
Additional File 2
Comparison of individual methods for interface residue prediction for porcine pancreatic trypsin (1avwa).
Click here for file
Additional File 3
Comparison of individual methods for interface residue prediction for porcine pancreatic elastase (1flee).
Click here for file
Additional File 4
Comparison of individual methods for interface residue prediction for kallikrein(1hiaa).
Click here for file
Additional File 5
Comparison of individual methods for interface residue prediction for subtilisin BPN' (2sice).
Click here for file
Additional File 6
Comparison of individual methods for interface residue prediction for carboxypeptidase A (4cpa).
Click here for file
Acknowledgments
The financial support through the NIH grant 1R21GM066387 is acknowledged by V. Honavar, D. Dobbs and R.L. Jernigan. We thank Dimitris Margaritis and other members of our research groups for helpful discussions. We also wish to thank the anonymous reviewers for valuable comments on the original version of this manuscript.
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| 15606919 | PMC544855 | CC BY | 2021-01-04 16:36:38 | no | BMC Bioinformatics. 2004 Dec 17; 5:205 | utf-8 | BMC Bioinformatics | 2,004 | 10.1186/1471-2105-5-205 | oa_comm |
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BMC NeurosciBMC Neuroscience1471-2202BioMed Central London 1471-2202-5-551558827810.1186/1471-2202-5-55Research ArticleXenon prevents cellular damage in differentiated PC-12 cells exposed to hypoxia Petzelt Christian [email protected] Per [email protected] Wolfgang [email protected]üller Jana [email protected] Wolfgang J [email protected] University Hospital Charité, Clinic for Anesthesiology and Intensive Care, Experimental Anesthesiology, 14050 Berlin, Germany2 Linde Gas Therapeutics, 18181-Lidingö, Sweden2004 8 12 2004 5 55 55 16 8 2004 8 12 2004 Copyright © 2004 Petzelt 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 neuroprotective effect of xenon has been demonstrated for glutamatergic neurons. In the present study it is investigated if dopaminergic neurons, i.e. nerve-growth-factor differentiated PC-12 cells, are protected as well against hypoxia-induced cell damage in the presence of xenon.
Results
Pheochromocytoma cells differentiated by addition of nerve growth factor were placed in a N2-saturated atmosphere, a treatment that induced release of dopamine, reaching a maximum after 30 min. By determining extracellular lactate dehydrogenase concentration as marker for concomitant cellular damage, a substantial increase of enzymatic activity was found for N2-treated cells. Replacement of N2 by xenon in such a hypoxic atmosphere resulted in complete protection against cellular damage and prevention of hypoxia-induced dopamine release. Intracellular buffering of Ca2+ using the Ca-chelator 1, 2-bis(2-Aminophenoxy)ethane-N,N,N',N'-tetraacetic acid tetrakis(acetoxymethyl) ester (BAPTA) reduced the neuroprotective effect of xenon indicating the essential participation of intracellular Ca2+-ions in the process of xenon-induced neuroprotection.
Conclusions
The results presented demonstrate the outstanding property of xenon to protect neuron-like cells in a hypoxic situation.
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Background
Originally, hypoxia/ischemia-induced alterations in neuronal function have been attributed to be an over-release of neurotransmitters, including dopamine and glutamate. Many studies have been performed on the mechanisms of glutamate-induced neuronal damage [1,2] but relatively few have investigated the hypoxia-induced damage in dopaminergic neurons [3-6]. In recent years several lines of evidence have suggested that effects other than excitotoxic mechanisms may also participate in hypoxia-induced cell damage such as cortical spreading depression [7,8]. Rat pheochromocytoma (PC-12) cells are catecholaminergic, excitable cells that have been widely used as an in vitro model for neuronal cells [9] possessing both D1- and D2-dopamine receptors [10]. In these cells hypoxia causes a transient release of dopamine resulting from a complex cellular response consisting of increased dopamine release and reduced uptake rate. Such increased dopamine concentration has been shown to be associated with cellular damage indicated by an elevated release of lactate dehydrogenase (LDH) from the cells [6,11].
Numerous approaches have been undertaken to reduce hypoxia-induced neurotoxicity [2,12]. The pathological increase of extracellular neurotransmitter concentration presents probably one of the first indicators for such damage although it is not clear to what extent it contributes directly. Thus, a reduction or even complete suppression of such an increase of neurotransmitter concentration after the primary neuronal damage would suggest a high probability for protection from the hypoxic insult. Recently, we have shown that the noble gas xenon prevents in hypoxic cortical neurons hypoxia-induced cell damage and glutamate release [13,14]. Such neuroprotective potential has been confirmed by Ma et al. [15] and Wilhelm et al.[16], and related to its property of being an NMDA-receptor antagonist. In the present paper, however, we show that also in the dopaminergic PC-12-system xenon exhibits profound neuroprotective properties for hypoxic cells thus underlining its usefulness as a general neuroprotectant.
Results
Release of dopamine under hypoxic conditions
Cells kept under normoxic conditions did not release dopamine during the time period studied. If, however, they were kept in an atmosphere consisting of 100% nitrogene, considerable amounts of dopamine were found in the extracellular space reaching a maximum at 30 min of incubation, followed by a subsequent decrease. If under the same conditions nitrogen was replaced by xenon, no such increase in dopamine concentration was found (Fig. 1a). The level of extracellular dopamine remained as low as in cells kept under normoxic conditions.
Hypoxia-induced cellular damage
In order to test if such hypoxia damaged the cells, extracellular LDH was determined after a two-hour period of treatment. A low level of LDH was found in cells kept under normoxic conditions whereas cells kept under nitrogen showed a significant release of LDH indicating severe cellular damage (Fig. 1b). If instead of nitrogen xenon was used to create such hypoxic condition, the LDH level remained at the same low level as in controls.
Effect of the dopamine reuptake inhibitor GBR 1209
Hypoxia-induced extracellular increase of dopamine could be caused either by elevated release of dopamine or by a reduced, or even inhibited, dopamine uptake. If hypoxia caused faster release but did not interfere with uptake, uptake-inhibitors would cause a higher concentration of dopamine in the extracellular space. On the other hand, if the release was constant but the re-uptake inhibited by hypoxia, additional inhibition of uptake by inhibitors would have no or little effect. In the presence of 5 nM of the dopamine reuptake inhibitor GBR 1209 the extracellular dopamine concentration did not change in a normoxic or xenon environment. However, in nitrogen the extracellular dopamine concentration did not reach exactly the same value as in pure nitrogen, the dopamine level was slightly but significantly reduced, thus supporting the view that hypoxia-induced extracellular dopamine increase was caused by an enhanced release of dopamine and – to a lesser extent – an interference with the uptake mechanism (Fig. 2).
Effects of the dopamine receptor antagonists SCH 23390 and sulpiride
To test if indeed the hypoxia-induced increase of extracellular dopamine itself caused the cell damage measured by the increase in extracellular LDH, dopamine receptor antagonists were used. Since they prevent dopamine binding they should provide protection of dopamine-induced damage. If the D1 receptor antagonist SCH 23390 was used during the incubation period, then at the highest dose of 10 nM, a reduction of nitrogen-induced external LDH-increase could be seen. However, even at this highest applied dose of SCH 23390, there was still only a less than 50% reduction in extracellular LDH (Fig. 3a). If, on the other hand, the D2 receptor antagonist sulpiride was used, no reduction in the nitrogen-induced LDH-release was found (Fig. 3b). Both compounds did not change the xenon-induced suppression of cellular damage.
Cellular damage induced by external addition of dopamine
To analyze if indeed the increased external dopamine was detrimental to cells, they were incubated in the presence of 100 nM dopamine, either for 30 min followed by 120 min in normal medium, or continuously for 150 min. As shown in fig. 4, column (c), even the 30 min incubation with 100 nm dopamine (the lesser of the two dopamine challenges) was sufficient to cause considerable cell damage. Such damage was further increased if dopamine was present for the whole period of time of 150 min (column (e). We asked then if xenon not only prevented the release of dopamine in a hypoxic situation but could even reduce the damage caused by external dopamine. Cells were incubated for 30 min in normal buffer containing 100 nM dopamine followed by 120 min in xenon-saturated buffer, without dopamine. As shown in column (d), the dopamine-induced damage to the cells as seen in column (c) was significantly reduced. If the cells were incubated for 150 min in xenon-saturated buffer containing dopamine, even under those conditions the damage was low compared to cells exposed to dopamine in normal buffer (column (f)).
Buffering of intracellular Ca2+-ions using BAPTA
In order to test if changes in intracellular Ca2+ were required for the neuroprotective effect of xenon, cells were incubated with the cell-permeant Ca-chelator BAPTA-AM. As shown in fig. 5, chelating intracellular Ca2+ does not damage the cells per se (control + 10 μM BAPTA). Surprisingly, such chelation reduces significantly the neuroprotective effect of xenon, indicating an essential role for intracellular Ca2+ for this effect to occur. A slight but significant reduction in cellular damage is observed when BAPTA-treated cells are incubated in nitrogen-saturated buffer.
Comparison with another dopaminergic cell system
To exclude that the results obtained were limited to the PC-12-system itself, hypoxia-induced dopamine release and cell damage was investigated in rat embryonic primary mesencephalic cell cultures that are known to contain 0.5 – 1.5% of dopaminergic cells [26]. As shown in fig. 6, in an hypoxic atmosphere a very similar pattern of dopamine and LDH release is obtained compared to PC-12 cells. Xenon prevents also in these primary cells the hypoxia-induced neurotransmitter and LDH release.
Discussion
In hypoxia/ischemia a key feature of secondary damage after the primary neuron-damaging event is the over-release of neurotransmitters [17]. Consequently, an interference with the hypoxia-induced release mechanism with respect to its control systems may be extremely useful to reduce cellular damage. The results presented here show that xenon has such properties, namely to prevent cellular damage and neurotransmitter release in a hypoxic situation thus qualifying it as an almost ideal early neuroprotectant. Concerning possible cellular targets for xenon, a first indication for the participation of Ca2+-regulated events was obtained when it was shown that xenon blocked cells in metaphase and that the block could be lifted by artificial small intracellular Ca2+- increases [18]. Since the CaM KII complex is known to play a decisive role in the metaphase/anaphase transition, it was tested if the CaMKII-inhibitor KN-93 had likewise metaphase-blocking properties. Such effects were obtained [19]. It is well known that in dopaminergic differentiated PC-12 cells, the CaMKII complex is involved in the regulation of neurotransmitter release [20-22] as well as its participation in a multitude of other Ca2+-dependent regulatory events [23]. Thus, it appears to be plausible that one of the targets for xenon might be the CaMKII complex, either directly or via interference with other Ca2+-dependent systems. One of those may be the Ca2+/calmodulin-activated calcineurin system that has been implicated in the regulation of monoamine release [24]. Alternatively, xenon might interact upstream of these regulatory systems with other Ca2+-dependent events required to occur in hypoxia-induced cell damage. Such a scenario is suggested by our demonstration that the neuroprotective effect of xenon is strongly reduced if PC-12 cells are loaded with BAPTA. Thus, at present all evidence obtained by us [13,14,18,19] and others [15,16] establish a complex and composite picture of targets susceptible to xenon including NMDA receptors, Ca2+-regulating and -regulated systems up to the activaton of transcription factors whereby such targets are probably not essentially and sequentially linked to each other.
To summarize briefly our main findings: (1) The presence of xenon blocks hypoxia-induced dopamine release in dopaminergic cells. (2) Hypoxia-induced dopamine increase is caused by an enhanced release of dopamine rather than a reduced uptake of dopamine. (3) When measuring LDH release as a marker of cellular damage, xenon was found to block such release, which suggests that xenon reduces hypoxia-induced cellular damage. (4) Increased extracellular dopamine can damage dopaminergic cells directly. This is mainly mediated by D1 receptor agonism rather than D2. (5) Such direct extracellular dopamine-induced damage can be reduced by the presence of xenon, even when the increase in extracellular dopamine has not been caused by an episode of cellular hypoxia. (6) The above described protective effects of xenon depend on the presence of calcium ions.
Further studies will show if indeed in the hypoxic cell multiple intracellular targets exist for xenon and how they are orchestrated together to result in cellular protection.
Conclusions
Based on the present results obtained with NGF-differentiated PC-12 cells and on the literature cited in this paper, xenon appears to be a neuroprotectant for a broad spectrum of neuronal cells; given its proven non-toxicity based on its long clinical use, it may come close to fulfilling the requirements for an ideal or "gold standard" neuroprotectant.
Methods
Cells
Rat pheochromocytoma cells (PC-12) were maintained in RPMI 1640 medium containing 5% fetal calf serum, 10% horse serum, at 37°C, 5% CO2. For experiments, cells were seeded in 24-well plates at a density of 1 × 105 cells/well and nerve-growth-factor (Promega, Heidelberg, Germany) was added (0.4 μg/ml) whereupon cells entered differentiation. They were used on day five after the addition of growth factor [25]. Primary dopaminergic cells from rat embryonic brain were prepared as described (26) and used on day 14.
Determination of dopamine and LDH
Hypoxia-treatment was performed as described [13]. Samples from individual wells were taken at the intervals indicated and deproteinated using 5% perchloric acid (1:1 = vol/vol). Supernatants were transferred to Eppendorff tubes and the same volume (0.5 ml) of 0.4 M perchloric acid was added, mixed on vortex and centrifuged (6000 rpm, 3 min) to remove cell debris. Dopamine concentration was determined by high-pressure liquid chromatography (Bio-Tek, Neufahrn, Germany) using an electrochemical detector (Biometra, Göttingen, Germany). Cellular damage after the experiment was assessed by measuring spectrophotometrically the concentration of LDH in the original supernatant, before the addition of perchloric acid (Roche Diagnostics, Mannheim, Germany).
Chemicals
Gases were supplied by AGA-Linde (Berlin, Germany). 1,2-bis(2-Aminophenoxy)ethane-N,N,N',N'-tetraacetic acid tetrakis(acetoxymethyl) ester (BAPTA-AM) was purchased from Molecular Probes, (Leiden, The Netherlands), and all standard chemical products were obtained from Merck (Berlin, Germany).
Statistical analysis
All experiments were repeated at least five times, i.e. in five different plates on five different days. The data were presented as means ± SEM. The results of multiple groups were analyzed using one-way ANOVA with Dunnett's multiple comparison post test or two-way ANOVA with Bonferroni posttests using GraphPad Prism version 3.00 for Windows, GraphPad Software, San Diego California USA. Differences with p values less than 0.05 were considered statistically significant.
Authors' contributions
CP conceived the study, participated in its design and coordination, carried out the cellular studies involving the various gas treatments, participated in the preparation of the cells, and drafted the manuscript. PB and WS participated in the design of the experiments and the gas applications, JM performed neurotransmitter analysis and LDH determinations, and WK participated in the design of the study and performed the statistical analysis. All authors read and approved the final manuscript.
Acknowledgements
The partial support of this work by Linde Gas Therapeutics is gratefully acknowledged.
Figures and Tables
Figure 1 A. Dopamine release from differentiated PC-12 cells under normoxic conditions, in N2, or in xenon. Whereas almost no dopamine was released from control cells during the two-hour period, a strong increase of extracellular dopamine was found when cells were kept in N2. When cells were maintained in a xenon-atmosphere, no dopamine release occurred, there was virtually no difference compared to controls. B. Assessment of cellular damage in PC-12 cells after two-hour incubation. If cells were kept in normal air, or in xenon, only a small amount of LDH was released. Much higher cellular damage was found when cells were incubated in N2. (n = 5; **P < 0.01 with respect to untreated controls).
Figure 2 Effect of the dopamine uptake inhibitor GBR 1209 on dopamine release in hypoxic cells. If PC-12 cells were incubated in the presence of 5 nM of the dopamine uptake inhibitor GBR 1209, no effect was observed in cells incubated under normoxic conditions or in a xenon atmosphere. However, a much higher extracellular dopamine concentration was found in cells incubated in N2 and GBR 1209 than in N2 alone, indicating that indeed the release was intensified under hypoxic conditions. (n = 4; P < 0.0001 with respect to untreated controls, analyzed by 2-way ANOVA and Bonferroni posttests between GBR1209-treated and untreated groups).
Figure 3 A. Effect of various concentrations of the D1-receptor antagonist SCH 23390 on cell survival after two-hour incubation. No effect was observed on PC-12 cells maintained under normal conditions or in xenon, however, for cells kept in nitrogen, a concentration-dependent decrease of cellular damage was found, indicating that the D1-receptor was involved to convey the cellular damage. (n = 4; **P < 0.01 and *P < 0.05 with respect to untreated controls). B. Effect of various concentrations of the D2-receptor antagonist sulpiride on cell survival after two-hour incubation. No protective effect of sulpiride was found for cells incubated in nitrogen, even a slight increase of cellular damage was seen with increasing concentrations of sulpiride.
Figure 4 Dopamine-induced cellular damage. Cells were incubated for 150 min with and without 100 nM dopamine and LDH-release was determined. (a) untreated control; (b) untreated cells kept for 30 min in normoxia followed by 120 min in xenon-atmosphere; (c) cells under normoxic conditions treated for 30 min with 100 nM dopamine followed by 120 min in normal medium; (d) cells under normoxic conditions treated for 30 min with 100 nM dopamine, followed by 120 min incubation in xenon-atmosphere in normal medium without dopamine; (e) cells under normoxic conditions treated for 150 min with 100 nM dopamine; (f) cells under normoxic conditions treated for 30 min with 100 nM dopamine, followed by 120 min incubation in xenon-atmosphere in medium containing 100 nM dopamine. No significant difference was found between (a) and (b) whereas the difference between (c) and (d), and (e) and (f) was highly significant (P < 0.0001).
Figure 5 Effects of intracellular Ca2+-buffering by BAPTA. If PC-12 cells were preloaded with 10 μM BAPTA, the neuroprotective effect of xenon was strongly reduced. At the same time, however, a small reduction in cellular damage was observed in nitrogen-treated cells. (n = 5; P < 0.0001 analyzed by 2-way ANOVA and Bonferroni posttests between BAPTA-treated and untreated groups; no significant difference was found when the nitrogen-group was compared to the nitrogen-BAPTA group).
Figure 6 A. Dopamine release from mesencephalic dopaminergic cells under normoxic conditions, in N2, or in xenon. Mesencephalic cells containing dopaminergic neurons were exposed on day 14 after preparation for two hours either to normal atmosphere, or xenon or nitrogen. Dopamine was not released from cells maintained under normal conditions or in xenon whereas a significant amount of dopamine was liberated from cells maintained in nitrogen. B. Cellular damage in mesencephalic dopaminergic cells after two-hour incubation. If cells were kept in normal air, or in xenon, only a small amount of LDH was released. Much higher cellular damage was found when cells were incubated in nitrogen. (n = 4; **P < 0.01).
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J Transl MedJournal of Translational Medicine1479-5876BioMed Central London 1479-5876-2-441561056010.1186/1479-5876-2-44ReviewFrom bench to clinic and back: Perspective on the 1st IQPC Translational Research conference Hörig Heidi [email protected] William [email protected] Columbia University Medical Center, Division of Surgical Science NY 10032 USA2 Sanofi-Aventis, Bridgewater NJ 08807 USA2004 20 12 2004 2 44 44 9 12 2004 20 12 2004 Copyright © 2004 Hörig and Pullman; licensee BioMed Central Ltd.2004Hörig and Pullman; 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.
Translational Research (TR) provides a set of tools and communication context for scientists and clinicians to optimize the drug discovery and development process. In the proceedings of a Princeton conference on this timely topic, the strengths and needs of this developing field were debated. Outcomes and key points from these discussions are summarized in this article which covers the topics of defining what we mean by translational research (both theoretically and in operational terms), ways in which to engender the TR mindset and embed it in organizations such as the pharmaceutical industry in order to optimize the impact of available technologies (including imaging methods), the scientific basis and under-pinnings of TR including genomics knowledge, information sharing, as well as examples of application to drug discovery and development. Importantly, it should be noted that collaborations and communications between the stakeholders in this field, namely academia, industry and regulatory authorities, must be strengthened in order for the promise of TR to be delivered as better therapies to patients.
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Introduction
There are many challenges facing pharmaceutical companies in the post-genome era not least of which is declining productivity and innovation. Not surprisingly, there is agreement between Industry, Academia and Regulatory communities that the drug discovery and development process needs to change in order to meet the future needs of patients with effective and desirable drugs. A key part of the strategic solution is to leverage the application of TR principles and practices, which if implemented will go a long way towards addressing the challenge posed by FDA's Critical Path Initiative [1] (for more detail on this initiative see section on "Optimizing the Impact of TR" and ). Successful drug development requires satisfying a matrix of domains from relevance to the disease and the drug-ability of the target through feasibility and convenience of drug delivery, demonstration of favorable benefit-risk profile in order to achieve to a drug label that reflects physician and patient acceptance. Herein lies a key role for TR in helping to navigate this journey.
In order to promote discussion on this topic, the International Quality and Productivity Center (IQPC) organized a Translational Research Conference (20–22 September 2004 Princeton, NJ) that hosted a small group of clinical and basic science researchers and individuals from the pharmaceutical industry. Among the topics discussed were how to define "Translational Research", how to expedite the transfer of pre-clinical findings to influence development plans, how to select biomarkers to ensure support for decisions, how new strategies can be effectively translated to practical tactics, and what team players and collaborations are necessary to conduct successful TR. Success factors identified include: Identification and validation of novel drug targets, development of robust and validated assays to screen drug leads for safety and potential efficacy in humans, and the identification of suitable patients for expedited but informative trials.
Defining Translational Research
While the goal of TR is to implement in vivo measurements and leverage preclinical models that more accurately predict drug effects in humans, TR itself can be defined in many ways. At its core however, is the thesis that information gathered in animal studies can be translated into clinical relevance and vice versa, thus providing a conceptual basis for developing better drugs. It could in fact be argued that the designation of a special term or definition for TR might be unnecessary or even misleading. Historically the term was assigned to create awareness and advocacy for the general public, clinicians and scientific communities, and especially for the government and other private sponsors [2] in this evolving discipline. Nonetheless, the basis for TR lies in sound scientific and clinical research principles.
Whatever the precise definition TR, it should serve as a forum to find a "common language" for clinicians and scientist in navigating the complexities of basic scientific approaches, data analysis and information processing. It clearly implies the need for an intensive training for scientists and clinicians in multiple disciplines to acquire expertise and experience to conduct TR. For the purposes of this symposium, the scope of translational research was defined as the application of scientific tools and methods to drug discovery and development. This can be achieved by integrating information concerning a) exposure (pharmacokinetics), b) biological activity (pharmacodynamics including safety profiles) delineating differences between species and leading to the validation of target and mechanism biomarkers, and c) outcomes leading to an understanding of efficacy and safety between species and ultimately to the qualification or linkage of biomarkers to clinical outcome (for a fuller discussion on Biomarkers and Surrogate Endpoints see definitions in [3]). Thus TR can be used to mitigate risk and enhance drug development opportunities.
Therefore, in taking a pragmatic or operational rather than a definitional approach, a key to a successful translation of non-human research to human clinical trials lies in the choice of biomarkers. While biological pathways tend to be homologous across species and more so than pharmacokinetic parameters such as absorption and clearance, animal models themselves have a poor record of predicting human disease outcome. Nonetheless, biomarkers are the key for prediction of biological activity if not drug efficacy in humans. At least three types of biomarkers can be identified: (1) target biomarkers measuring the interactions between a drug and its target; (2) mechanism biomarkers measuring their downstream biological effects and (3) outcome biomarkers that reflect efficacy and safety. A second dimension can also be ascribed to biomarkers to help drug developers assign risk assessment to such approaches. This sub-classification links desired utility to points on a risk continuum; e.g. low, medium and high, in which 'low' describes a biomarker applied solely to animal models for example for selecting compounds for progression into humans, whereas 'medium' is association with utility for some aspects of early clinical profiling of efficacy and safety including across species correlation, and 'high' is associated with reproducibility and qualification as an outcome or even regulatory tool in humans.
Additionally, TR itself undergoes an evolution from pathfinding (hypothesis generating) to discovery research, to development, and finally to application. Each of these operational phases is amenable to being evaluated or supported by biomarkers, either for the definition of objectives, proof of principle or in assessing risk and feasibility. Consequently the right choice of biomarkers can help drive decision-making and lower the costs and cycle-time for progression of a new drug from the bench into the clinic. In summary, whatever the definition or classification ultimately used, in practical terms translational tools should be developed and applied on a "fit for purpose" basis with prior assessment and agreement of attendant risks.
Optimizing the Impact of Translational Research
Traditional Research and Development (R&D, also referred to as Discovery and Development) paradigms have accentuated the boundaries between the territories of discovery and development worlds and have not been conducive to bridging key transition points. This is unfortunate since the development world tends to lag behind advances made in discovery, a point recognized by FDA in launching the Critical Path Initiative [1]. In brief, this initiative challenges Industry and others to develop and implement better tools, such as biomarkers, trial modeling and simulation and other solutions, in order to optimize the development and regulatory stages of a product's life.
While advances have been made on streamlining forward progression of R&D through organizational linkages, what has not happened to the same degree is a bi-directional flow of information, namely flow of information from the clinic (e.g. clinical validation or lack thereof) back into the hands of the discovery scientist. The consequence of this is that the biological models used to qualify drug candidates may fail to be predictive of subsequent drug responses in the clinical setting. Thus a practical outcome of TR is to improve the overall probability for technical success (POS) in drug development.
Consequently the next paradigm for R&D optimization depends not only on leveraging emerging technologies such as pathway mapping and in silico modeling, but also the need to empower key scientists and clinicians with the task of enhancing the prediction and iteration learning cycle. Since there are different organizational solutions for embedding the TR mindset within an organization, a key element is to provide TR expertise to drug development teams. Furthermore, innovation and productivity values are critically linked through information exchange. Rapid iteration (e.g., learn-confirm cycles) and transfer of knowledge gained from prototype development experience will enable more rapid compound redesign against the highly desired target and be reflected as enhanced innovation. On the productivity side, the tools outlined in the Critical Path Initiative [1], once effectively implemented, will lead to enhanced development productivity but only if information exchange occurs efficiently across different functions. Hence a backbone for TR is support by user-friendly informatics systems.
The journey however starts at understanding the scientific foundations of physiology and pathophysiology, thus providing a rational linkage between the gene, its expressed product, disease expression and ultimately outcome. The discipline of biomarker identification and development as mentioned previously encompasses these principles and is a core tool in the TR scientist's armamentarium.
Biomarkers (which are not necessarily Surrogate endpoints and few are in fact) are key tools for escorting the drug candidate from the bench to the bedside and back. That is they can be both animal "diagnostic" as well as human "diagnostic" tools. A key implementation tool is therefore to identify early on which biomarkers may be of value and to study these in the relevant animal models, that is, specifically include them in preclinical screening paradigms, as well as identify their role (e.g., go / no go decision factors) in the clinical development plan. Biomarkers, which include imaging techniques as well as protein and genetic markers, may fulfill several roles in R&D from compound screening and selection through dose justification, decision-making and risk mitigation, however the key is to overtly link them to the discovery and development plans with a priori agreed performance characteristics, such that there is agreement on the utility of the marker.
There are many good examples of the value or non-value of preclinical models in predicting subsequent human response and safety. The journey from preclinical experience to the clinic is a well-worn one (e.g., Xenograft testing for oncology), albeit without the degree of overall predictiveness we would desire. On the other hand there is a marked paucity of examples in which clinical experience or observation was translated back into a legitimate drug target and discovery effort (e.g. Viagra). Thus, a major opportunity lies in both developing more sensitive and specific animal models of disease (e.g. knock in/out) as well as fully leveraging novel clinical observations. At the same time it is the ultimate validation in the clinic that counts, and rapid feedback of that information will allow the conditional probabilities and learning cycle to be enhanced. By enabling these principles through organizational and cultural change, the impact of TR will be determined by direct impact on high-quality mid-phase transitions as well as reduced cycle-times and resource burdens.
Basic science, genomics and Translational Research
The era of genome-scale biology has seen an increase in, and production of, vast amounts of biological data together with an extensive increase in biology-oriented databases. To make the best use of biological databases and the knowledge they contain, different kinds of information from different sources must be integrated in ways that make sense to biologists. A major component of the integration effort is the development and use of annotation standards such as ontologies. Ontologies offer a conceptualization of domains of knowledge and facilitate both communication between researchers and the use of domain knowledge by computers for multiple purposes. Therefore, the Gene Ontology (GO) project was founded in 1998, in an attempt to provide consistent descriptors for gene products, in different databases; and to standardize classifications for sequences and sequence features. Since then, the GO Consortium has grown to include many databases, including several of the world's major repositories for plant, animal and microbial genomes [4]. Despite vast differences in genome size among various species, genes can be highly conserved at the level of protein sequence allowing searching for an unknown human protein function in yeast. As new genome sequences are being rapidly generated, and where comparative genome analysis requires the integration of data from multiple sources, it is especially relevant to provide rigorous ontologies that can be shared by the scientific community at large.
In the past, biological processes and the underlying genes, proteins, other molecules and environmental factors, have been studied separately more than on an integrated basis. The challenge, however, for future research on human disease is to understand not only the mechanistic basis, but also the underlying dynamics of gene product expression. Thus, biological research should emphasize the analysis of pattern of gene expression over individual measurements.
GO has been developed to predict behavior of entire biological systems, being assigned to three aspects: (1) Molecular Function describes activities, such as catalytic or binding activities, at the molecular level, e.g. kinase activity. (2) Biological Process describes biological goals accomplished by one or more ordered assemblies of molecular functions, e.g. 'cell death' can have both subtypes, such as 'apoptosis', and subprocesses, such as 'apoptotic chromosome condensation'. (3) Cellular Component describes locations, at the levels of subcellular structures and macromolecular complexes, e.g. 'nuclear inner membrane' with the synonym 'inner envelope' [4].
The powerful use of comparative gene expression analysis in human disease was exemplified with a recent study on gene expression profiles of gastric cancer patients and their correlation to survival. Leung et al. [5] have shown that Phospholipase A2 group IIA (PLA2G2A) expression is associated with prolonged survival and less frequent metastasis by studying gene expression patterns in human gastric cancers. This observation was confirmed in an independent set of patient samples by using quantitative RT-PCR. Beyond its potential diagnostic and prognostic significance, this result suggested that the activity of PLA2G2A may suppress progression or metastasis of human gastric cancer.
In summary, the application of mathematical models and computer simulations to analyze gene expression profiles and to compare complex data sets of various origins may provide new insight into the pathogenesis of cancer progression and metastasis. The Gene Ontology (GO) project provides structured, controlled vocabularies and classifications that cover several domains of molecular and cellular biology and are freely available for community use in the annotation of genes, gene products and sequences.
Translational Research in Drug Discovery: Strategies for Complex Systems
Cancer vaccines are promising therapeutics designed to elicit immune responses against antigens expressed by tumor cells. However, vaccines that have worked well in preclinical models have not translated into consistent responses in the clinic. Since vaccines are comprised of multiple components, multiple immunological endpoints are used to identify the least effective vaccine components in cancer patients. Post-clinical research strategies are subsequently designed with a focus on improving the least effective vaccine components.
To improve the performance of cancer vaccines in the clinics, which are traditionally judged by clinical endpoints, novel endpoints and biomarkers are needed to assist in understanding why cancer vaccines are not working. From clinic to bench, a systematic strategy is needed for pre-clinical optimization that addresses vaccine limitations identified in the clinics; and from bench to clinic, performance criteria need to be established for a follow-up clinical study. After gathering the therapeutic options, testing has to be prioritized on the basis of: a) already available data; b) availability of the therapeutic modality; c) models and assays available internally; d) turnaround time; and e) on the patent landscape.
Prioritization and rapid evaluation of novel therapeutics will decrease the turnaround time and facilitate decision-making. However, several tools are needed to make this a reality. For example, complex therapeutic strategies require biomarker or even surrogate endpoints from clinical trials to direct development of second-generation therapeutics. The rapid qualification and choice of surrogate endpoints should be based on knowledge gathered by an "early-stage therapeutic opportunities database". This comprehensive database should include data on therapeutic targets, models, assays and published results and indeed the plethora of new therapeutic strategies in preclinical stages can only be managed by accessing informative databases. Moreover, pre-clinical compound optimization can be facilitated by establishing quantitative endpoints of short duration and lastly go / no go decision points must be established for surrogate endpoints and clinical responses in animal models.
However, several current issues of scientific basis also have to be addressed, such as the importance of clinical surrogate endpoints, the relevance of animal models, lack of concordances between assays, and the lack of concordance between surrogate endpoints and the clinical response, in order to improve cancer vaccine development strategies.
Applying Translational Research to Drug Development
A core principle of TR revolves around validation of targets, biomarkers and treatment modalities in humans. These activities and drug development itself cannot be undertaken without patients or clinical data. How TR can be integrated in a multi-center, multi-cultural organization involving patient accrual from more than 38 different countries worldwide, for the research and treatment of cancer can be exemplified by EORTC , a non-profit organization conducting more than 100 clinical trials and treating 7000 cancer patients yearly.
Advancement in basic science and immunology and an overwhelming revolution of biotechnology have changed the targets and endpoints in cancer trials from the mere assessment of cytotoxicity to defined mechanisms for potential anti-tumor effect. That is in the era of "targeted therapies" molecular therapeutics are now being designed to target "strategic" checkpoints that underlie the malignant phenotype. The challenges to be met are: 1) dealing with new compounds affecting novel molecular targets, 2) innovation in design and analysis of clinical trials, 3) cooperation between translational researchers and network of clinical investigators and 4) informed patients. The major concerns in conducting clinical trials are rising costs coupled with efficacy rates as low as 5% in cancer patients, making signal to noise detection not only difficult but expensive.
The need for research on tumor tissue requires the set-up of tumor banks and the associated administrative burden often discourages young oncologists. EORTC established a tumor bank comprising real tissue samples but including a "virtual review" by pathologists. This ensures the availability of a well-categorized and prognostically evaluated collection of primary tumors and allows an online-searchable bank for researchers to access. Indeed the tumor bank harbors paraffin-embedded tumors, as well as frozen tumor tissues and storage of tissue is de-centralized at the institute where it is collected. To assure equal quality of tissue, which in outcome of scientific experiments can be compared, standardization of the collection and storage methods is fundamental. Therefore, protocols for storage, retrieval and tracking of tissues will be standardized and implemented in all participating laboratories.
Access to the tumor bank allows screening of many available tumor samples for the expression of molecular targets and will help to unravel novel biomarkers for diagnosis and treatment. Such access will allow us to overcome the missed opportunities due to lack of tissue collection in clinical trials, which could have allowed better pre-screening of potentially responsive patients based on expression of certain biomarkers e.g., expression of bcl-2, and the treatment of target positive patients may have ensured a better clinical outcome in this target class.
The challenge of testing promising new modalities for the cure of disease that had shown efficacy in experimental models lies in a lack of understanding of the underlying mechanism, heterogeneity of human genetic backgrounds and a lack of suitable controls in human studies. Strategies have been developed at the NIH for the global monitoring of patients by studying, with high-throughput technology, the systemic effects of treatments as well as their effect within the target organ. For this bedside to bench effort, a systematic sampling of human tissues of local (site of immunogen application), systemic (circulation) and peripheral (tumor site) origin needs to be standardized to ensure high quality of samples avoiding degradation of protein, RNA and DNA. This TR approach allows experimental studies in human samples during or after therapy through amplification of transcripts for analysis of minimal sample tissue, and the application of monitoring techniques for genetic profiling. Further, proteomic-based approaches allow following the kinetics of the mechanism of actions of therapeutics.
Studying the effects of treatment in a bedside to bench approach provides markers for the characterization of disease process and/or testing hypotheses generated by experimental models. Therefore, the nature of research in the clinical setting can realistically be described as 'hypothesis generating", rather than 'hypothesis driven', through a discovery-driven approach. Analysis of the genetic background can reveal polymorphism of genes involved in immune reactions, such as cytokines and their receptors, which might influence the outcome of immunological interventions in different patient populations [6].
Analysis of disease heterogeneity can be approached by transcriptional analysis, through linear amplification of RNA and subsequent analysis by cDNA array and transcriptome array, and/or functional protein analysis, through protein characterization by proteomics [2,7]. Numerous tumor-antigen based cancer vaccine studies have shown that there is a functional dissociation between systemic circulating cytotoxic T cells and tumor infiltrating T cells (TIL). Tumor antigen-specific T cells have been demonstrated to have a quiescent phenotype and consequently cell cycle activation requires antigen-specific stimulation, as well as non-specific stimulation by IL-2 [8]. In addition, the local release of immune inhibitory factors by tumor cells is influencing the T cell phenotype and cytotoxicity leading either to tumor regression or recurrence [2]. To understand these complex mechanisms, it is important to study the tumor microenvironment by collection of large libraries of relevant clinical specimen, such as excisional biopsies or fine needle aspirates (FNA). FNA have the advantage to allow serial sampling of the same tumor site over time and treatment and to allow a prospective follow up of a given lesion. Studying of the tumor microenvironment will provide invaluable insights into mechanisms involved in disease progression and/or changes affected by therapy, in terms of genes whose expression changed due to (1) genetic instability, (2) immune selection or (3) immune regulation.
Despite the many obstacles in monitoring therapeutic effect in early phase clinical trials and the lack of hypothesis, the scientific significance of these trials should be reviewed assuming that the new treatment will not be beneficial. Desirable outcomes include learning about the disease process, the primary goal of the therapy and the reasons for its failure. Another concern should be if we have taken advantage of the patient population accrued at least to learn something, although independent of treatment, about the disease process itself. Clinical trials should therefore be designed, within ethical constructs, to look at questions beyond the ones related solely to treatment. This can be achieved through (1) establishment of libraries of relevant clinical samples for immediate or future studies, (2) prospective collection of data into a consistent format, and (3) tight link between clinical and scientific data.
Developing better therapies for chronic inflammatory diseases also exemplifies the use of the latest technological advances in TR such as proteomics, transcriptomics and cellomics, for identification or application of biomarkers. Chronic inflammation frequently precedes the development of cancer in adults, such as lung [9], esophageal, gastric and pancreatic cancers. This may be due to a switch from apoptotic (scheduled) to necrotic (unscheduled) tumor cell death induced by mechanisms related to the chronicity of the inflammatory response. Acute inflammatory processes caused by viral or bacterial infections are in most cases effectively cleared by the immune system of a competent host. Some infections and other causes of inflammation such as solar exposure to the skin, prolonged tobacco smoke or chemicals, can also lead to prolonged inflammatory processes. In these chronic up-regulated situations, products of cyclooxygenase activity, or nitric oxide accumulating at the local inflammatory site lead to augmented cell proliferation and death. This is often be linked to hypermethylation of promoter regions in tumor-suppressor and/or pro-apoptotic genes. Persistence of defects in the apoptotic machinery provokes necrotic cell death and the release of cellular contents, which in turn enhances cell growth, cancer progression and infiltration of leukocytes including tumor-associated mast cells and macrophages.
Several factors, such as: 1) the nuclear protein HMGB1, 2) the S100 family of molecules; 3) purine metabolites, ATP, AMP and uric acid, and 4) heat shock proteins have emerged as relevant mediators or "endogenous damage or danger signals" to recruit inflammatory cells, to promote wound healing and associated stromagenesis, angiogenesis; and ultimately to modulate immune functions [10]. Until recently, methods to measure necrotic death in patients were not available. The application of proteomics to identify factors, such as HMGB1 in serum of cancer patients, has revealed elevated serum levels in patients with metastatic melanoma, pancreatic cancer and others [10]. The correlation of these serological markers of necrotic cell death with histological patterns, genetic resistance to apoptotic death in animal models could lead to novel targets for immune therapy, such as antibodies to HMGB1, in order to interrupt the "circolo vizioso" of this "addiction to death" which promotes tumor growth [9].
Current attempts for cancer therapy focused on vaccination to antigenic targets or application of cytokines have resulted in measurable anti-tumor reactivity in the blood; however, these therapies have mostly failed to show a correlation with tumor outcome or progression. Therefore, to more completely understand and identify factors assessing tumor death could inform and drive the development of more effective biological therapies for cancer patients. Sample acquisition in the blood includes serum/protein collection for Seldi-Tof mass spectrometry; and the collection of cells for microarray, proteomics, and high contents screening via cellomics. Protein chip Seldi-Tof MS has been already successfully used to discriminate serum expression profiles in various cancer types [11-13]. The complexity of these advanced, high-throughput technologies will exponentially increase the amount of data, with the consequence that the main activities of future biological and medical laboratories will be in data analysis and integration rather than in data collection. Therefore, specialized teams are required for collaboration efforts in order to manage data warehousing, mining and analysis, and thus establishing networks for the identification and application of biomarkers.
Beside proper study design, the models chosen to perform data classification and to estimate classification errors are highly critical for the complex data analysis. The identification of diagnostic markers for cancer, or markers to identify responders vs. non-responders to therapy requires systematical analysis of healthy vs. diseased, then of benign inflammatory disease vs. malignant cancer. Thus, methods to perform statistical analysis (e.g. permutation, randomization) are powerful, intuitive and provide an objective position from which to assess results. To handle these complex data analysis problems, the University of Pittsburgh has formed the Pittsburgh Supercomputing Center (PSC) headed by Dr. Arthur W. Wetzel, in a joint effort with Carnegie Mellon University and Westinghouse Electric Company, and is to date the most powerful open-resource computer available.
Imaging tools and Technologies for Translational Research
There are many examples of the value of weaving molecular imaging into Investigational New Drug Development. At the same time, the scale of the initial investments required vs. perceived benefits may not gain the necessary support of decision makers for application into development programs. There is a clear need to educate on the power and limitations of nuclear imaging techniques within the context of enhancing new drug development. Within this context, a primary goal for TR is to emphasize the cultural and operational shifts required of various stakeholders including academia, in order to better partner with industry.
The term imaging covers a range of available techniques, including discovery autoradiography, small animal imaging (PET and MRI), traditional anatomical imaging (Ultrasound, MRI, CT), functional imaging (MRI, PET, SPECT) and many new tracers are available as are techniques with increased sensitivity to enable micro-doing studies (AMS) [14]. Nuclear imaging techniques are powerful tools and can be used for a number of development objectives. These include a number of goals described below.
Firstly demonstrating drug penetration into the tissue of interest and co-localization or binding with the intended target through receptor occupancy (e.g., labeled ligand displacement), including describing dose vs. target occupancy curves remains a key objective an done used frequently in early clinical research. A second objective involves the quantification of a compound's pharmacokinetic (PK) profile using radio-labeled compound, an analysis that can be performed on a region of interest basis e.g. to assess time on target as well as potential therapeutic benefits vs. side effects. Additionally, imaging can be used to quantify pharmacodynamic (PD) effects of drug action and their relationship to administered dose. In combination, PK/PD information thus derived can be used to select a dose with which to test the clinical hypothesis or help quantify the therapeutic index. From a TR perspective all these techniques can be applied in the discovery and preclinical phases to facilitate compound selection and optimization as well as in the clinical phases.
A key question emerges in applying these technologies: "How best to get it done" and the debate of internal imaging centers vs. external networks and academic relationships quickly emerges. On balance, it is clear that there is not one ideal solution here rather in general a collaborative approach between industry and academia is recommended. As a consumer of medical imaging, industry is a critical player in driving innovation and the paradigm shift towards more frequent yet appropriate utilization. However, a partnership approach ultimately generates better value and cost-effectiveness for the Imaging discipline as a whole.
Conclusions and path forwards
TR is an approach to foster communication between the scientific community and clinical practitioners. To maximize the value this can bring requires that public and governmental education has to be improved in order to leverage understanding and advocacy. There are many benefits to be accrued from this, not least of which being for the patient that is waiting for meaningful therapeutic advances. New drugs have to be developed fast and show effect on the right target at the earliest possible stage of development in order for industry to become more innovative and productive and medicines to be less expensive.
Amongst other specific aspects required, are the strengthening of educational opportunities for physician scientists to help prepare them to conduct effective TR. At the same time, discovery science should be conducted by scientists who have been trained in relevant disciplines including cell biology and pharmacology as well as molecular biology. This in turn requires grant support for TR-related projects. Specifically, young scientific investigators should have more access to grants from governmental bodies and foundations in order to conduct research on clinical samples. This funding is largely in the hands of government leadership. Other points for disseminated education include the availability of a plethora of tools available to conduct and advance TR and development opportunities that include high quality clinical sample collection.
Lastly, since TR is information intensive, considerable efforts are required to provide accessible databases and share knowledge. To help ameliorate this gap and provide access to information derived from human experimentation and to optimize the communication between clinicians and scientist, Dr. Marincola founded the Journal of Translational Medicine, an Open Access, peer-reviewed online journal, so that more therapeutic insights may be derived from new scientific ideas – and vice versa .
In conclusion, TR represents a team effort, since no single constituency can be fluent in all aspects, and thus a concerted effort is needed amongst translational researchers to convince stakeholders and legislators of the need to support TR efforts, and thus maximize its potential.
Acknowledgements
The conference was organized by IQPC's Stacey Mankoff, Managing Director, and chaired by Francesco M. Marincola, MD, Director of Immunogenetics, NIH. Thanks go to both these individuals for shaping the program and chairing the meeting. In addition thanks are due to all the speakers, listed below, who contributed to the meeting. We would also like to thank Brian Swanson PhD, for critical review of the manuscript.
Roger L. Aamondt, PhD, Chief, Resources Development Branch, NCI.
David Botstein, PhD, Director and Anthony B Evnin Professor of Genomics, Lewis-Sigler Institute of Integrative Genomics, Princeton University, NJ.
Joan Dunbar, PhD, Director, Biotechnology Development, Wayne State University, MI
Alexander M. M. Eggermont, MD, PhD, President, EORTC, Daniel den Hoed Cancer Center, Erasmus Medical College, The Netherlands.
Frank Harrison PhD, Senior Director IS, Sanofi-Aventis
Bruce Littman, MD, Executive Director of Experimental Medicine, Pfizer, Groton, CT
Michael T. Lotze, MD, Director, Translational Research, Molecular Medicine Institute, University of Pittsburgh, PA
P. David Mozley, MD. Medical Advisor for Imaging Technologies, ELI LILLY & Co., Indianapolis, IN
Francesco M. Marincola, MD, Director of Immunogenetics, Department of Transfusion Medicine, NIH.
Philip Oldfield PhD, MSc, Scientific Director, Analytical Chemistry and Bioanalysis, CTBR Bio Research Inc Canada
Michael A. Perricone, PhD, Scientific Associate Director, Genzyme Corporation, Framingham, MA.
William Pullman MD PhD, Senior VP, Global Head Clinical Pharmacology Sanofi-Aventis Bridgewater, NJ
Edward A. Sausville, MD, PhD, Associate Director, Clinical Research, Greenbaum Cancer Center, University of Maryland.
Evan Siegel PhD, President and CEO Ground Zero Pharmaceuticals
Susan Smith, MSc, Scientific Director CTBR BIO Research, Canada
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| 15610560 | PMC544857 | CC BY | 2021-01-04 16:39:24 | no | J Transl Med. 2004 Dec 20; 2:44 | utf-8 | J Transl Med | 2,004 | 10.1186/1479-5876-2-44 | oa_comm |
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BMC Cardiovasc DisordBMC Cardiovascular Disorders1471-2261BioMed Central London 1471-2261-4-231559600410.1186/1471-2261-4-23Research ArticleChanging treatment patterns for coronary artery revascularization in Canada: the projected impact of drug eluting stents Halpern Michael T [email protected] Michael [email protected] Mary Ann [email protected] Miguel A [email protected] Exponent, Alexandria VA, USA2 Boston Scientific Corporation, Natick MA, USA2004 13 12 2004 4 23 23 2 4 2004 13 12 2004 Copyright © 2004 Halpern et al; licensee BioMed Central Ltd.2004Halpern 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
To evaluate current treatment patterns for coronary artery revascularization in Canada and explore the potential impact of drug eluting stents (DES) on these treatment patterns.
Methods
Eleven cardiologists at multiple Canadian academic centers completed a questionnaire on coronary artery revascularization rates and treatment patterns.
Results
Participating physicians indicated slightly higher rates of PTCA, CABG, and stent implantation than reported in CCN publications. Participants estimated that 24% of all patients currently receiving bare metal stents (BMS) would receive DES in the first year following DES approval in Canada, although there was a large range of estimates around this value (5% to 65%). By the fifth year following DES approval, it was estimated that 85% of BMS patients would instead receive DES. Among diabetic patients, estimates ranged from 43% in the first year following approval to 91% in the fifth year. For all patients currently receiving CABG, mean use of DES instead was estimated at 12% in the first year to 42% at five years; rates among diabetic patients currently undergoing CABG were 17% in the first year to 49% in the fifth year.
Conclusions
These results suggest a continued increase in revascularization procedures in Canada. Based on the panel's responses, it is likely that a trend away from CABG towards PTCA will continue in Canada, and will be augmented by the availability of DES as a treatment option. The availability of DES as a treatment option in Canada may change the threshold at which revascularization procedures are considered.
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Background
Coronary artery disease (CAD) is a common condition in western society [1]. Treatment of CAD often involves surgical revascularization, that is, removal of coronary artery stenoses to restore sufficient myocardial blood flow. Currently, there are three major treatment options for coronary artery revascularization: coronary artery bypass grafting (CABG), percutaneous transluminal coronary angioplasty (PTCA), and coronary artery stenting [2]. Treatment choice is based on a variety of factors, including patient age, comorbidities, extent of disease (i.e., number and location of affected coronary arteries), and disease severity.
Treatment choices and treatment patterns for CAD have changed over the past several years, and are likely to evolve further in the next few years. Drug eluting stents (DES) are a newly available treatment modality. DES are stents that incorporate bioactive coatings (polymer or non-polymer) permitting the release of associated molecules to attenuate the processes of restenosis. Preliminary clinical data suggests that use of DES can substantially reduce the rates of restenosis seen following implantation of bare metal stents but at much lower incidences of severe procedure-related complications as compared to CABG surgery [3,4]. DES may also be an important treatment option for populations such as individuals with diabetes and multi-vessel disease, who appear to have better outcomes with CABG [2].
It is likely that the availability of DES will change treatment patterns for patients with CAD [5]. To better understand current treatment patterns for CAD and the potential role of DES in Canada, we developed and administered a questionnaire to a panel of Canadian cardiologists. This manuscript describes the estimates regarding current and future CAD treatment patterns in Canada provided by this panel.
Methods
The objective of this study was to obtain opinions and estimates from Canadian cardiologists regarding the annual rates of PTCA, CABG, and stenting in Canada, treatment patterns associated with these procedures, and the potential future impact of DES on coronary revascularization treatment patterns. The results from this questionnaire were used in the development of health economic models used to evaluate these stents for use among Canadian patients.
The questionnaire asked participating physicians to assess the projected annual procedure rates for CABG, PTCA and stent implantations published by the Cardiac Care Network of Ontario (CCN) Target-Setting Working Group [6]. In addition to the estimated rates of the revascularization procedures, respondents were also requested to provide a lower and upper bound for each rate. Additional sections of the questionnaire provided recommendation rates for repeat revascularization procedures (following restenosis) depending on the type of first procedure. For example, physicians were asked for the percentage of patients they would recommend CABG after restenosis following PTCA. In the final sections, the questionnaire requested information on the projected use of DES among patients currently receiving bare metal stents or CABG. The questionnaire instructed respondents that if they agreed with provided reference values (based on published estimates) for a particular item, they could leave that item blank. However, respondents free to provide estimates that were greater, lesser, or unchanged compared to the reference values.
Cardiologists at multiple academic centers distributed across Canada were identified for this study. These physicians were contacted, provided a brief introduction to the study, and offered a study honorarium of $400 dollars to participate. The questionnaire was distributed to physicians willing to participate. Completed questionnaires were collected and data was entered into Excel. Summary statistics including mean, median, maximum, and minimum values were computed for the various estimates provided by the participating physicians. The summary statistics were returned to those doctors who completed questionnaires. Study participants were then asked to comment on the summary results and indicate any questions or comments on the results. At the time of the study, only one drug-eluting stent (CYPHER™, Cordis Corporation) had been approved for use by Health Canada (Nov. 2002).
Results
Study participants
A total of 18 Canadian physicians were contacted and invited to participate. Eleven physicians agreed to participate and complete the questionnaire, a response rate of 61%. All eleven physicians were male, specialized in cardiology, and had experience in this specialty ranging from 6 to 30 years. The average length of time specializing in cardiology was 12.8 years. The average age of participating physicians was 44.0 years. In each of the result tables, the number of panel members responding to each item is reported.
Procedure annual rates
The CCN Target-Setting Working Group (TSWG) projected annual rates of CABG surgery and PTCA of 110 and 160 per 100,000 Canadians, respectively [6]. Based on responses from the questionnaire, participating physicians reported slightly higher mean estimates of 112.7 (range of 100 to 150) for CABG and 172.3 (range of 125 to 200) for PTCA (Table 1). The TSWG also estimated that the rate of stent implantations among PTCA cases is over 90%. Questionnaire results indicated a similar proportion of patients receiving stents (92.1%, range 90% to 99%).
Table 1 Estimated rates of CABG, PTCA, and Stenting
Mean Median Min Max # of Panel Members Responding
Annual rate of CABG per 100,000 (CCN estimate: 110)
estimate 112.7 110 100 150 11
not lower than 91.1 90 60 110 9
not higher than 131.1 120 110 190 9
Annual rate of PTCA per 100,000 (CCN estimate: 160)
estimate 172.3 180 125 200 11
not lower than 147.8 150 110 180 9
not higher than 214.4 200 160 300 9
Percent of PTCA patients receiving stents (CCN estimate: >90%)
estimate 92.1% 90.0% 90.0% 99.0% 11
not lower than 81.8% 84.5% 70.0% 90.0% 8
not higher than 96.8% 96.0% 94.0% 100.0% 8
Recommendations for subsequent revascularization procedures
Table 2 presents results for recommendations from participating physicians for patients who need a subsequent revascularization procedure after having received either stenting or CABG. For patients requiring subsequent revascularization after stenting, PTCA would be recommended for approximately 40% of cases. Brachytherapy was the second most frequently recommended procedure (23%), followed by similar rates for CABG and a second stenting. For revascularization following CABG surgery, stenting was recommended for almost 80% of patients while PTCA and CABG were recommended for 6.8% and 13.6% of patients, respectively.
Table 2 Subsequent revascularization procedures following stenting or CABG
Mean Median Min Max # of Panel Members Responding
Percent of patients receiving each procedure following initial stenting
PTCA 38.5% 40.0% 9.0% 75.0% 11
Stenting 18.4% 15.0% 0.0% 65.0% 11
CABG 19.8% 15.0% 1.0% 50.0% 11
Brachytherapy 23.3% 20.0% 0.0% 90.0% 11
Percent of patients receiving each procedure following initial CABG
PTCA 6.8% 5.0% 0.0% 15.0% 11
Stenting 79.5% 80.0% 55.0% 90.0% 11
CABG 13.6% 10.0% 5.0% 40.0% 11
Projected use of DES among Canadian CAD patients
Tables 3 through 6 present results from the cardiologist panel regarding the projected use of DES once after they are approved in Canada. For these projections, we separately asked the panel members to provide estimates on the proportion of patients currently receiving bare metal stents who would likely receive DES instead versus the proportion currently undergoing CABG who would likely receive DES instead. We also requested rates DES adoption separately for the entire Canadian CAD population versus the subpopulation of Canadian CAD patients with diabetes, as the diabetic population is at higher risk for adverse clinical outcomes [7] and therefore may have different treatment patterns. In all cases, responses for the proportion of patients likely to receive DES were requested for all patients in the specified population as well as separately for patients with single- vs. multi-vessel CAD. Projections were requested annually for the first five years following approval of DES in Canada.
Table 3 Estimated Percentage of BMS Patients Likely to Receive DES by Year Following DES Approval
Mean Median Min Max # of Panel Members Responding
% of Bare Metal Stent Patients Receiving DES, 1st Year Following Approval
all patients 24.0% 22.5% 5.1% 65.0% 10
all patients with single-vessel 18.8% 12.6% 5.0% 50.0% 8
all patients with multi-vessel 32.9% 25.0% 5.0% 80.0% 6
% of Bare Metal Stent Patients Receiving DES, 2nd Year Following Approval
all patients 36.6% 40.0% 10.2% 65.0% 10
all patients with single-vessel 28.4% 30.4% 10.0% 50.0% 9
all patients with multi-vessel 43.9% 40.0% 5.1% 100.0% 8
% of Bare Metal Stent Patients Receiving DES, 3rd Year Following Approval
all patients 57.1% 60.0% 20.5% 80.0% 10
all patients with single-vessel 53.5% 60.0% 20.0% 80.9% 9
all patients with multi-vessel 61.2% 65.0% 10.3% 100.0% 8
% of Bare Metal Stent Patients Receiving DES, 4th Year Following Approval
all patients 76.7% 80.0% 50.8% 91.0% 10
all patients with single-vessel 69.6% 80.0% 30.0% 91.0% 9
all patients with multi-vessel 85.0% 80.0% 80.0% 100.0% 7
% of Bare Metal Stent Patients Receiving DES, 5th Year Following Approval
all patients 85.0% 90.0% 49.0% 100.0% 10
all patients with single-vessel 81.7% 90.0% 49.0% 100.0% 9
all patients with multi-vessel 88.4% 90.0% 60.0% 100.0% 8
Table 4 Estimated Percentage of Diabetic BMS Patients Likely to Receive DES by Year Following DES Approval
Mean Median Min Max # of Panel Members Responding
% of Diabetic Bare Metal Stent Patients Receiving DES, 1st Year Following Approval
all patients 43.2% 40.0% 10.2% 90.0% 11
all patients with single-vessel 39.5% 30.0% 10.2% 80.0% 9
all patients with multi-vessel 61.4% 50.0% 30.0% 100.0% 7
% of Diabetic Bare Metal Stent Patients Receiving DES, 2nd Year Following Approval
all patients 60.6% 60.0% 25.5% 100.0% 11
all patients with single-vessel 57.9% 50.0% 25.5% 100.0% 9
all patients with multi-vessel 75.0% 75.0% 40.0% 100.0% 7
% of Diabetic Bare Metal Stent Patients Receiving DES, 3rd Year Following Approval
all patients 77.9% 80.0% 50.0% 100.0% 11
all patients with single-vessel 75.7% 80.0% 50.0% 100.0% 9
all patients with multi-vessel 81.4% 80.0% 50.0% 100.0% 7
% of Diabetic Bare Metal Stent Patients Receiving DES, 4th Year Following Approval
all patients 86.1% 90.0% 60.0% 100.0% 11
all patients with single-vessel 85.2% 90.0% 60.0% 100.0% 9
all patients with multi-vessel 87.1% 90.0% 60.0% 100.0% 8
% of Diabetic Bare Metal Stent Patients Receiving DES, 5th Year Following Approval
all patients 90.9% 90.0% 70.0% 100.0% 11
all patients with single-vessel 88.0% 90.0% 70.0% 100.0% 10
all patients with multi-vessel 89.0% 90.0% 70.0% 100.0% 10
Table 5 Estimated Percentage of CABG Patients Likely to Receive DES by Year Following DES Approval
Mean Median Min Max # of Panel Members Responding
% of CABG Patients Receiving DES, 1st Year Following Approval
all patients 12.3% 5.4% 0.0% 50.0% 11
all patients with single-vessel 7.8% 5.1% 0.0% 20.0% 9
all patients with multi-vessel 15.7% 6.0% 0.0% 80.0% 8
% of CABG Patients Receiving DES, 2nd Year Following Approval
all patients 17.5% 12.6% 5.0% 50.0% 10
all patients with single-vessel 9.7% 10.0% 2.0% 20.0% 8
all patients with multi-vessel 21.4% 15.1% 5.0% 80.0% 8
% of CABG Patients Receiving DES, 3rd Year Following Approval
all patients 31.7% 30.0% 5.0% 90.0% 10
all patients with single-vessel 29.1% 27.7% 2.0% 90.0% 8
all patients with multi-vessel 28.9% 25.0% 5.0% 90.0% 8
% of CABG Patients Receiving DES, 4th Year Following Approval
all patients 37.3% 33.0% 5.0% 90.0% 10
all patients with single-vessel 32.0% 30.2% 5.0% 90.0% 8
all patients with multi-vessel 33.2% 30.0% 5.0% 90.0% 8
% of CABG Patients Receiving DES, 5th Year Following Approval
all patients 42.1% 40.2% 5.0% 90.0% 10
all patients with single-vessel 35.1% 30.2% 5.0% 90.0% 7
all patients with multi-vessel 37.6% 30.0% 5.0% 90.0% 7
Table 6 Estimated Percentage of Diabetic CABG Patients Likely to Receive DES by Year Following DES Approval
Mean Median Min Max # of Panel Members Responding
% of Diabetic CABG Patients Receiving DES, 1st Year Following Approval
all patients 16.9% 10.0% 1.1% 65.0% 10
all patients with single-vessel 17.4% 5.1% 1.1% 50.0% 7
all patients with multi-vessel 19.5% 10.0% 1.1% 80.0% 9
% of Diabetic CABG Patients Receiving DES, 2nd Year Following Approval
all patients 26.0% 17.5% 5.1% 80.0% 10
all patients with single-vessel 21.3% 15.2% 5.0% 50.0% 8
all patients with multi-vessel 21.1% 15.0% 5.1% 80.0% 9
% of Diabetic CABG Patients Receiving DES, 3rd Year Following Approval
all patients 33.5% 25.0% 10.0% 90.0% 10
all patients with single-vessel 30.1% 25.2% 5.0% 90.0% 8
all patients with multi-vessel 26.3% 17.5% 5.1% 90.0% 9
% of Diabetic CABG Patients Receiving DES, 4th Year Following Approval
all patients 42.6% 40.0% 10.0% 90.0% 10
all patients with single-vessel 37.0% 32.8% 5.0% 90.0% 8
all patients with multi-vessel 34.0% 30.0% 10.2% 90.0% 9
% of Diabetic CABG Patients Receiving DES, 5th Year Following Approval
all patients 48.6% 50.0% 10.0% 90.0% 10
all patients with single-vessel 41.4% 45.0% 5.0% 90.0% 8
all patients with multi-vessel 42.6% 37.5% 15.0% 90.0% 10
Tables 3 and 4 present estimated percentage for all CAD patients and diabetic patients (respectively) who are currently receiving bare metal stents but are likely to receive DES once approved. As presented in Table 3, the mean estimated percentage for all CAD patients in the first year of approval is 24%. A large range was present around this mean, from a minimum of 5.1% to a maximum of 65%. However, the median (22.5%) was similar to the mean, suggesting that outliers did not substantially skew the mean value. Patients with single-vessel disease were less likely to receive DES (18.8%), while those with multi-vessel disease were more likely (32.9%). The proportion of patients projected to receive DES rather than bare metal stents increased with each subsequent year after DES approval. In each year, a greater proportion of multi-vessel disease patients are projected to receive DES than are single vessel disease patients. During the fifth year following approval, the panel estimated that 85% of all bare metal stents patients are likely to receive DES instead. Ranges around the annual mean values continued to be large, with the minimum estimate being 49% and the maximum estimate of 100%.
Projected use of DES among diabetic patients who currently receive bare metal stents is presented in Table 4. Among patients with diabetes, the estimated percentage likely to receive DES is higher than the corresponding values of the overall population. In the first year following DES approval, 43.2% of patients with diabetes who would have received bare metal stents are projected to receive DES instead. While the median proportion of diabetic patients receiving DES in this first year (40%) is similar to the mean, suggesting that outliers do not skew the projections, a very large range of responses was present (10.2% to 90%). The estimated proportion of patients receiving DES rather than bare metal stents in the first year was 39.5% for single vessel disease patients with diabetes, and 61.4% for multi-vessel disease patients. As with the overall population of CAD patients currently receiving bare metal stents (Table 3), the proportion of patients with diabetes receiving DES instead of bare metal stents increases in each subsequent year, and the percentage is greater for multi-vessel disease patients than for single vessel disease patients. In the fifth year following DES approval, it is estimated that 90.9% of patients with diabetes who would have received bare metal stents will instead receive DES (range 70% to 100%).
Table 5 presents estimates from the cardiologist panel for CABG patients who are likely to receive DES after approval. For each year, the proportion of CABG patients who would instead receive DES is approximately half the proportion of bare metal stent patients who would receive DES instead (Table 3). Of all CABG patients, 12.3% are estimated to likely receive DES during the first year following approval in Canada. The range of estimates for receipt of DES rather than CABG was substantial, from a minimum of 0% to a maximum of 50%. The median estimate, 5.4%, is lower than the mean, suggesting that higher estimates may be skewing the mean. In the first year following approval, 7.8% of single vessel disease patients and 15.7% of multi-vessel disease patients would receive DES rather than CABG. In years three through five after DES approval, the estimated proportions of single vessel disease and multi-vessel disease patients likely to receive DES rather than CABG are both less than the proportion among the overall CABG population. This is due to missing data, in that some panel members provided projections only for the overall population and/or one of the population subgroups. In these cases, the relative projections for single vessel and multi-vessel disease patients cannot be directly compared to the estimates for the overall population.
Similar to DES adoption among bare metal stent patients, the likelihood of DES use among CABG patients increases with each year after approval. At year five, 42% of all CABG patients are likely to receive DES compared to 85% of bare metal stent patients. In all years except year three, the proportion of single vessel disease CABG patients instead receiving DES is less than the proportion for multi-vessel disease CABG patients.
Table 6 provides the estimated rates of DES use for patients with diabetes currently receiving CABG. In the first year following approval, 16.9% of patients with diabetes who would have received CABG are projected to likely to receive DES instead. The range of estimates around this value is large (1.1% to 65.0%). The mean estimate of DES adoption among diabetic CABG patients increased each year, and is larger each year than the corresponding mean estimate for the overall population of patients who would receive CABG. However, the estimated proportion of patients with diabetes receiving DES instead of CABG is less than the estimated proportion for bare metal stent patients.
Rates for single and multi-vessel disease patients with diabetes are similar; however, as noted above, missing data makes comparison of these subpopulations to the overall diabetic population difficult. During the fifth year following approval, an estimated 48.6% of diabetic patients who would have received CABG surgery are instead projected receive DES.
Recommended use of DES among Canadian CAD patients
Tables 3 through 6 present the proportion of bare metal stent and CABG patients who are likely to receive DES rather than these other revascularization procedures. In a final question to the cardiologist panel, we asked for estimates of the proportion of bare metal stent and CABG patients in the overall CAD population who should receive DES rather than these other procedures. In requesting this additional information, the questionnaire specified that respondents could indicate that the proportion of patients who should receive DES is the same as or different from the proportion that are likely to receive DES (as presented in Tables 3 and 5). The questionnaire also specified that in estimating the proportion of patients who should receive DES instead of bare metal stents of CABG, panel members should assume that funding is available for this intervention. Thus, this question addressed the projected use of DES in a best-case scenario, without economic restrictions.
The estimated percentages of patients who should receive DES are summarized in Table 7. During the first year of approval, the mean estimated percentage of bare metal stent patients who should receive DES is 42.8%; this is close to double the estimate of the proportion of bare metal stent patients who are likely to receive DES during the first year following approval (24.0%, Table 3). The estimated percentage of CABG patients who should receive DES during the first year following approval is 16.8%, an increase of 37% over the proportion of CABG patients likely to receive DES that year (12.3%, Table 5). These estimated percentages of patients who should receive DES increase with each year after DES approval. At year five, the panel indicated that 86.8% of bare metal stent patients and 43.7% of CABG patients should be receiving DES. The median responses are very similar to these values, suggesting that outliers are not distorting the presented means. However, while the range around the mean proportion of bare metal stent patients who should receive DES has decreased (minimum 60.8%, maximum 100%), the range around the proportion of CABG patients who should receive DES remains very large (5% to 90%). Thus, there are considerable differences in opinion regarding the appropriate patients to convert from CABG to DES.
Table 7 Estimated Percentage of BMS and CABG Patients who Should Receive DES by Year Following DES Approval*
Mean Median Min Max
% of Patients Who Should Receive DES, 1st Year Following Approval
Bare metal stent patients 42.8% 30.0% 5.1% 100.0%
CABG patients 16.8% 10.0% 5.0% 50.0%
% of Patients Who Should Receive DES, 2nd Year Following Approval
Bare metal stent patients 56.0% 50.0% 10.2% 100.0%
CABG patients 25.5% 20.0% 5.0% 80.0%
% of Patients Who Should Receive DES, 3rd Year Following Approval
Bare metal stent patients 70.1% 90.0% 30.0% 100.0%
CABG patients 31.4% 30.0% 5.0% 90.0%
% of Patients Who Should Receive DES, 4th Year Following Approval
Bare metal stent patients 79.7% 90.0% 40.6% 100.0%
CABG patients 37.8% 35.0% 5.0% 90.0%
% of Patients Who Should Receive DES, 5th Year Following Approval
Bare metal stent patients 86.8% 90.0% 60.8% 100.0%
CABG patients 43.7% 40.0% 5.0% 90.0%
*In answering this question, respondents were asked to assume that funding for DES was available. All questions were responded to by all 11 members of the study panel.
Discussion
This study presents results from a panel of Canadian cardiologists on treatment patterns for coronary artery revascularization and the potential future adoption of DES in these treatment patterns. Previous reports have debated whether the rate of coronary revascularization in Canada is likely to decrease [8] or increase [9] during the present decade. The estimated procedure rates provided by the panel were slightly higher than those from the CCN, suggesting a continued increase in revascularization procedures. In addition, multiple reports have indicated that PTCA is replacing CABG among broad populations of patients requiring coronary revascularization, and CABG is being performed more frequently among higher risk patients [10]. Based on the panel's responses, it is likely that a trend away from CABG towards PTCA will continue in Canada, and will be augmented by the availability of DES as a treatment option.
There are a number of limitations associated with this study. The panel members were recruited from academic medical centers and thus may be more familiar with and more likely to use newer technologies. This may limit the generalizability of the rates provided by the panel to the overall population of Canadian cardiologists and may explain the differences between the panel's estimates and those of the CCN. The study panel was also relatively small; this small sample size may result in estimates that are subject to change if a larger population of cardiologists is surveyed.
Despite these limitations, the results of the panel questionnaire indicate that DES will be an important treatment option for Canadian CAD patients, both among patients currently receiving bare metal stents and for patients currently undergoing CABG surgery. It is difficult to assess the validity of these results, as they relate to future events. A recent report by Poses et al. indicated that physicians were likely to underestimate survival for medically managed CAD patients and overestimate the benefits for such procedures [11]. If this finding applies to the present study, then the rate of DES adoption may be lower than that reported. However, other reports have suggested that coronary revascularization procedures are currently underused, with resulting adverse clinical outcomes [12]. Even if the adoption rates are lower than the projected values presented in Tables 3 through 6, DES is likely to be a commonly used treatment modality. Further, the availability of this less invasive yet more efficacious treatment option may address the potential underuse issues, resulting in greater adoption rates than reported by the panel. The recommended adoption rates presented in Table 7 may then be more realistic estimates for the future use of DES.
Little information is available regarding the impacts of "converting" patients from CABG to stent implantation. Lee et al. evaluated in the impact of bare metal stent use among patients who were at high operative risk or refused CABG [13]. In the Lee et al. study, stent implantation was reported to be safe and clinically beneficial [13]. Use of DES as a treatment option is likely to improve clinical outcomes while maintaining the safety of this less invasive revascularization approach.
We requested information separately for the projected use of DES among individuals with diabetes. Previous studies have reported that CAD patients with diabetes have better outcomes following CABG than with PTCA [14,15]. Available data also suggest that use of bare metal stents improves outcomes among patients with diabetes compared to angioplasty alone [16], although it is unclear whether or not diabetics have worse outcomes following stenting than do non-diabetics [7,17]. While few published data are yet available regarding outcomes among individuals with diabetes following DES implantation, reductions in subsequent restenoses and revascularizations in the general population receiving DES may also occur in the diabetic population. The cardiologist panel felt that DES would become a frequently used treatment option in this population, with adoption rates surpassing those of the overall CAD population.
Comparing the estimated proportion of patients who the panel indicated were likely to receive DES versus the proportion the panel reported "should" receive DES provides interesting findings. The proportion of bare metal stent patients that the panel indicated should receive DES (Table 7) is greater than the proportion who are likely to receive DES (Table 3) for each of the first five years following approval. These estimated proportions become approximately equal at five years following approval (85.0% likely to receive DES, 86.8% should receive DES). A number of factors may influence the difference in proportions between patients who are "likely to" versus "should" receive DES, such as available funding and attitudes towards adoption of new technologies. The estimated proportion of CABG patients who should receive DES (Table 7) is greater than the proportion that are likely to receive DES (Table 5) during the first two years following approval. For years three through five, the "likely to" and "should" proportions are approximately equal for the CABG population. This more rapid convergence of projected rates may reflect the perceived benefits of the less invasive stenting with DES compared to CABG as well as the potential cost savings from DES versus CABG.
A number of reports have indicated that the rate of coronary revascularization procedures in Canada is less than that in the U.S. Bourassa et al. reported that more anginal symptoms were present in Canadian patients prior to revascularization compared to U.S. patients, although Canadian patients apparently experienced greater improvements in quality of life following revascularization procedures [18]. The availability of DES as a treatment option in Canada may change the threshold at which revascularization procedures are considered. The projected uptake rates presented in Tables 3 through 6 certainly indicate that DES is likely to be used for a substantial proportion of revascularization procedures. It will therefore be important to evaluate the impact of this new technology on patient-reported outcomes, such as satisfaction with treatment, satisfaction with the medical care system (e.g., time until treatment), and change in health-related quality of life. These metrics will help to assess further the potential benefits of DES in Canada.
Conclusions
Cardiologists at tertiary care hospitals in Canada expect the use of drug-eluting stents (DES) for coronary artery revascularization to increase over the next five years. DES will both be used instead of bare metal stents and an alternative to CABG surgery. This increase in DES use will increase initial procedure-related costs compared to bare metal stents, but is likely to decrease subsequent costs due to the decreased need for repeat revascularizations. Medical care decision makers and planners need to prepare for this increased use, in terms of both facility and budget allocation as well as staffing availability and training.
Competing interests
This study was performed under a research contract from Boston Scientific Corporation. MH has received research funding from Boston Scientific. ML, MAC, and MV are employees of Boston Scientific Corporation. Boston Scientific is providing the article-process charge for this manuscript. Boston Scientific holds a number of patents on the TAXUS Express2 Paclitaxel-Eluting Coronary Stent System. Publication of this article may result in increased consulting work for Exponent, Inc.
Authors' contributions
MH directed the overall study and participated in all aspects. ML and MV participated in study design and questionnaire development. MAC participated in data analysis. All authors participated in preparation of the manuscript and read and approved the final manuscript.
Pre-publication history
The pre-publication history for this paper can be accessed here:
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| 15596004 | PMC544858 | CC BY | 2021-01-04 16:30:02 | no | BMC Cardiovasc Disord. 2004 Dec 13; 4:23 | utf-8 | BMC Cardiovasc Disord | 2,004 | 10.1186/1471-2261-4-23 | oa_comm |
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Virol JVirology Journal1743-422XBioMed Central London 1743-422X-2-11563163110.1186/1743-422X-2-1ResearchThe involvement of survival signaling pathways in rubella-virus induced apoptosis Cooray Samantha [email protected] Li [email protected] Jennifer M [email protected] Enteric, Neurological, and Respiratory Virus Laboratory, Health Protection Agency, 61 Colindale Avenue, London NW9 5HT, UK2 Department of Infectious Diseases, Virology Section, Guy's, King's and St. Thomas' School of Medicine, St. Thomas' Hospital, London SE1 7EH, UK3 Present address: Department of Virology, 3rd Floor, Wright Flemming Institute, Imperial College Faculty of Medicine, Norfolk Place, London W2 1PG, UK2005 4 1 2005 2 1 1 22 11 2004 4 1 2005 Copyright © 2005 Cooray et al; licensee BioMed Central Ltd.2005Cooray 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.
Rubella virus (RV) causes severe congenital defects when acquired during the first trimester of pregnancy. RV cytopathic effect has been shown to be due to caspase-dependent apoptosis in a number of susceptible cell lines, and it has been suggested that this apoptotic induction could be a causal factor in the development of such defects. Often the outcome of apoptotic stimuli is dependent on apoptotic, proliferative and survival signaling mechanisms in the cell. Therefore we investigated the role of phosphoinositide 3-kinase (PI3K)-Akt survival signaling and Ras-Raf-MEK-ERK proliferative signaling during RV-induced apoptosis in RK13 cells. Increasing levels of phosphorylated ERK, Akt and GSK3β were detected from 24–96 hours post-infection, concomitant with RV-induced apoptotic signals. Inhibition of PI3K-Akt signaling reduced cell viability, and increased the speed and magnitude of RV-induced apoptosis, suggesting that this pathway contributes to cell survival during RV infection. In contrast, inhibition of the Ras-Raf-MEK-ERK pathway impaired RV replication and growth and reduced RV-induced apoptosis, suggesting that the normal cellular growth is required for efficient virus production.
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Introduction
Rubella virus (RV) is the sole member of the Rubivirus genus of the Togaviridae. It has a positive-sense single stranded RNA genome that is 9762 nucleotides (nt) in length and contains two non-overlapping open-reading frames (ORFs). The 5' proximal ORF encodes the p200 polyprotein precursor for the nonstructural proteins (NSPs) p150 and p90 [1,2]. The 3' proximal ORF encodes the structural proteins: capsid (C), and glycoproteins E1 and E2 [3,4].
RV infection usually causes mild disease with few complications. However, infection during the first trimester of pregnancy results in fetal infection, and in more than 75% of cases this leads to the development of congenital abnormalities. These abnormalities include sensorineural deafness, mental retardation, and congenital heart defects, and are collectively termed congenital rubella syndrome (CRS) [5]. The cellular mechanisms activated by RV, which lead to the disruption of organogenesis, are not fully understood. However, in permissive cell cultures, the cytopathic effect (CPE) of RV has been shown to be due to caspase-dependent apoptosis [6-12]. Apoptosis is a key component of developmental processes in mammals, which functions to delete vestigial structures, control cell number and remodel tissues and organs [13]. Thus, it has been proposed that RV-induced apoptosis may cause irreparable damage to fetal tissues, resulting in the abnormalities observed in CRS [12]. However, the outcome of RV infection is likely to depend on multiple signaling events that control the balance between cell death and cell survival.
Eukaryotic cells contain a large number of mitogen activated protein kinase (MAPK) signaling cascades that are activated in response to growth factors, cytokines and stress stimuli such as viral infection and UV irradiation. In common with apoptotic proteins, MAPKs are highly conserved and ubiquitously expressed [14,15]. These cascades integrate external stimuli and transmit signals to the nucleus resulting in the activation of transcription factors, which regulate expression of genes required for proliferation, differentiation, survival and apoptosis. Two well-studied mitogenic pathways are the phosphoinositide 3-kinase (PI3K)-Akt pathway and the Ras-Raf-MEK-ERK pathway, which are central to cell survival and proliferative signals respectively.
PI3Ks phosphorylate plasma membrane inositol lipids at the 3' position of the inositol ring. These 3'phosphoinsoitides recruit proteins such as Akt and phosphoinositide dependent kinases 1 and 2 (PDK1/2) to the plasma membrane via their pleckstrin homology (PH) domains [16,17]. At the plasma membrane PDK1/2 activate Akt through phosphorylation at Ser473 and Thr308. Activated Akt promotes cell survival by phosphorylating and inhibiting a number of pro-apoptotic proteins including BAD, caspase-9, GSK-3β and Forkhead transcription factors [18,19].
The Ras-Raf-MEK-ERK is a classical MAPK pathway where growth factor-receptor interactions trigger intracellular activation of the small G-protein Ras. Ras recruits and directly activates the MAPK kinase kinase (MAPKK) Raf, which phosphorylates and activates the MAPK kinase (MAPKK) MEK1/2, which in turn activate the MAPK ERK1/2. Activated ERK1/2 translocates to the nucleus where it can activate a number of transcription factors including c-myc, c-jun, and Elk-1, which regulate cell cycle progression responses [20].
Activation of PI3K-Akt and Ras-Raf-MEK-ERK signaling cascades during virus infection is thought to play an important role not only in cellular growth and survival, but also in virus replication and growth during both acute and chronic virus infections [21-25]. This study was carried out to examine the role of PI3K-Akt and Ras-Raf-MEK-ERK signaling during RV infection in RK13 cells. The PI3K inhibitor LY294002 and the MEK inhibitor U0126 were used to investigate PI3K-Akt and Ras-Raf-MEK-ERK signaling respectively during RV replication, growth and induction of apoptosis. Apoptosis was measured in RV-infected cells by caspase activity and cell viability assays, DNA fragmentation analysis, and trypan blue exclusion staining. Involvement of PI3K-Akt and Raf-Raf-MEK-ERK signaling in RV-induced apoptosis was also examined by expression of constitutively active Akt and MEK in RV-infected cells.
Results
Phosphorylation of Akt, ERK1/2 and their downstream targets during RV infection
The effect of RV infection on PI3K-Akt and Ras-Raf-MEK-ERK pathways was investigated by examining the expression and phosphorylation profiles of Akt, ERK1/2 and their downstream targets. Cell lysates from RV and mock infected RK13 cells were collected 12–96 hours post-infection (p.i.), separated by SDS-PAGE, and analyzed for total and phosphorylated Akt and ERK1/2 by Western blotting. Phosphorylated Akt and ERK1/2 could be detected in RV-infected cells from 48 hours p.i., and band intensity increased from 48–96 hours p.i. compared to total levels (Fig. 1A). Phosphorylated Akt and ERK2 (but not ERK1) were detected in the mock-infected cells at 96 hours p.i. but not before, whereas total levels of Akt and ERK 1/2 were detectable at all time points (Fig. 1A). Treatment of RV-infected cells with PI3K inhibitor LY294002 and MEK1/2 inhibitor U0126 completely inhibited activation of Akt and ERK1/2 respectively (data not shown).
Figure 1 Kinase phosphorylation during RV infection. Serum-starved RK13 cells were mock infected or infected with RV at an m.o.i. of 4 PFU/cell. At indicated time points cell lysates were collected and proteins (30 μg/lane) were separated by SDS-PAGE, and analysed by Western blotting using phospho-specific antibodies. Blots were also probed with anti-tubulin antibody to demonstrate equal loading. A – Total and phosphorylated Akt and ERK (24–96 hours p.i.). B – Total and phosphorylated Akt, ERK, and p70S6K, and phosphorylated GSK-3β and c-myc. The data were consistently repeated in two independent experiments.
The phosphorylation of Akt and ERK and their downstream targets p70S6K, GSK-3β, c-myc and BAD were also examined by Western blotting between 12–96 hours p.i. (Fig. 1B). Phosphorylated Akt and ERK1/2 were detectable in RV-infected cells at 48 and 36 hours p.i. respectively. p70S6K is phosphorylated by FRAP/mTOR downstream of Akt at Thr389 and at Thr421/Ser42, downstream of the Ras-Raf-MEK-ERK pathway. Phosphorylation at Thr389 was observed at 12, 24, 60, 84 and 96 hours p.i. (Fig. 1B). Phosphorylation of the Thr421/Ser42 site was observed at all time points, although increases in band intensity could be seen at 12, 24, 60, 84 and 96 hours p.i., mirroring the phosphorylation at Thr389. Phosphorylation of Thr421/Ser424 but not Thr389 was observed in the mock-infected cells, albeit at a lower level than in RV-infected cells.
The phosphorylation of GSK-3β, downstream of Akt, increased from 12 and 96 hours p.i. and was similar to that of Akt. Phosphorylation of BAD, another substrate for Akt, however, could not be detected in RV-infected or mock-infected cells. The phosphorylation of c-myc, a transcription factor activated by ERK1/2 phosphorylation, decreased between 12 and 96 hours p.i., in contrast to the phosphorylation profile of ERK1/2. GSK-3β and c-myc were also detectable in the mock-infected cells at 96 hours p.i.
The effects of LY294002 and U0126 on cell viability in RV-infected cells
RV induces apoptosis in RK13 cells with characteristic morphological and biochemical features [6,8,9]. The XTT assay was used to examine the effect of RV infection and LY29002 and U0126 treatment on cellular metabolism over time. XTT is a tetrazolium salt, which is cleaved by the succinate dehydrogenase system of mitochondria in metabolically active cells, to yield a soluble orange formazan product. A decrease in the intensity of formazan was used to monitor changes in cellular metabolism and cell viability in RV-infected cells by spectroscopy.
Cellular viability during RV infection did not appear to be disrupted, supporting previous observations which reported that a large number of monolayer cells remain in tact and do not rapidly undergo apoptosis in RV infected cells [9,12] (Fig. 2). LY294002 treatment of RK13 cells reduced cell viability by 20%, which remained constant throughout the 12–96 hour period. Cell viability was reduced to 60% in the presence of both RV and LY294002. Thus the combined effect of PI3K inhibition and RV-infection caused a significant reduction in cell viability.
Figure 2 The effect of PI3K and MEK1/2 inhibition on cell viability during RV infection. Serum-starved RK13 cells were mock infected or infected with RV at an m.o.i of 4 PFU/cell with or without LY294002 (30 μM) or U0126 (15 μM). At indicated time points cell viability was determined by XTT assay. Tetrazolium salt (XTT) and electron coupling reagent were added directly to cells, and after 24 hours the absorbance at 405–690 nm was determined. Data represent mean ± S.E. from three independent experiments.
As Ras-Raf-MEK-ERK signaling is crucial to the regulation of cell growth in many cell lines, inhibition of this pathway often has detrimental effects. A typical dose-response curve can be seen with MEK inhibitor U0126 in RK13 cells, with cell viability completely abolished by 60–72 hours p.i. (Fig. 2). With the addition of RV, the U0126 curve moved to the right, the effect of the drug was delayed by approximately 12 hours.
Inhibition of PI3K results in an increase in the speed and magnitude of RV-induced apoptosis
To evaluate the role of PI3K-dependent signaling during RV infection, the effects of PI3K inhibitor LY294002 on the development of RV-induced apoptosis were examined, 12–96 hours p.i., by caspase activity assay, trypan blue exclusion staining, DNA fragmentation and light microscopy. (Fig. 3A–D). RV-induced apoptotic signaling has been reported to occur between 12–24 hours p.i., with peak caspase activity occurring around 72 hours p.i. at a multiplicity of infection (MOI) of 3 PFU/cell [6]. Fig. 3A shows that with a MOI of 4 PFU/cell the peak of RV-induced caspase activity occurs earlier at 60 hours p.i. When RV infection was carried out in the presence of LY294002, the maximum caspase activity increased by 53.9 % (P < 0.05) and occurred 12 hours earlier than with RV alone (Fig. 3A).
Figure 3 The effect of PI3K and MEK1/2 inhibition on RV-induced apoptosis. Serum-starved RK13 cells were mock infected or infected with RV at an m.o.i of 4 PFU/cell with or without LY294002 (30 μM) or U0126 (15 μM). Cells were harvested and analyzed for markers of apoptosis. A – At indicated time points, cell lysates were collected and incubated with artificial caspase substrate Ac-DEVD-pNA. Free pNA due to caspase cleavage was measured at an absorbance of 405 nm. Data represent mean ± S.E. from three experiments, *P < 0.05. B – The number of measurable dead floating cells in the cell culture supernatant was determined by trypan blue exclusion staining at indicated time points. Data represent mean ± S.E. from three experiments, *P < 0.05. C – Total DNA was extracted from detached and monolayer cells at 72 hours p.i. and apoptotic DNA fragments were resolved on a 1.5% agarose gel, stained with ethidium bromide, and visualized using UV transillumination. Molecular size markers were run in the left hand lane. D – Light microscopy photographs of cell monolayers at 72 hours p.i., at a magnification of 20X.
This increase in speed and magnitude of RV-induced apoptosis is more strikingly observed in Fig. 3B, which shows the number of dead floating cells by trypan exclusion staining in the culture supernatant fluid of RV infected and LY294002 treated cells. LY294002 treatment doubles (and at 84 hours p.i. triples) the number of floating cells produced in RV-infected cells. Increases in the number of apoptotic floating cells are statistically significant at 84 and 96 hours p.i. (P < 0.05). Fragmented DNA patterns can be seen at 72 hours p.i. with both RV and RV in the presence of LY294002 (Fig. 3C). However, the interesting feature of these apoptotic ladders is that in RV-infected cells, a significant proportion of genomic DNA is still intact, whereas when RV-infected cells are also exposed to LY294002, the majority of the genomic DNA is fragmented. The morphological changes caused by RV-infection and LY294002 were examined by light microscopy (Fig. 3D). At 72 hours p.i. CPE and induction of apoptosis by RV can be clearly seen. RV-induced CPE is characterized in the earlier stages by clumps of apoptotic cells, surrounded by healthy cells. In the later stages the cell sheet is completely destroyed and the majority of cells have become apoptotic floaters [6]. In the presence of LY294002, RV-infected cells are almost all dead by 72 hours p.i., resembling the later stages of RV-induced CPE.
LY294002-only treatment of RK13 cells did not induce apoptosis as evidenced by the lack of caspase activity (Fig. 3A), DNA fragmentation (Fig. 3C), and measurable floating cells (data not shown). Morphological examination of LY294002 treated RK13 cells show the cell monolayers were in tact with no visible cytotoxicity (Fig. 3D).
Inhibition of MEK1/2 reduces RV-induced apoptosis
The role of Ras-Raf-MEK-ERK signaling in RV-induced apoptosis was investigated using MEK inhibitor U0126 as described above for LY294002 (Fig. 3A–D). U0126 treatment reduced caspase activity in RV-infected cells by 51.9% (P < 0.05), with a low peak occurring at 48 hours p.i. (Fig. 3A). The number of dead floating cells in RV and U0126-treated cells was slightly lower than in RV-infected cells at all time points (Fig. 3B). DNA fragmentation was observed in both RV-infected cells and RV in the presence of U0126 (Fig. 3C), although the presence of the drug also appeared to result in smearing of high molecular weight DNA, characteristic of necrosis [26,27]. The detrimental effect of U0126 on RK13 cell morphology is shown in Fig. 3D; this correlates with the rapid decline in cell viability.
Inhibition of MEK1/2 inhibits RV replication and growth
To examine the effect of LY294002 and U0126 on RV replication and growth, RV-infected and drug-treated cell culture supernatants were tested for RV capsid gene expression by RT-PCR, and virus growth by TCID50 assay 24–96 hours p.i.. The capsid gene is the first gene to be transcribed from the second ORF encoding the structural proteins. Therefore detection of capsid RNA by RT-PCR is a good measure of RV replication [1,28]. In RV-infected cells simultaneously treated with LY294002, levels of RV capsid RNA increased over time, as in RV-infected cells (Fig. 4A). In the presence of U0126, however, levels of capsid RNA were reduced, and remained lower than that seen at 24 hours p.i. in RV-infected cells.
Figure 4 The effect of PI3K and MEK1/2 inhibition on RV growth and replication. Serum-starved RK13 cells were infected with RV at an m.o.i of 4 PFU/cell with or without LY294002 (30 μM) or U0126 (15 μM). Cell culture supernatants were extracted from cells at indicated time points. A – RV RNA was extracted from virus-infected cell culture supernatants and the capsid gene was amplified by RT-PCR as described under 'Experimental Procedures'. B – Monolayers of RK13 cells in 96-well plates were infected with RV-infected cell culture supernatants, and virus titers were determined using the TCID50 assay. Results are representative of a least two independent experiments.
Both LY294002 and U0126 affected virus growth (Fig. 4B). During RV-infection of RK13 cells with 4 PFU/cell of virus, virus titers reached 108 TCID50/ml by 96 hours p.i. However, in the presence of U0126 the titer was approximately 102 lower at 24 hours p.i., 103 lower at 48 hours p.i., and 104 lower at 72–96 hours p.i. LY294002 reduced virus growth to a similar extent, but unlike with U0126, by 96 hours p.i. the virus titer recovered slightly.
Constitutively active Akt and MEK1/2 enhance RV-induced apoptosis
To determine the importance of PI3K-Akt and Ras-Raf-MEK-ERK in the transduction of cell survival and proliferative mechanisms during RV-infection, RK13 cells were transiently transfected with constitutively active forms Akt and MEK. Significant expression of both proteins was seen after 24 hours (Fig. 5A). Over-expression of both activated Akt and MEK enhanced RV-induced caspase activity (Fig. 5B). RV infection in the presence of the empty pUSEamp(+) control vector slightly decreased caspase activity. Caspase activity following Lipofectamine treatment alone or pUSEamp(+) transfection was below that of the mock-infected cells (data not shown).
Figure 5 Over-expression of Akt and MEK enhances RV-induced apoptosis. RK13 cells were transfected with eukaryotic expression vector pUSEamp(+) containing constitutively active HA-tagged MEK1 or myristoylated myc-tagged Akt1 under the control of a CMV promoter, or with an empty pUSEamp(+) control. A – Expression of MEK1 and Akt1 was determined by Western blotting. Cell lysates were collected 24 hours post-transfection and 30 μg protein separated by SDS-PAGE and transferred to nitrocellulose membranes. MEK1 and Akt1 were detected by anti-HA and anti-myc antibodies respectively. B – RK13 cells in 6-well plates were transfected with Akt, MEK or pUSEamp(+) control constructs for 24 hours and subsequently infected with RV or mock-infected. 24 hours later cell lysates were collected and tested for caspase activity using artificial caspase substrate Ac-DEVD-pNA.
Discussion
We have previously shown that RV induces caspase activation during the early stages of infection in vitro, prior to the appearance of morphological apoptotic changes [6]. In this study we demonstrated that, in common with other viruses such as Coxsackievirus B3 virus, human cytomegalovirus, influenza virus A, and respiratory syncitial virus (RSV) (Cooray, 2004; Johnson et al., 2001; Opavsky et al., 2001; Pleschka et al., 2001), signaling downstream of PI3K stimulates a survival response in the cell following RV infection and that signaling downstream of MEK1/2 is required for RV replication, growth and induction of apoptosis.
Analysis of phosphorylation profiles during RV infection demonstrated that the presence of the virus stimulated an increase in the phosphorylation of ERK1/2, Akt, and Akt target GSK-3β over time. The presence of phosphorylated Akt (and occasionally ERK2) at 96 hours p.i. in the mock-infected cells, suggests that cell survival mechanisms may be activated in older uninfected cell cultures. The phosphorylation pattern of downstream target p70S6K did not follow that of Akt and ERK1/2. Apart from being phosphorylated by ERK1/2 and mTOR/FRAP downstream of Akt, p70S6K can be phosphorylated by an array of different proline-directed kinases, including PDK1, PKCζ, JNK and cdc2 which may explain this difference [29-33].
The phosphorylation of c-myc, a downstream target of ERK1/2, did not follow the same pattern. Levels of phosphorylated c-myc decreased as infection progressed, which was probably due to its targeted degradation or the action of cellular phosphatases. RV infection has been observed to slow cell cycle progression both in vivo and in vitro [12,34]. As c-myc is a transcription factor that stimulates cell cycle progression, its de-phosphorylation or degradation as RV infection progresses supports these observations. The expression and activity of c-myc and other downstream transcription factors in relation to the cell cycle during RV-infection requires further investigation. Phosphorylation of BAD, downstream of Akt, could not be detected in RV-infected cells (data not shown). However, BAD is not ubiquitously expressed and therefore may not be produced in the rabbit kidney epithelial cells (RK13) used [16].
Inhibition of PI3K signaling with LY294006 significantly increased the speed and magnitude of RV-induced apoptosis as shown by increased caspase activity, dead floating cells, apoptotic laddering of genomic DNA and decreased cell viability. Thus, RV-induced apoptotic signaling appears to be held in check by host cell survival signals downstream of PI3K. Although inhibition of PI3K did not affect RV replication, virus growth was affected. The speed of apoptotic monolayer death may have prevented production of optimal virus titers.
The importance of PI3K survival signaling has been observed with other viruses. Recently phosphorylation of Akt, GKS3β and PKCζ (another downstream target of PI3K signaling), has been demonstrated in Vero E6 cells early during infection with severe acute respiratory syndrome (SARS)-associated corona virus (CoV) [35]. However, unlike in this study the survival response due to PI3K-Akt signaling was deemed to be weak, as LY294002 treatment did not result in an increase in apoptotic DNA laddering. PI3K, Akt and NFκB have also been shown to be activated prior to epithelial cell apoptosis in RSV-infected cells [36]. As with RV, inhibition of PI3K increased the speed and magnitude of RSV-induced apoptosis, although host-cell survival was suggested to occur prior to apoptotic signaling, as opposed to RV where caspase activation and Akt phosphorylation occur concomitantly [6]. PI3K-Akt signaling has also been shown to reduce coxsackievirus B3 (CVB3)-induced apoptosis. However, in contrast to RSV, the replication of CVB3 was also reduced, suggesting that PI3K-Akt survival signals may also serve as a defense mechanism against virus infection [37].
Inhibition of the MEK1/2 in RK13 cells by U0126 resulted in necrotic monolayer destruction and a significant decrease in cell viability. XTT assay and light microscopy demonstrated that RV infection appeared to delay the effect of U0126. As discussed above, RV infection stimulates ERK activity, downstream of MEK, and may therefore counteract the effect of the inhibitor. Despite this, U0126 impaired RV replication, growth, and induction of apoptosis. Therefore it appears that although RV infection slows the cell cycle progression, cells must be cycling and metabolizing normally for RV replication to occur.
ERK1/2 phosphorylation has also been observed during infection with a number of other viruses, and inhibition of ERK1/2 signaling by U0126 has consistently been shown to be detrimental to virus growth. Infection of Jurkat cells with CVB3, for example, leads to up-regulation of ERK1/2 phosphorylation, and elevated levels of phosphorylated ERK1/2 have been observed in the myocardium of mice susceptible to CVB3-induced myocarditis [38]. Treatment of cultured cells with U0126 reduced CVB3 titers and inhibited the release of virus progeny [38,39]. Similarly, HCMV infection in human embryonic lung fibroblasts (HELs) has been shown to stimulate biphasic activation of MEK1/2 and ERK1/2, and treatment of infected cells with U0126 reduced viral DNA replication, protein production and virus titer [40]. Influenza A virus infection in vitro has also been shown to stimulate biphasic activation of MEK1/2 and ERK1/2, and U0126 treatment prevented export of ribonucleoprotein complexes from the nucleus and inhibited virus production [24]. Inhibition of MEK1/2 during HIV infection has been demonstrated to reduce infectivity, but unlike the other viruses mentioned herein, did not affect protein levels or virus production [25]. These findings, along with the results of this study, suggest that signaling downstream of MEK1/2 and ERK1/2 is important for viral infectivity and efficient virus replication and growth in vitro.
Over-expression of Akt and MEK1/2 increased RV-induced caspase activity in RK13 cells. This response was not due to the transfection procedure, as the increase in caspase activity was not observed in the pUSEamp(+) or lipofectamine controls. Such a response is also seen in malignant cells, which are more readily killed by apoptotic stimuli. Thus, the over-expression of these mitogenic pathways may have resulted in a cell survival response whereby a negative feedback loop occurred that sensitized cells to RV-induced apoptosis. In order to study this further, it would be necessary to construct stable cell lines over-expressing active Akt and ERK1/2 as well as their dominant negative mutants and other signaling proteins.
It is clear from the results of this and previous studies that the outcome of RV infection in vitro depends on numerous signaling events. It has been suggested that RV capsid protein, when anchored to the ER can independently induce apoptosis in culture (Duncan et. al, 2000). However this has not been confirmed by other groups and there is conflicting evidence that virus replication and the presence of the RV NSPs (which are necessary for replication) is required [10,12,41]. Interestingly the NSP p90 has been shown to interact with the retinoblastoma (pRB) cell cycle-regulatory protein and the cytokinesis regulatory protein citron-K kinase (CK), and it has been suggested that this may perturb the cell cycle [42,43]. How these interactions interfere with signaling pathways and modulate cellular responses, however, remains to be determined.
In relation to CRS, study of the expression and localization of apoptotic and mitogen activated signaling proteins in RV-infected fetal tissues would be necessary to confirm the theory that the pathogenesis of the disease is related to perturbation of the cell cycle. However as CRS is now rare in the UK and work with fetal tissues is tightly regulated, such a study would be hard to carry out. In vivo studies are difficult, as a reliable animal model does not exist for CRS. However, it may be possible to extrapolate findings from cell culture systems. We used RK13 cells because they are the best cells in which to detect rubella-induced apoptosis; further studies are required to confirm our findings in primary human embryonic cells.
Materials and methods
Chemical Compounds
Stock concentrations of PI3K inhibitor LY294002 [2-(4-Morpholinyl)-8-phenyl-1-4H-1-benzopyran-4-one] and MAPK/MEK inhibitor U0126 [1, 4-Diamino-2, 3-dicyano-1, 4-bis (2-aminophenylthio) butadiene] (Calbiochem, UK) were made up in dimethyl sulfoxide (DMSO). In all experiments LY294002 and U0126 were used at concentrations of 30 μM and 15 μM respectively.
Cell Culture & Viral Infection
Mycoplasma-free rabbit kidney epithelial (RK13) cells were obtained from the European Collection of Cell Cultures and cultured as previously described (3). RV (wild type strain RN) was propagated as previously described (3). For infection, cells were grown to confluence in minimal essential medium (MEM) supplemented with 15 mM L-glutamine and 5% FCS (v/v) (Invitrogen, UK) at 37°C in 5% CO2 in air, and serum starved overnight. Cells were infected with RV at a MOI of 4 plaque forming units (PFU) per cell and maintained in MEM with 1% FCS until harvested at indicated time points. Where appropriate kinase inhibitors (LY294002 and U0126) were added to the media at the same time as the virus, and were present during subsequent incubation periods. Mock-infected cells were treated and harvested in the same manner as those infected, except that MEM without virus was used during the infection. RV titers, in the presence of inhibitors, were determined by TCID50 assay in RK13 cells as the sample number was too large to perform plaque assays. Inhibitor, virus and serum concentrations were optimized to ensure that the effect of both the virus and the inhibitors could be monitored.
Transfection
Control and expression plasmids [pUSEamp(+), and constitutively active HA-Akt1 and Myc-MEK1 in pUSEamp(+)] were purchased from Upstate Biotechnology Inc. (UK). RK13 cells were grown to confluence in 25 cm2 tissue culture flasks and transiently transfected with 0.25 μg of control or expression plasmids. Tranfections were carried out in serum-free MEM using Lipofectamine Plus (Invitrogen, UK), according to the manufacturer's instructions. For optimal transfection, cell monolayers were incubated with the DNA-liposome mixture for 5 hours at 37°C. Following transfection, the DNA liposome complexes were removed and replaced with fresh medium. After 24 hours, RV was added to cells, which were maintained on MEM with 1% serum (as above). After an additional 24 hours, cells were analyzed for protein expression by Western blot analysis, and for apoptosis by caspase activity assay.
Western Blot Analysis
Polyclonal anti-PI3K p85, anti-HA Tag, anti-myc Tag, and monoclonal anti-β-tubulin antibodies were from Upstate Biotechnology inc. (UK). Polyclonal anti-caspase-3 antibody was from Sigma (UK). All other primary antibodies were purchased from Cell Signaling Technology (UK). Cells were treated as described above and at indicated times post-infection (p.i.), washed in PBS and harvested in cell lysis buffer [50 mM Tris, 150 mM NaCl, 1% Triton-X-100, 2 mM EDTA, 2 mM EGTA, 100 μM protease inhibitor cocktail, and 100 μM each of phosphatase inhibitor cocktails 1 and 2 (Sigma, UK)]. Protein concentrations were determined using the BioRad assay (BioRad, Hemel Hemstead, UK), and equal protein loading was determined by Coomassie staining (Invitrogen, Paisley, Scotland). Lysates were electrophoresed on 12% Bis-Tris polyacrylamide gels (Invitrogen, UK) and transferred onto Hybond™ ECL nitrocellulose or PVDF membranes (Amersham Biosciences, UK). Membranes were blocked with 5% non-fat dried milk in PBS containing 0.1% Tween-20, and subsequently incubated with primary antibody (1:1000) overnight at 4°C. Specific antibody binding was detected using horseradish peroxidase conjugated anti-rabbit or anti-mouse IgG (1:2000) (Dako, UK), and immunoreactive bands were visualized using the ECL detection system according the manufacturer's instructions (Amersham Biosciences, UK).
XTT Assay
RK13 cells were grown to confluence in 96-well tissue culture plates at 37°C in 5% CO2 in air. Cells were treated, in a final volume of 100 μl, with RV and kinase inhibitors as described above. At indicated times p.i., 50 μl of labeling mixture containing XTT (sodium 3'- [1-(phenylaminocarbonyl)-3, 4-tetrazolium]-bis (4-methoxy-6-nitro) and coupling reagent PMS (N-methyl dibenzopyrazine methyl sulphate) (Roche Applied Science, Mannheim, Germany) was added directly to the wells to give final concentrations of 0.3 mg/ml and 2.5 μg/ml respectively. Plates were incubated in a humidified atmosphere (37°C, 5% CO2) for 24 hours. The absorbance of the formazan product was measured at a test wavelength of 450 nm and a reference wavelength of 690 nm.
Caspase Activity Assay
DEVD specific caspase activity assay (Promega, UK) was carried out as previously described (3). Briefly, RK13 cells were grown to confluence, and treated with RV, LY294002, and U0126 (as above). Cell lysates were collected at indicated times p.i. and stored at -70°C until required. For the assay, lysates were incubated with colorimetric substrate DEVD-p-NA for 4 hours at 37°C, and absorbance of free pNA cleaved by endogenous caspases-3 and -7 was measured at 405 nm.
DNA Fragmentation Analysis
Analysis of apoptotic DNA fragmentation was carried out as previously described (3). Briefly, RK13 cells in 6-well plates were treated with RV, LY294002 and U0126 as above, and harvested 72 hours p.i. Total cellular DNA was extracted from 2 × 106 cells according to the manufacturer's instructions (Calbiochem, Nottingham, UK). Nucleic acids were precipitated using 3 M sodium acetate, 2-propanol, and ethanol. DNA pellets were dried and re-suspended in 10 mM Tris pH 7.5, 1 mM EDTA. Ladder fragments were electrophoretically separated on 1.5% Tris-Acetate EDTA (TAE) agarose gels. Gels were stained in ethidium bromide solution (5 mg/ml) and fragmented DNA was visualized under UV light.
Examination of floating cells
Floating dead cells in the supernatant following infection with RV or drug treatment (as described above) were quantified by trypan blue exclusion staining. The morphological changes to the cells were examined by light microscopy using a Nikon Eclipse TS100 light microscope. Pictures of cells were taken at a magnification of 20X using a Nikon COOLPIX 4500 digital camera and processed with Adobe Photoshop 7.0 software.
RV Capsid RT-PCR
Total RNA was extracted from 100 μl tissue culture supernatants, collected at indicated times p.i., using a silica-guanidinium isothiocyanate method [44]. Prior to reverse transcription, RV RNA was heated to 95°C for 1 minute and kept on ice. RNA was transcribed to cDNA using Superscript III RNase H- reverse transcriptase (Invitrogen, UK). Reverse transcription was performed in 20 μl reaction volumes containing 200 U enzyme, 10 μl sample RNA, 0.5 mM of each dNTP, and 5 pmoles external reverse primer (5'-CCTGTACGTGGGGCCTTTAA-3'). RNA bound to cDNA in RNA-DNA hybrids was removed by incubation of the cDNA with RNase H (Roche Diagnostics, UK) for 20 minutes at 37°C. PCR amplification was carried out using a GC-Rich PCR System (Roche Diagnostics, UK). In the PCR reaction 10 μl cDNA was added to 40 μl of PCR reaction mix to give final concentrations of 1X GC-Rich PCR buffer, 1.5 mM MgCl2, 0.2 mM each dNTP, 0.5 M GC-rich resolution solution™, 0.5 pmole of forward and reverse primers (5'-TAGGAGGTGCCGCCATATCA-3' and 5'-CCTGTACGTGGGGCCTTTAA-3' respectively), and 2U Taq polymerase and a mixture of proof-reading polymerases. The cycling conditions, as recommended by the manufacturer were: 95°C for 3 minutes followed by 10 cycles of 95°C for 30s, 57°C for 30s, 72°C for 1 minute; and 25 cycles of 95°C for 30s, 57°C for 30s, 72°C for 1 minute (plus an additional 5 seconds per cycle), and a final extension of 72°C for 7 minutes. Amplified capsid product (1053 b.p.) was electrophoretically separated on 1% Tris-Borate (TBE) agarose gels, stained with ethidium bromide solution (5 mg/ml) and visualized under UV light.
Authors' Contributions
SC conceived of the study, carried out the virological and biochemical assays and drafted the manuscript. JL participated in the design of the study. JMB participated in design and coordination of the study and helped to draft the manuscript. All authors read and approved the final manuscript.
Acknowledgements
We would like to thank Dr. Simon Cook for helpful discussions on this work. This work was supported by a grant from the Medical Research Council.
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| 15631631 | PMC544859 | CC BY | 2021-01-04 16:39:01 | no | Virol J. 2005 Jan 4; 2:1 | utf-8 | Virol J | 2,005 | 10.1186/1743-422X-2-1 | oa_comm |
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Nutr Metab (Lond)Nutrition & Metabolism1743-7075BioMed Central London 1743-7075-1-161561757310.1186/1743-7075-1-16ResearchDietary isoflavones alter regulatory behaviors, metabolic hormones and neuroendocrine function in Long-Evans male rats Lephart Edwin D [email protected] James P [email protected] Trent D [email protected] Lihong [email protected] Kenneth DR [email protected] Gina [email protected] William R [email protected] Department of Physiology and Developmental Biology, Brigham Young University, Provo, UT, USA2 The Neuroscience Center, Brigham Young University, Provo, UT, USA3 Biomedical Sciences, Colorado State University, Fort Collins, CO, USA4 Department of Pediatrics, Children's Hospital Medical Center, Cincinnati, OH, USA5 Pharmacology & Toxicology, University of Utah, College of Pharmacy, Salt Lake City, UT, USA2004 23 12 2004 1 16 16 4 10 2004 23 12 2004 Copyright © 2004 Lephart et al; licensee BioMed Central Ltd.2004Lephart 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
Phytoestrogens derived from soy foods (or isoflavones) have received prevalent usage due to their 'health benefits' of decreasing: a) age-related diseases, b) hormone-dependent cancers and c) postmenopausal symptoms. However, little is known about the influence of dietary phytoestrogens on regulatory behaviors, such as food and water intake, metabolic hormones and neuroendocrine parameters. This study examined important hormonal and metabolic health issues by testing the hypotheses that dietary soy-derived isoflavones influence: 1) body weight and adipose deposition, 2) food and water intake, 3) metabolic hormones (i.e., leptin, insulin, T3 and glucose levels), 4) brain neuropeptide Y (NPY) levels, 5) heat production [in brown adipose tissue (BAT) quantifying uncoupling protein (UCP-1) mRNA levels] and 6) core body temperature.
Methods
This was accomplished by conducting longitudinal studies where male Long-Evans rats were exposed (from conception to time of testing or tissue collection) to a diet rich in isoflavones (at 600 micrograms/gram of diet or 600 ppm) vs. a diet low in isoflavones (at approximately 10–15 micrograms/gram of diet or 10–15 ppm). Body, white adipose tissue and food intake were measured in grams and water intake in milliliters. The hormones (leptin, insulin, T3, glucose and NPY) were quantified by radioimmunoassays (RIA). BAT UCP-1 mRNA levels were quantified by PCR and polyacrylamide gel electrophoresis while core body temperatures were recorded by radio telemetry. The data were tested by analysis of variance (ANOVA) (or where appropriate by repeated measures).
Results
Body and adipose tissue weights were decreased in Phyto-600 vs. Phyto-free fed rats. Food and water intake was greater in Phyto-600 animals, that displayed higher hypothalamic (NPY) concentrations, but lower plasma leptin and insulin levels, vs. Phyto-free fed males. Higher thyroid levels (and a tendency for higher glucose levels) and increased uncoupling protein (UCP-1) mRNA levels in brown adipose tissue (BAT) were seen in Phyto-600 fed males. However, decreased core body temperature was recorded in these same animals compared to Phyto-free fed animals.
Conclusions
This study demonstrates that consumption of a soy-based (isoflavone-rich) diet, significantly alters several parameters involved in maintaining body homeostatic balance, energy expenditure, feeding behavior, hormonal, metabolic and neuroendocrine function in male rats.
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Background
Some phytochemicals are considered to be endocrine disrupters that mimic or modulate the physiological effects of steroid hormones, especially that of estrogens [1,2]. Of all estrogenic endocrine disrupters examined thus far, phytoestrogens have been extensively studied [1-6].
Phytoestrogens represent hundreds of molecules possessing non-steroidal, diphenolic structures found in many plants (e.g. fruits, vegetables, legumes, whole-grain and especially soy food products) that have similar chemical and structural properties to those of estrogens [1-4]. There are three main classifications of phytoestrogens: 1) isoflavones (derived principally from soybeans), 2) lignans (found in flaxseed in large quantities) and 3) coumestans (derived from sprouting plants like alfalfa) [2-6].
Of these three main classifications, human consumption of isoflavones has the largest impact due to its availability and variety in food products containing soy. Furthermore, the phytoestrogens principally derived from soy foods have received prevalent usage due to their 'health benefits' of decreasing: a) age-related diseases (cardiovascular & osteoporosis), b) hormone-dependent cancers (e.g. breast & prostate) and c) postmenopausal symptoms [2-6]. However, little is known about the influence of dietary (soy-derived) phytoestrogens on neuroendocrine, hormone and metabolic parameters. In spite of this fact, the Food and Drug Administration (FDA) in the United States in October of 1999 authorized the use of-on food labels- the health claim that: soy protein can reduce the risk of coronary heart disease by lowering blood cholesterol levels (when included in a diet low in saturated fat and cholesterol) [5].
The purpose of this study was to examine, in a comprehensive manner, important hormonal and metabolic health issues by testing the hypotheses that dietary soy-derived phytoestrogens influence: 1) body weight and adipose deposition, 2) food and water intake, 3) metabolic hormones (i.e., leptin, insulin, T3 and glucose levels), 4) brain neuropeptide Y (NPY) levels, 5) heat production [in brown adipose tissue (BAT) quantifying uncoupling protein (UCP-1) mRNA levels] and 6) core body temperature. This was accomplished by conducting longitudinal studies where male Long-Evans rats were exposed (from conception to time of testing or tissue collection) to a diet rich in phytoestrogens vs. a diet low in phytoestrogens.
Methods
Animals
Long-Evans male and female rats [10 per sex at 50 days old] were purchased from Charles River Laboratories (Wilmington, MA, USA) for breeding. These animals were caged individually and housed in the Brigham Young University Bio-Ag vivarium and maintained on an 11-hour dark 13-hour light schedule (lights on 0600–1900). The animals and methods of this study were approved by the institute of animal care and use committee (IACUC) at Brigham Young University (BYU).
Treatment-Diets
Upon arrival all animals were allowed ad libitum access to either a commercially available diet with high phytoestrogen levels (Harlan Teklad Rodent Diet 8604, Madison, WI, USA) containing 600 micrograms of phytoestrogens/gram of diet [or specifically this diet is high in isoflavones, 600 parts per million or ppm]; referred to hereafter as the Phyto-600 diet, or a custom phytoestrogen-free diet; referred to hereafter as the Phyto-free diet, obtained from Ziegler Bros. (Gardner, PA, USA) and water [7]. In the Phyto-free diet, the phytoestrogen concentrations were below the detectable limits of HPLC analysis [7]. The content and nutrient composition of these diets is described in detail elsewhere [7]. The diets were balanced and matched for equivalent percentage content of protein, carbohydrate, fat, amino acids, vitamins and minerals, etc. [7]. Circulating phytoestrogen serum levels from rats maintained on these diets (lifelong) have been reported previously by our laboratory using GC/MS analysis [7]. The animals were time mated within their respective diets so that the offspring of these pairings would be exposed solely to either the Phyto-600 or Phyto-free diet. Parameters were measured and/or the male rats were sacrificed and blood and tissues collected mainly at 33, 55 or 75 days of age; other ages were tested where indicated. Serum was prepared and stored at -20°C until assayed for metabolic hormones. For this study serum isoflavone levels are shown in Figure 1 from animals at 75 days of age. Male rats were only examined in this study since the influence of the estrous cycle on several of the measured parameters is unknown.
Figure 1 Serum Isoflavone Levels in Phytoestrogen-rich (Phyto-600) vs. Phytoesterogen-low (Phyto-Free) Fed Male Rats at 75-Days of Age. For each phytoestrogen measured the Phyto-600 animals displayed significantly higher (** p < 0.01) isoflavone levels compared to Phyto-free fed rats. ODMA = O-desmethylangolensin. Equol levels in Phyto-600 animals accounted for approximately 78% of the total phytoestrogen levels.
Weight measurements
Body weights and food intake were measured on a Mettler 1200 balance [in grams (g) ± 1 g; St. Louis, MO, USA], white and brown adipose tissue and prostate weights were measured on a Sartorious balance [in milligrams (mg) ± 1 mg; Brinkman Inst. Co., Westbury, NY, USA]. Water intake was measured in drinking tubes [in milliliters (ml) ± 1 ml]. White adipose tissue (WAT) was dissected inferior to the kidneys and superior to the testes in the abdominoplevic cavity (representing a majority of intra-abdominal WAT) and then weighed in grams ± 0.01 g. Brown adipose tissue was dissected from between the scapular blades (inter-scapular region) and weighed in milligrams (mg) ± 1 mg.
Metabolic Hormones
Serum leptin and insulin levels were determined by kits purchased from Linco Res. Inc. (St. Charles, MO, USA) [from arterial blood samples of 33 and 55 day-old male animals and venous blood samples collected from 75 day-old rats. This was due to exhausting the arterial supplies from the available blood samples for other assays and thus venous blood was assayed at 75 days of age]. Serum thyroid (T3) levels were assayed by a kit purchased from Diagnostic Systems Labs. Inc. (Webster, TX, USA) and glucose levels were detected by a kit (#510) purchased from Sigma Chem. Co. (St. Louis, MO, USA).
Hypothalamic NPY Levels
Subsequent to blood collection (above), after the animals were sacrificed, brains were removed rapidly, frozen on dry ice and then stored at -80°C until microdissection. Coronal slices 300 μm thick were sectioned on a microtome cryostat. The paraventricular nucleus, arcuate nucleus and median eminence regions of the hypothalamus were microdissected by punch technique and homogenized in 100 μl of 0.1 M HCl. Tissue protein was determined by the Lowry method [8] and NPY was measured using a solid-phase radioimmunoassay in Protein G-coated 96-well plates, as described previously [9]. The NPY antiserum was used at a final concentration of 1:16,000. The sensitivity of the assay is 0.2 pg, with an intra-assay coefficient of variation of 8 %. All samples were run in duplicate in the same assay to avoid inter-assay variation.
Body temperature
Body temperature was monitored by radio telemetry by implanting a very small electronic chip [under the skin above the left thoracic cavity near the heart] that measured and transmitted core body temperature (± 0.1°C) to a notebook-sensor monitor (BioMedic Data Systems Inc., Seaford, DE, USA) within 2 seconds and repeated measurements were made throughout the day and/or the duration of the experiments.
Body Heat Production
Uncoupling protein (UCP-1) mRNA levels were determined in brown adipose tissue (BAT) collected from the interscapular region of each male rat. The BATs were homogenized in Trizol reagent (Invitrogen, Carlsbad, CA, USA) and total RNA was extracted. Two micrograms (2 μg) of total RNA were reverse transcribed (RT) for 60 min at 42°C using Superscript II (Invitrogen, Carlsbad, CA, USA) (200 U). Each 20 μl reaction contained 0.1 M DTT (2 μl), 10 mM dNTP mix (1 μl), 10X PCR buffer (2 μl), random decamers (0.4 μl), and RNaseOUT (Invitrogen) (40 U). A duplex PCR reaction was then performed on each RT product, with 18S rRNA serving as the internal control. Each 50 μl reaction contained the RT product (2 μl), UCP-1 primers (2 μl), 18S primers [2 μl of 3:7 ratio of 18S primer to18S competimer (Ambion, Austin, TX, USA)], 10X PCR buffer (5 μl), 10 mM dNTP mix (0.625 μl), 32P-dCTP (0.1–0.15 μl), and Jumpstart Taq polymerase (Sigma Chem. Co., St. Louis, MO, USA) (1 U). Each tube was then subjected to the following protocol: 95°C for 5 min, 20 cycles of 94°C for 30 sec, 60°C for 30 sec, 72°C for 45 sec, followed by 72°C for a final 10 min. interval. With this profile, the UCP-1 and 18S fragments were amplified within the linear range (20 cycles for UCP-1). The primers for UCP-1 were GTGAAGGTCAGAATGCAAGC (sense) and AGGGCCCCCTTCATGAGGTC (antisense), the resultant fragment was 197 bp. The sequence of the UCP-1 fragment was verified by the DNA Sequencing Center at BYU. The PCR products were then subjected to non-denaturing polyacrylamide gel electrophoresis and the gels were exposed to autoradiographic film. Optical density (O.D.) of each band was determined using the NIH imaging system (Version 1.61). For each sample the O.D. ratio UCP:18S was determined. Each RT-PCR protocol was repeated and O.D. ratio values averaged over at least two runs.
Statistical Analysis
All data are presented as the mean ± SEM with p < 0.05 deemed significant. The data were tested by analysis of variance (ANOVA) (or where appropriate by repeated measures), followed by pairwise comparisons (via Neuman-Keuls analysis) to detect significant differences between the diet treatment groups (p < 0.05).
Results
Body Weight, White Adipose Tissue Weight and Food/Water Intake
When food and water intake was measured in young adult animals, surprisingly the Phyto-600 fed males displayed slight but significantly higher food (Figure 2A) and water (Figure 2B) consumption compared to Phyto-free fed males [for food intake: Phyto-600 = 24.3 vs. Phyto-free = 21.7 grams/day (p < 0.05) and for water intake: Phyto-600 = 37.7 vs. Phyto-free = 31.2 ml/day (p < 0.05).
Figure 2 Effects of Dietary Phytoestrogens on Food and Water Intake in 75 Day-Old Male Long-Evans Rats. Males fed a phytoestrogen-rich (600) diet displayed significantly greater (* p < 0.05) food (A) and water intake (B) compared to males fed a phytoestrogen-free (Free) diet. The average food and water intake represents the volumes consumed over 3 consecutive days.
The effects of dietary phytoestrogens on body weights in pre-, early adult and young adult age male rats are shown in Figure 3. At every age examined (i.e., 33, 55 and 75 days old), males exposed to the Phyto-free diet displayed significantly higher body weights (around 10–15%) compared to animals fed the Phyto-600 diet.
Figure 3 Effects of Dietary Phytoestrogens on Body Weight in Male Long-Evans Rats fed either a phytoestrogen-rich (600) or a phytoestrogen-free (Free) diet. At 33, 55 and 75 days-old Free-fed male body weights (* p < 0.05) were significantly greater compared to 600-fed male values.
White adipose tissue (WAT) weights were not measured in 33 day-old animals, since relatively little fat deposition was observed in the abdominopelvic cavity (especially around the reproductive structures) at this age. However, at 55 and 75-days of age, males fed the Phyto-free diet displayed significantly higher white adipose tissue weights (approximately 50% greater) compared to Phyto-600 values (Figure 4).
Figure 4 Effects of Dietary Phytoestrogens on White Adipose Tissue in Male Long-Evans Rats fed either a phytoestrogen-rich (600) or a phytoestrogen-free (Free) diet. At 55 and 75 days post-birth white adipose tissue weight was significantly greater in Free-fed males (** p < 0.01) compared to 600-fed male values.
Circulating Leptin, Insulin, Glucose and Brain NPY Levels
In 33, 55 and 75 day-old male rats, circulating leptin and insulin levels were within the normal ranges (as described by the vendor's assay kit values), however, at each age males fed the Phyto-free diet displayed significantly higher leptin (Figure 5A) and insulin (Figure 5B) levels compared to Phyto-600 values. Notably, the leptin levels significantly increased with age that corresponded with significantly higher white adipose tissue deposition seen in these animals.
Figure 5 Plasma Leptin (A) and Insulin (B) Levels from 33, 55 or 75 day-old Male Long-Evans Rats fed either a phytoestrogen-rich (Phyto-600) or a phytoestrogen-free (Phyto-Free) diet. At 33, 55 and 75 days of age, males fed the phytoestrogen-free (Free) diets displayed significantly higher leptin and insulin levels (* p < 0.05, ** p < 0.01) compared to males fed the Phyto-600 (600) diet.
From the animals collected on 33, 55 and 75 days of age not enough serum was left after other assays were performed to quantify glucose levels on these days. However, circulating glucose levels were assayed in non-fasting 65, 80 or 110 day-old animals, Phyto-600 fed males displayed slightly higher values (that were not significantly different) compared to Phyto-free fed males [age 65 days old- Phyto-600 = 113.5 (± 4.4) vs. Phyto free = 93.5 (± 9.0) mg/dl, n = 8 per group; age 80 days old- Phyto-600 = 137.2 (± 3.8) vs. Phyto-free = 122.5 (± 3.9) mg/dl, n = 10 per group; age 110 days old- Phyto-600 = 123.5 (± 5.0) vs. Phyto-free = 113.8 (± 3.6) mg/dl, n = 5 per group, (mean ± SEM), data not shown graphically].
Since leptin plays an important role in regulating brain NPY levels that in turn influences food/water intake, NPY levels were determined in three hypothalamic regions [i.e., the periventricular nucleus (PVN), median eminence (ME) and the arcuate nucleus (ARC)] in 75 day-old males exposed to the diet treatments. In the PVN and ARC (but not the ME) NPY levels were significantly higher (by approximately 40 %) in Phyto-600 fed males vs. the Phyto-free male values (Figure 6).
Figure 6 Dietary Phytoestrogens Influence on Brain NPY Levels in 75 day-old Male Long-Evans Rats. In the paraventricular (PVN) and arcuate (ARC) nucleus, males fed the Phyto-600 (600) diet displayed significantly greater NPY levels (* p < 0.05) compared to males fed the Phyto-Free (Free) diet. In the median eminence (ME) no significant differences were observed between male rats fed 600 vs. the Free diet.
Circulating Thyroid (T3), UCP-1 mRNA Levels and Core Body Temperature
In non-fasting young adult rats at 65 and 110 days of age, circulating thyroid (T3) levels were determined from venous blood samples. Phyto-600 fed males displayed significantly higher T3 levels compared to Phyto-free fed values [age 65 days old- Phyto-600 = 2.4 ± 0.2 vs. Phyto-free = 1.5 ± 0.4 pg/ml (± SEM), n = 8 per diet treatment; age 110 days old- Phyto-600 = 1.9 ± 0.4 vs. Phyto-free = 0.8 ± 0.3 pg/ml, n = 5 per group, (mean ± SEM) data not shown graphically].
The effects of dietary phytoestrogens on uncoupling protein-1 (UCP-1) mRNA levels in brown adipose tissue (BAT) from 75 day-old animals is shown in Figure 7. Phyto-600 fed males displayed significantly higher (≈ 2-fold) UCP-1 mRNA levels in BAT compared to Phyto-free values. Notably, the BAT weights of Phyto-600 animals were significantly less (by approximately 1/3) to that of Phyto-600 males (Phyto-600 = 293.6 ± 22.1 vs. Phyto-free = 437.5 ± 25.9 mg (mean ± SEM), n = 8 per group.
Figure 7 Dietary Phytoestrogens Influence on Uncoupling Protein-1 (UCP-1) mRNA Levels in Brown Adipose Tissue (BAT) from males fed either a phytoestrogen-rich (Phyto-600) or a phytoestrogen-free (Phyto-free) diet. For each sample, the optical density (O.D.) signal ratio of BAT UCP-1 mRNA abundance to ribosomal 18S RNA intensity was determined. The results represent the mean O.D. values and S.E.M. of 8 independent samples by diet treatment of at least two runs of the RT-PCR protocol. Males fed the Phyto-600 diet expressed significantly higher BAT UCP-1 mRNA levels (* p < 0.05) compared to Phyto-free values.
Finally, when core body temperatures were recorded during a 24-hour interval, Phyto-free fed males displayed, in general, slight but significantly higher values compared to Phyto-600 animals during the dark phase of the light/dark cycle when rodents are most active (Figure 8). However, during one time point during the dark cycle (3 am) and one time point during the light phase of the cycle (3 pm) Phyto-600 males displayed slight but significantly higher core body temperatures vs. Phyto-free values.
Figure 8 Dietary Phytoestrogens Influence on Core Body Temperature in 75 day-old rats. In general, during the dark phase of the light cycle, Phyto-free fed males displayed significantly higher core body temperatures (* p < 0.05) compared to Phyto-600 fed values. However, near the end of the dark (at 3 am) and light (at 3 pm) phase of the dark/light cycle Phyto-600 fed males displayed significantly higher core body temperatures (▲ p < 0.05) compared to Phyto-free values.
Discussion
Estrogen is known to play a dual role in regulating body weight, food intake and adipose tissue deposition. On the one hand, estrogens decrease food intake, increase locomotor activity and hence decrease body weight [10,11]. However, adipose tissue deposition increases with puberty and early pregnancy in women, suggesting that estrogens influence body fat accumulation [12]. Additionally, in aging, estrogens promote adipose deposition and insulin resistance [13]. Conversely, results from aromatase, FSH and ER-knockout studies indicate that estrogens regulate adiposity where the complete lack of estrogens or blocking estrogen hormone action increases adipose tissue deposition [14-18], whereas, estrogen replacement in these models decreases adiposity. Notably, in the present study, male rats fed the Phyto-600 diet displayed significantly decreased adipose tissue and body weights compared to Phyto-free fed animals. While there is not extensive data on phytoestrogens and metabolism, other investigators have reported that genistein, increases lipolysis and decreases lipogenesis in rodent adipocytes [19] by a tyrosine kinase independent mechanism and these estrogen mimics inhibit glucose uptake by altering membrane-associated glucose transporters [20,21]. Thus, our data suggests that dietary soy phytoestrogens significantly decrease: 1) body and adipose tissue weights and 2) circulating leptin and insulin levels (that correspond with adipose deposition) compared to Phyto-free fed animals, implying that the hormonal action of phytoestrogens is beneficial to body fat regulation. Recent studies imply that insulin helps to regulate leptin expression in humans [22] and estrogens appear to enhance the action of insulin [23,24]. This may account for the decreased incidence of obesity in Asian countries where isoflavone consumption is high compared to Western countries. Decreased adipose tissue deposition by decreasing lipogenesis and increasing lipolysis may help to prevent insulin resistance (by reducing body fat) and the estrogenic actions of dietary phytoestrogens may augment the efficiency of insulin.
It was previously observed in our laboratory that dietary phytoestrogens significantly alter food and water intake [7,25,26]. The differential effects of the Phyto-free vs. Phyto-600 diets observed in the present studies on hypothalamic NPY levels, circulating insulin and leptin concentrations and food intake are consistent with the well established interrelationships among these parameters. Thus, relative to animals maintained on the Phyto-free diet, food intake was significantly increased in animals fed the Phyto-600 diet. Phyto-600-fed rats also exhibited higher concentrations of NPY in the arcuate and paraventricular nuclei of the hypothalamus. It is well established that NPY neurons whose perikarya reside in arcuate nucleus and project to PVN comprise an extremely important orexigenic neural pathway [27]. It therefore appears likely that at least one factor contributing to the higher food intake in Phyto-600-fed rats is the increased levels of NPY in this system.
The present studies also suggest a mechanism that may underlie the diet-induced effects on NPY (i.e., plasma insulin and leptin concentrations were significantly reduced in the Phyto-600 fed rats, relative to the Phyto-free animals). A number of previous studies have demonstrated a reciprocal relationship between circulating insulin and leptin titers and NPY concentrations in PVN. Thus, experimentally-induced reductions in either insulin [28] or leptin [29] are associated with increased pre-proNPY messenger RNA expression in arcuate nucleus and increased NPY levels in PVN, and moreover, it has been proposed that reductions in insulin and leptin that occur physiologically, e.g., with food deprivation, provide an important signal to the NPY system to initiate feeding [27,29]. Hence, taken together, the present findings suggest that by reducing secretion of insulin and/or leptin, chronic consumption of the Phyto-600 diet results in up-regulation of the orexigenic NPY circuit in the hypothalamus, which in turn stimulates food intake (and water consumption, since rodents and humans display prandial characteristics).
While it is clear that thyroid hormone levels are influenced by estrogens where increases are seen in T3 and T4, presumably by increasing in the production of thyroid binding globulin in the liver [30], the published data examining thyroid function and hormone levels are problematic at best in the soy research field due to the history of soy food formulations, parameters examined and iodine deficiencies [6,31,32]. In agreement with more recent studies, our results demonstrate that circulating T3 levels increase with soy consumption [33], and "personal communication- Dr. David Baer-USDA". Furthermore, there appears to be a link between increased thyroid levels with soy consumption and cardiovascular protection in lowering serum cholesterol levels [6] and thyroid hormones along with estrogens protecting against osteoporosis [34]. However, in animals consuming the Phyto-600 diet (that displayed higher T3 levels) we observed a lower core body temperature compared to Phyto-free fed rats. In subsequent (unpublished) studies, we have consistently recorded slight (approximately 0.5°C) but significantly lower core body temperatures in Phyto-600 vs. Phyto-free fed rats during pregnancy. This suggests that the overall effect on body temperature via these estrogen mimics in the soy-rich diet may act primarily by increasing cutaneous vasodilation, thus decreasing core body temperature. Animal studies have shown that estrogens can act centrally (in the preoptic/anterior hypothalamus) or peripherally to regulate body temperature [35,36]. Support for this view is seen in humans where changes in skin blood flow via cutaneous vasodilation during the menstrual cycle and in hormone replacement therapy studies correspond with estrogen levels [36,37]. Also, one report showed that soy-derived phytoestrogens have a similar effect to our findings where ovariectomized rats fed a soy diet displayed an approximate 0.8°C decrease in skin temperature, whereas, estradiol treatment decreased temperature values by 1.4°C [38]. Finally, in association with temperature regulation, several studies have reported that soy consumption may be an effective therapy for relief of hot flushes in women [39]. Finally, the various metabolic parameters examined in a global fashion seem to suggest that declines in insulin and leptin levels are the dominant systemic regulators in regard to body weight, since overall the Phyto-600 animals weigh less compared to Phyto-free fed animals. However, the present findings also suggest that body temperature is reduced in Phyto-600 fed animals vs. Phyto-free fed animals and previous behavioral studies suggest that Phyto-600 animals exhibit more locomotor active vs. Phyto-free fed animals [7,50] (see summary Figure 9).
Figure 9 Summary of Dietary Phytoestrogen (Isoflavone) Influences on Metabolic and Hormonal Parameters. The left-hand column: Phyto-600 arrows are relative to the effects seen in Phyto-free fed animals-right-hand column [in other words, relative to one another]. * = general increase in body temperature compared to Phyto-600 values
Uncoupling proteins (UCP-1 through UCP-5) are expressed in various tissues from many different species (mammals, birds, fish, insects and plants) that play important (but controversial) role(s) in the regulation of energy expenditure, or thermogenesis [40,41]. Uncoupling protein-1 is expressed mainly in BAT. When the influence of dietary phytoestrogens on UCP-1 mRNA levels in BAT was examined, Phyto-600 fed male rats, expressed significantly higher levels of the uncoupling protein (approximately 2-fold) compared to Phyto-free values (but BAT weights were significantly less in the Phyto-600 vs. Phyto-free fed males). To date, we are unaware of any studies that have investigated this aspect of soy consumption on thermogenesis. The decrease in BAT mass in Phyto-600 animals but increased expression of UCP-1 may represent a compensation mechanism for energy expenditure, and there are several neural inputs and hormonal factors that influence UCP-1 in BAT that make it difficult to differentiate the regulatory aspects of UCP-1 expression. For example, sympathetic denervation of inter-scapular BAT markedly reduced UCP-1 mRNA levels and estrogen, T3 and adrenergic agents [norepinephrine (NE)] stimulate UCP-1 expression in BAT [42,43]. In fact, it has been reported that T3 synergizes with NE to increase UCP-1 in BAT and stabilizes its mRNA transcripts [44]. These factors overlap with the changes seen in Phyto-600 fed vs. Phyto-free fed rats, in the present study, where T3 levels were increased and, presumably, along with the estrogenic influence of circulating isoflavones resulted in stimulating UCP-1 expression in BAT. Previously, we have not observed any significant alterations in circulating estradiol (or LH) levels in Phyto-600 vs. Phyto-free fed intact males [7]. Conversely, it has been reported that increases in hypothalamic NPY decrease UCP-1 [and reduces sympathetic outflow to BAT, but increases adipose tissue lipoprotein lipase activity] [30]. Also, plasma leptin levels are thought to stimulate UCP-1 in BAT [45,46], results opposite, in general, to that obtained in the present study. Based upon the obtained data sets, it is difficult to identify a common stimulatory or inhibitory pattern for the expression of UCP-1 in BAT of soy fed animals and especially define a functional role for the physiological properties associated with these UCPs in thermoregulation. Therefore, it is reasonable to speculate that multiple factors act collectively to regulate UCPs in BAT that in turn contribute to adaptive changes in body temperature.
Conclusions
This study demonstrates that consumption of a widely used commercially available soy-based rodent diet, (i.e., the Phyto-600 diet rich in isoflavones), alters several hormonal, metabolic and neuroendocrine parameters involved in maintaining body homeostatic balance, energy expenditure and feeding behavior in male rats. Further research is warranted in examining the important aspects of the neuroendocrine and metabolic influences of dietary phytoestrogens via the consumption of soy in humans and laboratory animals. This is especially true when diet is usually not considered as an influencing factor in the experimental design [47-50].
Abbreviations
neuropeptide Y (NPY), white adipose tissue (WAT), brown adipose tissue (BAT), uncoupling proteins (UCP), phytoestrogen-rich diet (Phyto-600), phytoestrogen free diet (Phyto-free), periventricular nucleus (PVN), median eminence (ME), arcuate nucleus (ARC), norepinephrine (NE), thyroid (T3, T4), National Institutes of Health (NIH), reverse transcriptase-polymerase chain reaction (RT-PCR), Food and Drug Administration (FDA), optical density (OD), luteinizing hormone (LH), follicle stimulating hormone (FSH), Brigham Young University (BYU), estrogen receptor (ER), high performance liquid chromatography (HPLC)
Competing interests
The author(s) declare that they have no competing interests.
Author's contributions
JPP, TDL, LB and GR contributed equally to this paper in various aspects of this study. KDRS carried out the isoflavone quantifications, WRC conducted the NPY analyses and EDL conceived of the study, designed, coordinated and drafted the manuscript along with the other authors.
Acknowledgements
This work was supported, in part, by grants from the USDA (2002-00798; EDL), the BYU Research Office (21-223566 & 19-223566; EDL) The Dean's Graduate Fellowship in Neuroscience (TDL and LB), and the National Institutes of Health (HD-13703; WRC)
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| 15617573 | PMC544860 | CC BY | 2021-01-04 16:37:45 | no | Nutr Metab (Lond). 2004 Dec 23; 1:16 | utf-8 | Nutr Metab (Lond) | 2,004 | 10.1186/1743-7075-1-16 | oa_comm |
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Nutr Metab (Lond)Nutrition & Metabolism1743-7075BioMed Central London 1743-7075-1-161561757310.1186/1743-7075-1-16ResearchDietary isoflavones alter regulatory behaviors, metabolic hormones and neuroendocrine function in Long-Evans male rats Lephart Edwin D [email protected] James P [email protected] Trent D [email protected] Lihong [email protected] Kenneth DR [email protected] Gina [email protected] William R [email protected] Department of Physiology and Developmental Biology, Brigham Young University, Provo, UT, USA2 The Neuroscience Center, Brigham Young University, Provo, UT, USA3 Biomedical Sciences, Colorado State University, Fort Collins, CO, USA4 Department of Pediatrics, Children's Hospital Medical Center, Cincinnati, OH, USA5 Pharmacology & Toxicology, University of Utah, College of Pharmacy, Salt Lake City, UT, USA2004 23 12 2004 1 16 16 4 10 2004 23 12 2004 Copyright © 2004 Lephart et al; licensee BioMed Central Ltd.2004Lephart 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
Phytoestrogens derived from soy foods (or isoflavones) have received prevalent usage due to their 'health benefits' of decreasing: a) age-related diseases, b) hormone-dependent cancers and c) postmenopausal symptoms. However, little is known about the influence of dietary phytoestrogens on regulatory behaviors, such as food and water intake, metabolic hormones and neuroendocrine parameters. This study examined important hormonal and metabolic health issues by testing the hypotheses that dietary soy-derived isoflavones influence: 1) body weight and adipose deposition, 2) food and water intake, 3) metabolic hormones (i.e., leptin, insulin, T3 and glucose levels), 4) brain neuropeptide Y (NPY) levels, 5) heat production [in brown adipose tissue (BAT) quantifying uncoupling protein (UCP-1) mRNA levels] and 6) core body temperature.
Methods
This was accomplished by conducting longitudinal studies where male Long-Evans rats were exposed (from conception to time of testing or tissue collection) to a diet rich in isoflavones (at 600 micrograms/gram of diet or 600 ppm) vs. a diet low in isoflavones (at approximately 10–15 micrograms/gram of diet or 10–15 ppm). Body, white adipose tissue and food intake were measured in grams and water intake in milliliters. The hormones (leptin, insulin, T3, glucose and NPY) were quantified by radioimmunoassays (RIA). BAT UCP-1 mRNA levels were quantified by PCR and polyacrylamide gel electrophoresis while core body temperatures were recorded by radio telemetry. The data were tested by analysis of variance (ANOVA) (or where appropriate by repeated measures).
Results
Body and adipose tissue weights were decreased in Phyto-600 vs. Phyto-free fed rats. Food and water intake was greater in Phyto-600 animals, that displayed higher hypothalamic (NPY) concentrations, but lower plasma leptin and insulin levels, vs. Phyto-free fed males. Higher thyroid levels (and a tendency for higher glucose levels) and increased uncoupling protein (UCP-1) mRNA levels in brown adipose tissue (BAT) were seen in Phyto-600 fed males. However, decreased core body temperature was recorded in these same animals compared to Phyto-free fed animals.
Conclusions
This study demonstrates that consumption of a soy-based (isoflavone-rich) diet, significantly alters several parameters involved in maintaining body homeostatic balance, energy expenditure, feeding behavior, hormonal, metabolic and neuroendocrine function in male rats.
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Background
Some phytochemicals are considered to be endocrine disrupters that mimic or modulate the physiological effects of steroid hormones, especially that of estrogens [1,2]. Of all estrogenic endocrine disrupters examined thus far, phytoestrogens have been extensively studied [1-6].
Phytoestrogens represent hundreds of molecules possessing non-steroidal, diphenolic structures found in many plants (e.g. fruits, vegetables, legumes, whole-grain and especially soy food products) that have similar chemical and structural properties to those of estrogens [1-4]. There are three main classifications of phytoestrogens: 1) isoflavones (derived principally from soybeans), 2) lignans (found in flaxseed in large quantities) and 3) coumestans (derived from sprouting plants like alfalfa) [2-6].
Of these three main classifications, human consumption of isoflavones has the largest impact due to its availability and variety in food products containing soy. Furthermore, the phytoestrogens principally derived from soy foods have received prevalent usage due to their 'health benefits' of decreasing: a) age-related diseases (cardiovascular & osteoporosis), b) hormone-dependent cancers (e.g. breast & prostate) and c) postmenopausal symptoms [2-6]. However, little is known about the influence of dietary (soy-derived) phytoestrogens on neuroendocrine, hormone and metabolic parameters. In spite of this fact, the Food and Drug Administration (FDA) in the United States in October of 1999 authorized the use of-on food labels- the health claim that: soy protein can reduce the risk of coronary heart disease by lowering blood cholesterol levels (when included in a diet low in saturated fat and cholesterol) [5].
The purpose of this study was to examine, in a comprehensive manner, important hormonal and metabolic health issues by testing the hypotheses that dietary soy-derived phytoestrogens influence: 1) body weight and adipose deposition, 2) food and water intake, 3) metabolic hormones (i.e., leptin, insulin, T3 and glucose levels), 4) brain neuropeptide Y (NPY) levels, 5) heat production [in brown adipose tissue (BAT) quantifying uncoupling protein (UCP-1) mRNA levels] and 6) core body temperature. This was accomplished by conducting longitudinal studies where male Long-Evans rats were exposed (from conception to time of testing or tissue collection) to a diet rich in phytoestrogens vs. a diet low in phytoestrogens.
Methods
Animals
Long-Evans male and female rats [10 per sex at 50 days old] were purchased from Charles River Laboratories (Wilmington, MA, USA) for breeding. These animals were caged individually and housed in the Brigham Young University Bio-Ag vivarium and maintained on an 11-hour dark 13-hour light schedule (lights on 0600–1900). The animals and methods of this study were approved by the institute of animal care and use committee (IACUC) at Brigham Young University (BYU).
Treatment-Diets
Upon arrival all animals were allowed ad libitum access to either a commercially available diet with high phytoestrogen levels (Harlan Teklad Rodent Diet 8604, Madison, WI, USA) containing 600 micrograms of phytoestrogens/gram of diet [or specifically this diet is high in isoflavones, 600 parts per million or ppm]; referred to hereafter as the Phyto-600 diet, or a custom phytoestrogen-free diet; referred to hereafter as the Phyto-free diet, obtained from Ziegler Bros. (Gardner, PA, USA) and water [7]. In the Phyto-free diet, the phytoestrogen concentrations were below the detectable limits of HPLC analysis [7]. The content and nutrient composition of these diets is described in detail elsewhere [7]. The diets were balanced and matched for equivalent percentage content of protein, carbohydrate, fat, amino acids, vitamins and minerals, etc. [7]. Circulating phytoestrogen serum levels from rats maintained on these diets (lifelong) have been reported previously by our laboratory using GC/MS analysis [7]. The animals were time mated within their respective diets so that the offspring of these pairings would be exposed solely to either the Phyto-600 or Phyto-free diet. Parameters were measured and/or the male rats were sacrificed and blood and tissues collected mainly at 33, 55 or 75 days of age; other ages were tested where indicated. Serum was prepared and stored at -20°C until assayed for metabolic hormones. For this study serum isoflavone levels are shown in Figure 1 from animals at 75 days of age. Male rats were only examined in this study since the influence of the estrous cycle on several of the measured parameters is unknown.
Figure 1 Serum Isoflavone Levels in Phytoestrogen-rich (Phyto-600) vs. Phytoesterogen-low (Phyto-Free) Fed Male Rats at 75-Days of Age. For each phytoestrogen measured the Phyto-600 animals displayed significantly higher (** p < 0.01) isoflavone levels compared to Phyto-free fed rats. ODMA = O-desmethylangolensin. Equol levels in Phyto-600 animals accounted for approximately 78% of the total phytoestrogen levels.
Weight measurements
Body weights and food intake were measured on a Mettler 1200 balance [in grams (g) ± 1 g; St. Louis, MO, USA], white and brown adipose tissue and prostate weights were measured on a Sartorious balance [in milligrams (mg) ± 1 mg; Brinkman Inst. Co., Westbury, NY, USA]. Water intake was measured in drinking tubes [in milliliters (ml) ± 1 ml]. White adipose tissue (WAT) was dissected inferior to the kidneys and superior to the testes in the abdominoplevic cavity (representing a majority of intra-abdominal WAT) and then weighed in grams ± 0.01 g. Brown adipose tissue was dissected from between the scapular blades (inter-scapular region) and weighed in milligrams (mg) ± 1 mg.
Metabolic Hormones
Serum leptin and insulin levels were determined by kits purchased from Linco Res. Inc. (St. Charles, MO, USA) [from arterial blood samples of 33 and 55 day-old male animals and venous blood samples collected from 75 day-old rats. This was due to exhausting the arterial supplies from the available blood samples for other assays and thus venous blood was assayed at 75 days of age]. Serum thyroid (T3) levels were assayed by a kit purchased from Diagnostic Systems Labs. Inc. (Webster, TX, USA) and glucose levels were detected by a kit (#510) purchased from Sigma Chem. Co. (St. Louis, MO, USA).
Hypothalamic NPY Levels
Subsequent to blood collection (above), after the animals were sacrificed, brains were removed rapidly, frozen on dry ice and then stored at -80°C until microdissection. Coronal slices 300 μm thick were sectioned on a microtome cryostat. The paraventricular nucleus, arcuate nucleus and median eminence regions of the hypothalamus were microdissected by punch technique and homogenized in 100 μl of 0.1 M HCl. Tissue protein was determined by the Lowry method [8] and NPY was measured using a solid-phase radioimmunoassay in Protein G-coated 96-well plates, as described previously [9]. The NPY antiserum was used at a final concentration of 1:16,000. The sensitivity of the assay is 0.2 pg, with an intra-assay coefficient of variation of 8 %. All samples were run in duplicate in the same assay to avoid inter-assay variation.
Body temperature
Body temperature was monitored by radio telemetry by implanting a very small electronic chip [under the skin above the left thoracic cavity near the heart] that measured and transmitted core body temperature (± 0.1°C) to a notebook-sensor monitor (BioMedic Data Systems Inc., Seaford, DE, USA) within 2 seconds and repeated measurements were made throughout the day and/or the duration of the experiments.
Body Heat Production
Uncoupling protein (UCP-1) mRNA levels were determined in brown adipose tissue (BAT) collected from the interscapular region of each male rat. The BATs were homogenized in Trizol reagent (Invitrogen, Carlsbad, CA, USA) and total RNA was extracted. Two micrograms (2 μg) of total RNA were reverse transcribed (RT) for 60 min at 42°C using Superscript II (Invitrogen, Carlsbad, CA, USA) (200 U). Each 20 μl reaction contained 0.1 M DTT (2 μl), 10 mM dNTP mix (1 μl), 10X PCR buffer (2 μl), random decamers (0.4 μl), and RNaseOUT (Invitrogen) (40 U). A duplex PCR reaction was then performed on each RT product, with 18S rRNA serving as the internal control. Each 50 μl reaction contained the RT product (2 μl), UCP-1 primers (2 μl), 18S primers [2 μl of 3:7 ratio of 18S primer to18S competimer (Ambion, Austin, TX, USA)], 10X PCR buffer (5 μl), 10 mM dNTP mix (0.625 μl), 32P-dCTP (0.1–0.15 μl), and Jumpstart Taq polymerase (Sigma Chem. Co., St. Louis, MO, USA) (1 U). Each tube was then subjected to the following protocol: 95°C for 5 min, 20 cycles of 94°C for 30 sec, 60°C for 30 sec, 72°C for 45 sec, followed by 72°C for a final 10 min. interval. With this profile, the UCP-1 and 18S fragments were amplified within the linear range (20 cycles for UCP-1). The primers for UCP-1 were GTGAAGGTCAGAATGCAAGC (sense) and AGGGCCCCCTTCATGAGGTC (antisense), the resultant fragment was 197 bp. The sequence of the UCP-1 fragment was verified by the DNA Sequencing Center at BYU. The PCR products were then subjected to non-denaturing polyacrylamide gel electrophoresis and the gels were exposed to autoradiographic film. Optical density (O.D.) of each band was determined using the NIH imaging system (Version 1.61). For each sample the O.D. ratio UCP:18S was determined. Each RT-PCR protocol was repeated and O.D. ratio values averaged over at least two runs.
Statistical Analysis
All data are presented as the mean ± SEM with p < 0.05 deemed significant. The data were tested by analysis of variance (ANOVA) (or where appropriate by repeated measures), followed by pairwise comparisons (via Neuman-Keuls analysis) to detect significant differences between the diet treatment groups (p < 0.05).
Results
Body Weight, White Adipose Tissue Weight and Food/Water Intake
When food and water intake was measured in young adult animals, surprisingly the Phyto-600 fed males displayed slight but significantly higher food (Figure 2A) and water (Figure 2B) consumption compared to Phyto-free fed males [for food intake: Phyto-600 = 24.3 vs. Phyto-free = 21.7 grams/day (p < 0.05) and for water intake: Phyto-600 = 37.7 vs. Phyto-free = 31.2 ml/day (p < 0.05).
Figure 2 Effects of Dietary Phytoestrogens on Food and Water Intake in 75 Day-Old Male Long-Evans Rats. Males fed a phytoestrogen-rich (600) diet displayed significantly greater (* p < 0.05) food (A) and water intake (B) compared to males fed a phytoestrogen-free (Free) diet. The average food and water intake represents the volumes consumed over 3 consecutive days.
The effects of dietary phytoestrogens on body weights in pre-, early adult and young adult age male rats are shown in Figure 3. At every age examined (i.e., 33, 55 and 75 days old), males exposed to the Phyto-free diet displayed significantly higher body weights (around 10–15%) compared to animals fed the Phyto-600 diet.
Figure 3 Effects of Dietary Phytoestrogens on Body Weight in Male Long-Evans Rats fed either a phytoestrogen-rich (600) or a phytoestrogen-free (Free) diet. At 33, 55 and 75 days-old Free-fed male body weights (* p < 0.05) were significantly greater compared to 600-fed male values.
White adipose tissue (WAT) weights were not measured in 33 day-old animals, since relatively little fat deposition was observed in the abdominopelvic cavity (especially around the reproductive structures) at this age. However, at 55 and 75-days of age, males fed the Phyto-free diet displayed significantly higher white adipose tissue weights (approximately 50% greater) compared to Phyto-600 values (Figure 4).
Figure 4 Effects of Dietary Phytoestrogens on White Adipose Tissue in Male Long-Evans Rats fed either a phytoestrogen-rich (600) or a phytoestrogen-free (Free) diet. At 55 and 75 days post-birth white adipose tissue weight was significantly greater in Free-fed males (** p < 0.01) compared to 600-fed male values.
Circulating Leptin, Insulin, Glucose and Brain NPY Levels
In 33, 55 and 75 day-old male rats, circulating leptin and insulin levels were within the normal ranges (as described by the vendor's assay kit values), however, at each age males fed the Phyto-free diet displayed significantly higher leptin (Figure 5A) and insulin (Figure 5B) levels compared to Phyto-600 values. Notably, the leptin levels significantly increased with age that corresponded with significantly higher white adipose tissue deposition seen in these animals.
Figure 5 Plasma Leptin (A) and Insulin (B) Levels from 33, 55 or 75 day-old Male Long-Evans Rats fed either a phytoestrogen-rich (Phyto-600) or a phytoestrogen-free (Phyto-Free) diet. At 33, 55 and 75 days of age, males fed the phytoestrogen-free (Free) diets displayed significantly higher leptin and insulin levels (* p < 0.05, ** p < 0.01) compared to males fed the Phyto-600 (600) diet.
From the animals collected on 33, 55 and 75 days of age not enough serum was left after other assays were performed to quantify glucose levels on these days. However, circulating glucose levels were assayed in non-fasting 65, 80 or 110 day-old animals, Phyto-600 fed males displayed slightly higher values (that were not significantly different) compared to Phyto-free fed males [age 65 days old- Phyto-600 = 113.5 (± 4.4) vs. Phyto free = 93.5 (± 9.0) mg/dl, n = 8 per group; age 80 days old- Phyto-600 = 137.2 (± 3.8) vs. Phyto-free = 122.5 (± 3.9) mg/dl, n = 10 per group; age 110 days old- Phyto-600 = 123.5 (± 5.0) vs. Phyto-free = 113.8 (± 3.6) mg/dl, n = 5 per group, (mean ± SEM), data not shown graphically].
Since leptin plays an important role in regulating brain NPY levels that in turn influences food/water intake, NPY levels were determined in three hypothalamic regions [i.e., the periventricular nucleus (PVN), median eminence (ME) and the arcuate nucleus (ARC)] in 75 day-old males exposed to the diet treatments. In the PVN and ARC (but not the ME) NPY levels were significantly higher (by approximately 40 %) in Phyto-600 fed males vs. the Phyto-free male values (Figure 6).
Figure 6 Dietary Phytoestrogens Influence on Brain NPY Levels in 75 day-old Male Long-Evans Rats. In the paraventricular (PVN) and arcuate (ARC) nucleus, males fed the Phyto-600 (600) diet displayed significantly greater NPY levels (* p < 0.05) compared to males fed the Phyto-Free (Free) diet. In the median eminence (ME) no significant differences were observed between male rats fed 600 vs. the Free diet.
Circulating Thyroid (T3), UCP-1 mRNA Levels and Core Body Temperature
In non-fasting young adult rats at 65 and 110 days of age, circulating thyroid (T3) levels were determined from venous blood samples. Phyto-600 fed males displayed significantly higher T3 levels compared to Phyto-free fed values [age 65 days old- Phyto-600 = 2.4 ± 0.2 vs. Phyto-free = 1.5 ± 0.4 pg/ml (± SEM), n = 8 per diet treatment; age 110 days old- Phyto-600 = 1.9 ± 0.4 vs. Phyto-free = 0.8 ± 0.3 pg/ml, n = 5 per group, (mean ± SEM) data not shown graphically].
The effects of dietary phytoestrogens on uncoupling protein-1 (UCP-1) mRNA levels in brown adipose tissue (BAT) from 75 day-old animals is shown in Figure 7. Phyto-600 fed males displayed significantly higher (≈ 2-fold) UCP-1 mRNA levels in BAT compared to Phyto-free values. Notably, the BAT weights of Phyto-600 animals were significantly less (by approximately 1/3) to that of Phyto-600 males (Phyto-600 = 293.6 ± 22.1 vs. Phyto-free = 437.5 ± 25.9 mg (mean ± SEM), n = 8 per group.
Figure 7 Dietary Phytoestrogens Influence on Uncoupling Protein-1 (UCP-1) mRNA Levels in Brown Adipose Tissue (BAT) from males fed either a phytoestrogen-rich (Phyto-600) or a phytoestrogen-free (Phyto-free) diet. For each sample, the optical density (O.D.) signal ratio of BAT UCP-1 mRNA abundance to ribosomal 18S RNA intensity was determined. The results represent the mean O.D. values and S.E.M. of 8 independent samples by diet treatment of at least two runs of the RT-PCR protocol. Males fed the Phyto-600 diet expressed significantly higher BAT UCP-1 mRNA levels (* p < 0.05) compared to Phyto-free values.
Finally, when core body temperatures were recorded during a 24-hour interval, Phyto-free fed males displayed, in general, slight but significantly higher values compared to Phyto-600 animals during the dark phase of the light/dark cycle when rodents are most active (Figure 8). However, during one time point during the dark cycle (3 am) and one time point during the light phase of the cycle (3 pm) Phyto-600 males displayed slight but significantly higher core body temperatures vs. Phyto-free values.
Figure 8 Dietary Phytoestrogens Influence on Core Body Temperature in 75 day-old rats. In general, during the dark phase of the light cycle, Phyto-free fed males displayed significantly higher core body temperatures (* p < 0.05) compared to Phyto-600 fed values. However, near the end of the dark (at 3 am) and light (at 3 pm) phase of the dark/light cycle Phyto-600 fed males displayed significantly higher core body temperatures (▲ p < 0.05) compared to Phyto-free values.
Discussion
Estrogen is known to play a dual role in regulating body weight, food intake and adipose tissue deposition. On the one hand, estrogens decrease food intake, increase locomotor activity and hence decrease body weight [10,11]. However, adipose tissue deposition increases with puberty and early pregnancy in women, suggesting that estrogens influence body fat accumulation [12]. Additionally, in aging, estrogens promote adipose deposition and insulin resistance [13]. Conversely, results from aromatase, FSH and ER-knockout studies indicate that estrogens regulate adiposity where the complete lack of estrogens or blocking estrogen hormone action increases adipose tissue deposition [14-18], whereas, estrogen replacement in these models decreases adiposity. Notably, in the present study, male rats fed the Phyto-600 diet displayed significantly decreased adipose tissue and body weights compared to Phyto-free fed animals. While there is not extensive data on phytoestrogens and metabolism, other investigators have reported that genistein, increases lipolysis and decreases lipogenesis in rodent adipocytes [19] by a tyrosine kinase independent mechanism and these estrogen mimics inhibit glucose uptake by altering membrane-associated glucose transporters [20,21]. Thus, our data suggests that dietary soy phytoestrogens significantly decrease: 1) body and adipose tissue weights and 2) circulating leptin and insulin levels (that correspond with adipose deposition) compared to Phyto-free fed animals, implying that the hormonal action of phytoestrogens is beneficial to body fat regulation. Recent studies imply that insulin helps to regulate leptin expression in humans [22] and estrogens appear to enhance the action of insulin [23,24]. This may account for the decreased incidence of obesity in Asian countries where isoflavone consumption is high compared to Western countries. Decreased adipose tissue deposition by decreasing lipogenesis and increasing lipolysis may help to prevent insulin resistance (by reducing body fat) and the estrogenic actions of dietary phytoestrogens may augment the efficiency of insulin.
It was previously observed in our laboratory that dietary phytoestrogens significantly alter food and water intake [7,25,26]. The differential effects of the Phyto-free vs. Phyto-600 diets observed in the present studies on hypothalamic NPY levels, circulating insulin and leptin concentrations and food intake are consistent with the well established interrelationships among these parameters. Thus, relative to animals maintained on the Phyto-free diet, food intake was significantly increased in animals fed the Phyto-600 diet. Phyto-600-fed rats also exhibited higher concentrations of NPY in the arcuate and paraventricular nuclei of the hypothalamus. It is well established that NPY neurons whose perikarya reside in arcuate nucleus and project to PVN comprise an extremely important orexigenic neural pathway [27]. It therefore appears likely that at least one factor contributing to the higher food intake in Phyto-600-fed rats is the increased levels of NPY in this system.
The present studies also suggest a mechanism that may underlie the diet-induced effects on NPY (i.e., plasma insulin and leptin concentrations were significantly reduced in the Phyto-600 fed rats, relative to the Phyto-free animals). A number of previous studies have demonstrated a reciprocal relationship between circulating insulin and leptin titers and NPY concentrations in PVN. Thus, experimentally-induced reductions in either insulin [28] or leptin [29] are associated with increased pre-proNPY messenger RNA expression in arcuate nucleus and increased NPY levels in PVN, and moreover, it has been proposed that reductions in insulin and leptin that occur physiologically, e.g., with food deprivation, provide an important signal to the NPY system to initiate feeding [27,29]. Hence, taken together, the present findings suggest that by reducing secretion of insulin and/or leptin, chronic consumption of the Phyto-600 diet results in up-regulation of the orexigenic NPY circuit in the hypothalamus, which in turn stimulates food intake (and water consumption, since rodents and humans display prandial characteristics).
While it is clear that thyroid hormone levels are influenced by estrogens where increases are seen in T3 and T4, presumably by increasing in the production of thyroid binding globulin in the liver [30], the published data examining thyroid function and hormone levels are problematic at best in the soy research field due to the history of soy food formulations, parameters examined and iodine deficiencies [6,31,32]. In agreement with more recent studies, our results demonstrate that circulating T3 levels increase with soy consumption [33], and "personal communication- Dr. David Baer-USDA". Furthermore, there appears to be a link between increased thyroid levels with soy consumption and cardiovascular protection in lowering serum cholesterol levels [6] and thyroid hormones along with estrogens protecting against osteoporosis [34]. However, in animals consuming the Phyto-600 diet (that displayed higher T3 levels) we observed a lower core body temperature compared to Phyto-free fed rats. In subsequent (unpublished) studies, we have consistently recorded slight (approximately 0.5°C) but significantly lower core body temperatures in Phyto-600 vs. Phyto-free fed rats during pregnancy. This suggests that the overall effect on body temperature via these estrogen mimics in the soy-rich diet may act primarily by increasing cutaneous vasodilation, thus decreasing core body temperature. Animal studies have shown that estrogens can act centrally (in the preoptic/anterior hypothalamus) or peripherally to regulate body temperature [35,36]. Support for this view is seen in humans where changes in skin blood flow via cutaneous vasodilation during the menstrual cycle and in hormone replacement therapy studies correspond with estrogen levels [36,37]. Also, one report showed that soy-derived phytoestrogens have a similar effect to our findings where ovariectomized rats fed a soy diet displayed an approximate 0.8°C decrease in skin temperature, whereas, estradiol treatment decreased temperature values by 1.4°C [38]. Finally, in association with temperature regulation, several studies have reported that soy consumption may be an effective therapy for relief of hot flushes in women [39]. Finally, the various metabolic parameters examined in a global fashion seem to suggest that declines in insulin and leptin levels are the dominant systemic regulators in regard to body weight, since overall the Phyto-600 animals weigh less compared to Phyto-free fed animals. However, the present findings also suggest that body temperature is reduced in Phyto-600 fed animals vs. Phyto-free fed animals and previous behavioral studies suggest that Phyto-600 animals exhibit more locomotor active vs. Phyto-free fed animals [7,50] (see summary Figure 9).
Figure 9 Summary of Dietary Phytoestrogen (Isoflavone) Influences on Metabolic and Hormonal Parameters. The left-hand column: Phyto-600 arrows are relative to the effects seen in Phyto-free fed animals-right-hand column [in other words, relative to one another]. * = general increase in body temperature compared to Phyto-600 values
Uncoupling proteins (UCP-1 through UCP-5) are expressed in various tissues from many different species (mammals, birds, fish, insects and plants) that play important (but controversial) role(s) in the regulation of energy expenditure, or thermogenesis [40,41]. Uncoupling protein-1 is expressed mainly in BAT. When the influence of dietary phytoestrogens on UCP-1 mRNA levels in BAT was examined, Phyto-600 fed male rats, expressed significantly higher levels of the uncoupling protein (approximately 2-fold) compared to Phyto-free values (but BAT weights were significantly less in the Phyto-600 vs. Phyto-free fed males). To date, we are unaware of any studies that have investigated this aspect of soy consumption on thermogenesis. The decrease in BAT mass in Phyto-600 animals but increased expression of UCP-1 may represent a compensation mechanism for energy expenditure, and there are several neural inputs and hormonal factors that influence UCP-1 in BAT that make it difficult to differentiate the regulatory aspects of UCP-1 expression. For example, sympathetic denervation of inter-scapular BAT markedly reduced UCP-1 mRNA levels and estrogen, T3 and adrenergic agents [norepinephrine (NE)] stimulate UCP-1 expression in BAT [42,43]. In fact, it has been reported that T3 synergizes with NE to increase UCP-1 in BAT and stabilizes its mRNA transcripts [44]. These factors overlap with the changes seen in Phyto-600 fed vs. Phyto-free fed rats, in the present study, where T3 levels were increased and, presumably, along with the estrogenic influence of circulating isoflavones resulted in stimulating UCP-1 expression in BAT. Previously, we have not observed any significant alterations in circulating estradiol (or LH) levels in Phyto-600 vs. Phyto-free fed intact males [7]. Conversely, it has been reported that increases in hypothalamic NPY decrease UCP-1 [and reduces sympathetic outflow to BAT, but increases adipose tissue lipoprotein lipase activity] [30]. Also, plasma leptin levels are thought to stimulate UCP-1 in BAT [45,46], results opposite, in general, to that obtained in the present study. Based upon the obtained data sets, it is difficult to identify a common stimulatory or inhibitory pattern for the expression of UCP-1 in BAT of soy fed animals and especially define a functional role for the physiological properties associated with these UCPs in thermoregulation. Therefore, it is reasonable to speculate that multiple factors act collectively to regulate UCPs in BAT that in turn contribute to adaptive changes in body temperature.
Conclusions
This study demonstrates that consumption of a widely used commercially available soy-based rodent diet, (i.e., the Phyto-600 diet rich in isoflavones), alters several hormonal, metabolic and neuroendocrine parameters involved in maintaining body homeostatic balance, energy expenditure and feeding behavior in male rats. Further research is warranted in examining the important aspects of the neuroendocrine and metabolic influences of dietary phytoestrogens via the consumption of soy in humans and laboratory animals. This is especially true when diet is usually not considered as an influencing factor in the experimental design [47-50].
Abbreviations
neuropeptide Y (NPY), white adipose tissue (WAT), brown adipose tissue (BAT), uncoupling proteins (UCP), phytoestrogen-rich diet (Phyto-600), phytoestrogen free diet (Phyto-free), periventricular nucleus (PVN), median eminence (ME), arcuate nucleus (ARC), norepinephrine (NE), thyroid (T3, T4), National Institutes of Health (NIH), reverse transcriptase-polymerase chain reaction (RT-PCR), Food and Drug Administration (FDA), optical density (OD), luteinizing hormone (LH), follicle stimulating hormone (FSH), Brigham Young University (BYU), estrogen receptor (ER), high performance liquid chromatography (HPLC)
Competing interests
The author(s) declare that they have no competing interests.
Author's contributions
JPP, TDL, LB and GR contributed equally to this paper in various aspects of this study. KDRS carried out the isoflavone quantifications, WRC conducted the NPY analyses and EDL conceived of the study, designed, coordinated and drafted the manuscript along with the other authors.
Acknowledgements
This work was supported, in part, by grants from the USDA (2002-00798; EDL), the BYU Research Office (21-223566 & 19-223566; EDL) The Dean's Graduate Fellowship in Neuroscience (TDL and LB), and the National Institutes of Health (HD-13703; WRC)
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| 0 | PMC544861 | CC BY | 2021-01-04 16:37:34 | no | Biomed Eng Online. 2005 Jan 6; 4:3 | latin-1 | Biomed Eng Online | 2,005 | 10.1186/1475-925X-4-3 | oa_comm |
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BMC Med GenetBMC Medical Genetics1471-2350BioMed Central London 1471-2350-5-281558831410.1186/1471-2350-5-28Research ArticleMen's values-based factors on prostate cancer risk genetic testing: A telephone survey Doukas David J [email protected] Yuelin [email protected] Family and Geriatric Medicine, and Institute for Bioethics, Health Policy, and Law, University of Louisville School of Medicine, Louisville, KY 40202, USA2 Center for Outcomes Research, The Children's Hospital of Philadelphia, Philadelphia, PA 19104, USA3 Anesthesia, University of Pennsylvania School of Medicine, Philadelphia, PA 19104, USA2004 10 12 2004 5 28 28 30 3 2004 10 12 2004 Copyright © 2004 Doukas and Li; licensee BioMed Central Ltd.2004Doukas and Li; 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 a definitive genetic test for Hereditary Prostate Cancer (HPC) is not yet available, future HPC risk testing may become available. Past survey data have shown high interest in HPC testing, but without an in-depth analysis of its underlying rationale to those considering it.
Methods
Telephone computer-assisted interviews of 400 men were conducted in a large metropolitan East-coast city, with subsequent development of psychometric scales and their correlation with intention to receive testing.
Results
Approximately 82% of men interviewed expressed that they "probably" or "definitely" would get genetic testing for prostate cancer risk if offered now. Factor analysis revealed four distinct, meaningful factors for intention to receive genetic testing for prostate cancer risk. These factors reflected attitudes toward testing and were labeled "motivation to get testing," "consequences and actions after knowing the test result," "psychological distress," and "beliefs of favorable outcomes if tested" (α = 0.89, 0.73, 0.73, and 0.60, respectively). These factors accounted for 70% of the total variability. The domains of motivation (directly), consequences (inversely), distress (inversely), and positive expectations (directly) all correlated with intention to receive genetic testing (p < 0.001).
Conclusions
Men have strong attitudes favoring genetic testing for prostate cancer risk. The factors most associated with testing intention include those noted in past cancer genetics studies, and also highlights the relevance in considering one's motivation and perception of positive outcomes in genetic decision-making.
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Background
There are several factors to consider in undergoing genetic testing for cancer risk: potential benefits, possible risks, psychological distress, and the uncertainty in subsequent decision-making about prophylactic interventions [1-9]. While the health professional's assessment of the potential benefits and harms frames the disclosure of informed consent, the patients' values and expectations are intrinsic on the decision-making process. Current understanding of these values and expectations has been primarily derived from patients considering genetic testing for breast and colorectal cancers [10]. It remains unclear how these same factors may influence men's decision making in testing for hereditary prostate cancer risk [2]. The question addressed in this article is what values and expectations influence the intention of men to undergo genetic testing for prostate cancer risk.
A definitive genetic test for prostate cancer is not clinically available yet. Current genetic tests are only conducted in research studies. Several potential genetic loci have been identified as linked to hereditary prostate cancer, including HPC1 [11], MXI1, KAI1, [12] and 1q42.2-q43 on chromosome 1q. In the future, a test (or set of tests) for hereditary prostate cancer risk may become available. Such testing may become an important tool in preventing prostate cancer, or be useful once prostate cancer is diagnosed (e.g., for treatment decisions). Further, it is prudent for physicians to be prepared for patient requests for genetic testing, even when there are no strong clinical indications. Learning why men would accept or refuse prostate cancer genetic risk information is therefore relevant to the future of testing, and its informed consent.
Informed consent for genetic testing for cancer risk is particularly controversial in cancers where knowledge of a positive test result does not provide opportunities for interventions for favorable outcomes, and a negative result does not provide reassurance [13]. High stated intention for genetic testing for prostate cancer risk (over 80%) has been reported in the past [14]. Identification of a man at genetic risk for prostate cancer presents an ambiguous dilemma: Should a positive result be followed by prophylactic surgery, medication, increased surveillance (via PSA testing or rectal examination), or standard screening recommendations? The knowledge gained through genetic screening may not necessarily lead to clear cut recommendations about what the patient should do next.
This study examines men's beliefs and values toward interest in prostate cancer genetic testing. A survey instrument was developed for men between 40 and 70 years of age, exploring their beliefs, attitudes, and concerns in considering a hypothetical blood test. Exploratory factor analysis was applied to identify the underlying factor dimensions. The relative importance of these factors was then compared to testing intention.
Methods
Study population
The Institutional Review Board (I.R.B.) of the University of Pennsylvania and US Department of Defense Human Subjects Review approved the study. Subjects for this study included healthy outpatient males, identified with the assistance of the institution's Office of Health Services Research for demographic characteristics of age, ethnicity, and absence of past or current history of prostate cancer. Subjects were sent a letter-invitation to participate with an opt-out telephone number to call. Inclusion criteria were that subjects must be English-speaking men in a large metropolitan East-coast city, between the ages of 40 and 70, with no current evident mental incapacity and no present or past personal history of prostate cancer. All others were excluded.
Prostate cancer genetic screening survey questionnaire
Survey development
A 53-item attitude survey instrument was developed. The items were selected by the collaborators from a pool of more than 100 preliminary items from the data resulting from 12 focus groups of 90 lay men regarding their attitude, beliefs, and concerns about prostate cancer genetic screening [15]. The statements were answered on a 1–5 Likert-type scale ("Strongly Disagree" = 1, "Disagree" = 2, "Neutral" = 3, "Agree" = 4, and "Strongly Agree" = 5). Twenty-one items were reverse phrased to counter balance directionality in the response scale. Items 1, 51, 52, and 53 were intent items: "I would want the genetic test for prostate cancer risk when it becomes available," "I would want this test if it could tell me that prostate cancer is more likely to happen earlier in my life," "I would want this test if it could tell me that prostate cancer is more likely to be more life threatening because I have the prostate cancer risk gene," and "I would want this test even if it does not tell me new information about how early or aggressive prostate cancer may be in my future," respectively.
Telephone interview
The survey was conducted using Computer-Assisted Telephone Interviewing software (MacCATI, Senecio Software). The survey instrument was pilot tested in face-to-face interviews of randomly selected men, age 40–70, in a primary care office prior to data collection, to verify understandability of the survey's content and format. For the telephone survey, a recruitment packet that included an informed consent letter was first mailed to the prospective participants. Instructions explained the goals of the study and gave them an option to opt-out with a toll-free phone call prior to their interview. An oral informed consent was completed prior to the telephone interview.
Missing data
The number of missing observations ranged from 0 to 13, with an average of 2.38. Missing data were imputed based on an imputation model that predicts the missing values of factors as predicted by all of the other responses, including the outcome (desire to be tested). The algorithm uses Markov Chain Monte Carlo methods to select at random a value from the distribution of the possible values predicted by the missing value model. This method differs in several respects from other methods of filling in for missing data, in that with each imputation a different value will be imputed for the missing value, thus ensuring an added dimension of variability in the resulting analyses. The imputation was repeated multiple times. Each imputation generated an imputed data set. The same factor analysis was applied and no statistically reliable differences were found across the imputed data sets. Thus, only the results from the first imputed data are reported here. The imputation was carried out by SAS PROC MI.
Factor analysis and reliability statistics
A maximum-likelihood factor analysis with oblique rotation was applied to the 49 non-intent questions to classify men's non-intent beliefs and attitudes according to their underlying dimensions. The four questions that directly probed men's expressed intent were considered a priori as a separate factor. The factor analysis involved methodological criteria for data reduction, which included the rules summarized in Tabachnick [16]. Items with factor pattern loading lower than .40 were dropped (less than 16% overlapping variance between the item and the associated factor). The most salient dimensions were then retained, accounting for at least 70% of the total variability. The internal consistency reliability was assessed by Cronbach's alpha coefficient [17]. Items that showed the highest factor pattern loadings for a particular factor were considered items that measure the attitude associated with that dimension [18]. Factor scores, with estimated scores on each of the individual factors had they been measured directly, were also derived by summing the raw scores of the items [19].
Results
Demographics
Interviews were completed with 400 respondents with a cooperation rate of 47% (1675 were contacted, 431 refused to participate either by phone prior to the interview, or at the time of the interview, and 844 were excluded due to no answer, disconnected telephones, and death). Table 1 summarizes the respondents' characteristics. Of note, another study by the authors revealed that no demographic factor had a moderating impact on intention, except one – in which higher levels of education correlated with diminished testing intention [20]. IRB constraints precluded non-respondent data collection for comparison.
Table 1 Respondent characteristics
Characteristics N (N = 400) %
Ethnicity
White 288 72
Black 87 22
Hispanic 5 1
Asian 8 2
Other 6 2
No response 6 2
Age
40–49 133 33
50–59 143 36
60–69 124 31
Education
< High school 86 22
High school graduate/some college 149 37
College graduate 141 35
Post-graduate degree 23 6
No response 1 0
Annual household income
$15,000 or less 17 4
$15,001 – $45,000 74 19
$45,000 – 75,000 84 22
$75,000 – $105,000 90 23
More than $105,000 107 28
No response 28 7
Marital status
Married 319 80
Steady relationship but not married 23 6
Separated or divorced 26 7
Single 25 6
Widowed 7 2
Testing intention
About 82% of men interviewed expressed that they "probably" or "definitely" would take the test if one were offered now. This high interest increased to 88% if a positive test result indicates elevated risk in the early onset of cancer; 93% if it indicates graver prognosis of cancer; and the stated interest dropped to a still appreciable 68% if no new information on timing or severity of prostate cancer is to be learned from the prospective test.
Subscales
Exploratory factor analysis identified four underlying factors that accounted for 76% of the total variability among the 49 items probing men's beliefs and attitudes. The four factors were 1) Motivation, i.e., those values relating how strongly the respondent wanted the test, and how strongly the opinions of professionals, spouse, family, relatives, and friends could have influenced the respondent's own strength of intent; 2) Consequences, which measured beliefs with respect to follow-up decision-making and management; 3) Distress, which assessed fear of losing health and life insurance, anxiety, and worsening of quality of life if tested positive; and 4) Positive Expectations, which described beliefs in how the test results will confer useful information in family risk and favorable outcomes. The four intent items were added separately as the fifth subscale 5) Intention directly probing the respondent's stated intent. Table 2 summarizes the subscales, their respective internal consistency, and the factor loadings of their constituent items.
Table 2 Subscales, internal consistency, and factor item loadings
Factors / statements (internal consistency statistics) Factor pattern loading
Motivation
Subscale 1: Motivation (alpha = 0.89, 37% variability)
Even if other relatives did not want me to, I would get genetic testing. 0.80
Even if my children did not want me to, I would get genetic testing. 0.77
I would get genetic testing if my friends wanted me to. 0.73
Even if my friends did not want me to, I would get genetic testing. 0.72
I would get testing if other relatives wanted me to. 0.68
Even if my wife or partner did not want me to, I would get genetic testing. 0.68
Even if a genetic testing specialist recommended against it, I would get genetic testing. 0.60
I would get testing if my children wanted me to. 0.59
Even if my doctor recommended against it, I would get genetic testing. 0.57
I would get testing if a genetic testing specialist recommended it. 0.46
I would get testing if my wife or partner wanted me to. 0.43
Consequences
Subscale 2: Consequences and actions after knowing the test result (alpha = 0.73, 23% variability)
I find that my concerns about getting prostate cancer interfere with my every day life. [R] 0.56
I don't want testing unless there is a prostate cancer cure. [R] 0.54
If I know I have the prostate cancer risk gene, it will make me feel guilty. [R] 0.53
I'll have to make a quick treatment decision if I know I have the prostate cancer risk gene. [R] 0.52
If I know I have the prostate cancer risk gene, I will make me want to end my life. [R] 0.51
If I don't have the prostate cancer risk gene, I will be able to put my mind at rest about prostate cancer. [R] 0.51
I don't want testing unless it can tell me whether I have prostate cancer now. [R] 0.49
I would not want to have children if I know I have the prostate cancer risk gene. [R] 0.44
I would want to put off testing as long as I can. 0.42
Distress
Subscale 3: Psychological distress (alpha = 0.73, 10% variability)
I am concerned I will lose or not be able to get LIFE insurance if I get the genetic testing for prostate cancer risk. [R] 0.64
If I know I have the prostate cancer risk gene, it will make me anxious. [R] 0.59
I am concerned I will lose or not be able to get HEALTH insurance if I get the genetic testing for prostate cancer risk. [R] 0.57
If I know I have the prostate cancer risk gene, I will feel worse about myself. [R] 0.46
My life will get worse if I know I have the prostate cancer risk gene. [R] 0.46
If I know I have the prostate cancer risk gene, it will change the way I think about the future. 0.45
Positive expectations
Subscale 4: Beliefs in favorable outcomes if tested (alpha = 0.60, 7% variability)
I believe this test could save my life. 0.52
The more I know about my risk for prostate cancer, the better I will feel about testing. 0.51
The test results might provide valuable information on prostate cancer risk to my family members. 0.45
If I know I have the prostate cancer risk gene, my doctor may want to do more tests. 0.42
Intent
Subscale 5: Intention (alpha = 0.79)
I would want the genetic test for prostate cancer risk when it becomes available. -
I would want the test if it could tell me that prostate cancer is more likely to happen earlier in my life. -
I would want this test if it could tell me that prostate cancer is more likely to be more life threatening because I have the prostate cancer risk gene. -
I would want this test even if it does not tell me now information about how early or aggressive prostate cancer may be in my future. -
[R] – Item reversed in coding for analysis
N.B. – Intent items were not included in the factor analysis, thus there are no available data on pattern loadings and variance.
The following factors did not load onto the four value-based factor domains, and were omitted from further analysis:
I want to wait on testing until it is shown to be very accurate.
I will not be able to keep my job, or get a promotion, if I know I have the prostate cancer risk gene.
If I know I have the prostate cancer risk gene, my doctor might pressure me to receive treatment.
Nothing can be done to prevent prostate cancer.
Changes in my lifestyle can reduce my risk of cancer.
I only want my family doctor to do this test for prostate cancer risk.
I don't want testing unless I can do something to prevent prostate cancer.
The government could use my test results in ways I do not want.
I often worry about getting prostate cancer
If I know I do not have the prostate cancer risk gene, I won't need rectal exams or PSA tests as often.
No matter my results, I would want testing if it helps find a cure.
I don't want the test if my health care coverage does not pay for it.
I would want to get tested because I just want to know if I have the gene for prostate
No one should give out my test results to anyone else without my permission.
I would get testing if my doctor recommended it.
Overall, the five subscales showed satisfactory internal consistency. The four items within Intention scale, although grouped together a priori for their content, showed a good internal consistency alpha coefficient at 0.79. Among the 49 items that probed men's beliefs, values, and attitudes, the 11 items that loaded high on Motivation accounted for most of the variability (37%) with a very high alpha coefficient (0.89). The nine items in Consequences accounted for the next largest amount of variability (23%) with an alpha coefficient of 0.73. The six items in Distress accounted for 10% of the variability with an alpha of 0.73. Finally, the four items in Positive Expectations accounted for 7% of the variability with an alpha of 0.60.
The factor pattern loadings reflect the correlation between an individual item and its subscale. For example, Table 2 shows that Motivation is strongly associated with the item "even if other relatives did not want me to, I would get genetic testing." (Loading value = 0.80). Respondents with high motivation tended not to be influenced by other relatives. Importantly, the less one is influenced by a relative's opinion, the more likely he is to be motivated to get testing. Conversely, a man who was easily influenced by his spouse or children was somewhat less likely to be motivated toward testing. This latter set of values may reflect the desire for more information and counsel.
The inter-correlations between the subscales are summarized in Table 3, and reveal how these subscales were associated with one another and how they affected intent. The respondents' motivation (regarding the influence of others in their decision) was positively correlated with intention to test (r = 0.69, p < 0.001). There was also a positive and statistically significant correlation between one's motivation and one's expectations that genetic screening may lead to favorable outcomes for the gene carrier and his family (r = 0.39, p < 0.001). Concerns about the consequences of a positive result, including the uncertainties of test validity and accuracy, and the availability of subsequent interventions, were positively correlated with distress (r = 0.34, p < 0.001) and diminished intention to test (r = -0.16, p < 0.01). Distress-based values were associated with diminished intention to test (r = -0.17, p < 0.001). Finally, respondents who expected favorable outcomes were associated with increased intention to test (r = 0.48, p < 0.001).
Table 3 Inter-correlations between subscales
Motivation Consequences Distress Positive Expectations
Motivation -
Consequences -0.08 -
Distress -0.19* 0.34* -
Positive Expectations 0.39* -0.02 -0.003 -
Intention 0.69* -0.16* -0.17* 0.48*
* p < 0.001
Discussion
These data demonstrate that men in the general public, aged 40 to 70 years without a personal history of prostate cancer, consider prostate cancer genetic testing related to four value-based factor domains, similar to past literature findings on genetic testing for hereditary cancer risk. The motivation factor, which measures values of influence by others, is the strongest decision factor in guiding their opting for the test. More than 80% of men interviewed would consider getting tested if the test was available now. Their stated intention, as measured by the four intent items, is highly correlated with how strongly they feel they are motivated toward the test and inversely related to family influences. Men with strong motivation to get tested also have significantly lower concerns about psychological distress and higher levels of positive expectations. The recommendations of physicians and geneticists are important to men's expressed motivation, although the professionals did not appear to be more influential than their kin.
A respondent is more likely to want the test if he believes that the test may be informative of family risk and may lead to early identification and prevention of cancer (as part of the Positive Expectations domain). The influences of kin, along with beliefs in family risk, highlight the importance of reviewing family-related risk information as part of genetic consultation and informed consent. Men undergoing informed consent for hereditary prostate cancer risk in the future not only should be provided information on what genetic testing can and cannot do for them, but also what the test results could mean for others surrounding them (as evidenced by the influences of family, etc.).
Prior hereditary breast cancer (BRCA) and colorectal cancer (CRC) literature has noted anecdotally that perception of benefit to one's family influences genetic test uptake. Eliciting patient perceptions of concerns regarding their family may be beneficial to consider in oncology genetic testing generally. Similar to this literature, intention was found to be influenced by the respondent's concerns about test validity, test accuracy, and by the availability of interventions that may lead to favorable outcomes. Not surprisingly, men who were concerned about potential psychological distress were less likely to want the test. One unanswered question is how men's anticipatory distress and expected adverse consequences may affect how family risk information is interpreted and discussed. Few men in our study anticipated high levels of distress. Although literature data clearly show elevated distress among patients and their family members [21]. More research is needed to better establish the family-risk construct and how it may be influenced by other beliefs and values.
The present study has limitations. Given the exploratory nature of factor analysis, these data are aimed at identifying coherent subsets of variables for data reduction, not at identifying specific attitude statements that discriminate skeptics from supporters. Nevertheless, the reduced set of 34 items is the most important among the administered 57 items, and comprises a coherent and reliable assessment tool of eliciting values and intention toward testing. This item set can thereby serve as a foundation for a confirmatory health beliefs model, using Structural Equation Modeling techniques to better elucidate the interactions of these value-based domains [22]. Also, we noted that this population had somewhat higher income and education levels than the overall Philadelphia Consolidated Metropolitan Statistical Area (CMSA). 51% of men had over $75,000 income, compared with the 32% in the Philadelphia CMSA 2000 census year dataset, and 41% had completed a Bachelor's degree or higher, compared with 28% in the CMSA. These differences may be due to affluent subjects living in suburban counties in the metropolitan Philadelphia area, who then self-select to be seen by physicians in the University of Pennsylvania system. As noted above, our prior work demonstrated no demographic differences except education (with more education correlating with diminished intention). Thus, we do not foresee an adverse impact of these discrepancies on the overall outcomes of our analysis [20].
Future directions of this research may include exploring the relationship between stated intent in prostate cancer genetic screening and actual testing behavior when testing is available. Studies have shown that expressed intention does not necessarily translate to actual behavior in taking genetic tests for breast and colorectal cancers [10,23-29]. The same discrepancy between attitude and behavior may exist when a test for prostate cancer is available for the general public. Our data suggest that potential psychological distress, worries about test validity, insurance, confidentiality, and the uncertainties in subsequent intervention decisions may need to be balanced with family considerations when testing becomes available [30].
Conclusions
Men in this survey voiced strong attitudes favoring future genetic testing for prostate cancer risk. In the past decade and a half, genetic testing for a variety of cancers concentrated on several key concepts: i.e., stigmatization, privacy, anxiety/stress, and the need to know. These notions of stigma and psychological impact were not as relevant in this population regarding prostate cancer risk genetic testing. For examples, the following statements did not show strong enough factor loadings to warrant their inclusion, such as "I will not be able to keep my job, or get a promotion, if I know I have the prostate cancer risk gene," "The government could use my test results in ways I do not want," "I often worry about getting prostate cancer," and "I would want to get tested because I just want to know if I have the gene for prostate cancer."
The most relevant aspect of data reported herein is that they begin to shed new light on the relevance of "others." How men were concerned about the impact on and the effects upon one's family were reflected in the factor analysis. As a result, future informed consent may likely include considerations of 1) how the test results will affect their own future lives, and 2) how the test results will affect their family members. The latter consideration is seldom brought into the informed consent process in the genetic counseling but may be relevant to the patient.
Competing interests
The author(s) declare that they have no competing interests.
Authors' contributions
DD designed the overall project study, collaborated in the data analysis, and participated in its coordination. YL designed and carried out the factor analysis statistics. Both authors drafted, read, and approved the final manuscript.
Pre-publication history
The pre-publication history for this paper can be accessed here:
Acknowledgments
This study was funded by the US Department of Defense Grant DAMD17-98-1-8527 [both authors]. The authors thank Drs. A. Russell Localio, James Coyne, Larry McCullough and Michael Fetters and for their helpful counsel and comments.
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Virol JVirology Journal1743-422XBioMed Central London 1743-422X-2-21563435610.1186/1743-422X-2-2MethodologyDevelopment of a real-time QPCR assay for the detection of RV2 lineage-specific rhadinoviruses in macaques and baboons Bruce A Gregory [email protected] Angela M [email protected] Margaret E [email protected] Timothy M [email protected] Department of Pathobiology, School of Public Health and Community Medicine, University of Washington, Seattle, WA 98195 USA2005 5 1 2005 2 2 2 16 11 2004 5 1 2005 Copyright © 2005 Bruce et al; licensee BioMed Central Ltd.2005Bruce 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 distinct lineages of rhadinoviruses related to Kaposi's sarcoma-associated herpesvirus (KSHV/HHV8) have been identified in macaques and other Old World non-human primates. We have developed a real-time quantitative PCR (QPCR) assay using a TaqMan probe to differentially detect and quantitate members of the rhadinovirus-2 (RV2) lineage. PCR primers were derived from sequences within ORF 60 and the adjacent ORF 59/60 intergenic region which were highly conserved between the macaque RV2 rhadinoviruses, rhesus rhadinovirus (RRV) and Macaca nemestrina rhadinovirus-2 (MneRV2). These primers showed little similarity to the corresponding sequences of the macaque RV1 rhadinoviruses, retroperitoneal fibromatosis herpesvirus Macaca nemestrina (RFHVMn) and Macaca mulatta (RFHVMm). To determine viral loads per cell, an additional TaqMan QPCR assay was developed to detect the single copy cellular oncostatin M gene.
Results
We show that the RV2 QPCR assay is linear from less than 2 to more than 300,000 copies using MneRV2 DNA, and is non-reactive with RFHVMn DNA up to 1 billion DNA templates per reaction. RV2 loads ranging from 6 to 2,300 viral genome equivalent copies per 106 cells were detected in PBMC from randomly sampled macaques from the Washington National Primate Research Center. Screening tissue from other primate species, including another macaque, Macaca fascicularis, and a baboon, Papio cynocephalus, revealed the presence of novel rhadinoviruses, MfaRV2 and PcyRV2, respectively. Sequence comparison and phylogenetic analysis confirmed their inclusion within the RV2 lineage of KSHV-like rhadinoviruses.
Conclusions
We describe a QPCR assay which provides a quick and sensitive method for quantitating rhadinoviruses belonging to the RV2 lineage of KSHV-like rhadinoviruses found in a variety of macaque species commonly used for biomedical research. While this assay broadly detects different RV2 rhadinovirus species, it is unreactive with RV1 rhadinovirus species which commonly co-infect the same primate hosts. We also show that this QPCR assay can be used to identify novel RV2 rhadinoviruses in different primate species.
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Background
Members of the Rhadinovirus genus of the gammaherpesviruses are lymphotrophic and are associated with a variety of lymphoproliferative diseases. Herpesvirus saimiri (HVS), the prototype rhadinovirus isolated from the South American squirrel monkey, causes fulminant T-cell lymphomas in closely related host species [1]. Kaposi's sarcoma-associated herpesvirus (KSHV)/human herpesvirus 8, the only known human rhadinovirus, is associated with classical and AIDS-related Kaposi's sarcoma, primary effusion lymphoma and multicentric Castleman's disease [2]. Other rhadinoviruses have been isolated from ruminants, including wildebeest, sheep and cows, that are associated with malignant catarrhal fever, a lymphoproliferative syndrome [3,4].
We and others have demonstrated the existence of two distinct lineages of KSHV-like rhadinoviruses in Old World non-human primates [5,6]. The rhadinovirus-1 (RV1) lineage includes KSHV and closely related homologs infecting different Old World non-human primate species. In macaques, the RV1 lineage is represented by retroperitoneal fibromatosis herpesvirus (RFHV) that was identified in retroperitoneal fibromatosis (RF) tumor lesions of two macaque species at the Washington National Primate Research Center (WaNPRC) [7,8]. The RV2 lineage in macaques includes rhesus rhadinovirus (RRV) which was first identified in co-cultures of peripheral blood mononuclear cells (PBMC) of rhesus macaques (M. mulatta) in the New England National Primate Research Center (NENPRC) [9] and pig-tailed macaque rhadinovirus/M. nemestrina RV2 (MneRV2) [5,10,11]. While sequence analysis of the RRV genome demonstrated a close similarity to KSHV [12,13], phylogenetic analysis of multiple gene sequences has grouped RRV and the closely related MneRV2 within the RV2 lineage distinct from RFHV and KSHV [5]. Although RV2 rhadinoviruses have been identified in all Old World non-human primates tested, including gorillas and chimpanzees, no evidence of a human homolog has so far been found [6,14-17].
While complete genomic sequences have been obtained for two closely related strains of the RV2 lineage rhadinovirus of rhesus macaques, RRV strain H26-95 from the NENPRC [13] and RRV strain 17577 from the Oregon National Primate Research Center (ONPRC) [12], little information is known regarding the sequences of RV2 rhadinoviruses from other macaque species, and assays to detect these rhadinoviruses have not been developed. Quantitative real-time PCR assays (QPCR) have been developed to specifically detect RRV in rhesus macaque samples [18,19], but these assays have not be shown to cross to other RV2 rhadinovirus species. Since the WaNPRC and other primate research centers in the US and abroad utilize macaque species other than rhesus for biomedical research, we decided to obtain sequence information from the RV2 rhadinovirus of pig-tailed macaques, MneRV2, in order to develop a general assay to detect RV2 rhadinoviruses from different macaque species. Our strategy was to identify gene sequences that were highly conserved between different RV2 species but not conserved within macaque RV1 rhadinoviruses, such as RFHVMn or RFHVMm, which are sometimes found in conjunction with RV2 rhadinovirus infections. Previous nucleotide sequence information for MneRV2 consisted only of a region of the DNA polymerase which had significant sequence similarity with the macaque RV1 rhadinoviruses, and therefore was unsuitable for the desired assay [5]. We analyzed several regions of the RV1 and RV2 rhadinovirus genomes as targets for a general RV2 specific assay and identified the ORF 59/60 junctional region as a suitable target. This region was highly conserved within macaque RV2 rhadinovirus species but not within macaque RV1 rhadinoviruses. In this paper, we report the development of a sensitive and specific TaqMan QPCR assay and its use in detecting and quantitating RV2 rhadinoviruses from different primate species.
Results
Identification of the ORF 59/60 junctional region from the RV1 and RV2 rhadinovirus species from Macaca nemestrina, RFHVMn and MneRV2
The ORF 59 and ORF 60 genes show high levels of homology between the related rhadinoviruses, KSHV and RRV, with 52% and 70% identity at the amino acid level, respectively [13]. Using the CODEHOP approach [20,21], we developed degenerate primers targeting conserved amino acid motifs "RDEL" (ORF 60) and "PQFV" (ORF 59) that would enable the amplification and sequence analysis of the ORF 59/60 junctional region of novel RV1 and RV2 rhadinovirus species as described in Materials and Methods. The CODEHOP primers were used in PCR amplification of DNA obtained from spleen tissue from 442N, a M. nemestrina, which has been previously shown to contain a co-infection of both MneRV-2 and RFHVMn rhadinoviruses [5]. PCR products from both MneRV2 and RFHVMn were obtained as described in Materials and Methods. Sequence analysis revealed a close similarity between the 833 bp of the ORF59/60 junctional region between the "RDEL" and "PQFV" motifs of MneRV2 and the corresponding region of RRV, with an 87% nucleotide identity. The 834 bp sequence of the RFHVMn junctional region was 67% identical to the corresponding region of KSHV and 60% identical to the RRV sequence. Phylogenetic analysis using DNA maximum-likelihood demonstrated a close clustering of the MneRV2 and RRV sequences, while the RFHVMn sequence clustered with the KSHV ORF59/60 sequence, as expected for the macaque homolog of KSHV (Figure 1).
Figure 1 Phylogenetic analysis of the nucleotide sequences of the ORF59/60 junctional region from various rhadinoviruses. Sequences of the PCR products obtained using CODEHOP PCR primers from the rhadinoviruses MneRV2 (M. nemestrina), MfaRV2 (M. fascicularis), PcyRV2 (Papio cynocephalus) and RFHVMn (M. nemestrina) were aligned with the corresponding published sequences for KSHV (homo sapiens; U93872, bp 96678–97514), RRV (M. mulatta; AF083501, bp 92374–93205), and HVS (squirrel monkey, HSGEND, bp 81608–82613) using ClustalW. Phylogenetic analysis was performed using the DNA maximum likelihood procedure from Phylip. The division of New and Old World primate hosts is indicated. The RV1 and RV2 lineages of the Old World primate rhadinoviruses are shown. Novel viruses identified with the RV2 QPCR assay are underlined.
TaqMan quantitative PCR (QPCR) assay specific for RV2 rhadinoviruses
Multiple alignment of the RRV and MneRV2 nucleotide sequences revealed large regions of identical sequences within both the ORF 59 and ORF 60 coding regions and the ORF 59/60 intergenic region. As shown in Figure 2, a region of 71 identical nucleotides in the MneRV2 and RRV sequences was identified at the 3' end of the ORF 60 gene and the adjacent intergenic region. This region was only 43% conserved with the corresponding sequence of RFHVMn. Using Primer Express software (Applied Biosystems), a set of PCR primers (RV2a and RV2b) and a probe (RV2-FAM) were identified for a TaqMan QPCR assay (71 bp amplicon) which would specifically detect these macaque RV2 rhadinoviruses and not cross to known RV1 rhadinoviruses (Fig. 2 and Table 1).
Figure 2 Primer location and specificity of the RV2 QPCR assay. Corresponding sequences from the end of ORF 60 and the adjacent intergenic region from different rhadinoviruses (see legend to Figure 1) were aligned. Rhadinovirus species and lineages are indicated. The primer set and probe were designed from the RRV and MneRV2 sequences. The RV2a primer and RV2-FAM probe were derived from the sense strand, as shown, while the RV2b primer was derived from the antisense strand. The alignment shows the mismatches between the primer and probe sequences and the MfaRV2 and PcyRV2 sequences identified with the RV2 assay in this study. Dots represent residues identical to those in the RRV sequence, and highlight the similarity of the primer sequences within the RV2 lineage of rhadinoviruses and the dissimilarity with members of the RV1 lineage of rhadinoviruses.
Table 1 PCR primers
Primer1 Gene Target Sequence2
RV2 QPCR Assay (Figure 2)
RV2a RV2 ORF 60 5'-TCTGAATATGTCACATCCGTTCATA-3'
RV2b RV2 ORF 59/60 intergenic 5'-GGCCCGGAAAATGAGTAACA-3'
RV2-FAM3 RV2 ORF 60 and 59/60 intergenic 5'-(6-FAM)-TGATCTGTAGTCCCCATGTGTCC-(BHQ-1)-3'
OSM QPCR Assay (Figure 3)
OSMa Exon 3 OSM 5'-CCTCGGGCTCAGGAACAAC-3'
OSMb Exon 3 OSM 5'-GGCCTTCGTGGGCTCAG-3'
OSM-FAM Exon 3 OSM 5'-(6-FAM)-TACTGCATGGCCCAGCTGCTGGACAA-(BHQ-1)-3'
ORF 59/60 CODEHOP Primers
RDELa4 ORF 60 bias5 KSHV 5'-CTTGCCAACGATTACATTTCCAGRGAYGARCT-3'
SRDEa4 ORF 60 bias RRV 5' CTGGCTAACGACTACATCTCCAGRGAYGARCT-3'
NFFEa ORF 60 bias KSHV 5'-GGCAGTTTCAAGGCTGTGAATTTYTTYGARCG-3'
PQFVb6 ORF 59 bias KSHV 5'-CCGTAAGAAATGGTGGTCCTGACRAAYTGNGG-3'
QFVRb6 ORF 59 bias RRV 5'-CCGTAGGCGATGGTCGTCCTAACRAAYTGNGG-3'
CFICb ORF 59 bias RRV 5'-TACAAAATACAGCGAGTGATANATRAARCA-3'
Gene-Specific Primer
MPVDb ORF 59 (RFHV/KSHV)7 5'-TGAAAATCCACAGGCATGAT-3'
1The terminal "a" or "b" in the primer name indicates the plus or minus sense of the gene transcription, respectively.
2IUB code for ambiguous nucleotides: R = A or G; Y = C or T; N = A, C, G, or T
3FAM indicates a TaqMan dual-labeled probe with the fluorescent dye 6-FAM at the 5' end and the "black hole quencher" (BHQ) dye at the 3' end.
4These CODEHOP primers target the same motif but are biased differently (see below).
5"bias" indicates that the 5' consensus region of the CODEHOP primer was derived from a particular sequence" see [20].
6These CODEHOP primers target the same motif but are biased differently.
7This primer sequence is identical to the RFHVMn, RFHVMm and KSHV sequences
TaqMan QPCR assay for the cellular gene, oncostatin M, to determine cell number
In order to determine viral copy number per cell, an additional TaqMan QPCR assay was developed to detect a single copy cellular gene, oncostatin M (OSM) [22]. We had previously determined the sequence of the OSM gene in an African green monkey which was highly conserved with the human gene (unpublished results). Using PCR primers derived from consensus sequences of the human and monkey gene, we determined the sequence of the entire OSM coding sequence of the M. nemestrina OSM gene (data not shown). Multiple alignment of the human, monkey and macaque OSM sequences revealed a region within exon 3 which was highly conserved. Using Primer Express software, a set of primers (OSMa and OSMb; 76 bp amplicon) and a probe (OSM-FAM) were identified (Fig. 3 and Table 1) which could be used to detect OSM DNA from macaque, monkey and human sources allowing quantitation of cell number in tissue samples.
Figure 3 Primer location and specificity for the OSM QPCR assay to detect cell copy number. Corresponding sequences from the third exon of the OSM gene from human, African green monkey (AGM) and pig-tailed macaque (Mn) are aligned with the positions of the OSM primer set and probe indicated. The OSMa primer and OSM-FAM probe were derived from the sense strand, as shown, while the OSMb primer was derived from the antisense strand.
QPCR Assay Development and Characterization
The RV2 and OSM QPCR assays were optimized using DNA obtained from the spleen of a rhesus macaque, MmuA01111, which we have previously determined to contain RRV DNA in a background of macaque genomic DNA [23]. Initially, a temperature gradient PCR was performed to determine annealing temperatures that gave a single PCR product. An annealing temperature of 62°C was chosen because that temperature was optimal in both the RV2 and OSM assays (data not shown). The magnesium chloride, nucleotide, primer and probe concentrations were then varied to determine conditions which gave optimal efficiency.
Standard curves were obtained from a dilution series using the optimal conditions for the RV2 and OSM assays as described in Material and Methods. For the RV2 assay, purified MneRV2 DNA obtained from a lytic infection of rhesus primary fetal fibroblasts (RPFF) was assayed in duplicate using 4-fold dilutions. As seen in Figure 4A, the assay was linear across a range of dilutions from less than 2 to more than 3.0 × 105 copies of MneRV2, with a slope of -3.320 (100% efficiency) and r2 = 0.997. For the OSM assay, MmuA01111 genomic DNA was assayed in duplicate using 4-fold dilutions, with the amount of DNA tested ranging from 0.06 ng (corresponding to 20 diploid OSM gene copies: equivalent to 10 cells) up to 1 μg (corresponding to 3.2 × 105 diploid OSM gene copies: equivalent to 1.6 × 105 cells). The assay was linear across this range with a slope of -3.322 (100% efficiency) and r2 = 0.999 (Fig. 4B).
Figure 4 Standard curves obtained from the RV2 rhadinovirus and OSM reference cellular gene assays. A) The standard RV2 assay was performed on purified MneRV2 DNA in a series of four-fold dilutions over the range of 2 copies to 3.0 × 105 copies of MneRV2. (slope = -3.320, 100% efficiency; r2 = 0.997). B) The standard OSM assay was performed on MmuA01111 spleen DNA in a series of four-fold dilutions over the range of 0.06 ng (20 diploid OSM gene copies) to 1 μg (3.2 × 105 diploid OSM gene copies). (slope = -3.322, 100% efficiency; r2 = 0.999)
To determine the linearity of the RV2 assay with a biologically relevant sample, DNA from the spleen of MmuA01111 which contains cells naturally infected with RRV was subjected to 4-fold dilutions while keeping genomic DNA levels constant at 1 μg per reaction by the addition of DNA from an uninfected animal. The results demonstrate that the assay was linear from less than 66 copies of RRV (256-fold dilution of MmuA01111 DNA in uninfected macaque DNA) to more than 1.7 × 104 RRV copies per μg genomic DNA (MmuA01111 DNA undiluted) with a slope of -3.318 (100% efficiency) and r2 = 0.988 (Fig. 5). This shows that the viral load determination would be accurate down to 410 RRV genomes/106 cells which is 1 viral copy per 2400 cells. The upper limit in this assay was determined to be greater than 110,000 viral genomes/106 cells which is the number of viral copies of RRV in 1 μg of DNA from the MmuA01111 spleen.
Figure 5 Biologically relevant standard curve obtained with the RV2 rhadinovirus assay using RV2 DNA in a constant amount (1 μg) of genomic DNA. DNA from MmuA01111 which was naturally infected with RRV was assayed in duplicate in four-fold dilutions made with uninfected macaque DNA. (slope = -3.318, 100% efficiency; r2 = 0.988].
To ensure that the RV2 assay does not detect RV1 viruses, the assay was performed using DNA from the human and macaque RV1 rhadinoviruses. A DNA sample from the KSHV infected BCBL-1 cell line [24] containing approximately 4 × 106 copies of the KSHV genome and a sample containing 109 copies of a PCR product of the ORF59/60 junctional region from RFHVMn were used as templates in the RV2 assay. The RV-2 QPCR assay was negative for these templates under the standard reaction conditions.
Identification of a novel RV2 rhadinovirus in Macaca fascicularis using the RV2 QPCR assay
Since the RV2 QPCR assay was based on consensus sequences shared by two distinct members of the RV2 lineage from M. mulatta and M. nemestrina, RRV and MneRV2, respectively, we tested to see if this assay could be used to identify a novel RV2 rhadinovirus in M. fascicularis. DNA was obtained from spleen tissue of Mfa95044, an M. fascicularis from the Tissue Distribution Program at the WaNPRC. Approximately 250 ng of spleen DNA produced a positive result in the RV2 QPCR assay with an average cycle threshold (CT) of 31.9 cycles. In order to prove that the assay detected a novel rhadinovirus, CODEHOP primers were used in a PCR amplification reaction with the Mfa95044 spleen DNA to obtain the ORF59/60 intergenic region of this rhadinovirus as described in the Materials and Methods. An 832 bp PCR product was obtained and sequenced. A comparison of this sequence with the corresponding region from RRV and MneRV2 showed 94% and 86% nucleotide identity, respectively. The nucleotide identity with the corresponding region in RFHV and KSHV was only 59% and 60%, respectively. Phylogenetic analysis showed a close clustering of the M. fascicularis sequence with the RRV sequence and a more distant relationship with the MneRV2 sequence, confirming its origin from an RV2 rhadinovirus of M. fascicularis, herein termed MfaRV2 (Figure 1). The evolutionary relationship of these rhadinovirus species mirrors that determined for the host macaque species themselves, where the M. mulatta and M. fascicularis have been shown to be more closely related to each other than to M. nemestrina [25]. Our data supports the hypothesis of a co-speciative divergence of the Old World primate rhadinoviruses and their hosts [26]
Identification of a novel RV2 rhadinovirus in the baboon, Papio cynocephalus, using the RV2 QPCR assay
To further determine the specificity of the RV2 QPCR assay, DNA obtained from lymphocytes of baboon Pcy78404 was tested for the presence of a related RV2 rhadinovirus species under the standard assay conditions. Approximately 250 ng of lymphocyte DNA produced a positive result with an average CT of 33.8 cycles. In order to determine the identity of the reactive DNA species, CODEHOP primers were used in a PCR reaction with the baboon DNA as template as described in Materials and Methods. A product was obtained that yielded an 834 bp sequence which was 83% identical to the ORF59/60 intergenic region of each of the macaque RV2 rhadinoviruses, RRV, MneRV2 and MfaRV2, and 58% identical to the corresponding region in both KSHV and RFHVMn. The baboon sequence clustered with the macaque RV2 rhadinovirus sequences confirming its origin from an RV2 rhadinovirus of the baboon (Papio cynocephalus), herein termed PcyRV2. Phylogenetic analysis demonstrated that while PcyRV2 clustered within the RV2 rhadinovirus lineage, it branched off separately from the macaque RV2 rhadinoviruses as expected for a baboon rhadinovirus (Fig. 1).
Previously, an RV2 rhadinovirus, PapRV2, was detected in a baboon (Papio anubis) [27], and a partial sequence of the DNA polymerase was obtained. In order to compare PcyRV2 with PapRV2, we utilized CODEHOP PCR primers [7] to amplify a region of the polymerase gene of PcyRV2 that could be compared to the sequence available for PapRV2. DNA sequence for 352 bp of the DNA polymerase gene was obtained. An alignment of this sequence with the corresponding sequence of the PapRV2 rhadinovirus revealed a 97% sequence identity with 11 nucleotide differences which altered one amino acid.
Specificity of the RV2 QPCR assay
In order to compare the ability of the RV2 QPCR assay to detect different rhadinovirus templates, test samples containing roughly equivalent viral copy numbers in a background of genomic DNA were prepared. DNA from purified MneRV2, DNA from MmuA01111 spleen which contains RRV, and DNA from Mfa95044 spleen which contains MfaRV2 were diluted in DNA from a virus negative macaque to have approximately the same virus load as that found in the baboon lymphocyte DNA containing PcyRV2. As shown in Figure 6, all four samples have relatively similar levels of the different viruses, as indicated by the similar CT values (30.3, MneRV2; 30.8, RRV; 31.6, MfaRV2; and 33.2, PcyRV2). The cumulative fluorescence curve for the MneRV2 and RRV samples were superimposable with slopes typical of those seen in the assays performed in Figures 4 and 5 which showed amplification efficiencies of 100%. In contrast, both the M. fascicularis and baboon templates produced fluorescence curves with significantly decreased slopes, indicating lower amplification efficiencies. The efficiencies of these PCR reactions were calculated to be approximately 81% (r2 = 0.900) for the MfaRV2 and 72% (r2 = 0.929) for the PcyRV2, however, the low levels of virus in these samples made it difficult to accurately determine the efficiencies, as indicated by the correlation coefficients.
Figure 6 Comparison of the RV2 QPCR assay using different rhadinovirus templates diluted in genomic DNA. The PcyRV2 results were obtained using 1 μg of spleen DNA from baboon, Pcy78404, naturally infected with PcyRV2. The other rhadinovirus DNA templates were diluted in uninfected macaque genomic DNA to yield approximately equivalent CT values. The MneRV2 results were obtained using DNA from purified MneRV2 in macaque genomic DNA. The RRV results were obtained using DNA from spleen of MmuA01111, naturally infected with RRV. The MfaRV2 results were obtained using DNA from spleen of Mfa95044, naturally infected with MfaRV2. The released reporter fluorophore is plotted as a function of the amplification cycle number.
The novel ORF 59/60 intergenic regions of MfaRV2 and PcyRV2 were aligned with the corresponding sequences of RRV, MneRV2, RFHVMn, and KSHV. Also aligned was a partial sequence of the ORF 59/60 region obtained from RFHVMm (see Materials and Methods). As shown in Figure 2, the MfaRV2 sequence contained single nucleotide mismatches with the RV2a primer and RV2-FAM probe; an exact match was seen with the RV2b primer. The PcyRV2 sequence contained the same nucleotide mismatches seen in MfaRV2 and additionally had a second nucleotide mismatch within both the RV2a primer and the RV2-FAM probe. An additional mismatch was found between the PcyRV2 sequence and the RV2b primer. These nucleotide mismatches correlated with the decreased amplification efficiency of the assay with this template, as shown in Figure 6.
RV2 QPCR screen of the prevalence of RV2 rhadinoviruses in macaques housed at the WaNPRC
DNA samples were obtained from PBMC of a random assortment of thirty macaques housed at the WaNPRC and analyzed using the standard RV2 and OSM QPCR assays. While all of the samples were positive for the single copy OSM gene, only six of the thirty macaques were positive for the presence of an RV2 rhadinovirus. In all of these six cases, both duplicate reactions in the assay were positive yielding average viral loads of 6–2300 per 106 cells (Table 2). However, in four of the six positive macaques, the RV-2 assay result was low and outside the linear range of the assay.
Table 2 RV2 rhadinovirus load in PBMC of 30 healthy macaques in the WaNPRC colony
Animal RV2 DNA load in PBMC (Viral copies per 106 cells; mean ± SD1)
M. nemestrina (pig-tail)
A98078 2300 ± 1200
F94132 650 ± 460
A98079 340 ± 49*
90152 5.8 ± 4.2*
16 other M. nemestrina Below the limit of detection
M. fascicularis (crab-eating)
98023 250 ± 96*
7 other M. fascicularis Below the limit of detection
Unknown macaque species
98062 57 ± 52*
1 other unknown species Below the limit of detection
% of all macaques testing positive 6/30 = 20%
1 Samples (1 μg) were assayed in duplicate and the means were determined. Standard deviations were calculated using the sum of the errors of the viral and OSM copy number determinations, as described in Materials and Methods.
* These results, while positive for both duplicates, were outside of the linear range of the assay.
Discussion
We have developed a TaqMan probe-based QPCR assay to quantitate the viral load of macaque rhadinoviruses belonging to the RV2 lineage of KSHV-like rhadinoviruses. The primers and probe for this assay were based on sequences within the 3' end of the ORF 60 coding sequence and the ORF 59/60 intergenic region which were identical between the pig-tailed and rhesus macaque rhadinoviruses, MneRV2 and RRV, respectively, but were not conserved with the corresponding macaque viruses from the RV1 lineage of KSHV-like rhadinoviruses RFHVMn and RFHVMm. We have also developed a TaqMan probe-based QPCR assay targeting the single copy cellular gene, OSM, to serve as an internal control for quantitating cell copy number. Both assays were designed to give 100% PCR efficiency at the same annealing temperature, are linear over more than 4 orders of magnitude and are sensitive enough to detect less than 20 copies of the DNA target. The RV2 assay is able to accurately detect less than 66 copies of viral DNA in a genomic DNA background, even when the viral load is as low as 1 copy per 2400 cells.
Quantitation of the cellular DNA and viral DNA copy numbers in a tissue sample provides a suitable method for comparing viral loads, even between samples of unknown purity or degradation status. Because of the small size of the amplicons for both assays, OSM (76 bp) and RV2 (71 bp), viral loads can even be determined in formalin-fixed paraffin embedded tissue in which significant degradation of the DNA has occurred. Due to the similarities in sequence of the human, macaque and African green monkey OSM genes, the OSM QPCR assay may be suitable for quantitation of DNA in tissue from a number of other Old World primate species.
We have screened DNA from a number of random PBMC samples from macaques at the WaNPRC for the presence of an RV2 rhadinovirus. We detected RV2 rhadinovirus DNA in 6 of 30 macaques; 4 of 20 M. nemestrina, 1 of 7 M. fascicularis and 1 of 2 macaques whose species is not known. In these macaques, the viral copy number was determined to range from 6–2300 per 106 cells. Although the copy number in the single positive M. fascicularis was calculated to be 250 viruses per 106 cells, this would be a low estimate due to the 81% efficiency of the amplification of that template, as discussed above. Our results for RV2 rhadinoviruses in the macaque species tested at the WaNPRC were similar to those determined for RRV in rhesus macaques at the Tulane National Primate Research Center [18]. In the Tulane study, a QPCR assay developed against the interleukin-6 homolog of RRV found infrequent and low levels of RRV in PBMC of healthy and SIV-infected rhesus macaques. Only two healthy macaques had detectable RRV DNA with levels of 320 and 880 genomes per 106 cells. In the other 28 animals, the RRV load was below the level of detection. While RRV was detected more frequently in SIV-infected macaques in this study, the virus load was similar to that seen in healthy macaques.
The Tulane RRV assay had a similar sensitivity to our RV2 assay, with a lower limit of one RRV genome per 10,000 cell equivalents however, it was designed to specifically target only RRV while our RV2 assay is capable of detecting RRV, MneRV2 and other macaque and baboon rhadinoviruses. In this report, we have used the RV2 assay to detect novel RV2 rhadinovirus homologs in both the spleen of a crab-eating macaque (Macaca fascicularis) and the lymphocytes of a baboon (Papio cynocephalus). The standard RV2 assay had an amplification efficiency less than 100% with the M. fascicularis and P. cynocephalus templates which cautions against its use for accurate quantitation of the MfaRV2 and PcyRV2 rhadinoviruses. The primer and probe binding regions of these two rhadinoviruses showed nucleotide mismatches which correlate with the decrease amplification efficiency of the assay.
We have shown that the RV2 QPCR assay is capable of detecting a novel RV2 rhadinovirus, PcyRV2, in a baboon. Previously, an RV2 rhadinovirus, PapRV2, was also detected in baboons by others [27] using the degenerate PCR primer approach targeting the DNA polymerase gene that we had originally developed to detect novel herpesviruses [7]. In order to compare the two baboon viruses, we have sequenced a region of the DNA polymerase gene of PcyRV2. An alignment of this sequence with the corresponding sequence of the PapRV2 rhadinovirus revealed a 97% sequence identity with 11 nucleotide differences. This nucleotide similarity is consistent with the origin of these two viruses from two related species of baboons; the PcyRV2 rhadinovirus was isolated from the baboon species Papio cynocephalus, while the PapRV2 rhadinovirus was isolated from the baboon species Papio anubis.
Conclusions
In this report, we describe a QPCR assay which provides a quick and sensitive method for screening RV2 rhadinoviruses found in the variety of non-human primate species commonly found in the National primate centers. While this assay broadly detects different RV2 rhadinoviruses species, it is unreactive with several RV1 rhadinovirus species. We also show that this QPCR assay can be used to identify novel RV-2 rhadinoviruses in primates.
Methods and Materials
Animals
Fresh frozen spleen tissue samples from Macaca nemestrina (Mne) 442N were provided by R. Shibata while at the National Institutes of Health, Bethesda, MD. This pig-tailed macaque had been experimentally infected with a pathogenic SHIV strain [28]. We have previously obtained PCR evidence for the presence of both RV1 and RV2 macaque rhadinoviruses, RFHVMn and MneRV2, respectively, in RF tumor and spleen tissue of this animal [5]. Fresh frozen RF tumor tissue from Macaca mulatta (Mmu) YN91-224, an SIV-infected rhesus macaque diagnosed with RF, was kindly provided by H. McClure, Yerkes National Primate Research Center. Fresh frozen spleen tissue samples were also obtained from Macaca mulatta (Mmu) A01111 at the WaNPRC, a rhesus macaque that had been experimentally infected with SIV which we have shown to be co-infected with the RV1 and RV2 macaque rhadinoviruses, RFHVMm and RRV, respectively (unpublished observations). Fresh frozen spleen tissue from a Macaca fascicularis (Mfa) 95044 and lymphocytes from a baboon (Papio cynocephalus) (Pcy78404) were kindly provided by H. Bielefeldt-Ohmann and C.-C. Tsai, respectively, from the WaNPRC. DNA from the PBMC of thirty random healthy colony macaques was also obtained from the virus screening program at the WaNPRC.
Cells
The KSHV-infected pleural effusion lymphoma cell line, BCBL-1, was obtained from D. Ganem (Howard Hughes Institute – UCSF), and was carried in RPMI 1640 supplemented with 10% fetal bovine serum, penicillin, streptomycin, glutamine, and β-mercaptoethanol. Rhesus primary fetal fibroblasts (RPFF) were kindly provided by Dr. Michael Axthelm (ONPRC).
Rhadinovirus
An isolate of MneRV2, was obtained from an M. nemestrina, MneJ97167, at the WaNPRC. The MneRV2 was used to infect cultures of RPFF and viral particles were harvested from culture supernatent by high speed centrifugation. Viral DNA used as positive controls in the PCR assays was obtained by disruption of the viral particles using phenol/chloroform and ethanol precipitation.
DNA samples
DNA was extracted from frozen tissues using standard proteinase K-phenol/chloroform extractions and concentrated by ethanol precipitation.
PCR amplification primers
The protein sequences of the ORF 59 and ORF 60 genes from KSHV and RRV were aligned using ClustalW. The consensus-degenerate hybrid oligonucleotide primer (CODEHOP) technique [20,21] was used to design two sets of degenerate PCR primers within both ORF 59 and ORF 60 that would enable the amplification and sequence analysis of the ORF 59/60 junctional region of novel RV1 and RV2 rhadinovirus species. The ORF 59 and ORF 60 genes are arranged in the same transcriptional orientiation in both RRV and KSHV. Two sense-strand CODEHOP primers, RDELa and SRDEa contained nucleotides encoding the highly conserved amino acid motif, Arg-Asp-Glu-Leu (RDEL; 8 fold degenerate), in ORF 60. Primer RDELa was biased toward the RV1 rhadinoviruses and contained a 5' consensus region derived from the KSHV sequence (Accession no. NC_003409). Primer SRDEa was biased toward the RV2 rhadinoviruses and contained a 5' consensus region derived from the RRV sequence (Accession no. AF210726). Two antisense-strand CODEHOP primers, PQFVb and QFVRb contained all coding possibilities for the highly conserved motif, Pro-Gln-Phe-Val (PQFV) in ORF 59 (16 fold degenerate), and were biased to the KSHV and RRV sequences, respectively (see Table 1). An additional anti-sense strand CODEHOP primer, CFICb (16 fold degenerate), was designed from a Cys-Phe-Ile-Cys (CFIC) motif in the ORF 59 gene, downstream of the PQFV motif and contained all coding possibilities for the CFIC motif and was biased to RRV.
Amplification of the ORF 59/60 junctional region of novel rhadinoviruses
To obtain the ORF 59/60 junctional regions between the RDEL motif of ORF 60 and the PQFV motif of ORF 59 of MneRV2, PcyRV2, RFHVMn, and RFHVMm, DNA was obtained from different sources and used in PCR amplification with different CODEHOP PCR primers. Reactions were performed in 1 μM forward and reverse primers, 200 μM each dNTP, 20 mM Tris-HCl (pH 8.4), 50 mM KCl, and 2.5 units Platinum Taq polymerase (Invitrogen) using a 55–70°C annealing temperature gradient (BioRad Icycler). For MneRV2, PCR amplification was performed on Mne442N spleen DNA using primers RDELa and PQFVb. For PcyRV2, PCR amplification was performed on lymphocyte DNA from baboon Pcy78404, using SRDEa and QFVRb. In both cases an ~830 bp PCR fragment was obtained and sequenced. To obtain the sequence of RFHVMn which had a low copy number, it was necessary to amplify the RDEL-PQFV region in two fragments. A CODEHOP primer NFFEa (See Table 1), downstream of the RDEL motif was designed and used in conjunction with PQFVb to amplify an ~600 bp product from the Mne442N DNA. From the sequence of this product a specific primer, MPVDb, was derived and used in conjunction with RDELa to obtain an overlapping ~400 bp product. A similar strategy was used with RF tumor DNA obtained from MmuYN91-224 to obtain sequence from the ORF 59/60 junctional region of RFHVMm, however, only the sequence from NFFEA to PQFVB was obtained for comparison purposes. The ORF 59/60 junctional region of MfaRV2 was also obtained in two fragments. An ~400 bp PCR product was obtained after amplification of spleen DNA from Mfa95044, using the RV2 QPCR assay primer RV2b (see QPCR assay below and Table 1) and CODEHOP primer RDELa. An overlapping ~1400 bp PCR product was obtained using the RV2 QPCR assay primer, RV2a, in conjunction with an additional CODEHOP primer, CFICb.
Sequence alignment and phylogenetic analysis
Nucleotide sequences were aligned using ClustalW and analyzed using the DNA maximum-likelihood program from the Phylip package, version 3.62 (University of Washington, Seattle). Phylogenetic tree output was produced using TreeView.
Real-time QPCR design
The RV2 assay was designed to amplify a 71-bp amplicon from the ORF 59/60 junctional region of macaque viruses belonging to the RV2 rhadinovirus lineage using consensus primers "RV2a" (forward primer 5'-TCTGAATATGTCACATCCGTTCATA-3') and "RV2b" (reverse primer 5'-GGCCCGGAAAATGAGTAACA-3') with a TaqMan probe "RV2" 5'-(6-FAM)-TGATCTGTAGTCCCCATGTGTCC-(BHQ-1)-3' (Table 1 and Figure 1). As an internal control for cellular DNA which would allow the determination of the viral copy number per cell, a QPCR assay was developed to detect exon 3 of oncostatin M (OSM), a single copy cellular gene [Rose, 1993 #18]). The OSM assay amplifies a 76-bp amplicon from the macaque OSM gene using "OSMa" (forward primer 5'-CCTCGGGCTCAGGAACAAC-3') and "OSMb" (reverse primer 5'-GGCCTTCGTGGGCTCAG-3') with a TaqMan probe "OSM" 5'-(6-FAM)-TACTGCATGGCCCAGCTGCTGGACAA-(BHQ-1)-3' (Table 1 and Figure 2)
Reactions (50 μl) contained approximately 250–1000 ng of template DNA, 1 μM forward and reverse primers, 100 nM probe, 200 μM each dNTP, 20 mM Tris-HCl (pH 8.4), 50 mM KCl, and 2.5 units Platinum Taq polymerase (Invitrogen). Magnesium chloride concentrations were 4.0 mM for the RV2 assay and 2.0 mM for the OSM assay. After activation of the polymerase by incubation for 1 minute at 95°C, amplification was performed on a Bio-Rad iCycler equipped with an optical module for 45 cycles of 95°C for 30 s, 62°C for 30 s and 72°C for 30 s. The copy number for each assay was calculated from the cycle threshold (CT) determined using the Bio-Rad software. The viral load was calculated as a cellular genome copy equivalent by using the formula:
Viral load (genome equivalent copies) = Viral copy number/diploid OSM copy number
Samples were assayed in duplicate and the means were determined. Standard deviations were calculated using the sum of the errors of the viral and OSM copy number determinations.
List of Abbreviations
AGM, African green monkey; CODEHOP, consensus-degenerate hybrid oligonucleotide primer; CT, cycle threshold; KSHV/HHV8, Kaposi's sarcoma-associated herpesvirus/human herpesvirus 8; Mfa, Macaca fascicularis; MfaRV2, Macaca fascicularis rhadinovirus-2; Mm/Mmu, Macaca mulatta; Mn/Mne, Macaca nemestrina; MneRV2, Macaca nemestrina rhadinovirus-2; ORF, open-reading frame; OSM, oncostatin M; Pcy, Papio cynocephalus; PcyRV2, Papio cynocephalus rhadinovirus-2; PCR, polymerase chain reaction; QPCR, quantitative PCR; RFHV, retroperitoneal fibromatosis herpesvirus; RRV, rhesus rhadinovirus; RV1, rhadinovirus-1; RV2, rhadinovirus-2;
Competing Interests
The author(s) declare that they have no competing interests.
Authors' Contribution
Design and conception of the study (AGB, TMR); development of the methods for amplification of the ORF59/60 regions (AGB, TMR); Development of the QPCR assays and quantitative analysis (AGB, AMB); Virus isolation and preparation (MET); Sequence analysis, alignment and phylogeny (AGB, AMB, TMR); Manuscript preparation (AGB, AMB, MET, TMR). All authors read and approved the final manuscript.
Acknowledgments
We would like to thank R. Shibata of the Laboratory of Molecular Microbiology, National Institute of Allergy and Infectious Disease, NIH (currently at Gilead Sciences), H. McClure at the YNPRC, and H. Bielefeldt-Ohmann and C.-C. Tsai at the WaNPRC for their generous gifts of tissue, and W. Morton, Director of the WaNPRC, for his continued interest and support. We would also like to acknowledge the excellent technical support of C. Saunders who performed the PBMC assays.
This work was partially supported by RR13154 and RR00166 from the National Center for Research Resources. T. Rose is the recipient of a K02 award, AI49275, from the National Institute for Allergy and Infectious Diseases.
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| 15634356 | PMC544863 | CC BY | 2021-01-04 16:38:59 | no | Virol J. 2005 Jan 5; 2:2 | utf-8 | Virol J | 2,005 | 10.1186/1743-422X-2-2 | oa_comm |
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CytojournalCytoJournal1742-6413BioMed Central London 1742-6413-2-11564413710.1186/1742-6413-2-1Case ReportDesmoplastic Infantile Ganglioglioma: cytologic findings and differential diagnosis on aspiration material Fadare Oluwole [email protected] M Rajan [email protected] Denise [email protected] Arthur W [email protected] Jung H [email protected] Idris Tolgay [email protected] Department of Pathology, Yale University School of Medicine, New Haven, CT, USA2005 11 1 2005 2 1 1 27 11 2004 11 1 2005 Copyright © 2005 Fadare et al; licensee BioMed Central Ltd.2005Fadare 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
Desmoplastic infantile ganglioglioma (DIG) is a rare WHO Grade I tumor of infancy that is characterized by large volume, superficial location, invariable supratentoriality, fronto-parietal lobe predilection and morphologically, by an admixture of astroglial and neuroepithelial elements in a desmoplastic milieu. With over 50 cases described, the histologic and radiographic spectrum of DIG has been well-characterized. The superficial location of DIGs may render them greatly amenable to preoperative assessment utilizing aspiration cytology; however, the cytologic features of this rare tumor have only been reported once previously.
Case Presentation
We present herein cytomorphologic findings from the intraoperative aspiration of a typical case of DIG diagnosed in a 1-year-old male. As evaluated on a single liquid-based preparation, the specimen showed low cellularity and was comprised predominantly of a population of dispersed (occasionally clustered) large neuronal cells with eccentrically located hyperchromatic nuclei (which were occasionally binucleated) and abundant unipolar cytoplasm. Rare smaller astroglial cells were intermixed. Despite the tumor's characteristic desmoplastic histologic appearance, no stromal fragments were identified on the aspiration material.
Conclusions
A differential diagnosis is presented and analyzed in detail and it is concluded that when these large neuronal cells are encountered in an aspirate of a brain mass in a child, a combination of clinical, radiologic and immunohistochemical parameters can eliminate most of the differential possibilities.
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Background
The clinicopathologic features of 11 examples of a distinctive pediatric tumor designated desmoplastic supratentorial neuroepithelial tumors of infancy (also known as desmoplastic infantile ganglioglioma, [DIG]) were originally described by Vandenberg et al in 1987 [1]. Since that seminal report, at least 40 additional cases have been described, such that the clinical, radiologic and histopathologic features of this tumor are now well-defined. An uncommon tumor that constituted less than 0.04% of all central nervous system (CNS) tumors in one series [2], DIG is classified as a Grade 1 tumor in the World Health Organization (WHO) classification of CNS tumors [3]. They most commonly occur in children less than 18 months of age [1] who typically present with symptoms related to an intracranial mass effect [3]. DIGs are generally of large size, are solid to cystic, show a predilection for the frontal and parietal cerebral lobes, and are typically superficially located with at least focal attachment to the overlying dura [1-3]. The superficial location of DIGs may render them greatly amenable to preoperative assessment utilizing aspiration cytology. However, there is a dearth of information on the cytomorphologic features of these tumors [4]. To contribute information of possible utility in their pre-operative or intra-operative assessment, we report herein cytomorphologic features associated with a typical case of DIG.
Case Presentation
A one-year-old boy was noted to have a striking increase in head circumference as compared to a previous measurement. Neurological examination and developmental status were normal at that point. Within 2 weeks, the patient deteriorated rapidly, with poor mobilization, feeding and verbalization. He was brought to the emergency room where an emergent computed tomographic scan showed a large left hemispheric cerebral mass (Figure 1-1) with an underlying cystic component and a more superficial area of bright enhancement; the rest of the brain showed massive edema. He was emergently admitted and within 24 hours, a gross resection of the tumor was carried out. At surgery, following a parietal craniotomy, 50–60 cc of straw colored fluid was aspirated from the cystic component through a taut dura. After the excision of the dura, the bright area of enhancement previously noted was an area of tumor attachment to the dura in the parietal region. Otherwise, the tumor showed a well-demarcated interface with the subjacent normal brain parenchyma and a complete gross resection was achieved. A follow-up magnetic resonance image at 12 months post-surgery showed no evidence of tumor recurrence. Functionally, the patient was felt to have a mild right hemiparesis and some probable language delay, but otherwise showed no neurological deficits.
Figure 1 Radiologic, cytologic and morphologic appearance of the tumor. 1: This computed-tomographic scan of the patient's cerebral mass shows a large cystic mass with peripheral enhancement at the solid portion which attached to the overlying dura; 2: In addition to scattered individual cells, variably sized clusters of neuronal cells were identified, all composed of cells with eccentrically located, occasionally binucleated hyperchromatic nuclei and abundant unipolar cytoplasm [original magnifications ×400]; 3: Occasional neuronal cells were binucleated (3a) while others showed bland nuclear features (3b) [original magnifications ×400]; 4: Scattered astroglial cells with more convoluted nuclear contours and less cytoplasm were also present. [original magnifications ×400]; 5: Typical histologic appearance of desmoplastic infantile ganglioglioma, showing scattered ganglion cells in a desmoplastic and fibroblastic, vaguely storiform background (original magnification ×200, inset ×400)
Materials and Methods
For cytology, a slide was prepared from 50–60 cc of straw-colored fluid utilizing the ThinPrep® 2000 Automated Slide Processor (Cytyc, Boxborough, MA) according to the manufacturer's instructions. For the tumor specimen, approximately 10 × 6.5 cm of fragmented gray and white cerebral tissue was received and entirely processed routinely: tissue sections were fixed in 10% neutral buffered formalin, processed, embedded in paraffin, sectioned to 4 μ-thick sections and stained with hematoxylin and eosin, Nissl stain and reticulin. The immunohistochemical profile of the tumor was evaluated on 4 μ thick, formalin-fixed, deparaffinized sections using a DAKO Autostainer (Carpinteria, CA, USA) based on the avidin-biotin-peroxidase complex with antibodies ki-67 (dilution 1:320, DakoCytomation Corp, Carpinteria, CA), synaptophysin (dilution 1:600, DakoCytomation) and glial fibrillary acid protein [GFAP] (dilution 1:10, DakoCytomation).
Pathologic findings
As evaluated on a single liquid-based preparation, the specimen showed low cellularity and was comprised predominantly of a population of dispersed (occasionally clustered) large neuronal cells (~70 μm diameter each) with round eccentrically placed uniform hyperchromatic nuclei, undulating and slightly convoluted nuclear membranes, and abundant unipolar granular cytoplasm (ganglion cells) (figures 1-2 and 1-3). Occasional cells were binucleated (figure 1-3a). A spectrum in the degree of nuclear membrane irregularities was noted, with most cells displaying irregular features as described above, while other cell showed bland nuclear features (figure 1-3b). However, all displayed nuclear polarity to the cytoplasm. Rare smaller cells interpreted as astroglial cells were interspersed between the larger cells. The latter cells showed nuclear hyperchromasia, more prominent irregularities in their nuclear membranes and a smaller cytoplasmic rim. (figure 1-4). Overall, significant proportions of both cellular populations showed varying degrees of degenerative changes manifested as lack of clear delineation of nuclear and cytoplasmic borders and a loss of nuclear detail. Several clusters were composed of large cells, and in these clusters, constituent large cells showed less cytoplasm but retained a unipolarity in relation to the nuclei and their nuclear features were identical to those of the more predominant population of large cells. Additionally, scattered foamy histiocytes were present. A finely granular background material consistent with necrosis was present, but there was no distinct neurofibrillary material.
Vascular structures or stroma were not present. Histologically, the tumor was partially attached to the dura and was present in the subarachnoid space. The bulk of the specimen was a variably cellular desmoplastic component whose predominant constituent cells were elongated spindle cells arranged in a reticulin-rich, storiform pattern (figure 1-5). At higher magnification, ganglion-type cells (Nissl stain positive) with 1–4 round nuclei, prominent nucleoli, and abundant unipolar cytoplasm were present (Figure 1-5, inset). Immature or abortive ganglion cells with enlarged single nuclei and markedly irregular nuclear membranes were rare but identifiable morphologically. Less "differentiated" aggregates of cells with hyperchromatic nuclei and minimal cytoplasm, as has been well-described in DIGs [1-3], were present. Mitotic figures were rare and small foci of necrosis were limited to the less differentiated component. Immunohistochemical stains for synaptophysin was positive in the ganglion cells only. GFAP was positive in astroglial elements within the desmoplastic regions; the latter was negative for synaptophysin. The ki-67 labelling index was 11.3% (evaluated in the area of greatest density of positive staining cells).
Discussion
The clinical presentation, radiographic appearance and histopathologic features of this case are entirely consistent with those described for desmoplastic infantile ganglioglioma [1-3]. There has been a significant evolution in the understanding of this rare tumor since its original description in 1987 [1]. DIGs are typically large supratentorial tumors that, at least as observed radiographically in one patient, are initially solid then become cystic [5]. Although this tumor is considered a grade 1 tumor based on the histopathologic features of cases described prior to the publication of the WHO monograph in 2000, at least one report has since documented anaplastic features in a case of DIG, which was ultimately fatal [6]. However, follow-up has generally been favorable following complete resection in the reported cases of DIG, with a median post-surgical interval of 8.7 years without metastases or recurrence in one series of 14 patients [2]. Additionally, in some cases, spontaneous regression of tumor following subtotal tumor resection has been documented [7,8].
The distinctive clinical features of DIG, being a typically superficially located tumor occurring in young children (with potentially unclosed fontanelles), may render them particularly amenable to pre-operative assessment using aspiration cytology. In addition, familiarization of practitioners with the cytopathologic features of DIG may be useful because 1) With the aforementioned cases of DIG regressing after subtotal resection [7,8], it might be unnecessary to aggressively resect these tumors to negative margins, and a preoperative aspiration diagnosis of DIG will be helpful in the neurosurgical planning and 2) In their intra-operative assessment, imprint cytopathology may potentially be more diagnostic than histopathology. However, to our knowledge, the cytologic features of DIG have been documented only once previously [4]. In that report, Hasegawa et al [4] reported aspiration and imprint cytology findings in two cases of DIG. Two distinct cellular populations were identified, a predominant population of small to intermediate sized astroglial cells and "a few" large cells with round nuclei, prominent nucleoli and profuse cytoplasm that was unipolar to the nuclei in all their illustrations. In the current case, the reverse was found, with the predominant cells being an identical population of large cells with round nuclei, prominent nucleoli and abundant unipolar cytoplasm, and only rare unequivocal astroglial cells. Most of the analysis of the aforementioned report [4] was on the imprint smears, and although a mixture of small and large cells were also identified on the aspiration smear, there was no stated assessment or low-power illustration of the relative ratio of small to large cells on the latter. In the current case, a cell-block for immunohistochemical confirmation of the nature of two-cell population was unavailable; however, the larger cells were positive for neurofilament (confirming their neuronal nature) while the smaller cells were positive for GFAP (confirming their astroglial nature) in the report of Hasegawa et al [4].
The finding of large cells with eccentrically located nuclei and abundant unipolar cytoplasm in an aspiration specimen of a cerebral mass occurring in a young person should generate a differential diagnosis that includes DIG, atypical teratoid/rhabdoid tumor (AT/RT), dysembroplastic neuroepithelial tumor (DNT), ganglioglioma, supratentorial primitive neuroectodermal tumour (PNET) with ganglionic differentiation (ganglioneuroblastoma), anaplastic large cell lymphoma and pleomorphic xanthoastrocytoma. In our opinion, clinical features as well as immunohistochemical analysis can significantly help reduce the likelihood for most of the aforementioned entities. The distinction of DIG from AT/RT is probably of the greatest prognostic significance, since in contrast to DIG, AT/RT is a highly malignant tumor that is almost uniformly fatal [9]. Although AT/RT occurs in infants or young children, most cases occur in the posterior fossa, in contrast to DIGs, which are invariably supratentorial [1-3]. Radiographically, AT/RT are typically not distinctly cystic, although necrosis may impart an irregularly cystic appearance.
Immunohistochemically, the rhabdoid cells of AT/RT co-express vimentin and epithelial membrane antigen [10], in contrast to the ganglion cells of DIG. However, rhabdoid cells may rarely express neurofilament, an immunophenotypic overlap with DIG.
Morphologically, the distinct cytoplasmic borders, "inclusion-like" cytoplasmic globule and overall dense eosinophilia of the cytoplasm of rhabdoid cells, in conjunction with the aforementioned clinicopathologic parameters, may help in their distinction from ganglion cells of DIG [9,10]. The separation of DIG from other tumors containing true ganglion cells based on cytomorphology alone would probably pose the greatest difficulty. These tumors include DNT, ganglioglioma, and supratentorial PNET with ganglionic differentiation; all have a predilection for, or at least may potentially occur in children. In addition to ganglion cells, cytomorphologic features of DNT include oligodendroglial-like cells arranged in lobules and neurons in abundant extracellular mucin or neurofibrillary material [11,12]; these findings were neither identified in the 2 cases of DIG reported by Hasegawa et al [4], nor the current case. The distinction of gangliogliomas form DIG based on cytomorphology alone may be impossible even in the presence of a significant stromal component on the aspirate. Gangliogliomas, like DIG may be solid to cystic and show desmoplasia, although in contrast to DIG, they have a predilection for the temporal lobe and most commonly occur in an age group slightly older than is typical for DIG [13]. However, it should be noted that conventional gangliogliomas and DIG may exist on a morphologic spectrum, and a case with morphologic features of both entities has been described [14]. Supratentorial PNET with ganglionic differentiation (ganglioneuroblastomas) also show significant clinicopathologic overlap with DIG, as they are supratentorial, occur in young children, may be cystic and may show desmoplasia [15]. Cerebral ganglioneuroblastomas are extremely rare, and in the absence of treatment-related cytodifferentiation, will show a significant neuronal component of smaller cells. However, the cytomorphologic features of pure ganglioneuroblastoma have not been well-characterized.
Other less likely differential considerations include pleomorphic xanthoastrocytoma and anaplastic large cell lymphoma (ALCL), both of which may contain large cells with eccentrically located nuclei and abundant cytoplasm. The temporal lobe predilection, lack of neuronal differentiation, presence of xanthomatous cells, smaller tumor size and older age of patients with pleomorphic xanthoastrocytoma should permit an easy distinction of the large cells in this tumor from those of DIG. Primary brain ALCL is exceedingly rare and generally occurs in older individuals, with a mean age of 29 years in one series [16]. Immunoreactivity for CD30, ALK and CD45 in ALCL and absence of similar immunoreactivity in DIG should facilitate a distinction in rare cases that occur in very young children.
When the cytomorphologic findings of DIG are described in more cases, it is likely that the cytologic spectrum will mirror the histologic heterogeneity of this tumor. For example, astroglial cells predominated in the two cases of Hasegawa et al [4] while the ganglion cells predominated in ours. In addition, it is conceivable that an aspirate would only capture the immature neuroepithelial cells which frequently characterizes DIG. Nonetheless, it is concluded that when the large neuronal cells are encountered in an aspirate of a brain mass in a child, a combination of clinical, radiologic and immunohistochemical parameters can eliminate most of the differential possibilities.
Competing interests
The author(s) declare that they have no competing interests.
Authors' contributions
All authors made substantial contributions to the intellectual content and/or presentation of the manuscript. ITO (cytologist), diagnosed the cytopathological aspects of the case and co-supervised the entire project. JHK (neuropathologist), diagnosed the histological aspects of the case and co-supervised the project. OF wrote the initial version of the manuscript. MRM, DH and AWZ collected pathological, clinical and/or photographical information and revised the manuscript.
Acknowledgement
Due to the archival nature of the case as well as the absence of any potentially identifying patient information, the acquisition of patient consent to specifically report the case was deemed unnecessary.
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| 15644137 | PMC544864 | CC BY | 2021-01-04 16:36:23 | no | Cytojournal. 2005 Jan 11; 2:1 | utf-8 | Cytojournal | 2,005 | 10.1186/1742-6413-2-1 | oa_comm |
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BMC PediatrBMC Pediatrics1471-2431BioMed Central London 1471-2431-4-241559835310.1186/1471-2431-4-24Research ArticlePsychological health of family caregivers of children admitted at birth to a NICU and healthy children: a population-based cross-sectional survey Klassen Anne F [email protected] Shoo K [email protected] Parminder [email protected] Sarka [email protected] Centre for Community Child Health Research, Department of Pediatrics, University of British Columbia, Vancouver, BC, Canada2 Centre for Healthcare Innovation and Improvement, Dept of Pediatrics, University of British Columbia, Vancouver, BC, Canada3 Evidence-Based Practice Centre, Dept of Clinical Epidemiology and Biostatistics, McMaster University, Hamilton, ON, Canada2004 14 12 2004 4 24 24 29 6 2004 14 12 2004 Copyright © 2004 Klassen et al; licensee BioMed Central Ltd.2004Klassen et al; licensee BioMed Central Ltd.This is an Open Access article distributed under the terms of the Creative Commons Attribution License (), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Background
There is little information in the research literature on how parents of children who spend time in a neonatal intensive care unit (NICU) adapt psychologically to the demands of caregiving beyond the initial hospitalization period. Our aim was to compare parents of NICU children with parents of healthy full-term children, looking specifically at the relationship between parental psychosocial health and child characteristics, as well as the relationship between important predictor variables and psychosocial health.
Methods
A cross-sectional survey was sent to parents as their child turned 3 1/2 years of age. The setting was the province of British Columbia, Canada. The sample included all babies admitted to tertiary level neonatal intensive care units (NICU) at birth over a 16-month period, and a consecutive sample of healthy babies. The main outcome was the SF-36 mental component summary (MCS) score. Predictor variables included caregiver gender; caregiver age; marital status; parental education; annual household income; child health status; child behavior; birth-related risk factors; caregiver strain; and family function.
Results
Psychosocial health of NICU parents did not differ from parents of healthy children. Child health status and behavior for NICU and healthy children were strongly related to MCS score in bivariate analysis. In the pooled multivariate model, parental age, low family function, high caregiver strain, and child's internalizing and externalizing behavioral symptoms were independently associated with lower psychosocial health. In addition, female gender was associated with lower psychosocial health in the NICU group, whereas lower education and child's problem with quality of life indicated lower psychosocial health in the healthy baby group.
Conclusions
Overall, parental gender, family functioning and caregiver strain played influential roles in parental psychosocial health.
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Background
Neonatal intensive care is associated with a range of long-term health problems such as cerebral palsy, mental retardation, deafness, blindness and milder but more common problems such as learning disabilities and behavioral problems [1-13]. Although these problems create challenges for the parent responsible for the day-to-day provision of care to their child at home, the impact of caregiving on the health of parents of children discharged from neonatal intensive care units (NICUs) remains an under-explored research topic. There is a literature that focuses on the early hospitalization period. These studies show that mothers of preterm infants experience more severe levels of psychological distress in the neonatal period than do mothers of healthy full-term infants [14-17]. In the few studies that compare the impact of caregiving on parents of children discharged from NICUs with parents of healthy full-term children, the addition of a preterm infant into the family has been shown to have negative repercussions for the family in some studies [18-21], but not in others [22-24]. In one of the few NICU studies where parental mental health was the primary outcome measure, mothers of high and low-risk very low birth weight infants were compared with parents of healthy full-term infants [17]. The authors report that early differences between the groups at one month and two years were no longer apparent by the age of three, although parenting stress remained high throughout.
In the present study, we sent a questionnaire booklet to mothers of all children admitted to a level III neonatal intensive care unit in the province of British Columbia (Canada) over a 16 month period to collect data on a range of factors in order to examine both neonatal and caregiver outcomes. Our study differs from other NICU follow-up studies in that it is population-based, focuses on preschool aged children and examines the full spectrum of NICU graduates. The aims of this paper are two-fold: (1) to compare psychosocial health of parents of NICU children with parents of healthy full-term children, looking specifically at the relationship between parental psychosocial health and child characteristics (i.e., health status, behavior problems, and birth-related risk factors); and (2) to identify predictors of parental psychosocial health (i.e., socioeconomic and demographic variables, child characteristics, caregiver strain, and family function).
Methods
Sample
Ethical approval was gained from the University of British Columbia and participating hospitals. Our NICU sample included 2221 surviving babies admitted for more than 24 hours to one of three level III NICUs in British Columbia (BC), Canada over a 16 month period (March 1996 to June 1997). These 3 hospitals (Children's and Women's Health Centre of BC, Royal Columbian Hospital, Victoria General Hospital) provided 100% of the tertiary care NICU beds in the province. The birth mothers' name and contact details were obtained from the health records department at two hospitals and manually extracted from ledgers of the third hospital. Our list of babies was matched with provincial mortality records to exclude any babies that had died after discharge from the NICU and thereby prevent questionnaires being sent to bereaved parents.
A comparison group of 718 healthy singleton full-term babies was recruited from the two hospitals with a hospital-based primary care unit (i.e., Children's and Women's Health Centre of BC and the Royal Columbian Hospital). This sample included all babies delivered over an 11 month period (March 1996 and January 1997) by primary care physicians at these two clinics. Babies with a sibling in the NICU sample and babies subsequently admitted to an NICU for more than 24 hours were excluded. Contact details for the mother were obtained from the health records department at one hospital, and directly from the primary care unit at the other.
We excluded from the sample 150 babies (123 NICU; 27 healthy children) who did not meet our inclusion criteria for the following reasons: parent did not speak English (n = 95); baby died (n = 34); mother died (n = 6); and not applicable (n = 1). In addition, we excluded cases where the questionnaire was completed on the wrong child (n = 7) and where a comparison baby was subsequently admitted to a NICU (n = 7). The overall response rate (after exclusions), was 55% (54.3% NICU, 56.9% healthy baby group). The response rate for located families (82.8% of the sample was located) was 67.4% (n = 1140) for the NICU group, and 66.4% (n = 393) for the comparison group. Five NICU respondents returned a signed consent form without a completed questionnaire and were dropped from the analysis. Seventy-five percent of parents provided permission for data linkage between the questionnaire data and CNN database. The NICU sample included 181 children that were part of a multiple birth group: 171 twins; and 10 triplets. Table 1 contains sample characteristics. Most questionnaires (98%) were completed by a biological parent, most often the mother (96%). The NICU sample was composed of 1.8% fewer biological parents; 2.6% more male respondents, and 11.9% more families who earned less than $50,000 per year.
Table 1 Characteristics of study sample
Group; no. (%) of subjects
NICU
N = 1135 Comparison
N = 393
Biological parent1 1091 (97.7) 389 (99.5)
Female1 1070 (95.4) 383 (98.0)
Married/common-law 962 (85.9) 344 (87.8)
Age of parent, years
19–29 195 (17.8) 61 (15.7)
30–39 704 (64.1) 265 (68.3)
≥ 40 199 (18.1) 62 (16.0)
Education level
University 373 (33.4) 146 (37.4)
Trade/technical school or community college 494 (44.3) 176 (45.1)
High school graduation 185 (16.6) 50 (12.8)
No high school diploma 64 (5.7) 18 (4.6)
Household income, $2 511 (48.1) 136 (3)
<30,000 247 (23.3) 58 (15.5)
30 – 49,999 264 (24.9) 78 (20.8)
50 – 79,999 333 (31.4) 145 (38.7)
80 > 218 (20.5) 94 (25.1)
Male children in the sample 633 (55.8) 198 (50.6)
Age of child, years
3 years 784 (69.3) 253 (64.4)
4 years 328 (29.0) 134 (34.1)
5 years 19 (1.7) 6 (1.5)
1p < .05 (chi-square, Fischer's exact test); 2p = .0018 (chi-square)
Materials
Our main measure of outcome was the SF-36 mental component summary (MCS) score [25,26]. The SF-36 is a well validated generic measure of adult physical and psychosocial health related quality of life (HRQL), which is composed of 36 items that measure 8 health domains. The MCS is computed from the following four domains: mental health (5 items); vitality (4 items); social functioning (2 items); and role limitations due to emotional problems (3 items). It has a mean of 50 and standard deviation of 10 and represents the mean and standard deviation of the general population (USA).
Child health status was measured using the Health Status Classification Preschool Version (HSCS-PS) [27]. This measure asks about twelve health status (HS) problems that we have grouped into the following 4 categories: neurosensory (i.e., seeing and hearing); motor development (i.e., getting around, using hands and fingers, taking care of self); learning (i.e., speaking, learning/remembering and thinking/solving problems); and quality of life (i.e., pain/discomfort, feelings, behavior and general health). Each attribute has 3 to 5 levels of severity ranging from normal function to severe functional limitations. For each category of health problems, we recoded the data into the following: no problem; a mild problem; or a moderate or severe problem.
Child behavior was measured with the Child Behavior Checklist 1.5–5 (CBCL/1.5–5) [28]. This questionnaire measures internalizing, externalizing and total problems, and scales can be scored categorically to indicate normal, borderline or clinical range scores.
Data for birth-related risk data were obtained from the Canadian Neonatal Network Study [29] for the NICU children whose parents provided written consent for data linkage. The following variables were examined: birthweight; gestational age; small for gestational age; multiple birth, apgar score less than 7 at 5 minutes; congenital anomalies; the presence of a major morbidity (i.e., a composite score for the presence of at least one of the following: chronic lung disease (at 36 weeks); severe intraventricular hemorrhage (≥ grade 3); nosocomial infection; necrotizing enterocolitis; retinopathy of prematurity (≥stage 3)); and neonatal illness severity score [30].
Caregiver strain was measured using the Parental Impact-Time (PTT) scale from the Infant Toddler Quality of Life Questionnaire [31]. This 7-item scale asks parents to indicate limitations in the amount of time in the past 4 weeks they had for their own personal needs due to problems with their child's health (e.g., physical, emotional, cognitive, behavior, temperament). Scores on these scales can range from 0 to 100, with lower scores indicating greater caregiver strain.
Family function was measured using the Family Assessment Device (FAD) [32]. Scores for this 12-item questionnaire can range from 0 to 36, with higher scores indicative of greater family dysfunction.
Procedure
A questionnaire booklet, which included the questionnaires described above, was sent to the address of the birth mother as her child turned 3 1/2 years of age. A consent letter was included to obtain permission to link the questionnaire data with hospital birth records. The primary caregiver in our study was defined as the person who, to that point in the child's life, had spent the most amount of time with the child. This could include the mother or father or another parent (e.g., grandparent, foster parent, guardian). We asked the primary caregiver (referred to in this paper as parent) to complete the questionnaire booklet and consent form. Non-respondents were sent a reminder letter, additional copies of the questionnaire booklet and a phone call as necessary. If the telephone number was not in service or reassigned, or a questionnaire booklet was returned to us from the post office, we implemented a comprehensive search strategy that involved searching the Internet and contacting the mothers' primary care physician.
Data analysis
To address the first objective, we compared the psychosocial summary score for the SF-36 questionnaire for parents of NICU children and parents of healthy children using student's T-test. T-test, ANOVA and the equivalent nonparametric tests, and Spearman correlation were used to explore relationships between MCS score and various child characteristics, including health status, behavior and birth-related risk factors. For health status and child behavior, we computed an effect size (mean difference divided by standard deviation of the group with no problems (health status) or with scores in the normal range (behavior)), to look at the magnitude of the difference in MCS score between subgroups for the NICU and healthy baby samples, and used the Cohen's guidelines for interpretation (0.2 is small, 0.5 is medium, 0.8 is large) [33].
To address the second objective, multiple regression analysis was used to examine the independent effects of, and proportion of variance in MCS scores explained by our predictor variables. For the analysis we examined a pooled model and a model where we stratified by group membership (i.e., NICU vs. healthy baby sample) to separately examine the contribution of each predictor variable for the two samples. Variables with significant (p < .05) or borderline p-values in bivariate analysis were included in the model. Certain birth-related risk factors (i.e., birthweight, congenital anomalies, illness severity score, and gestational age) were entered into the model on the basis of clinical rather than statistical importance, however, no effects were found. Potential predictor variables include the following: caregiver's gender; caregiver's age (continuous); marital status (married or common-law versus other), caregiver's education (less than high school graduation vs. other); annual household income (< or > $30,000); child health status (i.e., neurosensory; motor development; learning; and quality of life problems); child behavior; caregiver strain (continuous); and family function (continuous). For child health status and behavior variables, no problem (health status) and scores in the normal range (behavior) were the reference categories, with mild and moderate/severe (health status) or borderline and clinical range scores (behavior) entered separately, or combined and entered as dichotomous variables. We computed effect sizes to interpret the significance of beta coefficients.
Results
Psychosocial health comparing NICU and healthy children
The unadjusted mean MCS score for parents of NICU children did not differ from parents of healthy children (48.2 versus 48.8; p = .305). We also compared MCS scores after adjusting for the three sample characteristics that differed between the two groups (i.e., proportion of biological parents; gender of subject; and those with lower household income), and no differences were found in the outcome variable.
Psychosocial health by child health status problem
On the HSCS-PS, 55.2% of healthy children had no health problems in any area, compared with 39.8% of NICU children (p < .001 on Chi-square). Table 2 shows the joint distribution of health status problems across the four categories for the NICU and healthy sample. These results show that the NICU sample had a higher proportion of children with more health status problems, as well as a higher proportion with moderate/severe versus mild problems.
Table 2 Distribution of children with health status problems across the 4 health status categories for NICU and healthy children
HSCS problems by domains NICU (N = 1104) Comparison (N = 386)
no problem N 438 215
% 39.67 55.7
1 mild problem N 309 111
% 27.99 28.76
2+ mild problems N 183 37
% 16.58 9.59
1 moderate/severe problem only N 40 7
% 3.62 1.81
1 moderate/severe problem + any mild N 69 15
% 6.25 3.89
2–3 moderate/severe problems N 60 0
% 5.43 0
4 moderate/severe problems in all domains N 5 1
% 0.45 0.26
p < 0.0001, chi-square
For parents of NICU children, for all 4 health status categories, parental MCS scores decreased as severity of the child health problem increased (see Table 3). Effect sizes comparing parents of children with no health status problems with parents of children with a moderate or severe health status problem were all moderate to large indicating important differences in parental mental health according to Cohen's benchmarks. The results for parents of healthy children show similar trends, with mainly moderate to large effect sizes.
Table 3 Parental mental health summary score, 95% confidence intervals, number of subjects, p-value and effect size for child health status category
Sample Type of HS problem None Mild Moderate or Severe p-value* Effect size
NICU children Neurosensory 48.4 (47.7, 49.0) n = 975 46.3 (41.7, 51.0) n = 33 41.9 (36.0, 47.8) n = 17 .023
.040 .63
Motor development 49.1 (48.5, 49.8) n = 789 45.8 (44.0, 47.6) n = 174 42.0 (38.8, 45.2) n = 63 <.001
<.001 .74
Learning/remembering 49.3 (48.6, 50.0) n = 623 47.5 (46.3, 48.8) n = 298 43.4 (40.9, 45.9) n = 109 <.001
<.001 .63
Quality of life 49.8 (49.0, 50.5) n = 659 46.3 (45.1, 47.4) n = 313 39.4 (35.7, 43.2) n = 59 <.001
<.001 1.11
Healthy children Neurosensory 48.9 (47.9, 49.8) n = 361 57.0 n = 1 31.1 n = 1 .120
.154 1.89
Motor development 49.2 (48.2, 50.3) n = 333 45.6 (42.6, 48.7) n = 31 37.9 (22.6, 53.2) n = 3 .018
.002 1.18
Learning/remembering 49.6 (48.5, 50.7) n = 266 46.9 (44.7, 49.1) n = 89 45.8 (39.1, 52.5) n = 14 .037
.025 .41
Quality of life 50.1 (49.0, 51.2) n = 271 45.6 (43.5, 47.8) n = 86 36.2 (23.8, 48.6) n = 8 <.001
<.001 1.58
* first based on Anova, second based on Kruskal-Wallis non-parametric test (in italics)
Psychosocial health by child behavior problem
Child behavior was strongly related to parental psychosocial health in both groups of parents (see Table 4). Parents whose child scored in the clinical range for internalizing and externalizing symptoms and the total problem score on the CBCL/1.5–5 had the lowest mean (i.e., poorest) MCS scores. The differences between this group and the group with children scoring in the normal range resulted in large effect sizes, indicative of clinically important differences in parental psychosocial health.
Table 4 Mean score, p-value and effect size for SF-36 psychosocial summary score comparing CBCL/1.5–5 normal with borderline and clinical groups
CBCL scale Normal Borderline Clinical p-value Effect size
NICU sample Internalizing 49.5 (48.9,50.2) n = 841 42.5 (39.6,45.5) n = 67 40.5 (37.6,43.4) n = 78 <.001 .95
Externalizing 48.9 (48.3,49.6) n = 925 41.6 (38.0,45.2) n = 45 35.0 (29.6,40.2) n = 32 <.001 1.43
Total 49.3 (48.6,49.9) n = 831 43.3 (39.7,47.0) n = 34 35.3 (31.0, 39.5) n = 39 <.001 1.45
Healthy children Internalizing 49.6 (48.6,50.6) n = 324 40.7 (36.8,44.6) n = 20 40.1 (34.0,46.2) n = 16 <.001 1.03
Externalizing 49.3 (48.3,50.3) n = 342 43.4 (37.0,49.8) n = 14 34.1 (22.5,45.8) n = 8 <.001 1.67
Total 49.4 (48.4,50.4) n = 330 41.5 (34.0,49.0) n = 12 36 (27.0, 44.0) n = 11 <.001 1.46
p-value based on Anova, (non-parametric tests: all p-values < .001)
Parental psychosocial health by birth-related risk factors
Within the NICU sample, MCS score did not vary by any birth-related risk factor (i.e., gestational age; small for gestational age; apgar score; multiple birth; the presence of a major morbidity; and neonatal illness severity score), with the exception of the presence of a congenital anomaly. For this variable, MCS scores were significantly lower in parents of children with versus without a congenital anomaly (mean difference = -3.8; p = .017; effect size = -.37). Children with a congenital anomaly (n = 87) had proportionally more mild and moderate/severe health status problems in all 4 categories (see Table 5).
Table 5 Number (%) of NICU children with and without a congenital anomaly to report a problem for each health status category and p-value for Chi-square test of significance
Type of HS problem Congenital anomaly None Mild Moderate or Severe p-value
Neurosensory No 715 (95.6) 25 (3.3) 8 (1.1) <.001
Yes 70 (83.3) 8 (9.5) 6 (7.1)
Motor development No 584 (78.3) 126 (16.9) 36 (4.8) <.001
Yes 48 (57.1) 17 (20.2) 19 (22.6)
Learning/remembering No 457 (60.9) 222 (29.6) 71 (9.5) <.001
Yes 36 (42.9) 30 (35.7) 18 (21.4)
Quality of life No 481 (64.2) 233 (31.1) 35 (4.7) <.001
Yes 38 (44.2) 34 (39.5) 14 (16.3)
Correlates of psychosocial health in general
In general, variables significantly associated with the MSC score in bivariate analysis were as follows: any health status problems (mean difference = -3.8; p < .001); neurosensory problems (mean difference = -3.7; p = 0.04); motor development problems (mean difference = -4.4; p < .001); learning/remembering problems (mean difference = -2.9; p < .001); poorer quality of life (mean difference = -4.8; p < .001); more internalizing behaviour symptoms (mean difference = -8.3; p < .001); more externalizing behavior symptoms (mean difference = -9.9; p < .001); household income below $30,000 per year (mean difference = -2.6; p < .001); female gender (mean difference = -2.6; p < .001);not living as common-law or married (mean difference = -3; p = .03); more caregiver strain (r = .41; p < .001); and lower family function (r = -.44; p < .001). Borderline significance was also found for less than high school education (mean difference = -2; p = .08).
We examined a pooled model (both groups together) for a direct comparison of the NICU and healthy groups after adjustment for other variables. Due to the low number of male respondents in the healthy group, we restricted the pooled multivariable analysis to only female respondents. Predictors significantly associated with the outcome were the following: parental age (Beta = 0.15; p = 0.001); internalizing behavior (Beta = -2.06; p = 0.017); externalizing behavior (Beta = -3.24; p = 0.004); parental strain (Beta = 0.15; p < 0.001); and family function (Beta = -0.53; p < 0.001). The pooled model also showed an interaction effect between NICU admission and education (less than high school) (Beta-education = -5.94 with p = 0.009; Beta-interaction = 7.28 with p = 0.005)(see Table 6.) For the NICU group, education did not show any effect in terms of difference in outcome, but for the healthy group, lower education was associated with a significantly lower mean MCS score. More specifically, for respondents with less than high school education, the healthy group reported lower MCS scores than did the NICU group. The results were not affected by exclusion of multiple births and cases of congenital anomalies from the analysis.
Table 6 Beta coefficients, 95% confidence intervals, standardized beta coefficients and p-values for predictor variables in the multiple regression models for pooled model
Pooled model
Variable Beta CI-low CI-high St. beta p-value
Intercept 34.21 30.26 38.17 <.0001
NICU -0.38 -1.32 0.55 -0.02 0.500
Parental age 0.15 0.08 0.23 0.08 0.001
Education -5.94 -9.67 -2.21 -0.13 0.009
Internalizing behavior -2.06 -3.47 -0.65 -0.07 0.017
Externalizing behavior -3.24 -5.08 -1.40 -0.08 0.004
Caregiver strain 0.15 0.13 0.18 0.26 <.0001
Family function -0.53 -0.60 -0.46 -0.32 <.0001
NICU-education interaction 7.28 3.01 11.56 0.14 0.005
Although other interaction terms with NICU status did not add any more significant results in the pooled model (non-significant partial F-test), we examined separate models for the NICU and the healthy baby group to further explore the association between gender and MCS score, and to evaluate the potential influence of congenital anomalies in NICU group.
Correlates of psychosocial health for NICU sample
Variables that were significantly associated with lower MCS scores at the bivariate level include the following: female caregivers (mean difference = -3.2; p = .037); household income below $30,000 per year (mean difference = -3.3 and p < .001); not living as common-law or married (mean difference = -5.1; p < .001); neurosensory problems (mean difference = -6.44; p = .011); motor development problems (mean difference = -7.1; p < .001); learning/remembering problems (mean difference = -5.9; p < .001); poorer quality of life (mean difference = -10.4; p < .001); more internalizing behaviour symptoms (mean difference = -9.03; p < .001); more externalizing behavior symptoms (mean difference = -13.9; p < .001); the presence of a congenital anomaly (mean difference = -3.8; p = .017); more caregiver strain (r = .411; p < .001); and lower family function (r = -.441; p < .001).
Predictors that were significant in the final regression model appear in Table 7. Female gender was an independent risk factor for lower MCS score: females scored on average 5.3 points (CI interval 2.5 to 8.0) lower, which represents a moderate effect size of 0.51 (when overall NICU parents group standard deviation (SD) 10.4 for MCS was used as the denominator). Scoring outside the normal range for internalizing and externalizing child behavior symptoms independently contributed to lower MCS scores (-1.9 and -2.8, both with wide confidence intervals), with the change representing small effect sizes of 0.18 and 0.27. More caregiver strain (i.e., lower PTT) was related with poorer MCS scores. A one point change in PTT corresponded to a 0.15 (CI: 0.11–0.19) change in MCS score. In NICU parents, the mean PTT was 86.9 and SD was 18.5. Therefore, 2 SD on the PTT would represent 5.5 points on the MCS, or an effect size of 0.53. The mean score for family function (FAD) was 8.1 and the SD was 6.4. A one point change in FAD corresponded to a 0.5 (CI: 0.62; 0.42) change in MCS. Therefore a 2 SD increase in family function score (i.e., poorer family functioning) would result in a 6.4 decrease (worsening) in MCS, representing a moderate effect size of .62. Overall, the adjusted R2 was .2884 (F = 73.96; df = 5; p = < .0001), with 5 out of 15 predictors included in the full model.
Table 7 Beta coefficients, 95% confidence intervals, standardized beta coefficients and p-values for predictor variables in the multiple regression models for both samples
NICU sample Healthy baby sample
Variable Beta CI-low CI-high St. beta p-value Beta CI-low CI-high St. beta p-value
Intercept 44.9 40.3 49.5 <.001 35.2 26.3 44.1 <.001
Parental age 0.3 0.1 0.5 0.1 .005
Female gender -5.3 -2.6 -8.0 -0.11 <.001
Education -5.00 -0.8 -9.1 -0.1 .019
Internalizing behavior -1.9 -0.1 -3.8 -0.06 .043 -4.0 -0.8 -7.2 -0.1 .014
Externalizing behavior -2.8 -0.4 -5.3 -0.07 .025
Caregiver strain 0.2 0.1 0.2 0.26 <.001 0.1 0.04 0.2 0.2 .003
family function -0.5 -0.4 -0.6 -0.32 <.001 -0.6 -0.4 -0.8 -0.4 <.001
Quality of life -6.9 -0.4 -13.4 -0.1 .039
Correlates of psychosocial health for healthy baby sample
Variables that were significantly associated with poorer SF-36 MCS scores at the bivariate level include the following: younger parental age (r = .19; p < .001); household income below $30,000 per year (mean difference = -4.6; p = .005); less than high school education (mean difference = -6.22; p = 0.065); not living as common-law or married (mean difference = -6.1; p = .005); motor development problems (mean difference = -11.3; p = .043); learning/remembering problems (mean difference for any problems versus none = -2.68, p = 0.021); poorer quality of life (mean difference = -13.9; p < .032); more internalizing behavior symptoms (mean difference = -9.5; p < .001); more externalizing behavior symptoms (mean difference = -15.2; p < .018); more caregiver strain (r = .385; p < .001); and lower family function (r = -.438; p < .001).
Predictors that were significant in the final regression model appear in Table 7. The model for parents of healthy children did not include female gender (because of low numbers) and externalizing behavior symptoms, and included several variables not predictive in the NICU model (i.e., parental age; education; quality of life). Both models included internalizing child behaviors, caregiver strain and family function.
In the healthy baby sample, younger parental age was related to poorer MCS score, with a one year change in age resulting in a 0.26 (CI: 0.08; 0.45) change in MCS. A ten year difference in age would correspond to a 2.6 difference in MCS, which would represent a small effect size of 0.27 (when the overall healthy baby parent group SD for MCS (9.6) was used as a denominator). Education was also associated with MCS. Compared with high school graduates, the MCS score for parents with less than a high school education were on average 5.0 lower (CI: 0.84; 9.1), which represents a moderate effect size of 0.52, although the effect could range from minimal to large due to lower precision of the beta estimate. Child internalizing symptoms, family function and caregiver strain were associated with parental MCS in a similar way as for NICU parents. However, due to lower numbers and resulting low precision in beta estimates, the effects ranged from minimal to large. Lower parent-reported child quality of life was also associated with a lower parental MCS. Parents who reported a problem with their child's quality of life had MCS scores that were 6.9 (CI: 0.37; 13.4) lower than parents who reported at least one quality of life problem compared with those who reported at least one problem. Again, due to the small numbers, the effect could range from minimal to large. In the final regression model, the adjusted R2 was .3046 (F = 25.97; df = 6; p < .0001), with 6 out of 16 predictors included in the full model.
Discussion
There is little information in the research literature on how parents of NICU children adapt psychologically to the demands of caregiving beyond the initial hospitalization period. We compared the psychosocial health of parents of NICU children with parents of a group of healthy full-term children using the SF-36, a popular generic measure of psychosocial HRQL. Although children admitted to a NICU at birth are at increased risk of a variety of long-term health problems, we did not find any difference in parental psychosocial health when the two groups were compared. This finding is in agreement with one of the few studies that measured mental health in parents of NICU children at preschool age. Singer et al. [17] reported that after the neonatal period, the mental health of mothers of low-risk infants did not differ from mothers of term infants, and by 3 years, they had lower levels of distress, which they suggest may be due to maternal relief after an initial period of fear and anxiety. Mothers of high-risk infants, in contrast, had more symptoms of distress at 2 years, more negative family impact at 2 and 3 years and more parental strains and illness stressors at 3 years. But by 3 years, their reported psychological distress did not differ from that of term mothers. The authors suggest that by 2 years, infant developmental scores are predictive of later outcomes, and many mothers of high-risk infants must relinquish their hopes for their children to "catch up" to healthy born children and that some psychological adaptation has taken place despite parental acknowledgment of greater family and parenting stressors. With our cross-sectional design, we are not able to confirm the trend noted by Singer, but given the lack of relationship between most birth-related risk factors and parental mental health, it is possible that the parents of high- and low-risk infants in our sample have adjusted over time.
Current health status, in bivariate analysis, was strongly related with parental psychosocial health. In both groups of parents, those whose child had a neurosensory, motor development, learning/remembering or quality of life problem had poorer psychosocial health than those with children with no problems in these areas. Child behavior was also strongly related to parental psychosocial health. More specifically, parents of children who scored in the borderline or clinical range for internalizing, externalizing and total behavior problems on the CBCL/1.5–5 reported poorer psychosocial health than parents of children who scored in the normal range. These findings were consistent across both samples of parents. The only birth-related risk factor associated with parental psychosocial health was the presence of a congenital anomaly. Here the effect size was small, but points to the possibility that a congenital anomaly may affect parents mental health adversely. Researchers have reached a consensus that a minimally important difference in HRQL is close to one half of a standard deviation [34]. The differences that we found for health status and behavior were substantially larger and therefore represent clinically important differences in parental psychosocial health. However, not all of these variables showed a significant effect in the multivariate analysis, and it is possible that these variables influence other, more proximal, variables that showed stronger effects on parental psychosocial health.
The factors associated with poorer psychosocial health in the multivariate models provide important information about correlates of adjustment for NICU and healthy baby families. In a more general pooled model, parental age, higher caregiver strain, lower family function, and child's internalizing and externalizing behavior were independently associated with poorer caregiver's mental health score. The effect of lower parental education was modified by NICU status of the child. In the healthy baby group, less than high school education indicated lower MCS score. Child externalizing behavior symptoms and female gender (parental) were associated with lower MCS scores in the NICU group, whereas lower parental age, less education and poorer child quality of life were associated with lower MCS in the healthy baby group. For both samples, as it is also seen in a pooled model results, low family function, high caregiver strain, and child's internalizing behavioral symptoms were independently associated with lower parental psychosocial health. For family function and caregiver strain, only a substantial departure from mean values (at least 2 SDs) would result in a clinically important moderate effect size for the NICU group. Our interpretation for the healthy baby sample is hampered by wide confidence intervals around the beta estimates, resulting in effect sizes that ranged from minimal to large. Internalizing behavior symptoms were associated with only a small effect on caregiver's MCS score, again with wide confidence intervals around the beta coefficients for both samples.
A recent publication outlines the integration of a number of theoretical models into one multidimensional model that can be used to describe the caregiving process [35]. This model includes the following constructs: background and context; child characteristics; caregiver strain; intrapsychic factors; coping/supportive factors; and health outcomes. Fitting our findings within this framework, we found that poorer psychosocial health in parents was associated with background/context variables (i.e., female gender, younger age, less education); child characteristics (i.e., poorer quality of life, more child behavior problems); caregiver strain; and coping/supportive factors (i.e., family function). We suggest that future research with NICU parents be conceptually based and measure constructs found in other research to be important to caregiver health.
Our study has several limitations. Because it is not possible to verify cause-effect using a cross-sectional design, we were only able to estimate the direct effect of a limited number of predictor variables on parental psychosocial health. While our study has helped to identify some possibly important caregiving variables, there are other variables important to caregiver health that we did not measure. For example, while it is possible that some parents of children with severe health problems may have received specialized or targeted services (health and/or social services) to help them cope with their child's health problems, we did not include measures to determine this. Another limitation concerns our response rate. Although it is within the range often obtained in a postal survey [36], non-response can introduce bias. Some non-respondents indicated (verbally or in writing) they were "too busy" to participate. It is also likely that some questionnaires returned to us blank were from non-English speakers. Where we had data and were able to explore response bias (NICU sample only), only a few differences in birth-related sample characteristics and outcome were found that suggests respondents had sicker babies [37]. However, our study findings about health outcomes of NICU graduates are in agreement with the larger NICU literature, so it is unlikely that the differences we found are entirely due to response bias.
Conclusion
Our findings would suggest that overall, parental gender, family functioning and caregiver strain played influential roles in parental psychosocial health. For child characteristics, current behavior was more influential than initial birth-related risk factors.
List of abbreviations
MCS – Mental Component Score
NICU – Neonatal intensive care unit
HS – Health status
FAD – Family Assessment Device
PTT – Parental Impact Time
Competing interests
The author(s) declare that they have no competing interests.
Contributions of each author
Anne Klassen contributed substantially to the study's conception and design, acquisition of data, analysis and interpretation of data; and she drafted and revised and gave final approval of the version to be published.
Shoo Lee contributed substantially to the study's conception and design, acquisition of data, analysis and interpretation of data; and revised the article critically for important intellectual content and gave final approval of the version to be published.
Parminder Raina contributed substantially to the analysis and interpretation of data; and revised the article critically for important intellectual content and gave final approval of the version to be published.
Sarka Lisonkova contributed substantially to the analysis and interpretation of data; and revised the article critically for important intellectual content and gave final approval of the version to be published.
Pre-publication history
The pre-publication history for this paper can be accessed here:
Acknowledgements
The Hospital for Sick Children Foundation (Toronto) provided an operating grant for this study. Anne Klassen was recipient of a Killam Postdoctoral Fellowship. From Canadian Institutes of Health Research, Anne Klassen holds a Senior Research Fellowship, and Parminder Raina holds an Investigator Award. We wish to thank Jeanne Landgraf, Saroj Saigal, Drs Mike Carkner and Michael Klein, and the Canadian Neonatal Network.
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| 15598353 | PMC544865 | CC BY | 2021-01-04 16:31:00 | no | BMC Pediatr. 2004 Dec 14; 4:24 | utf-8 | BMC Pediatr | 2,004 | 10.1186/1471-2431-4-24 | oa_comm |
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Reprod Biol EndocrinolReproductive biology and endocrinology : RB&E1477-7827BioMed Central London 1477-7827-2-821559834710.1186/1477-7827-2-82ResearchThe association between male infertility and sperm disomy: Evidence for variation in disomy levels among individuals and a correlation between particular semen parameters and disomy of specific chromosome pairs Tempest Helen G [email protected] Sheryl T [email protected] Maria [email protected] Dimitra [email protected] David [email protected] Xiao P [email protected] Darren K [email protected] Cell and Chromosome Biology Group, Department of Biological Sciences, Brunel University, Uxbridge, UK2 112 Harley Street, London, UK3 Medicines Control Agency, Market Towers, 1 Nine Elms Lane, London, UK4 Department of Biosciences, University of Kent, Canterbury, CT2 7NZ, UK2004 14 12 2004 2 82 82 20 8 2004 14 12 2004 Copyright © 2004 Tempest et al; licensee BioMed Central Ltd.2004Tempest 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 association between infertility and sperm disomy is well documented. Results vary but most report that men with severely compromised semen parameters have a significantly elevated proportion of disomic sperm. The relationship between individual semen parameters and segregation of specific chromosome pairs is however less well reported as is the variation of disomy levels in individual men.
Methods
In order to address these questions the technique of fluorescent in-situ hybridisation (FISH) was utilised to determine the disomy levels of chromosomes X, Y and 21 in 43 sperm samples from 19 infertile males. The results generated from this study were analysed using logistic regression.
Results
In this study we compared levels of sperm concentration, motility and morphology with levels of sperm disomy for chromosome 21 and the sex chromosomes. Our results suggest that there is considerable variation in disomy levels for certain men. They also suggest that oligozoospermic males have significantly elevated levels of sex chromosome disomy but not disomy 21; they suggest that severe asthenozoospermic males have significantly elevated levels of disomy 21 but not sex chromosome disomy. Surprisingly, severe teratozoopsermic males appeared to have significantly lower levels of sperm disomy for both the sex chromosomes and chromosome 21.
Conclusion
We suggest that the association between sex chromosome disomy and oligozoospermia may be due to reduced recombination in the XY pairing region and discuss the relevance of our findings for the correlations between sperm disomy and sperm motility and morphology.
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Background
The relationship between male infertility and elevated proportions of sperm with extra or missing chromosomes in any given ejaculate is now extensively documented. There have been over 30 studies that have investigated this effect [e.g. [1-7]], and the majority have suggested a highly significant relationship between decreased semen quality parameters and increased sperm disomy. At least three studies however [3,8,9] have suggested that there is only a moderate increase in disomy associated with male infertility and a further three have found no significant relationship [2,10,11]. The reasons for these apparent discrepancies between groups are not clear although they may reflect laboratory-specific differences in stringency of scoring criteria, collection of semen samples after different periods of abstinence and/or criteria for patient selection differing from study to study. An alternative explanation however is that, among individuals and individual patient cohorts, some men have elevated levels of sperm disomy associated with infertility whereas others do not. If this is the case, there are a number of possible explanations; perhaps environmental influences could play a role. Indeed, a number of synthetic chemicals have been shown to be able to mimic endogenous hormones and affect the normal pattern of reproductive development [12]. In humans, levels of sperm disomy can be increased by environmental factors such as alcohol abuse and heavy smoking [13,14]. Intrinsic factors such as age and DNA polymorphisms have also been implicated. Indeed age and its effect on sperm disomy is well established [15,16]; Abruzzo et al. [17] found no effect of Y chromosome alphoid array size on Y chromosome non-disjunction, however Hobbs et al. [18] recently identified a genetic polymorphism involved in folate metabolism as a significant risk factor for trisomy 21.
A number of authors [4,11,19-21] refer to "severe oligoasthenoteratozoospermia (OAT)." Pang et al. [4] defined OAT as a sperm concentration of less than 15 million per ml, motility of less than 41% and normal morphology of less than 4.4%. This phenotype has been associated with increases in sperm disomy levels of around tenfold compared to normal controls [4]. Other papers however are less descriptive about the semen parameters in their patient cohort, and few studies set out to establish any relationship between individual semen parameters and the frequency of disomy of specific chromosomes. Exceptions to this include two studies that have examined patients with teratozoospermia alone [7,22]. Further studies, demonstrated a negative correlation between sperm disomy for sperm concentration [7,23,24]. Correlations were also found between disomy and progressive motility [24,25], disomy and teratozoospermia [7,25]. Viville et al. [22] analysed four individual patients presenting with four different types of total teratozoospermia. In that study, no significant difference was reported for three patients however one patient with macrocephalic spermatozoa had an aneuploidy rate of around 90%, demonstrating a significant correlation with morphology for patients with macrocephalic spermatozoa.
In most of the above studies either semen parameters and or aneuploidies for individual chromosome pairs were grouped together and thus not considered individually. Moreover, cases where males have given multiple samples are rare and thus there are few occasions where the individual specific parameters have been compared on a sample-by-sample basis. Establishing chromosome-specific and parameter-specific correlations between male infertility and percentage of aneuploid sperm in an ejaculate is a preliminary step towards understanding the mechanisms of the association between male infertility and chromosome segregation. In this study, our results provide evidence for a variation in rates of disomy for individual men and a correlation between specific semen parameters and individual chromosome disomies.
Methods
Patient cohort and experimental design
A series of males undergoing infertility treatment with a range of andrological phenotypes were assessed for conventional semen parameters and for sperm disomy. All patients were attending IVF clinics in the central London area. Semen samples were taken, with patients' informed consent, from 19 different men on 43 occasions from infertility clinics in central London. None had known constitutional karyotypic abnormalities or Y chromosome deletions. We received 1 sample each from 12 men, 2 samples from one man, 3 samples from 2 men, 4 samples from one man, 6 samples from 2 men and 7 samples from one man. In some men one, two or three of the semen parameters measured (concentration, motility and morphology) were within the normal range; these were hence placed in a control group. In other cases (test group) individual parameters were in the abnormal range (see subsequent section for andrological criteria). Given that some samples were taken from individual patients on several occasions, sometimes males appeared in the control group for some samples and in the test group for others. We restricted our molecular cytogenetic studies to chromosome 21 and the sex chromosomes for three reasons. First, according to previous studies [26-28] these are the most prone to non-disjunction in sperm and hence the most likely to give significant results. Second, the sheer number of sperm that needed to be scored per individual to establish statistically significant results precluded the study of large numbers of chromosome pairs. Finally these pairs are the most clinically significant as they lead to common mutant phenotypes among liveborns. That is, unlike most trisomies that abort in the first trimester, trisomies of the sex chromosomes and chromosome 21 frequently go to term and can lead to Klinefelter Syndrome and Down Syndrome respectively.
Semen analysis
Men were required to abstain from ejaculating for between 2 and 5 days prior to providing a sample for the study. Samples were produced on site into sterile 60 ml containers and kept at room temperature for up to 60 minutes to allow for liquefaction. Semen parameters were then analysed according to guidelines defined by WHO [29]. Patients were assessed for sperm quality using WHO guidelines and Kruger strict criteria for assessment of morphology. Concentration, percent motility, forward progression and the percentage of normal morphology were noted. The same operator performed all analyses. Sperm morphology was assessed on unstained samples, using phase contrast microscopy at a magnification of × 640. Evaluation of normal forms was based on Kruger strict criteria as described by Menkveld et al [30].
Individual samples were then placed in three occasions into a "test" or "control" group. On the first occasion, the control group had a sperm concentration of ≥ 20 million/ml and the test group <20 million/ml. On the second occasion, the control group had forward motility of ≥ 20% and the test group <20%. On the third occasion the control group had a normal morphology of ≥ 4% and the test group <4%. The cut-off points for considering individual samples as being in the test or control groups were based on WHO guidelines for oligozoospermia, severe asthenozoospermia, and severe teratozoospermia and were comparable to those used in other studies for sperm disomy [e.g. [4]]. In each case the sample was assessed by fluorescent in-situ hybridization (FISH) for the proportion of disomic sperm.
FISH analysis
FISH analysis was performed according to Griffin et al [15]. Briefly samples were prepared as follows: samples were washed in a buffer solution (10 mM Tris HCl, 10 mM NaCl, pH 8.0), smeared onto clean microscope slides, and dehydrated in an alcohol series. Slides were then air dried, and sperm heads were swelled by successive incubations in 0.1 M DTT (30 minutes) and 0.1 M LIS (1 hour). Slides were then dehydrated in an alcohol series and air dried ready for subsequent FISH studies. Three colour FISH was carried out for chromosomes 21, X and Y in each patient using directly labelled commercially available probes (Vysis Inc., Downers Grove, Il, USA). Spectrum Orange LSI 21 DNA probe, Spectrum Green CEP Y (satellite III) DNA probe, and a combination of the CEP X centromeric alpha-satellite probes one labelled in Spectrum Orange and one in Spectrum Green were used to give a yellow colour. The protocol followed was identical to that of Griffin et al. [15] with the exception that the colour combinations (above) used.
Approximately 5,000 sperm were scored per patient by two or more independent observers. The proportion of aneuploid sperm per sample for each chromosome was noted.
Statistical Analysis
The hypotheses of interest were whether the rate of disomy was significantly different for the test and control groups in terms of sperm concentration, morphology or motility. To this end, in the first case, all oligospermic patients designated "O" (i.e. O, OT, OA and OAT in table 1) were in the test group and the remainder were in the control group. In the second case, the test group were severe asthenozoospermic patients designated "A" (A, OA, AT and OAT – control group were the remainder) and, in the third case, the test group were severe teratozoospermic patients designated "T" (T, OT, AT and OAT – control group were the remainder). Thus six patients (5, 9, 10, 12, 14 and 17) appeared in more than one group on at least one occasion. Data on the rate of disomy were generated for chromosome 21 and the sex chromosomes. To test these hypotheses six logistic regression models were fitted in the statistical software package SAS (Version 8.2). For each model an Odds Ratio, a 95% confidence interval and a p-value were calculated. A confidence interval that does not contain 1 implies that there is evidence that the disomy rates were significantly different for that comparison and hence has a corresponding p-value < 0.05.
Table 1 Incidence of sperm disomy for the sex chromosomes and chromosome 21 and the semen analysis in 43 men.
Patient number % disomy sex chromosomes % chromosome 21 disomy Total cells scored count (million/ml) Motility (%) % abnormal forms Semen analysis
1 0.50% 0.43% 5097 6.9 26 97 OT
2 1.02% 0.10% 5000 6.6 48 98 OT
3 0.29% 0.29% 4864 128 63 86 N
4 0.35% 0.20% 4790 13.5 17 98 OAT
5a 0.17% 0.24% 5400 61 24 95 N
5b 0.22% 0.10% 5092 37.5 7 95 A
5c 0.12% 0.10% 5000 22 45 99 T
5d 0.06% 0.14% 5000 63 13 94 A
6a 0.14% 0.08% 5000 48 44 98 T
6b 0.12% 0.04% 5000 40 50 99 T
7 0.18% 0.20% 5000 4.8 <10 95 OA
8 0.74% 0.00% 544 <0.01 <10 100 OAT
9a 0.73% 0.25% 5066 17 18 96 OAT
9b 0.45% 0.24% 5056 12 11 97 OAT
9c 0.30% 0.14% 5084 21 30 96 T
9d 0.10% 0.22% 5000 80 8 98 AT
9e 0.18% 0.12% 5063 13.3 26 97 OT
9f 0.24% 0.12% 5004 7.9 42 98 OT
10a 0.24% 0.38% 5000 25 48 99 T
10b 0.02% 0.04% 5000 8 38 97 OT
10c 0.16% 0.12% 5000 22 41 98 T
11 0.17% 0.09% 5275 41 46 93 N
12a 0.96% 1.46% 4050 69 10 91 A
12b 0.44% 0.11% 5449 10.3 26 87 O
12c 0.30% 0.12% 5048 9.5 11 97 OAT
12d 0.24% 0.14% 5018 63 17 97 AT
12e 0.18% 0.08% 5031 32 41 96 T
12f 0.12% 0.06% 5061 57 37 96 T
12g 0.14% 0.18% 5000 24 58 87 N
13 0.06% 0.02% 5000 96 60 92 N
14a 0.65% 0.20% 3521 12 36 97 OT
14b 0.24% 0.08% 5009 7.5 53 99 OT
14c 0.14% 0.10% 5001 37 27 97 T
14d 0.08% 0.14% 5037 16 45 98 OT
14e 0.20% 0.04% 5000 30 40 100 N
14f 0.27% 0.37% 4108 16 38 94 O
15 2.00% 0.59% 5293 14 57 96 OT
16 0.31% 0.21% 5120 47 49 89 N
17a 0.24% 0.24% 5000 18 53 92 O
17b 0.08% 0.04% 5000 49 44 96 T
17c 0.08% 0.04% 5000 44 56 92 N
18 0.10% 0.26% 5000 123 10 96 AT
19 0.30% 0.18% 5107 52 46 89 N
Patient number: Letters after patient numbers indicate consecutive samples from the same patient e.g. 10b is the second sample from patient 10
Semen Analysis: O = oligozoospermia (sperm concerntration <20 million/ml); A = severe asthenozoospermia (formward motility <40%); T = severe teratozoospermia (normal motility <4% by strict Kruger criteria); N = normozoospermia (>20 million/ml concentration; >40% formward motlity; >4% normal morphology).
Results
A total of 209,188 spermatozoa were scored (approximately 5,000 per sample). The total rate of disomy for the sex chromosomes and chromosome 21 was found to be 0.3% (633/209,188) and 0.19% (398/209,188) respectively. Disomy levels ranged from 0.02% – 2.00% for the sex chromosomes and 0.00% – 1.46% for chromosome 21. For the patients who gave multiple samples, individual disomy rates were surprisingly varied: For instance patient 9 (who gave 6 samples) had sex chromosome disomy frequencies ranging from 0.1% and 0.73%. The results of each individual sample are presented in Table 1 where, in each case, the semen parameters as well as the sperm disomy rates for each individual chromosome are given. The results of the logistic regression analysis (table 2) clearly demonstrate that men with oligozoospermia (figure 1a), (sperm concentration < 20 million/ml) have significantly elevated levels of sex chromosome disomy (Odds Ratio 2.39, p < 0.0001) in their sperm compared to men with normal sperm count levels (sperm concentration ≥ 20 million/ml). As the lower limit of the 95% confidence interval for the Odds Ratio is 2.04 these data suggest that the rate of sperm disomy is likely to be at least twice as high in test patients compared to controls. Further analysis revealed that this increase was largely accounted for by an increase in XY disomy, which is usually associated with non-disjunction errors of meiosis I (data not shown). Conversely there was no evidence of a significant association between oligozoospermia and sperm disomy for chromosome 21. For the motility data (figure 1b) however, the opposite situation pertained. That is, there was no significant difference between sperm disomy levels for the sex chromosomes (XY, XX or XY disomy) whereas men with motility of < 20% (asthenozoospermia) had significantly elevated levels of chromosome 21 disomy compared to controls (Odds Ratio 1.75, p < 0.0001), (table 2). Finally (and surprisingly) men with severe teratozoospermia (figure 1c), (< 4% abnormal forms) had significantly reduced levels of sperm disomy for both pairs of chromosomes compared to controls. (Sex chromosome disomy, Odds Ratio 1.22, p = 0.013, Chromosome 21 disomy, Odds Ratio = 1.54, p=<0.0001) (table 2). Figure 2 shows examples of normal and XY disomic sperm.
Table 2 Logistic regression analysis of individual disomy rates compared to semen parameters. a. Sperm concentration, b. sperm motility, c. sperm morphology. (C) = control values, (T) = test values.
a. Sperm concentration
Chromosome Mean disomy SD (4 dp) Odds Ratio (95% Confidence Interval) p-value
21 (C) 0.19%
(T) 0.20% 0.0018
0.0028 1.14 (0.94, 1.40) 0.18
Sex (C) 0.20%
(T) 0.48% 0.0046
0.0015 2.39 (2.04, 2.81) < 0.0001
b. Motility
Chromosome Mean disomy SD Odds Ratio (95% Confidence Interval) p-value
21 (C) 0.16%
(T) 0.28% 0.0013
0.0037 1.75 (1.43, 2.14) < 0.0001
Sex (C) 0.30%
(T) 0.37% 0.0037
0.0029 1.12 (0.95, 1.34) 0.19
c. Morphology
Chromosome Mean disomy SD Odds Ratio (95% Confidence Interval) p-value
21 (C) 0.24%
(T) 0.16% 0.0039
0.0013 1.54 (1.26, 1.89) < 0.0001
Sex (C) 0.33%
(T) 0.28% 0.0039
0.0024 1.22 (1.04, 1.43) 0.013
Figure 1 a) Mean sperm disomy frequencies for the sex chromosomes and chromosome 21 in two groups with sperm concentration of ≥ 20 million/ml and < 20 million/ml. b) Mean sperm disomy frequencies for the sex chromosomes and chromosome 21 in two groups with percentage of motility of ≥ 20% motility and < 20% sperm motility. c) Mean sperm disomy frequencies for the sex chromosomes and chromosome 21 in two groups with ≥ 4% normal morphology and < 4% normal morphology. Error bars represent SEM.
Figure 2 Image of five sperm with chromosome X labelled in yellow, Y in green and 21 in red. The sperm at the bottom left and top centre-left are XY disomic.
Discussion
To the best of our knowledge, this is the first report that demonstrates a relationship between individual clinically defined semen parameters and segregation of specific chromosome pairs. The relationship between sex chromosome disomy and the failure of spermatocytes to complete spermatogenesis is manifested by significantly higher sex chromosome disomy levels in oligozoospermic men. These results are similar to those of Rives et al. [23], Vegetti et al. [24], Calogero et al. [7] who also reported a relationship between sex chromosome disomy and sperm concentration. Here however we also report the absence of such an effect for chromosome 21 and this leads us to propose the hypothesis that this effect may be restricted to the sex chromosome bivalent. This could be confirmed by further studies using probes for different autosomes and studies are ongoing in this regard. A common mechanism leading to both XY disomy and failure of spermatogenesis is aberrant pairing in the pseudoautosomal region. That is, Hassold et al. [31] demonstrated that men with paternally derived Klinefelter syndrome arose through a reduction or absence in pairing in the pseudoautosomal region. Furthermore, the term "obligatory" has often been used to describe the need for X and Y to pair and recombine in order for spermatogenesis to proceed properly [32]. In the light of our results therefore we propose that a common mechanism leading to oligozoospermia and increased levels of XY disomy involves a perturbation in the mechanisms of synapsis and/or recombination in the XY paring region. In order to test this hypothesis, in future studies, we will use single sperm PCR [33] using primers both within and outside the XY paring region. A significant difference between oligozoospermic males compared to controls would provide evidence to support our hypothesis.
Our results also suggest a significant association between asthenozoospermia (poor motility) and non-disjunction of chromosome 21. This is similar to reports by Vegetti et al. [24] but in this case, we found no such association with the sex chromosomes. One possible explanation is that over expressed genes on chromosome 21 significantly impair the formation of the sperm midpiece through which sperm motility is mediated. This seems unlikely however since chromosome 21 is a gene-poor chromosome and there are thought to be few genes expressed in the spermatocyte itself that impact on spermiogenesis. It is also possible that there are gene products (e.g. micro tubular or motor proteins) common to both normal chromosome segregation of chromosome 21 (or the acrocentric (non-Y) chromosomes, or the autosomes in general) and normal formation of the structures that mediate sperm motility. We could establish the extent to which this effect is widespread in other autosomes by similar experiments using probes for other chromosomes; again these studies are ongoing. A final possibility is that our results represent a statistical anomaly. While correlations for individual males who have given multiple (four or more) samples are relatively consistent for sperm concentration, they are less so for motility (see table 1). If disomy were related to motility by a genetic cause, then would expect a consistency in chromosomal aneuploidies from individual patients who gave multiple samples. In patient 5 however, his highest motility sample of 45% also had the lowest proportion of autosomal disomy (0.1%) and the highest (0,24%) in a "normal" motility sample. There was also considerable evidence of varying disomy levels when motility remained relatively constant for instance patient 10, had normal motility in all samples, the disomy frequency ranged from 0.12 – 0.38%. Clearly therefore further studies are necessary before a stronger relationship between autosomal sperm disomy and asthenozoospermia can be established.
The apparent inverse association between sperm morphology and chromosome segregation was surprising and it is, again, possible that this is a statistical anomaly. Indeed although a number of studies have found no significant correlation between morphology and disomy [24,34-36], others suggest a positive correlation between disomy and abnormal morphology [7,25]. The high level of significance, the fact that the effect is clear in two separate chromosome pairs and the fact that different effects were seen for concentration and morphology would argue that this is a genuine phenomenon. Moreover this is one of the few studies that has used repeat samples from individual patients and, in some cases, the same individual appeared in different groups depending on his semen parameters at the time of donation. In other words disomy levels appear not to be consistent among individuals, rather they relate more to their semen parameters on any given day, perhaps as a result of extrinsic factors. Other studies have reported that teratozoospermic males have elevated levels of sperm disomy, Calogero et al. [7] found a correlation between increased sperm disomy levels and teratozoospermia as did Viville et al. [22] but only an association with macrocephalic spermatozoa. For the most part however these individuals were defined as "OAT" i.e. also oligozoospermic and asthenozoospermic and thus it is possible that the association of teratozoospermia alone was not measured fully. Future studies warrant investigating this further using more chromosome pairs and individuals who display severe teratozoospermia but normal levels of sperm concentration and motility.
Conclusions
In conclusion we provide evidence that sperm disomy levels can vary considerably between samples from the same man, the reasons for this are unclear but one possible explanation is the involvement of extrinsic factors or lifestyle changes. Such differences provide hope for possible treatment regimes to improve disomy rates. The evidence of correlations between individual semen parameters and increased disomy of individual chromosome pairs, while statistically significant, warrants further investigation. Closer correlations of disomy rates in men with defects in only one of the three criteria used to measure semen quality will form the basis of our future investigations. In future studies it is also likely that we would include a second control group of normal, fertile donors not attending fertility clinics. Ethical considerations precluded this in this case. Through these studies, a closer understanding of the mechanistic basis of the relationship between chromosome segregation and infertility will be achieved.
Authors' contributions
HGT- performed the majority of FISH experiments, scoring of semen samples, collected data generated within this study and assisted in drafting the manuscript. SH- performed all semen assessments. MD and DC- performed FISH experiments and acted as independent scorers of semen samples. DW- performed statistical analysis. XPZ- provided patient samples with signed informed consent. DKG- conceived the study and participated in its design and drafted the manuscript. All authors have read and approved the final manuscript.
Acknowledgements
Signed written consent was obtained from all of the individuals who have participated within this study.
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| 15598347 | PMC544866 | CC BY | 2021-01-04 16:36:43 | no | Reprod Biol Endocrinol. 2004 Dec 14; 2:82 | utf-8 | Reprod Biol Endocrinol | 2,004 | 10.1186/1477-7827-2-82 | oa_comm |
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Reprod Biol EndocrinolReproductive biology and endocrinology : RB&E1477-7827BioMed Central London 1477-7827-2-821559834710.1186/1477-7827-2-82ResearchThe association between male infertility and sperm disomy: Evidence for variation in disomy levels among individuals and a correlation between particular semen parameters and disomy of specific chromosome pairs Tempest Helen G [email protected] Sheryl T [email protected] Maria [email protected] Dimitra [email protected] David [email protected] Xiao P [email protected] Darren K [email protected] Cell and Chromosome Biology Group, Department of Biological Sciences, Brunel University, Uxbridge, UK2 112 Harley Street, London, UK3 Medicines Control Agency, Market Towers, 1 Nine Elms Lane, London, UK4 Department of Biosciences, University of Kent, Canterbury, CT2 7NZ, UK2004 14 12 2004 2 82 82 20 8 2004 14 12 2004 Copyright © 2004 Tempest et al; licensee BioMed Central Ltd.2004Tempest 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 association between infertility and sperm disomy is well documented. Results vary but most report that men with severely compromised semen parameters have a significantly elevated proportion of disomic sperm. The relationship between individual semen parameters and segregation of specific chromosome pairs is however less well reported as is the variation of disomy levels in individual men.
Methods
In order to address these questions the technique of fluorescent in-situ hybridisation (FISH) was utilised to determine the disomy levels of chromosomes X, Y and 21 in 43 sperm samples from 19 infertile males. The results generated from this study were analysed using logistic regression.
Results
In this study we compared levels of sperm concentration, motility and morphology with levels of sperm disomy for chromosome 21 and the sex chromosomes. Our results suggest that there is considerable variation in disomy levels for certain men. They also suggest that oligozoospermic males have significantly elevated levels of sex chromosome disomy but not disomy 21; they suggest that severe asthenozoospermic males have significantly elevated levels of disomy 21 but not sex chromosome disomy. Surprisingly, severe teratozoopsermic males appeared to have significantly lower levels of sperm disomy for both the sex chromosomes and chromosome 21.
Conclusion
We suggest that the association between sex chromosome disomy and oligozoospermia may be due to reduced recombination in the XY pairing region and discuss the relevance of our findings for the correlations between sperm disomy and sperm motility and morphology.
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Background
The relationship between male infertility and elevated proportions of sperm with extra or missing chromosomes in any given ejaculate is now extensively documented. There have been over 30 studies that have investigated this effect [e.g. [1-7]], and the majority have suggested a highly significant relationship between decreased semen quality parameters and increased sperm disomy. At least three studies however [3,8,9] have suggested that there is only a moderate increase in disomy associated with male infertility and a further three have found no significant relationship [2,10,11]. The reasons for these apparent discrepancies between groups are not clear although they may reflect laboratory-specific differences in stringency of scoring criteria, collection of semen samples after different periods of abstinence and/or criteria for patient selection differing from study to study. An alternative explanation however is that, among individuals and individual patient cohorts, some men have elevated levels of sperm disomy associated with infertility whereas others do not. If this is the case, there are a number of possible explanations; perhaps environmental influences could play a role. Indeed, a number of synthetic chemicals have been shown to be able to mimic endogenous hormones and affect the normal pattern of reproductive development [12]. In humans, levels of sperm disomy can be increased by environmental factors such as alcohol abuse and heavy smoking [13,14]. Intrinsic factors such as age and DNA polymorphisms have also been implicated. Indeed age and its effect on sperm disomy is well established [15,16]; Abruzzo et al. [17] found no effect of Y chromosome alphoid array size on Y chromosome non-disjunction, however Hobbs et al. [18] recently identified a genetic polymorphism involved in folate metabolism as a significant risk factor for trisomy 21.
A number of authors [4,11,19-21] refer to "severe oligoasthenoteratozoospermia (OAT)." Pang et al. [4] defined OAT as a sperm concentration of less than 15 million per ml, motility of less than 41% and normal morphology of less than 4.4%. This phenotype has been associated with increases in sperm disomy levels of around tenfold compared to normal controls [4]. Other papers however are less descriptive about the semen parameters in their patient cohort, and few studies set out to establish any relationship between individual semen parameters and the frequency of disomy of specific chromosomes. Exceptions to this include two studies that have examined patients with teratozoospermia alone [7,22]. Further studies, demonstrated a negative correlation between sperm disomy for sperm concentration [7,23,24]. Correlations were also found between disomy and progressive motility [24,25], disomy and teratozoospermia [7,25]. Viville et al. [22] analysed four individual patients presenting with four different types of total teratozoospermia. In that study, no significant difference was reported for three patients however one patient with macrocephalic spermatozoa had an aneuploidy rate of around 90%, demonstrating a significant correlation with morphology for patients with macrocephalic spermatozoa.
In most of the above studies either semen parameters and or aneuploidies for individual chromosome pairs were grouped together and thus not considered individually. Moreover, cases where males have given multiple samples are rare and thus there are few occasions where the individual specific parameters have been compared on a sample-by-sample basis. Establishing chromosome-specific and parameter-specific correlations between male infertility and percentage of aneuploid sperm in an ejaculate is a preliminary step towards understanding the mechanisms of the association between male infertility and chromosome segregation. In this study, our results provide evidence for a variation in rates of disomy for individual men and a correlation between specific semen parameters and individual chromosome disomies.
Methods
Patient cohort and experimental design
A series of males undergoing infertility treatment with a range of andrological phenotypes were assessed for conventional semen parameters and for sperm disomy. All patients were attending IVF clinics in the central London area. Semen samples were taken, with patients' informed consent, from 19 different men on 43 occasions from infertility clinics in central London. None had known constitutional karyotypic abnormalities or Y chromosome deletions. We received 1 sample each from 12 men, 2 samples from one man, 3 samples from 2 men, 4 samples from one man, 6 samples from 2 men and 7 samples from one man. In some men one, two or three of the semen parameters measured (concentration, motility and morphology) were within the normal range; these were hence placed in a control group. In other cases (test group) individual parameters were in the abnormal range (see subsequent section for andrological criteria). Given that some samples were taken from individual patients on several occasions, sometimes males appeared in the control group for some samples and in the test group for others. We restricted our molecular cytogenetic studies to chromosome 21 and the sex chromosomes for three reasons. First, according to previous studies [26-28] these are the most prone to non-disjunction in sperm and hence the most likely to give significant results. Second, the sheer number of sperm that needed to be scored per individual to establish statistically significant results precluded the study of large numbers of chromosome pairs. Finally these pairs are the most clinically significant as they lead to common mutant phenotypes among liveborns. That is, unlike most trisomies that abort in the first trimester, trisomies of the sex chromosomes and chromosome 21 frequently go to term and can lead to Klinefelter Syndrome and Down Syndrome respectively.
Semen analysis
Men were required to abstain from ejaculating for between 2 and 5 days prior to providing a sample for the study. Samples were produced on site into sterile 60 ml containers and kept at room temperature for up to 60 minutes to allow for liquefaction. Semen parameters were then analysed according to guidelines defined by WHO [29]. Patients were assessed for sperm quality using WHO guidelines and Kruger strict criteria for assessment of morphology. Concentration, percent motility, forward progression and the percentage of normal morphology were noted. The same operator performed all analyses. Sperm morphology was assessed on unstained samples, using phase contrast microscopy at a magnification of × 640. Evaluation of normal forms was based on Kruger strict criteria as described by Menkveld et al [30].
Individual samples were then placed in three occasions into a "test" or "control" group. On the first occasion, the control group had a sperm concentration of ≥ 20 million/ml and the test group <20 million/ml. On the second occasion, the control group had forward motility of ≥ 20% and the test group <20%. On the third occasion the control group had a normal morphology of ≥ 4% and the test group <4%. The cut-off points for considering individual samples as being in the test or control groups were based on WHO guidelines for oligozoospermia, severe asthenozoospermia, and severe teratozoospermia and were comparable to those used in other studies for sperm disomy [e.g. [4]]. In each case the sample was assessed by fluorescent in-situ hybridization (FISH) for the proportion of disomic sperm.
FISH analysis
FISH analysis was performed according to Griffin et al [15]. Briefly samples were prepared as follows: samples were washed in a buffer solution (10 mM Tris HCl, 10 mM NaCl, pH 8.0), smeared onto clean microscope slides, and dehydrated in an alcohol series. Slides were then air dried, and sperm heads were swelled by successive incubations in 0.1 M DTT (30 minutes) and 0.1 M LIS (1 hour). Slides were then dehydrated in an alcohol series and air dried ready for subsequent FISH studies. Three colour FISH was carried out for chromosomes 21, X and Y in each patient using directly labelled commercially available probes (Vysis Inc., Downers Grove, Il, USA). Spectrum Orange LSI 21 DNA probe, Spectrum Green CEP Y (satellite III) DNA probe, and a combination of the CEP X centromeric alpha-satellite probes one labelled in Spectrum Orange and one in Spectrum Green were used to give a yellow colour. The protocol followed was identical to that of Griffin et al. [15] with the exception that the colour combinations (above) used.
Approximately 5,000 sperm were scored per patient by two or more independent observers. The proportion of aneuploid sperm per sample for each chromosome was noted.
Statistical Analysis
The hypotheses of interest were whether the rate of disomy was significantly different for the test and control groups in terms of sperm concentration, morphology or motility. To this end, in the first case, all oligospermic patients designated "O" (i.e. O, OT, OA and OAT in table 1) were in the test group and the remainder were in the control group. In the second case, the test group were severe asthenozoospermic patients designated "A" (A, OA, AT and OAT – control group were the remainder) and, in the third case, the test group were severe teratozoospermic patients designated "T" (T, OT, AT and OAT – control group were the remainder). Thus six patients (5, 9, 10, 12, 14 and 17) appeared in more than one group on at least one occasion. Data on the rate of disomy were generated for chromosome 21 and the sex chromosomes. To test these hypotheses six logistic regression models were fitted in the statistical software package SAS (Version 8.2). For each model an Odds Ratio, a 95% confidence interval and a p-value were calculated. A confidence interval that does not contain 1 implies that there is evidence that the disomy rates were significantly different for that comparison and hence has a corresponding p-value < 0.05.
Table 1 Incidence of sperm disomy for the sex chromosomes and chromosome 21 and the semen analysis in 43 men.
Patient number % disomy sex chromosomes % chromosome 21 disomy Total cells scored count (million/ml) Motility (%) % abnormal forms Semen analysis
1 0.50% 0.43% 5097 6.9 26 97 OT
2 1.02% 0.10% 5000 6.6 48 98 OT
3 0.29% 0.29% 4864 128 63 86 N
4 0.35% 0.20% 4790 13.5 17 98 OAT
5a 0.17% 0.24% 5400 61 24 95 N
5b 0.22% 0.10% 5092 37.5 7 95 A
5c 0.12% 0.10% 5000 22 45 99 T
5d 0.06% 0.14% 5000 63 13 94 A
6a 0.14% 0.08% 5000 48 44 98 T
6b 0.12% 0.04% 5000 40 50 99 T
7 0.18% 0.20% 5000 4.8 <10 95 OA
8 0.74% 0.00% 544 <0.01 <10 100 OAT
9a 0.73% 0.25% 5066 17 18 96 OAT
9b 0.45% 0.24% 5056 12 11 97 OAT
9c 0.30% 0.14% 5084 21 30 96 T
9d 0.10% 0.22% 5000 80 8 98 AT
9e 0.18% 0.12% 5063 13.3 26 97 OT
9f 0.24% 0.12% 5004 7.9 42 98 OT
10a 0.24% 0.38% 5000 25 48 99 T
10b 0.02% 0.04% 5000 8 38 97 OT
10c 0.16% 0.12% 5000 22 41 98 T
11 0.17% 0.09% 5275 41 46 93 N
12a 0.96% 1.46% 4050 69 10 91 A
12b 0.44% 0.11% 5449 10.3 26 87 O
12c 0.30% 0.12% 5048 9.5 11 97 OAT
12d 0.24% 0.14% 5018 63 17 97 AT
12e 0.18% 0.08% 5031 32 41 96 T
12f 0.12% 0.06% 5061 57 37 96 T
12g 0.14% 0.18% 5000 24 58 87 N
13 0.06% 0.02% 5000 96 60 92 N
14a 0.65% 0.20% 3521 12 36 97 OT
14b 0.24% 0.08% 5009 7.5 53 99 OT
14c 0.14% 0.10% 5001 37 27 97 T
14d 0.08% 0.14% 5037 16 45 98 OT
14e 0.20% 0.04% 5000 30 40 100 N
14f 0.27% 0.37% 4108 16 38 94 O
15 2.00% 0.59% 5293 14 57 96 OT
16 0.31% 0.21% 5120 47 49 89 N
17a 0.24% 0.24% 5000 18 53 92 O
17b 0.08% 0.04% 5000 49 44 96 T
17c 0.08% 0.04% 5000 44 56 92 N
18 0.10% 0.26% 5000 123 10 96 AT
19 0.30% 0.18% 5107 52 46 89 N
Patient number: Letters after patient numbers indicate consecutive samples from the same patient e.g. 10b is the second sample from patient 10
Semen Analysis: O = oligozoospermia (sperm concerntration <20 million/ml); A = severe asthenozoospermia (formward motility <40%); T = severe teratozoospermia (normal motility <4% by strict Kruger criteria); N = normozoospermia (>20 million/ml concentration; >40% formward motlity; >4% normal morphology).
Results
A total of 209,188 spermatozoa were scored (approximately 5,000 per sample). The total rate of disomy for the sex chromosomes and chromosome 21 was found to be 0.3% (633/209,188) and 0.19% (398/209,188) respectively. Disomy levels ranged from 0.02% – 2.00% for the sex chromosomes and 0.00% – 1.46% for chromosome 21. For the patients who gave multiple samples, individual disomy rates were surprisingly varied: For instance patient 9 (who gave 6 samples) had sex chromosome disomy frequencies ranging from 0.1% and 0.73%. The results of each individual sample are presented in Table 1 where, in each case, the semen parameters as well as the sperm disomy rates for each individual chromosome are given. The results of the logistic regression analysis (table 2) clearly demonstrate that men with oligozoospermia (figure 1a), (sperm concentration < 20 million/ml) have significantly elevated levels of sex chromosome disomy (Odds Ratio 2.39, p < 0.0001) in their sperm compared to men with normal sperm count levels (sperm concentration ≥ 20 million/ml). As the lower limit of the 95% confidence interval for the Odds Ratio is 2.04 these data suggest that the rate of sperm disomy is likely to be at least twice as high in test patients compared to controls. Further analysis revealed that this increase was largely accounted for by an increase in XY disomy, which is usually associated with non-disjunction errors of meiosis I (data not shown). Conversely there was no evidence of a significant association between oligozoospermia and sperm disomy for chromosome 21. For the motility data (figure 1b) however, the opposite situation pertained. That is, there was no significant difference between sperm disomy levels for the sex chromosomes (XY, XX or XY disomy) whereas men with motility of < 20% (asthenozoospermia) had significantly elevated levels of chromosome 21 disomy compared to controls (Odds Ratio 1.75, p < 0.0001), (table 2). Finally (and surprisingly) men with severe teratozoospermia (figure 1c), (< 4% abnormal forms) had significantly reduced levels of sperm disomy for both pairs of chromosomes compared to controls. (Sex chromosome disomy, Odds Ratio 1.22, p = 0.013, Chromosome 21 disomy, Odds Ratio = 1.54, p=<0.0001) (table 2). Figure 2 shows examples of normal and XY disomic sperm.
Table 2 Logistic regression analysis of individual disomy rates compared to semen parameters. a. Sperm concentration, b. sperm motility, c. sperm morphology. (C) = control values, (T) = test values.
a. Sperm concentration
Chromosome Mean disomy SD (4 dp) Odds Ratio (95% Confidence Interval) p-value
21 (C) 0.19%
(T) 0.20% 0.0018
0.0028 1.14 (0.94, 1.40) 0.18
Sex (C) 0.20%
(T) 0.48% 0.0046
0.0015 2.39 (2.04, 2.81) < 0.0001
b. Motility
Chromosome Mean disomy SD Odds Ratio (95% Confidence Interval) p-value
21 (C) 0.16%
(T) 0.28% 0.0013
0.0037 1.75 (1.43, 2.14) < 0.0001
Sex (C) 0.30%
(T) 0.37% 0.0037
0.0029 1.12 (0.95, 1.34) 0.19
c. Morphology
Chromosome Mean disomy SD Odds Ratio (95% Confidence Interval) p-value
21 (C) 0.24%
(T) 0.16% 0.0039
0.0013 1.54 (1.26, 1.89) < 0.0001
Sex (C) 0.33%
(T) 0.28% 0.0039
0.0024 1.22 (1.04, 1.43) 0.013
Figure 1 a) Mean sperm disomy frequencies for the sex chromosomes and chromosome 21 in two groups with sperm concentration of ≥ 20 million/ml and < 20 million/ml. b) Mean sperm disomy frequencies for the sex chromosomes and chromosome 21 in two groups with percentage of motility of ≥ 20% motility and < 20% sperm motility. c) Mean sperm disomy frequencies for the sex chromosomes and chromosome 21 in two groups with ≥ 4% normal morphology and < 4% normal morphology. Error bars represent SEM.
Figure 2 Image of five sperm with chromosome X labelled in yellow, Y in green and 21 in red. The sperm at the bottom left and top centre-left are XY disomic.
Discussion
To the best of our knowledge, this is the first report that demonstrates a relationship between individual clinically defined semen parameters and segregation of specific chromosome pairs. The relationship between sex chromosome disomy and the failure of spermatocytes to complete spermatogenesis is manifested by significantly higher sex chromosome disomy levels in oligozoospermic men. These results are similar to those of Rives et al. [23], Vegetti et al. [24], Calogero et al. [7] who also reported a relationship between sex chromosome disomy and sperm concentration. Here however we also report the absence of such an effect for chromosome 21 and this leads us to propose the hypothesis that this effect may be restricted to the sex chromosome bivalent. This could be confirmed by further studies using probes for different autosomes and studies are ongoing in this regard. A common mechanism leading to both XY disomy and failure of spermatogenesis is aberrant pairing in the pseudoautosomal region. That is, Hassold et al. [31] demonstrated that men with paternally derived Klinefelter syndrome arose through a reduction or absence in pairing in the pseudoautosomal region. Furthermore, the term "obligatory" has often been used to describe the need for X and Y to pair and recombine in order for spermatogenesis to proceed properly [32]. In the light of our results therefore we propose that a common mechanism leading to oligozoospermia and increased levels of XY disomy involves a perturbation in the mechanisms of synapsis and/or recombination in the XY paring region. In order to test this hypothesis, in future studies, we will use single sperm PCR [33] using primers both within and outside the XY paring region. A significant difference between oligozoospermic males compared to controls would provide evidence to support our hypothesis.
Our results also suggest a significant association between asthenozoospermia (poor motility) and non-disjunction of chromosome 21. This is similar to reports by Vegetti et al. [24] but in this case, we found no such association with the sex chromosomes. One possible explanation is that over expressed genes on chromosome 21 significantly impair the formation of the sperm midpiece through which sperm motility is mediated. This seems unlikely however since chromosome 21 is a gene-poor chromosome and there are thought to be few genes expressed in the spermatocyte itself that impact on spermiogenesis. It is also possible that there are gene products (e.g. micro tubular or motor proteins) common to both normal chromosome segregation of chromosome 21 (or the acrocentric (non-Y) chromosomes, or the autosomes in general) and normal formation of the structures that mediate sperm motility. We could establish the extent to which this effect is widespread in other autosomes by similar experiments using probes for other chromosomes; again these studies are ongoing. A final possibility is that our results represent a statistical anomaly. While correlations for individual males who have given multiple (four or more) samples are relatively consistent for sperm concentration, they are less so for motility (see table 1). If disomy were related to motility by a genetic cause, then would expect a consistency in chromosomal aneuploidies from individual patients who gave multiple samples. In patient 5 however, his highest motility sample of 45% also had the lowest proportion of autosomal disomy (0.1%) and the highest (0,24%) in a "normal" motility sample. There was also considerable evidence of varying disomy levels when motility remained relatively constant for instance patient 10, had normal motility in all samples, the disomy frequency ranged from 0.12 – 0.38%. Clearly therefore further studies are necessary before a stronger relationship between autosomal sperm disomy and asthenozoospermia can be established.
The apparent inverse association between sperm morphology and chromosome segregation was surprising and it is, again, possible that this is a statistical anomaly. Indeed although a number of studies have found no significant correlation between morphology and disomy [24,34-36], others suggest a positive correlation between disomy and abnormal morphology [7,25]. The high level of significance, the fact that the effect is clear in two separate chromosome pairs and the fact that different effects were seen for concentration and morphology would argue that this is a genuine phenomenon. Moreover this is one of the few studies that has used repeat samples from individual patients and, in some cases, the same individual appeared in different groups depending on his semen parameters at the time of donation. In other words disomy levels appear not to be consistent among individuals, rather they relate more to their semen parameters on any given day, perhaps as a result of extrinsic factors. Other studies have reported that teratozoospermic males have elevated levels of sperm disomy, Calogero et al. [7] found a correlation between increased sperm disomy levels and teratozoospermia as did Viville et al. [22] but only an association with macrocephalic spermatozoa. For the most part however these individuals were defined as "OAT" i.e. also oligozoospermic and asthenozoospermic and thus it is possible that the association of teratozoospermia alone was not measured fully. Future studies warrant investigating this further using more chromosome pairs and individuals who display severe teratozoospermia but normal levels of sperm concentration and motility.
Conclusions
In conclusion we provide evidence that sperm disomy levels can vary considerably between samples from the same man, the reasons for this are unclear but one possible explanation is the involvement of extrinsic factors or lifestyle changes. Such differences provide hope for possible treatment regimes to improve disomy rates. The evidence of correlations between individual semen parameters and increased disomy of individual chromosome pairs, while statistically significant, warrants further investigation. Closer correlations of disomy rates in men with defects in only one of the three criteria used to measure semen quality will form the basis of our future investigations. In future studies it is also likely that we would include a second control group of normal, fertile donors not attending fertility clinics. Ethical considerations precluded this in this case. Through these studies, a closer understanding of the mechanistic basis of the relationship between chromosome segregation and infertility will be achieved.
Authors' contributions
HGT- performed the majority of FISH experiments, scoring of semen samples, collected data generated within this study and assisted in drafting the manuscript. SH- performed all semen assessments. MD and DC- performed FISH experiments and acted as independent scorers of semen samples. DW- performed statistical analysis. XPZ- provided patient samples with signed informed consent. DKG- conceived the study and participated in its design and drafted the manuscript. All authors have read and approved the final manuscript.
Acknowledgements
Signed written consent was obtained from all of the individuals who have participated within this study.
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| 15644139 | PMC544867 | CC BY | 2021-01-04 16:36:40 | no | Retrovirology. 2005 Jan 11; 2:1 | latin-1 | Retrovirology | 2,005 | 10.1186/1742-4690-2-1 | oa_comm |
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RetrovirologyRetrovirology1742-4690BioMed Central London 1742-4690-1-441560147410.1186/1742-4690-1-44ResearchHIV-1 nef suppression by virally encoded microRNA Omoto Shinya [email protected] Masafumi [email protected] Yutaka [email protected] Yuko [email protected] Harumi [email protected] Ebiamadon Andi [email protected] Nitin K [email protected] Yoichi R [email protected] Molecular Biology and Retroviral Genetics Group, Graduate School of Pharmaceutical Sciences, Nagoya City University, Nagoya 467-8603, Japan2 Division of Nutritional Sciences, Graduate School of Pharmaceutical Sciences, Nagoya City University, Nagoya 467-8603, Japan3 Department of Molecular Diagnostics, Fields of Pathology, Nagoya University Graduate School of Medicine, Nagoya 464-8550, Japan4 Department of Pathology, Fujita Health University School of Medicine, Toyoake, Aichi 470-1192, Japan5 Research and Scientific Developments Division, Molecular Bio/Sciences Limited, 124 MCC Road, Calabar, Cross River State, Nigeria6 Retroviral Genetics Division, Center for Virus Research, Westmead Millennium Institute, Westmead Hospital, Westmead NSW 2145, Sydney, Australia2004 15 12 2004 1 44 44 24 8 2004 15 12 2004 Copyright © 2004 Omoto et al; licensee BioMed Central Ltd.2004Omoto 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
MicroRNAs (miRNAs) are 21~25-nucleotides (nt) long and interact with mRNAs to trigger either translational repression or RNA cleavage through RNA interference (RNAi), depending on the degree of complementarity with the target mRNAs. Our recent study has shown that HIV-1 nef dsRNA from AIDS patients who are long-term non-progressors (LTNPs) inhibited the transcription of HIV-1.
Results
Here, we show the possibility that nef-derived miRNAs are produced in HIV-1 persistently infected cells. Furthermore, nef short hairpin RNA (shRNA) that corresponded to a predicted nef miRNA (~25 nt, miR-N367) can block HIV-1 Nef expression in vitro and the suppression by shRNA/miR-N367 would be related with low viremia in an LTNP (15-2-2). In the 15-2-2 model mice, the weight loss, which may be rendered by nef was also inhibited by shRNA/miR-N367 corresponding to suppression of nef expression in vivo.
Conclusions
These data suggest that nef/U3 miRNAs produced in HIV-1-infected cells may suppress both Nef function and HIV-1 virulence through the RNAi pathway.
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Background
The human immunodeficiency virus (HIV), which infect humans cause acquired immunodeficiency syndrome (AIDS), which has reached pandemic levels in some societies, especially those in Southern Africa and Southeast Asia [1]. Given the immensity of HIV pandemic, the development of a rather safe and cheap, effective therapeutics, has become the main focus [2]. Several strategies attempted to control the spread of AIDS have not shown major breakthrough and the vaccines have shown little promise as far as their efficacy is concerned. However, one approach used extensively in other diploid organisms, which now has tremendous potential to encourage antiviral defense against HIV appears to be double stranded RNA-dependent post-transcriptional gene silencing or RNA interference (RNAi).
RNAi is a defense mechanism against aberrant transcripts that may be produced during viral infection and mobilization of transposons [3,4]. The RNAi pathway has been implicated in silencing transposons in the C. elegans germline [5,6], silencing stellate repeats in the Drosophila germline, and the response against invading viruses in plants [7]. Post-transcriptional regulation by RNAi is mediated by small non-coding RNAs (~25-nucleotides; nt). Small interfering RNAs (siRNAs) are short RNA duplexes that direct the degradation of homologous transcripts [8]. In contrast, the single stranded microRNAs (miRNAs) bind to 3' untranslated regions of mRNA with complementarity of 50 to 85% to give translational repression without target degradation [9]. The mature miRNA (~25-nt) is produced by processing of ~70-nt precursor stem-loop hairpin RNAs (Pre-miRNA) by Dicer [10,11]. At the moment several human diseases, including spinal muscular atrophy (Paushkin et al., 2002), fragile X mental retardation [13,14] and chronic lymphocytic leukemia [15] have been identified as illnesses in which miRNAs or their machinery might be implicated. However, up until now there has been no clear-cut scientific proof that establishes the exact correlation between miRNAs and human infectious diseases such as AIDS.
One of the human immunodeficiency virus type 1 (HIV-1) coding accessory genes, nef, is located at the 3' end of the viral genome and partially overlaps the 3'-long terminal repeat (LTR). The nef gene is uniquely conserved in HIV-1, HIV-2 and simian immunodeficiency virus (SIV) and is not essential but important for viral replication in vivo [16]. The nef gene is expressed during HIV infection and often accounts for up to 80% of HIV-1 specific RNA transcripts during the early stages of viral replication [2]. Our own investigations have shown that defective variants of nef dsRNA containing the 3'-LTR regions, obtained from long-term non-progressor (LTNP) AIDS patients, actually inhibited the transcription of HIV-1 [17]. Furthermore, cis-expression of mutated F12-HIV-1 nef inhibits replication of highly productive NL43-HIV-1 strain, which is not related to down-regulation of CD4 [18,19]. It has been demonstrated that F12 nef gene cloned from the provirus of naturally occurring HUT-78 T cells infected with the supernatant of the peripheral blood mononuclear cells (PBMCs) of an HIV-seropositive non-producer patient, induces a block of viral replication [19]. Thus, it has been suggested that nef RNAs may be a cis-regulatory factor for HIV-1 replication [20].
In the current study, we have established the link between miRNAs and HIV infections by demonstrating that nef-derived miRNAs are produced in HIV-1-infected cells. The results presented here show that nef short hairpin RNAs (shRNAs) corresponding to the nef miRNAs efficiently block RNA stability or mRNA translation, perhaps an indication that HIV-1 regulates its own replication by using nef miRNAs.
Results and Discussion
Identification of a candidate of miRNAs in HIV-1-infected cells
Very recently, the Epstein-Barr virus (EBV)-encoded miRNAs were identified. Thus, during the preliminary stages of this study, our curiosity was fixed on the need to find out if indeed there was any relationship between nef miRNAs and HIV-1-infected cells. To achieve this purpose, we extracted total RNA from HIV-1 IIIB strain persistently infected MT-4 T cells and northern blot analysis was performed using eight probes against the nef coding region, as shown in Figure 1A. Analyses using several anti-sense probes, small RNA molecules approximately ~25-nt in size were detected as well as HIV-1 major transcripts, 9.1, 4.3 and 1.8 kb bands (Fig. 1B). Similar results were obtained with total RNA from HIV-1 SF2 strain infected MT-4 T cells (data not shown). RNA samples treated with a mixture of the single stranded specific RNases A and T1 also generated ~25-nt RNAs that hybridized in northern blots with the sense probes against the same nef region. However HIV-1 major transcripts were not detected (data not shown), indicating that the structure of the small RNA molecules could be double-stranded RNAs (miRNAs). Some variability was observed when the quantity of the miRNAs was compared with the total of the major transcripts. A maximum of 3.2% of miRNAs was detected by using #367 probe when compared with total HIV-1 transcripts, and the minimum of 0.3% was detected by using probe# 90 (Fig. 1B).
Figure 1 Detection of HIV-1 nef miRNAs and inhibition of Nef expression by nef RNAs. (A) Schematic representation of HIV-1 nef in its genome and synthetic DNA probes (#) used in this study. (B) Total RNAs were extracted from MT-4 T cells persistently infected with HIV-1 IIIB, separated on a 15% polyacrylamide-7 M urea gel, and subjected to northern blot analysis. The approximate sizes of the three classes of HIV-1 transcripts and small RNAs are indicated on the right. The loading control was rRNA stained with ethidium bromide. Relative expression (%) of nef small RNAs to the three classes of HIV-1 transcripts is at the bottom of figure. (C) Schematic representation of effector plasmids (E) H1 promoter-driven shRNA expression plasmids. Reporter (R) Nef-EGFP expression plasmid (pYM2.2) is also shown. (D) Inhibition by sinefs in pH1 plamids of Nef-EGFP expression. Either sinef, siluc or siegfp in each plasmid was transfected into Jurkat T cells in the presence of either pYM2.2 or control pEGFP-N1. At 36 h after transfection EGFP-positive cells were counted by flow cytometry. Data represent the relative activity of EGFP-positive cells where the percentage of positive cells in the sample transfected with pYM2.2 or pEGFP-N1 plus pcDNA3.1 or si(-) in pH1 plasmid was scored as 100%. Data are averages of three independent experiments + SD. Bars, SD. (E) Immunoblot analysis showing inhibition of YM2.2 expression by different nef shRNAs. Jurakat T cells were transfected with pYM2.2 and pH1/sinefs or siluc plasmid, cellular lysates were prepared 48 h after transfection, and immunoblotted with rabbit serum against Nef (upper panel) and anti-β-actin antibody (lower panel). The β-actin expression shows equal loading of all samples.
To randomly clone the nef miRNA, ~25-nt RNAs were gel purified, cloned and sequenced. The sequences from the nef miRNA clones were 5'-acugaccuuuggauggugcuucaa-3' or similar ones, corresponded to the nucleotides approximately 420 to 443 conserved region of nef (miR-N367). The most notable feature of this analysis is that it has proven beyond reasonable doubts that nef-derived miRNAs are produced in HIV-1 infected cells.
Inhibition of Nef by plasmids-encoding siRNA/miRNA
To examine inhibition of nef expression by the nef miRNA, we constructed eight shRNAs homologous to the native miRNA or probes used in Figure 1A[21]. Although it has been reported that three to four mutations in the sense strand derived from miRNA could have the potential to control unmutated 21-nt stem loop [22], we investigated whether the native shRNA-expressing plasmid can effectively reduce nef gene expression or not (Fig. 1C and 1D). We used egfp or luc gene (pH1/siegfp or luc) as a positive or negative control. All the shRNA-expressing plasmids including the controls were co-transfected into Jurkat T cells with pYM2.2 and cell fluorescence resulting from the expression of EGFP reporter gene was quantified by flow cytometry. The sinef176, 190, 367/miR-N367 and control siegfp all showed efficient reduction, but the sinef 007, 084, 299, 468 and 580 constructs gave only modest reductions, and no suppression was observed with si(-) and luc (Fig. 1D and Table 1). Immunoblot analysis using anti-Nef rabbit serum also confirmed the inhibition of Nef-EGFP expression by sinef 176, 190 and 367/miR-N367 (Fig. 1E).
Table 1 Relation between AIDS clinical courses and nef dsRNA or siRNA/miRNA in suppression of nef gene expression
Nef inhibitor Nef expressed by: Target region Clinical courses
Nef-EGFP HIV-1 IIIB PFV/nef
dsRNA* Human
SF2 ++ † ++ ND‡ Full-length (including miR-N367) ND
1-3-3 ++ - ND U3 deleted Rapid progressor, Died within 3 years
4-2-1 ND + ND U3 region (including miR-N367) Rapid progressor, Died within 3 years
15-2-2 ++ +++ ND U3 region (including miR-N367) Non progressor with low plasma viremia
16-1-1 ND + ND U3 deleted Non progressor with low plasma viremia
jw95-1 ND + ND U3 region (including miR-N367) Non progressor with undetectable viremia
siRNA 15-2-2 model mouse
dsnef + + + Full-length ND
sinef007 + ND ND Upstream of U3 region ND
sinef084 + ND ND Upstream of U3 region ND
sinef176 +++ +++ ++ Upstream of U3 region§ ND
sinef190 +++ +++ +++ Upstream of U3 region ND
sinef299 + ND ND U3 region (aa 83–135) ND
sinef367/miR-N367 ++ +++ ++ U3 region No weight loss
sinef468 + ND ND U3 region ND
sinef580 + ND ND U3 region ND
*dsRNA to LTNPs' nef has been described (Yamamoto et al., 2002).
† -, negative; +, 0 to 50 % inhibition; ++, 50 to 75% inhibition; +++, 75 to 100 % inhibition.
‡ ND, not done.
Inhibition of Nef expression by STYLE vector-encoding nef siRNA/miR-N367
To assess the effects of nef miR-N367 in vivo, we constructed a prototype foamy virus (PFV)-based live vector. The PFV vector expressed HIV-1 SF2 nef gene as a reporter and the STYLE vector expressed shRNA as effectors. The full-length nef gene was inserted into the bel-2 portion of a PFV clone (22) in frame to obtain pPFV/nef (Fig. 2A). The pPFV/nef was transfected into BHK cells and treated with the histone deacetylase inhibitor, trichostatin A (TA) [23]. The viral supernatant, which contained approximately 5 × 106 infectious units (IFU), was collected at 72 h after transfection. For preparation of the STYLE vectors to deliver the shRNAs, the env gene portion of pSKY3.0 was replaced with the shRNA expression cassette under the control of the H1 promoter. The pSTYLE was produced (Fig. 2A), and transfected into the FFV envelope-expressing packaging cells, CRFK sugi clone # 6, in the presence of TA. The transfected CRFKsugi clone #6 had 99% FFV Env positive cells when analyzed by flow cytometry. The viral supernatant with a titer of ca. 1 × 105 IFU was collected at 72 h post-transfection.
Figure 2 Inhibition of nef expression in human T cells by nef siRNAs. (A) Schematic representation of shRNA-expressing STYLE vector (E) and HIV-1 SF2 nef gene expressing pPFV/nef vector (R). The helper plasmid, pFFenv expresses FFV envelope protein under the control of CMV promoter. (B) Detection of expression of nef mRNA and integration of vectors. STYLE, SKY3.0, PFV/nef, PFV or mock was used to infect Jurkat T cells and the infected cells were cultured for 2 weeks. After 2 weeks, gag and nef mRNA expression was measured by RT-PCR. Genomic DNA of LTR, gag and nef of STYLE or PFV/nef were also detected by PCR. β-actin was used as a control. (C) Inhibition of Nef-EGFP expression by nef siRNA-expressing STYLE in Jurkat T cells. The pYM2.2 was transfected into each of the STYLE or mock-infected Jurkat T cells and EGFP-expressing cells were counted by flow cytometry at 48 h after transfection. Data represent the relative activity of EGFP-positive cells, where the percentage of positive cells in the sample transfected with pYM2.2 upon the STYLE-si(-) infected cells was scored as 100%. (D) Inhibition of HIV-1 transcription and replication by nef STYLE-367. HIV-1 IIIB persistently infected MT-4 T cells were transfected with the pLTRSF2 reporter and β-gal expressing control pCMVβ plasmids at 72 h after infection with STYLE. At 48 h post-transfection, Luc activity was measured and normalized as Luc values (Luc/β-gal). Absolute levels of Luc activity in the samples of pLTRSF2 plus SRYLE-si(-) were 16,311 + 1,253 or 783 + 87 light units for STYLE-367/miR-N367 transfectants. Data represent the relative Luc activities where the percentage of positive cells in the samples infected with the STYLE-si(-) was scored as 100%. After 48 h, p24 antigen was also measured in the cell culture supernatant of STYLE-infected Jurkat T cells. Data are averages of three independent experiments + SD. Bars, SD. (E) Inhibition of nef expression by nef siRNA in Jurkat T cells. Cells were infected with PFV/nef 48 h after infection with the STYLE and then subjected to semi-quantitative RT-PCR analysis. Data represent the relative expression of mRNA, where the percentage of positive cells in the sample of mock-infected cells (E: Mock) relative to the PFV/nef (R: PFV/nef) infected cells was scored as 100%. Data averages were derived from three independent experiments + SD. Bars, SD.
The expression of viral mRNAs and integrated DNAs from either the PFV/nef or STYLE vectors was confirmed by infection of Jurkat T cells. The mRNAs and genomic DNA were extracted from the infected cells at 2 weeks post-infection. The PFV/nef-expressed gag and nef mRNAs and the STYLE-expressed gag mRNA were detected after amplification of these regions using reverse transcription (RT)-PCR. The integration of the DNAs into the genome of Jurkat T cells was also confirmed by PCR of the LTR, gag and/or nef regions (Fig. 2B). The control SKY3.0 and PFV-infected cells were both negative for nef mRNA and integrated DNA (Fig. 2B). The integrated DNA was also detected by southern blot analysis with genomic DNA of either PFV/nef or STYLE-infected cells (data not shown). Expression of shRNAs (~22-nt) was also confirmed in STYLE-infected cells by northern blot analysis (data not shown). Expression of Nef protein in PFV/nef-infected cells was also detected with specific rabbit anti-Nef serum in immunoblots (data not shown).
To evaluate whether the STYLE encoding siRNA could inhibit the expression of the nef gene in cultured human T cells, pYM2.2 was transfected into each of the STYLE-infected Jurkat T cells (m.o.i. = ca. 0.1). The most efficient sinef176, 190 and 367/miR-N367 vectors for reduction in nef expression (Fig. 1D and 1E) were selected for this experiment. The EGFP-positive cells were counted by flow cytometry at 48 h after transfection. Expression of Nef-EGFP fusion protein was reduced drastically following treatment with either the STYLE-176 (74 + 3.2) or 190 (51 + 4.2) and also reduced with 367/miR-N367 (32 + 2.3%). Reduction was insignificant with either the STYLE-si(-) (0 + 0.7) or STYLE-luc (7 + 0.9%) controls (Fig. 2C).
The in vitro inhibitory effects of STYLE encoding nef siRNA on HIV-1-infected cells were evaluated in Luc assays and using MT-4 T cells persistently infected with HIV-1 IIIB. Cultivation of the STYLE infected cells for 72 h followed by transfection with the pLTRSF2 and culture for another 48 h showed that STYLE-176, 190 and 367/miR-N367 all significantly (p < 0.005) suppressed Luc activity when compared to controls (Fig. 2D). HIV-1 p24 Gag was also significantly inhibited in the culture supernatant by infection with STYLE-176, 190 and 367/miR-N367 when compared to controls (p < 0.001) (Fig. 2D). These data suggested that shRNA/miR-N367 could inhibit HIV-1 transcription and replication in intact HIV-1-infected human T cells.
Jurkat T cells that had been transduced with nef shRNA for 48 h were infected with the PFV/nef. Semi-quantitative RT-PCR analysis revealed that while treatment with STYLE-190 dramatically reduced the expression of both nef and gag mRNAs of the PFV/nef, the expression of nef mRNA was also drastically suppressed by STYLE-176 and 367/miR-N367 (Fig. 2E). However the STYLE-si(-) and luc controls showed ~10% suppression of nef and ~20% suppression of gag mRNAs (Fig. 2E), which was probably a result of interference following super-infection. Nonetheless, both nef transcription and PFV/nef replication were substantially inhibited by STYLE-176, 190 and 367/miR-N367.
Inhibition of Nef expression by siRNA/miR-N367 in mice
Since different host gene products are required for siRNA-mediated RNAi and miRNA-mediated translational repression with let-7 and lin-4 in C. elegans, the two RNAs may not have the same functions in vivo [24]. To test this point, we investigated the efficacy of miR-N367 using STYLE-367 in mammalian tissues. The study mice were group 1 = PFV/nef-infected (n = 6); group 2 = PFV/nef and control STYLE-luc infected (n = 6); group 3 = PFV/nef and STYLE-367-infected (n = 8); and group 4 = STYLE-367-infected (n = 6). Identical study groups were used for both Balb/c and C3H/Hej mouse strains. Nef protein expressing lymphocytes were quantified by histochemical analysis using F3 Nef monoclonal antibody (mAb) or anti-Nef rabbit serum 2 days after PFV/nef infection. Nef protein was detected by immunofluoresence assay in the subcapsular area of the spleens of groups 1 or 2 Balb/c mice, but not groups 3 or 4 (Fig. 3A and Table 2). No positive cell staining was observed using normal rabbit serum as a primary antibody (Fig. 3A). To test the expression of nef, nested RT-PCR was also done on day 2 to evaluate the degree of nef mRNA expression in the spleen, liver, adipose tissues and hematopoietic cells in groups 1–4. The nef mRNA was significantly expressed in liver and hematopoietic cells of Balb/c mice in groups 1 and 2, but not in the group 3 animals that were STYLE-367 infected (Table 2). Tissues from group 4 did not show any nef bands after RT-PCR (data not shown).
Figure 3 In vivo effects of miR-N367. (A) Distribution of Nef positive staining cells in the subcapsular area of groups 2 or 3 mouse spleens at 2 days after infection with PFV/nef. Anti-Nef rabbit serum or normal rabbit serum was used as a primary antibody. (B) Immunofluorescence for 305 mAb positive staining cells in the subcapsular area of groups 2 or 3 mouse spleens at 2 days after infection with PFV/nef and immunoperoxidase staining by 305 mAb in cells of interfollicular area of HIV-1 uninfected human spleen and tonsillar follicle. (C) Short term body weights of PFV/nef-infected Balb/c mice. The body weights of the PFV/nef-infected mice (group 1, n = 6, solid circle), the PFV/nef-infected followed by the STYLE-luc-infected mice (group 2, n = 6, solid triangle), the PFV/nef-infected followed by the STYLE-367-infected mice (group 3, n = 8, open triangle) and the STYLE-367-infected mice (group 4, n = 6, open circles) were measured from days 0 to 5. (D) Long term body weights of PFV/nef-infected C3H/Hej mice. Treatment of each group and numbers of mice were same as (C). Bars, SD. *; p < 0.05, **; p < 0.01 (relative to group 3). (E) Immunoperoxidase staining by 305 mAb and anti-Nef rabbit serum in cells of mouse or human adipose tissue. Arrows show positively stained areas. Magnification, X 20 (A and B); X 20 and X 200 (E).
Table 2 Histochemical detection and RT-PCR amplification of Nef from human and mouse tissues
Tissues Histochemistry* RT-PCR†
F3 Anti-Nef rabbit serum 305 nef gag PPARγ
Human (HIV-1 uninfected)
Spleen -§ - + - -‡ ND
Tonsillar follicle - - + - - ND
Liver - - + - - ND
Adipose tissue (Salivary gland) - - + - - +
Bone marrow - - + - - ND
Bronchi - - + - - ND
Thyroid gland - - + - - ND
Heart muscle - - + - - ND
Prostate gland - - + - - ND
Testis - - + - - ND
Colon mucosa - - + - - ND
Lung - - + - - ND
Adrenal gland - - + - - ND
Brain (Cerebrum cortex) - - + - - ND
Mouse Group 1 and 2
Spleen + + + + + ND
Liver ND ND + + + ND
Hematopoietic cells ND ND + + + ND
Adipose tissue (Intestine) + + + - - -
Mouse Group 3
Spleen - - + ± ± ND
Liver ND ND + - - ND
Hematopoietic cells ND ND + - - ND
Adipose tissue (Intestine) - - + - - +
*Histological analysis was performed with human or each group of mouse tissues by using F3 anti-Nef mAb, anti-Nef rabbit serum or 305 mAb as a primary antibody. For secondary antibody, FITC or peroxidase-conjugated antibody was used.
†HIV-1 nef, PFV gag (‡HIV-1 gag for human tissues), and PPARγ mRNA expression were detected by RT-PCR with mRNA from human or each group of mouse tissues.
§+, positive; -, negative; ND, not done.
Because extracellular Nef is internalized into human and mouse lymphocytes and macrophages [25-27], we examined putative Nef receptor molecule (Ner) expression with 305 mAb [27] in both mouse and human tissues by histochemical analysis. In mice, 305 mAb positive lymphocytes were detected in the subcapsular area of the spleens by immunofluoresence assay (Fig. 3B) and liver and hematopoietic cells (Table 2) in groups 1, 2 and 3, indicating that detection of antigen by 305 mAb was not altered by Nef expression. In HIV-1 uninfected humans, the 305 mAb positive cells were detected by immunoperoxidase staining in spleen (red pulp), tonsillar follicle (germinal center), liver (Kupffer cells), salivary gland (germinal center and adipose cells), bronchi (smooth muscle cells), lung (stroma cells), thyroid gland (colloid), heart muscle (smooth muscle cells), prostate gland (smooth muscle cells), colon mucosa (intestinal absorptive and muscle cells), testis (basement membrane of tubuli seminiferi), adrenal gland (adipose cells), and brain (cerebrum cortex and cortical cells) (Fig. 3B and Table 2).
Since Nef suppressed PPARγ expression and reduced fatty acid levels in vitro [29-32], we monitored the expression of PPARγ mRNA and body weights of mice. Significant PPARγ mRNA expression in intestinal adipose tissue of group 3, but not group 1 and 2, was detected on day 2 (Table 2). All Balb/c mice in group 1 showed sedation and a drastic loss of weight from days 1 to 3 (day 1, p = 0.003; day 2, p = 0.021; day 3, p = 0.032 relative to mice in group 3) (Fig. 3C). Similar results were obtained in group 2 (Fig. 3C). However, group 3 mice infected with STYLE-367 did not appear to be sedated and had no drastic loss of weight (Fig. 3C). The group 4 animals, which were not infected with the PFV/nef but treated with STYLE-367, had no changes in either behavior or weight (Fig. 3C). In longitudinal examinations done during the post-infection period, the animals in groups 1 and 2 had recovered the lost weight (Fig. 3D). Similar results were obtained in group 2 from day 1 to 5 (day 1, p = 0.037; day 3, p = 0.044; day 5, p = 0.048 relative to mice in group 3) in the C3H/Hej mouse groups (Fig. 3D). To assess the above in vivo results, expression of nef mRNA was examined in adipose tissues (Table 2). As shown in Table 2, although mRNAs of nef and gag were not detected in mouse adipose tissues, 305 mAb and anti-Nef rabbit serum positive staining cells were detected in mouse group 1 and 2 adipose tissues (Fig. 3E and Table 2). Considering that the 305 mAb positive staining adipocytes appeared in mouse as well as human tissues (Fig. 3E and Table 2), these data suggest that the interaction between 305 and soluble Nef detected in adipose tissues may be responsible for the weight loss observed in mice.
In this study, whereas siRNA has been reported to inhibit hepatitis B virus replication in vivo (33–34), our results show that nef-derived miRNAs are produced in HIV-1 infected cells, and support the possibility that miRNA and siRNA may be functionally identical, at least in a retrotransposon such as HIV. Recent studies have revealed that miRNAs and siRNAs could block mRNA expression by similar mechanisms [9] and that siRNAs could function as miRNAs [35] and EBV-encoded miRNAs were found [36]. Our results reported here are consistent with these previous observations and are suggestive of the fact that nef miR-N367 could regulate nef expression even in vivo. In our unpublished data, HIV-1 LTR promoter activity was inhibited by miR-N367 (nt number 379 to 449 of SF2 nef, 71-nt) expression, of which activity was dependent on negative responsive element (NRE) of U3 region (our unpublished data). Although no mismatch shRNA against region #367 was active, the miR-N367 from HIV-1 genome may have some mismatches and effectively inhibit HIV-1 transcription. Further the effects of siRNAs of Tc1, in particular those to the terminal inverted repeats derived from read-through transcription of entire transposable elements, were presented for silencing transposase gene expression by RNAi machinery in germ lines of C. elegans, [37]. Taken together, it could be implied from these and our other results that miRNAs produced in HIV-1-infected cells can efficiently block not only Nef function but also HIV-1 replication through RNAi, which renders persistently low pathogenic infection latent as observed in an LTNP of 15-2-2 (see Table 1). It is equally important that although the weight loss reported here occurred only temporarily in vivo, however the inserted nef gene in the foamy retrotransposon may represent miRNAs which could inhibit nef mRNA expression by presumably an identical mechanism to that observed of siRNAs. Thus, RNAi might serve as a new sequence-specific therapeutic arsenal in AIDS prevention and possibly treatment.
Overall, our results indicate that nef shRNA transduced into T cell line inhibited HIV-1 transcription. Further, nef miRNAs could be produced from infected T cells and can block the trans-activity of Nef as well as HIV-1 replication on its own via the cis-action of nef. These functions of nef via RNAi pathways may allow persistently low pathogenic or latent infection as observed in HIV-infected non-progressors. Cumulatively, these data suggest that Nef may be involved in both viral replication and the disease progression, the findings, which may facilitate new strategies for HIV control in vivo.
Materials and Methods
Patient details
Patient selection is showed in Table 1. These SF2 (HIV-1 subtype B prototype) was included as a control nef sequence, because of the inclusion of viruses, which were also subtype B. The SF2 contained full-length nef reading frame as indicated in Table 1. Patients 1-3-3, 4-2-1 (Table 1) are rapid progressors infected with HIV-1. These patients were infected in 1984–1985 and died within 3 years of primary infection with >1 × 106 viral copies and CD4+ T cell count of 75 and 110/ml blood. Patients 15-2-2 and 16-1-1 (Table 1) are slow progressors, who were infected in 1984 and have survived HIV-1 infection with high and stable CD4+ T cell counts (690 and 760/ml blood) with low (<5000 copies) plasma viremia. All these patients acquired virus through homosexual sex. JW95-1 (Table 1) is a boy who was infected from his mother via breast feeding. The child was infected in 1983 and has survived disease free with high CD4+ T cell count (890/ml blood) with undetectable viremia. Human samples were obtained from a donor after informed consent.
Cells and viruses
HeLa and BHK cells were grown in Dulbecco's modified Eagle Medium (DMEM) (GIBCO, Grand Island, NY) supplemented with 10% heat-inactivated fetal bovine serum (FBS) and antibiotics. CRFK cells were grown in Iscove's Modified Dulbecco's Medium (IMDM) (GIBCO) with 10% FBS and antibiotics. Jurkat T cells and MT-4 T cells persistently infected with HIV-1 IIIB strain were cultured in RPMI-1640 medium (GIBCO) supplemented with 10% FBS and antibiotics. The packaging cells (CRFKsugi) were made by transfecting CRFK cells with 10 μg of the pFFenv with Lipofectin Reagent (Invitrogen) and selecting transformants after culture for 14 days with 25 μg/ml of hygromycine B (Invitrogen). After 14 days, FFV Env protein expression was measured by flow cytometry and immunoblot analyses with FFV-infected cat B serum [34]. The pPFV/nef (10 μg) was transfected into BHK cells and pSTYLE/si (10 μg) was transfected into CRFKsugi cells with Lipofectin Reagent. The transfected cells were cultured for 72 hr, and the viral supernatant was collected and filtered through a 0.45 μm pore size Millex-GP filter (Millipore, Bedford, MA). Vector stocks were stored at -70°C prior to use. Viral titers were determined as described previously [21]. Cells were infected with PFV/nef and/or SKY/si at an m.o.i. of ca. 0.1 in the presence of 4 μg/ml of polybrene and infected cells were cultured at a density of 1 × 106 cells per ml for 3 days.
The details of plasmid constructs and the primer sequences used in cloning strategies are shown in supplementary file (see Additional file: 1).
Flow cytometric analysis
Flow cytometry was performed with a FACS Calibur (Becton Dickson, San Jose, CA) as described previously [17].
Luc assay and Immunoblotting
Firefly Luc assay was performed using the Luciferase Assay System (Promega) as described previously [17]. Immunoblotting was performed essentially as described previously by Otake et al. [28].
P24 ELISA
The concentration of p24 supernatant was determined by an antigen capture assay (Beckman Coulter, Fullerton, CA) according to the manufacturer's instructions.
Confocal laser microscopy analysis
Confocal laser microscopy analysis was performed as described previously [28].
Northern blot analysis
Total RNAs were extracted from HIV-1 IIIB or SF2 persistently infected or uninfected MT-4 T cells using TRIzol reagent (Invitrogen). Approximately 40 μg of total RNA was treated with RNase A and T1 (Sigma, St. Louis, MO) as described previously [17], subjected to electrophoresis on a 15% polyacrylamide-7 M urea gel and electroblotted to HybondN+ (Pharmacia, Uppsala, Sweden) for 4 hr at 400 mA. RNAs were immobilized by UV crosslinking and baking for 1 hr at 80°C. Hybridization was done with an ECL direct Kit (Pharmacia). Synthetic DNA probes were labeled with horseradish peroxidase. The sequence for synthetic sense DNA probes for northern blot analysis are as follows: #007 (5'-gcgtcgacggcaagtggtcaaaacgta-3'); #084 (5'-gcgtcgacgccagcagcagatggggtg-3'); #176 (5'-gcgtcgacgtgcctggctagaagcaca-3'); #190 (5'-gcgtcgacgcacaagaggaggaga-3'); #299 (5'-gcgtcgacgactggaagggctaatttg-3'); #367 (5'-gctcgacggctacttccctgattggc-3'); #468 (5'-gcgtcgacggtagaagaggccaatgaa-3'); #580 (5'-gcgtcgacgcatttcatcacatggccc-3'). RNAs were cloned by 5'RACE System (Invitrogen, CA., USA) with a slight modification in that primers were used that were the same as the synthetic DNA probes as described above that are abbreviated as #primer. In brief, gel purified small RNAs were annealed with #primer and first strand cDNA was synthesized with SuperScript II RT (Invitrogen, CA., USA). Afrer RNase H and T1 treatment, a homopolymeric tail was added to the 3'-end of the cDNA using terminal deoxynucleotidyl transferase and dCTP. After ethanol precipitation, PCR amplification was done with abridged anchor primer and #primer. Then the PCR products were obtained using abridged universal amplification primer and #primer. The PCR fragments were digested with SalI and cloned into SalI site of pBluescript SK(-), followed by sequence analysis. The secondary structures of RNAs were predicted by GENETYX-MAC program (Software Development Co. Ltd, Tokyo, Japan).
Semi-quantitative RT-PCR analysis
Semi-quantitative RT-PCR analysis was performed using the ThermoScript RT-PCR System (Invitrogen, CA., USA) according to the manufacturer's protocol with the following primers: III (5'-atcatgggccaaagagaattc-3') and IV (5'-aaatttcactcaatcgagcc-3') for FFV LTR, VI (5'-aggacctgaaaggcatg-3') and VII (5'-ttgttgagatcgtcccg-3') for FFV gag, VIII (5'-tgtggtggaatgccactag-3') and IX (5'-attgtcatggaattttgta-3') for PFV LTR, XI (5'-tcttacagaccagtaacaa-3') and XII (5'-gtcaatcattacatctgca-3') for PFV gag, XIII (5'-aactactagtacccttcagg-3') and XIV (5'-aaaactcttgctttatggcc-3') for HIV-1 gag, XV (5'-atgggtggcaagtcaaaacg-3') and XVI (5'-tcagcagtctttgtagtactccg-3') for HIV-1 nef, XVII (5'-gttatgggtgaaactctgggagat-3') and XVIII (5'-atgttcctgaacataatcgtc-3') for PPARγ, XIX (5'-gacaacggctccggcatgtgcaaag-3') and XX (5'-ttcacggttggccttagggttcag-3') for β-actin, respectively. The nested PCR followed RT reaction was performed as described previously [34]. PCR products were quantified with the NIH image program. Relative mRNA expression was calculated as percentage expression using the following formula: integrating number of nef or gag bands/integrating number of β-actin X 100.
In vivo studies and tissue analyses
Balb/c and C3H/Hej mice were raised under specific pathogen-free (SPF) conditions. Mice were infected with 1 ml of 105 IFU of PFV/nef and STYLE-367 by intravenous (i.v.) injection. RT-PCR analyses were performed 2 days after infection. For histological analysis, cryostat sections were prepared from both human and mouse tissues. The fixed sections were rinsed with PBS and incubated with 5% BSA for at least 1 hr to inhibit nonspecific binding of antibodies. Sections were incubated overnight at 4°C with anti-Nef rabbit serum, F3 or 305 mAb, and incubated with peroxidase or FITC conjugated secondary antibodies. The washed sections were incubated in 0.03% 3,3'diaminobenzidine (Sigma) solution in 0.05 M Tris buffer with 0.01% H2O2 for development of peroxidase activity. After counterstaining with hematoxylin or methylgreen, the sections were dehydrated and mounted.
Statistical methods
Data were analysed using a one-way ANOVA analysis with a post-hoc Fisher's test. P values of 0.05 or more were determined for that of cut off.
List of abbreviations used
HIV-1 human immunodeficiency virus type 1
miRNA microRNA
nt, nucleotides
LTNP long-term non-progressors
shRNA short hairpin RNA
AIDS acquired immunodeficiency syndrome
RNAi RNA interference
siRNA small interfering RNA
LTR long terminal repeat
SIV simian immunodeficiency virus
PBMCs peripheral blood mononuclear cells;
EBV Epstein-Barr virus;
EGFP enhanced green fluorescence protein
PFV prototype foamy virus
TA trichostatin
A IFU infectious units
FFV feline foamy virus
RT reverse transcription
m.o.i., multiplicity of infectionm
Ab monoclonal antibody;
Ner Nef receptor molecule;
NRE negative responsive element.
Competing interests
The authors declare that they have no competing interests.
Authors' contributions
S.O. carried out northern analyses, immunoblot analyses, RNAi assays and was involved in the construction of plasmids. M.I. and Y.T. participated in in vivo studies and tissue analyses. Y.I. and H.O. participated in data validation and overall experimental design. E.A.B. and N.K.S. carried out the clinical, sequencing, and virological studies and the writing of the manuscript. Y.R.F. participated in the design of the study and coordinated it. All authors read and approved the final manuscript.
Supplementary Material
Additional file 1
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Acknowledgements
We thank S. Hatama, T. Yamamoto, R. Shimizu, M. Sugiyama, Y. Mitsuki and Y. Yasui for excellent technical assistance; T. Kawamura, N. Okada and H. Okada for financial supports; K. Otake for technical support of flow cytometric analysis; Y. Murase and N. Takeo, for primary experiments; M. Kameoka for gift of Gal4 plasmid; and K. Imakawa for supplement of technical advices of Luc assay.
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| 15601474 | PMC544868 | CC BY | 2021-01-04 16:36:37 | no | Retrovirology. 2004 Dec 15; 1:44 | utf-8 | Retrovirology | 2,004 | 10.1186/1742-4690-1-44 | oa_comm |
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J Negat Results BiomedJournal of Negative Results in Biomedicine1477-5751BioMed Central London 1477-5751-3-71561324710.1186/1477-5751-3-7ResearchDespite WT1 binding sites in the promoter region of human and mouse nucleoporin glycoprotein 210, WT1 does not influence expression of GP210 Olsson Magnus [email protected] Milton A [email protected] Jacqueline [email protected] Jonathan D [email protected] Peter [email protected] Department of Cell and Molecular Biology, Section for Cell and Developmental Biology, Lund University, Sweden2 Mount Sinai School of Medicine, 1425 Madison Avenue, New York, NY 10029, USA2004 21 12 2004 3 7 7 5 7 2004 21 12 2004 Copyright © 2004 Olsson et al; licensee BioMed Central Ltd.2004Olsson 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
Glycoprotein 210 (GP210) is a transmembrane component of the nuclear pore complex of metazoans, with a short carboxyterminus protruding towards the cytoplasm. Its function is unknown, but it is considered to be a major structural component of metazoan nuclear pores. Yet, our previous findings showed pronounced differences in expression levels in embryonic mouse tissues and cell lines. In order to identify factors regulating GP210, the genomic organization of human GP210 was analyzed in silico.
Results
The human gene was mapped to chromosome 3 and consists of 40 exons spread over 102 kb. The deduced 1887 amino acid showed a high degree of alignment homology to previously reported orthologues. Experimentally we defined two transcription initiation sites, 18 and 29 bp upstream of the ATG start codon. The promoter region is characterized by a CpG island and several consensus binding motifs for gene regulatory transcription factors, including clustered sites associated with Sp1 and the Wilms' tumor suppressor gene zinc finger protein (WT1). In addition, distal to the translation start we found a (GT)n repetitive sequence, an element known for its ability to bind WT1. Homologies for these motifs could be identified in the corresponding mouse genomic region. However, experimental tetracycline dependent induction of WT1 in SAOS osteosarcoma cells did not influence GP210 transcription.
Conclusion
Although mouse GP210 was identified as an early response gene during induced metanephric kidney development, and WT1 binding sites were identified in the promoter region of the human GP210 gene, experimental modulation of WT1 expression did not influence expression of GP210. Therefore, WT1 is probably not regulating GP210 expression. Instead, we suggest that the identified Sp binding sites are involved.
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Introduction
Nuclear pore complexes (NPCs) provide the only known gateway for transport of RNAs to the cytoplasm and bidirectional transport of proteins between the nucleus and the cytoplasm. The NPC in vertebrates has an estimated mass of approximately 125 Mda. Structural studies suggest an octagonal rotational symmetry framework, from which 50–100-nm long fibrils extend into the nucleoplasm and cytoplasm. A comprehensive inventory of all NPC constituents has been made for yeast [1] and metazoans [2]. A polypeptide profile from purified rat liver NPCs revealed ~50 putative nucleoporins [3].
In the list of metazoan nucleoporins, there are only two integral membrane proteins, gp210 [4-6] and POM121 [7,8]. Both have been localized to the NPC structure, each with a distinct membrane topology and amino acid motifs. Primarily due to their location, both proteins are presumed to anchor NPCs by the nuclear envelope and to assemble nucleoporins postmitotically. No binding partners have so far been identified for either of these proteins. The 121-kDa pore membrane protein POM121 [7,8] is located in the pore membrane domain of the NPC with a short (29 residues) N-terminal tail protruding into the lumen of the nuclear envelope, with the C-terminus facing the cytoplasm [8]. POM121 contains a C-terminal tandem sequence repeat of a core XFXFG motif interrupted by hydrophilic spacers. These motifs typical for nucleoporins and have been shown to interact with components of the soluble transport machinery [3,9].
In contrast to POM121, gp210 has an inverted topology with its main bulk residing in the lumen of the NE and only a short 58 residue C-terminal portion facing the NPC [5,6]. The amino acid-sequence of gp210 lacks pentapeptide repeats indicating no direct interaction with the mobile receptors directing nucleocytoplasmic transport [5,10]. A 23-amino-acid hydrophobic peptide residing in the luminal part of gp210 has been predicted to be involved in formation of new pores acting as a nuclear membrane fusion agent [5,11]. It has also been experimentally shown that the C-terminus of gp210 is involved in nuclear pore dilation [11], even though this is not a conserved sequence in different species [12]. Remarkably, it has also been shown that gp210 is essential for viability of human HeLa cells and C. Elegans [13]. A fraction of the cellular pool of gp210 can form dimers that may constitute a lumenal submembranous protein skeleton [14].
The primary sequence of gp210 is known for rat [5] and mouse [10]. Interestingly, whereas several nucleoporins found in vertebrates have homologues in the completed yeast genome, no such similarities have so far been detected for POM121 or gp210. Possibly, this could be related to the fact that the yeast nuclear membrane does not break down during cell division, and assembly regulators are not needed. In a comprehensive analysis of a highly enriched NPC fraction, presumably containing all yeast NPC proteins [1], only three transmembrane nucleoporins were detected, but these have no resemblance with gp210 or POM121. Thus, if the role of POM121 or gp210 in metazoans is to anchor the NPC, different proteins or mechanisms should be involved in the anchorage of yeast NPCs. Mouse gp210 was initially identified as an early response gene to induction of metanephric kidney development and data from other embryonic tissues confirmed the differential distribution of its mRNA [10] and protein [15]. This suggested a novel cell-type specific regulation of gp210. It was thus of interest to characterize the promoter region of the human GP210 gene.
In the current study we present the genomic structure of the human integral membrane glycoprotein 210 gene (GP210), the open reading frame sequence and a promoter region analysis. This was done in silico by taking advantage of the available human genomic sequence. Transcription start sites were determined experimentally by RNA ligase mediated rapid amplification of cDNA ends. Computer-assisted searches of the promoter sequence indicated putative consensus binding sites for transcription factors involved in tissue specific gene regulation. We also identified of shared putative cis-acting elements in the human promoter and its mouse counterpart. Several putative Wilms tumor suppressor binding 1 sites were found. Nevertheless, experimental overexpression of WT1 in SAOS osteosarcoma cells did not influence GP210 mRNA expression.
Results
Organization of the human GP210 gene
We initially assumed that mouse gpP210 was a member of a yet undiscovered large family of tissue-specific nuclear pore membrane proteins, and initially named it POM210 [10] to emphasize the similar subcellular distribution with POM121 [7]. Since current data suggest a surprisingly low amount of pore membrane proteins both in vertebrates and yeast, renaming is unnecessary. A BLAST homology search was performed using the mouse gp210 cDNA sequence (POM210, accession AF113751) against the working draft sequences of the human genome. This identified a completed contig-component (clone RP11-220D14, accession AC090942.1) localized to chromosome 3, in a region defined by three genomic markers (stSG4499, Cda14e10 and WI-9637). These markers were cytogenetically positioned to 3p25.1. By comparing to the mouse cDNA sequence and taking advantage of the exon/intron prediction program provided by the Genscan web server, 40 exons covering 102551 bp were defined (see Table 1, additional file 1). The exons ranged between 63 (exon 24) and 251 (exon 36) bp. All exon-intron junctions conformed to the consensus splice donor (GT) and acceptor (AG) sites, except for the splice donor sites of intron 7 (AT) and10 (GC). The introns sizes were between 74 (intron 38) and 20198 bp (intron 1). Introns were classified relative to codon interruption, as follows: phase 0 (no codon interruption), phase 1 (interruption between first and second base) and phase 2 (after second base). Exons were interrupted by introns of all phases. Most introns were of phase 0 (55%). A number of efforts where made to identify alternatively spliced products using PCR with primer pairs directed to high probability putative splice variant exons. However, no such variants could be found. Manually, we could identify one single polyadenylation signal (AATAAA) 1423 nucleotides downstream of the translation stop codon (Fig. 1). The exons formed an ORF of 5664 bp including the stop codon.
Figure 1 Genomic organization of GP210 and a model of the deduced amino acid chain of GP210. Exons (black boxes) and intron sizes are scaled individually. In silico predictions of a signal peptide, the transmembrane region and 12 putative N- linked glycosylation sites (N).
Primary structure of GP210
The deduced amino acid sequence of human GP210 contains 1887 residues, predicting a molecular mass of 205 198 Da and a pI of 6,41 of the non-processed protein. Alignment to the corresponding mouse and rat sequences displayed a high degree of homology (91,8% similarity and 88,9% identity compared to the primary structure of the rat protein). One insertion, an alanine at position 1858, makes the GP210 one residue longer compared to rat and mouse GP210. A signal peptide cleavage consensus site could be defined between residues 25 and 26 using the SignalP algorithm (Fig. 1). This signal sequence shows no resemblance to previously reported GP210 sequences. Hydrophobicity values along the deduced amino acid chain identified several putative membrane spanning regions. One of these (residues 1809 to 1828) corresponded to a domain mapped in the rat orthologe [5], leaving 59 residues facing the nuclear pore. Motifs found scanning the sequence through the ExPASy-Prosite database included 12 potential N-glycosylation sites (outlined in Fig. 1), and numerous putative consensus sites for various kinase related phosphorylations. Two cAMP- and cGMP dependent protein kinase sites (residues 1089, 1874), two tyrosine kinase sites (residues 227, 922), 30 protein kinase C phosphorylation sites and 26 casein kinase II phosphorylation sites were found. The sites associated to PKC and CK2 phosphorylation were evenly distributed throughout the sequence. Blast homology searches revealed a vast number of EST clones containing GP210 sequence and an 871 amino acid partial sequence of a hypothetical human protein KIAA09906 (accession Xp051621) identical to the C-terminal end of the full length translated GP210 open reading frame.
Identification of the transcription start
The sequence 1 kb upstream of the ATG start codon possesses neither TATA or CAAT boxes, but contains scattered initiator (Inr) elements (consensus Py Py C A N T/A Py Py) [16]. In order to determine the transcription start site (tss), we therefore performed RNA ligase mediated rapid amplification of cDNA ends [17]. By Northern blotting using a 935 bp cDNA probe (nt 4286–5220), a single transcript of 7,3 kb was seen in two different tumor cell lines (Fig 2). The size of the mRNA corresponds to the sum of the open reading frame and 3'-UTR including a potential poly A tail. Since expression was much more abundant in HeLa cells than in Wilms tumor cells, we used HeLa cell total RNA as a template. The nested PCR (see material and methods) gave a major specific cDNA product of approximately 80 nucleotides (fig. 3, lane 2). Sequence analysis of 10 independently ligated PCR products obtained using nested adapter- and gene specific primers (outlined in fig. 3) revealed two different amplified, GP210 specific fragments of similar length, indicating two alternative tss (Fig. 4). Out of ten clones sequenced, eight ended at position -29 and two at position -18 upstream ATG. In an identical analysis using poly-A+ RNA from a human fetal myoblast cell line G6, 7 clones ended at position -29 and 3 at position -18 upstream ATG. These findings argue for a major tss at -29. In addition, to confirm our findings we performed sequence homology searches in the human EST clone database at NCBI and elsewhere. The results revealed no reported cDNA sequences located upstream of the experimentally determined start site.
Figure 2 Northern blot analysis of HeLa cell and WCCS-1 mRNA. A 935 bp GP210 specific cDNA probe was hybridized to 10 μg of HeLa and WCCS-1. Loading and RNA quality was controlled by a methyleneblue staining of 18 and 28s rRNA.
Figure 3 Determination of 5 prime end of GP210 mRNA. Transcription start site were determined analyzing nested PCR products generated with gene (fig. 5) and adapter specific primers. Lane 1, Marker. Lane 2, Nested PCR product obtained using two primer pairs specific to sequences within the adapter and the first exon. Lane 3, Negative water control. Lane 4, Positive control using bacterial adapter ligated cDNA and specific primers. Lane 5, Marker.
Figure 4 Promoter analysis human GP210 and homology to its mouse counterpart. A 500 bp sequence upstream of translation start site was analyzed for the presence of consensus transcription factor binding sites. Upper lane is the human sequence, and lower sequence is mouse. Homology in the (GT)n repeat between human and mouse genomic sequences and its position relative to the translation start codon. The translation start site is in bold and numbered +1. Homology to the mouse sequence is marked in grey, Outer gene specific primer (ogsp) and inner gene specific primer (igsp) used for transcription start definition using RLM-RACE are underlined with arrows. The transcription initiation sites are positioned with empty arrows and the start nucleotide is in bold. Putative transcription binding motifs are underlined. Some elements for different transcription factors overlap. Only sense strand binding sites were considered. Legend: Sp1, Simian-virus-40-protein 1; EGR2, Early growth response gene 2; WT1, Wilms' tumor zink finger protein 1; PuF, c-myc purine-binding transcription factor.
Promoter sequence analysis
Analysis of the sequence surrounding the translation start site with the GRAIL program predicted a 1236 bp long CpG island, with a GC content of 75.3% starting 434 bp upstream of ATG, covering the first exon and extending into the first intron. We used a variety of promoter and transcription factor binding site algorithms to analyse the region upstream the start site for GP210, including TESS and Matinspector. By selecting for perfect matching and human consensus motifs a restricted number of putative transcription factor binding sites were found. Using these criteria, 22 motifs recognized by 14 different factors were defined on the sense strand, evenly distributed within 1000 bp proximal to the translation start codon (See Table 2, additional file 2). Seven Sp1 binding motifs were identified [18], five of them clustered in a region spanning 315 bases, starting eight nucleotides upstream of the major tss (Fig. 4). Four putative binding motifs for EGR1/WT1 (consensus GXGXGGGXG) were mapped within the same promoter region, starting at positions -47, -70, -76 and -283. Two of these matched completely with this consensus sequence, whereas two contained one mismatch in the 9bp-binding motif (positions -71 and -284 respectively). In addition, we found a WT1 binding site in the antisense strand (pos. -112). Only a few other upstream regulatory elements were defined within and proximate to the CpG rich region. We found one binding motifs associated to Ets-2 [19], one to the c-myc purine-binding transcription factor PuF [20] and one to the early growth response gene 2 (table 2, Fig. 4).
A sequence of 9433 bp (c047302867. Contig1) was found in the Mouse Genome Sequencing Consortium (MGSC) database. This sequence mapped to mouse chromosome 6 and contained the first exon, part of the first intron, and 3 kb of the upstream promoter region of mouse GP210. Similar to human, a GPC island containing 644 bp and with a GC content of 74% was found starting 268 nt upstream of the start codon and extending into the gene. Pairwise ClustalW alignment of this genomic clone showed 54% homology to its human counterpart within the first 500 bp upstream of the translation start (fig. 4). In the same region as in human we found putative EGR1/WT1 binding sequences. The sequence at positions -40 to -32 matched completely with the consensus sequence. The sequences starting at -47, -76, and -283 had one, two and one mismatches, respectively. As in the human sequence, an additional putative WT1 binding site was located in the antisense strand starting at position -83, but it contained a single nucleotide insertion. An Ets-2 motif, identical to the motif starting at -500 in the human sequence, was found starting at position -390 in the mouse sequence. In addition, a 40 bp (GT)n repetitive sequence was located about 1700 bp upstream of the ATG both in the human and mouse promoter (Fig. 5). This repetitive sequence is known to exist redundantly in the genome [21], and has been reported to be a binding element for WT1 [22,23].
Figure 5 Alignment of mouse and human sequences demonstrating a conserved region about 1700 bp upstream of ATG.
To determine whether the putative WT1 binding sites in the GP210 promoter might correspond to functional regulation of GP210 by WT1, we used a model system for the examination of WT1 target genes in which WT1- isoform A, devoid of a 17 amino acid insert and a KTS insert in the zinc finger region, was expressed upon removal of tetracycline from the growth media. Figure 6 shows in a triplicate experiment that the induction of WT1 (Lanes 4–6) in these cells did not alter GP210 transcription. An actin control showed comparable loading and integrity of mRNA. These data suggest that GP210 is not a target of WT1.
Figure 6 WT1 isoform A does not regulate expression of GP210. SAOS cells conditionally expressing WT1 isoform A were grown in the presence (Lanes 1–3) or absence (lane 4–6) of tetracycline to induce expression of WT1. Triplicate plates of cells were harvested for total RNA and subjected to sequential northern blot analysis with the GP210 probe, WT1probe and actin control.
Discussion
The present study describes the genomic structure of the human nucleoporin GP210 gene, including its exon and intron sizes, intron/exon junctions and the 5' UTR sequence. Transcription start sites were determined experimentally by RNA ligase mediated rapid amplification of cDNA ends. Analysis of the promoter sequence identified a number of putative binding motifs for factors involved in tissue- or cell -type specific gene regulation. Strikingly, we could identify five putative Wilms tumor 1 (WT1) suppressor protein binding sites, four Sp1 biding sites, and one ETS binding site in a range of 315 bases just upstream of the translation initiation site. Some of these were conserved in the mouse promoter region.
Mouse GP210 was initially identified as an early response gene to induction of embryonic kidney tubule development [10,15], suggesting that transcription factors regulating conversion of mesenchyme to kidney tubules are involved in its activation. Transcription factors implicated in early kidney tubule development include members of the myc family, Pax-2, hox a11 and Hoxd11, lmx-1b, HNF-1a, Pod-1, and WT1 [24-29]. Except for the WT1 binding site, putative binding motifs for these factors were lacking in the promoter region of the GP210 gene. Experimentally we found that WT1 does not influence GP210 expression in human osteosarcoma cells. It is thus more likely that Sp1 and some member of the ETS transcription factor family are the positive regulators of GP210.
WT1 is a zinc finger transcription factor known to exist in different isoforms due to alternative pre-mRNA splicing. DNA binding specificity is determined by insertion or removal of three amino acids between zinc finger III and IV (referred to as WT1(+KTS) and WT1(-KTS)). The -KTS isoform have been reported to repress or activate target genes containing variations of an EGR1 related, GC-rich motif (consensus GXGXGGGXG) in their promoter [24]. Other biological activities have been suggested for the +KTS isoform [26]. Mutations in the WT1 gene has been shown in a small proportion of nephroblastomas, an embryonic kidney tumor, as well as in other tumor types, such as leukemia, mesothelioma and desmoplastic small round cell tumor. The restricted expression pattern in the mouse embryonic kidney and the failure of kidney development in WT1 null mice shows that WT1 is important for mesenchyme-to-epithelial transition, especially for early organogenesis of kidney and gonads [29]. It is thus of considerable importance to identify downstream target genes for WT1. This could include the gene for GP210, but presumably not other nucleoporins. In the only previously reported nucleoporin promoter region, of mouse nup358, several binding sites for Sp1 but none for WT1 were detected [25,30].
Sp1 is a transcription factor included in a small protein family (Sp1, Sp2, Sp3, and Sp4), whose members are binding to cis-elements widely distributed in different types of transcription control regions [25]. Although traditionally considered as an activator for house keeping genes, it has become increasingly clear that Sp1 can act as a cell specific regulator of gene expression. Differential expression levels of Sp1 during nephrogenesis [31] and hematopoietic development [32] have been reported. Along with specific post-translational modifications, the substantial differences in the expression patterns of Sp1 suggest that Sp1 can induce specific gene expression in embryonic tissues, including GP210 in the kidney.
We also found a putative ETS-2 binding site in the mouse and human promoter for gp/GP210. Ets-2 is a widely distributed member of the ETS family of transcription factors characterized by a unique winged helix-turn-helix domain, which specifically interacts with DNA sequences containing the purine-rich core motif, GGAA/T. Since several ETS family members binds to the same core motif it has been difficult to determine specific target genes for each member, gene targeting in mice implicates ETS-2 as an activator of metalloproteinases in placenta (MMP-3, MMP-9 and MMP-13) and a regulator of hair development [33]. Elevated ETS-2 expression can reverse ras dependent transformation in cell lines [34]. In contrast, a high expression of ETS-2 is needed to maintain the transformed state of human prostate cells [35]. These data suggests multiple roles for ETS-2 during development and cancer. Interestingly, a binding site for Pea3, a member of the ETS family, has been noted in the WT1 promoter, and Pea3 was found to transactivate the Wt1 promoter [36].
Our promoter region analyses, which identify WT1, SP1 and Ets-2 as putative transcription factors regulating GP210 expression are descriptive. GP210 expression in the developing kidney resembles that of E-cadherin, which has been show to be a bone fide WT1 target gene [24]. WT1 A isoform, which lacks a 17 amino acid insert and the KTS insert in the zinc finger region, did not influence GP210 expression in SAOS osterosarcoma cells, in a system using tetracycline-induced repression of expression. In vivo, expression of WT1 appears very early during nephrogenesis, and is downregulated when GP210 expression increases [10,15,28,29]. Based on these findings, it was difficult to predict whether WT1 is involved in the positive or negative regulation of GP210. Our data do not exclude the possibility that demonstrate that the WT1 A isoform in different setting can regulate GP210 expression. It is also possible that other isoforms of WT1 regulate GP210.
The amino acid sequence of human GP210 revealed potential sites for phosphorylation and glycosylation. The role for phosphorylation of nuclear envelope associated proteins is not well understood, but is presumed to have a function in mitotic events [37,38]. Non-membrane nucleoporins 153, 214 and 358 are phosphorylated throughout the cell cycle, but hyperphosphorylated during cell division. In contrast, GP210, was in the same study specifically phosphorylated during mitosis and one single consensus Ser1880-Pro1881 motif could be detected as a target for cyklin-B-p34cdc2 kinase and MAP kinase in vitro [39]. A comparison of the cytoplasmic domain in mouse, rat and human reveals that this serine-proline dipeptide located seven amino acids downstream of the carboxyl terminus is conserved. Whether the many putative phosphorylation sites in GP210 are actively regulated has to be experimentally determined. Restricted to the lumenal region of GP210, there are 12 potential putative acceptor residues for N-linked oligosaccharides. This is one residue less than in the rat homologue [5], but the remaining 11 seem to be located at conserved locations. The binding of GP210 to the lectin ConA suggests presence of high mannose-type oligosaccharides in mature GP210 [4], but there are no reports on functional aspects of this posttranslational modification.
Materials and methods
Wilms tumor and HeLa cell cultures
WCCS-1 Wilms tumor cells [40] and HeLa cells were cultured in Dulbecco's modified medium containing 10% heat- inactivated Fetal Calf Serum (FCS) and in the presence of 1% HEPES. RNA from WCCS-1 and HeLa cells was isolated from 5 × 107 cells using the RNAeasy midi kit (Qiagen) following the recommendations of the manufacturer, including a DNAse treatment step to avoid chromosomal DNA contaminations. Northern blotting of total RNA from cells was performed as described [41]. To visualize 18 and 28s rRNA, a control RNA filter lane were immersed in 5% acetic acid followed by colorization in 0,5 MNaAc (pH 5,2), 0,4% methyleneblue for 15 min. cDNA probes used: a 935 bp PCR generated GP210 probe (nt 4286–5220), a 1.8 kb human β-Actin control probe (Clontech) and a 1023 bp PCR generated human WT1 probe covering the 3' end of the ORF and 523 bp of the 3' untranslated region thereby hybridizing to all known splice variants [42]. Probes were labelled with [α32P] dCTP by random priming using Megaprime DNA labeling system kit (Pharmacia). Filters were hybridized in 20 mM Na2HPO4 (pH 7.2), 7% SDS at 65°C for 18 hours. After washing in 20 mM Na2HPO4 (pH 7.2), 5% SDS at 65°C for 2 × 60 min followed by 2 × 60 min in 20 mM Na2HPO4 (pH 7.2), 1% SDS at 65°C the filters were exposed to Hyperfilm-MP films (Amersham) for 5 days at -70°C in the presence of intensifying screens. Band intensities were quantified using a PhosphoImager 400S (Molecular Dynamics, Sunnyvale, CA).
Tetracycline-regulated expression of WT1 in human osteosarcoma cells
WT1-A SAOS cells were constructed from a cell line harboring the tetracycline-repressor-VP16 fusion protein [43], transfecting the parental cell with a construct harboring WT1 isoform A (-17amino acids, -KTS) linked 3' to the CMV minimal promoter and tetracycline operators [44]. Conditional expression of WT1-A was demonstrated by immunoblotting. WT1-A-SAOS cells were maintained in Dulbecco's modified Eagle's medium supplemented with 10% fetal calf serum, 1∞ penicillin/streptomycin, 0.3 mg/mL L-glutamine and 0.5 mg/mL G418. All cells were cultured at 37°C in a 5% CO2 atmosphere. For the induction, cells (at 70% confluence) were washed twice with PBS and refed with fresh media in the absence or presence of 1 μg/mL of Tetracycline. After 18 hours, the cells were washed twice with PBS and total RNA was isolated using TRIzol reagent (Invitrogen, Carlsbad, CA) according to the manufacture's instructions. 4 μg of the RNA were resolved on formaldehyde-containing agarose gels and transferred to Nytran membranes (Schleicher and Schuell, Keene, NH). Hybridization was performed in ULTRAhyb buffer (Ambion, Austin, TX) at 42°C. Briefly, filters were prehybridized in ULTRAhyb buffer for 6 hours followed by an overnight hybridization at 42°C with the 935 bp hGP210cDNA probe. A WT1 cDNA probe (exons 5 to 10) and a cDNA probe for actin were used as controls. Membranes were exposed to BIOMAX MS films (Kodak, Rochester, NY) at -80°C in the presence of intensifying screens. Probes were stripped by boiling the membranes in 0.1% sodium dodecyl sulfate/ standard saline citrate solution for 10 minutes.
Oligo-capping
To determine transcription start, the RNA Ligase Mediated Rapid Amplification of cDNA Ends (RML-RACE) kit (Ambion) was used according to the instructions of the manufacturer's. Briefly 5 μg of Hela cell RNA or 2–5 μg of poly-A+ RNA from partially differentiated human G6 satellite cells [45] was treated with calf intestinal phosphatase (CIP) at 37°C for 60 min. RNA from G6 cells was kindly provided by Donald Gullberg at ICM, Uppsala University, Sweden. The mixture was phenol:chloroform (1:1) extracted followed by ethanol precipitation. The RNA was subsequently incubated with Tobacco Acid Phosphatase (TAP) at 37°C for 60 min. A 45 nt adapter RNA oligonucleotides (5'GCUGAUGGCGAUGAAUGAACACUGCGUUUGCUGGCUUUGAUGAAA3') was ligated to the CIP/TAP treated 5' RNA end using T4 ligase. cDNA was generated using random decamers and MMLV reverse transcriptase at 42°C for one hour. The nested PCR were performed using the advantaq 2 polymerase system (Clontech) and the following primers: in the first PCR reaction the outer adapter (5'GCTGATGGCGATGAATGAACACTG3') was combined with the gene specific outer primer (5'CAGCAGCACTTTGGGGATGTTGAG3'), in the second PCR the inner adapter primer (5'CGCGGATCCGAACACTGCGTTTGCTGGCTTTGATG-3') was used in combination with the gene specific inner primer (5'CCCGCCGCCAACAGCACCGACAGC3'). The conditions for both PCR reactions were as follows: 94°C for one min (hot start) followed by 95°C for 20 s, 68°C for 60 s, repeated 35 cycles. After the final cycle, the reactions were extended for an additional 5 min at 68°C. PCR products were analysed on a 2% agarose gel, ligated into the pCRII vector (Invitrogen) and sequenced using M13 primers.
Sequencing
Sequencing was performed using ABI PRISM and the Dye Terminator cycle sequencing kit according to the manufacturer's directions (Perkin Elmer) and analysed by an automated ABI-310 fluorescent-dy sequencer (Applied Biosystems).
Bioinformatics and sequence analyses
Exon-intron boundary predictions were done manually and using the Genscan web server at MIT . Open reading frame finding and all sequence analyses were done using the MacVector 6.5.3 sequence analysing software (Oxford Molecular Group). The BLAST (Basic Local Alignment Search Tool) server of the National Center of Biotechnology Information (NCBI) and The Mouse Genome Sequencing Consortium (MGSC) database was used for sequence alignments. Signal peptide prediction was done using the SignalP v1.1 at WWW Prediction Server (Center for Biological Sequence Analysis, and membrane topology predictions at the HMMTOP server using the TopPred2 program [46]. Transcription factor mapping in the 5' untranslated region of hGP210 was analysed in TESS and Matinspector [47] search programs. Additional amino acid sequence patterns and domains were analysed using the PROSITE database . Prediction of GPC islands and detection of repeats in sequences analysed were done with the GRAIL program [48].
Supplementary Material
Additional File 1
Table 1. Organization of the human GP210 gene including exon/intron sizes, splice acceptor consensus sequences and intron phases.
Click here for file
Additional File 2
Table 2. Predicted cis-acting elements of the human GP210 promoter. The start sites of the elements are indicated as 5' end of consensus sequence and relative to translation start as +1. The compilation was made using the TESS and MatInspector analyze programs. Results are restricted to human species and perfect match except for four WT1 binding sites indicated as a boxed nucleotide. Some abbreviations: EGR2, Early growth response gene 2; WT1, Wilms' tumor zinc finger protein 1; PuF, c-myc purine-binding transcription factor; IL-6. RE-BP, IL-6 Response element-Binding protein; NF-1, Nuclear factor 1; Ets-2, proto-oncoprotein; USF2, upstream stimulating factor.
Click here for file
Acknowledgements
We thank Dr. Donald Gullberg (Uppsala University) for cell lines and help with analyses of promoter regions. Supported by Barncancerfonden (Stockholm), and NIH grant CA 59998.
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| 15613247 | PMC544869 | CC BY | 2021-01-04 16:37:33 | no | J Negat Results Biomed. 2004 Dec 21; 3:7 | utf-8 | J Negat Results Biomed | 2,004 | 10.1186/1477-5751-3-7 | oa_comm |
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BMC Chem BiolBMC Chemical Biology1472-6769BioMed Central London 1472-6769-4-11558829210.1186/1472-6769-4-1Research ArticleRedesigned and chemically-modified hammerhead ribozymes with improved activity and serum stability Hendry Philip [email protected] Maxine J [email protected] Tom S [email protected] Trevor J [email protected] CSIRO Molecular Science, PO Box 184 North Ryde NSW 1670, Australia2 School of Biochemistry and Molecular Genetics, University of New South Wales, Sydney NSW 2052, Australia2004 9 12 2004 4 1 1 11 6 2004 9 12 2004 Copyright © 2004 Hendry et al; licensee BioMed Central Ltd.2004Hendry 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
Hammerhead ribozymes are RNA-based molecules which bind and cleave other RNAs specifically. As such they have potential as laboratory reagents, diagnostics and therapeutics. Despite having been extensively studied for 15 years or so, their wide application is hampered by their instability in biological media, and by the poor translation of cleavage studies on short substrates to long RNA molecules. This work describes a systematic study aimed at addressing these two issues.
Results
A series of hammerhead ribozyme derivatives, varying in their hybridising arm length and size of helix II, were tested in vitro for cleavage of RNA derived from the carbamoyl phosphate synthetase II gene of Plasmodium falciparum. Against a 550-nt transcript the most efficient (t1/2 = 26 seconds) was a miniribozyme with helix II reduced to a single G-C base pair and with twelve nucleotides in each hybridising arm. Miniribozymes of this general design were targeted to three further sites, and they demonstrated exceptional cleavage activity. A series of chemically modified derivatives was prepared and examined for cleavage activity and stability in human serum. One derivative showed a 103-fold increase in serum stability and a doubling in cleavage efficiency compared to the unmodified miniribozyme. A second was almost 104-fold more stable and only 7-fold less active than the unmodified parent.
Conclusion
Hammerhead ribozyme derivatives in which helix II is reduced to a single G-C base pair cleave long RNA substrates very efficiently in vitro. Using commonly available phosphoramidites and reagents, two patterns of nucleotide substitution in this derivative were identified which conferred both good cleavage activity against long RNA targets and good stability in human serum.
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Background
Hammerhead ribozymes were discovered as self-cleaving motifs in a number of small, circular, pathogenic RNAs in plants [1-3]. Uhlenbeck [4] showed that the ribozyme was able to act in a bimolecular fashion as a true enzyme, ie each ribozyme was able to cleave multiple substrates. Haseloff and Gerlach [5] divided the hammerhead into a form in which the majority of the conserved nucleotides were located on the enzyme strand, with the only sequence requirements for the substrate being UH (H = A, U or C) [6-8]. Since 1988 this configuration, as shown in Figure 1, has been the paradigm for hammerhead ribozyme design. Hammerhead ribozymes are sequence-specific RNA cleaving agents with the potential to control the expression of genes by eliminating specific RNAs. This can be achieved by expressing the ribozyme within the target cell or by delivering it to the cell as a preformed entity. One of the difficulties associated with delivering preformed ribozymes is their instability in vivo, since RNA is degraded very rapidly by ribonucleases present in cells and extracellular fluids. Significant segments of ribonucleotides in the hammerhead ribozyme can be replaced with more nuclease-resistant analogues like DNA, phosphorothioate linkages, or 2' O-methyl analogues; however, within the conserved core of the hammerhead, the majority of ribonucleotides are sensitive to modification. Yang et al [9] demonstrated that predominantly DNA ribozyme analogues with at least 4 ribonucleotides (G5, G8, A9 and A15.1 or G15.2 numbered according to [10]) displayed measurable cleavage activity (albeit reduced 5000-fold). Phosphorothioate modification of DNA hybridising arms and three of the conserved pyrimidines (C3, U4 & U7) resulted in significant increase in stability in human serum with a 6-fold loss in cleavage activity [11]. In the context of 2'-O-methyl substituted ribozyme analogues, at least 5 unmodified ribonucleotides (G5, G8, A9, A15.1 and G15.2) were required for activity [9]. Paolella et al [12] identified a minimum set of 6 ribonucleotides, U4, G5, A6, G8, G12 and A15.1, in the conserved domain in which substitution with 2' O-allyl ribonucleotides inhibited activity. Eckstein's group [13,14] showed that good activity and stability in foetal calf serum could be achieved with 2'-amino-2'-deoxyuridines at U4 and U7, and 2'-fluoro-2'-deoxycytidine at C3. Hammerheads in which helix II was shortened to only two base pairs, and all the pyrimidines were 2'-fluoro or 2'-methoxyethoxy derivatives except for U4 and U7 which were 2'-amino-2'-deoxy, were only 2–3 fold less active than the unmodified parent hammerhead [15], but showed a 104-fold increase in nuclease stability. A study of the effects of various modified nucleotides on stability and activity of the 2'-O-methyl-pyrimidine modified hammerhead found a number of modifications, including 2'-amino-2'-deoxyuridine, 2'-O-methyl-uridine, and 2'-C-allyl-uridine at positions U4 and U7, supported good rates of cleavage [16]. These modifications result in a greater than 103-fold increase in stability in human serum, while the addition of an inverted thymidine at the 3' end of the oligonucleotide (a 3'-3' linkage) further improved the stability by two orders of magnitude.
Figure 1 Schematic representation of Mrz-12/12-A bound to substrate S-30. Helices I and III are formed between the ribozyme and substrate. The standard hammerhead (eg Rz-12/12) has a 4 base pair helix II in place of the single g-c pair in the miniribozyme. The minizyme (Mz-12/12) has no helix II, but has a loop sequence gtttt connecting bases A9 and G12. Upper-case letters represent ribonucleotides, and lower-case letters represent deoxyribonucleotides. Nucleotides which have been further modified in this study are shown in blue.
Our laboratory is interested in the relationship between hammerhead design and reactivity. We have described a number of ribozyme derivatives that appear to have promise as RNA cleavage agents. Minizymes (Mz) possess a non-base-pairing tetranucleotide linker in place of helix II [17]. In general, such minizymes are less active than standard hammerheads, although in some instances they show comparable cleavage rate constants [18,19]. Miniribozymes (Mrz) have a single G10.1–C11.1 base pair joined by a flexible linker in place of helix-loop II [19]. Asymmetric hammerheads are those in which the 5' hybridising arm is restricted to around 5 or 6 nucleotides; this modification eliminates the decrease in cleavage rate that occurs with standard hammerheads when the length of helix I, formed upon binding the substrate, increases to greater than about 6 base pairs [20,21]. The purpose of this study was to investigate the ability of these various derivatives to cleave an RNA molecule (sequence derived from the mRNA of the cpsII gene of the Malaria-causing organism Plasmodium falciparum [22]) in the context of two substrates, a 30-mer and a 550-mer. Having optimised the cleavage activity of the ribozyme, we planned to chemically modify the nuclease sensitive RNA nucleotides, using readily available protected nucleoside phosphoramidites, to extend the life of the ribozyme in the presence of human serum.
Results
Ribozyme design
The primary target site for cleavage in this study was a previously identified site [23] in the cps II mRNA [22] of Plasmodium falciparum. The chosen target site, centred at position 3733 in the nucleotide sequence (Genbank reference L32150), has the local sequence 5' UAA CUU AUC AAG GUC* AAG AAC AUG AUG UUC 3', where the site of cleavage is denoted by the asterisk. A number of ribozyme designs were tested for their ability to cleave this RNA sequence either as a short (30-mer) oligonucleotide or in a transcribed RNA segment (550 nt). These designs included standard hammerhead ribozymes (Rz) which are defined as those with a helix II consisting of four base pairs closed at the end with a four-nucleotide loop, minizymes (Mz) [17] which lack helix II and instead the two segments of conserved nucleotides are linked between A9 and G12 with a non-base-paired linker (in this case consisting of 5 nucleotides), and finally miniribozymes (Mrz) [19] which have a single G-C base pair replacing helix II and in this instance a linker sequence consisting of four deoxythymidines. In this communication the core of each ribozyme is flanked by hybridising arms composed of DNA of various lengths, where the length is given in the ribozyme's name (e.g. Rz-6/12 has 6 nt in its 5' hybridising arm and 12 nt in its 3' arm). Hammerhead-ribozyme derivatives of these designs were tested for their ability to cleave the 30-mer substrate at pH 7.6, 37°C and 10 mM MgCl2 under pseudo first-order conditions with an excess of ribozyme. Ribozyme concentrations ranged from 50 nM to 10 μM. Cleavage data fitted well to single exponential curves to yield observed rate constants which were plotted against the ribozyme concentration in each experiment to determine the apparent dissociation constant ("Kd") and the maximum cleavage rate constant (kmax) for each ribozyme-substrate pair (Table 1). In terms of catalytic efficiency, the most efficient ribozyme was the standard hammerhead Rz-12/12. Its efficiency is due to a very low "Kd" of 7 nM, despite displaying a kmax some 5-fold less than Mrz-12/12. The highest kmax was displayed by Rz-6/12; however it had a "Kd" about 60-fold greater than Rz-12/12.
Table 1 Kinetic parameters for RNA/DNA unmodified hammerhead ribozyme derivatives.
Ribozyme S-30 S-550
kmax (min-1) "Kd" nM kmax/"Kd" (min-1μM-1) kmax (min-1) "Kd" nM kmax/"Kd" (min-1μM-1)
Rz-12/12 0.8 ± 0.1 7 ± 12 114 ± 200 0.7 ± 0.1 2600 ± 900 0.3 ± .2
Mz-12/12 0.22 ± 0.08 31 ± 6 7 ± 4 0.12 ± 0.01 240 ± 50 0.5 ± .2
Mrz-8/8 0.56 ± 0.06 210 ± 80 2.7 ± 1.3 0.005 ± 0.001 2200 ± 500 0.002 ± .001
Mrz-12/12 4.2 ± 0.3 75 ± 30 56 ± 26 1.6 ± 0.1 120 ± 50 13 ± 6
Rz-6/12 9 ± 1 400 ± 200 22 ± 13 0.6 ± 0.1 2800 ± 900 0.2 ± .1
Cleavage conditions; 37°C, pH 7.6, 10 mM MgCl2, ribozyme is in large excess over RNA substrates S-30 (30 nt) and S-550 (550 nt).
The abilities of all the ribozymes to cleave the same target in the context of transcribed RNA (550 nt) was also examined. Mrz-12/12, by virtue of only a modest decrease in kmax, and marginal increase in "Kd", was by far the most efficient of all the designs tested. In contrast Mrz-8/8, Rz-12/12 and Rz-6/12 displayed "Kd"'s between 2 and 3 mM, which, in the case of Rz-12/12, is an increase of about 400-fold. Interestingly the kmax values displayed by Rz-12/12 and Rz-6/12 were within experimental error, and were the same as displayed by Rz-12/12 for cleavage of the short substrate.
Cleavage of other targets by the miniribozyme
We examined whether the effectiveness of the miniribozyme design was limited to this target site. Miniribozymes, with long (>10 nucleotides) hybridising arms were designed to cleave RNAs of interest to other projects in the laboratory. Tet Mrz was a 53-mer oligonucleotide with conserved bases of RNA and hybridising arms and stem loop II composed of DNA. This Mrz, with hybridising arms of 18 and 19 nucleotides, was targeted to cleave the GUC triplet at position 60 of the Tetrahymena IVS ribozyme [24]. It cleaved the 388-nt transcript, L-21 ScaI, with a rate constant of 4.0 ± 0.2 min-1 (t1/2 = 10 seconds), to about 70%, at pH 7.6, 37°C and 10 mM MgCl2. Another miniribozyme with 14-mer arms (HC Mrz), targeting a segment of Hepatitis C polyprotein mRNA, was tested against a synthetic 29-mer substrate. Under our standard conditions about 70% of the substrate was cleaved, and the cleavage rate (>5 min-1) was too fast to be measured reliably. In contrast, the standard ribozyme HC Rz cleaved 78% of the same substrate with a rate constant of only 0.2 ± 0.02 min-1. Finally, PDGF MRz, with hybridising arms each of 10 ribonucleotides, was tested against both 25-mer synthetic and 707-nt transcribed substrates. Under standard conditions, the 25-mer was ~ 80% cleaved with a rate constant > 5 min-1. Reducing the magnesium ion concentration to 1 mM yielded a rate constant of 2.8 min-1 and 63% cleavage for the 25-mer substrate, and 1.6 min-1 and 45% cleavage for the 707-nt transcript.
Identification of nuclease susceptible sites in human serum
Mrz-12/12 was the most efficient cleaver of the 550-nt cpsII RNA transcript and was used as the platform to test the effect of chemical modification on cleavage activity and nuclease stability. Firstly, the stability of unprotected Mrz-12/12 in RPMI + 10% human serum was determined using 32P 5'-end labelled ribozyme at 37°C (Figure 2). Approximately 90% of the miniribozyme is degraded in 10 seconds, and no full-length material is observed after 10 minutes. Initially there are three main sites of cleavage, at U4, U7 and C15.2. After 10 minutes, nearly all the end-labelled material co-migrates with the band generated by alkaline digestion at U4. That initial, major product has a half-life of about two hours under these conditions, as it is slowly converted to a product one nucleotide shorter, ie its 3' end corresponds to C3 in the original miniribozyme.
Figure 2 Degradation of 5' end-labelled Mrz-12/12 A in RPMI + 10% human serum at 37°C. Time of incubation in indicated above each lane. OH-indicates an alkaline digest of the same material. FL indicates the position of the full-length miniribozyme. The position of the fragments terminating at each of the ribonucleotides is indicated by the letters adjacent to the alkaline digest.
Nuclease resistant modifications
Commercially available modified phosphoramidites were used to generate hammerhead derivatives which were expected to be protected from nuclease degradation. The effect of the various modifications on the cleavage ability are given in Table 2. In these experiments the concentration of the miniribozyme was fixed at 1 μM and the substrate S-30 at 5 nM. The nucleotide most sensitive to chemical modification was U4. All the 2' modifications tested (amino, deoxythymidine, deoxyuridine, and O-methyl, shown as D, F, G and H, respectively, in Table 2) diminished activity by more than 10-fold. Only the presence of a phosphorothioate linkage between U4 and G5 preserved the activity of the unmodified ribozyme. The ready availability of 2'-O-methyl phosphoramidites, coupled with the previous demonstrations [9,16] of tolerance to that modification at C3, U7 and C15.2, lead us to synthesise Mrz-J, which we expected to have reasonable activity and nuclease stability. Its cleavage rate constant was actually twice that observed for the unmodified ribozyme, and its stability in serum was increased about 103-fold. The kinetics of degradation in serum (Figure 3) were not straight-forward, displaying a rapid initial decay of about 25% of the starting material, followed by an approximately first-order decay with a half-life around 30 minutes. This second phase accounted for approximately 50% in the total starting material, ie after about 4 hours approximately 25% of the full-length material remained intact and thereafter decayed only very slowly. There was a single major product, corresponding to cleavage at U4, observed over the six hours of the experiment.
Table 2 Cleavage Rate constants for cleavage of S-30 by Chemically Modified Mrz-12/12.
Miniribozyme (Mrz-12/12) C3 U4 U7 C15.2 3' end Cleavage rate constant (min-1) Relative Stability
A - - - - - 4.2 ± 0.3 1
B F NH2 NH2 F - 0.30 ± 0.05 -
C - NH2 NH2 - - 0.18 ± 0.01 -
D - NH2 - - - 0.30 ± 0.03 -
E - - NH2 - - 7.7 ± 0.8 -
F - dT - - - 0.014 ± 0.005 -
G - dU - - - 0.024 ± 0.007 -
H - OMe - - - 0.01 ± 0.007 -
I - ps - - - 3.9 ± 0.06 -
J OMe ps OMe OMe psps 7.3 ± 0.7 1400
K OMe NH2 OMe OMe - 0.6 ± 0.1 8600
Rz-12/12-L - NH2 - - - 0.05 ± 0.009 -
Cleavage conditions; 37°C, pH 7.6, 10 mM MgCl2. [Rz] = 1 μM, [S30] = 5 nM. (- = unmodified, ie 2'OH). F = 2'-fluoro, NH2 = 2'-amino, dT = 2'-deoxythymidine, dU = 2'-deoxyuridine, OMe = 2'-O-methyl, ps = 3' phosphorothioate linkage. Stability is defined as the time required in 10% human serum to degrade 75% of full-length ribozyme, relative to unmodified miniribozyme A.
Figure 3 Degradation of Mrz-12/12 J in RPMI + 10% Human Serum. Experimental conditions are as described in Figure 2.
The single degradation site in Mrz-J suggested that a more robust modification at this position would have a significant effect on its lifetime in serum. Mrz-12/12 K was synthesised with a 2'-amino modification at the U4 position and with a 2'-O-methyl modification at each of the three other conserved pyrimidines. Protection of the 3' end was omitted from Mrz-12/12 K because it did not appear to contribute significantly to the stability observed in Mrz-12/12 J. As expected, the cleavage activity of Mrz-12/12 K was significantly reduced compared to Mrz-12/12 J (Table 2), but stability in serum was greatly improved with only minor losses apparent after 5 hours incubation at 37°C (Figure 4). Even after 24 hours in 10% serum, approximately 25% of the radioactivity co-migrated with the full-length material, representing an almost 104-fold increase in stability. Even in the absence of 3' terminal protection there was no 3' exonuclease activity apparent. The amount of 32P label appearing in the gel lanes remained relatively constant throughout the experiment implying a lack of significant phosphatase activity in the serum. The main sites for degradation were the remaining unprotected (purine) ribonucleotides.
Figure 4 Degradation of Mrz-12/12 K in RPMI + 10% Human Serum, and by alkaline digest. Experimental conditions are as described in Figure 2.
The modifications to the conserved nucleotides in this study were all made in the context of a miniribozyme. Compared to a standard hammerhead, the catalytic domain is expected to be more conformationally flexible, and therefore it should not be assumed that all the changes described here can be directly applied to standard hammerheads with a helix II of four base-pairs. However, as for the miniribozyme, a standard ribozyme containing a 2'-amino modification at U4, was about 15-fold less efficient at cleaving S-30, (kobs = 0.05 min-1, Rz-12/12-L, Table 2), compared to the unmodified ribozyme (Rz-12/12).
Discussion
Work in this laboratory [20,21] and elsewhere [25] has demonstrated that the cleavage kinetics of any conventional hammerhead ribozyme are significantly inhibited when the length of helix I exceeds about 6 bp. This has been ascribed to an interaction between helices I and II which stabilises an inactive conformation [20]. The angle between helices I and II changes with metal ion concentration [26], and we postulated that a similar change was required for the transformation between more and less active conformations of the ribozyme [20]. This general model is supported by more recent observations using a variety of techniques [27-31] which conclude that the dominant ground-state conformation of the hammerhead is inactive and is in equilibrium with the active form. The results of the present study are in accord with these conclusions.
It is commonly observed, for conventional hammerhead ribozymes, that cleavage efficiencies for long transcripts are about 2 orders of magnitude lower than for short substrates [32]. This has been observed here also for ribozyme derivatives of both the conventional design and of the short-armed (8-nt) miniribozyme (Table 1). In these cases the cause appears to reside largely in the apparent dissociation constant "Kd". In contrast, the longer-armed miniribozyme (Mrz-12/12) shows excellent cleavage kinetics against both short and long substrates. It appears that the more flexible miniribozyme is better suited to binding to the target in the context of a long RNA. These data can be interpreted according to a simple model in which ribozymes possessing a stable helix II form a more rigid three-dimensional structure [33,34] which does not bind strongly to the long, folded substrate. When binding is achieved at very high ribozyme concentrations, the ribozyme-substrate complex is sterically hindered to such an extent that the complex is locked into a poorly active conformation. Hence Rz-6/12 and Rz-12/12 display similar maximal rate constants. In contrast, the Mrz lacking helix II is more flexible and readily adapts to binding the folded substrate with relatively minor effects on kobs and "Kd". This observation is not specific to target or cleavage triplet, since four unrelated targets, including three long transcripts, one with an AUC cleavage triplet, were cleaved with rate constants much higher than typically reported for cleavage even of short substrates. It is worth noting the magnitude of the observed cleavage rate constants; under our standard conditions these miniribozymes cleave their targets with half-lives in the range of < 5 to 25 seconds.
The unmodified Mrz-12/12 was very unstable in 10% human serum. Degradation occurred by endonucleolytic cleavage at the 3' side of pyrimidine nucleotides. Since the hybridising arms and helix-loop II are composed of DNA, the four remaining ribo-pyrimidine nucleotides in the conserved domain were the critical residues for stabilisation. Modification of all four ribopyrimidines with 2'-aminouridine and 2'-fluorocytidine, (Mrz-12/12 B), resulted in a more than 10-fold decrease in maximum cleavage rate constant. This contrasts with some previous results where a U4, U7-amino derivatised ribozyme with a standard helix II was only marginally diminished in activity [14], and a U4, U7-amino derivatised ribozyme [16], in which all the remaining pyrimidines were modified with 2'-O-methyl groups, was only two-fold less active than the parent ribozyme. The study by Heidenreich et al [14] was confounded by the fact that the cleavage kinetics were measured against a long transcript and are relatively slow compared with rates typically observed for short substrates, and therefore it seems likely that access to the transcript, rather than chemistry of cleavage, may have been rate determining in that case. In a later report [15], a modification pattern identical to miniribozyme B (ie C3, C15.2-fluoro and U4, U7-amino) but in a ribozyme with a standard helix II resulted in a more than 10-fold loss in turnover number (kcat). The series of singly and doubly substituted miniribozymes (C-E) showed clearly that a 2'-aminouridine at position 4 was solely responsible for the loss in activity. This is consistent with the result of Beigelman et al [16], but, in contrast to that result, the cleavage activity of the miniribozyme was not rescued by the addition of an amino group at U7. A phosphorothioate linkage between U4 and G5 (Mrz-12/12 I) was the only modification tested here that did not suppress cleavage activity. This is in accord with interference studies [35,36] identifying U4 as insensitive to phosphorothioate modification. The results for dU and 2'O-methyl substitutions contrast with Beigelman et al [16] where, in a background of 2'O-methyl-pyrimidines, the effect of those substitutions at U4 were relatively minor.
In serum, Mrz-12/12 J was degraded by cleavage at the U4 phosphorothioate, where an initial, quite rapid degradation was followed by a slower step which is not complete at 6 hours. Chemically synthesised phosphorothioate linkages are a mixture of two enantiomers and it is likely that the multiphase kinetics observed were due in part to the different susceptibility of the two isomers (Rp and Sp) to the nucleases present in the serum [37,38]. Mrz12/12 K has 2'-amino modification at U4, is very stable in serum, and is only 7-fold less active than the unmodified parent. The products of nuclease cleavage of Mrz-12/12 K indicate that degradation occurred by cleavage at the unprotected ribopurine sites. This derivative was not protected at the 3' terminus, but unlike earlier work [11,16,38], there was no evidence in this experiment for 3'-DNA exonuclease activity.
In recent times RNAi has surplanted catalytic RNA as the method of choice for suppression of gene expression in many eukaryotic cells [39]. Results published to date suggest that it may be effective in many settings, including possibly as a therapeutic. However, the mechanistic details of RNAi activity are quite complex and are still being unravelled, and it seems likely that there will be cell types, perhaps whole classes of organisms, that lack the necessary cellular machinery. For this reason, it may be premature to abandon the autonomous catalytic RNAs. During the 1990's, many studies aiming to demonstrate hammerhead ribozymes as gene silencing agents in vivo were not successful. The results obtained here, and in other recent studies on the effect of non-conserved sequences on the kinetics of cleavage [25,40,41], are beginning to reveal shortcomings of many studies of that era. That is the in vivo catalytic efficiency of the hammerheads under study was compromised by their simplistic design, which was based on in vitro studies performed with very short substrates. We were too quick to ascribe poor cleavage of transcripts to "accessibility" issues, and too slow to undertake comprehensive studies to understand the phenomenon. The miniribozyme design, and the effective nuclease protection afforded by the simple chemical modifications described here, provide an opportunity to revisit cellular and in vivo experiments with hammerheads that are around two orders of magnitude more efficient.
Conclusions
Long RNA substrates are much more effectively cleaved by miniribozymes than similarly targeted standard hammerheads. The presence of a stable helix II in the standard hammerhead appears to inhibit binding of the ribozyme to the substrate, and prevent the conformational mobility required for activity. Miniribozymes are affected by serum nucleases in a similar way to standard hammerheads, with cleavage occurring at the 3' side of unprotected ribopyrimidines. This work shows that it is readily possible to modify RNA/DNA hammerhead ribozymes, using commercially available reagents, to yield greatly improved nuclease resistance and retention of significant cleavage activity. These modified miniribozymes represent excellent candidates for cellular and in vivo studies on suppression of gene expression.
Methods
Oligonucleotide synthesis
Oligoribonucleotides were synthesised using DNA phosphoramidites and ancillary reagents supplied by Perkin Elmer (Applied Biosystems Division, Foster City, CA), RNA and modified phosphoramidites from Glen Research (Sterling, VA) and using an Applied Biosystems Model 394 DNA synthesiser. Phosphorothioates were introduced by oxidation with Beaucage reagent (Glen Research, Sterling, VA). Deprotection and purification of oligonucleotides were as described previously [20]. The purity of each oligonucleotide was checked by labelling its 5'-end with 32P phosphate using T4 polynucleotide kinase (New England Biolabs, Beverly, MA, USA) and [γ-32P]-ATP (Du Pont, Wilmington, DE), followed by electrophoresis in a 15% polyacrylamide gel containing 7 M urea and visualisation using a Molecular Dynamics PhosphorImaging system (Sunnyvale, CA). The concentrations of the purified oligonucleotides were determined by UV spectroscopy using the following molar extinction coefficients for the various nucleotides at 260 nm: A, 15.4 × 103; G, 11.7 × 103; C, 7.3 × 103; T/U, 8.8 × 103 l mol-1 cm-1. All oligonucleotides were stored in distilled, deionised and autoclaved water at -20°C.
The 550-nt CPSII substrate was transcribed by T7 RNA polymerase from the vector pCPS3b.1 [22] after linearisation with Eco RI restriction enzyme using an Ampliscribe T7 kit (Epicentre Technologies, Madison, WI, USA). The transcription reaction contained CTP, GTP and ATP (unlabelled) at 5 mM, UTP at 0.5 mM and 32P UTP at about 1 nM. The transcription was for 90 minutes at 37°C, followed by the addition of 0.5 μL of DNAaseI and incubation for a further 20 minutes. The mixture was extracted three times with a 1:1 phenol/chloroform mix and once with chloroform, before precipitation of the transcript by the addition of 1/3 volume of 7.5 M ammonium acetate and incubation on ice for at least 2 hours. After recovery of the transcript by centrifugation and washing, the degree of incorporation of 32P-labelled uridine was measured by Cerenkov counting of the transcript and the unincorporated UTP. The Tetrahymena intervening sequence transcript was generated from the ScaI digested pT7L-21 vector [24] (ATCC 40291) using the same method. The PDGF transcript was generated by T3 RNA polymerase transcription from the Eco RI digested vector pBSPDGF-LA using an Ampliscribe T3 kit (Epicentre Technologies, Madison, WI, USA). The vector pBSPDGF-LA was generated by insertion of the Xba I/Eco RI fragment from pmetPDGF-LA [42] into a similarly digested pBluescribe vector.
Oligonucleotide sequences
Deoxyribonucleotides are denoted by lower case letters, ribonucleotides by uppercase letters, modified nucleotides are shown in bold, Cf = 2'-fluoro-2'-deoxycytidine, Cme = 2' O-methylcytidine, Uam = 2'-deoxy-2'-aminouridine, Ume = 2' O-methyluridine, ps = phosphorothioate linkage. S-30, UAA CUU AUC AAG GUC* AAG AAC AUG AUG UUc, * denotes site of cleavage; Mrz-8/8, atgttctt CUGAUGA gttttc GAAAC cttgat; Mrz-12/12, catcatgttctt CUGAUGA gttttc GAAAC cttgataagt; Mz-12/12 catcatgttctt CUGAUGA gtttt GAAAC cttgataagt; Rz-12/12, catcatgttctt CUGAUGA GUCC UUUU GGAC GAAAC cttgataagt; Rz-6/12, gttctt CUGAUGA GUCC UUUU GGAC GAAAC cttgataagt; Mrz-12/12-A (= Mrz-12/12), catcatgttctt CUGAUGA gttttc GAAAC cttgataagt; Mrz-12/12-B, catcatgttctt CfUamGAUamGA gttttc GAAACf cttgataagt; Mrz-12/12-C, catcatgttctt CUamGAUamGA gttttc GAAAC cttgataagt; Mrz-12/12-D, catcatgttctt CUamGAUGA gttttc GAAAC cttgataagt; Mrz-12/12-E, catcatgttctt CUGAUamGA gttttc GAAAC cttgataagt; Mrz-12/12-F, catcatgttctt CtGAUGA gttttc GAAAC cttgataagt; Mrz-12/12-G, catcatgttctt CuGAUGA gttttc GAAAC cttgataagt; Mrz-12/12-H, catcatgttctt CUmeGAUGA gttttc GAAAC cttgataagt; Mrz-12/12-I, catcatgttctt CUpsGAUGA gttttc GAAAC cttgataagt; Mrz-12/12-J, catcatgttctt CmeUpsGAUmeGA gttttc GAAACme cttgataagtpstpst; Mrz-12/12-K, catcatgttctt CmeUamGAUmeGA gttttc GAAACme cttgataagt; Rz-12/12-L, catcatgttctt CUamGAUGA GUCC UUUU GGAC GAAAC cttgataagt; Tet Mrz, gcaatctattggtttaaa CUGAUGA gttttc GAAAC tagctaccaggtgcatg 3'; HC Mrz, 5' gtcgccacgacgac CUGAUGA gttttc GAAAC gttcccgctggt 3'; HC Rz, 5' gtcgccacgacgac CUGAUGA GGCC GAAA GGCC GAAAC gttcccgctggt 3'; HC S29, 5' ACCAGCGGGAACGUCGUCGUCGUGGCGAc 3'; PDGF Mrz, 5' CAGCUUCCUC CUGAUGA ggtaac GAAAU GCUUCUCt 3' ; PDGF S25, 5' GAAGAGAAGCAUCGAGGAAGCUGUc 3'.
Cleavage kinetics
Cleavage kinetics were studied at 37°C, pH 7.6 and 10 mM MgCl2 under conditions of ribozyme excess; the substrate concentrations varied between 4 and 20 nM, and the ribozyme concentrations were between 20 nM and 10 μM. The short, synthetic substrates were labelled at their 5' ends using polynucleotide kinase (Roche Molecular Biochemicals) and γ-32P ATP. The long, transcribed substrates were uniformly labelled by including α-32P UTP in the transcription reaction. The ribozymes and 32P-labelled substrates were mixed together in 20 μL of 50 mM Tris buffer and heated to 85°C for two minutes, then incubated at 37°C for 2 minutes. The tube containing the mix was centrifuged, and 2 μL was removed and put into 4 μL quenching solution which contained 90% formamide, 20 mM EDTA and 0.01 % xylene cyanol and bromophenol blue. The reaction was initiated by the addition of 2 μL of 100 mM MgCl2, and 2 μL samples were removed at various times and quenched as described above. The reaction products were separated using 15% denaturing polyacrylamide gels (containing 7 M urea), and then imaged using a Molecular Dynamics PhosphorImager. At each time-point, the amount of the substrate cleaved was calculated and plotted versus time. The data were fitted by a least-squares method using the program MacCurveFit [43], to an equation of the form: Pt = P∞-(exp(-kobst)PΔ) where Pt is the amount of product at time t, P∞ is amount of product generated in the exponential phase of the reaction, kobs is the first-order rate constant for the reaction, and PΔ is the difference in the amount of product at t = 0 and P∞. Some reactions resulted in biphasic kinetics in which the first-order phase described above was followed by a slower step which was accommodated adequately in the curve-fitting process by the addition of a linear term, Pt = {P∞-(exp(-kobst)PΔ)} + d*t. Apparent dissociation constants "Kd" and the maximal first-order rate constants kmax were determined from the plot of kobs versus ribozyme concentration according to the simple binding isotherm: kmax = (kobs * [Rz])/("Kd" + [Rz]) by least-squares fitting using the program MacCurveFit.
Serum stability
Ribozymes labelled at their 5' end with 32P phosphate were dissolved in RPMI medium (Gibco) at 2 μM and the reactions were initiated by adding human serum (pooled human serum, Red Cross Blood Bank, Sydney, NSW), to a final concentration of 10%. A 2 μL sample was removed immediately and the remainder incubated at 37°C with samples removed at the times indicated in Figures 2,3,4. The samples were quenched by addition to 4 μL of 90% formamide containing 20 mM EDTA and 0.01% bromophenol blue and xylene cyanol. The products of nuclease digestion in each sample were separated on 15% polyacrylamide gels containing 7 M urea and imaged using a Molecular Dynamics PhosphorImager.
Authors' contributions
The authors jointly conceived and designed this study. PH synthesised the ribozymes and performed the kinetic and stability analyses. All authors read and approved the final manuscript.
==== Refs
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| 15588292 | PMC544870 | CC BY | 2021-01-04 16:30:50 | no | BMC Chem Biol. 2004 Dec 9; 4:1 | utf-8 | BMC Chem Biol | 2,004 | 10.1186/1472-6769-4-1 | oa_comm |
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Epidemiol Perspect InnovEpidemiologic perspectives & innovations : EP+I1742-5573BioMed Central London 1742-5573-1-61560691310.1186/1742-5573-1-6SoftwareWINPEPI (PEPI-for-Windows): computer programs for epidemiologists Abramson Joseph H [email protected] School of Public Health and Community Medicine, Hebrew University, Jerusalem, Israel2004 17 12 2004 1 6 6 10 6 2004 17 12 2004 Copyright © 2004 Abramson; licensee BioMed Central Ltd.2004Abramson; 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 WINPEPI (PEPI-for-Windows) computer programs for epidemiologists are designed for use in practice and research in the health field and as learning or teaching aids. They aim to complement other statistics packages. The programs are free, and can be downloaded from the Internet.
Implementation
There are at present four WINPEPI programs: DESCRIBE, for use in descriptive epidemiology, COMPARE2, for use in comparisons of two independent groups or samples, PAIRSetc, for use in comparisons of paired and other matched observations, and WHATIS, a "ready reckoner" utility program. The programs contain 75 modules, each of which provides a number, sometimes a large number, of statistical procedures. The manuals explain the uses, limitations and applicability of specific procedures, and furnish formulae and references.
Conclusions
WINPEPI provides a wide variety of statistical routines commonly used by epidemiologists, and is a handy resource for many procedures that are not very commonly used or easily found. The programs are in general user-friendly, although some users may be confused by the large numbers of options and results provided. The main limitations are the inability to read data files and the fact that only one of the programs presents graphic results. WINPEPI has a considerable potential as a learning and teaching aid.
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Background
This paper describes the WINPEPI (PEPI-for-Windows) programs recently added to the PEPI suite of computer programs for epidemiologists, and discusses some of their uses and limitations. The programs were developed for use in practice and research in the health field and as learning or teaching aids.
PEPI (an acronym for Programs for EPIdemiologists) grew from a set of programs for programmable pocket calculators that was published in 1983 to "make life easier for investigators, extend the use of appropriate analytic methods, and enable researchers to concentrate on substantive issues rather than on procedural technicalities" [1]. The first version of PEPI appeared in 1993 [2], and was followed by version 2 (where the name "PEPI" was first used, in 1995) [3], version 3 (in 1999) [4], and version 4 (containing 43 programs, in 2001) [5].
The original programs were DOS-based. The first WINPEPI program, WHATIS, was included in version 4 of the PEPI package, a review of which stated: "WHATIS, the only Windows program, is our pick for the best program in PEPI. If all the programs could be converted into the WHATIS type of format, PEPI will be a truly outstanding package!" [6]. Four WINPEPI programs, containing 75 modules, have so far been issued. They provide many procedures not offered by the DOS-based programs, but do not include all those provided by the latter (which can be run in Windows as DOS applications, complementing the WINPEPI programs).
Implementation
There are at present four WINPEPI programs, DESCRIBE, COMPARE2, PAIRSetc, and WHATIS. The programs are free, and can be downloaded from the Internet. New versions have been issued at frequent intervals. Comprehensive manuals are provided. These furnish full information about each module, including explanations of the uses, limitations and applicability of specific procedures, and formulae or references.
DESCRIBE
DESCRIBE has 14 modules for use in descriptive epidemiology. It can appraise rates or proportions and categorical or numerical data (including survival data), examine a sequence of rates or other values (including the appraisal of seasonal variation), perform direct and indirect standardization, estimate prevalence from a cluster or stratified sample or by the capture-recapture method, determine required sample sizes, and appraise screening or diagnostic tests (with procedures for use in meta-analyses of studies of these tests).
COMPARE2
COMPARE2 has 28 modules for use in comparisons of two independent groups or samples, and may be used to analyze cross-sectional, cohort and case-control studies, and trials. It can compare proportions or odds, risks, rates, and categorical and numerical data (including survival data), appraise the effect of misclassification, and determine power and sample size for a variety of tests.
The program can deal with stratified data, analyzing the combined strata as well as each stratum; it permits the control of possible confounding by the stratifying variable or variables, and the assessment of heterogeneity as an indication of effect modification. It can be used in meta-analyses, to compare study results and, if warranted, combine them.
PAIRSetc
PAIRSetc has 29 modules for use in comparisons of paired and other matched observations, such as matched-control trials and cohort studies, matched case-control studies, before-after studies, and reliability studies that compare replicate observations or methods of measurement. The "etc" in its name indicates the program's ability to deal with matched sets larger than pairs. The program can compare dichotomous, categorical and numerical data (including paired survival data), appraise the effect of misclassification, and determine power and sample size for a variety of tests and for measuring kappa or intraclass correlation coefficients. Like COMPARE2, PAIRSetc can deal with stratified data.
WHATIS
WHATIS is a "ready reckoner" utility program with four modules. It provides a calculator (expression evaluator) that stores values and formulae, enabling them to be recalled when needed, and it computes confidence intervals for a variety of statistics, P-values corresponding to given values of z, t, chi-square, or F, or vice versa, and time-spans.
The modules
Some of the modules have very specific purposes; for example, to determine the sample size required to perform a specific test with a given power or precision, or to appraise the effect of misclassification in a given situation by computing the "true" findings that would give rise to the observed findings. Other modules provide many statistical procedures, as is illustrated by the following summaries of what two of the richer modules do. A module may not only provide numerous tests and measures, it may also use alternative methods of estimation.
Comparison of proportions or odds (module A of COMPARE2)
After entry of a 2 × 2 table, this module provides exact one-tailed and two-tailed tests (Fisher's, mid-P, and Overall's continuity-corrected tests and Tocher's test), chi-square tests (with and without Yates's, Upton's, and Haber's corrections), an optional equivalence test, the ratio of proportions (with its standard error and 90%, 95% and 99% confidence intervals and Jewell's low-bias estimator), the difference between proportions (with its standard error and 90%, 95% and 99% confidence intervals computed by Fleiss's procedure and by Wilson's score method, without and with a continuity correction), the odds ratio (with 90%, 95% and 99% confidence intervals – Cornfield's and exact Fisher's and mid-P intervals – and Jewell's low-bias estimator), Yule's Q, phi, and lambda. Separate results are shown for studies in which inverse sampling was used.
For stratified data, the combined analysis provides Fisher and mid-P exact and Mantel-Haenszel tests, an optional equivalence test, three estimators of the overall ratio of proportions and of the overall difference between proportions (precision-based, Mantel-Haenszel, and DerSimonian-Laird estimators, with 90%, 95%, and 99% confidence intervals), and four estimators of the overall odds ratio (conditional and unconditional maximum-likelihood estimators, a Mantel-Haenszel estimator, and a DerSimonian-Laird estimator, with 90%, 95%, and 99% Fisher's, mid-P, Mantel-Haenszel, Cornfield-Gart, and Dersimonian-Laird intervals), heterogeneity tests and measures (H and I-squared, with their 95% confidence intervals), and (for meta-analysis) estimates of the fail-safe N and two tests for a skewed funnel plot (regression asymmetry and adjusted rank correlation tests).
Appraisal of numerical data (module D of DESCRIBE)
This module appraises a frequency distribution, and also appraises a sequence of numbers. It describes the frequency distribution in terms of its central tendency (the mean, with its standard error and 90%, 95% and 99% confidence intervals, three robust estimators of the mean, the geometric mean, and the median, with its 95% confidence interval) and dispersion (quantiles, standard deviation, variance, mean deviation from the mean, and median absolute deviation from the median), and it performs the Grubbs test for outliers. The shape of the frequency distribution is appraised in terms of symmetry or skewness (Bowley's quartiles-based skewness coefficient, Randles-Fligner-Policello-Wolfe test, Wilcoxon signed-rank test of symmetry around the sample median) and peakedness or flatness (Moors octiles-based kurtosis coefficient, Kolmogorov-Smirnov test for an even distribution). The shape of the frequency distribution is pictured in box-and-whisker diagrams, for both raw and log-transformed data. Two tests for normality (Lilliefors and D'Agostini-Pearson tests) are applied to the raw and log-transformed data. The median or mean can be compared with a hypothetical value (using a t-test and Wilcoxon's signed-ranks test), and the Poisson dispersion test for heterogeneity is done (appropriate only if the values that were entered are counts).
If a sequence of numbers is entered, it is tested for randomness (two runs tests, an up-and-down-runs test, and the mean square successive difference test), trend (Mann-Kendall and Cox-Stuart tests – including a test controlling for seasonal variation), a change-point, and centrifugality. The module provides Sen's estimator of slope, parametric and nonparametric linear regression analyses, and Spearman, Kendall's, and Pearson's correlation coefficients, and it smooths the curve, using procedures based on running medians and on Fourier transforms. Regression lines, smoothed curves, and the change-point are shown in a graph.
Operating the Programs
There is no special installation procedure; the programs need only be put in a folder of the user's choice.
The appropriate program and module must first be selected. As an aid, a Pepi Finder (a Windows help file, FINDER.HLP) is provided; it is called up by clicking on its icon, and can be printed for easy reference. The Pepi Finder is an alphabetical index that shows which programs and modules deal with a specified procedure, measure, or kind of study. As seen in the excerpt shown in Figure 1, the four WINPEPI programs are colour-coded. The Finder may point to more than one module; the entry for "Case-control study, unmatched", for example, is "COMPARE2 C,G". When COMPARE2 is opened (Figure 2) it is clear that its module G is designed for a case-control study with more than two exposure categories. The index also includes procedures provided by PEPI DOS programs (shown in italics) but not by WINPEPI programs.
Figure 1 PEPI FINDER: Excerpt.
Figure 2 COMPARE2: Opening screen.
Each program has an opening screen (Figure 2) that displays a main menu and a top menu. Except in WHATIS, data entry is possible only after a selection has been made; a data-entry screen then appears. As an example, if option F of COMPARE2 is selected, i.e. "Categorical data (2 × k table)" (see Figure 2), the opening screen is replaced by the data-entry screen shown in Figure 3.
Figure 3 COMPARE2: Data-entry screen (for 2 × k table).
The programs do not read data files, but require the entry of data that have already been counted or summarized, either manually or by using statistical software that processes primary data. The data can be entered at the keyboard, or (in multiple-entry boxes for the entry of tables) can be "pasted" from a file in which they are available. Once entered, tabular data can be pasted to a text file for future re-use by pasting. Alternative forms of data are often accepted, e.g. numerators instead of rates or proportions, and either individual or grouped observations. Warning messages are shown if obvious errors are made when entering data or if essential items are omitted.
Simple on-screen instructions are provided, using simple language. For example, dichotomous variables are referred to as "yes-no" variables, and metric-scale observations, continuous or discrete, as "numerical". The term "rate" is used both for rates that have person-time denominators (e.g. incidence density) and for measures whose denominators are numbers of individuals (e.g. prevalence and risk); when the distinction is important, this is indicated. The instructions make use of terms well-known to epidemiologists, such as "case-control study", "exposed" and "not exposed", and "risk factor". (If the programs are used outside an epidemiological context, allowance must be made for their epidemiological labels.)
To simplify operation, the program generally performs and reports all the prescribed procedures that the data will permit, without requiring choices by the user. But some options may be offered. In Figure 2, for example, three options are shown: the categories may be nominal or ordinal, the scores allotted to the categories can be changed, and there is an option for performing a very specific kind of follow-up study. If "nominal" is checked instead of "ordinal", the instructions change, and the only option is for the partitioning of chi-square. Clicking on an option may modify the procedures a module performs, the manner in which the computation is done (e.g. depending on whether number-of-individuals or person-time denominators are entered, or whether a normal distribution can be assumed), and the data requirement (e.g. monthly or weekly or daily data for the appraisal of seasonal variation). Choice of an option may also modify the output. For example, the module that does a meta-analysis of studies of screening or diagnostic tests and produces forest plots for sensitivity, etc., permits optional display or suppression of the detailed numerical results for all studies.
Pop-up hints and help screens are provided.
Results are shown in an output screen (Figure 4), from which it is easy to return to the main menu or the previous screen. Results automatically go to the Windows clipboard, from which they can be pasted to other files. Clicking on "View" in the top menu displays all results obtained in the current session. "Print" options are offered. By clicking on "Note" in the top menu, it is possible to add comments to the results, for pasting, printing, or saving. A "Repeat" button is provided, permitting repeated analyses of the same data with changed options.
Figure 4 COMPARE2: Results screen (for 2 × k table).
All results are saved in a disk file, unless the user changes this default. The WINPEPI package contains a utility program (JOINTEXT) that can merge result files.
DESCRIBE (but no other WINPEPI program) displays graphs – box-and-whisker plots, survival curves, seasonal peaks, regression lines, smoothed curves, forest plots, scattergrams, summary ROC curves, and graphs showing required sample sizes under different conditions. In most of the graphs, numerical values can be read by mouse-clicking at any location, optionally after magnifying a segment (zooming). Specimen graphs are shown in Figures 5 to 8 Figure 5 shows the number of clusters required for a cluster-based prevalence study (with stipulated requirements) for a true prevalence ranging from 5 to 20 per 100; the number can be read by clicking on the graph. Figure 6 shows a series of numerical observations, with regression lines, smoothed curves, and the change-point. Figure 7 shows post-test probabilities and net gain for a diagnostic test with a given likelihood ratio, for a range of pretest probabilities. Figure 8 shows a comparison of ROC curves, for use in appraising the effect of a covariate on the accuracy of a diagnostic test.
Figure 5 Number of clusters required for a cluster-based prevalence study.
Figure 6 A series of numerical observations. The straight lines are simple linear and nonparametric regression lines; the curved lines represent smoothing by two different methods; the red triangle marks the first point at which there is a significant change.
Figure 7 Post-test probabilities and net gain for a diagnostic test. Positive likelihood ratio = 10. The net gain is the absolute difference between pretest and post-test probabilities.
Figure 8 Comparison of ROC curves.
Documentation
Comprehensive manuals are provided. These furnish full information about each module, including explanations of the uses, limitations, and applicability of specific procedures, and formulae or references. The Pepi Finder serves as an index to the manuals.
Discussion
Criteria for the appraisal of statistical software for epidemiology [7] include not only its capabilities, but also "smoothness of the installation, simplicity of the interface, ease of use, completeness and statistical quality of the documentation, completeness and appearance of statistical graphics, accuracy of statistical computations".
The WINPEPI programs are easy to install and easy to use (with the reservations discussed below). Their documentation is very detailed and (at the price of repetitiveness) includes a separate self-contained description of each module. A regrettable shortcoming of WINPEPI is that only one of the programs, DESCRIBE, presents graphic results. This is because DESCRIBE is the only 32-bit program, and the graph unit used by WINPEPI [8] is appropriate only for 32-bit programs. As for accuracy, the programs have been tested extensively, and all errors found have been promptly corrected; but (to cite the PEPI manual), it unfortunately remains a truism that no computer software can be entirely problem-free.
But the WINPEPI programs do not provide data management facilities, and some other software package must be used if the data require processing. An epidemiologist or student whose data have been stored and maybe processed in another package, and who is well versed in the use of that package, may therefore have no need for the WINPEPI programs, despite their ease of operation, except when these do analyses not done by the other package. The WINPEPI programs aim "to complement – not replace – other statistics packages" [5].
Also (unlike the DOS-based PEPI programs for multiple logistic and Poisson regression analyses), the WINPEPI programs do not read data files. Data must be entered each time a program is used. This drawback is partly overcome by the possibility of pasting tabular data into data-entry boxes. But data entry can be tiresome, and users accustomed to programs that use data files may find it particularly vexatious. On the other hand, for some purposes keyboard entry may be seen as a boon: "Although conventional statistical software packages are adequate when you have a data set to work with, they are not always helpful when you need to do keyboard entry of data and rapidly perform simple analyses. For instance, you may want to replicate some analyses from a journal article and compute a Mantel-Haenszel odds ratio, or you may want to compute the sample size for your study while writing a grant proposal. Maybe you want to demonstrate to your students the impact of increasing sample size on the confidence intervals of a proportion. Perhaps you are a student and would like to do your epidemiology or biostatistics homework with some easy-to-use analytical routines... It is in this niche area that PEPI scores!" [6].
A criticism of version 3 of PEPI as being insufficiently user-friendly [9] led to a major revision in version 4. In the WINPEPI programs, user-friendliness is maximized by the provision of the Pepi Finder, simple on-screen instructions, pop-up hints and help screens, and warning messages, by streamlined data-entry procedures, which accept alternative forms of data, by the automatic saving of results, by the ease with which results can be recalled, annotated, printed, and pasted, and sometimes by the provision (in the output screens) of comments on the applicability of specific results.
Unfortunately the wide variety of statistical procedures that is offered makes the WINPEPI programs less convenient to use; versatility carries a price. Even the provision made for the entry of alternative forms of data, meant as a convenience, necessitates a decision and may hence be an inconvenience – for example, a simple comparison of two proportions (using module A of COMPARE2) requires a choice between entry of four frequencies, of numerators and denominators, or of proportions and denominators.
The DOS-based PEPI package elicited the comments "there are so many modules that sometimes it is difficult to remember which one to use" [10] and, with less restraint, "it is comprised of a large number of separate modules, which can make it a pain to use" [11]. The Pepi Finder was introduced (in version 3 of the package) to mitigate this problem. The advent of the WINPEPI programs, with their added statistical procedures, increased the potential for confusion and hence the value of the Finder, both for finding what program and module to use, and as an index to the detailed descriptions supplied in the manuals.
The possibility of confusion is of course much reduced by the fact that related modules – for example, those concerning comparisons of two independent samples – are concentrated in the same WINPEPI program. Having opened the appropriate program, the user need only click on the kind of analysis that is required. But even that may tax some users. In COMPARE2, for example, a choice between modules B and D (see Figure 2) requires an awareness of whether the denominators are number-of-individuals or person-time ones.
A further penalty for WINPEPI's versatility is that users may be confused by the large number of results in the output, some of them of little or no obvious relevance. As described above, module A of COMPARE2 (for a 2 × 2 table), for example, provides numerous "exact" and chi-square tests, and three measures of association, with confidence limits computed by different methods, as well as other results, including some that are valid only if inverse sampling was used. Similarly, module D1 of PAIRSetc (for paired numerical observations) provides three tests, six intraclass coefficients and a number of other measures of agreement, appropriate for different purposes. For this reason, every WINPEPI manual carries the admonition: "This program offers more options than most users will ever need, and will usually display more results than are needed. Ignore the options and results you don't require". (This of course assumes that the user knows what he or she wants.)
But while all the results cannot be of interest to an ordinary user, each of them may be of interest to some users. As pointed out in a review of epidemiological software [11], "what one person might call 'statistical clutter' might be desirable to other people or even to that person if the person learned about that statistic". A review of PEPI says "Will you need all the programs in PEPI? Probably not. We have, for example, never used the Jonckheere-Tepstra test for trend or the Kullback-Leibler distances. However, more is good..." [6]. If a user wishes only to compute kappa, it can do no harm if the output provides extra results that draw attention to the fact that kappa has a ceiling value, or that its value can be adjusted to avoid paradoxical results. The user may be stimulated to use some of the additional procedures, after (if necessary) learning more about them. The manuals carry the warning: "It is unwise to use a statistical procedure whose use one does not understand. This manual cannot supply this knowledge, and it is certainly no substitute for the basic understanding of statistics and epidemiological thinking that is essential for the wise choice of methods and the correct interpretation of their results".
The provision of alternative tests, and estimators based on alternative methods, may of course be confusing, whatever explanatory comments may be offered in the output or the manuals. But it may permit a knowledgeable user to select the method most appropriate in a particular situation, and it serves as a reminder to the less knowledgeable user that different methods exist, based on different assumptions and using different models, most of them yielding approximations, and none of them having absolute validity for all purposes, and as a warning that caution is indicated if different methods lead to very different conclusions. "Exact" results computed in different ways differ, and "exact" probabilities and confidence intervals are not always preferable to probabilities and confidence intervals computed in other ways.
The length of the list in the Pepi Finder testifies to the wide variety of statistical routines offered. "The programs cover an amazing array of applications", says one review [6]. PEPI has repeatedly been called a "Swiss army knife" of utilities for epidemiologists and biomedical researchers [6,12,13]. One reviewer added, "one will find here more analytic options for a simple 2 × 2 or 2 × K table than will probably be needed during an entire epidemiology career" [13]. Another compared several packages when estimating sample size for a matched case-control study, and "found that PEPI provides an output richer than others do. This feature is common to other programs in PEPI" [14].
PEPI is of course very far from being a complete compendium of statistical routines for epidemiologists. It does not, for example, provide Cox regression, log-linear analysis, multiple regression analysis, procedures for the study of disease clustering, and many other procedures of interest to epidemiologists [7,11], which must be sought elsewhere. But it is a handy resource for many routines that are not very commonly used or very easily found, such as those concerned with misclassification, meta-analysis, reliability studies, the appraisal of screening and diagnostic tests, the equivalence of two proportions or means, cluster samples, inverse sampling, capture-recapture studies, serial correlation of residuals, skewed funnel plots, direct standardization using age intervals as weights, smoothing of curves, generalized odds ratios for ordinal data, quantitative measures of heterogeneity, harmonic analysis in the study of seasonality, and bias-adjusted and prevalence-adjusted bias-adjusted estimates of kappa (a feature picked out as "unique" in one comparison of PEPI with other epidemiological software [11]).
From the viewpoint of veterinary epidemiologists, a shortcoming of WINPEPI is its use of "person-time" and not "animal-time". But they are doubtless used to this.
WINPEPI's potential as a learning and teaching aid is worth stressing. Students welcome the facts that the package is free and requires no special installation procedure, and that (unlike major general-purpose statistical packages) it uses epidemiological language and provides results that are meaningful to epidemiologists. They rapidly learn to use the Pepi Finder and the programs themselves. They find the programs easy to use, although they may at first be confused by the multiplicity of modules and results; but they rapidly learn to focus on the specific modules and results that interest them, and to disregard others. At the same time, the rich output may serve to acquaint the student with other measures and tests, and excite interest in them. The weight the programs give to measures of association and their confidence intervals may help to counteract the belief that significance testing is the be-all and end-all of an analysis.
"PEPI facilitates a ready understanding of important epidemiologic concepts, unfettered by the complexities of statistical programming", says a reviewer [6]. With appropriate data, for example, the Mantel-Haenszel results provided by COMPARE2's module B can serve as an object lesson on the assessment of confounding and effect modification, the control of confounding, and appraisal of the defensibility of a summary odds or risk ratio. The student can concentrate on analysis and interpretation, with no need to get involved in data management, sorting and tabulation.
A useful feature is that, by clicking on the "Repeat" button and making changes to the data or options, students can easily do "what if?" exercises [15]. For example, they can easily learn, by manipulating data, how differences in prevalence or the number of controls per case can alter the required sample size, or how consideration of cost can alter sample size decisions in stratified sampling, or how the sensitivity or specificity of measures can alter a prevalence estimate or an odds or risk ratio. The sensitivity analysis provided by a module in COMPARE2 can demonstrate how markedly a single aberrant result can affect the results of a meta-analysis. Using the "misclassification" modules, it may be a salutary experience for students – and possibly also for some more experienced epidemiologists – to learn that an observed prevalence of 120 per 1000, using a measure whose sensitivity and specificity are 90%, points to a true prevalence of only 25 per 1000, or to find how inaccurate their guesses about the effect of misclassification on an odds or risk ratio can be.
A recent epidemiology textbook makes frequent use of PEPI in its exercises "to relieve students from some of the tedium and anxiety of hand calculation, while opening up possibilities of using advanced techniques that might not otherwise be available. It is time to familiarize even introductory students to these essential tools of the trade" [16].
Conclusions
WINPEPI complements other statistics packages. It is versatile, providing a wide variety of statistical routines commonly used by epidemiologists, but is far from being a complete compendium of such routines. It is a handy source of many procedures that are not very commonly or easily found.
The programs are in general user-friendly, although some users may be confused by the large numbers of options and results provided. The main limitation is the inability to read data files, but tabular data can be entered by pasting, and for some purposes keyboard entry of data is an advantage. Only one of the programs presents graphic results.
WINPEPI has a considerable potential as a learning and teaching aid.
Availability and requirements
The current version (at the time of this writing) of the software is available for free download as an additional file (WINPEPI.ZIP) attached to this article. It includes the programs, their manuals, and the Pepi Finder. Subsequent versions will be available at http://www.brixtonhealth.com for free download. Information about the latest WINPEPI version can be found at http://www.sagebrushpress.com/pepibook.html, where the DOS-based programs are available for free download.
The programs and manuals are copyrighted, but may be freely copied and distributed for personal use; they may not be exploited commercially without permission.
COMPARE2, PAIRSetc, and WHATIS are 16-bit programs (written in Delphi version 1) that can be run in any version of Windows. DESCRIBE is a 32-bit program (written in Delphi version 5), and can be run in any version of Windows except Windows 3.
The manuals for DESCRIBE, COMPARE2, and PAIRSetc are in PDF format, and can be read or printed with Adobe Acrobat. WHATIS is documented in the version 4 manual [5].
Competing interests
The author wrote the WINPEPI programs and manuals and is co-author of the DOS-based programs and manual, and hence may be biased in their favour.
Supplementary Material
Additional File 1
WINPEPI package. WINPEPI programs, with manuals and Pepi Finder.
Click here for file
Acknowledgements
The author is indebted to Paul Gahlinger, who wrote the original DOS-based programs and introduced him to the mysteries of Pascal programming, to Kevin Sullivan, who suggested the creation of a Windows version, and to Garry Anderson, Mark Myatt, Ray Simons, and many other colleagues and users of the programs, for their suggestions, criticism, and practical help.
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Ann Clin Microbiol AntimicrobAnnals of Clinical Microbiology and Antimicrobials1476-0711BioMed Central London 1476-0711-4-11563435710.1186/1476-0711-4-1ReviewHelicobacter pylori and gastroduodenal pathology: New threats of the old friend Ahmed Niyaz [email protected] Leonardo A [email protected] Pathogen Evolution Group, Centre for DNA Fingerprinting and Diagnostics (CDFD), Hyderabad, India2 Department of Biomedical Sciences, University of Sassari, Sassari, Italy2005 5 1 2005 4 1 1 29 11 2004 5 1 2005 Copyright © 2005 Ahmed and Sechi; licensee BioMed Central Ltd.2005Ahmed and Sechi; 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 human gastric pathogen Helicobacter pylori causes chronic gastritis, peptic ulcer disease, gastric carcinoma, and mucosa-associated lymphoid tissue (MALT) lymphoma. It infects over 50% of the worlds' population, however, only a small subset of infected people experience H. pylori-associated illnesses. Associations with disease-specific factors remain enigmatic years after the genome sequences were deciphered. Infection with strains of Helicobacter pylori that carry the cytotoxin-associated antigen A (cagA) gene is associated with gastric carcinoma. Recent studies revealed mechanisms through which the cagA protein triggers oncopathogenic activities. Other candidate genes such as some members of the so-called plasticity region cluster are also implicated to be associated with carcinoma of stomach. Study of the evolution of polymorphisms and sequence variation in H. pylori populations on a global basis has provided a window into the history of human population migration and co-evolution of this pathogen with its host. Possible symbiotic relationships were debated since the discovery of this pathogen. The debate has been further intensified as some studies have posed the possibility that H. pylori infection may be beneficial in some humans. This assumption is based on increased incidence of gastro-oesophageal reflux disease (GERD), Barrett's oesophagus and adenocarcinoma of the oesophagus following H. pylori eradication in some countries. The contribution of comparative genomics to our understanding of the genome organisation and diversity of H. pylori and its pathophysiological importance to human healthcare is exemplified in this review.
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Introduction
Helicobacter pylori is a bacterium that colonizes the harshly acidic milieu of the human stomach. More than half of the world's population carries this infection. Infection rates vary among the developed and developing countries of the world. H. pylori infection is on a steep decline in most of the western countries mainly due to the success of combination therapies and improved personal hygiene and community sanitation to prevent re-infection. However, the situation is not improving in many of the developing countries due to failure of treatment regimes and emergence of drug resistance. The infection in some cases leads to chronic superficial gastritis, chronic active gastritis, peptic ulcer disease and gastric adenocarcinoma [1-4]. One of the most distinctive features of H. pylori is the genetic diversity between clinical isolates obtained from different patient populations. Most H. pylori isolates can be discriminated from others by DNA profiling [5-8] or sequencing of corresponding genes due to mainly a high degree of sequence divergence between orthologs (3–5%) [9,10]. Also, H. pylori has a panmictic or freely recombining population structure [11] and is naturally competent [12]. These characteristics facilitate an inter-strain recombination due mainly to horizontal exchange of alleles from other strains colonising the same niche, which is extremely common in H. pylori chromosome. However, such genetic recombinations in the H. pylori genome might not be deleterious because they occur in the plasticity zone, a special cluster of DNA rearrangements that protects the essential complement of genes by acting as a bed for foreign DNA insertion or abrogation. DNA loss and rearrangement are therefore a norm for H. pylori, and flexibility and diversity in gene content may contribute to bacterial fitness in different members of the diverse human host population. Post genomic analyses have revealed interesting attributes of H. pylori pathogenicity and novel mechanisms of causation of ulcer disease and cancer have been envisaged. Efforts to know the cause and potential benefits of the genetic diversity of this bacterium has led to some interesting discoveries relating to its co-evolution with the human host, microevolution while during infection and quasi-species development, virulence determinants and eradication strategies. Recent studies reviewed herein collectively aim at testing the speculation about whether H. pylori may be beneficial to human health in certain circumstances and whether eradication of this organism is always necessary? Epidemiological studies are needed in the context of such intriguing hypotheses. Results obtained from such studies might enable the development of a high-throughput screening system for high-risk groups within the huge population of H. pylori-infected individuals. Recent studies have shown that H. pylori infection protects against gastro-oesophageal reflux and oesophageal carcinoma. So it will be important to selectively eradicate H. pylori in people that are at the highest risk of developing gastric carcinoma. Eradication of highly pathogenic H. pylori specifically from high-risk groups would markedly reduce the worldwide incidence of ulcer and gastric cancer.
Epidemiology and Evolution
H. pylori infection is usually acquired during childhood, where transmission occurs predominantly within families [13]. A couple of recent studies demonstrated the possible co-existence of a large array of clonal lineages within H. pylori populations that are evolving in each individual separately from one another [14,10]. It is therefore probable that via this semi-vertical transmission of H. pylori strains, there are distinct sets of H. pylori genotypes colonising different human populations. With different strains evolving separately of one another and the fact that H. pylori is a genetically diverse (panmictic) organism, distinct genotypes have been found to be associated with particular geographic regions [15,16]. For example, the shuffling of variant regions within the vacA gene (a gene encoding a vacuolating cytotoxin) within a local H. pylori population has led to predominant vacA genotypes being characteristic of isolates from different geographic regions. In addition, worldwide studies encompassing H. pylori isolates from many geographic regions have demonstrated weak clonal groupings and geographic partitioning of H. pylori isolates [9,17]. If recombination only occurs between a resident H. pylori population, exchange of genetic sequences can genetically homogenise this population. As H. pylori is naturally competent and recombination occurs frequently [11], specific genotypes associated with different geographic regions occur as a result of this homogenising force.
Introduction of polymorphisms and sequence variants from one H. pylori population from a particular geographic region to another H. pylori population from another geographic region via human migration makes the association of particular genotypes with specific geographic locations more difficult. Although the introduction of new polymorphisms into a particular H. pylori population poses a problem with identifying specific genotypes within certain geographic locales, it may, however, provide information on the ancestry of the hosts in whose stomachs the strains were carried. Studies have been aimed at demonstrating the path of human migration to Latin America with conflicting results regarding whether European or Asian populations brought H. pylori to South America [16,11]. However, a recent and comprehensive study by Flaush et al. [19] demonstrated that sequence analysis of H. pylori isolates recovered from twenty-seven countries displayed geographic partitioning. Thus, polymorphisms within the H. pylori genome can serve as useful markers for studying ancient human migrations. However, a mix up of H. pylori strains between migrated and native populations can sometimes complicate analysis. Accordingly, the study of migrated populations that have remained isolated from the native populations is essential.
Genome organization, genetic diversity and microevolution
Since its successful isolation in 1983 by Warren and Marshall, H. pylori has been linked to various pathologies and a strong association with gastric carcinoma and mucosa-associated lymphoid tissue lymphoma [1,3] has been established. However, although H. pylori is definitely responsible for these diseases, only less than 10% of people colonized with H. pylori portray disease symptoms. This suggests that specific H. pylori strains may be responsible for virulence in different hosts. Many studies have shown that certain allotypes of the vacA gene and the presence a functional cagA gene are associated with an increased risk of peptic ulceration and gastric cancer, respectively [20-22]. However, these correlations vary based on the host population studied and efforts to correlate other H. pylori alleles with clinical diseases have failed.
So how do a few H. pylori strains trigger higher virulence as compared to other strains? Current approaches in functional genomics based on protein- protein interaction and microarray based transcription profiling are helping to decode this mystery. Functional genomics often uses the gene chip based expression profiling to provide a condition-dependent and time-specific genome wide profile of an organism's transcriptome [23,24]. Whereas, comparative genomics juxtaposes two or more genome sequences at the level of gene content and organization [25,26]. Both the approaches harness extensive computer algorithms and in silico modelling to summarise gene encoded (or putative) functions. H. pylori has been the first prokaryote wherein full genome sequence of two different patient isolates [J99 and 26995] were characterized and compared [27,28]. As H. pylori is a freely recombining or panmictic organism [11] the question of whether the two genome sequences would accurately represent the myriad of genetic diversity found among the strains was posed. Since the 2 sequenced strains were obtained a decade apart from the two different continents and cultured from the lesions of different gastric disorders, it has been widely assumed that their genome sequences will more likely portray the genetic diversity exhibited by clinical isolates. Comparative genomic analysis of the two completely sequenced strains revealed a significant amount of genetic variation between their genomes. For instance, the J99 genome is shorter (1,643,831 bp) than that of strain 26695 (1,667,867 bp) and has 57 less predicted ORF's [27,28]. Strains 26695 and J99 contain 110 and 52 strain specific genes, respectively [29,30], in which more than a half reside within a locus termed the plasticity cluster. A recent approach helped revised annotation and comparison of the two sequenced H. pylori genomes [31] and reclassified the coding sequences. Based on this study the total number of hypothetical proteins was reduced from 40% to 33%. A large amount of size variation was also discovered between orthologous genes mostly due to natural polymorphisms arising as a result of natural transformation and free recombination within H. pylori chromosome. Recombinational events including the presence of insertion elements, pathogenicity islands, horizontally acquired genes (restriction recombination genes), mosaics and chromosomal rearrangements were frequently annotated in subsequent bioinformatics based attempts. It has been argued that such diversity is a result of a lack of direct competition between strains, even when resident within different individuals within the same community [32]. However, a recent study has demonstrated that integration of foreign gene fragments acquired via natural transformation is often prevented by the well-developed restriction-modification systems in H. pylori genome [33]. It has been demonstrated that H. pylori has extensive, non-randomly distributed repetitive chromosomal sequences, and that recombination between identical repeats contributes to the variation within individual hosts [34]. That H. pylori is representative of prokaryotes, especially those with smaller (<2 megabases) genomes, that have similarly extensive direct repeats, suggests that recombination between such direct DNA repeats is a widely conserved mechanism to promote genome diversification [33]. In addition, although H. pylori has been termed as a panmictic organism [11,35], it is surprising that clonal lineages within H. pylori populations exist [36-39]. Recent reports demonstrated that H. pylori in some populations shows a clonal descent and suggest that a large array of H. pylori clonal lineages co-exist, which evolve in isolation from on another [14]. Moreover, in certain parts of the world H. pylori isolates have been shown to exhibit little genetic heterogeneity, based on fingerprint profiles [17,40]. Functional genomics, utilising microarray technology, has provided researchers with a powerful tool to investigate the genetic diversity of clinical isolates [41], the transcriptional profiles of isolates grown under different conditions [42], the identification of strain-specific and species-specific genes [29,30] and the diversity between strains giving rise to differing clinical illnesses [43]. One of the interesting findings using microarray based genotyping has been the discovery that H. pylori isolates undergo 'microevolution' and give rise to sub-species during prolonged colonisation of a single host [44-46]. The presence of stable sub-species within a single individual suggests an adaptation of a H. pylori population to specific host niches, facilitated by unknown advantages conferred to them by select plasticity region genes. Bjorkholm et al. [45] demonstrated that several loci differed within two genetically related isolates from the same host, one major difference being the presence of the cag pathogenicity island (cag PAI) in one isolate and not the other. As the cagA gene and cag PAI are principle virulence factors within the strains, the excision or abrogation of the cag PAI within a strain may indicate that attenuating the virulence of a strain could be a favourable adaptation.
The conundrum of strain diversity: How many more genome sequences do we need to understand this bug?
Within the bacterial populations, genome content may not be fixed, as changing selective forces favour particular phenotypes; however, organisms well adapted to particular niches may have evolved mechanisms to facilitate such plasticity. The highly diverse H. pylori is a model for studying genome plasticity in the colonization of individual hosts. For H. pylori, neither point mutation, nor intergenic recombination requiring the presence of multiple colonizing strains, is sufficient to fully explain the observed diversity.
The two H. pylori genomes sequenced to date are each from ethnic Europeans, and genomic comparisons modelled on these data are sufficient to identify novel loci from new strains, especially from understudied Asian populations. However, these genome sequences may not be fully representative of the entire diversity of the gene pool. Identification and characterization of such loci which are more abundant in the Asian gene pool may lead to newer insights into the mechanisms of H. pylori colonization, carriage and virulence in the countries of Asia which are more seriously under threat from H. pylori. Therefore, future high throughput efforts involving a large number of strains are clearly needed. Taking the Indian example for instance, according to the Ethnologue database , there are about 1683 languages and dialects ('mother tongues') in this country and H. pylori diversity therefore can be assumed to coincide with this figure. So one has to roughly look at the inter-strain genomic diversity contributed by approximately 1683 different strains representing each dialect and or a community.
Nonetheless, genotypic data from each geographic area or a community is extremely vital and might constitute a missing piece of a large, biologic jigsaw puzzle.
Natural competence and transformation
Independent of the other two pathogenesis associated type IV systems, H. pylori harbours a dedicated type IV apparatus, the comB gene cluster [47] linked to the natural transformation and competence. The comB gene cluster is essential for the bacterium to take up plasmid and chromosomal DNA during natural transformation. To identify the genes essential for natural transformation competence in H. pylori, genetic approach of transposon shuttle mutagenesis has been used and the comB locus was located, consisting of orf2 and comB1-comB3 [48,49]. This cluster contains four tandemly arranged genes, ORF2, comB1, comB2 and comB3 as a single transcriptional unit. Subsequently, the components of comB cluster namely Orf2, comB1, comB2 and comB3 were renamed (according to homology with the Agrobacterium tumifaciens type IV secretory apparatus) as comB7, comB8, comB9 and comB10 respectively (Figure 1). Another ORF in HP26695, HP0017 was found to be homologous to the virB4 gene in Agrobacterium tumifaciens type IV secretory apparatus and was named as comB4 [50]. From this study it also appeared that each of the gene products of ORFs comB8 to comB10 were absolutely essential for the development of natural transformation competence. It appears that the comB transformation apparatus has evolved conservatively and is typically present in all the strains. This conservation is interestingly in agreement with the need for genomic fluidity in H. pylori where deletions and rearrangements due to natural transformation and transposition are the norm. This is therefore necessary for the pathogen to keep the gene content flexible and as diverse as possible to probably acclimatise itself to diverse host niches during the process of infection. Both these systems, the cag-PAI encoded type IV export system and the transformation-associated type IV system seem to act completely independently, since the deletion of one system from the chromosome does apparently not affect the function of the other system.
Figure 1 Different outcomes of H. pylori infection. Some studies argue that eradication of H. pylori might trigger some of the worst forms of heart burns and increased acidity, leading ultimately to oesophageal cancer and or GERD.
Pathogenic apparatuses
The cag pathogenicity island
Molecular analysis of bacterial transport has been attempted in several bacterial pathogens. Among such transport systems, Type IV secretion systems have been described in greater detail in diverse bacteria. In H. pylori, 3 different kinds of type IV secretion apparatuses have been identified. The first such secretion system identified in H. pylori was the one comprised of 29 genes encoding the cag pathogenicity island (cag-PAI). One of the principal virulence factors of H. pylori, the cagA antigen is contained in the 40 kb cag-PAI. The tyrosine-phosphorylated cagA protein is translocated to the epithelial cells by the type IV secretion system (forming a sort of syringe like structure) [51-53]. Upon tyrosine phosphorylation, the cagA protein elicits growth factor like stimuli in epithelial cells (hummingbird phenotype) coupled with interleukin-8 induction for the recruitment of neutrophils. Mutations in several genes of the cag-PAI interfere with tyrosine phosphorylation and induction of interleukin-8 secretion [54]. In recent studies, in order to analyse which genes of the cag-PAI are essential for cagA translocation and/or interleukin 8 induction, a complete mutagenesis of the cag-PAI was performed [55]. In general, it appears that most of the cag genes are involved in assembly and arrangement of the secretory apparatus. Five of these genes namely HP0524 (virD4), HP0525 (virB11), HP0527 (virB10), HP0528 (virB9) and HP0544 (virB4/cagE) constitute the main apparatus of the type IV secretory system of H. pylori [56]. All these genes except HP0524 are associated with IL8 production [55]. However, the presence of strains eliciting IL 8 responses irrespective of intactness of the cag-PAI underlines the fact that it's 'not' the only factor linked to IL8 secretion [57].
Very recently, and for the first time, ultrastructure analysis of the surface of H. pylori 26695 has revealed a sheathed, surface organelle, coded by the cag-PAI genes, HP0527 (forms sheath around the pilus needle) and HP0532/ cagT (forms the base of the pilus) [58]. This structure, although uncommon, could be the special adaptation of H. pylori to the host niches and this might mediate biological as well as transport functions of the cag-PAI encoded proteins. Computational analyses to predict the macromolecular assemblies of such apparatuses are needed to have a more simplified understanding of the entire model of the H. pylori type IV secretion mechanism
Link with cancer: the oncogenic cagA protein
A large-scale prospective study revealed that the risk for development of gastric carcinoma was much greater in the H. pylori-infected population than in the H. pylori-uninfected population [59]. The cagA gene of H. pylori is assumed as partially responsible for eliciting signaling mechanisms that lead to the development of gastric adenocarcinoma. Based on the carriage of a functional cagA as a marker for the cag PAI, the H. pylori species is divided into cagA-positive and cagA-negative strains. The cagA-positive strains are associated with higher grades of gastric or duodenal ulceration and are more virulent than the cagA-negative strains [60]. Some epidemiological studies have demonstrated roles of cagA positive H. pylori in the development of atrophic gastritis, peptic-ulcer disease and gastric carcinoma [61,62]. The cagA gene product, cagA, is translocated to the gastric epithelial cells to undergo tyrosine phosphorylation by SRC family kinases [63]. Tyrosine phosphorylation is known to occur at the EPIYA motifs on the cagA. The cagA protein upon phosphorylation binds and activates a SHP2 phosphatase that acts as a human oncoprotein. As SHP2 transmits positive signals for cell growth and motility, deregulation of SHP2 by cagA is an important mechanism by which cagA-positive H. pylori promotes gastric carcinogenesis. Cag A is noted for its variation at the SHP2 binding site and, based on the sequence variation, it is sub- classified into two main types – East-Asian cagA and Western cagA. East-Asian cagA shows stronger SHP2 binding and greater biological activity than Western cagA. In East-Asian countries, endemic circulation of H. pylori strains that carry biologically active forms of cagA might underlie the high incidence of gastric carcinoma. One puzzling attribute of H. pylori infection is why some populations with high incidences of H. pylori infection, such as those in Japan and Korea, have high incidences of gastric carcinoma, whereas other highly infected populations, such as populations in central Africa, do not. Possible reasons could be the differences in genetic susceptibility among populations, environmental factors such as dietary habits, and strain differences of H. pylori. Among these, diversity of cagA in H. pylori strains might be involved in determination of the type and severity of disease. As discussed above, East-Asian and Western forms of cagA possess the distinctly structured tyrosine phosphorylation/ SHP2-binding sites – EPIYA-D and EPIYA-C, respectively [64]. Notably, the grades of inflammation, activity of gastritis, and atrophy are significantly higher in patients with gastritis who were infected with the East-Asian cagA-positive strain than in patients infected with the cagA-negative or Western cagA-positive strain [65]. Furthermore, the prevalence of the East-Asian cagA-positive strain is associated with the mortality rate of gastric cancer in Asia. Therefore, populations infected with East-Asian cagA positive H. pylori are at greater risk for gastric cancer than those infected with Western cagA-positive strains.
Among Western CagA species, the number of EPIYA-C sites directly correlates with levels of tyrosine phosphorylation, SHP2-binding activity and morphological transformation [64]. Furthermore, molecular epidemiological studies have shown that the number of EPIYA-C sites is associated with the severity of atrophic gastritis and gastric carcinoma in patients infected with Western CagA-positive strains of H. pylori [66].
The number-2 virulence determinant: vacuolating cytotoxin (VacA) of H. pylori
H. pylori has a single copy of the vacA gene. Screening of H. pylori chromosomal fragments permitted the identification of a 3864-base pair open reading frame (vacA) that encoded the vacuolating cytotoxin [67]. The sequence of the vacA gene includes a 33 amino acid signal sequence. With the exception of a hydrophobic region at the N terminus, the mature 90-kDa protein (amino acids 34 to 842) is mainly hydrophilic [67]. The cytotoxic activity of VacA has been shown to increase substantially under acidic conditions. VacA protein, a secreted 95 kD peptide, varies in the signal sequence (alleles s1a, s1b, s1c, s2) and/or its middle region (alleles m1, m2) between different H. pylori strains. The different combinations of s and m regions determine the production of cytotoxic activity. Strains with the genotype s1 m1 produce high levels of vacuolating cytotoxin in vitro. Strains with the genotype s2 produce an inactive toxin. Whereas, strains with the genotype m2 produce toxic activity with a different target cell specificity from those of m1 genotype. Genotypic variations in the vacA gene structure specific to a geographic locale have been recognised. While the vacA m1a allele is specific for the European strains [16], the vacA m1b genotype is typical of the Asian strains [68]. Yet another signal region genotype, s1c is also characteristic of the East Asians [69]. Among other functions, VacA selectively inhibits the invariant chain (Ii)-dependent pathway of antigen presentation mediated by the MHC class II and might induce apoptosis in epithelial cells. VacA, so far mainly regarded as a cytotoxin of the gastric epithelial cell layer, now turns out to be a potent immunomodulatory toxin, targeting the adapted immune system. Thus, in addition to the well-known vacuolating activity, VacA has been reported to induce apoptosis in epithelial cells, to affect B lymphocyte antigen presentation, to inhibit the activation and proliferation of T lymphocytes, and to modulate the T cell-mediated cytokine response.
The plasticity region cluster
The fascinating genomic landmark discovered post genomic era in both the sequenced strains is the one where 48% and 46% of the strain specific genes are located in J99 and 26695 respectively. This region is called as the 'plasticity zone' [28]. Genome sequence comparisons have revealed that nearly half of the strain specific genes fall in this zone. Recently, a new type IV secretion apparatus has been located in this plasticity zone [70]. This type IV cluster is comprised of 7 genes, homologous to the vir B operon of A. tumifaciens carried in a 16.3 kb genomic fragment called tfs3 (Figure 1). This cluster was discovered by Kersulyte et al. as a result of subtractive hybridization and chromosome walking and sequence homology. They also tested conservation of this island in clinical isolates and found that full length and partially disrupted tfs3 occur in 20% and19% of the strains respectively, from Spain, Peru, India and Japan. Although there is no correct role assigned to this cluster, it might be an unusual transposon linked to many deletion events occuring in the plasticity region that contribute to bacterial fitness in diverse host populations via exercising flexibility in gene content and gene order. The plastic nature of H. pylori and the evidence of horizontal transfer of genes from other H. pylori isolates and bacterial species could explain the ability of this organism to persist in a changing environment and why only a subset of clinical isolates exert an adverse effect on patients.
Link with cancer: are plasticity region genes involved?
The plasticity region as a whole displays certain characteristics of pathogenicity islands [71] with relatively low G+C content (35%) compared to the rest of the genome (39%). This region is about 45 kb long in J99 and 68 kb long in strain 26695. Genomic analysis revealed the region to be highly mosaic with a majority of the genes being transcribed suggesting their functional role. They also express protein level homology to various other recombinases, integrases and topoisomerases [72] accounting to natural transformation and recombination. In addition to these, many ORFs were identified as differentially expressed (JHP0927-JHP0928-JHP0931 and JHP-042-JHP0944-JHP0945-JHP0947-JHP0960). They share same chromosomal orientation and therefore they potentially represent a bacterial operon. It is interesting to study the expression or suppression of these ORFs in strains linked to different clinical conditions. Recent studies have posed a possibility to explore the presence of any new pathogenicity markers in the plasticity zone, although the functions of most of the putatively encoded proteins in this cluster are unknown. But they are thought to play a role in increasing the virulence capacity of H. pylori strains either directly or by encoding factors that could lead to variance in the clinical out come of the infection. More interestingly, it is also noted that some of the genes of the plasticity regions were co-inherited along with cagA. However their co-association with the disease status or with the severity of gastric inflammation was not established either due to small sample size or lack of clinical information [73]. Interestingly, a novel pathogenicity marker, JHP947 has been detected within the plasticity zone [74]. Many genes putatively linked to the development of gastric cancer have been assigned to the plasticity zone [72]. Researchers have looked for genetic markers in H pylori strains isolated from patients with gastric extranodal marginal zone B cell lymphoma (MZBL) of the mucosa-associated lymphoid tissue (MALT)-type and strains from age matched patients with gastritis only [75]. Two ORFs were significantly linked with gastric MZBL over gastritis strains: JHP950 (74% v 49%) and JHP1462 (26% v 3%). JHP950 proved specific for gastric MZBL when tested against a group of strains from patients with duodenal ulcer and patients with adenocarcinoma, with significant prevalence (49% and 39%, respectively), and is therefore the candidate marker for gastric MZBL. Interestingly, the candidate ORF JHP950 is located in the plasticity region of the J99 genome [75,76]. In view of such findings it can be speculated that some members of the plasticity region cluster provide selective advantage to some of the strains to adapt to changing host niches and become more and more invasive. In what way such advantage is gained? This needs to be discovered.
Do we need to eradicate H. pylori from this earth?
How long humans carried H. pylori is still controversial. However, it is accepted that this organism has colonized humans possibly for many thousands of years, and the successful persistence of H. pylori in human stomach for such a long period may be because this organism offers some advantages to the host. Unfortunately, the H. pylori infection is on steep decline in the western world. This is mainly due to the success rate of combination therapies and subsequent prevention of re-infection due to improvement in sanitation and personal hygiene. This may seem good news to many gastro-enterologists around the world, but having a H. pylori infection may be advantageous. A study has shown that H. pylori produces a cecropin-like peptide (antibacterial peptide) with high antimicrobial properties [77]. A German study revealed that children infected with H. pylori were less likely to have diarrhoea than children without an infection [78], implying that H. pylori may have beneficial properties to human hosts. Interestingly, there has been a marked decline in the instances of peptic ulcer disease and gastric cancer in the 20th century. Concurrent with this is a dramatic increase in the incidences of gastro-oesophageal reflux disease (GERD), Barrett's oesophagus and adenocarcinoma of the oesophagus in Western countries [79]. This observation led to the speculation that H. pylori may in some way be associated with these diseases and perhaps capable of preventing their onset. Studies have also shown that cagA+ H. pylori strains have a more protective effect than cagA- strains [80]. The presence of cagA+ H. pylori strains can reduce the acidity of the stomach, and it is believed that the raising of the pH by H. pylori prevents GERD, Barrett's oesophagus and adenocarcinoma of the oesophagus (Figure). Conversely, arguments have been made that, although H. pylori may prevent these reflux-associated diseases, the risks of acquiring gastric cancer via H. pylori infection far outweigh any possible benefits it may provide [81]. However, it has been stated that, if H. pylori does provide protection from GERD, the notion of restriction of anti-H. pylori treatment to only a few cases (peptic ulcer disease and MALT lymphoma) could be justified [82]. In spite of this controversy, recent reports have demonstrated a protective role for H. pylori in erosive reflux oesophagitis [83-85]. However, as safe and potent anitsecretory drugs to prevent gastro-oesophageal reflux are available [86] it seems unjustified to use a dangerous organism that has been associated with extremely dangerous outcomes such as a carcinoma.
On the other hand, eradication also is not an ultimate choice. Some ulcers recur even after successful eradication of H. pylori in non-users of non-steroidal anti-inflammatory drug (NSAID). In addition, the incidence of H. pylori-negative, non-NSAID peptic ulcer disease (PUD) (idiopathic PUD) is reported to increase with time. Moreover, it appears that H. pylori-positive ulcers are not always H. pylori-induced ulcers because there are two paradoxes of the H. pylori myth, first the existence of H. pylori-positive non-recurring ulcer and secondly, recurring ulcer after cure of H. pylori infection. To summarise, H. pylori is not the only cause of peptic ulcer disease. Therefore, it is still necessary to seriously consider the need for eradication in all cases of PUD, which may exist even after the elimination of H. pylori.
Conclusion and expert opinion
In our opinion, the intricacies of the role of H. pylori in health and disease may be fully ascertained only if we analyze genetic diversity of the pathogen as juxtaposed to the host diversity and the environment (food and dietary habits). A possible working hypothesis (that we are currently nurturing) may be that among the ocean of molecular host-pathogen interactions that could potentially occur with micro-evolution of this bacterium during long term colonization, some could prove advantageous where the bacterium and the host negotiate nearly a 'symbiotic' and balanced relationship. Such a 'friendship' might have taken thousands of years to develop. If so, why has this bacterium survived for such a long time? Microbes that have long been persisted in humans may be less harmful than recently emerged microbes, such as the human immunodeficiency viruses (HIV). This suggests that the colonization may either be beneficial or of low biological cost to the host. In addition to characterization of bacterial virulence apparatuses that are for sure linked to disease outcome, host responses to such factors must also be examined hand in hand, to completely ascertain mechanisms that lead to gastroduodenal disease. For instance polymorphisms linked to the host immune apparatus, such as IL-1β, TNF-α, and IL-10, which are responsible for elevated proinflammatory potential of the strains. These polymorphisms increase the risk for atrophic gastritis and distal gastric adenocarcinoma among H. pylori-infected persons. Cancer of stomach is a highly lethal disease and establishment of H. pylori as a risk factor for this malignancy deserves an approach to identify persons at increased risk; however, infection with this organism is extremely common and most colonized persons never develop cancer. Thus, screens to identify high-risk subpopulations must use high-resolution biological markers. Fortunately, this task appears to be highly simplified due to the availability of biological tools, which were never thought in the past. Genome sequences (H. pylori, human, C. elegans), quantitative phenotypes (cagA phosphorylation, oipA frame status, vac Aallele status), and practical animal models (Mongolian gerbils) can be harnessed to decipher the molecular basis of H. pylori-associated malignancies, which should have direct clinical applications. It is important to gain more insight into the pathogenesis of H. pylori-induced gastric adenocarcinoma, not only to develop more effective diagnostics and treatment for this common cancer, but also to validate the role of chronic inflammation in the genesis of other tumours of the alimentary tract.
Acknowledgement
Authors would like to thank Prof. Seyed E. Hasnain for his guidance, encouragement and for discussions. We are very much thankful to Farhana Kauser for her help in preparation of this manuscript.
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BMC Public HealthBMC Public Health1471-2458BioMed Central London 1471-2458-4-691562740110.1186/1471-2458-4-69DatabaseThe Latin American Social Medicine database Eldredge Jonathan D [email protected] Howard [email protected] Holly S [email protected] Janis [email protected] Celia [email protected] Kevin [email protected] Jonathan [email protected] The University of New Mexico Health Sciences Library and Informatics Center, MSC09 5100, 1 University of New Mexico, Albuquerque, New Mexico 87131-0001, USA2 The University of New Mexico Department of Family & Community Medicine, MSC09 5040, 1 University of New Mexico, Albuquerque, New Mexico 87131-0001, USA2004 31 12 2004 4 69 69 16 7 2004 31 12 2004 Copyright © 2004 Eldredge et al; licensee BioMed Central Ltd.2004Eldredge et al; licensee BioMed Central Ltd.This is an Open Access article distributed under the terms of the Creative Commons Attribution License (), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Background
Public health practitioners and researchers for many years have been attempting to understand more clearly the links between social conditions and the health of populations. Until recently, most public health professionals in English-speaking countries were unaware that their colleagues in Latin America had developed an entire field of inquiry and practice devoted to making these links more clearly understood. The Latin American Social Medicine (LASM) database finally bridges this previous gap.
Description
This public health informatics case study describes the key features of a unique information resource intended to improve access to LASM literature and to augment understanding about the social determinants of health. This case study includes both quantitative and qualitative evaluation data. Currently the LASM database at The University of New Mexico brings important information, originally known mostly within professional networks located in Latin American countries to public health professionals worldwide via the Internet. The LASM database uses Spanish, Portuguese, and English language trilingual, structured abstracts to summarize classic and contemporary works.
Conclusion
This database provides helpful information for public health professionals on the social determinants of health and expands access to LASM.
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Background
Public health practitioners have long recognized the connections between patients' socioeconomic conditions and their health [1-8]. Yet these practitioners and their empirically oriented researcher colleagues have faced difficulties in establishing the precise linkages between socioeconomic variables and sub-optimal health status. Social medicine is a diverse field that studies these relationships between society (and its socioeconomic conditions) and the health of populations. In Latin America, social medicine consists of a widely respected and influential field of research, teaching and professional practice [9]. Professionals working in this field seek to identify and to understand better the linkages between socioeconomic conditions and patients' health.
Until recently, however, most of the knowledge base in this discipline has remained largely unknown outside Latin America. Language barriers and disincentives to distribute this information more widely are two major reasons for this lack of awareness. Some readers might have first learned about Latin American social medicine (LASM) through recent critical reviews [9] or through a special issue of the American Journal of Public Health that focused on LASM [10,11].
LASM traces its historic origins to European researchers such as Rudolf Virchow and the belief systems of indigenous cultures. Both the European and indigenous sources of current social medicine practices emphasized the importance of linking social conditions to health status. Contemporary social medicine in Latin America continues to emphasize these linkages between social conditions and the health of populations. Social medicine professionals participate in a wide array of settings in Latin America and represent diverse specialties. Their integration into healthcare systems has varied by era and country [12].
Construction and content
Innovative approaches to disseminating work in LASM have become increasingly available due to Internet technology. The project "Enhanced Access for Latin American Social Medicine" at The University of New Mexico with funding from the U.S. National Library of Medicine seeks to make information on the connections between social conditions and health problems available to a wide audience. The project has sought to bridge the prior information gap primarily through delivering structured abstracts of social medicine publications in Spanish, English, and Portuguese via the Internet on the LASM database beginning in 2001. Other goals of this project include: publishing full text social medicine electronic journals on behalf of medical societies in Latin America; and, maintaining a repository for key classic and contemporary social medicine publications.
Structured abstracts are posted in three languages in the LASM database at The University of New Mexico for both the classic and contemporary social medicine literatures. The first phase of this project involved preparing and posting the structured abstracts of 25 landmark books, 50 book chapters, and 100 journal articles from the classic social medicine literature in Spanish, Portuguese, and English.
A peer selection committee identified and agreed upon the specific selections of classic books, book chapters, and journal articles to be abstracted for this project. Table 1 lists the members of this committee, representing institutions in Brazil, Chile, Colombia, Cuba, Ecuador, Mexico, the United States, and Venezuela [13]. Representative examples of some of the classic books [14-16], book chapters [17-19], and articles [20-22] can be found in the list of references following this article.
Table 1 Members of the Peer Selection Committee
Country Name Institution
Brazil Emerson Elias Merhy University of Campinas, São Paulo
Chile Alfredo Estrada L. Investigation and Training Group in Social Medicine, Santiago
Colombia Saul Franco Agudelo National School of Public Health, Bogotá
Cuba Francisco Rojas Ochoa National School of Public Health, Havana
Ecuador Jaime Breilh Health & Research Advisory Center, Quito
Mexico Ángeles Garduño Autonomous Metropolitan University-Xochimilco, Mexico City
Mexico Asa Cristina Laurell Secretariat of Health, Mexico City
Mexico Francisco Mercado Martínez University of Guadalajara, Guadalajara
Peru Marcos Cueto Peruvian University "Cayetano Heredia," Lima
United States Elizabeth Fee National Library of Medicine, Bethesda, MD
United States Norman Frankel American Medical Association, Chicago, IL
United States Allen Jones American Public Health Association, Washington, DC
United States Antonio Ugalde University of Texas, Austin, TX
Venezuela Oscar Feo Department of Public Health, Maracay
Venezuela Maria Urbaneja Latin American Social Medicine Association and Foreign Ministry of the Venezuelan national government, Caracas
The contemporary literature summarized in the LASM database has been drawn primarily from the 12 journals currently or previously published in Latin America. Table 2 lists the 12 journals, with their titles translated into English. These specific journals also have been identified and approved by the peer selection committee. The website that hosts the LASM database provides further information about this committee's members, including their institutional affiliations and areas of research. The peer selection committee consists of experts in social medicine and information technology. The LASM steering committee based at The University of New Mexico meets with the peer selection committee twice a year via online conferencing to decide on selection policies, actual lists of resources slated for inclusion in the LASM database, and administrative matters regarding the project [23].
Table 2 Current journal subscriptions monitored for noteworthy articles on social medicine
Country Title
Argentina Cuadernos Médico Sociales (Medico-Social Notebooks) 197?-
Argentina Salud Problema y Debate (Health – Problem and Debate) +
Brazil *Cadernos de Saúde Pública (Notebooks of Public Health) 1985-
Brazil Ciência & Saúde Coletiva (Science and Collective Health) 1996-
Brazil Interface (Interface) 1997-
Brazil Revista Brasileira de Epidemiología (Brazilian Journal of Epidemiology) 1998-
Brazil Saúde e Sociedade (Health and Society) 1992-
Brazil Saúde em Debate (Health in Debate) 1976-
Cuba Archivos del Ateneo Juan César García (Archives of the Juan César García Circle) 2000-
Cuba *Revista Cubana de Medicina Tropical (Cuban Journal of Tropical Medicine) 1996-
Cuba Revista Cubana de Salud Pública (Cuban Journal of Public Health) 1975-
Mexico Salud Problema (Health – Problem) +
Notes: * Indexed by the MEDLINE database from the National Library of Medicine + Start date occurred during 1900s but exact date not known
As a pilot, the project has made two social medicine journals available in electronic full text format. Several issues of these journals, Saúde em Debate (Brazil) and SaluCo Bulletin (Cuba), are available from the host website. The host website also posts structured abstracts of important articles from these full text journals.
The LASM database encompasses several broad themes within LASM. Subject areas include the history, theories, methodologies, and organizational dimensions of social medicine. Other subjects pertain to institutional analysis, social/critical epidemiology, and strategic planning. Table 3 summarizes the specific topics emanating from these broad subject areas. Readers will recognize that many of the subjects have direct bearing on the health of populations, such as health disparities and managed care.
Table 3 Subjects covered by Latin American social medicine
Health policy analysis Violence and health
Medical education reform Mental health services and mental health policies
Primary care research and preventive services Determinants of mental illness in race, ethnicity, social class, or gender
Strategic planning Indigenous, complementary and herbal medicine
Environmental health Social, environmental and nutritional causes of infant and perinatal mortality
Labor and health Economic development, demographic change, and aging
Ethnic/racial disparities and health Socioeconomic barriers to cancer prevention
Social class disparities and health Social processes of alcohol and drug abuse
Gender disparities and health Chronic illnesses
Infant and perinatal mortality Urban health
International health Managed care
The LASM database is a web application developed using the Cold Fusion application server. The data are stored in a Microsoft SQLServer relational database. The data sources are the original Latin American publications, which are summarized in Spanish, Portuguese, and English languages in structured abstract format. The English-language structured abstracts are quality checked by the principal investigator, who reviews the abstracts for substantive content, and then by the librarian investigator who reviews the structured abstracts for final quality assurance purposes. Each record contains fields for author, title (book, book chapter, or article), place of publication, publisher and structured abstracts in each language. Records contain volume, number, and pagination when applicable. All records contain terms the from Medical Subject Headings (MeSH) system, a controlled vocabulary developed and maintained by the National Library of Medicine. Users can search the database on controlled fields of author, title, and MeSH terms in any of the three available languages. Full text abstract searching is also available. In addition to the abstract searching facilities, the LASM database can be browsed alphabetically by title. The browsing interface offers a convenient way to become familiar with the extent and diversity of the LASM literature.
Utility and discussion
From January 2002 through December 2003, a total of 17, 853 visits were made to the website hosting the LASM database. The largest numbers of visits in descending rank order originated from Brazil, Mexico, Argentina, Spain, and Colombia. A preliminary, qualitative evaluation has been favorable. Following completion of this project, we will conduct and publish a comprehensive summative evaluation. A total of 250 structured abstracts in Spanish, Portuguese, and English had been posted to the LASM database as of June 30, 2004. The host website presents more detailed information about this project, as well as the structured abstracts themselves.
The LASM database comprises a dynamic and searchable web application containing structured abstracts in English, Portuguese, and Spanish. It is designed using industry standard web application design principles, but its content is unique to the LASM domain.
A database searching design and utility problem unique to the LASM database, and other web applications like it, relates to the problem of web-based multilingual searching. Widely available search engines cannot preprocess a multilingual language translation of search terms (e.g. retrieving results containing the Spanish word "pública" for the English search term "public"). Additionally, search engines do not recognize that the unaccented "publica" (as it might be entered by an English speaker as a search string) might be the same word as the Spanish accented "pública" and therefore will not return the user's expected search result. Key combinations and modifiers that are used to create special and accented characters in word processing programs like Microsoft Word often do not work in browser based search and form fields. Our primary instruction to users for constructing search terms containing special and accented characters (i.e., diacritics) suggests that they use a separate text editor that accepts keyboard modifiers to construct special and accented characters to build the search term and then copy and paste the completed search string into the field. On our search pages we also list common special and accented characters in Spanish and Portuguese that users can copy and paste into browser based search fields.
Although there are no other alternatives for entering special and accented characters into browser form fields, both methodologies are somewhat cumbersome. Therefore, to improve the searching utility of the LASM database we also preprocess entered search strings to allow the search engine to perform selected character substitution on the entered search string and attempt to solve the accented character problem from the search engine side. All search strings are first passed through a regular expression routine, which substitutes single character search engine wildcards for the following characters: A, E, I, O, U, a, e, i, o, u, N, C, n, c. This effectively removes potential special and accented character misspellings. To return to the previous example, a user might enter the search string "publica" ("public" in English) in a search field as an attempt to find article titles that contain the Spanish or Portuguese word "pública". Properly spelled, "Pública" uses the accented character "ú" rather than "u" (ASCII 163 rather than ASCII 117). Unfortunately, current search engines are not intelligent enough to infer that "pública" is a match for the search string "publica". Therefore, the literal search for publica (unaccented) will not return any search results that contain "pública" although there are hundreds of instances of the word "pública" in the LASM database.
In the case of "publica", the preprocessed search string that is finally submitted to the search engine is "p*bl***". The search engine will return all seven-letter word matches that contain the letters p, b, and l in the first, third, and fourth positions respectively. The net effect of this technique is to under-specify the search result. That is, the search engine may possibly return records that contain other words that happen coincidentally to match the submitted search string. On the other hand, the returned result set can be guaranteed to contain the desired search result.
Problems created from under-specifying the search are limited based on experience gained from using this technique. The positional constraints of submitted characters within the search string generally are restrictive enough to prevent most problems. Specifically, within a limited domain search surface like the LASM database, the likelihood of the occurrence of most alternative word matches is very low.
The character substitution methodology described here is not perfect, and many other alternative strategies for addressing the multilingual search problem have been explored by LASM technical staff. Most of the alternative strategies considered, however, involved much higher costs in terms of acquiring or developing specialized search engine capabilities or much higher abstract preparation costs. Therefore, we chose the character substitution strategy as a compromise between implementation cost and search utility.
Conclusions
Internet technology via websites and web browsers has created numerous opportunities for public health colleagues to inform one another and to collaborate across wide geographic space. The LASM database clearly demonstrates the efficacy of the Internet for communicating its informative structured abstracts posted in the Spanish, Portuguese, and English languages. This database provides useful information that would otherwise be unavailable for public health professionals on the social determinants of health. Furthermore, it expands access to LASM through its inclusion of both the classic and contemporary literature.
Availability and requirements
Anyone with access to the World Wide Web and a web browser can access all structured abstracts in the LASM database . As the data reported above indicate, the LASM database already has been accessed steadily since its initial small-scale publicity began in 2002. We hope that public health readers will utilize the LASM database to improve their research, teaching, and practice.
Competing interests
Neither the authors nor any support personnel possess competing interests related to the LASM database or to this article's publication.
Authors' contributions
JE conceived of and wrote the initial version of this article, served as an early collaborator on and helped design the project, was responsible as an investigator for acquiring journals for the Latin American social medicine collection, provided quality assurance editing on structured abstracts, played a major role in the formative evaluation of the project, and coordinated all revisions to this article. HW initiated and designed the project, served as principal investigator, obtained funding, translated and edited abstracts in the English-language, and edited this manuscript. HSB served as co-principal Investigator, helped design this project, obtained funding, and led the effort for the formative evaluation of this project. JT helped administer the project and edited this manuscript. CI coordinated the project, wrote Spanish-language structured abstracts, and provided reference materials for this article. KW and JT developed all programming aspects of the website including the search strategies, managed the web interface and underlying database and contributed the text for portions of this article.
Pre-publication history
The pre-publication history for this paper can be accessed here:
Acknowledgements
The authors wish to thank Christee King and Robert de Lancey for their assistance in this project and for supplying needed information.
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| 15627401 | PMC544873 | CC BY | 2021-01-04 16:28:47 | no | BMC Public Health. 2004 Dec 31; 4:69 | utf-8 | BMC Public Health | 2,004 | 10.1186/1471-2458-4-69 | oa_comm |
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BMC Public HealthBMC Public Health1471-2458BioMed Central London 1471-2458-4-631561323410.1186/1471-2458-4-63Research ArticleAssociation between mortality from suicide in England and antidepressant prescribing: an ecological study Morgan Oliver WC [email protected] Clare [email protected] Azeem [email protected] Health and Care Division, Office for National Statistics, 1 Drummond Gate, London, United Kingdom2 Department of Primary Care and Social Medicine, Imperial College, Charring Cross Hospital, London, United Kingdom2004 21 12 2004 4 63 63 12 8 2004 21 12 2004 Copyright © 2004 Morgan et al; licensee BioMed Central Ltd.2004Morgan 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
Antidepressant prescribing has been increasing in England. Studies in other countries suggest that while this may be associated with reduced suicide rates, it may also be associated with increased fatal poisoning from antidepressant drugs. We therefore conducted an ecological study to assess the association between prescription rates for antidepressants and suicide or fatal antidepressant-related poisoning in England.
Methods
The Office for National Statistics provided information on the number of suicides, antidepressant-related poisoning deaths and populations for England between 1993 and 2002. The Department of Health supplied data on prescriptions for all antidepressants dispensed in England. Associations between prescriptions and deaths were assessed using Spearman's rank correlation coefficient.
Results
There were 46,747 suicides, 3,987 deaths involving tricyclic antidepressants and 430 involving selective serotonin re-uptake inhibitors and other antidepressants. Increased antidepressant prescribing was statistically associated with a fall in suicide rates (Spearman's rs = -0.73, p = 0.02) and fatal poisoning involving tricyclic antidepressants (rs = -0.64, p = 0.05). In contrast, increased prescribing of selective serotonin re-uptake inhibitors and other antidepressants was statistically associated with an increase in fatal poisoning involving these drugs (rs = 0.99, p < 0.001).
Conclusion
Increased prescribing of antidepressants may indicate improved diagnosis and treatment of depression in primary care. Our analysis suggests that this was accompanied by lower suicide rates. A decrease in poisoning deaths involving tricyclic antidepressants may suggest a change in preference for using serotonin reuptake inhibitors and other antidepressant drugs for high-risk patients. This may also partially explain the increase in deaths involving these drugs. Due to the ecological nature of the design, we cannot say conclusively whether reduced suicide rates are a direct consequence of increased antidepressant prescribing rates. To confirm these associations, individual level data on prescribing and suicide is needed.
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Background
Around 5000 people take their own lives in England every year [1]. The Government's White Paper Saving Lives: Our Healthier Nation sets a target to reduce suicide deaths by 20% by 2010 [2]. One of the most common risk factors amongst suicide victims is a major depressive episode (present in about 60–90% of victims) [3]. Treatment of depressed patients can reduce suicide risk by half [3]. Hence, increased diagnosis and treatment of depression in primary care is a key element in reducing suicide risk.
Studies in Sweden [4], Denmark [5], Finland [5], Norway [5] and Australia [6] suggest that increased used of antidepressants drugs is related with lower suicide rates. However, other studies have also reported a positive association between increased prescribing of antidepressants and higher death rates from antidepressant overdose in Finland [7], Norway [8], Australia [9] and England [10]. In contrast, studies in Italy [11], Austria [12] and Ireland [13] have not observed increased suicide or fatal antidepressant poisoning following increases in antidepressant prescribing.
In England, prescriptions for antidepressants have been increasing [14,15]. We conducted an ecological study to assess trends in prescription rates for antidepressants in England and rates for suicide and fatal antidepressant-related poisoning.
Methods
Suicide
Suicide deaths were defined as deaths where the coroner has given a verdict of suicide or where an open verdict was reached in a death from injury or poisoning. This is because it is thought that most open verdicts are cases where the harm was self-inflicted but there was insufficient evidence to prove that the deceased deliberately intended to kill themselves [16]. Open verdicts account for about 30% of male and 40% of female suicide deaths [16]. Suicide deaths were identified using the International Classification of Diseases codes shown in Table 1.
Table 1 ICD-9 and ICD-10 classification for suicides
Description ICD-9 ICD-10
All suicides E950–E959, E980–E989 excluding E988.8 with verdict pending X60–X84, Y10–Y34 excluding Y33.9 with verdict pending
Non-drug poisoning suicide As above excluding E950.0–E950.5 and E980.0–E980.5 As above excluding X60–X64 and Y10–Y14
Antidepressant-related deaths
The Office for National Statistics has stored drug-poisoning mortality data for England and Wales from 1993 onwards in a dedicated database [17]. The database contains data on cause of death, individual characteristics (age and sex) as well as textual information from the death certificate. This textual information has been examined to identify and code the substances involved in the death. All drugs mentioned are also coded to British National Formulary categories where appropriate.
Drug poisoning deaths were defined using the International Classification of Diseases codes shown in Table 2. Antidepressant-related deaths were defined as any drug poisoning death where an antidepressant drug was mentioned on the death certificate, with or without mentions of alcohol or other drugs. Antidepressant drugs were further classified according to their BNF categories (Table 3).
Table 2 ICD-9 and ICD-10 classification for drug poisoning
Description ICD-9 ICD-10
Mental and behavioural disorders due to drug use (excluding alcohol and tobacco) 292, 304 305.2-9 F11–F16, F18–F19
Accidental poisoning by drugs, medicaments and biological substances E850–E858 X40–X44
Intentional self-poisoning by drugs, medicaments and biological substances E950.0–E950.5 X60–X64
Poisoning by drugs, medicaments and biological substances, undetermined intent E980.0–E980.5 Y10–Y14
Assault by drugs, medicaments and biological substances E962.0 X85
Table 3 British National Formulary Categories for Antidepressant Drugs
BNF Category Description
4.3.1 Tricyclics and related antidepressants
4.3.2 Monoamine oxidase inhibitors
4.3.3 Selective serotonin re-uptake inhibitors
4.3.4 Other antidepressants
Prescriptions for antidepressant drugs
The Department of Health supplied data on prescriptions for all antidepressants dispensed in England between 1993 and 2002. Prescription information is derived from the Prescription Cost Analysis (PCA) system, which collects data from all prescriptions dispensed in the community [18]. This includes community pharmacists, dispensing doctors and prescriptions submitted by prescribing doctors for items personally administered (i.e. given by the doctor during a consultation). PCA data also includes prescriptions written in Wales, Scotland, Northern Ireland and the Isle of Man but dispensed in England. Drugs dispensed in hospital or private prescriptions are not included.
Analysis
Directly age-standardised rates for suicide and antidepressant-related deaths were calculated using the European Standard Population. Populations for England between 1993 and 2002 were interim revised estimates published by ONS in September 2003. Prescription rates were presented as number of prescriptions per 100 population. Statistical associations between prescription rates and directly age-standardised rates for antidepressant-related poisoning deaths and suicides were assessed using Spearman's rank correlation coefficient.
Results
In England between 1993 and 2002, there were 46,747 suicides. Suicide was twice as common in men as in women. There were 3,987 deaths involving tricyclic antidepressants (TCAs, BNF 4.3.1,) and 430 involving selective serotonin re-uptake inhibitors and other antidepressants (SSRIs & others, BNF 4.3.3 & 4.3.4). The number of antidepressant-related deaths was similar for men and women. Changes in age-specific rates were similar across all age groups for suicide and antidepressant-related deaths.
Between 1993 and 2002, age-standardised mortality rates for suicide decreased from 98.2 to 84.3 per million population (Table 4). Rates for TCA poisoning also decreased from 8.6 to 5.3 per million while mortality rates for SSRIs & others increased from 0.2 to 1.8 per million. During the same period, prescriptions per 100 population for all antidepressants increased almost two and a half times from 22.4 to 53.2 per 100 population (Table 4). While there was a modest increase for TCAs from 17.5 to 19.9 per 100 population, prescriptions for SSRIs and others increased more than seven-fold from 4.6 to 33.1 per 100 population.
Table 4 Age-standardised mortality rates for suicide and antidepressant-related poisoning deaths and prescriptions per 100 population in England
Year Age-standardised mortality rates per million population Prescriptions per 100 population
Suicide TCAs SSRIs All TCAs SSRIs
1993 98.2 8.6 0.2 22.4 17.5 4.6
1994 94.8 8.7 0.1 24.5 17.9 6.2
1995 95.6 8.7 0.4 27.4 18.3 8.7
1996 90.6 9.7 0.4 30.9 18.8 11.8
1997 92.8 9.4 0.6 34.7 19.3 15.1
1998 95.7 8.8 0.7 37.9 19.7 17.9
1999 95.1 8.0 1.1 41.2 19.7 21.2
2000 90.2 6.9 1.3 44.9 19.7 25.0
2001 84.9 5.8 1.8 49.3 19.8 29.3
2002 84.3 5.3 1.8 53.2 19.9 33.1
Note: Directly standardised to the European Standard Population
Spearman's rank correlation indicate that rates for all suicides were inversely related to prescribing rates for all antidepressants combined, rs = -0.73, p = 0.016 (Table 5). Mortality rates for non-drug poisoning suicides showed a strong statistical association with prescription rates for all antidepressants, rs = -0.89, p = 0.005. Antidepressant-related poisoning mortality rates were also inversely statistically associated with prescription rates for all antidepressants combined, but the association was much weaker, rs = -0.45, p = 0.187. As most antidepressant-related poisoning deaths were due to TCAs, the relationship between TCA deaths and prescribing were similar to all antidepressants combined. In contrast, SSRI and other-related deaths increased and prescriptions for SSRIs & others both increased during the study period rs = 0.99, p < 0.001.
Table 5 Spearman's rank correlation coefficients and p-values for directly age-standardised suicide and antidepressant poisoning rates and prescription rates, England 1993 to 2002
Cause of death Prescriptions rs p
All suicides All antidepressants -0.73 0.016
Non-drug poisoning suicide All antidepressants -0.89 0.005
Antidepressant-related poisoning deaths All antidepressants -0.45 0.187
Tricyclic antidepressant -related poisoning deaths Tricyclic antidepressants -0.64 0.05
Selective serotonin re-uptake inhibitor and other antidepressant-related poisoning deaths Selective serotonin re-uptake inhibitors and other antidepressants 0.99 <0.001
Antidepressant only poisoning deaths* All antidepressants -0.53 0.12
Tricylclic antidepressant only poisoning deaths* Tricyclic antidepressants -0.71 0.022
Selective serotonin re-uptake inhibitor and other antidepressant only poisoning deaths* Selective serotonin re-uptake inhibitors and other antidepressants 0.94 <0.001
* Where no other drugs were mentioned on the death certificate
A previous analysis found that about a third of antidepressant-related deaths also have other drugs or substances mentioned on the death certificate (26% of deaths involving TCAs, 72% involving SSRIs and others) [19]. Hence, death certification data may over-estimate deaths attributable to antidepressant overdose. When we excluded deaths involving other substances from our analysis, antidepressant mortality rates were more closely related with antidepressant prescribing (TCAs rs = -0.71, p = 0.022, SSRIs & others rs = 0.94, p < 0.001).
Discussion
Increased prescribing of antidepressants was statistically associated with reduced suicide mortality rates. TCA-related poisoning deaths also decreased during the study period although the evidence for a statistical association with prescribing rates was weaker. Increased prescribing of SSRIs and other antidepressants was statistically associated with increased mortality rates involving SSRI & other-related poisoning, although the rates were much smaller than for TCA-related poisoning or suicide. Because our study uses population level data, we cannot conclude that these associations are necessarily causal.
Limitations
Although the ONS drug poisoning database is the most complete record of drug poisoning statistics available, about 10% of these deaths have no specific information about drug(s) taken [1]. Inconsistency in the investigation and recording of drug-poisoning deaths may mean that not all antidepressant-related deaths are identified [17]. Prescribing Cost Analysis data do not record why the drug was prescribed and antidepressants are increasingly used for conditions other than depression [20]. This may lead to an over-estimation of their use in the treatment of depression. Furthermore, prescribing data does not provide information on the age or sex of the patient. Both of these variables are likely to be associated with prescribing and suicide or poisoning, and may have introduced confounding into our analysis.
Further bias may have been introduced due to prescriber and patient factors. Doctors may perceive SSRIs and other antidepressants to have clinical advantages over TCAs due to their lower toxicity, possibly making them more popular for treating newly diagnosed (an uncontrolled) depression [21], treating individuals who are at a greater risk of overdose [22] or individuals who have not previously responded to treatment.
Interpretation
Assessing the impact of increased antidepressant prescribing is difficult. This is because antidepressants are both a treatment and method for suicidal behaviour. Suicide is also closely linked to availability of methods [23], generating concern that increased prescribing of antidepressants may increase fatal poisoning rates. There has also been concern that some antidepressants, particularly the SSRIs, may actually precipitate suicide behaviour [24]. In a recent article, Jick et al suggested that both older and newer antidepressants were associated with 3–4 fold increased risk of suicidal behaviour in the first month after starting treatment [25]. Hence, determining the balance of risk and benefit of treatment with antidepressants is not straightforward. Using a theoretical model based on paediatric trails of antidepressants drugs and increased prevalence of suicidal thoughts and self-harm, Gunnell and Ashby suggest that compared to 1991, antidepressants may have contributed to an excess of 388 suicides (95% credibility interval -202 to 704) in 2002 [26].
However, our study indicates that at a population level, increased antidepressant prescribing in England was statistically associated with lower rates of suicide and antidepressant-related poisoning. One explanation for our findings is that SSRIs and other antidepressants are being used in preference to TCAs to treat depression. The perceived side effects of TCAs means they are often prescribed or taken at sub-therapeutic doses [21,27], leading to poor management of depression and increased risk of suicide [28,29]. Furthermore, the lower toxicity profiles of the newer antidepressants may also lead to preferential use of SSRIs for high-risk patients, which may in turn explain the increase in deaths associated with SSRIs and other antidepressants. Some support for this is provided by a previous analysis of antidepressant-related deaths during the same study period, which showed a decrease in antidepressant-related deaths per million prescriptions for all TCAs [19].
Nevertheless, the clinical benefits of antidepressant drugs for the management of depression and prevention of suicide are still unclear. A recent Cochrane review found little difference between antidepressants and active placebos on improvement of mood [30]. (Active placebos contain a drug, which is not thought to have an effect on the disorder being treated, but which mimics the effect of taking an active substance). The possibility that antidepressants have limited effectiveness on treatment of depression suggests that we should be cautious when drawing conclusions about the relationship between antidepressant prescribing and suicide. Increased antidepressant prescribing may be a marker of improved diagnosis and treatment of depression in primary care with greater use of non-pharmacological and psychosocial interventions [6]. Alternatively, suicide rates may reflect other trends during the study period: unemployment, which is associated with suicide [31], fell from about 10% to below 5% during the study period [32]. In contrast, consumption of alcohol, mentioned in 28% of antidepressant poisoning deaths, remained relatively stable [33]. Hence, the ecological nature of our data means that we cannot say conclusively whether reduced suicide rates is a direct consequence of increased antidepressant prescribing rates.
Conclusions
The public health impact of increased prescribing of antidepressants remains unclear, including whether this leads to lower suicide rates, and whether this could be a marker for improved diagnosis and treatment of depression in primary care. Our analysis of data for England between 1993 and 2002 suggest that increased prescribing of antidepressants was associated with lower suicide rates and probably lower rates of poisoning involving TCAs. This may have been due to a change in preference for SSRIs and other antidepressants for high-risk patients but also to other secular trends in areas like unemployment. The increase in prescribing rates does however probably explain the increase in fatal poisonings involving SSRIs and other antidepressants, which are generally considered to be less toxic than older agents. Further analysis of individual level data, possibly from longitudinal general practice patient data as well as from primary care based clinical trials, are needed to provide better evidence of the role of antidepressants in reducing suicide.
Competing interests
The author(s) declare that they have no competing interests.
Authors' contributions
OM designed the study, conducted the analysis and drafted the manuscript. CG participated in conducting the analysis and drafting the manuscript. AM participated designing the study 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
Oliver Morgan is funded by the National Health Service London Deanery of Postgraduate Dental and Medical Education. Azeem Majeed holds a Primary Care Scientist award funded by the Department of Health.
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| 15613234 | PMC544874 | CC BY | 2021-01-04 16:28:47 | no | BMC Public Health. 2004 Dec 21; 4:63 | utf-8 | BMC Public Health | 2,004 | 10.1186/1471-2458-4-63 | oa_comm |
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BMC BioinformaticsBMC Bioinformatics1471-2105BioMed Central London 1471-2105-5-1751552750910.1186/1471-2105-5-175Research ArticleComputation of elementary modes: a unifying framework and the new binary approach Gagneur Julien [email protected] Steffen [email protected] Cellzome AG, Meyerhofstr. 1, 69117 Heidelberg, Germany2 Max Planck Institute for Dynamics of Complex Technical Systems, Sandtorstr. 1, D-39106 Magdeburg, Germany2004 4 11 2004 5 175 175 28 6 2004 4 11 2004 Copyright © 2004 Gagneur and Klamt; licensee BioMed Central Ltd.2004Gagneur and Klamt; 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
Metabolic pathway analysis has been recognized as a central approach to the structural analysis of metabolic networks. The concept of elementary (flux) modes provides a rigorous formalism to describe and assess pathways and has proven to be valuable for many applications. However, computing elementary modes is a hard computational task. In recent years we assisted in a multiplication of algorithms dedicated to it. We require a summarizing point of view and a continued improvement of the current methods.
Results
We show that computing the set of elementary modes is equivalent to computing the set of extreme rays of a convex cone. This standard mathematical representation provides a unified framework that encompasses the most prominent algorithmic methods that compute elementary modes and allows a clear comparison between them. Taking lessons from this benchmark, we here introduce a new method, the binary approach, which computes the elementary modes as binary patterns of participating reactions from which the respective stoichiometric coefficients can be computed in a post-processing step. We implemented the binary approach in FluxAnalyzer 5.1, a software that is free for academics. The binary approach decreases the memory demand up to 96% without loss of speed giving the most efficient method available for computing elementary modes to date.
Conclusions
The equivalence between elementary modes and extreme ray computations offers opportunities for employing tools from polyhedral computation for metabolic pathway analysis. The new binary approach introduced herein was derived from this general theoretical framework and facilitates the computation of elementary modes in considerably larger networks.
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Background
The background section presents the importance of computing elementary modes for metabolic system analysis, its computational difficulties and the existence of various known algorithms. A theoretical section brings these algorithms into a unified framework. In a following section we introduce a new approach, called the binary approach. Although relying on concepts introduced in the theoretical section, this section gives enough practical details to be stand-alone for the implementer. Results obtained from example networks and a conclusion section close the article.
Definition and benefits of elementary modes
We consider a metabolic network with m metabolites and q reactions. Reactions may involve further metabolites that are not considered as proper members of the system of study. The latter metabolites, considered to be buffered, are called external metabolites in opposition to the m metabolites within the boundary of the system, called internal metabolites. The stoichiometry matrix N is an m × q matrix whose element nij is the signed stoichiometric coefficient of metabolite i in reaction j with the following sign convention: negative for educts, positive for products. Some reactions, called irreversible reactions, are thermodynamically feasible in only one direction under the normal conditions of the system. Therefore, reaction indices are split into two sets: Irrev (the set of irreversible reaction indices) and Rev (the set of reversible reaction indices). A flux vector (flux distribution), denoted v, is a q-vector of the reaction space q, in which each element vi describes the net rate of the ith reaction. Sometimes we are interested only in the relative proportions of fluxes in a flux vector. In this sense, two flux vectors v and v' can be seen to be equivalent, denoted by v ≃ v', if and only if there is some α > 0 such that v = α · v'.
Metabolism involves fast reactions and high turnover of substances compared to events of gene regulation. Therefore, it is often assumed that metabolite concentrations and reaction rates are equilibrated, thus constant, in the timescale of study. The metabolic system is then considered to be in quasi steady state. This assumption implies Nv = 0. Thermodynamics impose the rate of each irreversible reaction to be nonnegative. Consequently the set of feasible flux vectors is restricted to
P = {v ∈ q : Nv = 0 and vi ≥ 0, i ∈ Irrev} (1)
P is a set of q-vectors that obey a finite set of homogeneous linear equalities and inequalities, namely the |Irrev| inequalities defined by vi ≥ 0, i ∈ Irrev and the m equalities defined by Nv = 0. P is therefore – by definition – a convex polyhedral cone [1].
Metabolic pathway analysis [2-5] serves to describe the (infinite) set P of feasible states by providing a (finite) set of vectors that allow the generation of any vectors of P and are of fundamental importance for the overall capabilities of the metabolic system. One of this set is the so-called set of elementary (flux) modes (EMs). For a given flux vector v, we note R(v) = {i : vi ≠ 0} the set of indices of the reactions participating in v. Hence, R(v) can be seen as the underlying pathway of v. By definition, a flux vector e is an elementary mode (EM) if and only if it fulfills the following three conditions [6,7]:
In other words, e is an EM if and only if it works at quasi steady state, is thermodynamically feasible and there is no other non-null flux vector (up to a scaling) that both satisfies these constraints and involves a proper subset of its participating reactions. Note that with this convention, reversible modes are here considered as two vectors of opposite directions.
The concept of elementary modes (and, with some restrictions, the very similar concept of extreme pathways [8-10]) has proven useful in many ways and has become an important theoretical tool for systems biology as well as for biotechnology and metabolic engineering (see review [5]). Because the metabolic network structure becomes now available at a genome-scale for an increasing number of microorganisms, this approach is well-suited to today's metabolic studies. Here, we give a short overview on the major applications and variants:
(1) Identification of pathways: The set of EMs comprises all admissible routes through the network and thus of "pathways" in the classical sense, i.e. of routes that convert some educts into some products [5].
(2) Network flexibility: The number of EMs is at least a rough measure of the network's flexibility (redundancy, structural robustness) to perform a certain function [11-13].
(3) Identification of all pathways with optimal yield: Consider the linear optimization problem, where all flux vectors with optimal product yield are to be identified, i.e. where the moles of products generated per mole of educts is maximal. Then, one or several of the EMs reach this optimum and any optimal flux vector is a convex combination of these optimal EMs [3,14].
(4) Importance of reactions: The importance or relevance of a reaction can be assessed by its participation frequency or/and flux values in the EMs.
(4a) Inference of viability of mutants: If a reaction is involved in all growth-related EMs its deletion can be predicted to be lethal, since all EMs would disappear [11].
(4b) A more quantitative measure of the importance of a reaction has been given by "control-effective fluxes" (CEFs, [11]). The CEFs take also the efficiency of each mode as well as the absolute flux values of the respective reaction in the EMs into account. CEFs have been used to predict transcript ratios [11,15].
(5) Reaction correlations: EMs can be used to analyze structural couplings between reactions, which might give hints for underlying regulatory circuits [14,16,17]. An extreme case is an enzyme (or reaction) subset (set of reactions which can operate only together) or a pair of mutually excluding reactions (two reactions never occurring together in any EM [10]).
(6) Detection of thermodynamically infeasible cycles: EMs representing internal cycles (without participation of external material or energy sources) are infeasible by laws of thermodynamics and thus reflect structural inconsistencies [18,19].
(7) The framework of pathway analysis also allows us to combine and to study stoichiometric constraints together with regulatory rules [20].
(8) Minimal cut sets: EMs allow for a computation of minimal cut sets that represent minimal cuts (deletion sets) in the network repressing certain metabolic functions [21].
(9) The α-spectrum has been introduced to quantify the involvement of extreme pathways in a particular flux distribution (e.g. from an experiment) [22]. Since the decomposition of a flux vector into extreme pathways is usually not unique, the α-spectrum specifies a range of possible weights for each extreme pathway. The same could be defined for EMs.
Computational limitations and algorithm variants
Due to the combinatorial explosion in the number of EMs in large networks [23], computing EMs is known to be a hard computational task, so far restricting elementary-mode analysis to medium-scale networks. Several algorithms (and derivations thereof) have been developed for computing EMs. The two most prominent ones are the algorithm elaborated by Schuster et al. [4] and the recently developed null-space approach by Wagner [24]. The latter considerably accelerated the computation speed and thus shifted the current limitation – at least for a typical PC – from computation time to the memory requirement.
Here we show that both the Schuster algorithm as well as that by Wagner can be embedded in a more general algorithmic framework stemming originally from computational geometry. These studies do not only give a summarizing point of view, they also lead to a crucial modification of the existing algorithms, decreasing the required memory for computing and storing EMs drastically.
Results
A unified framework
Elementary modes as extreme rays in networks of irreversible reactions
In the particular case of a metabolic system with only irreversible reactions, the set of admissible reactions reads:
P = {v ∈ q : Nv = 0 and v ≥ 0} (3)
Compared with (1) P is in this case a particular, namely a pointed polyhedral cone (an example is depicted in Figure 1). This geometry can be intuitively understood, noting that there are certainly 'enough' intersecting half-spaces (given by the inequalities v ≥ 0) to have this 'pointed' effect in 0: P contains no real line (otherwise there coexist x and -x not null in P, a contradiction with the constraint v ≥ 0). The figure even suggests that a pointed polyhedral cone can be either defined in an implicit way, by the set of constraints as we did until now, or in an explicit or generative way, by its 'edges', the so-called extreme rays (or generating vectors) that unambiguously define its boundaries. In the following, we show that elementary modes always correspond to extreme rays of a particular pointed cone as defined in (3) and that their computation therefore matches to the so-called extreme ray enumeration problem, i.e. the problem of enumerating all extreme rays of a pointed polyhedral cone defined by its constraints. An overview on general and current issues on extreme ray enumeration can be found in [25]. For the sake of consistency, we use this reference as a main source and adopt the same mathematical notations.
Figure 1 A pointed polyhedral cone. Dashed lines represent virtual cuts of unbounded areas
The pointed polyhedral cone is the central mathematical object throughout this work; therefore we shall introduce more precise definitions and results surrounding it.
P is a pointed polyhedral cone of d if and only if P is defined by a full rank h × d matrix A (rank(A) = d) such that,
P = P(A) = {x ∈ d : Ax ≥ 0} (4)
The h rows of the matrix A represent h linear inequalities, whereas the full rank mention imposes the "pointed" effect in 0. Note that a pointed polyhedral cone is, in general, not restricted to be located completely in the positive orthant as in (3). For example, the cone considered in extreme-pathway analysis may have negative parts (namely for exchange reactions), however, by using a particular configuration it is ensured that the spanned cone is pointed [8].
Now we must characterize the extreme rays. A vector r is said to be a ray of P(A) if r ≠ 0 and for all α > 0, α · r ∈ P(A). We identify two rays r and r' if there is some α > 0 such that r = α · r' and we denote r ≃ r', analogous as introduced above for flux vectors. For any vector x in P(A), the zero set or active set Z(x) is the set of inequality indices satisfied by x with equality. Noting Ai• the ith row of A, Z(x) = {i : Ai•x = 0}. Zero sets can be used to characterize extreme rays. For simplicity, we adopt in this document the following characteristic ([25] for example) as a working definition of extreme rays.
Definition 1: Extreme ray
Let r be a ray of the pointed polyhedral cone P(A). The following statements are equivalent:
(a) r is an extreme ray of P(A)
(b) if r' is a ray of P(A) with Z(r) ⊆ Z(r') then r' ≃ r
Since A is full rank, 0 is the unique vector that solves all constraints with equality. The extreme rays are those rays of P(A) that solve a maximum but not all constraints with equalities. This is expressed in (b) by requiring that no other ray in P(A) solves the same constraints plus additional ones with equalities. Note that in (b) Z(r) = Z(r') consequently holds.
An important property of the extreme rays is that they form a finite set of generating vectors of the pointed cone ([25] for example): any vector of P(A) can be expressed as a non-negative linear combination of extreme rays, and the converse is true: all non-negative combinations of extreme rays lie in P(A). Moreover, the set of extreme rays is the unique minimal set of generating vectors of a pointed cone P(A) (up to positive scalings).
Lemma 1: EMs in networks of irreversible reactions
In a metabolic system where all reactions are irreversible, the EMs are exactly the extreme rays of P = {v ∈ q : Nv = 0 and v ≥ 0}.
Proof: P is the solution set of the linear inequalities defined by where I is the q × q identity matrix. Since it contains I, A is full rank and therefore P is a pointed polyhedral cone. All v ∈ P obey Nv = 0, thus the 2m first inequalities defined by A hold with equality for all vectors in P and the inclusion condition of Definition 1 can be restricted to the last q inequalities, i.e. the inequalities corresponding to the reactions. Inclusion over the zero set can be equivalently seen as containment over the set of non-zeros in v, i.e. R(v). Consequently, e ∈ P is an extreme ray of P if and only if: for all e' ∈ P : R(e') ⊆ R(e) ⇒ e' = 0 or e' ≃ e, i.e. if and only if e is elementary. Thus, all three conditions in (2) are fulfilled.
The general case
In the general case, some reactions of the metabolic system can be reversible. Consequently, A does not contain the identity matrix and P (as given in (1)) is not ensured to be a pointed polyhedral cone anymore [7]. Because they contain a linear subspace, non-pointed polyhedral cones cannot be represented properly by a unique set of generating vectors composed of extreme rays, albeit a set of generating vectors exists, sometimes also called convex basis [7]. One way to obtain a pointed polyhedral cone from (1) is to split up each reversible reaction into two opposite irreversible ones. Note that this operation is completely analogous to a transformation step used in linear programming to obtain a linear optimization problem in canonical form: free variables v are also split into two variables v+ and v- with v = v+ - v- and v+, v- ≥ 0 [26]. It has been noticed earlier that this virtual split does not change essentially the outcome: the EMs in the reconfigured network are practically equivalent to the EMs from the original network [10]. Here we prove and precisely characterize this result.
We first introduce some notations. We denote the original reaction network by S and the reconfigured network (with all reversible reactions split up) by S'. The reactions of S are indexed from 1 to q. Remember that Irrev denotes the set of irreversible reaction indices and Rev the reversible ones. An irreversible reaction indexed i gives rise to a reaction of S' indexed i. A reversible reaction indexed i gives rise to two opposite reactions of S' indexed by the pairs (i,+1) and (i,-1) for the forward and the backward respectively. The reconfiguration of a flux vector v ∈ q of S is a flux vector v' ∈ Irrev ∪ Rev × {-1;+1} of S' such that
Let N' be the stoichiometry matrix of S'. N' can be written as N' = [N - NRev] where NRev consists of all columns of N corresponding to reversible reactions. Note that if v is a flux vector of S and v' is its reconfiguration then Nv = N'v'.
If possible, i.e. if v' ∈ Irrev ∪ Rev × {-1;+1} is such that for any reversible reaction index i ∈ Rev at least one of the two coefficients v'(i,+1) or v'(i,-1) equals zero, then we define the reverse operation, called back-configuration that maps v' back to a flux vector v such that:
Theorem 1: EMs in original and in reconfigured networks
Let S be a metabolic system and S' its reconfiguration by splitting up reversible reactions. Then the set of EMs of S' is the union of
a) the set of reconfigured EMs of S
b) the set of two-cycles made of a forward and a backward reaction of S' derived from the same reversible reaction of S
Proof: see Methods.
Thus, the set of EMs of the original network is equivalent (up to the two-cycles) to the set of EMs in the reconfigured network and therefore can be seen as a reduced set of extreme rays of the pointed convex polyhedron as defined by:
P = {v' ∈ q + |Rev| : N'v' = 0 and v' ≥ 0} (5)
Hence, EMs computation can be derived from any extreme ray enumeration algorithm applied to the reconfigured network and followed by vector back-configuration and the elimination of meaningless vectors, namely the two-cycles.
Note that exactly the same procedure – splitting reversible reactions into two irreversible ones – was carried out also in the original work of Clarke [27] on stability analyses in stoichiometric networks. Clarke called the extreme rays of the corresponding cone (5) extreme currents. Thus, extreme currents are identical to the EMs in the reconfigured network and, hence, also (up to the 2-cycles) equivalent to the EMs from the original network
All known algorithms for computing EMs are variants of the Double Description Method
In the following we present a simple yet efficient algorithm for extreme ray enumeration, the so-called Double Description Method [28]. We show that it serves as a common framework to the most prominent EM computation methods. To reach this generality, we concentrate on mathematical operations regardless to the actual data-structures used in the implementation. Therefore we manipulate objects such as matrices, vectors or inequalities and leave their implementation into tableaus, arrays and so on to the next section.
A generating matrix R of a pointed polyhedral cone P(A) is a matrix such that P(A) = {x ∈ d : x = Rλ for some λ ≥ 0}. The pair (A,R) is called a Double Description pair, or DD pair. As mentioned above, the extreme rays form the unique set of minimal generating vectors of P(A) and thus, considered as set of d-vectors, the extreme rays of P(A) form the columns of a generating matrix R that is minimal in terms of number of columns. The pair (A,R) is then called a minimal DD pair.
The strategy of the Double Description Method is to iteratively build a minimal DD pair (Ak, Rk) from a minimal DD pair (Ak - 1, Rk - 1), where Ak is a submatrix of A made of k rows of A. At each step the columns of Rk are the extreme rays of P(Ak), the convex polyhedron defined by the linear inequalities Ak. The incremental step introduces a constraint of A that is not yet satisfied by all computed extreme rays. Some extreme rays are kept, some are discarded and new ones are generated. The generation of new extreme rays relies on the notion of adjacent extreme rays. Here again, for the sake of simplicity, we adopt a characteristic ([25] for example) as a working definition of adjacent extreme rays.
Definition 2: Adjacent extreme rays
Let r and r' be distinct rays of the pointed polyhedral cone P(A). Then the following statements are equivalent:
(a) r and r' are adjacent extreme rays
(b) if r" is a ray of P(A) with Z(r) ∩ Z(r') ⊆ Z(r") then either r" ≃ r or r" ≃ r'
Initialization
The initialization of the double description method must be done with a minimal DD pair. One possibility is the following. Since P is pointed, A has full rank and contains a nonsingular submatrix of order d denoted by Ad. Hence, (Ad, Ad-1) is a minimal DD pair which works as initialization and leads directly to step k = d. Note that there is some freedom in choosing a submatrix Ad or some alternative starting minimal DD pair.
Incremental step
Assume (Ak - 1, Rk - 1) is a minimal DD pair and consider a kth constraint defined by a not yet extracted row of A, denoted Ai•. Let J be the set of column indices of Rk - 1 and rj, j ∈ J, its column vectors, i.e. the extreme rays of P(Ak - 1), the polyhedral cone of the previous iteration. Ai• splits J in three parts (Figure 2) whether rj satisfies the constraint with strict inequality (positive ray), with equality (zero ray) or does not satisfy it (negative ray):
Figure 2 Double description incremental step. The scene is best visualized with a polytope; consider the cube pictured here as a 3 projection of a 4 polyhedral cone. Extreme rays from the previous iteration are {a,b,c,d,e,f,g,h} whose adjacencies are represented by edges. For the considered constraint, whose null space is the hyperplane depicted by the bold black border lines, b and f are positive rays, a and c are zero rays, d, e, g and h are negative rays. b, f, a and c satisfy the constraint and are kept for the next iteration. {f,e} and {f,g} are the only two pairs of adjacent positive/negative rays and only they give rise to new rays: i and j at the intersection of the hyperplane and the respective edges. The new polytope is then defined by its extreme rays: {a,b,c,f,i,j}.
J+ = {j ∈ J : Ai•rj > 0}
J0 = {j ∈ J : Ai•rj = 0} (6)
J- = {j ∈ J : Ai•rj < 0}
Minimality of Rk is ensured in considering all positive rays, all zero rays and new rays obtained as combination of a positive and a negative ray that are adjacent to each other [25]. For convenience, we denote by Adj the index set of the newly generated rays in which every new ray is expressed by a pair of indices corresponding to the two adjacent rays combined. Hence, Rk is defined as the set of column vectors rj, j ∈ J' with
The incremental step is repeated until k = h i.e. having treated all rows of the matrix A. The columns of the final matrix Rm are the extreme rays of P(A).
Computing EMs
The Double Description Method together with Theorem 1 offers a framework for computing EMs. The only steps to include are a reconfiguration step that splits reversible reactions and builds the matrix A, and a post-processing step that gets rid of futile two-cycles and computes the back-configuration. The dimension of the space is given by the number of reactions in the reconfigured network: q' = q + |Rev|. This results in the general algorithmic scheme as given in Table 1 (from here, all variables for the reconfigured network are written without prime):
Table 1 General double description method for EM computation.
N ← reconfigured stoichiometry matrix [N - NRev]
A ← [NT -NT I]T Reconfiguration
Aq ← q independent rows of A
R ← Aq-1 Initialization
for each unprocessed row Ai• of A do
J+ ← {j ∈ J : Ai•rj > 0}
J0 ← {j ∈ J : Ai•rj = 0}
J- ← {j ∈ J : Ai•rj < 0}
R' ← {rj : j ∈ J0 ∪ J+}
For (j+, j-) ∈ J+ × J- do Processing of constraints in a given order
If and adjacent in R then Adjacency test
end if
end for
R ← R'
End for Gaussian combination step
R ← R \ { futile two-cycles }
R ← back-configuration of R Back-configuration
As mentioned in the introduction section, the two most efficient algorithms for computing EMs available are the recently introduced null-space approach [24] and the Schuster algorithm [6], that we call "canonical basis approach" (implemented, for example, in METATOOL [29] version 4.3 and FluxAnalyzer version 5.0 [30]). Both algorithms handle reversible reactions directly. A direct handling of reversible reactions, meaning without network reconfiguration, is feasible in each setting and has been described in the respective original articles. This requires adapted adjacency tests. However, it does not affect the overall strategy. For simplicity, we describe these algorithms with networks of irreversible reactions only (the issue of reversible reactions is discussed below). We are now able to see that the algorithms of Schuster and Wagner differ basically only in the chosen initialization for R.
The canonical basis approach (Schuster approach; CBA)
The matrix I represents q independent rows extracted from A = [NT -NT I]T and can thus be used for Aq. The matrix Aq-1 = I-1 = I gives the q extreme rays that obey to these q independent constraints and works as initialization of R.
The remaining constraints are 2m linear inequalities defined by Nr ≥ 0 and -Nr ≥ 0, i.e. m equalities: Nr = 0. The processing of an equality constraint is done in a single pass by only keeping rays of J0 instead of J+ ∪ J0. This is achieved by replacing the line R' ← {rj : j ∈ J+ ∪ J0} with R' ← {rj : j ∈ J0} in the part "Processing of constraints in a given order" in Table 1. Note that in the original Schuster algorithm the values of Arj (required for the Gaussian combination step) are explicitly stored throughout the algorithm (in the left-hand side of the tableau [4]) and adapted after each iteration.
The null space approach (Wagner approach; NSA)
The idea there is to initialize R by a well-defined kernel (or null-space) matrix K of N with a particular structure (the transposed KT is in (reduced) row-echelon form):
which can be computed, for example, by the MATLAB command null(N,'r'). One can assume N to be of rank m, the opposite case being discussed below ("On redundancies and network compression"). This implies to be of size m × (q - m) and the identity of size q - m. This structure is obtained by allowing a reordering of the rows of K, i.e. of the reaction indices. Without losing generality, one can assume that the reactions corresponding to the block I are indexed from 1 to q - m. Consider the (q + m) × q matrix . For all x in P(Aq + m), there is some vector λ ≥ 0 such that x = Kλ. Reciprocally, for all λ ≥ 0, the vector x = Kλ lies in P(Aq + m). Thus (Aq + m, K) is a DD pair. Since K is a kernel matrix, its columns are independent vectors therefore (Aq + m, K) is a minimal DD pair. K as defined in (8) works as initial value for R. Hence, the initialization in this setup delivers directly k = q + m solved constraints.
The remaining constraints are m linear inequalities defined by ri ≥ 0, i = q - m + 1...q. The Gaussian elimination step simplifies too
The right hand-side is practically a positive combination of the two vectors and , because is positive and negative due to the definitions of J+ and J-.
Adjacency tests
Here we give explicitly the adjacency test in the case of reconfigured networks for each setup. Variants handling reversible reactions directly were introduced for CBA and NSA. They lead in general to more complex algorithmic steps for a little (at most 2-fold) memory gain.
The test is used when processing the constraint k + 1 to check whether two extreme rays r and r' of the cone P(Ak) are adjacent. The adjacency test is based on Definition 2(b). Note that for a given extreme ray r of the cone P(Ak), the considered zero set Z(r) is defined over the k constraints Ak.
CBA: As mentioned above, in a CBA setup, equality constraints are solved within a single iteration. After the l-th iteration step, k = q + 2l constraints are processed, therefore . The last 2l constraints are satisfied with equality for all computed rays. We denote by Zu(v) the Zero set of a vector v over the u first constraints. Here, with u = q it matches to the set of non-participating reactions in v. The adjacency test is then equivalent to the search of a third extreme ray r" such that Zq(r) ∩ Zq(r') ⊆ Zq(r"). If such an r" exists, then r and r' are not adjacent.
NSA: After the l-th iteration step in an NSA setup, k = q + m + l constraints including p = q - m + l sign constraints are processed. Thus . The last 2m constraints are satisfied with equality for all computed rays. Therefore, the adjacency test is then equivalent to the search of a third extreme ray r" such that
Zp(r) ∩ Zp(r') ⊆ Zp(r") (10).
Thus, for NSA we only have to check the first p(q - m ≤ p ≤ q) elements of the rays, in contrast to all q elements for CBA. This is one reason behind the relative velocity of NSA compared to CBA.
On redundancies and network compression
It is common practice to reduce the problem of extreme ray enumeration by restricting the input set to the set of irredundant constraints [25]. Although the general problem of extreme ray enumeration is non-polynomial, the reduction into irredundant constraints is equivalent to linear programming and therefore of polynomial complexity. To our best knowledge, this important pre-processing has never been spelled out explicitly in the context of EM computation. However some network simplification steps have been proposed earlier [4,30] that deeply relate to the notion of redundancy removal. These simplifications include three heuristics that reduce the size of the original stoichiometric matrix N and thus the input size of the problem: the detection of conservation relations, of strictly detailed balanced reactions and of enzyme subsets.
Conservation relations of metabolites are captured as linear dependencies between rows of the stoichiometry matrix N (thus, in the left null-space of N; [31]). This implies that some of the equality constraints in Nr = 0 are linearly dependent. Satisfying a maximal linearly independent subset of these equations suffices to satisfy all equations. Therefore the problem can be reduced to , where is the reduced stoichiometry matrix. For example, in Figure 3a, metabolites B and C build up one conservation relation and thus one of these metabolites can be removed. Note that conservation relations need not to be considered explicitly in the null-space approach since their removal does not affect the computed null-space matrix.
Figure 3 Small example networks illustrating redundancies. For explanations see text.
Conservation relations only consider redundancies among the equalities. The general approach handles also inequality constraints. Strictly detailed balanced reactions [32] and enzyme subsets [29] are particular cases of such redundancies. Strictly detailed balanced reactions are reactions with null flux at any steady-state. Many of them can be identified as null row vectors of K, the kernel matrix of N, and can be eliminated from the system. A non-trivial example is shown in Figure 3b, where R1 is strictly detailed balanced and would be detected by using the kernel matrix. However, there may be further reactions with a fixed zero-flux in steady state that cannot be identified by K. Some of those can be found by a simple analysis of N. For example, all the uni-directional reactions pointing into an internal sink (or emanating from a source) are certainly not participating in any steady-state flux (Figure 3c).
An enzyme subsets is defined as a group of reactions with relative constant flux ratio at steady state. Many of them can be identified as row vectors of K differing only in a scalar factor α. Reactions R1, R2 and R5 in Figure 3d would represent one enzyme subset. Assume one works on the reconfigured network and reactions R1 and R2 are members of the same enzyme subset. Thus, at steady state, we have for the respective rates r1 = α · r2. If α > 0, the constraints r1 ≥ 0 and r2 ≥ 0 are redundant, r1 ≥ 0 being sufficient. In that case the practice is to lump both reactions into one lowering the number of reactions (and often also of the metabolites). If α < 0, the constraints r1 ≥ 0 and r2 ≥ 0 imply r1 = r2 = 0, hence, a special case of strictly detailed balanced reactions. In this case we say that the reactions contradict each other. Both reactions are not used and can be eliminated from the system as reactions R1 and R4 in Figure 3e.
We identified another kind of redundancies. We call a metabolite M uniquely produced (respectively consumed) if only one single reaction, say i, can produce (respectively consume) M for several consuming (respectively producing) it (see Figure 3f). In that case, balancing metabolite M at steady-state implies that ri is always non-zero whenever the other reactions connected to M are active. We can therefore lump each reaction consuming (respectively producing) M with reaction i and remove metabolite M, decreasing the dimension of the problem further (see also the example in Figure 5 which is discussed below). Note that some enzyme subsets and strictly detailed balanced reactions can be seen as special cases of this type of redundancy.
Figure 5 Example network. Full structure (a), compressed structure (b) and compressed structure with split reversible reaction R4c (c).
Elimination of redundancies and network compression should be done in a pre-processing step leading to a compressed network structure. Thereby, it is important to detect and remove such redundancies iteratively until no further redundancy can be found. A MATLAB function compressSMat which removes all redundancies discussed above in an iterative fashion can be obtained from the corresponding author. After the computation of EMs, lumped reactions can be expanded to their single components.
There is a general approach for identifying redundancies in a set of linear constraints that uses linear programming, for example with the software redund distributed together with the software lrs [33]. This approach does not require any iterative process, but only identify redundant inequalities. Rows of A can be eliminated but no consequent column-wise reduction is done. Therefore, a simple redundancy removal is not as powerful as the accompanying network compressions presented above. The method however has the advantage to be systematic and might lead in the future to further network simplifications not yet identified.
The binary approach
General idea
Using the reconfigured network with only irreversible reactions we have shown that the most important algorithms for EM computation belong to the same general framework. However, the original algorithms from Schuster and Wagner operate directly on the original network without splitting reversible reactions. At a first look, this seems to be more efficient since the dimension (number of reactions) is lower, decreasing seemingly also the memory requirement and the costs for adjacency tests. However, using the reconfigured network S' offers great simplifications. First, as already mentioned in an earlier section, the adjacency tests are easier to handle. The most important advantage, however, is the following. For the CBA in S' it follows that all non-zero elements of a ray rk will be retained if a new ray is obtained by combining rk with another (adjacent) ray because only positive combinations of rays are performed. The same holds for the NSA with respect to the p already processed inequality (irreversibility) constraints. This is of great importance since the adjacency test requires the information on zero/non-zero places in the rays only.
We illustrate this idea for NSA because this approach turned out to be more efficient than CBA. We assume that N has full rank m, i.e. there is no conservation relation. In this section, all variables correspond again to the network with split reversible reactions.
As described above, for an initialization of R we use a kernel matrix K of N having form (8):
Note that we use here the transposed representation of the tableau compared to Wagner's original article [24]. Since by eq. (9) only positive column combinations are performed during the algorithm, no negative number can show up in the upper part (consisting of q - m rows (reactions)) during the next iterations. The first row to be processed now is p = q - m + 1. Using the general algorithmic scheme provided above all rays with non-negative entries at row p are retained and all negative entries can be combined with positive ones that are adjacent to them to obtain a zero at position p.
Assuming that the procession of the p-th row leads to a collection of t rays, we have:
The upper part, R1, contains the p processed rows which only contain non-negative values. Again, positive combinations of rays performed during the next iterations lead in the upper part to sums of non-negative numbers. Hence, it is easy to keep track of the zeroes in the upper part R1 by the use of bit masks. After the procession of the p-th inequality constraint the p-th row (i.e. the first row of R2) can be transformed to its binary representation and moved from R2 to R1. Using a binary representation for R1 has many advantages:
(i) For the next row p + 1 to be processed we have to perform the adjacency test for pairs of vectors , . This test only requires the first p elements of these rays (see (10)), hence, exactly the columns of R1. Test (10) can then be written as a simple (and fast) bit operation. Two distinct vectors , are adjacent if and only if for all vectors rk distinct from and , it holds:
( taken from R1; r1...p denotes the first p elements of r). Of course, the identical terms in the parentheses are computed only once.
(ii) Combination step of two adjacent rays (eq. (9)) reduces for the part in R1 to a simple OR operation, which is already computed for (13). The other (real number) components of the two rays (contained in R2) are combined as usual by eq. (9).
(iii) Bit operations as applied in R1 are not only fast, they are numerically exact in contrast to operations on real numbers.
(iv) The binary representation requires much less memory. Taking a typical 64-bit floating-point variable, storing R1 binary takes only 1.6 % of the memory needed for real numbers. Taking into account that in the worst case (all reactions reversible) the number of reactions in the reconfigured network is twice of that from the original one we still have a reduction in memory requirements of more than 96%. Note that R2 is empty at the end of the algorithm, hence, all EMs are then stored binary.
Bitmap representations of EMs have already been used in earlier implementations for accelerating the adjacency (elementarity) tests. However, binary tableaus had then been stored and updated in parallel to the full (real number) tableau of EMs which is not necessary here.
After the whole processing, EMs (extreme rays) are obtained for the reconfigured network S' as binary vectors. Binary patterns of EMs are completely sufficient for many applications of EMs (see discussion). However, a well-known lemma ([25] for example) ensures that this information is also sufficient to retrieve the real values up to a positive scalar:
Lemma 2
In a d-dimensional Euclidean space, let r be a ray of the pointed polyhedral cone P(A). The following statements are equivalent:
(a) r is an extreme ray of P(A)
(b) rank(AZ(r)) = d - 1
Each obtained binary vector provides the zero set Zq(e) and its complement the reaction set R(e) of an EM e in the reconfigured network S'. Lemma 2 says that the equation and therefore
NR(e)eR(e) = 0 (14)
admit a one-dimensional solution space, i.e. the dimension of the null space of NR(e) is 1. NR(e) denotes the m × |R(e)| sub-matrix of N containing all those reactions (columns) of N which are involved in e. Solving the homogeneous linear system (14) gives a vector that can be normalized and properly oriented for example by dividing it by the value on its first participating reaction (see the example below). The reconstruction process reflects the fact that an EM is – up to a scalar – determined by its participating reactions.
In a second post-processing step, we transform the (real number) EMs of S' back into their representation in the original network S by using the rules given before Theorem 1. Note that it is also possible to transform first the binary EMs from S' into the binary EMs of S and then to reconstruct the real numbers (by using eq. (14) for the stoichiometric matrix of the non-reconfigured network S; see pseudo-code). In both cases, if the original network had been compressed during pre-processing, the EMs can finally be expanded to their corresponding modes in the uncompressed network.
Pseudo-code of the binary (null-space) approach
Using the results of the previous sections we are now able to give a pseudo-code of the binary (null-space) approach (Figure 4). The code follows MATLAB style, which provides a convenient and comprehensible notation for operations on vectors and matrices. We use several native MATLAB routines (written in bold). For concision, we also make the use of some other routines (indicated in italic). The code of the latter routines is not given here explicitly but their names and accompanying comments should allow the reader to implement them. For readers not familiar with MATLAB notation we give in the Methods section some basic explanations which should suffice for understanding the pseudo-code.
Figure 4 Pseudo-code: Core algorithm for computing elementary modes with the binary approach.
Note that the pseudo-code in Figure 4 is not given in its computationally most efficient form. It should just present the basic structure of the algorithm. There are two important issues in the algorithm we still have to discuss.
Minimal number of zeros in extreme rays (maximal pathway length)
In the null-space approach, the m equality constraints are always solved for each ray during the procession of sign constraints. Since any ray satisfies by Lemma 2 at least a total of q - 1 constraints, this implies that at least q-1-m sign restrictions are solved by equality. Hence each ray contains at least q-1-m zero-places. This fact can be used as a shortcut when checking the adjacency of two rays (see pseudo-code). At the end of the algorithm, it follows that the maximal pathway length |R(e)|max, that is the maximal number of involved reactions in an EM, reads (cf. [7]):
|R(e)|max = q - (q - m - 1) = m + 1 (15)
Initialization of R
As for the non-reconfigured network, the initialization of R for the reconfigured network can be done with a null space matrix K' of N' having the special structure (8). Several of such kernel matrices may exist. We are interested in such a one that contains as many zeros as possible because the number of zeros in the starting tableau R has great impact on the number of ray combinations to be performed. For this purpose, it can be exploited that very sparse vectors of the null space of N' (not contained in the null space of N) are known, namely the two-cycles emerging by splitting up reversible reactions. We detail in Method section a technique that incorporates as many two-cycles as possible into K to construct K'.
Simple example
This section is devoted to illustrate our binary approach for computing elementary modes. Figure 5(a) shows a simple example network consisting of four metabolites (A,B,C,D) and 7 reactions (R1...R7), whereof R5 is reversible. The stoichiometric matrix N of this network reads accordingly:
Using our rules for removing redundancies, this network can be compressed as depicted in Figure 5(b). Metabolite A is uniquely produced, hence, R1 and R2 can be combined to R1c and reactions R1 and R3 are lumped into R2c. R3c and R4c correspond to the original reactions R4 and R5, respectively. Finally, R6 and R7 are enzyme subsets and are combined to R5c. Metabolites A and D can be removed, since they do not occur in any reaction anymore. Thus, the network dimension could be reduced by two metabolites and two reactions. The stoichiometric matrix NC of the compressed system reads:
From this compressed network, we can compute a null space matrix having structure (8), here even without permuting rows (reactions):
KC would be the starting tableau in the original null-space approach. Applying our binary approach we have now to split the (only) reversible reaction R4c in the compressed network (Figure 5(c)). This results in the stoichiometric matrix NC', where R4cb denotes the additionally introduced column of the backward direction of R4c:
Now we need to determine a null space matrix KC' of NC', if possible in the sparse form as in eq. (M1) (Methods section). KC – as given in (18) – contains only irreversible reactions in the identity sub-matrix. Therefore, without further rearrangements, we can already use it to construct KC' as described in the Methods section. We introduce an additional row in the identity sub-matrix of KC (corresponding to R4cb) and an additional column representing the two-cycle from the split reversible reaction R4c:
KC' is now a proper initialization for the R tableau according to (11). The first four rows (in the identity sub-matrix) can be seen as already completed, we therefore denote the starting tableau as R4. According to (12) we can divide R4 into a binary (a non-zero entry is demarked by "×") and unprocessed real number part:
We proceed now with the 5-th row (R4c). All columns with non-negative entries in R4c are retained (columns 1 and 4). Columns 2 and 3 have a negative entry at position R4c and are therefore combined with 1 and 4 to obtain a zero at position R4c. In the binary sub-tableau, the combination step is a simple OR operation. Thereby, using the obtained binary patterns, the adjacency test (13) must be performed for each pair of combined columns. Here, all 4 possible pairs are adjacent. Accordingly, after completing row 5, tableau R5 has 6 columns and reads:
Now we have already reached the last iteration step where R5c – the last row in real number format – is processed. Columns 1–5 are retained and column 6 is combined with columns 1,3 and 5. However, the column pairs (1,6) and (3,6) are not pairs of adjacent rays. This can be detected in two alternative ways. The usual way is that both column pairs violate condition (13) because of column 4. The second and quicker way is to observe that the minimal number of zeros in this network is 3 (q'-m-1 = 6-2-1) and that their respective combinations would give columns with only 2 zeros. These combinations are therefore not included in the tableau. We obtain:
Tableau R6 is the binary representation of the EMs (extreme rays) from the split compressed network. Now, the post-processing begins. First, we remove the spurious 2-cycle (second column in R6) raised by splitting R4c. Then, rows R4c and R4cb are combined by an OR operation and row R4cb is dropped. Note, if a completely reversible elementary mode exists in the non-split network, it would lead to two EMs – one for each direction – in the split network. In such a case, either both are kept or only one, then marked as reversible EM. We have now obtained the 5 EMs of the compressed network as binary vectors:
Here, it is easy to reconstruct the real numbers of the EMs from their binary patterns. For illustrating the general case, we reconstruct the first mode e1 using eq. (14):
The dimension of the null space of , hence of the solution space of eq. (25) is 1 (as it is for all EMs). A scalable solution vector is (2,1,1)T, normalizing to the first component yields the unique solution (1,0.5,0.5)T. Thus, the first EM in the compressed network is e1 = (1,0,0,0.5,0.5)T. Reminding that we lumped the original reactions R1 and R2 into R1c and R6 and R7 into R5c, we can finally reconstruct the original elementary mode from the uncompressed network, that is R1 + R2 + 0.5 × R5 + 0.5 × R6 + 0.5 × R7.
Results from real networks
We implemented the binary null-space approach (binary NSA) in MATLAB (Mathworks Inc.) and incorporated it into the FluxAnalyzer [30,34]. The function includes a pre-processing step where the network is compressed as described. Some sub-routines of the algorithm are performed by compiled C-code (via MATLAB MEX interface), since this proved to accelerate the implementation drastically. In order to check the capabilities of our algorithm we computed the elementary modes in realistic and large metabolic networks. The three networks (S1-S3) considered here are variants from a model of the central metabolism of Escherichia coli investigated originally in [11,23]. For considering networks with different complexities we inserted an increasing number of substrate uptake or/and product excretion (pseudo) reactions, which increase the number of EMs much faster than the insertion of internal reactions. For a (rough) comparison with the original NSA we used the program coverN (developed by Clemens Wagner and co-workers; available upon request from [email protected], which is also implemented in MATLAB and uses external C-files for some sub-routines. The original as well as the binary CBA algorithm proved to be slower than both methods of NSA (not shown).
Table 2 summarizes the computations. As a first result, it can be noted that redundancy removal and network compression during pre-processing results in much smaller networks. Note that the dimensions of the compressed networks of S1 and S2 are even lower than given in [23] due to the additional removal of uniquely produced/consumed metabolites. A lower number of reactions reduces the dimension of the null-space (hence, the number of iterations) and, in particular, the effort for adjacency tests. Generally, the proportion of the pre-processing on the overall computation time is negligible.
Table 2 Computations of elementary modes in a realistic metabolic network (central metabolism of Escherichia coli). Computations were performed on a typical PC with AMD Athlon XP 3000 + CPU and 1 GB RAM. Abbreviations: Form = formiate, Ac = acetate, Glc = glucose, Succ = succinate, Asp = aspartate, Glyc = glycerol, Eth = ethanol, Lac = lactate, CO2 = carbon dioxide.
S1 S2 S3
substrates Glc Glc, Succ, Glyc, Ac Glc, Succ, Glyc, Ac, Asp
products Ac, Form, Eth, Lac, CO2 Ac, Form, Eth, Lac, CO2 Ac, Form, Eth, Lac, CO2, Succ
#reactions (q)
# metabolites (m) 106 (28 reversible)
89 110 (28 reversible) 89 112 (28 reversible)
89
compressed network:
# reactions
# metabolites
42 (17 reversible)
25
47 (17 reversible)
26
51 (17 reversible)
28
final number of elementary modes 27,100 507,632 2,450,787
binary NSA NSA binary NSA NSA binary NSA NSA
computation time 0.16 min (9.63 sec) 0.54 min (32.20 sec) 51.20 min 116.77 min 1546 min (25.78 h) not finished
back transformation 0.13 min (7.97 sec) 2.57 min 13 min
total computation time 0.29 min (17.60 sec) 0.54 min (32.20 sec) 53.77 min 116.77 min 1559 min (25.98 h)
Comparing the required computation times, the binary NSA seems to be slightly faster than the original NSA. This observation should not be considered as a general result, since we cannot exclude that there are different potentials in optimizing the source code of coverN and in FluxAnalyzer, respectively. Besides, different row orders in the starting tableau can generally result in different computation times. However, it seems that the original and the binary NSA are comparable with respect to computation time. The adjacency tests in the binary null-space approach need to consider more elements (due to the split of reversible reactions) but are simpler to perform because preliminary modes from a previous iteration cannot lose their elementary property. Note also that implementing the full algorithm in C (and not only parts of it as in coverN and FluxAnalyzer) might further accelerate the computation considerably.
Using a special null space matrix K' as initialization of R (as explained in the Methods section) contributes considerably to a reduced computational effort. We can estimate this by the total sum over the number of candidates Pi occurring in the tableau before iteration i. In S1, for example, . Computing instead an arbitrary null-space matrix K' for N' (e.g. via MATLAB null command) results in a more dense initialization for R and the naive initialization would lead to . The larger numbers of candidates increase the costs for adjacency tests and accordingly the running time drastically. This underlines that the success of the null-space approach (in its original or binary form) depends strongly on the initially chosen null space matrix.
Generally, computing the stoichiometric coefficients of the EMs from their binary patterns is in larger networks in low proportion to the overall computation time (S3: ca. 0.8%).
Whereas the computational demands seem to be comparable for both null-space approaches, the memory requirements for the binary NSA are much lower, in particular during the last iterations. For this reason, the 2.45 millions of EMs from network S3 could be computed on a typical PC, whereas the original NSA ends in the 26-th iteration step (from a total of 28) due to memory overflow.
Discussion
Elementary modes are smallest functional sub-networks, which can be interpreted geometrically as extreme rays from a pointed convex cone (corresponding to the network with split reversible reactions). The computation of extreme rays has been intensively studied by the polyhedral computation community and we think that the metabolic community can benefit from it. We shall also mention another abstraction of elementary modes within the framework of matroid theory [35]. In an oriented vector matroid, the elementary modes correspond to the positive circuits (or positive cycles), which are minimal dependent sets. In fact, an elementary mode is a minimal linearly dependent set of the column vectors of the stoichiometric matrix (in the reconfigured network with only non-negative coefficients). This has been mentioned only rarely so far [36]. Matroid theory could be a source for new theoretical investigations on elementary modes and could lead to further improvements in the computation procedure as well as to new applications in the sense of metabolic pathway analysis.
Adjacent extreme rays can also be detected by an algebraic characterization that completes Definition 2 [25]:
(c) r and r' are extreme rays and the rank of the matrix AZ(r) ∩ Z(r') is d-2
In practical cases the characterization of adjacency is mostly computed in its combinatorial form than its algebraic one [25]. However, improvements could be done by using both characterizations. In fact, the test on EM length done before the actual adjacency test in our MATLAB pseudo-code is a consequence of the algebraic test. A striking feature of the algebraic test is that it only requires access to the two rays tested for adjacency (r and r') and to the fixed size matrix A, in practice to the stoichiometry matrix. In comparison, the combinatorial test implies a loop over all other rays (r"). Therefore, the algebraic test could be suited for distributed computing.
Some theoretical issues of the combinatorial complexity of EMs were discussed in [23]. An upper bound B for the number of EMs is (reversible modes are counted only once):
Assuming that no conservation relations occur in the stoichiometric matrix, we obtain:
Note that q and m should be taken from the non-split, compressed network to obtain the lowest upper bound. In larger, realistic networks, even if compressed, the values for B explode quickly. Fortunately, the actual number of modes in real networks proved to be much smaller than the boundary (cf. B ≈ 2.54 · 1011 for S1 in Table 2), although it grows also exponentially. One reason is that many routes are not admissible due to violation of the sign restrictions. Another reason is the low connectivity of many metabolites leading to sparse stoichiometric matrices.
A third reason is related to short pathway length. The upper bound reflects the case where all EMs have maximal pathway length |R(e)|max which is, by eq. (15), m + 1. However, many EMs, if not all, have a lower length immediately reducing the possible number of modes [23]. The pathway length distribution of the E. coli modes on glucose (network S1) is shown in Figure 6. The maximal length of an EM in the uncompressed network is m + 1 = 89 + 1 = 90. Modes that are not involved in biomass synthesis, in particular, are much smaller. In terms of linear algebra this means that there exist vector sets W containing fewer than m + 1 column vectors of N that are linearly dependent. In polyhedral computation this phenomenon is known as degeneracy. Generally, degenerate systems may cause annoying difficulties and must be handled often differently to non-degenerate systems, albeit they reduce here the number of modes. The algorithms related to EM computation may be, in general, especially suited for computing extreme rays in such strongly degenerate systems, whereas other programs may be better suited for only weakly degenerate problems. For example, the software lrs [33] implements the so-called reverse search enumeration algorithm [37] that is polynomial for non-degenerate cases. Note that the new binary approach as introduced herein can easily be adapted for computing extreme rays of any pointed cone as given in eq. (4) and may therefore improve the performance of extreme ray computation in many other applications.
Figure 6 Pathway length distribution in elementary modes of E. coli. (Substrate: glucose; network S1 in Table 2).
Albeit the general framework was formulated long time ago, the explicit introduction of the null-space approach was an important mile-stone in accelerating the computation of EMs. The binary null-space approach as introduced herein increases the efficiency of this approach also with respect to the memory requirements and enables now to compute EMs in networks significant larger as those investigated before. A simple computation gives the number of about 85 millions of EMs in a network of 100 (compressed) reactions that can be stored in 1 GB RAM (cf. compressed and reconfigured S3: q' = 51 + 17 = 68). Of course, only a fraction of this amount can be stored during the algorithm due to other (partially large) temporary variables. Besides, reactions that are not yet processed are still stored as real numbers. The amount M of memory required for storing E modes after the procession of p reactions (stored binary) is (assuming 64-bit real numbers)
M = E · (p + 64 · (q - p)). (28)
It depends on the evolution of the number of EMs during the algorithm where the maximal memory demand occurs. Generally, much larger networks can now be treated.
Conclusions
The four main results of this work are: (i) showing the equivalence between extreme rays and elementary modes, (ii) showing that algorithms for computing elementary modes can be seen as variants of the double description method for computing extreme rays in pointed polyhedral cones, (iii) introduction of a general framework and of new methods for redundancy removal and network compression, (iv) introduction of the new binary approach for computing extreme rays and elementary modes.
The binary approach computes elementary modes as binary patterns of participating reactions that are sufficient to compute the respective stoichiometric coefficients in a post-processing step. For many applications – following the computation – it is even sufficient to operate on the binary patterns of EMs. Among all applications of EMs presented in the introduction section, only the identification of all pathways with optimal yield, the "control-effective fluxes", and the α-spectrum need the explicit (real number) coefficients, i.e. the reaction rates, in the EMs. Whenever needed, the explicit representation of an EM can be determined (possibly temporarily) from its binary pattern.
The binary approach decreases the memory demand up to 96% without loss of speed and without loss of information giving the most efficient method available for computing elementary modes to date. The limiting step in computing elementary modes has thus been shifted back to the computation time. Parallelization – as investigated within the traditional, not-binary, schema in [38] – might lead to a further acceleration bringing us again a step closer to the complete set of EMs in genome-scale metabolic networks.
Methods
Proof of Theorem 1
We prove first that each case a) and b) defines EMs of S'. Let e' be a flux vector defined by either case a) or b). Clearly N'e' = 0 and e' ≥ 0. In case b) e' is not elementary only if the single forward or backward reaction balances all internal metabolites, i.e. if the reaction includes not any internal species. We can safely exclude this pathologic case by considering that N does not contain a null column. Therefore, e' is elementary. In case a), assume e' is not elementary, i.e. there exists a non-null flux vector x' of S' not equivalent to e' such that x' ≥ 0, N'x' = 0 and R(x') ⊆ R(e'). By definition of the reconfiguration, for each i ∈ Rev, at least one among e'(i,+1) or e'(i,-1) equals zero and this holds consequently also for x'. Thus one can define e and x, the back-configurations of e' and x'. Now, by definition, e is an EM of S and is not equivalent to x, Nx = 0, xi ≥ 0i for i ∈ Irrev and R(x) ⊆ R(e), a contradiction.
Hence each case a) and b) defines EMs of S'. We prove now that there is no other case. Assume there exists e' neither defined by a) nor b), such that e' ≥ 0, N'e' = 0 and e' elementary. For each i ∈ Rev at least one among e'(i,+1) and e'(i,-1) equals zero (otherwise the two-cycle defined on reaction i would satisfy the constraints and involve only a subset of the reactions of e'). Thus the back-configuration e of e' can be defined. By definition, e is not an EM of S. There exists x not equivalent to e such that Nx = 0, xi ≥ 0i for i ∈ Irrev and R(x) ⊆ R(e). The reconfiguration x' of x is such that x' is not equivalent to e', x' ≥ 0, N'x' = 0 and R(x') ⊆ R(e'), a contradiction.
Initialization of the R tableau in reconfigured networks
As in the case of non-reconfigured networks, we must initialize R in reconfigured networks as a null space matrix K' of N' having the special structure (8), i.e. . Several kernel matrices having this form can exist. Here we are interested in such a one that contains as many zeros as possible because the number of zeros in the starting tableau R has a great impact on the number of ray combinations to be performed. For this purpose, we can exploit the fact that we already know |Rev| many very sparse vectors of the null space of N', namely the two-cycles emerging by splitting up reversible reactions. Our goal is therefore to incorporate many (if possible all) of these vectors into K to obtain K'. For this purpose, we first compute the kernel matrix K of N. Then, by simple linear combinations of columns (analogous to the well-known computation of a row-echelon form of a matrix) and possibly by permutation of rows in K, we try to obtain , where only irreversible reactions (rows) are contained in the identity matrix I. If this is possible then we can easily include the backward directions of reversible reactions (as rows) and the two-cycles (as columns) into K yielding K':
The first q - m columns in K' correspond to the original columns in K, but contain additionally zeros for the inserted backward reaction of originally reversible reactions. These columns are obviously linearly independent and are contained in the null space of N'. Sub-matrix is a |Rev| × |Rev| identity matrix whose rows correspond to the backward directions of split reactions. Finally, C is a |Rev| × m sub-matrix which complements in such a way that they represent together the two-cycles of the split reactions. (Thus, each column ci in C contains only zeros, except a unity at that row, which corresponds to the forward direction of the split reversible reaction i. See also the example network.) I and yield together the new I', whereas and C represent together of K'. Thus, K' contains q - m + |Rev| linearly independent (basis) vectors of the null space of N' and is in form (8).
To our experience, in most realistic networks, a matrix K' as in (M1) can be found. Using instead an arbitrary K' can lead to a much larger computation effort because much more candidates are computed at an early state (see real network examples).
A further simple strategy avoiding that many rays are computed early is to sort the rows in ascending with respect to the number of their non-zero entries.
In case it is not possible to arrange only irreversible reactions into the sub-matrix I of K, we can nevertheless find a matrix K' with the same basic structure as in (M1). However, for some originally reversible reactions, the forward (in I) and backward (in ) direction will then be contained in I'. For each of those, the two-cycle cannot be represented by C and (because the row of the forward direction is contained in I' and not in K') and another corresponding column in C has to be constructed. Assume a reversible reaction is contained as j-th row in I. Assume further that the inserted backward direction of this reaction corresponds to the k-th row of . For the k-th column ck of C we can then chose the j-th column of multiplied by -1, i.e. . Together with the k-th column in , this gives a null space vector of N', which is linearly independent of the others and can therefore serve as basis vector in K'. The vector ck is now probably not that sparse. However, it enables us to retain the 2-cycles at least for those split reactions whose forward direction is not contained in I.
A MATLAB function initializeR that provides a proper initialization of R as described above (starting with the stoichiometric matrix N and the indices of the reversible reactions) can be obtained from the corresponding author.
Short introduction into MATLAB notation
Numeric variables in MATLAB can be scalars, vectors or two-dimensional arrays (i.e. matrices). To be more precise, a scalar in MATLAB is actually a 1 × 1 array and a vector is a 1 × n or n × 1 array. Size and type of a variable are automatically declared (or changed) by assignments to it. The following examples illustrate how to assign or access values of variables:
• scalar: a = 1;
• b(3) = 5; the value 5 is assigned to the third element of (vector) b.
• c(1:3) = [5,8,9]; here, "1:3" expresses "from 1 to 3", thus, 5, 8 and 9 are assigned to the first three elements of vector c. It is also possible to use an array of integers to access the elements of a vector, e.g. a = [2,3,4]; b = [1,3]; c = a(b). Vector c reads then [2,4].
• mat(2,5) = 3; value 3 is assigned to the element in the second row and fifth column of matrix mat.
• mat1(3,:) = mat2(5,:); the values of the fifth row of matrix mat1 is copied into the third row of matrix mat2. Here, the colon operator ":" expresses "all elements of the respective dimension" (here: columns). Of course, it must be ensured that mat1 and mat2 have the same number of columns.
• a = mat(7,1:3); the first three elements of the seventh row of matrix mat are assigned to a which is now a 3-element vector.
• a= [17,34,39]; a(2)= []; deletes the second element of a and shifts all elements behind one position back, i.e. vector a reads now [17,39].
The pseudo-code given in Figure 4 in the main text uses several basic routines pre-defined in MATLAB (written in bold) :
• c = length(a); if a is a vector (as in all cases in the pseudo-code) then length returns the number of elements in a.
• c = find(a); if a is a vector (as in all cases in the pseudo-code) then find returns all positions in a which are not zero. Example: find([23,0,5,9,0]) returns (1, 3, 4).
• c = or(a,b) returns the result of the logical OR operation applied element-wise to a and b. a and b can be scalars, vectors or matrices and must have the same size. Example: if a = [1,0,29], b = [1,0,0] then or(a,b) returns [1,0,1]. In the pseudo-code, we use this routine exclusively for OR-operations of bit masks (arrays with only "ones" and "zeros").
• c = zeros(m,n) returns a matrix of size m × n filled with zeros.
• c = null(a) returns a null-space matrix of matrix a.
• c = intersect(a,b) returns the intersection of elements in vectors a and b.
• c = all(b) returns "1" if all entries in vector b are not zero and "0" otherwise.
List of abbreviations
EM(s): Elementary Mode(s) also known as Elementary Flux Mode(s).
Authors' contributions
Both authors contributed equally to this work, the starting idea of the binary approach coming from a discussion between them. JG mainly established the relationships between extreme ray and elementary modes computation. SK mainly devised and implemented the binary null-space algorithm. Both authors prepared the manuscript jointly.
Acknowledgements
We thank Clemens Wagner for providing us with the software coverN and Axel von Kamp for helpful discussions. We are grateful to Georg Casari and Toby Mathieson for reading the manuscript.
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| 15527509 | PMC544875 | CC BY | 2021-01-04 16:36:38 | no | BMC Bioinformatics. 2004 Nov 4; 5:175 | utf-8 | BMC Bioinformatics | 2,004 | 10.1186/1471-2105-5-175 | oa_comm |
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BMC GenetBMC Genetics1471-2156BioMed Central London 1471-2156-5-341561757110.1186/1471-2156-5-34Research ArticleThe exceptionally high rate of spontaneous mutations in the polymerase delta proofreading exonuclease-deficient Saccharomyces cerevisiae strain starved for adenine Achilli Alessandro [email protected] Nabil [email protected] Enrico [email protected] Giorgio [email protected] Angela [email protected] Youri I [email protected] Nora [email protected] Dipartimento di Genetica e Microbiologia, Università di Pavia, Pavia, Italy2 Department of Molecular Pharmacology, St. Jude Children's Research Hospital, Memphis, TN 38105, USA3 Dipartimento di Biologia Animale e Genetica, Università di Firenze, Firenze, Italy4 Dipartimento di Biologia Cellulare e Molecolare, Università degli Studi di Perugia, Perugia, Italy5 Eppley Institute for Research in Cancer and Department of Biochemistry and Molecular Biology, University of Nebraska Medical Center, Omaha, NE 68198, USA2004 23 12 2004 5 34 34 13 7 2004 23 12 2004 Copyright © 2004 Achilli et al; licensee BioMed Central Ltd.2004Achilli 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
Mutagenesis induced in the yeast Saccharomyces cerevisiae by starvation for nutrilites is a well-documented phenomenon of an unknown mechanism. We have previously shown that the polymerase delta proofreading activity controls spontaneous mutagenesis in cells starved for histidine. To obtain further information, we compared the effect of adenine starvation on mutagenesis in wild-type cells and, in cells lacking the proofreading activity of polymerase delta (phenotype Exo-, mutation pol3-01).
Results
Ade+ revertants accumulated at a very high rate on adenine-free plates so that their frequency on day 16 after plating was 1.5 × 10-4 for wild-type and 1.0 × 10-2 for the Exo- strain. In the Exo- strain, all revertants arising under adenine starvation are suppressors of the original mutation, most possessed additional nutritional requirements, and 50% of them were temperature sensitive.
Conclusions
Adenine starvation is highly mutagenic in yeast. The deficiency in the polymerase delta proofreading activity in strains with the pol3-01 mutation leads to a further 66-fold increase of the rate of mutations. Our data suggest that adenine starvation induces genome-wide hyper-mutagenesis in the Exo- strain.
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Background
Mutagenesis in stationary-phase cells has attracted much attention recently. Historically, most studies on spontaneous mutation rates in bacteria and unicellular eukaryotes were conducted in exponentially growing cells, even though it was well known that bacteria and yeast, in their natural habitat, spend most of the time in the stationary phase [1,2]. There were relatively few papers about the accumulation of spontaneous mutations in non-dividing or poorly dividing cells [3] until the publication of the Cairns' paper "The Origin of Mutants" [4]. The paper showed that mutations can arise in a non-growing bacterial population and also suggested their adaptive nature. Since then, the parameters of spontaneous mutagenesis in unicellular organisms under conditions of limited growth have been examined in numerous studies [5,6]. Starvation for amino acids and bases has been used largely because of the relative ease of studying reversions of nutritional markers in both bacteria and yeast [4,7-9]. We previously reported that the replicative DNA polymerases δ and ε are involved in the control of mutability in non-dividing yeast cells. We have shown that strains with the mutational inactivation of proofreading exonucleases (pol3-01 and pol2-4 mutations, correspondingly) retained their mutator phenotypes, compared to each other and to the wild-type strains, upon histidine starvation [10]. This result was consistent with the earlier studies of the effect of the cdc2-1 mutation, allele of the POL3 [11].
We studied the effect of adenine starvation on the reversion of an auxotrophic strain carrying the ade5-1 allele. We show that adenine starvation induced a high level of reversion. The effect was further elevated 66 fold in the pol3-01 mutator strain, de3-01-CG. In addition to reversions, additional mutations throughout the genome were induced. The latter was suggested by the fact that most Ade+ revertants were also auxotrophic for additional nutritional requirements and 50% of them were temperature-sensitive. These data suggested that reversions to prototrophy in the strain de3-01-CG, under adenine deprivation, are not adaptive, as was observed for histidine starvation [12]. We propose that adenine starvation of strains auxotrophic for this nutrient leads to perturbations of replication and/or DNA repair synthesis (for example, nucleotide pool deprivation or imbalances, which results in high rates of mutagenesis). The proofreading exonuclease activity of polymerase δ is an important factor that protects resting cells from this mutagenesis.
Results
Ade+ revertants rates in the strains CG379-3-29(LR) and in de3-01-CG
First, we tested the effect of adenine starvation on reversion rates in a Hall's experiment [8], as we did in a previous study on histidine starvation [10,12]. The rate of accumulation of Ade+ revertants on SD medium with limited amount of adenine was high: all colonies of the wild-type strain CG379-3-29(LR) on day 11, and its pol3-01 derivative with defect in proofreading by polymerase δ (strain de3-01-CG) on day 7, had one or more papillae, thus making it impossible to estimate the reversion rate. Therefore, we used the medium without any adenine (see "Methods"). High reversion rates were observed again, but an estimation of the mutation rate was now possible. Ade+ revertants begun to appear on SDNA-ade plates on day 9 and on day 6 for the strains CG379-3-29(LR) and de3-01-CG, respectively, and continued to accumulate up to the end of the experiment (Figure 1). The cumulative reversion rate on day 16 was 1.5 × 10-4 for CG379-3-29(LR) and 1.0 × 10-2 for de3-01-CG. The presented reversion frequencies are the total number of Ade+ revertants at the end of the experiment per cells plated (not per survived cells). There are several reasons why we did the calculations in this way. We do not know exactly when the mutational event occurred since the Ade+ revertants growth rates were somewhat different (see below); we did not know the exact number of viable cells, due to ongoing residual divisions and cell death. In the latter respect, the strains CG379-3-29(LR) and de3-01-CG behaved very differently. The de3-01-CG cells stopped dividing on day 3 after plating and began to die. The strain CG379-3-29(LR) continued to multiply slowly and its survival was higher than survival of the de3-01-CG strain. These two parameters are documented in Figure 2 (dependence of number of cells on days of incubation) and in Figure 3 (cell viability versus time of incubation).
Figure 1 Accumulation of Ade+ revertants in the strains CG379-3-29 (LR) and de3-01-CG during starvation on SDNA-ade plates The revertant rate for the strains CG379-3-29 (LR) (squares ■) and de3-01-CG (triangles ▲) is given as the total number of revertant colonies per plated cell. The mean values from 3 independent experiments are reported on a logarithmic scale (Y axis). Vertical bars represent the standard errors of the mean.
Figure 2 Number of cells/unit on SDNA-ade in the strains CG379-3-29(LR) and de3-01-CG The number of cells per unit was counted at the microscope (400×) in order to estimate the post-plating cellular divisions on SDNA-ade in the strains CG379-3-29 (LR) (squares ■) and de3-01-CG (triangles ▲).
Figure 3 Cell viability in the strains CG379-3-29 (LR) and de3-01-CG during starvation on adenine-free plates The squares (■) represent values for CG379-3-29 (LR), while the triangles (▲) are values for de3-01-CG. The mean values from three independent experiments are reported. The vertical bars correspond to the standard errors of the mean.
The differences in the growth rate among Ade+ revertants made the estimation of the exact reversion rates in the logarithmic growth phase almost impossible. In one fluctuation test where 50 independent cultures of the de3-01-CG strain were analysed, we observed that the variability coefficient (σ × 100/X; where σ is the deviance and X is the mean) drastically dropped on day three. This suggests that the majority of Ade+ revertants that arose during the logarithmic growth phase were able to give visible colonies by day 2. Therefore, we decided to calculate an approximate reversion rate on day 2 by the P0 method and obtained a reversion rate of 2.0 × 10-7, 4.5 orders of magnitude lower than the rate calculated on day 16 (see Figure 1). It was more difficult to determine the exact reversion rate in the logarithmic phase of growth for the CG379-3-29(LR) strain. Since the strain is not a spontaneous mutator, a very high number of cells had to be plated on SDNA-ade dishes to get reliable estimates. In a fluctuation experiment with 107 cells/dish, we did not observe any Ade+ revertant colonies out of 50 independent cultures after two days of incubation at 28°C, implying a reversion rate lower than 2.0 × 10-9. Therefore, in this wild-type strain, starvation for adenine likely induced an accumulation of Ade+ revertants at a level that is several orders of magnitude higher than in standard growth conditions.
Molecular and growth characteristics of Ade+ revertants
We compared the ade5-1 sequence from the CG379-3-29(LR) strain with the sequence of the ADE5,7 gene in SGD and found complete identity except for a C to A transversion at position 1158 (amino acid position 386). This transversion introduced the TAA ochre codon instead of the TAC tyrosine codon, generating a nonsense mutation.
DNA sequencing of the ade5-1 allele in 19 Ade+ revertants of the de3-01-CG strain isolated on day 14 showed that they were all suppressors. Among 17 independent Ade+ clones isolated on day 2 of a fluctuation experiment with the de3-01-CG (logarithmic cultures) strain, 6 were locus revertants and the rest were suppressors. The revertants obtained after starvation for adenine differed from each other in growth characteristics. Most of them gave visible colonies 6 to 8 days after plating on SDNA-ade. All of them had lower growth rates on both YEPD and SDNA+ade, than the parent de3-01-CG strain. The colonies were visible on YEPD at the stereomicroscope for even the best growing revertants 1 day later than colonies of the parent strain, de3-01-CG. These initial observations prompted a more detailed examination of the growth characteristics in different conditions.
Are Ade+ revertants adaptive?
The extremely high rate of Ade+ revertants observed during prolonged incubation on selective medium could be the consequence of either "adaptive" or genome-wide mutagenesis in a strain with the pol3-01 mutation. To discriminate between the two possibilities, we tested the occurrence of temperature-sensitive (ts) and nutritional mutants among revertants and among Ade- survivors. The results are described below.
a) ts mutants
The frequency of ts mutants in independent experiments was estimated using a random sample of de3-01-CG Ade+ revertants, Ade- survivors isolated on day 14 and Ade+ revertants obtained from exponentially growing cells. The data are reported in Table 1. Fifty-one percent of Ade+ revertants and thirty-nine percent of Ade- survivors (i.e. non-mutated cells from aged and starved colonies) were ts mutants, whereas the number of ts mutants among exponentially growing cells was negligible. We also tested 100 Ade+ revertants of the strain CG379-3-29(LR) starved for adenine and we did not find a single ts mutant.
Table 1 Ts mutant frequency among: Ade+ revertants, Ade- survivors and colonies from the log phase. The frequency of ts mutants in three independent experiments was estimated using a random sample of de3-01-CG Ade+ revertants, Ade- survivors isolated on day 14 and Ade+ revertants obtained from exponentially growing cells. A hundred Ade+ revertants of the strain CG379-3-29(LR) starved for adenine were used as controls. The percentage of ts mutants with respect to the number of tested colonies is reported in parentheses.
Revertants/Survivors No. of tested colonies No. of ts mutants
Starvation condition day 14
de3-01-CG Ade+ 489 249 (51%)
de3-01-CG Ade- 151 59 (39%)
CG379-3-29(LR) Ade+ 100 0
Log phase cells
de3-01-CG Ade+ 1070 7a
a The percentage has not been calculated since the ts mutants are likely to be a single clone.
b) Nutritional mutants
To detect nutritional mutants we first used the replica plating technique ("Methods"), as we did for ts mutants. By this method, the fraction of nutritional mutants among de3-01-CG Ade+ revertants was fifty-nine percent (20 out of 34 revertants tested); all were leaky nutritional mutants.
We did not systematically test the ts phenotype in these experiments, but we noticed that the two phenotypes, ts and the nutritional requirement, did not necessarily correlate.
To get a quantitative estimation of the severity of nutritional defects in Ade+ revertants and Ade- survivors, we compared their fitness ratios (number of cells per colony on SDNA+ade/number of cells per colony on YEPD; see "Methods") with the control strain, de3-01-CG. We presented the growth rate estimations for strain de3-01-CG and for two Ade+ revertants in Figure 4. For all strains there was always less vigorous growth on SDNA+ade than on YEPD, however, the difference for Ade+ revertants is much more distinct than the rate in the parent strain; the exponential growth phase ended on day 4. For this reason, we compared the fitness ratios for all strains on day 4. The data are reported in Tables 2 (Ade+ revertants) and 3 (Ade- survivors). Sixteen out of 17 Ade+ revertants (94%) and 7 out of 12 Ade- survivors had a two times lower fitness ratio than the control strain. It is important to note that all of the strains were respiratory competent, as determined by the 2,3,5-triphenyltetrazolium chloride (TTC) test. We concluded that Ade+ revertants acquired one or more nutritional requirements.
Figure 4 Growth curves of the strains de3-01-CG and of two Ade+ revertants on YEPD and SDNA+ade plates The number of cells/colony (fitness) on SDNA+ade (dotted lines) and on YEPD (continuous lines) is plotted against days in culture. We compared the strain de3-01-CG (triangles ▲) with two Ade+ revertants (strain 10, open circles ○; strain 15, closed circles ●).
Table 2 Number of cells/colony (fitness) of de3-01-CG Ade+ revertants (1–17). We estimated the colonies' fitness as the number of cells per colony 30 on SDNA+ade as well as on YEPD after four days of incubation at 28°C. The strain de3-01-CG was used as control; see "Methods" for details.
Strain/Revertant No. of cells /colony (×107) on: Fitness ratio SDNA+ade/YEPD
YEPD SDNA+ade
de3-01-CG 4.380 1.700 0.388
1 1.700 -a -a
2 0.070 -a -a
3 -b -b -b
4 2.300 0.040 0.017
5 3.300 0.079 0.024
6 0.200 0.026 0.130
7 0.870 0.026 0.030
8 0.730 0.200 0.274
9 0.260 -a -
10 3.900 0.200 0.051
11 1.700 0.300 0.176
12 1.700 0.210 0.124
13 2.600 0.100 0.038
14 1.600 0.200 0.125
15 0.700 0.080 0.114
16 0.500 0.080 0.160
17 1.700 0.210 0.124
a Hardly visible at the stereomicroscope after 4 days of incubation at 28°C, diameter not estimable.
b Not yet visible at the stereomicroscope after 4 days of incubation at 28°C. Visible colonies developed by day 8.
Table 3 Number of cells/colony (fitness) of Ade- survivors isolated on day 14 from SDNA-ade dishes. see Table 2.
Strain/Survivor No. of cells/colony (×107) on: Fitness ratio SDNA+ade/YEPD
YEPD SDNA+ade
de3-01-CG 4.380 1.700 0.388
101 3.320 -a -a
102 1.700 0.100 0.059
103 1.120 0.400 0.357
104 0.750 -a -a
105 1.200 0.120 0.100
106 1.140 0.710 0.623
107 0.080 0.006 0.075
108 0.040 0.013 0.325
109 0.040 0.013 0.325
110 0.002 -b -b
111 3.320 -b -b
112 0.420 0.420 1.000
a Hardly visible at the stereomicroscope after 4 days of incubation at 28°C, diameter not estimable.
b Not yet visible at the stereomicroscope after 4 days of incubation at 28°C. Visible colonies developed by day eight.
With the replica-plating technique we estimated that the proportion of leaky nutritional mutants among revertants was 59% (see above). When the fitness ratios were determined, we concluded that 16 out of 17 Ade+ revertants tested (94%) were nutritional mutants. We suggest that the differences in the estimates may be due to the inaccuracy of the replica plating technique. We did not find a single nutritional mutant among 12 log-phase revertants (data not shown).
In the next experiment, cells of the de3-01-CG strain were plated on SDNA without ade and containing four additional amino acids: val, ile, met, arg. We tested the proportion of clones requiring at least one of these amino acids by replica-plating and found that 10.9% of Ade+ revertants and 7.9% of Ade- survivors were auxotrophs for the selected four amino acids (Table 4; see "Methods" for more details).
Table 4 Frequency of auxotrophic mutants among de3-01-CG Ade+ revertants and Ade- survivors. Auxotrophic mutants were detected by replica plating de3-01-CG Ade+ revertants and de3-01-CG Ade- survivors on SDNA+ade and on SDNA+ade + (val, ile, met, arg). The percentage of auxotrophic mutants with respect to the number of tested colonies is reported in parentheses.
No. of replicated colonies No. of auxotrophic mutants
de3-01-CG Ade+ revertants 375 41 (10.9%)
de3-01-CG Ade- survivors 128 10 (7.8%)
Does hypermutagenesis in the de3-01-CG strain under adenine starvation occur under starvation for other nutrilites?
To answer this question we evaluated the rates of reversion to prototrophy from histidine and tryptophan auxotrophy, respectively (the his7-2, frameshift allele; trp1-289, nonsense allele), by the same method that we used to evaluate the rates of accumulation of the Ade+ revertants. We have previously shown that the reversion rate of the his7-2 allele in non-growing de3-01-CG cells was about 1.6 × 10-7 on day 9 [10], therefore, we plated 1.0 × 106 cells/plate for our determinations. This density was optimized to avoid errors in rate estimation arising at higher densities due to cannibalism. The data reported in Table 5 suggest that histidine and tryptophan starvation was much less mutagenic than adenine starvation.
Table 5 Accumulation of de3-01-CG revertants under different selective conditions. The mean values of three experiments as well as the standard errors of the mean are reported; for each experiment, 15–20 dishes were done. The reversion rates are given as the total number of revertant colonies per plated cell.
Selective Condition Days after plating
6 8 9 10 12 16
SDNA-trp 0.20 × 10-6 ± 1.00 0.26 × 10-6 ± 0.58 0.26 × 10-6 ± 0.58 0.73 × 10-6 ± 1.30 0.73 × 10-6 ± 1.30 0.73 × 10-6 ± 1.30
SDNA-adea 2.50 × 10-4 ± 1.00 5.00 × 10-4 ± 0.60 -b 7.80 × 10-4 ± 0.90 1.70 × 10-3 ± 0.50 1.00 × 10-2 ± 0.30
SDNA- his 0.06 × 10-6 ± 0.58 0.12 × 10-6 ± 0.58 0.18 × 10-6 ± 1.17 0.18 × 10-6 ± 1.17 0.18 × 10-6 ± 1.17 0.18 × 10-6 ± 1.17
a These values are from Figure 1.
b Not determined.
Discussion
In our previous studies we observed that the 3'→5' exonuclease activity of the polymerases δ and ε are both involved in correcting errors in yeast cells starved for histidine [10]. Here we investigated the effects of adenine starvation and the role of polymerase δ poofreading activity in resting cells starved for adenine.
At first we used the Hall's test [8], the same experimental approach as the previous paper [10], which allows us to detect revertants as papillae. As mentioned in the "Results" section, with the de3-01-CG strain, every colony had one or more papillae, making an estimation of the reversion rates by this method impossible. Therefore, we studied the rate of accumulation of revertants on SDNA-ade plates, a medium completely devoid of adenine. We have shown that the reversion rate rate in the wild-type strain CG379-3-29(LR) on day 16 was almost 5 orders of magnitude higher than the mutation rate estimated in a fluctuation test (where only the data of the first two days were considered). The high rate of mutations during adenine starvation was further elevated in the de3-01-CG strain, reaching 1% of the plated cells, which means an increase of 66 times with respect to the CG379-3-29(LR) strain. This implied that the proofreading activity of polymerase δ prevented a majority of mutations in resting cells, as in growing cells [13].
One possible explanation for the extremely high reversion frequency observed is that starvation of strains auxotrophic for adenine leads to perturbations of DNA replication and repair (for example, due to nucleotide pool deprivation or imbalances which are known to be mutagenic in yeast [14,15]). In the yeast S. cerevisiae the consequences of adenine starvation on mutagenesis were previously studied by Korogodin et al. [16]. The authors investigated the reversion rates of the ade2-192 allele (a missense mutation) in different strains and found that the lowest adenine concentration tested resulted in a 150-fold increase in locus revertants rate, while the suppressors rate was almost constant. When ade2-192 cells entered the stationary phase, their color shifted from white to red, a color which is due to a pigment that accumulates in ade2 mutants when the adenine biosynthetic pathway is in operation. Korogodin et al. [16] suggested, therefore, that the ade2-192 allele is derepressed under the condition of adenine deprivation and proposed that mutagenesis resulted from some process associated with transcription. Indeed, it is now well known that transcription-coupled mutagenesis occurs in yeast [17].
The data presented in this paper allow us to conclude that the high frequency of reversion to adenine prototrophy cannot be explained by transcription-coupled mutagenesis at the specific location of the ade5-1 allele in our experimental conditions. Instead, genome-wide mutagenesis occurred in the de3-01-CG strain under adenine deprivation. This is suggested by the high rate of ts and nutritional mutants among de3-01-CG Ade+ revertants as well as Ade- survivors. It is possible that genome-wide mutagensis occured in CG379-3-29(LR) as well but its lower mutability could have made difficult to detect ts and nutritional mutants even under adenine starvation. The observed mutation rates in the de3-01-CG strain were so high that we can characterize adenine deprivation as one of the most powerful mutagens for adenine auxotrophic strains. We can also conclude that under adenine starvation the 3'→5' exonuclease activity of polymerase δ prevented errors leading to the reversion of the ade5-1 strain to prototrophy along with the prevention of numerous genome-wide errors. Indeed, the rate of the ade5-1 reversion, under the conditions of adenine starvation was increased 66-fold in the pol3-01 strain. Apparently, the high rate of reversion was a sign of mutational catastrophe, since most of the revertants were ts or auxotrophs due to additonal mutations that occurred elsewhere in the genome.
High mutation rates, similar to what is described in the present paper, were reported by Bresler et al. [18]. The authors showed that bacterial cells grown on thymine-limited medium were often auxotrophs for more than one nutritional requirement, and that most mutants selected for other markers (such as streptomycin resistance) had additional mutations leading to auxotrophy. They observed, for example, that in an E. coli strain the percentage of streptomycin-resistant mutants with a nutritional requirement was 97.8%. This proportion is similar to the 94% of nutritional mutants obtained for de3-01-CG Ade+ revertant clones in our study; however, Bresler et al. [18] performed experiments with replicating cells.
To the best of our knowledge, the high spontaneous frequency of ts mutations observed in the present work have never been reported. Hartwell [19], in a study of cells heavily treated with N-methyl-N'-nitro-N-nitrosoguanidine, found that 1% were ts mutants, which is fifty times lower than the proportion of ts mutants among the Ade+ revertants reported here.
We can give a rough estimation of mutation rate per gene in the de3-01-CG auxotrophic strain under adenine starvation from the proportion of ts mutants (51%) among Ade+ revertants. The ts phenotype is supposed to be the consequence of mutations in essential genes. According to Winzeler et al. [20], the number of essential genes in S. cerevisiae is about 1,000. If one Ade+ revertant cell has a probability of 0.51 to be a ts mutant, then the probability for any one essential gene to mutate to a ts phenotype is 0.51/1000, i.e. 5.1 × 10-4. Harris and Pringle [21] observed that only a fraction of essential genes could be identified by ts mutations in S. cerevisiae. Since ts mutations are only a portion of total mutations, it is likely that each mutation rate in an essential gene was higher than the rate of ts mutations. This high mutation rate might be one of the reasons for a decrease in the viability of the pol3-01 strain under adenine starvation (see "Results"). We previously calculated that overall a 0.01 level of mutability leads to a drop of viability to 5% [22].
Finally, we wish to address a further important issue. In the present paper, we show that yeast haploid cells may sustain a very high genetic load even if their viability and fitness were reduced. Indeed, it is likely that the much lower survival of the mutator strain de3-01-CG, with respect to CG379-3-29 (LR) on adenine-free plates, was due to a high rate of mutations in the essential genes. One may wonder how some haploid cells can survive with such a high genetic load. One possibility is that under adenine starvation most mutations are base changes which could not have an appreciable lethal effect; actually, we found high rates of ts mutants which should be due to base changes. In natural conditions, yeast cells are diploids and, therefore, they could accumulate more variability, given that, according to Hartwell [19], 99% of ts mutations are recessive. Therefore, yeast diploids can afford much higher rates of mutagenesis [23-25]. The same holds true for other eukaryotic organisms. Pimpinelli et al. [26] showed that Aspergillus nidulans diploid conidia subjected to several cycles of 6-N-hydroxylaminopurine-induced mutagenesis (a base analog that induces only base substitutions) differ from each other for about ten lethals and, therefore, for a large number of mutations, perhaps several hundreds, without any viability reduction.
Conclusions
In conclusion, we have demonstrated that: i) adenine starvation strongly induces reversion to Ade+ phenotype in the wild-type strain; ii) a defect in the proofreading exonuclease activity of DNA polymerase δ due to the pol3-01 mutation leads to a further 66-fold elevation of the Ade+ mutagenesis; and iii) under adenine starvation, the mutagenesis in the de3-01-CG strain is genome-wide, therefore, Ade+ reversions in this strain are not adaptive.
Methods
Strains
The S. cerevisiae strains used were: CG379-3-29(LR) [MATα ade5-1 leu2-3,112 Δ ura3 bik1::ura3 29 (LR) his7-2 trp1-289 CAN1 lys2-Tn5-13]; and de3-01-CG [same as CG379-3-29(LR) but pol3-01] [27].
Media
YEPD medium (1% Yeast Extract, 2% Pepton, 2% glucose) and the synthetic SD medium (6.7% Yeast Nitrogen Base, 2% glucose) were used throughout the work. In some experiments, SD was solidified with 1.5% Noble Agar (Sigma, SDNA) (see below). We named the SDNA medium containing all the nutrilites required by the strain SDNA+ade. When adenine was not included we named it SDNA-ade.
Reversion rates and isolation of revertants
The accumulation of revertants under the starvation condition was investigated on SD with a limited amount of adenine (20 μg/l, SD.lim), as well as on SDNA-ade. On SD.lim we counted the number of colonies with Ade+ papillae throughout the experiment [8]. On SDNA-ade plates, the number of Ade+ revertants was evaluated as follows: 10,000 and 1,000 cells/plate were plated for the strains GC379-3-29(LR) and de3-01-CG, respectively, and incubated at 28°C; the revertant colonies were scored at the stereomicroscope (20×). For each experiment, 15–20 plates were set up. Noble agar was used to limit unwanted cellular divisions. The reversion rate is given as the total number of revertant colonies per plated cell. As explained in the "Results" section, we did not correct for the residual divisions or for the surviving fraction.
We estimated the total number of post-plating cellular divisions on SDNA-ade plates by comparing the number of cells immediately after plating and in the following days. The dishes were observed at the microscope (400×) and the number of cells per unit was counted. Here we considered a unit any single cell as well as more cells clustered together. For each experimental point, at least 200 units were counted and the mean values calculated.
To estimate the surviving fraction of cells and to characterise Ade- survivors, we plated an adequate number of cells on SDNA-ade. We needed to rescue Ade- survivors colonies from SDNA-ade dishes to determine viable cells and to isolate Ade- clones for further characterisation. In order to do this, we have cut off two small pieces of agar in some dishes. This procedure created wells where we put adenine, which spread across plates without washing away the colonies. The procedure allows a correct estimation of surviving cells.
The reversion rate during the logarithmic growth phase was estimated by the fluctuation test using the P0 method [28,29].
To obtain Ade+revertants for further characterization we transferred Ade+ colonies to YEPD medium. We were careful to pick up cells from the revertant colony by touching only the colony's surface with a needle at the stereomicroscope. Control experiments have shown that this procedure is reliable in our hands (data not shown). The phenotypic as well as the molecular analysis of Ade+ revertants that arose in the logarithmic phase of growth was done on revertants isolated on day 2 in a fluctuation experiment. Only 1 colony per dish was picked up and isolated on YEPD. The analysis of Ade+ revertants arisen in starved cells was done on a random sample of revertants isolated on day 14, since we were sure that the great majority of them derived from mutational events that occurred in cells starved for adenine (see "Results").
Determination of growth of revertants on SDNA-ade
To determine the time of appearance of Ade+ revertant colonies as well as their growth rate on SDNA-ade, we either plated 100 cells per dish or streaked suspensions (105 cells/0.1 ml), incubated at 28°C and scored at the stereomicroscope (20×).
Detection of mutants with nutritional requirements
To detect leaky nutritional mutants among Ade+ revertants and Ade-survivors, we estimated the colonies' fitness as the number of cells per colony [30] on SDNA+ade as well as on YEPD on fourth day after plating. Fifty cells of each strain were plated on Petri dishes containing 25 ml of YEPD and SDNA+ade, respectively. The plates were then incubated at 28°C and scored at the stereomicroscope for the appearance of colonies. Most colonies appeared by 4 days (see "Results") but the plates were incubated for longer to score slowly growing colonies. The diameters of 50 randomly chosen colonies were measured, and the mean value estimated. The mean value was used to calculate the approximate colony volumes, assuming their hemispheric shape as was done previously by Wloch et al. [30]. To obtain the number of cells per colony, we divided the colony volume by 1.1 × 10-7 μl (by the volume of a haploid yeast cell) [31]. We then calculated the ratio of the revertant colonies' fitness on SDNA+ade to that on YEPD (SDNA+ade/YEPD) and compared it to that of the control strain. We arbitrarily considered those strains whose SDNA+ade/YEPD fitness ratio was at least twice lower than that of the strain de3-01-CG to be nutritional mutants. The comparison of SDNA+ade/YEPD ratios allowed us to detect nutritional mutants quantitatively but this approach was rather cumbersome. Alternatively, to screen more revertants, we used a qualitative test. The cells were point-inoculated by a needle on YEPD plates, which were then incubated for several days at 28°C. Thereafter, they were replica-plated on YEPD and SDNA+ade. After 2 days, the growth spots on both media were compared with those of the control strain. A revertant was considered a nutritional mutant when its SDNA+ade/YEPD growth ratio was reduced drastically (judged from visual inspection) with respect to that of the control strain.
The detection of respiration-deficient strains (petite) was done by the 2,3,5-triphenyltetrazolium chloride (TTC) test as in Ogur et al. [32].
Detection of nutritional mutants auxotrophic for valine, isoleucine, methione, arginine
Strain de3-01-CG cells were plated on SDNA-ade plus other randomly chosen aminoacids [SDNA-ade + (val, ile, met, arg)]. On the day 14, the Ade+ colonies were marked and adenine was added in two wells in agar as described above. The plates were incubated for further 6 days to allow Ade- survivors to form colonies. Then they were replica plated on SDNA+ade and on SDNA+ade + (val, ile, met, arg) to detect auxotrophic mutants.
Detection of temperature-sensitive (ts) mutants
Ade+ revertants as well as Ade- survivors isolated on SDNA-ade on day 14 were tested for the presence of ts mutations. To obtain Ade- survivors, Ade+ colonies were marked and then adenine was added to two wells of SDNA-ade plates seeded with 1,000 cells/plate on day 14. Dishes were incubated for 6 days more and previously non-marked colonies were isolated on YEPD. Colonies were then tested for their Ade- phenotype on SDNA-ade.
To detect ts mutants, cells from a fresh culture were point inoculated by a needle on YEPD plates, which were incubated for several days at 28°C. Colonies were then replica plated on two YEPD dishes: one of them was incubated at 28°C while the other one at 37°C for two days. The first replica was always incubated at 37°C. A strain was considered to be a ts mutant if in two days it was able to grow at 28°C but not at 37°C. The strains CG379-3-29(LR) and de3-01-CG were included as controls.
Determination of the nucleotide sequence of the ADE5,7 locus in CG379-3-29(LR) and Ade+ revertants strains
Genomic DNA from S. cerevisiae was purified using the NucleoSpin® Tissue kit (Macherey-Nagel).
The ADE5,7 locus (from 490 bp upstream of the start codon to 473 bp downstream of the stop codon) was amplified from genomic DNA of the CG379-3-29(LR) strain using primers ADE5-F1 (5'-CAAAAGTAGAAGACCCCC-3') and ADE5-R1 (5'-CCATTCATCAATTACGG-3'). The PCR reaction was performed according to standard protocol, using the proofreading-proficient Pfx DNA polymerase (Invitrogen). The PCR produced a DNA fragment of 3383 bp, which was purified from an agarose gel by the High Pure PCR Product Purification Kit (Roche). The nucleotide sequence of both strands of the purified DNA fragment was determined by the Sanger method [33], with fourteen different synthetic primers, using the BigDye Terminator Cycle Sequencing Kit (Applied Biosystem).
The sequence of the amplified fragment from the CG379-3-29(LR) strain was compared with the sequence of the ADE5,7 gene in the Saccharomyces Genome Database [34] by the BLAST algorithm [35].
DNA fragments of the ADE5,7 alleles in Ade+ revertant strains were obtained by PCR amplification, using purified genomic DNA as template and the primers ADE5-F4 (5'-CCG TAA ACA TAG GAA TCG-3') and ADE5-R4 (5'-TTG TAC GAG ATT GTT ACC-3'). PCR, performed as previously described, generated a 398 bp long DNA fragment, encompassing the ade5,7 mutation in the CG379-3-29 strain. The purified DNA products were sequenced using the same primers, ADE-F4 and/or ADE5-R4.
Authors' contributions
AA performed molecular analysis of Ade+ revertants and AA, AL, NB and GM studied mutagenesis during starvation. YIP constructed the pol3-01 strain, participated in the design of the study and writing the manuscript. EC performed sequencing of the ade5-1 allele and molecular analysis of Ade+ revertants. NB, GM and YIP coordinated the study. AA, AL, EC participated in the design of the study.
All authors read and approved the final manuscript.
Acknowledgements
We thank Matteo Panizzi and Marianna Paulis for their skilful technical assistance.
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| 15617571 | PMC544876 | CC BY | 2021-01-04 16:38:16 | no | BMC Genet. 2004 Dec 23; 5:34 | utf-8 | BMC Genet | 2,004 | 10.1186/1471-2156-5-34 | oa_comm |
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BMC Health Serv ResBMC Health Services Research1472-6963BioMed Central London 1472-6963-4-351560147310.1186/1472-6963-4-35Study ProtocolEffects of home visits by home nurses to elderly people with health problems: design of a randomised clinical trial in the Netherlands [ISRCTN92017183] Nicolaides-Bouman Ans [email protected] Rossum Erik [email protected] Gertrudis IJM [email protected] Paul [email protected] Dept. of Epidemiology, Faculty of Health Sciences, Maastricht University, P.O. Box 616, 6200 MD Maastricht, The Netherlands2 Dept. of Health Care Studies, Medical Sociology, Faculty of Health Sciences, Maastricht University, P.O. Box 616, 6200 MD Maastricht, The Netherlands3 Dept. of General Practice, Faculty of Medicine, Maastricht University, P.O. Box 616, 6200 MD Maastricht, The Netherlands2004 15 12 2004 4 35 35 8 10 2004 15 12 2004 Copyright © 2004 Nicolaides-Bouman 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
Preventive home visits to elderly people by public health nurses aim to maintain or improve the functional status of elderly and reduce the use of institutional care services. A number of trials that investigated the effects of home visits show positive results, but others do not. The outcomes can depend on differences in characteristics of the intervention programme, but also on the selection of the target population. A risk group approach seems promising, but further evidence is needed. We decided to carry out a study to investigate the effects in a population of elderly with (perceived) poor health rather than the general population. Also, we test whether nurses who are qualified at a lower professional level (home nurses instead of public health nurses) are able to obtain convincing effects. The results of this study will contribute to the discussion on effective public health strategies for the aged.
Methods/design
The study is carried out as a parallel group randomised trial. To screen eligible participants, we sent a postal questionnaire to 4901 elderly people (70–84 years) living at home in a town in the south of the Netherlands. After applying inclusion criteria (e.g., self-reported poor health status) and exclusion criteria (e.g., those who already receive home nursing care), we selected 330 participants. They entered the randomisation procedure; 160 were allocated to the intervention group and 170 to the control group. The intervention consists of (at least) 8 systematic home visits over an 18 months period. Experienced home nurses from the local home care organisation carry out the visits. The control group receives usual care. Effects on health status are measured by means of postal questionnaires after 12 months, 18 months (the end of the intervention period) and after 24 months (the end of 6-months follow-up), and face-to-face interviews after 18 months. Data on mortality and service use are continuously registered during 24 months. A cost-benefit analysis is included.
The design and setting of the study, the selection of eligible participants and the study interventions are described in this article. Other included items are: the primary and secondary outcome measures, the statistical analysis and the economic evaluation.
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Background
The number of elderly people is increasing. Due to the ageing population, more demands are made on health care services [1]. In the last two decades, preventive programmes have been developed aiming at reducing health care cost and improving the independent functioning of elderly people. One of such programmes is home visitation by public health nurses of elderly people living in the community. This aims to maintain or improve the functional abilities and well-being of elderly people and reduce the use of institutional care services. Such programmes for elderly people are part of national policy in several countries, including the UK, Denmark and Australia. However, the results of trials on the effects of home visits have been inconsistent [2]. Investigators are still in search of the most effective strategy.
In the past years 3 reviews were published on the effects of preventive home visits to elderly people living in the community [2-4]. These used different methodological approaches. Apart from a number of similar trials, each review also included a series of different trials, depending on the inclusion criteria and the date of publication. Van Haastregt et al [2] reviewed 15 studies and concluded that no clear evidence exists for the effectiveness of the visits: the observed effects are considered to be fairly modest and inconsistent. Nine trials reported at least one (significant) favourable effect and 6 trials reported no effects. In most of the studies the intervention was aimed at the general population aged 65 years or over, without any selection. The other 2 reviews [3,4] included a meta-analysis of the data and were more positive about the effects of the home visits. Stuck et al [4] indicated that home visits can reduce the risk of functional decline and nursing home admission, provided that the interventions are based on a multi-dimensional geriatric assessment and include multiple follow-up visits. Home visiting programmes improved functional status more in people with the lowest mortality risk (younger population, < 80 years). Elkan et al [3] reported a favourable effect on mortality and nursing home admissions among members of the general population and frail older people who are at risk of adverse outcomes. However, they did not find improvement in functional status.
One can argue about the differences in the approach of each review, but in general the results of the home visiting studies are heterogeneous with respect to the different outcome measures. Many factors can play a role in the effectiveness of the interventions, including the target population, characteristics of the intervention, the persons carrying out the visits and the compliance to the given advice.
Research in the Netherlands showed that preventive home visits do not seem to be useful for the general population of elderly people [5]. In that trial, experienced public health nurses visited the intervention group (n = 300) at least four times a year over a period of 3 years. The control group (n = 300) received usual care. After 3 years, no or hardly any effects were demonstrated on the health and service use of the total group of visited elderly (see table 1). However, a subgroup analysis indicated that the visits seemed to be effective for elderly with a poor (perceived) health status. Visited persons with poor health at baseline scored considerably better on several health measures (e.g., functional status) compared to similar persons in the control group. Mortality rates after three years were lower (24% versus 40%) and substantial effects were found for referrals to outpatient clinics (61% versus 79%) and also for hospital admissions, especially re-admissions. In the intervention subgroup 47% were admitted at least once to the hospital, with a total of 1,134 days; in the control subgroup these figures were 74% and 2,043 days (table 1). These effects emerged already during the first year of the intervention period [6].
The probable usefulness of home visits for a high risk group was confirmed in five controlled studies [7-11]. However, the results of three other trials did not support this assumption [12-14]. Although home visits for a restricted population seem a promising approach, further evidence is needed. The findings of the earlier Dutch subgroup analysis were based on a relatively small number of subjects (53 in the control and 57 persons in the intervention group). Therefore, we decided to carry out a new trial in which the risk group approach is tested in a larger population of those with (perceived) poor health. At the same time, we appointed nurses who are qualified at a lower professional level (enrolled home nurses instead of public health nurses) to carry out the visits. An experienced public health nurse will supervise them.
This study will investigate the effects of systematic home visits by home nurses to elderly people with (perceived) health problems in terms of their health status, the use of care services and the cost-effectiveness. We expect that the visits will improve the functional abilities, perceived health and quality of life of the participants. We also hypothesize that they will reduce specialist care, institutionalisation, especially hospital (re-) admissions, and total health care expenditures. Evidence regarding the usefulness of the proposed risk group approach is needed to decide on the future implementation of the visits. This article presents the design of this new trial.
Methods/design
Study design and setting
The study is carried out as a parallel group randomised trial. It is conducted in co-operation with a large home care organisation in the south of the Netherlands (Sittard and surroundings). The addresses we used to screen eligible persons for the study were drawn from the population register of the municipality. After the screening procedure we randomised 330 elderly. Effects of the intervention are measured by means of postal questionnaires after 12 months, 18 months (at the end of the intervention period) and after 24 months (at the end of a 6-months follow-up period) and by face-to-face interviews after 18 months. Mortality and data on the use of care services are continuously registered over the 24-months research period. A cost-benefit analysis is also included. The design of the study is shown in figure 1. The design is, unless otherwise mentioned, carried out according to plan.
The study has obtained the approval by the Medical Ethical Committee of Maastricht University/Academic Hospital Maastricht.
Identification of eligible participants
We sent a postal questionnaire to 4901 elderly people between the age of 70 and 84 years who were still living at home. These lived in 14 districts in the research area. We included districts with close proximity to the centre of town where the home care organisation is situated. In this way we limited the travelling time of the nurses to carry out the visits. Districts with large industrial areas were excluded.
Reminders were sent after 2–3 weeks to 45% of the elderly. The response rate was 76% after about 6 weeks. The response rates per district fluctuated between 65% and 81%. The average time to fill out the questionnaire was about 30 minutes. The elderly could do this by themselves or, if they needed help, with the assistance of family, friends or volunteers. A list of names and addresses of volunteers was added to the questionnaire. Even if persons did not want to participate in the study, we kindly requested them to fill out the questionnaire and return it to us. A postage free envelope was included.
The questionnaire was used as a screening instrument and also served as a baseline measurement for the participants of the trial. Among the respondents (n = 3,689, see figure 1), we found 872 persons who reported their health status as poor (on a scale ranging from 1–10 points, report marks 1–5 are considered poor, 6–7 fairly good, and 8–10 good). Our previous home visitation study indicated positive effects for this subgroup. Five persons did not sign the informed consent form and 273 persons with a poor health status did not want to participate in the study. Of the remaining 594 persons, we excluded those who already received home nursing care at baseline, in order to avoid contamination of (other) nursing care. Referral to nursing services after the start of the intervention period has no consequences for the scheduled home visits in the intervention group. It is regarded as a possible effect of the intervention and it is registered as outcome in terms of service use. Persons on a waiting list for admission to nursing homes or homes for the elderly were also excluded. The local independent committee dealing with applications for the use of care services already granted them this service. It is likely that most of them already receive regular supervision of professional caregivers. Six persons were excluded on the advice of their GPs. They were severely or terminally ill and would probably die within 6 months. On the basis of these exclusion criteria, a total of 102 persons were excluded.
After applying the in- and exclusion criteria, 492 persons were eligible to take part in the study. However, we excluded 162 more persons for the following reasons: their GP did not want to co-operate with the study (n = 139), respondents had too many missing values on the functional status scale (n = 11), the health insurance company was unknown (n = 1) or it was uncertain whether the health insurance company would be willing to co-operate (n = 11). The health insurance companies of 96% of the finally selected participants had already given consent to provide us with data on health service use. As we selected persons whose GP was willing to co-operate, relevant health care data from the GP practices are available for all participants. A flow diagram of the selection of participants is shown in figure 1. Finally, 330 persons entered the randomisation procedure. In consideration of the available working hours of the nurses, the maximum number of participants to receive home visits was 160. The control group was hence set at 170.
Sample size consideration
We calculated the sample size from the data of our previous home visitation study in the Netherlands [5]. Participants were categorized on the primary outcome measure self-rated health, perceiving their health status as (a) better or the same compared to the start of the study, or (b) worse or deceased. We expect to demonstrate a difference of 20% between the study groups (65% score (a) in group I versus 45% in group II). Based on a 0.9 power to detect a significant difference (α = 0.05, one-sided), 104 participants are required for both study groups. Accounting for a loss to follow up of 30%, we planned to enrol 150 participants per group. This number is also large enough (again extrapolated from our own data) to detect differences in specialist care and institutionalisation (e.g., to detect a difference in mean hospital days of 10 days over a 1.5 year period).
Based on data of the selection criteria we estimated that about 10% of the screened population was eligible for the study (including informed consent). Therefore, we needed to mail a minimum of 3,500 questionnaires to persons aged 70–84 years living in the community. To account for unforeseen circumstances, we decided to send out about 5,000 questionnaires. After applying the inclusion and exclusion criteria, and taking into account the GPs' willingness to co-operate with the study, there were sufficient participants eligible for the study to raise the selected number to 330. This slightly increased the power of the study.
Randomisation
The baseline measurements included questions on relevant prognostic factors related to the health status and service use. Before randomisation we divided the 330 participants into two groups: couples (n = 46) and those for whom this did not apply (n = 284). In this way we made sure that eligible persons who lived together, were always allocated to the same study group (in order to avoid contamination of the intervention). The 23 couples were divided into 3 strata on the basis of their (added) score on functional status (0–4, 5–7 or more than 7 out of 18 activities that cannot be carried out independently). The other 284 participants were stratified into 8 strata based on 3 prognostic factors – two health-variables and one service use-variable:
1. functional status (0–2 or more than 2 activities that the elderly cannot carry out independently)
2. changes in health during the 3 months prior to completion of the questionnaire (same/better or worse)
3. contact with a medical specialist in the 3 months prior to completion of the questionnaire (contact yes or no).
The participants in each of the 8 strata were then randomised into either a control or intervention group using a computer generated randomisation list with a block length of 4 [15]. Randomly, we allocated 160 persons to the intervention group and 170 persons to the control group. Table 2 shows the baseline characteristics of the intervention and control group. The study groups are well matched; there were hardly any differences between the groups at the start of the study.
The participants in the intervention group were assigned to one of the three home nurses. This depended on the location of their GP practice, as each nurse was assigned to a number of GP practices. We assumed that this would facilitate the co-operation with the GPs. Each nurse was responsible for 52–56 elderly during the intervention period.
Study interventions
Our previous study showed that positive effects of the visits for people with a poor health status emerged already within 1.5 years. In the new trial the intervention period is restricted to this period. At the same time we increased the frequency of the visits. This enables the nurses to intervene more promptly on identified problems and risks, and to establish a position of trust in a shorter time period. Experienced home nurses therefore visit the intervention group at least 8 times over an 18 months period. If necessary, extra visits can be made. The duration of the visits can last between 60 and 90 minutes. Participants in the control group receive usual care. As before, they can use or apply for all available services in the area.
Three nurses work half time for the trial. An experienced public health nurse supervises the visits on a weekly basis. All 3 home nurses are recruited from the co-operating home care organisation. Home nurses, as well as public health nurses, are well trained to conduct home visits. They are embedded in community care organisations that traditionally have preventive tasks. The home nurses are not part of a multidisciplinary team, but advice can be obtained from in-home specialists within the home care organisation, e.g., a dietician, a diabetes specialist and an occupational therapist. A nurse geriatric specialist from the local hospital can also be consulted, if necessary. At regular intervals, once every 6–8 weeks during the intervention period, he also advises the nurses on important geriatric issues.
Home visit protocol
The home visits can be described as "systematic home visits to elderly people with health problems carried out by a home nurse". The 3 most important elements of the visits are (1) to detect problems or risks, (2) to give advice and (3) to refer to other professional or community services. This brief description is applicable to all home visiting studies that have been carried out so far. However, there are large differences in the protocols that have been used in earlier studies, ranging from an interview to collect information on social and health conditions [16] to a 'multidimensional geriatric assessment' in which medical, functional, psychosocial, and environmental evaluation of the problems and resources are assessed [12,17]. Earlier studies did not show any clear relation between the structure of the visits and the effects. So far, the active components of the intervention are not known yet, but a number of elements seems to be of importance for the contents of the visits. We tried as much as possible to include these elements into the protocol: e.g., face-to-face assessment, good communication between the nurse and the elderly including an empathic attitude by the nurse, an individual plan, a client-centred approach, good compliance with the given advice and multiple visits [4,18].
The visits are carried out in a systematic way according to a nursing model [19] that distinguishes 4 steps: diagnosis, planning of activities, carrying out the activities and evaluation.
Diagnosis
Our starting-point is a client-centred approach. The elderly can indicate which problems they experience and which needs they have. The EasyCare Questionnaire [20,21], an elderly assessment system, is used to detect further problems. Also, additional checklists are used on a variety of topics: e.g., vision, hearing and use of medication. A number of instruments are used for further diagnostic assessment: the get-up-and-go test [22], the Geriatric Depression Scale [23] and the Mini Mental State Examination [24]. During the visits no physical examination takes place, as the home nurses are not qualified to do so. If necessary, the elderly are referred to their GPs.
Planning of activities
An individual plan for each elderly person is set up. The activities are planned in agreement with the elderly, as this will improve compliance. We only included elderly with a poor (perceived) health, hence a broad range of problems can come forward, including physical, mental as well as social problems. Guidelines on a number of geriatric topics are used for advice and referral regarding problems and risks that are identified. A Handbook of Nursing Diagnosis [25] is also used to set up goals and interventions. A maximum of three problems (and 2 interventions per problem) is being dealt with at one visit. Among the planned activities are referrals to professional or community services, and advice or information is given regarding, e.g., nutrition, social and physical activities and home aids.
Carrying out the activities
The elderly are primarily themselves responsible to carry out the planned activities. The home nurse only supports the elderly. In order to improve compliance, the nurses contact the elderly by telephone 1 to 4 weeks after each visit, depending on the type of advice. They ask whether the advice has been followed, and if not, what the impediments are and if further assistance is necessary. The participants are offered consultation with the nurses by telephone each morning between 9.00 – 9.30 hours.
Evaluation
The evaluation of each home visit takes place at the next visit. The cycle is then repeated and new or old, but not solved, problems can be dealt with.
In the 3-months period before the start of the visits, the home nurses were actively involved in the development of the visiting protocol. They also received relevant training in communication skills and using assessment tools. They took courses on several subjects, e.g., relevant geriatric health topics, behaviour change and the usage of the Handbook of Nursing Diagnosis [25]. Several pilot visits were carried out, in which different aspects of the protocol were trained, e.g., using assessment tools and measuring instruments.
Communication between the nurses and the GPs is according to the 'normal' communication lines between nurses of the home care organisations and the GPs. Before the start of the study all GPs received a list of eligible participants registered at their practice, to screen very ill persons. After randomisation a definite list of participants was sent to them, but no reference was made to which treatment group they belong. The allocation of the participants to the 2 groups was disclosed after conclusion of the first 3 home visits. The GPs then received an overview of all treated problems for each participant in the intervention group, including the accompanying recommendations and the results of the interventions. The GPs were asked for their comments or suggestions and in this way they could become involved, if they wanted to. A similar overview will be sent to them for visits 4–6 and 7–8.
Process evaluation
All elements of the intervention are monitored as part of a process evaluation. This includes the registration of topics discussed at each visit, treated problems, advice given and referral to other services. The evaluation of each visit is registered at each next visit and includes the compliance with the given advice. Reasons for non-compliance are noted. The nurses' experiences with the visiting protocol, the role of the supervising public health nurse and the patient's experiences with the home visits will be assessed at the end of the intervention period by means of face-to-face-interviews.
Other aspects of the intervention process assessed are: the time spent on the visits, including the travelling and preparation time and the time spent on telephone contacts. Elements of the telephone conversation after each visit, most importantly whether the elderly complied with the given advice, are registered.
Detailed analyses of the intervention process and outcome data might help to identify which programme characteristics are related to possible favourable effects of the visits and may result in the development of more effective interventions. It might also provide additional information for the possible implementation of the visits in daily practice.
Outcome measures
The primary health related outcome measures are: self-rated health, functional status, quality of life and changes in self-reported problems. In addition, a variety of other health measures (secondary outcome measures) will be assessed. Information will be obtained, among other things, on health complaints, medication use, and loneliness and mental health. The municipality will supply mortality data (secondary outcome measure) over the entire research period.
The use of services relates to the frequency and duration of care from the following services: domestic and community nursing care, GP, physiotherapy, day care in institutional care settings, hospital outpatient clinics, hospital, nursing home, home for the elderly, use of aids and modifications to the home. The primary outcomes for service use are specialist medical care and hospital (re-) admission. The health insurance companies will supply data on the use of services over the two-year research period. Additional data not covered by the health insurance companies, will be supplied by GPs, the hospital, the home care organisations, etc. Table 3 shows an overview of the outcome measures, their operationalisation and at which time points the measures are carried out.
Statistical analyses
The main analyses will be conducted according to the intention-to-treat principle. Analysis of primary and secondary endpoints will be performed using relevant significance tests (e.g., chi-square, t-test or analysis of variance). Regression techniques will be used, if necessary, to estimate the effects for the various outcome measures, adjusting for small differences between the groups at the start of the study. In addition, we will conduct per-protocol analyses; these are restricted to those participants who complied fully with the intervention protocol and outcome measurements. Preplanned subgroup analysis will be performed for the following subgroups: living alone/together; health deterioration over the previous 3 months; functional status and locus of control. Differences in approaches between the nurses will be investigated.
Economic evaluation
A cost-effectiveness analysis will be carried out in which we consider costs from a societal perspective. The economic evaluation will measure and evaluate the 'real' costs. In this study we will include direct health care costs, i.e. costs made for the home visit programme and health care costs made by the participants. Costs of the intervention programme consist of costs for the screening procedure, salaries of the nurses, travel expenses, costs of training sessions for the nurses, etc. Health care costs include costs of inpatient and outpatient treatment, consultation by GPs and other medical practising specialists, physiotherapy, medication, professional home care, nursing home, meals on wheels, aids and appliances, etc. In order to estimate the costs, the quantity of each resource will be multiplied by its assigned unit cost of price.
Direct non health care costs (e.g., the travel costs made by participants) are not included. These should preferably be gathered prospectively by means of a cost-diary [26]. We considered this too burdensome for the participants. Indirect health care costs (costs which are made during extra gained years of life) and indirect non health care costs (the value of production lost to society due to illness-related absence from work and days of inactivity) are often also included in an economic evaluation. We decided however not to include those costs, because of their limited relevance in a population of retired elderly people.
Time plan for this study
The screening procedure was carried out in the fall of 2002. In January 2003 we sent a letter to the elderly notifying them that they were selected to participate in the study and whether they would receive home visits or not. In February the home visits started. They are carried out according to plan and will end in September 2004. The first effect evaluation, 12 months after the start of the intervention period, has taken place: 302 questionnaires were sent out in March 2004. The response rate was 95%. Since the beginning of the intervention period, a total of 24 participants died and 4 persons withdrew from the study. Three more effect evaluations will take place: two evaluations after the intervention period in October 2004 (a postal questionnaire and a face-to-face interview) and one after the 6 months follow-up in March 2005 (postal questionnaire).
Discussion
The use of postal questionnaires turned out to be a good and inexpensive method to screen elderly people – there were more than sufficient eligible persons to participate in the research project. The response rate was high and less than one percent of the questionnaires were omitted due to too many missing values. For most of the variables, the percentage of missing values varied between 0 and 2 per cent. Media coverage shortly before sending out the questionnaires and accompanying letters from the municipality and the university may have contributed to the high response rate. It is not certain whether the results are comparable to other (larger) towns in the Netherlands. The response rate of the postal questionnaires used for the first effect evaluation (12 months after the start of the study) was 95%. Nearly all included elderly seemed to be motivated to participate.
We selected elderly with a poor perceived health status, because we expected the home visits to be more beneficial for this group rather than for those who are still in good health [5]. Results from the data analysis of the first postal questionnaire (the screening instrument and the baseline measurement) showed that the eligible persons indeed scored worse on most health related variables, including functional status, mental health and social functioning [27].
We considered including a third group of elderly with poor health status to receive home visits from voluntary workers. This was, however, not feasible, mainly because the number of participants with a participating GP was too low. The frequency of the visits and the level of professionalism, nurses versus voluntary aids (usually without any professional qualifications), could be a topic of study in another trial depending on the outcome of this study.
Competing interests
The author(s) declare that they have no competing interests.
Authors' contributions
Erik van Rossum and Paul Knipschild were responsible for the research question. They contributed to drafting of the study protocol, as did Ruud Kempen. Ans Nicolaides-Bouman made the first draft of this paper. The other 3 authors commented on it and approved the final version.
Pre-publication history
The pre-publication history for this paper can be accessed here:
Acknowledgements
The home care organisation 'Thuiszorg Westelijke Mijnstreek': Harry Heykens, Jan Houwen, Yvonne Monse, Ria Claessens, Ine Janssen and Hanneke de Jongh
The municipality of Sittard-Geleen-Born: Berry van Rijswijk, Piet Veugen, Daphne Kagelmaker and Jack Ehlen
Other co-operating parties in the Sittard area: Health Insurance Companies, GPs, Maasland Hospital; Herbert Habets and Wilma Krämer, RIO and Associations for the Elderly
Research assistance: Annemarie Spaninks, Truus Custers, Marion Gijbels, Carla Verheggen and Marijke Mol.
Sponsor: ZonMw, the Hague, the Netherlands.
Figures and Tables
Figure 1 Study design The summary of the trial design includes: the screening procedure, the randomisation, the intervention and the points in time at which the effects of the intervention are measured.
Table 1 Mortality and use of services (percentages) for all participants and for those who rated their health status as poor at the start of the study
Results of study by van Rossum et al [5] Total population Population with poor perceived health
4 home visits a year over 3 years in intervention group; no visits for the control group (usual care) Intervention group
(n = 292) Control group
(n = 288) Intervention group
(n = 57) Control group
(n = 53)
Mortality 41 (14%) 49 (17%) 14 (24%) 21 (40%)
Referrals to outpatient clinics 132 (55%) 166 (66%) 27 (61%) 38 (79%)
Hospital (re-) admissions
number of days 121 (41%)
3,838 133 (46%)
4,789 27 (47%)
1,134 39 (74%)
2,043
Admission to home for the elderly 20 (7%) 18 (6%) 12 (21%) 7 (13%)
Table 2 Baseline characteristics of study participants
Characteristic Intervention group n = 160 Control group n = 170
Age 75.8 (3.7) 75.6 (3.9)
Gender
male 64 (40%) 68 (40%)
female 96 (60%) 102 (60%)
Composition of household
alone 53 (34%) 61 (36%)
together 103 (66%) 108 (64%)
Education
primary school 60 (39%) 65 (39%)
lower/middle professional education 81 (52%) 92 (55%)
higher professional education 15 (10%) 11 (6%)
Self-rated health*
1–4 62 (39%) 67 (39%)
5 98 (61%) 103 (61%)
Functional status**
Adl-dependencies 0 73 (46%) 81 (48%)
1–11 86 (54%) 89 (52%)
Iadl-dependencies 0–1 76 (49%) 83 (50%)
2–7 79 (51%) 82 (50%)
Total number of dependencies 0–2 83 (53%) 92 (55%)
3–18 75 (47%) 76 (45%)
Health change in previous 3 months
same/better 85 (53%) 82 (48%)
worse 75 (47%) 88 (52%)
Health affects social participation
often 82 (53%) 87 (51%)
sometimes 49 (32%) 50 (29%)
never 24 (16%) 33 (19%)
Contact GP in last 3 months
yes (no is remaining %) 140 (88%) 150 (88%)
Contact specialist in last 3 months
yes (no is remaining %) 108 (69%) 117 (70%)
Hospital admission in last 3 months
yes (no is remaining %) 21 (13%) 24 (14%)
Use of home care
yes (no is remaining %) 64 (40%) 61 (37%)
* Indicated by a report mark on a scale ranging from 1 to 10 points. Participants with a poor health status were included (report mark below 6).
** Refers to 11 activities of daily living (Adl) and 7 instrumental activities of daily living (Iadl) or housekeeping activities (GARS). Adl / Iadl-dependencies: indicates the number of activities for which the elderly are dependent on others in order to carry out the activity.
Table 3 Outcome measures and their operationalisation
Outcome measure Operationalisation* Measurement**
Self-rated health*** report mark between 1–10 0, 1, 2, 3
Functional status*** GARS [28], score 18–72
Adl, subscore 11–44
Iadl, subscore 7–28 0, 1, 2, 3
Quality of life*** Rand-36 [29], 1item SF-20 [30], 2 subscales, score 0–100 0, 1, 2, 3
Changes in self-reported problems*** 3 main problems, 3-points scale 0, 1, 2, 3
Health complaints SCL-90 [31], 2 subscales 4
Depressive complaints GDS [23], score 0–15 4
Mental status MMSE-12 [32], score 0–12 4
Locus of control Mastery Scale [33], score 7–35 4
Social support SSL12-I [34], score 12–48 4
Loneliness Loneliness Scale [35], score 0–11 4
Medication volume, costs 4
Aids and modifications to the home type, costs 4, 5
Mortality number 5
Use of extramural and institutional care, e.g., medical specialist help*** and hospital (re-) admission*** e.g., number of contacts GPs, days in hospital, costs 5
* the underlined scores indicate the most favourable score on the specific scale
** 0 = postal questionnaire at the start of the study
1 = postal questionnaire after 12 months
2 = postal questionnaire after 18 months (at the end of the intervention period)
3 = postal questionnaire after 24 months (at the end of a 6-months follow-up period)
4 = face-to-face interview after 18 months
5 = continuous registration by services over a 24-months period
*** primary outcome measures
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| 15601473 | PMC544877 | CC BY | 2021-01-04 16:03:29 | no | BMC Health Serv Res. 2004 Dec 15; 4:35 | utf-8 | BMC Health Serv Res | 2,004 | 10.1186/1472-6963-4-35 | oa_comm |
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BMC MedBMC Medicine1741-7015BioMed Central London 1741-7015-3-31563893610.1186/1741-7015-3-3CommentaryBlood pressure demographics: nature or nurture ... ... genes or environment? Tomson Joseph [email protected] Gregory YH [email protected] University Department of Medicine, City Hospital, Birmingham B18 7QH, England, UK2005 7 1 2005 3 3 3 22 12 2004 7 1 2005 Copyright © 2005 Tomson and Lip; licensee BioMed Central Ltd.2005Tomson and Lip; 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 is a growing worldwide problem associated with an increased risk of cardiovascular morbidity and mortality. However, the rates of prevalence of hypertension are higher in some populations than others. Although ethnic and genetic factors have been implied in the past to explain this, the environmental influence and psychosocial factors may play a more important role than is widely accepted. Examining the non-genetic influences in future hypertension research may be necessary in order to clearly define the local blood pressure demographics and the global hypertensive disease burden.
Hypertensionprevalencegeneticenvironmentpsychosocial factors
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Hypertension is a common problem, with a consistent and continuous risk of cardiovascular disease and stroke associated with rising blood pressure levels [1]. Furthermore, effective treatment of blood pressures has been shown to cause reductions in morbidity and mortality from cardiovascular disease and stroke. The modern management of hypertension is even more complex, with the emergence of newer therapies, ageing populations and new clinical trial evidence, as well as the need for multiple agents to achieve target blood pressures, which are much lower than they used to be in the past [1].
The consequences of poor blood pressure control are huge. As high blood pressure is the most important risk factor for cardiovascular disease, it has been calculated that by achieving the target of 140 mmHg, there would be a reduction of 28–44% in stroke and 20–35% in ischaemic heart disease depending on the age. This would prevent approximately 21400 stroke deaths and 41400 ischaemic heart disease deaths each year – and these translate to approximately 42800 strokes and 82800 ischaemic heart diseases saved, making a total of 125600 events saved per year in the United Kingdom alone [2]. Even white coat hypertension is by no means a benign condition [3]. By 2020, the world population would be an estimated 7.8 billion people and hypertension currently is 'estimated' to affect about 1 billion worldwide – this figure will be rising. The growing numbers and the lack of concerted effort to tackle the burden of hypertension makes depressing reading.
Nonetheless, what is more intriguing and perhaps still not fully explained, is why some populations seem to have a much higher population prevalence of hypertension as compared to others. For instance, the prevalence and incidence of hypertension differs between the non-westernised and westernised populations. Even within the western world, the Afro-Caribbean or African-American black population has a higher prevalence of hypertension and target organ damage related to it, as compared to white Europeans or Americans [4]. Differences also exist within the same region, for example, with people of Eastern European origins having a higher prevalence of hypertension compared to elsewhere in Europe [5]. Understanding the reason(s) behind these geographical and ethnic differences would help devise effective measures in primary prevention.
Cooper et al [6], writing in BMC Medicine, address the issue of whether there is a truly genetic predisposition or perhaps an environmental influence is to blame for higher rates of prevalence of hypertension seen in some of these ethnic populations. In a well-designed pooled analysis, incorporating eight studies involving 8 white and 3 black populations from the North American, European and African populations – a dataset of nearly 85,000 patients – Cooper et al [6] examined patterns of blood pressure distribution in the different ethnic groups across three continents. They found a wide variation in hypertension prevalence among white and black racial groups, and the rates among blacks were not unusually high when viewed internationally. They therefore suggest that the impact of environmental factors among black and white populations may have been under-appreciated. Specifically, and perhaps contrary to expectations, the prevalence of hypertension was lower amongst the white peoples in Northern America and Canada, as compared to Europe.
Does this take us back to the drawing board? Perhaps environmental factors do play a more major role in developing hypertension than is widely accepted. Indeed, does urbanisation per se together with the unhealthy lifestyle and diet in the western world increase the risk of hypertension, compared to the rural, 'low stress', healthier lifestyle and dietary habits in Africa? Perhaps the genotype of black subjects was not idealised for the 'pro-hypertension' environment of the western world, leading to the greater risk of developing hypertension amongst blacks in the western world. This 'genetic predisposition' of certain ethnic groups, coupled with the 'wrong' environment, leads to an unhealthy combination that predisposes to cardiovascular disease [7]. However, the sociological definition of an ethnic group would be "people of the same race or nationality who share a common and distinctive culture", as it is impossible to consistently classify people by race. Genetic analyses have found more genetic variation within one ethnic group than between one group and another [8]. Therefore, race or ethnicity may appear to be more defined by customs, traditions, language and history than purely by genotype alone. Indeed, classification of race or ethnicity or skin colour, for example, is pretty subjective, imprecise and unreliable. Evidence for this exists in the differences in coronary risk factors in Indians, Pakistani and Bangladeshi populations in a British city, eventhough together these people might have been classed as 'Indo-Asian' but are clearly different [9]. Similarly, a Scottish Highland crofter is quite different from a Swede businessman, who would again be quite different from a Greek fisherman, although all would be ethnically classified as 'white caucasian'.
Can this 'wrong genotype in the wrong environment' hypothesis be applied to hypertension in black African-Americans? Efforts have already been made to understand the reasons behind the higher prevalence of hypertension in African Americans, with the underlying assumption that there may a genetically determined predisposition as compared to their white counterparts; however, no convincing data are available [10]. This may be because multiple genes determine human hypertension, at least in the vast majority of cases [11], and genetic factors have not been able to fully account for the differences in blood pressure prevalence between ethnic groups. Furthermore, elsewhere, black populations migrating to countries like UK have been shown to have similar blood pressures compared to the white UK population [12].
Numerous potential explanations for the higher prevalence of hypertension in blacks have been proposed. Genetic mechanisms have been used to explain familial aggregation of hypertension in Jamaican blacks and the intra-class correlation of systolic blood pressures amongst black twins [13,14]. Low renin levels noted in the USA black population have been hypothesised to be the result of a genetic 'maladaptation' which though benefited their earlier black ancestors to survive the torment of a transatlantic voyage under slavery, later turned out to be detrimental to survival due to the resultant avid salt retention [15]. Indeed, increased sodium sensitivity, abnormalities in sodium transport, increased vascular responsiveness to pressor stimuli, association between stresses of low socio-economic status and hypertension, and insulin resistance have also been suggested. Furthermore, ethnic differences in response to anti-hypertensive therapy are also well documented [4], with a potent effects of diuretics and calcium antagonists in black patients, compared to a relatively poor response to beta blockers and drugs that act on the renin-angiotensin system (such as the ACE inhibitors and angiotensin receptor blockers).
Notwithstanding the shortcomings of a retrospective, pooled study, with varying criteria for inclusion, and the intra-, inter- and cross-study observational errors, Cooper et al [6] have shown that the burden of hypertension seems to be more amongst the white population in Europe and that the global rates of hypertension amongst the black population are less in comparison. This study therefore sets the stage for a closer examination of data across geographic areas and calls for more stringent, standardised and possibly nationalised surveillance of blood pressure trends. If nothing else, the data suggests that inferences from cross-sectional studies done in certain geographic areas of a differing socio-economic stature cannot be extrapolated as logical benchmarks for other areas. Unintentionally perhaps, it beckons 'the scientist' to take a second look into the effect of other non-genetic mechanisms to explain the paradoxical findings. Surely more studies of a larger size are needed to confirm these implications.
Evidence that environmental and particularly psychosocial factors are important in the development of hypertension also comes from a series of epidemiological studies conducted in the early part of the last century, which have shown that urbanisation and adoption of a westernised life style leads to blood pressure rises. Many of these studies have been conducted in Africa, where the rural populace has a relatively low prevalence of hypertension. For example, primitive black populations living in more frugal circumstances in rural Africa have been shown to have low blood pressures. Evidence suggests that there are some populations that exist who are naïve to hypertension and related morbidity. Interestingly, their blood pressures hardly rise with increasing age, a common response to age in many other (urbanised?) communities [16-18]. The most important characterising feature of these populations were that, their lifestyle was traditional and they had not adopted or been under the influence of beliefs, customs or practices of another culture alien to theirs; the so-called 'unacculturated societies', with a close resemblance to the 'hunter-gatherer' lifestyle of primitive man. The other interesting feature observed was the constancy of electrolyte intake in the diet in these populations, which was in sharp contrast to the more 'developed' Western populations [19].
Migration, not withstanding the complexity of the studies in these populations, has been shown to significantly affect blood pressures. A study examining the migrant islanders into New Zealand showed raised population blood pressures, as well as an increased slope of the age-blood pressure relationship [20]. The Kenyan Luo migration study examined migration of rural tribes to the capital city Nairobi, also found that urbanised populations had higher mean blood pressures [21]. Chronic and excessive alcohol ingestion may also adversely affect blood pressure [22]. The relationship of hypertension with obesity has been demonstrated but weight loss seemed to have more pronounced blood pressure reductions in whites rather than blacks.
The issue of the influence of colour of the skin to blood pressures is even more complex. On the one hand, studies in America have shown relationship between the dark skin and blood pressures, leading to some suggesting that the link is genetic. In contrast, some argue that it is a manifestation of the stress and social pressure of having a dark skin that causes this. In Cuba, for example, where communist principles are considered to have broken the racial barriers, the ethnic differences in blood pressures were shown to be small, supporting the latter argument [23]. In addition, there may even be an effect of neighbourhoods or the social environment on blood pressure and cardiovascular disease [24,25].
Thus, the process by which a society becomes more economically advanced or "developed" seems to be associated closely with rates of hypertension prevalence. Indeed, lifestyle and dietary changes related to the so-called "development" seem linked to the prevalence of hypertension. In the INTERHEART population case-control study [26], which was an investigation into the association of psychosocial risk factors in patients with acute myocardial infarction, examining 11119 cases and 13648 controls from 52 countries, demonstrated higher prevalence of all four 'stress factors' (stress at home, at work, financial stress and major life events) in these patients, with consistency across regions, ethnicity and gender. Though data implicating stress as being contributory to the development of high blood pressures are limited and perhaps even hard to establish, a causal relationship between stress and developing high blood pressure does not appear to be an illogical assumption.
There remain many uncertainties to the relative importance and contribution of environmental versus genetic influences on the development of blood pressure – there is more than likely an influence from both. However, there is now evidence to necessitate increased attention in examining the non-genetic influences on blood pressure, a neglected area of hypertensive research but perhaps a goldmine for establishing causal influences. As stated earlier, the future of hypertension research should focus on more standardised and comparable protocols, with comparable designs in data collection. Multi-centric data collection with a view to establishing local or national blood pressure demographics is crucial for the formal assessment of the global hypertension burden and the implementation of cost-effective primary preventive measures.
Pre-publication history
The pre-publication history for this paper can be accessed here:
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| 15638936 | PMC544878 | CC BY | 2021-01-04 16:27:56 | no | BMC Med. 2005 Jan 7; 3:3 | utf-8 | BMC Med | 2,005 | 10.1186/1741-7015-3-3 | oa_comm |
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BMC GenomicsBMC Genomics1471-2164BioMed Central London 1471-2164-5-921558832010.1186/1471-2164-5-92Research ArticlePolymorphic segmental duplications at 8p23.1 challenge the determination of individual defensin gene repertoires and the assembly of a contiguous human reference sequence Taudien Stefan [email protected] Petra [email protected] Klaus [email protected] Kathrin [email protected] Markus [email protected] Karol [email protected] Atsushi [email protected] Shuichi [email protected] Adam [email protected] Ivan F [email protected] Nobuyoshi [email protected] Roman [email protected] Matthias [email protected] Genomanalyse, Institut für Molekulare Biotechnologie, Beutenbergstr. 11, D-07745 Jena, Germany2 Keio University School of Medicine, 35 Shinanomachi, Shinjuku-ku, Tokyo 160-8582, Japan3 Wellcome Trust Sanger Institute, Hinxton Cambridge CB10 1SA, UK4 Institut für Humangenetik und Anthropologie, Friedrich-Schiller-Universität Jena, Kollegiengasse 10, D-07743 Jena, Germany2004 10 12 2004 5 92 92 25 8 2004 10 12 2004 Copyright © 2004 Taudien et al; licensee BioMed Central Ltd.2004Taudien 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
Defensins are important components of innate immunity to combat bacterial and viral infections, and can even elicit antitumor responses. Clusters of defensin (DEF) genes are located in a 2 Mb range of the human chromosome 8p23.1. This DEF locus, however, represents one of the regions in the euchromatic part of the final human genome sequence which contains segmental duplications, and recalcitrant gaps indicating high structural dynamics.
Results
We find that inter- and intraindividual genetic variations within this locus prevent a correct automatic assembly of the human reference genome (NCBI Build 34) which currently even contains misassemblies. Manual clone-by-clone alignment and gene annotation as well as repeat and SNP/haplotype analyses result in an alternative alignment significantly improving the DEF locus representation. Our assembly better reflects the experimentally verified variability of DEF gene and DEF cluster copy numbers. It contains an additional DEF cluster which we propose to reside between two already known clusters. Furthermore, manual annotation revealed a novel DEF gene and several pseudogenes expanding the hitherto known DEF repertoire. Analyses of BAC and working draft sequences of the chimpanzee indicates that its DEF region is also complex as in humans and DEF genes and a cluster are multiplied. Comparative analysis of human and chimpanzee DEF genes identified differences affecting the protein structure. Whether this might contribute to differences in disease susceptibility between man and ape remains to be solved. For the determination of individual DEF gene repertoires we provide a molecular approach based on DEF haplotypes.
Conclusions
Complexity and variability seem to be essential genomic features of the human DEF locus at 8p23.1 and provides an ongoing challenge for the best possible representation in the human reference sequence. Dissection of paralogous sequence variations, duplicon SNPs ans multisite variations as well as haplotypes by sequencing based methods is the way for future studies of interindividual DEF locus variability and its disease association.
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Background
Despite the tremendous efforts and successful completion of the Human Genome Project by April 14th 2003, a set of recalcitrant gaps remain in the euchromatic part of the final human genome sequence. One obvious reason for these gaps is that the appropriate regions are enriched in sequences that are not tolerated by the cloning systems. The second possibility is that even if clones are available and amenable for sequencing, their sequences cannot be unambiguously aligned due to gap flanking segmental duplications. Generally, those duplicons are defined by >90% sequence identity and lengths of >1 kb and about 87% of all human ones are longer than 50 kb [1]. In these regions with nucleotide identities up to >99% over several kb it is nearly impossible to decide whether very similar sequences represent distinct loci or different alleles of a single locus. Here, sequencing of a single chromosomal haplotype is a straightforward approach to achieve a „consistent“ assembly. It was successfully applied to decipher intrachromosomal duplications of the human Y [2]. If, however, duplications are located on autosomes and their copy numbers vary interindividually, as shown for regions in 15q11-q13 [3], the situation becomes even more complicated and requires the extra effort of resolving haplotype differences that result from the diploid nature of the underlying BAC library. In the Williams-Beuren syndrome (WBS) region on human chromosome 7, only extensive redundant sequencing from a single BAC library led to a representative sequence [4]. Alternatively, monospermic complete hydatidiform moles [5,6] and hamster somatic cell hybrids [7] provide access to fully homozygous genomes or individual autosomes, respectively.
It is a fact that structural variations between chromosomal haplotypes complicate the sequence assembly and lead to the formation of de facto gaps [1,8]. The more haplotypes are represented by BAC clones, the more de facto gaps may be formed. In the case of unresolved segmental duplications, usually a large number of clones has been sequenced with high accuracy [9] and the clone coverage of the loci is well above-average of the entire human genome. However, no contiguous tiling path can be build and gaps remain. Nevertheless, the available data are an invaluable resource for the investigation of individual genetic variations in duplicated regions and of their association with diseases.
One of those complex regions is located in 8p23.1 at 6.3 – 8.3 Mb of the July 2003 human reference sequence (NCBI Build 34; UCSC version hg16, Fig. 1A). In the Golden Path assembly [10], there are 22 finished clones from five different libraries and 20 working draft or predraft clones (<4x coverage shotgun; four different libraries) grouped on both sides of a recalcitrant gap at 7.5 Mb. Another 10 finished clones from four different libraries are not included in the hg16 assembly but map to the 8p23.1 locus. Several attempts to close this gap have failed due to the highly repetitive structure of the flanking sequences. The gap flanking regions harbor defensin (DEF) genes, encoding a group of small cationic peptides with characteristic three intramolecular disulfide bonds. These peptides play a prominent role in innate immunity to defend bacterial and viral infections in animals, plants and insects [11]. Furthermore, in humans, loss or down regulation of DEF genes is shown to be related with cancer, such as renal cell carcinoma [12-14], prostate cancer [14] and bladder tumors [15]. Two different DEF gene clusters can be distinguished: DEF cluster a contains the genes DEFB1, DEFA6, DEFA4, DEFA1, DEFT1, DEFA3 and DEFA5; DEF cluster b comprises the genes DEFB109p, DEFB108, DEFB4, DEFB103, SPAG11, DEFB104, DEFB106, DEFB105, and DEFB107 (Fig. 2). DEF cluster b is duplicated in reverse complementary orientation on either side of the gap, forming the distal cluster b1 and the proximal cluster b2.
Figure 1 Alternative alignments of the 8p23.1 DEF locus. (A) July 2003 UCSC version hg16 [10] chr8:6,258,283-8,262,034. Only finished clones are shown and arranged by libraries, which are indicated by background colors: RP11 = gray, bottom; SCb = white, middle; other (CTB, CTD, GS, RP13) = gray, top. Defensin gene clusters are shown as arrows, repeat blocks are indicated as striped boxes, (+) strand is above the black line, (-) strand is below the black line, same stripe patterns indicate similar structures. The light blue background indicates the distal repeat region for chromosomal rearrangements [16]. (B) Revised alignment of the 8p23.1 DEF locus, containing an additional 360-kb-contig and five clones which cannot be aligned; colors: black = aligned as in Fig. 1A; orange = clones not present in the UCSC browser; red = clones with different positions in both alignments, blue = clones presented in the UCSC browser but excluded in the revised assembly. The yellow vertical bar in DEF a illustrates the widening of the DEF cluster a as a result of the alternative alignment of [GenBank:AF200455] / [GenBank:AF238378] (see text). Clone (number) GenBank accession.version / library: (1) [GenBank:AC018398] / RP11, (2) [GenBank:AF287957] / CTD, (3) [GenBank:AF233439] / GS, CTD, (4) [GenBank:AF200455] / SCb, (5) [GenBank:AF238378] / SCb, (6) [GenBank:AF228730] / SCb, CTB, (7) [GenBank:AF215847] / CTB, (8) [GenBank:AC130339] / RP13, (9) [GenBank:AC130360] / RP11, (10) [GenBank:AC130367] / RP11, (11) [GenBank:AC134395] / RP11, (12) [GenBank:AC134683] / RP11, (13) [GenBank:AC285443] / SCb, (14) [GenBank:AC202031] / SCb, (15) [GenBank:AC134684] / RP11, (16) [GenBank:AC084121] / RP11, (17) [GenBank:AC144950] / RP11, (18) [GenBank:AC130365] / RP11, (19) [GenBank:AC131269] / RP11, (20) [GenBank:AC105233] / RP11, (21) [GenBank:AC068020] / RP11, (22) [GenBank:AC068353] / RP11, (23) [GenBank:AF298854] / SCb, (24) [GenBank:AF205406] / SCb, (25) [GenBank:AF314060] / GS, (26) [GenBank:AF314059] / SCb, (27) [GenBank:AF252831] / SCb, (28) [GenBank:AF189745] / SCb, (29) [GenBank:AF252830] / SCb, (30) [GenBank:AC148106] / RP11, (31) [GenBank:AC105214] / RP11, (32) [GenBank:AC092766] / RP11
Figure 2 Genes and pseudogenes in DEF clusters a and b. Names correspond to the Vertebrate Genome Annotation, intergenic distances are scaled 1:10. Defensin and defensin like genes and pseudogenes are written in black, novel defensin genes and pseudogenes are underlined, other genes / transcripts are indicated in gray. DEF cluster a: The presence of four copies of the DEFA1/DEFTP tandem and the DEFA3 gene in [GenBank:AF200455] requires the illustrated clone alignment, resulting in a "widening" of the hg16 assembly, pictured by the striped gray box (corresponding to the yellow bar in Fig.1). Analysis of the intergenic distances (data not shown) suggests, that [GenBank:AF238378] harbors copies 2 and 4 of the DEFA1/DEFTP tandem whereas copy 3 is missing (dotted line). Since both clones are derived from the same library (SCb), either copy 3 is lost during the cloning process or the clones represent different alleles. DEF cluster b: The DEF cluster b is illustrated in the orientation of DEF cluster b1.
Interestingly, the DEF cluster region was identified as the distal breakpoint (REPD) of a 4.7-Mb segment inversion, identified as a common polymorphism with frequencies of 39% and 26% in the Japanese population and in Europeans, respectively [16-18]. Although the inversion itself apparently do not have any pathological effects, in heterozygous female carriers unequal recombinations can occur, leading to three macrorearrangements – inv dup del (8p); +der(8)(8p23.1pter) and del (8)(p23.1p23.2) – related to severe disease phenotypes. The fact that low copy repeats (LCR) flanking the DEF clusters represent the essential sites for such recombination events is a strong argument to resolve the structure of the LCRs themselves as well as the genomic organization of the entire region. Also, genes of both DEF cluster a and b vary interindividually in their copy numbers. This was shown by somatic cell hybrid mapping for DEFA1, DEFA3 (DEF cluster a; 2–3 copies each) [19] and by a combination of multiplex amplifiable probe hybridisation and semiquantitative fluorescence in situ hybridization for DEFB4, DEFB103, DEFB104 (DEF cluster b; 2–12 copies each) [20]. It is generally assumed that this variability crucially contributes to the differences in the innate immunity network between individuals and influences predisposition and susceptibility for diseases.
The polymorphic nature of this locus suggested to us that the pool of clones presented in the hg16 assembly should be aligned in a different way. Our alternative assembly creates more DEF cluster copies and better reflects the individual variability of the locus. In addition, comparative sequence analysis of DEF genes in our closest relative, the chimpanzee (Pan troglodytes), both revealed the differences in the defensin protein panel of both species and showed that DEF clusters are also multiplied in the ape.
Moreover, extraction of single nucleotide polymorphisms (SNPs) from overlapping regions of clones harboring DEF genes provided haplotypes which were analyzed for their ratio in individuals and used for the determination of individual gene copy numbers.
Results
Revision of the hg16 assembly
In the framework of the International Human Genome Sequencing Consortium we sequenced 19 out of 32 BAC clones mapping to the 8p23.1 DEF region (for linking clone numbers with accession numbers see legend of Fig. 1; accession numbers and corresponding clone names are given in Additional file 1). In addition to the existing clone alignments of DEF clusters b1 and b2, in our sequence assembly a consistent 360-kb-contig comprising five clones was built that also contains a DEF cluster b. Interestingly, this additional contig could neither be joined unambiguously to cluster b1 nor to cluster b2. This is in contradiction to the hg16 assembly where clones 13 and 14 of the novel contig are positioned in DEF cluster b1. This disagreement prompted us to evaluate carefully the alignment of the entire region. For this, we used all 32 finished clones which map in the region. To circumvent the problems of an automatic assembly in repeat rich, duplicated regions, the clones were manually joined according to the following criteria: only single base exchanges and insertions/deletions in repeat stretches were tolerated and joins were not performed if the total ratio of single base differences in the overlapping clone portions was >0.8%.
The result is an alternative alignment shown in Fig. 1B, which differs from the hg16 assembly in three major points and is supported by a detailed repeat and SNP/ haplotype analysis:
(1) Clones 23–27 not present in the hg16 assembly were located distally of the gap, whereas clones 11, 12 and 17 were excluded since they cannot be aligned according to our criteria. Clones 13 and 14 were moved to the new 360-kb-contig (see below).
(2) In the framework of the human and vertebrate analysis and annotation initiative (HAVANA) [21], where gene structures are annotated on the basis of human interpretation of combined supportive evidence generated during sequence analysis, we manually annotated seven DEF gene containing clones, five of them located in DEF cluster a. We found that clone 4 contains four copies of a DEFA1 / DEFTP tandem and one copy of DEFA3, whereas clone 5 harbors only two DEFA1 / DEFTP tandems and the DEFA3 gene (Fig. 2). Consequently, in our assembly, clone 4 is aligned to clone 5 in a way that results in four copies of the DEFA1/DEFTP tandem instead of three copies in the hg16 assembly. Thus, this region will be "widened" by shifting all proximally adjacent clones for 23,270 bp. Analysis of the intergenic distances between the DEF genes suggests that clone 5 harbors copies 2 and 4 of the four DEFA1 / DEFTP tandems. Furthermore, hitherto undiscovered, additional DEF genes and pseudogenes could be annotated: clones 3–5 harbor the DEFA7 gene [GenBank:A98570], [GenBank:A98571] coding for a novel protein, similar to DEFA4, as well as pseudogenes DEFAP1, DEFAP2 and DEFAP3 similar to DEFA (Fig. 2). Our annotation of the entire DEF cluster region is submitted to the Vertebrate Genome Annotation database [22].
(3) Our assembly of the 32 finished clones mapping to the locus created an additional 360-kb-contig which consists of five clones representing an additional, third DEF cluster copy named b3. Since the repeat in clone 30 is located on the (-)-strand, the contig cannot be joined to any of the repeats at either side of the gap but instead has to be located within the gap in unknown orientation.
Low copy repeat analysis
The DEF locus contains several genomewide low copy repeats (LCR) (Fig. 3). The repeats form clusters of up to 165 kb length and paralogs exist on other chromosomes as well as on chromosome 8 at about 12 Mb. In silico identification of these paralogs was in good accordance with fluorescence in situ hybridization (FISH) experiments using clones from the 8p23.1 DEF locus as probes (Additional files 3 and 4). Five types of LCRs can be distinguished:
Figure 3 DEF gene cluster flanking repeat blocks in 8p23.1 and their paralogs on other genomic loci. Repeats and repeat clusters are drawn as striped bars as in Fig. 1. Inverted repeat pairs of the paralogs are highlighted in gray.
Type I
The 89.6 kb cluster consists of 4.7 subunits of a 19.2 kb repeat. Each complete subunit contains DEFA1 and DEFTP. The incomplete fifth copy harbors DEFA3. The content of interspersed repeats is low (20%); the nucleotide identity between the subunits is 99%. The repeat subunits of cluster I are unique to this locus and do not show significant homologies to other regions of the human genome.
Type II
Repeats of type II flank DEF clusters b, do not show substructures and do not contain genes. In contrast to repeat units of type I they differ in length and orientation, contain inversions and show a high degree of interspersed repeats (55%). The considerable length differences are caused by an initial partial duplication and/or subsequent deletions. The nucleotide identity of the repeats is 96–99% except for II.1, located adjacent to DEF cluster a, which exhibits an identity of ~92% to repeats II.2-6.
Blat search of the longest repeat (II.2, 82 kb) revealed that repeats of type II are also present at about 12 Mb on chromosome 8p23.1 as well as on other human chromosomes, e.g. 3, 4, 7, 11, 12 and 16. The paralogs vary in length (25–133 kb) and show nucleotide identities of 93–95% compared to II.2 on 8p23.1. On chromosomes 3, 4 and 11, they are arranged as inverted repeats. The distances in between "repeat pairs" vary remarkably: Whereas in 11q13.2-11q13.4 and 4p16.2-16.1 the parts are separated by 4 and 5 Mb, respectively, the repeats on chromosome 3 are located in 3p12.3 and 3q13.31, framing 51 Mb on both arms of the chromosome.
Type III
Located adjacent to DEF clusters b, they differ in length and orientation and consist of various numbers of 7.7 kb-subunits showing a low content of interspersed repeats (17%). The nucleotide identity of two subunits of a single type III cluster is >99%, whereas the identity of two subunits of different clusters is in the range of 96–98%. Each of the 7.7 kb-subunits harbors one copy of a gene for the hypothetical protein FLJ10408 [GenBank:NM_018088]. The coding sequences of the gene copies differ and in some cases the reading frame contains premature termination codons. Blat search of the type III repeat subunit revealed three paralogs in the human genome. Two of these are located on chromosome 8p23.1 at about 12 Mb (flanked by repeats of type II), the third at 12p13.31. The paralogs are slightly rearranged in comparison to the subunits of III.1-5.
Type IV
Repeats of this type partially cover the DEF clusters b (containing DEFB109P and DEFB108, Fig. 2) and do not show substructures. The content of interspersed repeats is about 30%; the nucleotide identity between repeats IV.1-3 is >98%. Paralogs of these repeats exist on chromosome 8 at about 12 Mb and on chromosome 12 with identities of about 96% to IV1-3.
Type V
These repeats are unique to the 8p23.1 DEF locus, show no substructures and contain 35–38% interspersed repeats as well as the major part of the DEF cluster b genes (all genes downstream of DEFB108, Fig. 2). The nucleotide identities between V.1, V.2 and V.3 are >98%.
SNPs and haplotypes
We manually inspected seven clones covering DEF cluster a and 16 clones covering DEF clusters b1, b2 and b3 for SNPs in exons and introns of all DEF genes. In total we found 270 overlap SNPs: 25 are located in coding sequences, comprising 16 nonsynonymous and nine synonymous changes. 36 SNPs were identified in untranslated regions, and 209 are located in introns. With respect to the coding SNPs in DEF clusters b and regarding 11 of 16 clones, six distinct haplotypes H1-6 can be defined (Table 1). One clone each supports haplotypes H3, H4 and H6, whereas haplotypes H1, H2 and H5 are found in either two or three clones. The remaining five clones harbor only parts of DEF genes rendering the unambiguous identification of coding SNP based haplotypes impossible. Examination of all SNPs leading to amino acid (aa) changes in defensins indicates that diversity in the peptides is not restricted to residues outside and in between the cystein motif, but also occurs in the vicinity of cysteins, or even a cystein itself is changed (rs1800968 in DEFB1; C67S; data not shown).
Table 1 SNPs and haplotypes H1-H6 extracted from DEF cluster b covering clones
Gene Pos. mRNA1 Haplotypes2 Change
H1 H2 H3 H4 H5 H6
DEFB107 13 G / / T G T F5V
DEFB105 107 C / C T C C P36L
DEFB106 125 T / C T T T Silent
DEFB104 42 A A A A A G V10I
DEFB4 78 T C C C C C Silent
120 T T T T C T Silent
275 C C C C T C 3'UTR
335 C C C C G C 3'UTR
DEFB108 138 A A A A T / Silent
111 C C C C T / Silent
97 G G A A G / G33S
DEFB109p 133 A A A / G / V45I
132 C C A / A / K44N
119 C C G / G / R40T
104 T T C / C / S35F
41 C C / / A / S14ochre
1 mRNA positions are referred to the following human mRNAs: DEFB107 = [GenBank:AY122467]; DEFB105 = [GenBank:NM_152250]; DEFB106 = [GenBank:NM_152251]; DEFB104 = [GenBank:NM_080389], DEFB4 = [GenBank:NM_004942]; DEFB108 = [GenBank:AF540980]; DEFB109p = [GenBank:AF540981].
2 Haplotypes are derived from the following clones (libraries): H1 = 9, 10 (both RP11); H2 = 8 (RP13), 18, 19 (both RP11); H3 = 11 (RP11); H4 = 12 (RP11); H5 = 13, 14, 29 (all SCb); H6 = 28 (SCb).
Chimpanzee defensin loci
In order to compare the human chromosome 8p23.1 DEF region to the orthologous locus in our closest relative, we both employed the chimpanzee (Pan troglodytes, ptr) whole genome shotgun (WGS) working draft (WD, [23]) and high quality chimpanzee BAC sequences. Close inspection of the chimpanzee WD scaffold 32935 (chain ID 462) revealed all orthologs of the genes in human DEF cluster a except DEFA3. In contrast to the human organization, ptrDEFA1 and ptrDEFTP were found as single copies. In order to check whether ptrDEFA1 and ptrDEFTP are also multiplied in the ape, but misassembled into a single locus, we inspected the NCBI trace archive [24] for chimpanzee WGS sequences covering the ptrDEFA1 locus. For a region of about 500 bp spanning exon 1 of ptrDEFA1, there are shotgun reads representing six different haplotypes. Since the sequences derived from one chimpanzee this is a clear indication that ptrDEFA1 is also multiplied in the ape. No evidence was found for the presence of ptrDEFA3. Concerning DEF cluster b we encountered the same problem: the cluster is represented only once in the chimpanzee WD (chain ID 900), but trace data inspection indicates the presence of several different haplotypes. Additionally, ptrDEFB108 and ptrDEFB109p are not covered by any chimpanzee WD sequences. As an alternative to the WD approach, we sequenced for ptrDEF cluster b three BAC clones. Examination of SNPs in overlapping regions (104 kb) of the three clones [GenBank:AC150655], [GenBank:AC150656], [GenBank:AC150657] revealed three different haplotypes originating from one chimpanzee. The detected aa changes in human and chimpanzee defensins are illustrated in Additional file 5. Interestingly, in the ptrDEFA5 protein, one of the disulfide bridging cysteins is changed into serine (C54S). In ptrDEFB108 the canonical cystein motif is truncated (R53opal) which suggests that ptrDEFB108 is a pseudogene, since also the start codon ATG is changed into GTG.
We also used the ptrDEF sequences for the detection of ancestral alleles of all 19 nonsynonymous and synonymous human DEF coding SNPs (Additional file 2).
Individual DEF haplotypes and copy numbers
In order to determine individual DEF copy numbers we PCR-amplified a 500 bp fragment of DEFB104 which contains 4 SNPs, and a 511 bp region of DEFB4 containing 5 SNPs in four individuals (Table 2). Three of the DEFB4 SNPs (Table 2, SNPs 5,6 and 7) were previously described in a haplotype study in different ethnic populations [25]. The PCR products were cloned, individual clones were sequenced and respective haplotypes were determined according to the base composition at polymorphic positions. Each individual tested bears between three and four different haplotypes of DEFB104 and two to four haplotypes of DEFB4. In different individuals the ratios of the single haplotypes vary remarkably. For instance, at DEFB104 the haplotype GAGC is found in all four individuals but compared to all other haplotypes at ratios of 2:3 (proband 2) to 1:7 (proband 1). Interestingly, this haplotype is also found in the trace archives of chimpanzee and baboon. Furthermore, ratios of the individual haplotypes of DEFB104 as well as of DEFB4 indicate different DEF cluster b numbers in the four individuals. While proband 3 bears five copies, proband 4 most probably harbors eight copies or multiples thereof.
Table 2 Haplotype based estimation of gene and cluster copy numbers
Haplotypes SNP1 Proband
DEFB104 1 2 3 4 1 2 3 4
1 C A A T 50 19 - 5
2 G A A T - - 16 -
3 G A G C 15 18 32 9
4 G G G C 65 10 16 58
5 G A A C - - 14 -
Ratios of single haplotypes 3:1:4 2:2:1 1:2:1:1 1:1:6
Minimal gene copy number 8 5 5 8
DEFB4 5 6 7 8 9 1 2 3 4
1 C C C G A 14 - 8 -
2 C T G G A - 17 9 5
3 T C C A G 8 - 8 -
4 T C C G G 24 68 11 30
Ratios of single haplotypes 2:1:3 1:4 1:1:1:1 1:6
Minimal Gene copy number 6 5 4 7
Minimal DEF cluster b copy number 8 5 5 8
1 SNP1: ss28489415, 2: rs2680507 = rs11774031, 3: ss28489416, 4: rs4259430, 5: rs2740090, 6: rs2740091, 7: rs2737912, 8: rs2737913, 9: rs2737531.
Discussion
The manual clone-by-clone alignment and gene annotation as well as detailed repeat and SNP/haplotype analyses significantly improved the assembly of the human DEF 8p23.1 locus. Eventhough the revised alignment (Fig. 1B) does not represent a gap-free version of the locus and in fact introduces a second de facto gap, it better reflects the region in the sense of a „human genome reference“, since the clones harboring copies of DEF clusters b derive from three libraries (RP11, RP13, SCb) and may therefore represent up to five alleles (library RP13 is represented by only one clone). Our assembly also reflects better the diversity of all available sequence data of this chromosomal region: 27 out of 32 finished clones are incorporated into the tiling path. The remaining five clones cannot be included in the assembly according to our quality criteria and therefore must be regarded as parts of additional copies or alleles. Furthermore we point out that the identification of a third copy of the DEF cluster b in the 360-kb-contig does not represent an allele of clusters b1 or b2 derived from an alternative library / donor, since besides four SCb clones one RP11 clone is incorporated. With respect to the RP11 library of which most of the DEF cluster b covering clones derive we conclude that at present sequence information of at least five variants of the cluster is available from a single individual: cluster b1 (clones 9, 10, 15); b2 (clones 16 – 20); b3 (clone 30); b4 (clone 11) and b5 (clone 12). All these results are in agreement with the reported interindividual variability of DEF cluster b genes [20].
Alignment and analysis of the intergenic distances of clones 4 and 5 (DEF cluster a; Fig. 2) show that clone 5 harbors copies 2 and 4 of the four DEFA1 / DEFTP tandems present in clone 4. Since both clones derive from the same library (SCb), we conclude that either copy 3 of the tandem was lost during the cloning process of clone 5 or the clones represent two different alleles of the same chromosomal locus. This perfectly agrees with the variation in the copy number of DEFA1 reported by Mars [19]. Moreover, the identification of new DEF genes and pseudogenes demonstrates the advantages of a curated manual annotation over automatic approaches.
The LCR analysis (Fig. 3) allows to draw conclusions about the role of these repeats in chromosomal rearrangement processes: The difference in nucleotide identities between LCR II.1 at one hand and II.2-6 on the other hand indicates that repeats II.2-6 might be involved in rearrangement events of DEF clusters b, whereas repeat II.1, separating DEF cluster a from the clusters b, has evolved independently from its paralogs. Aditionally, for repeats on chromosome Y, a similar genomic structure as for inverted LCR type II is described in the literature: a 300-kb inverted repeat flanks a 3.5 Mb region that occurs in opposite orientations in different individuals [2]. This supports the assumption that also type II repeats may be involved in homologous recombination events resulting in chromosomal macrorearrangements including inversions, even pericentromeric ones. In particular, this may hold true for the polymorphic 4.7-Mb inv dup del (8p) reported in the literature [16-18]: LCRs of type II are located on chromosome 8p both at 6.9–8.0 Mb and 11.9–12.6 Mb and therefore separated from each other in a range of 3.9–5.7 Mb. Thus, they can be supposed to be inversion breakpoints in REPP and REPD.
The function of the protein encoded by FLJ10408 located in LCR type III is unknown, but the genomic arrangement facilitates proteome plasticity by multiple copies of the same gene.
SNP detection and correct assignment to regions with segmental duplications is not trivial and hampered by paralogous sequence variations [26], duplicon SNPs and multisite variations [6]. Moreover, there is considerable evidence that gene conversion [27,28] promotes allele plasticity in duplicated regions. This is illustrated by the fact that in the UCSC browser ~2800 SNPs from dbSNP [29] are assigned to DEF cluster a (224 kb) and the two DEF clusters b (196 kb each) resulting in a SNP density of 1 SNP per 220 bp. Close examination indicates that the SNPs are arbitrarily allocated to the two DEF cluster b loci present in the hg16 assembly. Therefore, in such regions, only manual clone-by-clone inspection as performed during our assembly process provides a reliable set of SNPs for the determination of haplotypes. Human SNPs such as rs1800968 in DEFB1, affecting cysteins (C67S) might be of functional relevance, since the cystein connectivity is assumed to determine the correct fold of the defensins which is essential to elicit chemotactic responses as shown for DEFB103 [30].
In order to answer the question whether the extraordinary complexity of the DEF locus is human specific, we closely inspected the orthologous region of the chimpanzee WD. In contrast to the human organization, ptrDEFA1 and ptrDEF cluster b were found as single copies. Familiar with the drawbacks of the WGS automatic assembly [31,32], we suspected these loci are also multiplied in the ape, but assembled wrongly into a single locus due to the high nucleotide identity. In accordance with this assumption, we identified WGS reads representing more than two haplotypes in one chimpanzee. In order to overcome the WD problems we sequenced BAC clones containing ptrDEF cluster b according to a high quality standard and conclude that it is at least duplicated. This suggests that the DEF locus of the chimpanzee is probably as complex as in humans.
The exceptional genomic complexity and heterogeneity of the human 8p23.1 DEF locus and the prominent position of defensins in the innate immunity framework raise the question whether individual patterns of haplotypes together with their variable copy number affect the functionality of the defensin system. A similar situation is found for a chemokine gene cluster where an individually variable gene copy number of CCL3-L1 regulates the gene's expression and is supposed to affect the susceptibility to and progression of inflammatory diseases [33]. Systematic typing of physically linked SNPs should allow to detect interindividual differences in haplotypes and locus copy numbers. Sequencing provides a robust method for the determination of haplotypes and their frequencies scalable to large numbers as required for association studies. As outlined above, SNP genotyping in duplicated regions is demanding and in addition to very careful initial data mining and laboratory practice requires methods allowing the quantitative assessment of allele ratios like dynamic allele-specific hybridization [34] and pyrosequencing [35] as well as of copy-number-variation like multiplex ligation-dependent probe amplification [36,37] and representational oligonucleotide microarray analysis [38,39]. In order to differentiate between valid SNPs, duplicon SNPs, paralogous sequence variations and multisite variations, complete hydatidiform moles or haploid genomes have to be included in upstream assay validation [6]. Nevertheless, for highly complex and polymorphic regions the significance of single SNP based assays may be insufficient. As shown in our approach, systematic typing of linked SNPs can overcome this limitation. An estimation of haplotype ratios provides information about the copy number, however, it depends on the number of individual clones analyzed. Our results confirm that the easy-to-handle „classical sequencing approach“ is a valuable tool for the determination of DEF gene variants, DEF haplotypes and DEF cluster copy numbers. More detailed analyses will give a catalog of haplotype combinations associated with different phenotypes and diseases. Finally, the presented work provides a set of SNPs and haplotypes suitable for future studies of interindividual DEF locus variability and its disease association.
Conclusions
Complexity and variability seem to be essential genomic features of the major human DEF locus and of – yet unknown – functional significance for the innate immunity framework. This is supported by our human-chimpanzee genomic comparison. In conclusion of the presented repeat analyses we propose a model of the repeat and DEF cluster organization (Additional file 6) that is consistent with the available sequence information and explains the observed extensive variability of the locus. Since all proposed structural elements of the highly complex locus are available at least once as finished sequence, no fosmid-end mapping problems have been observed (International Human Genome Sequencing Consortium, unpublished results). This is a strong indication that despite there are at least two de facto gaps no essential elements of the DEF locus are missing in the human reference sequence. In comparison to its actual representation, our revised clone alignment clearly better represents the 8p23.1 complexity and improves the human reference sequence as an invaluable resource for the investigation of individual genetic variations. Finally, the presented work provides a set of SNPs and haplotypes as well as a robust sequencing based method suitable for future studies of interindividual DEF locus variability and its disease association.
Methods
Alignment revision
Clone alignments were performed using the GAP4 assembly program, version 6 [40], using the sequences of the GenBank versions listed in the legend of Fig. 1 and Additional file 1. Additionally, for all clones sequenced at the IMB Jena the original GAP4 projects including the trace data were used. The clones were joined allowing only single base exchanges and insertions/deletions in repeat stretches. Joins were not performed if the total ratio of single base differences in the overlapping clone portions exceeded 0.8%.
Repeat analysis
LCRs were identified by application of the Miropeats program [41] to the revised alignment. The repeat sequences were extracted from the revised alignment of joined clones following the positions of the Miropeat's output and checked for their nucleotide identity using sim2 [42] and Blast 2.0 [43,44]. Interspersed repeats in the repeat blocks were identified by RepeatMasker (Version: 20040306-web; [45]. Paralogs of the repeat blocks were identified by Blat [46] to the July 2003 UCSC version h16 [10]. The DNA sequences in between the Blat match limits were fetched from the browser and also analyzed for their similarity to the DEF cluster repeats by Blast and sim.
Chimpanzee BAC clones
The clones were identified by Blast of the revised alignment consensus to the chimpanzee BAC end sequence database [47]. Subcloning was performed into pUC18 followed by sequencing using dye terminator chemistry and ABI 3730/3700 technology. Base calling and assembly were performed by Phred/Phrap and GAP4 was used for editing and finishing in accordance to the Human Genome Project standards [48].
Sequence annotation
The gene annotation was performed by using the automated sequence annotation system RUMMAGE [49] and ANA_NOTES, SPANDIT and LACE as a local client of the HAVANA pipeline at the Sanger Institute (Hinxton, UK; [21]. Detailed descriptions of the analysis tools are given by [50] and [51].
Haplotypes and copy numbers
Genomic DNA was extracted from the blood of four male volunteers using the QIAamp DNA Blood Kit (Qiagen). PCR was performed in a total volume of 25 μl using ReadyToGo PCR beads (Amersham) with 5 pmoles of each primer and 100 ng of DNA. Cycling conditions were 94°C for 30 sec followed by 35 cycles with 94°C for 20 sec, 58°C for 30 sec and 72°C for 60 sec, plus a final 72°C extension for 10 min. Oligos used were for DEFB4 GGCGATACTGACACAGGGTT (sense) and ATGGGGAAGGTCAAGGAATC (antisense) and for DEFB104 TTCTGTAGCCCCAACACCTC (sense) and GGTGCCAAGGACATCTAGGA (antisense), respectively. PCR products were cloned into PCRTopo2.1 (Invitrogen) and individual clones were sequenced as described above.
List of abbreviations
BAC Bacterial artificial chromosome
DEF Defensin
FISH Fluorescence in situ hybridization
GAP Genome assembly program
HAVANA Human and vertebrate analysis and annotation initiative
LCR Low copy repeat
NCBI National Center for Biotechnology Information
PCR Polymerase chain reaction
ptr Pan troglodytes (chimpanzee)
SNP Single nucleotide polymorphism
UCSC University of California Santa Cruz
WD Working draft
WGS Whole genome shotgun
Author's contributions
ST performed the assembly revision, the gene annotations, the LCR, SNP and chimpanzee sequence analyses and drafted the manuscript. PG performed mapping and repeat analyses in the 8p23.1 region, completed by IFL's FISH experiments. KR contributed to the sequencing process of clones in the region. AS, SA, NS, KS and MS accounted for the assembly revision and the LCR analyses. AF and RS supported the gene annotations and the DEF structure and function discussions. KH and KR were in charge for the individual haplotype and copy number determinations. MP conceived, designed and coordinated the project.
Supplementary Material
Additional File 1
Accession numbers, libraries and clone names of all clones shown in main text, Fig. 1.
Click here for file
Additional File 3
LCR of clones SCb-561b17 [GenBank:AF238378]; green/yellow signals) and CTB-415D8 [GenBank:AF228730]; red signals) visualized by FISH on metaphase chromosomes according to standard protocols [52,53]. Metaphase spread after DAPI counter stain (A) and color inversion (B). Targeted chromosomes are numbered in B. Any FISH signal is shown quadruplicated within the metaphase spread on four chromatides from two homologue chromosomes. Probe CTB-415D8 generated strong FISH signals with declining intensity at 8p23, 4p16, 11q13.3 and 3q21, corresponding to the in silico identified LCR type II paralogs (see Fig. 3, main text). Additional weaker signals at 3p12-13, 7q21, 11p15, 12p13 and 16p13.3 are also an indication for LCR type II and III paralogs. In contrast, the single locus of SCb-561b17 at 8p23 highlighted by open triangles corresponds to the unique LCR type I. Double signals, resulting from two close located targets at 4p16, 8p23 and 3q21 are marked by asterisks (compare also Additional_file_4).
Click here for file
Additional File 4
Resolving LCR type II and III "pairs" on chromosomes at approx. 900 band stage. Probe CTB-415D8 [GenBank:AF228730]; LCR type II and III) generates two clearly separated FISH signals at 4p16 and 8p23, respectively (red signals marked by double arrows): In contrast, probe SCb-561b17 [GenBank:AF238378]; LCR type I) yield a single signal at 8p23, solely (green/yellow signal, open triangle), that is co-localized with the telomeric signal of probe CTB-415D8 [GenBank:AF228730]. Signals with lower intensity are indeterminable in this picture.
Click here for file
Additional File 5
DEF aa sequences with highlighted residues (bold) different between human and chimpanzee. Boxes: human aa – human position – chimpanzee aa. All aa positions refer to the following human protein accessions: DEFA6 = [GenBank:NP_001917]; DEFA4 = [GenBank:NP_001916]; DEFA1 = [GenBank:NP_004075]; DEFA5 = [GenBank:NP_066290]; DEFB1 = [GenBank:NP_005209]; DEFB107 = [GenBank:AAM93909]; DEFB105 = [GenBank:NP_689463]; DEFB103 = [GenBank:NP_061131]; DEFB4 = [GenBank:NP_004933]; DEFB108 = [GenBank:AAN33116]. Aa for the chimpanzee orthologs ptrDEFA6, ptrDEFA4, ptrDEFA1, ptr novel defensin similar to DEFA4, ptrDEFA5 and ptrDEFB1 (ptrDEF cluster a) are deduced from the chimpanzee WD and might therefore include sequencing errors. Those for ptrDEFB1, ptrDEFB107, ptrDEFB105, ptrDEFB103, ptrDEFB4 and ptrDEFB108 (DEF cluster b) are derived from high quality BAC sequences and the appropriate traces were visually inspected. The gray shadow indicates the motif of six cystein residues (except for DEFB107 with only five cysteins).
Click here for file
Additional File 2
Synonymous and non synonymous changes by SNPs in human DEF genes and their ancestral alleles by comparison to chimpanzee sequences.
Click here for file
Additional File 6
Predicted genomic organization of the human 8p23.1 DEF locus. For simplicity only the DEF clusters (arrows) as well as LCRs type II (rectangles) are shown. Black: high quality sequence available; Gray: hypothetical structures, no finished sequence available. In addition to a 'minimal' DEF locus consisting of one a and two b clusters (middle), individual loci may have incorporated variable numbers (F, R) of additional b clusters in either orientation. The proposed duplicon consists of two inverted LCRs flanking a DEF cluster b (top/bottom). The orientation of any DEF cluster b can change either by inverted duplication/crossover (i) or homologous recombination within inverted LCRs (x, right). Moreover, the proposed genomic structure indicates that even in a 'minimal' DEF locus one or both DEF clusters may be deleted due to homologous recombination between direct LCR copies (Δ). Sequence features of the most distal LCR (II.1; see text and Fig. 3) suggest, that it may be less often involved in recombination or gene conversion events.
Click here for file
Acknowledgements
The authors thank Ivonne Heinze, Dorothee Lagemann and Uta Petz for their skillful technical assistance. We would like to thank Jennifer Ashhurst, Tim Hubbard and James Gilbert (Sanger Institute, Hinxton, UK) for their support in installing and maintaining a local client of the HAVANA pipeline. Clones 4, 5, 6, 13, 14, 24, 25, 26, 27, 28, 29 (Fig. 1 and Additional file 1) were provided by Brian Schutte (Dept. of Pediatrics, University of Iowa, USA). Chimpanzee BACs [GenBank:AC150655], [GenBank:AC150656], [GenBank:AC150657] were provided by Asao Fujiyama (RIKEN Genomic Science Center, Japan). We acknowledge the support by the German Human Genome Project grants 01KW9706 and 01KW0002.
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| 15588320 | PMC544879 | CC BY | 2021-01-04 16:39:23 | no | BMC Genomics. 2004 Dec 10; 5:92 | utf-8 | BMC Genomics | 2,004 | 10.1186/1471-2164-5-92 | oa_comm |
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BMC NephrolBMC Nephrology1471-2369BioMed Central London 1471-2369-5-181561056210.1186/1471-2369-5-18Case ReportSuccessful recovery of infective endocarditis-induced rapidly progressive glomerulonephritis by steroid therapy combined with antibiotics: a case report Koya Daisuke [email protected] Kazuyuki [email protected] Ryuichi [email protected] Masakazu [email protected] Department of Medicine, Shiga University of Medical Science, Seta, Otsu, Shiga 520-2192, Japan2 Internal Medicine II, Asahikawa Medical College, Asahikawa, Hokkaido 078-8510, Japan2004 21 12 2004 5 18 18 3 8 2004 21 12 2004 Copyright © 2004 Koya et al; licensee BioMed Central Ltd.2004Koya 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 mortality rate among patients with infective endocarditis, especially associated with the presence of complications or coexisting conditions such as renal failure and the use of combined medical and surgical therapy remains still high. Prolonged parenteral administration of a bactericidal antimicrobial agent or combination of agents is usually recommended, however, the optimal therapy for infective endocarditis associated with renal injury is not adequately defined.
Case presentation
Patient was a 24-years old man who presented to our hospital with fever, fatigue, and rapidly progressive glomerulonephritis. He had a history of ventricular septum defect (VSD). A renal biopsy specimen revealed crescentic glomerulonephritis and echocardiogram revealed VSD with vegetation on the tricuspid valve. Specimens of blood demonstrated Propionibacterium Acnes. The intensive antibiotic therapy with penicillin G was started without clinical improvement of renal function or resolution of fever over the next 7 days. After the short-term treatment of low dose of corticosteroid combined with continuous antibiotics, high fever and renal insufficiency were dramatically improved.
Conclusion
Although renal function in our case worsened despite therapy with antibiotics, a short-term and low dose of corticosteroid therapy with antibiotics was able to recover renal function and the patient finally underwent tricuspid valve-plasty and VSD closure. We suggest that the patients with rapidly progressive glomerulonephritis associated with infective endocarditis might be treated with a short-term and low dose of corticosteroid successfully.
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Background
Infective endocarditis has been classified as acute or subacute-chronic based on the clinical presentation and often presents extracardiac findings such as fever, anorexia, weight loss, malaise, and night sweats [1]. The prognosis of infective endocarditis has been shown to be strongly influenced by the complication of congestive heart failure and stroke [1]. Furthermore, glomerulonephritis, especially rapidly progressive glomerulonephritis, is also one of the complications associated with poor prognosis [2]. Infective endocarditis-induced rapidly progressive glomerulonephritis is treated with antibiotics alone, but it sometimes results in end-stage renal failure [2]. Although effective strategies to treat rapidly progressive glomerulonephritis have not been established, steroid therapy, immunosuppressive therapy, and plasmapheresis in addition to antibiotic therapy has been shown to be beneficial [3]. Here, we report a case of rapidly progressive glomerulonephritis associated with infective endocartitis in which the clinical symptoms were successfully improved by the treatment with short-term steroid therapy.
Case Presentation
A 24-year old man was admitted to our hospital because of macrohematuria and general malaise, along with insidious deterioration of renal function. The patient had been diagnosed as having ventricular septum defect (VSD) without complications and had been well six months before admission, when the patient presented a temperature of 38.0°C and a productive cough. One month before admission, the patient was admitted elsewhere because of fever, general malaise, and macrohematuria. The temperature was 38.6°C, the pulse was 120 beats per minute, and respirations were 24 times per minute. The blood pressure was 130/58 mmHg. On physical examination, the patient appeared acutely ill and pansystolic murmur of Levine III/IV was noted at the 4th left sternal border (LSB). The urine was positive for hematuria (+++) and protein (+++); the sediment contained 150–160 RBC/hpf and 10–15 granular casts/hpf. Laboratory test revealed 2.04 mg/dl of serum creatinine, 33.3 mg/dl of BUN, and 9.0 g/dl of hemoglobin. Echocardiogram demonstrated VSD and vegetation on the tricuspid valve associated with regurgitation. The patient was transferred to our hospital for evaluation of renal dysfunction.
On the first hospital day, the temperature was 39.4°C and the physical examination demonstrated pansystolic murmur at the 4th LSB again, but no lymphoadenopathy and no localizing signs for a focus of infection. Laboratory findings were: anemia (Hb 8.6 g/dl, Ht 26.5%), presence of d-dimer (10.3 mg/ml), BUN 41 mg/dl, serum creatinine 2.49 mg/dl, proteinuria 0.96 g/24 h, 24 h Ccr 30.0 ml/min, hypocomplementemia (C3 29 mg/dl, CH50 16.5 U/ml), positive for inflammatory sign (CRP 2.8 mg/dl, ESR 104 mm/h), and positive for cryoglobulinemia (Table 1). Antibodies for DNA, RNP, Sm, and myeloperoxidase and proteinase 3 ANCA were normal (Table 1). Echocardiogram again showed vegetation on tricuspid valve and TR (Figure 1), suggesting the presence of right-sided bacterial endocarditis. However, there were no histories of tooth extraction, skin injury, or drug addiction and serology for viruses including HIV-1 was negative. Specimens of blood were obtained 5 times for culture and demonstrated Propionibacterium Acnes. A renal biopsy specimen showed typical crescentic glomerulonephritis (Figure 2A) with interstitial inflammatory cell infiltration on PAS staining. Ten of eighteen glomeruli had cellular crescents without fibrocellular and fibrous crescents. Immune reactants such as C3 (Figure 2B) and Ig M were found in peripheral capillary walls and in the mesangium.
Table 1 Laboratory Values on Admission
Hematocrit (%) 26.5 IgG (mg/dl) 2707
Hemoglobin (g/dl) 8.6 IgM (mg/dl) 768
White cells (per mm3) 6100 IgA (mg/dl) 449
Total protein (g/dl) 6.8 C3 (mg/dl) 29
Total bilirubin (mg/dl) 0.3 C4 (mg/dl) 35
Aspartate aminotransferase (U/liter) 29 Rheumatoid factor (U/ml) 483
Alanine aminotransferase (U/liter) 29 Anti-dsDNA (U/ml) negative
Lactate dehydrogenase (U/liter) 666 Anti-GBM (U/ml) negative
Alkaline phosphatase (U/liter) 75 MPO-ANCA titer negative
Creatine phosphokinase (U/liter) 34
Urea nitrogen (mg/dl) 41
Creatinine (mg/dl) 2.49 Urinary sediment
Sodium (mmol/liter) 137 Erythrocytes +++
Potassium (mmol/liter) 4.7 Leukocytes -
Calcium (mg/dl) 8.1 Cylinders +
C-reactive protein (mg/dl) 2.8
Figure 1 Vegetation on tricuspid valve by echocardiography. Arrow denotes the vegetation.
Figure 2 Crescentic glomerulonephritis induced by infective carditis on PAS staining and IF. A. PAS staining demonstrated circumferential and cellular crescent formation with interstitial nephritis. B. IF demonstrated C3 positive staining in mesangial area.
Bacterial endocarditis complicated with rapidly progressive glomerulonephritis was diagnosed and the intensive antibiotic therapy with penicillin G was started, without clinical improvement of renal function (on day 7 serum creatinine 6.21 mg/dl) or resolution of fever over the next 7 days. The initial antibiotics were replaced with ampicillin and imipenem in addition to low dose of corticosteroid treatment initiated with intravenous methylprednisolone 0.5-g per day for 3 consecutive days followed by oral prednisolone 30-mg, 20-mg, and 10-mg per day each for 3 days, respectively. On day 16, laboratory test demonstrated normalization of inflammatory signs (CRP 0.5, ESR 11 mm/h), serum complements, and circulating immune complex and negative for blood culture and cryoglobulin. After the short-term treatment of low dose of corticosteroid combined with continuous antibiotics, high fever and renal insufficiency were dramatically improved (Figure 3). The antibiotic therapy lasted for 40 days until the day of the tricuspid valve-plasty and VSD closure. The patient was finally able to undergo the surgical operation, resulting in successful recovery from endocarditis-induced rapidly progressive glomerulonephritis. He is now well and laboratory test showed normal serum creatinine 0.72 mg/dl, while echocardiogram demonstrated mild regurgitation of tricuspid valve.
Figure 3 Clinical course. PCG; penicillin G, ABPC; ampicillin, IPM; imipenem
Discussion
Therapy with a bactericidal antimicrobial agent or combination of agents is usually effective [1-3], although in some cases antibiotic therapy fails, resulting in end-stage renal failure requiring dialysis therapy. Here, we present a patient complicated with VSD who developed rapidly progressive glomerulonephritis accompanying right sided-subacute bacterial endocarditis caused by Propionibacterium acnes. Although Propionibacteium acnes is considered to be contaminant, it has been found to be a pathogen of infective endocarditis in spite of its weak virulence [4]. Furthermore, case-reports of shunt nephritis associated with Propionibacterium acnes were also reported [5-7]. Membranoproliferative glomerulonephritis is the lesion most frequently seen in shunt nephritis, but in some patients in whom untreated and inadequately treated bacteremia persists, mild renal involvement may progress to the development of severe impairment such as crescents and sclerotic glomeruli, possibly through the prolonged immune-mediated pathogenesis [8]. In the present case, the prolonged exposure to the weak pathogen resulted in the development of crescentic glomerulonephritis in association with circulating immune complexes and cryoglobulinemia. Moreover, in the present case, the antibiotic therapy alone was only able to suppress circulating bacteremia, but failed to decrease the size of vegetation and the nest of bacteria. However, the clinical improvement of our case was thought to be a delayed response to continued antibiotic therapy and the addition of anticoaglants [3,9,10]. Some case reports also showed that immunosuppressive therapies such as plasmapheresis, cyclophosphamide, and azathioprine with antibiotics could recover renal dysfunction of infective endocarditis-induced crescentic glomerulonephritis [3,11]. Recently, a short-tem and low dose of anti-inflammatory corticosteroid has also shown to be potentially effective in reducing the risk of death in patients with sepsis [12,13]. In conclusion, we suggest that patients with rapidly progressive glomerulonephritis associated with infective endocarditis might be treated with a short-term and low dose of corticosteroid successfully, in the case presenting the clinical and biological evidence of immune-mediated pathogenesis with the prolonged duration of the illness.
Competing interests
The author(s) declare that they have no competing interests.
Authors' contributions
Koya D and Shibuya K cared the patients, took the picture, and wrote the paper.
Haneda M and Kikkawa R discussed the case.
Pre-publication history
The pre-publication history for this paper can be accessed here:
Acknowledgements
Written consent was obtained from the patient for publication of the study in The BMC Nephrology.
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| 15610562 | PMC544880 | CC BY | 2021-01-04 16:32:51 | no | BMC Nephrol. 2004 Dec 21; 5:18 | utf-8 | BMC Nephrol | 2,004 | 10.1186/1471-2369-5-18 | oa_comm |
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BMC Pregnancy ChildbirthBMC Pregnancy and Childbirth1471-2393BioMed Central London 1471-2393-4-271561055610.1186/1471-2393-4-27Case ReportPotential of essential fatty acid deficiency with extremely low fat diet in lipoprotein lipase deficiency during pregnancy: A case report Tsai Elaine C [email protected] Judy A [email protected] Megan Y [email protected] Gregory J [email protected] Alan [email protected] John D [email protected] Department of Medicine, University of Washington, Seattle, Washington, USA2 Veterans Affairs Puget Sound Health Care System, 1660 S. Columbian Way (152E), Seattle, Washington, USA3 Oregon Health Sciences University, Portland, Oregon, USA2004 20 12 2004 4 27 27 28 7 2004 20 12 2004 Copyright © 2004 Tsai et al; licensee BioMed Central Ltd.2004Tsai 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
Pregnancy in patients with lipoprotein lipase deficiency is associated with high risk of maternal pancreatitis and fetal death. A very low fat diet (< 10% of calories) is the primary treatment modality for the prevention of acute pancreatitis, a rare but potentially serious complication of severe hypertriglyceridemia. Since pregnancy can exacerbate hypertriglyceridemia in the genetic absence of lipoprotein lipase, a further reduction of dietary fat intake to < 1–2% of total caloric intake may be required during the pregnancy, along with the administration of a fibrate. It is uncertain if essential fatty acid deficiency will develop in the mother and fetus with this extremely low fat diet, or whether fibrates will cross the placenta and concentrate in the fetus.
Case presentation
A 23 year-old gravida 1 woman with primary lipoprotein lipase deficiency was seen at 7 weeks of gestation in the Lipid Clinic for management of severe hypertriglyceridemia that had worsened with pregnancy. While on her habitual fat intake of 10% of total calories, her pregnancy resulted in an exacerbation of the hypertriglyceridemia, which prompted further restriction of fat intake to < 2% of total calories, as well as administration of gemfibrozil at a lower than average dose. The level of gemfibrozil, as the active metabolite, in the venous and arterial fetal cord blood was within the expected therapeutic range for adults. The clinical signs and a biomarker of essential fatty acid deficiency, namely the ratio of 20:3 [n-9] to 20:4 [n-6] fatty acids, were closely monitored throughout her pregnancy. Despite her extremely low fat diet, the levels of essential fatty acids measured in the mother and in the fetal blood immediately postpartum were normal. Normal essential fatty acid levels may have been achieved by the topical application of sunflower oil.
Conclusions
An extremely low fat diet in combination with topical sunflower oil and gemfibrozil administration was safely implemented in pregnancy associated with the severe hypertriglyceridemia of lipoprotein lipase deficiency.
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Background
Primary lipoprotein lipase (LPL) deficiency is a rare autosomal recessive disorder characterized by severe hypertriglyceridemia, due to the accumulation in plasma of chylomicrons and very low density lipoproteins (VLDL) that result from the absence of LPL activity [1]. The estimated frequency of this disorder is <1 per million with the carrier frequency at about 1 in 500. Clinically significant hypertriglyceridemia usually manifests early in childhood with dietary fat intolerance, including recurrent episodes of abdominal pain and acute pancreatitis, failure to thrive, eruptive xanthoma and hepatosplenomegaly. Very severe hypertriglyceridemia during pregnancy can occur and is associated with significant maternal morbidity and fetal mortality [2-6]. Overproduction of hepatic VLDL in the presence of decreased LPL activity contributes to the marked increase in plasma triglyceride (TG) levels during pregnancy [7,8].
The management of severe hypertriglyceridemia in a pregnant patient with LPL deficiency is directed toward preventing pancreatitis in the mother and delivery of a healthy infant. Lowering of plasma TG in the prevention of pancreatitis is managed primarily by dietary fat restriction, but additional TG lowering may be required and has been reported with use of fibrates, such as gemfibrozil [6,9,10]. Two major questions arose during the treatment aimed at lowering the severe hypertriglyceridemia in this pregnant LPL-deficient patient. First, would essential fatty acid (EFA) deficiency develop in the mother and fetus as a result of severe maternal dietary fat restriction? Second, would gemfibrozil cross the placenta and concentrate in the fetus? The strategies utilized to prevent EFA deficiency and the fetal nutritional information obtained from studies at birth will address these questions and concerns.
Case presentation
Clinical history
The proband presented to her pediatrician at age 3 month with failure to thrive. She was evaluated at the Children's Hospital Medical Center in Seattle, where an elevated TG level of 14,000 mg/dl (158 mmol/L) suggested hyperchylomicronemia with LPL deficiency. Plasma post-heparin LPL activity was absent, consistent with defective catabolism of TG rich particles. Further study at the University of Washington of her post-heparin plasma at age 19 revealed absent LPL activity due to a defective LPL protein, while her hepatic lipase activity was normal [11]. She was a compound heterozygote with two missense mutations in the LPL gene (Trp86-Arg/His136-Arg) [12]. On a self-selected low fat diet (< 20% of dietary calories) she was able to maintain her TG < 1000 mg/dl (11.3 mmol/L) throughout a healthy and normal childhood and adulthood. Because of her excellent compliance with low dietary fat intake and active physical lifestyle, she had never developed clinical pancreatitis. Throughout the years, there were few episodes of mid epigastric abdominal discomfort that subsided with short periods of fasting. She had developed eruptive xanthoma briefly when oral contraceptives were used.
Pregnancy course
At the age of 23, the proband presented at week 7 of gestation for management of anticipated worsening of hypertriglyceridemia in pregnancy. She had been in excellent physical condition and had continued her routine 10–20% fat diet during the first trimester (Figure 1). Her TG was 396 mg/dl (4.5 mmol/L) at week 7 of gestation. When she retuned to the University of Washington Medical Center (UWMC) 5 weeks later, her TG levels had started to rise and despite further restriction of dietary fat to < 10% of calories, the level had risen to 3705 mg/dl (41.9 mmol/L) by week 16. At week 28, she developed her first episode of mid epigastric abdominal pain without elevated serum amylase or pancreatic lipase levels, consistent with subclinical pancreatitis. Remission of the symptoms occurred within 2–3 days of a near zero dietary fat intake as an outpatient. Subsequent reduction to less than 2% of dietary fat was implemented with a liquid formula by the following week, to decrease the risk of recurrent abdominal pain in the setting of extremely elevated TG levels (3000–6000 mg/dl [33.9–67.8 mmol/L]). Gemfibrozil at a low dose of 300 milligram (mg) twice a day was also initiated at week 29 to prevent a further upward trend in TG in the third trimester. The dosage was increased to 300 mg three times a day a week later. This appeared to stabilize her TG in the 5000–6000 mg/dl (56.5 – 67.8 mmol/L) range until week 34 when she developed severe abdominal pain. Initial evaluation at her local hospital revealed a TG level of 6,050 mg/dl (68.4 mmol/L) and elevated pancreatic lipase (680 IU/dl) and amylase (1336 IU/L). She was transferred and admitted to the UWMC and placed on intravenous fluids. Two days later, her pancreatitis subsided and she was placed back on the <2% fat diet and 900 mg/day of gemfibrozil. A second episode of pancreatitis a few days later prompted re-admission to UWMC for labor induction at the 35th week of gestation. A 5 lb 3 oz baby girl with a 5-minute Apgar score of 9 was delivered vaginally. A short time after the delivery, the baby was briefly intubated for about 48 hours due to respiratory distress but did well subsequently. The patient's plasma TG rapidly decreased to 2015 mg/dl (22.8 mmol/L) within the first postpartum 24 hours, accompanied by improved abdominal symptoms. Resumption of low fat solid food brought back the symptoms of pancreatitis and she was placed back on the IV fluids followed by a more gradual incremental introduction of oral intake. Along with 1,200 mg/day of gemfibrozil, she had complete resolution of abdominal symptoms by postpartum day-8 and eventual resolution two weeks after discharge of peri-pancreatic fluid accumulation demonstrated by CT imaging studies. Now, 11 years later, the proband and her daughter are both healthy and doing well. The proband's TG levels are back to baseline and stable on 10–20% fat diet. Her daughter has had normal TG and cholesterol levels on regular diet.
Figure 1 Serum triglyceride level and corresponding dietary fat intake and gemfibrozil administration during pregnancy *Dietary fat was expressed as % of total caloric intake.
Eruptive xanthomas, which are associated with hypertriglyceridemia, developed on the proband's buttocks at week 20 and subsequently spread to the upper arms and medial aspects of thighs as her TG levels rose. Peculiarly, palmar xanthoma, typical of remnant removal disease (type III hyperlipidemia), also developed at week 27. A concomitant ex vivo investigation of the mechanisms contributing to palmar xanthoma in the proband, who has an apo E 4/E3 phenotype, revealed an enhanced macrophages uptake of the TG rich lipoproteins as a result of an unusual enrichment of these lipoproteins with apo E during pregnancy [13].
Gestational EFA profiles
Because of concern for unfavorable fetal neurological development due to EFA deficiency, EFA profile was monitored in the mother at each visit starting at gestational week 23. The initial analyses were performed at the Clinical Nutrition Research Unit (CNRU), Harborview Medical Center campus of the University of Washington. After separation from cells, the fatty acids from the phospholipid fraction of the plasma were extracted and subsequently measured by capillary gas chromatography. In addition to the total amount and % of each FA, the ratio of eicosatrienoic acid (ETA, 20:3(n-9)) to arachidonic acid (AA, 20:4(n-6)) was calculated (Figure 2). This ratio was used as an index to the patient's EFA status. By week 26, the ratio had risen from 0.032 to 0.052 (Fig 2), suggesting a trend to a less EFA abundant state [14]. Topical application of sunflower oil containing large amounts of polyunsaturated fatty acids (PUFA) was initiated as a non-oral route for supplementing EFA because of its reported success in EFA deficient subjects [15]. With 460 mg per day of sunflower seed oil (approximately 240 mg of linoleic acid) applied to her arms and trunk, her EFA profile appeared to improve with the ratio stabilizing at 0.08 at 31 weeks. A peak to a ratio of 0.09 occurred at week 34 possibly due to irregular uses of topical PUFA (Figure 2).
Figure 2 Essential fatty acid profile in maternal blood EFA: essential fatty acids, from sunflower seed oil. Measurement was made in the phospholipid fractions. 20:3(n-9): eicosatrienoic acid (ETA). 20:4(n-6): arachidonic acid (AA).
Immediately postpartum, placental fetal blood and maternal plasma was obtained for total fatty acid analysis (Figure 3). These FA were measured by capillary gas/liquid chromatography at the Oregon Health Sciences University and expressed as % of total FA. In spite of low levels of n-6 and n-3 fatty acids in maternal blood and similarly decreased levels of PUFA precursors (linoleic [LA] and α-linolenic acid [ALA]) in cord blood samples compared to the reported normal reference range [16], there were abundant long chain PUFA (such as arachidonic acid [AA]) in the fetal circulation. This suggested that either the topical application of sunflower seed oil during the late stage of pregnancy prevented EFA deficiency or that the fetus had increased capacity for obtaining EFA from the mother.
Figure 3 Fatty acid composition in maternal and cord plasma LA: linoleic acid. ALA: α-linolenic acid. ETA: eicosatrienoic acid. AA: arachidonic acid.
Gemfibrozil in fetal circulation
To examine whether there might be excessive accumulation of gemfibrozil in the newborn baby, fetal cord blood was obtained at the time of delivery and gemfibrozil levels and its active compound, metabolite III, were measured. The analysis was performed as a courtesy of the Research Lab at the Parke-Davis Pharmaceutical (Ann Arbor, Michigan) by high performance liquid chromatography (HPLC) and revealed similar concentrations of the drug and its active metabolites in both umbilical vein and artery, which were within the normal reference range for adults (Figure 4).
Figure 4 Gemfibrozil metabolite III levels in the fetal cord blood *Ref. range: 0.5 – 40 μg/ml
Conclusions
Children with primary LPL deficiency can be effectively managed on fat-restricted diets and grow normally into adulthood. However, they can present with extreme elevation of TG levels with serious acute pancreatitis. This LPL-deficient subject developed severe hypertriglyceridemia in early pregnancy, with eruptive xanthomas and pancreatitis. With the diligent efforts from the patient, her family, and a team of specialists in lipid metabolism, dietetics, high-risk obstetrics and gastroenterology, a successful outcome was achieved. Outcome goals were clearly set at the onset of her pregnancy care, including nutritional management of the expected rise in triglyceride levels associated with the estrogen surge of pregnancy to prevent acute pancreatitis, and avoidance of clinical EFA deficiency in both the mother and the fetus.
Pregnancy and hypertriglyceridemia
Pregnancy-induced hypertriglyceridemia is estimated to be the cause in 4–6% of all pancreatitis cases during pregnancy, while most cases result from cholelithiasis [4]. Hypertriglyceridemia-related pancreatitis in pregnancy also has been reported due to other causes of severe hypertriglyceridemia [17]. Successful management requires early detection of signs and symptoms of acute pancreatitis often accompanied by increases in serum lipase and amylase levels and characteristic findings in imaging studies. Once the pancreatitis is suspected, these individuals should be admitted for aggressive medical management including intravenous hydration concurrent with no oral intake of solids or liquids. Obstructive processes in the biliary system need to be ruled out specifically since treatment modalities are quite different.
Pregnancy and LPL deficiency
Pregnancy is a well known situation in which the physiologic estrogen surge profoundly alters the TG-rich lipoprotein metabolism, resulting in a gradual rise in TG levels over the course of non-complicated pregnancy, peaking at the level of 200–300 mg/dl (2.26 – 3.39 mmol/L) at term [17]. During the first two trimesters of pregnancy, adipose fat storage, as maternal fuels, occurs in preparation for an active transfer of maternal glucose, amino acids, and free fatty acids (FFA) across the placenta for accelerate fetal growth in late phase of gestation [18]. In late gestation, adipose tissue lipolysis is greatly augmented generating FFA and glycerol, for further hepatic VLDL production, contributing to the flux of circulating TG-fatty acids in pregnancy [18,19]. Greater concentration of chylomicrons from dietary fat as a result of maternal hyperphagia in late pregnancy also contributes to the circulating TG-rich lipoprotein pool [18,19], and provides alimentary substrates for VLDL production [20,21]. LPL activities in the liver, heart, and particularly adipose tissue are, however, reduced by an estimated total of 85% [19,22] in late gestation. Concomitantly, clearance of circulating TG-rich lipoproteins is reduced in late pregnancy. Hepatic lipase activity is decreased as well and could explain the observation of parallel TG-enrichment of LDL and high-density lipoproteins (HDL) particles during normal gestation. All these changes take place to ensure a stable supply of fuel substrates across the placenta for normal fetal development while preserving maternal metabolic homeostasis [18,19].
Very low fat diet and EFA deficiency
Arachidonic acid [AA, 20:4(n-6)], an important precursor of the prostaglandin compounds, cannot be synthesized de novo from FFA in mammals and must be derived from another EFA in the diet, namely linoleic acid [LA, 18:2(n-6)]. In the case of life long low oral fat intake, as in our patient, clinical EFA deficiency might occur with depletion of n-3 and n-6 FA stored in adipose tissue. Therefore, her source of EFA would be entirely from recent dietary intake and deficiency might occur sooner than in individuals with normal LPL and abundant EFA storage [23]. Eicosatrienoic acid [ETA, 20:3(n-9)], on the other hand, is not an EFA because it can be synthesized in mammals from palmitic acid [16:1(n-9)]. In the event of diminishing pool of both n-3 and n-6 fatty acids due to absence or deficiency in the diet, more ETA are produced and the amount parallels the degree of deficiency [24-26]. EFA deficiency syndrome commonly results from a combined deficiency in both n-3 and n-6 fatty acids. A ratio of ETA to AA > 0.2, is suggestive of EFA deficiency [24-26]. Clinical manifestations in EFA deficiency are unusual on a diet containing > 2% of the calories as linoleic acid [27]. While the clinical symptoms of dryness and desquamation of the skin are annoying at best, a more serious consequence could be impaired fetal brain and visual development. The proband did not develop signs of clinical EFA deficiency, nor did the ratio of 20:3(n-9) to 20:4(n-6) exceed 0.2 at any stage of her pregnancy, although an upward trend did occur. Additionally, the report that infants fed a formula low in EFA grew poorly and developed multiple medical complications was a concern [28]. Several reports have documented a reversal of biochemical and clinical manifestations of EFA deficiency in infants and adults by cutaneous administration of EFA-rich oil, such as sunflower oil [29-35]. Therefore, application of sunflower oil to the proband's skin was initiated at week 25 and may have had prevented progression of EFA deficiency in mother, as suggested by the stabilization of the 20:3(n-9) to 20:4(n-6) ratio. Surprisingly, we found low levels of n-3, n-6, and PUFA precursor levels in the cord blood taken during the delivery, and yet there was abundant long chain PUFA in the infant circulation. This would suggest that other adaptive mechanisms were involved in maintaining the critical levels of long chain EFA in fetal circulation in the face of inadequate maternal supply.
Use of gemfibrozil in LPL deficiency
Use of TG lowering drugs, such as gemfibrozil (a fibrate), can be used to directly lower the triglyceride level in the prevention of acute pancreatitis. Pregnancy induces hepatic production of TG-rich VLDL and may respond to fibrates through inhibition of hepatic production of VLDL. Gemfibrozil, which is an FDA category C drug, has not been observed to be associated with adverse drug effects in reports of pregnancy-related severe hypertriglyceridemia [6,10,36,37]. During the last few weeks of her gestation low dose gemfibrozil in our subject seemed to have stabilized her TG level (Fig 1), which might otherwise have continued to rise due to the estrogen effect on hepatic VLDL production in the third trimester. A lower than usual dose of gemfibrozil was used due to the concern for excess placental transfer of its metabolites that has been reported in pregnant cats [38]. Analysis of the parent compound and metabolites did not detect excessive accumulation in the fetal cord circulation in contrast to the reports in animal models. While this observation needs to be independently confirmed, adverse drug effects in the infants born to mothers on gemfibrozil or other fibrates have not been reported. Moreover, gemfibrozil has been used and appears to be free of short-term side effects in pediatric populations [39-42]. Therefore, low dose gemfibrozil may be safe for use during the last trimester in hyperlipidemic patients at high risks for acute pancreatitis.
In conclusion, a successful pregnancy outcome was achieved in our LPL deficient patient, confirming previous reports [6,43] that aggressive lipid lowering strategies under the supervision of experienced health care providers works in this high risk setting. Although the patient developed pancreatitis during her pregnancy, the use of an extremely low fat diet together with a fibrate helped limit the increase in the triglycerides, and her pancreatitis was neither life threatening nor adversely affected fetal survival. Sunflower oil applied topically may have helped prevent EFA deficiency in both the mother and fetus. Use of gemfibrozil did not appear to have any adverse effect on the child. Thus, the use of these two therapeutic approaches appears safe and appropriate in the medical management of pregnancy-associated severe hypertriglyceridemia, where EFA deficiency and recurrent pancreatitis are major concerns.
Competing interests
The author(s) declare that they have no competing interests.
Authors' contributions
ECT was a senior fellow in Metabolism, Endocrinology and Nutrition and drafted the manuscript. JAB was the dietitian. Both MSV and GJA contributed to the measurement of fatty acids. GJA was also a consulting scientist in fatty acid metabolism. AC and JDB were the faculty associated with the case at the UW GCRC. JDB conceived of the research, supervised the fellow, and coordinated the manuscript revisions.
Pre-publication history
The pre-publication history for this paper can be accessed here:
Acknowledgments
ECT is supported by grants from the American Diabetes Association and Seattle Epidemiologic Research and Information Center. A portion of these studies was performed in the University of Washington General Clinical Research Center NIH MO-1 RR37, and by the Clinical Nutrition Research Unit DK035816. Written consent was obtained from the patient for publication of study.
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| 15610556 | PMC544881 | CC BY | 2021-01-04 16:32:03 | no | BMC Pregnancy Childbirth. 2004 Dec 20; 4:27 | utf-8 | BMC Pregnancy Childbirth | 2,004 | 10.1186/1471-2393-4-27 | oa_comm |
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BMC GenomicsBMC Genomics1471-2164BioMed Central London 1471-2164-6-11562740410.1186/1471-2164-6-1Research ArticleBioinformatic mapping of AlkB homology domains in viruses Bratlie Marit S [email protected]øs Finn [email protected] Department of Cancer Research and Molecular Medicine, Faculty of Medicine, MTFS, Norwegian University of Science and Technology, N-7489 Trondheim, Norway2005 3 1 2005 6 1 1 17 8 2004 3 1 2005 Copyright © 2005 Bratlie and Drabløs; licensee BioMed Central Ltd.2005Bratlie and Drabløs; 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
AlkB-like proteins are members of the 2-oxoglutarate- and Fe(II)-dependent oxygenase superfamily. In Escherichia coli the protein protects RNA and DNA against damage from methylating agents. 1-methyladenine and 3-methylcytosine are repaired by oxidative demethylation and direct reversal of the methylated base back to its unmethylated form. Genes for AlkB homologues are widespread in nature, and Eukaryotes often have several genes coding for AlkB-like proteins. Similar domains have also been observed in certain plant viruses. The function of the viral domain is unknown, but it has been suggested that it may be involved in protecting the virus against the post-transcriptional gene silencing (PTGS) system found in plants. We wanted to do a phylogenomic mapping of viral AlkB-like domains as a basis for analysing functional aspects of these domains, because this could have some relevance for understanding possible alternative roles of AlkB homologues e.g. in Eukaryotes.
Results
Profile-based searches of protein sequence libraries showed that AlkB-like domains are found in at least 22 different single-stranded RNA positive-strand plant viruses, but mainly in a subgroup of the Flexiviridae family. Sequence analysis indicated that the AlkB domains probably are functionally conserved, and that they most likely have been integrated relatively recently into several viral genomes at geographically distinct locations. This pattern seems to be more consistent with increased environmental pressure, e.g. from methylating pesticides, than with interaction with the PTGS system.
Conclusions
The AlkB domain found in viral genomes is most likely a conventional DNA/RNA repair domain that protects the viral RNA genome against methylating compounds from the environment.
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Background
The purpose of this study has been to identify domains with homology to AlkB in viral genomes, in order to get a better understanding of distribution and possible function of such domains. The AlkB protein of E. coli, and probably most of its homologues, is involved in repair of alkylation damage in DNA and RNA. It repairs 1-methyladenine and 3-methylcytosine by oxidative demethylation and direct reversal of the methylated base back to its unmethylated form. Recently the protein was identified as a member of the 2-oxoglutarate (2OG)- and Fe(II)-dependent oxygenase superfamily [1-3]. The catalytic reaction requires molecular oxygen, Fe2+ and 2-oxoglutarate, which is subsequently converted into succinate, CO2 and formaldehyde [4].
The 2OG-FeII oxygenase superfamily is widespread in Eukaryotes and bacteria [1], and is currently the largest known family of oxidising enzymes without a heme group [5]. The 3D structure of several of these oxygenases is known, and they share a common fold with a structurally conserved jelly roll β-sheet core with flanking α-helices. Very few residues are totally conserved across these structures, basically just the residues involved in coordination of the Fe(II) ion and the 2-oxoglutarate.
AlkB-like genes are widespread in most types of organisms except Archaea. However, whereas bacteria normally have just one or at most two AlkB homologues [6], multicellular Eukaryotes tend to have several homologues. In the human genome at least 8 different AlkB homologues (ABHs) have been identified [7]. These homologues seem to have slightly different properties with respect to substrate preference and subcellular localisation, and this may be a reason for the proliferation of ABHs e.g. in humans. However, a detailed functional mapping of all ABHs has not yet been carried out.
A sequence alignment of known ABHs identifies very few residues as totally conserved, basically just a HxD motif, a H and a RxxxxxR motif. These residues are also conserved in the more general 2OG-FeII oxygenase superfamily as described above, except for the final R. The first three residues (HxD and H) are involved in Fe(II)-coordination, whereas the first R is involved in 2OG-coordination. The final R is most likely involved in AlkB-specific substrate binding.
In addition to DNA repair, it has been shown that E. coli AlkB and the human AlkB homologue hABH3 may be involved in RNA repair. When expressed in E. coli both AlkB and hABH3 reactivate methylated RNA bacteriophage MS2 in vivo. This illustrates that direct repair may be an important mechanism for maintenance of RNA in living cells [4]. RNA repair proceeds by the same mechanism as DNA repair. Repair of damaged RNA was previously considered very unlikely, due to the natural redundancy of RNAs in a cell [8]. However, RNA is essential for cell function: unrepaired RNA can lead to miscoded or truncated proteins, and alkylated RNA could signal cell cycle checkpointing or apoptosis [9]. Consequently the occurrence of RNA repair does not come as a great surprise. The mechanism of direct reversal of methylation as used by AlkB homologues is particularly important for RNA repair, as it means that single-stranded regions may be repaired without introducing strand breaks. Repair of alkylation damage in DNA and RNA has recently been reviewed [10].
AlkB homologues have also been found in plant viruses. It has been suggested that methylation may be used in host-mediated inactivation of viral RNAs, and that AlkB homologues in some plant viruses may be used to counteract such defence mechanisms [1]. However, no detailed study of this has been published.
The research project reported here has focused on a better understanding of the distribution and potential function of putative AlkB homology domains by using in silico mapping of viruses in which such domains have been found, as well as related viruses.
Results
The general mapping strategy of the project was to identify viral genomes with AlkB homology domains, identify common features of these genomes, and subsequently find additional genomes with similar features, but without AlkB homology domains. This data set could then be used to analyse the properties and distribution of AlkB-like domains in viruses, as a basis for generating hypotheses about the evolution and function of these domains.
Identification of relevant viral protein sequences
The PSI-Blast search for viruses in the NCBI nr protein sequence database was initiated with ALKB_ECOLI (NCBI gi113638), restricted to residues 110 to 210 and using the default inclusion threshold of 0.005 on E-values. The chosen residue range corresponds to the most conserved region in AlkB homologues [10].
The PSI-Blast search converged after 4 iterations, and included 43 hits below the 0.005 inclusion threshold, from 22 different ssRNA positive-strand viruses. The AlkB homologues were found in viruses belonging to Allexi, Ampelo, Carla, Fovea, Mandari, Potex, Tricho and Vitiviruses, all of which are known to infect plants [11].
In all of these viruses the AlkB domain is a part of the replicase polyprotein, which normally consists of a viral methyltransferase domain (MT), a viral helicase domain (HEL) and a RNA-dependent RNA polymerase domain (RdRp). Therefore separate PSI-Blast searches for the individual components of the replicase polyprotein were also initiated. All searches were done with PSI-Blast using the default inclusion threshold (E-value of 0.005). The searches for MT and HEL domains were initiated using residue ranges 449–841 and 1938–2178 respectively from Grapevine leafroll-associated virus 3 (Ampelovirus, NCBI gi29650261). The search for RdRp was initiated with residue range 1361–1798 from Soil-borne cereal mosaic virus (Furovirus, NCBI gi11546056). These sequences were chosen based on the output from the previous AlkB search. This gave a library of protein sequences with either AlkB, MT, HEL or RdRp domains, the general composition of which is illustrated in Figure 1. From this library a subset was generated, consisting of all sequences containing MT, HEL and RdRp domains. This included processed (cleaved) polyprotein sequences where RdRp was found as a separate subsequence. However, whenever possible, the protein sequence corresponding to the genomic sequence was used. The final library, described in Table 1 and in Additional file 1, consisted of 146 sequences from a large number of different viruses.
Figure 1 PSI-Blast search results shown as a Venn diagram. Initial searches using methyltransferase, helicase and RdRp domains retrieved 163, 175 and 237 sequences, respectively. A total of 146 sequences contained all three domains, and 22 of these also contained an AlkB domain.
Table 1 Summary of Pfam domains
Classification Pfam domainsb
Host Family Genus na AB OT PC A1 ot
Plant Bromoviridae Alfamovirus 1
Bromovirus 4 4
Cucumovirus 3
Ilarvirus 11
Oleavirus 1
Unassigned 1
Closteroviridae Ampelovirus 4 2
Closterovirus 5 1
Crinivirus 4
Flexiviridae 1 Allexivirus 5 1
Mandarivirus 1 1
Potexvirus 17 3
Flexiviridae 2 Capillovirus 3 3 3
Carlavirus 6 5 5 6
Foveavirus 6 5 6 6
Trichovirus 2 2 2
Vitivirus 2 2
Unassigned 2 1 2 2
Tymoviridae Maculavirus 1 1
Marafivirus 3 3 3
Tymovirus 7 7 3
Unassigned Benyvirus 2 2
Furovirus 4
Hordeivirus 1
Idaeovirus 1
Pecluvirus 1
Pomovirus 4
Tobamovirus 18
Tobravirus 3
Unassigned 2
Invertebrate Tetraviridae Betatetravirus 1
Unassigned 1
Vertebrate Togaviridae Alphavirus 17 17 2
Unassigend Hepatitis E-like 2 2
a Number of sequences.
b Number of sequences with each domain type, excluding the common MT, HEL and RdRp domains (AB – AlkB, OT – OTU, PC – Peptidase C, A1 – A1pp, ot – other).
The library of protein sequences was screened for known domains in Pfam. This identified Pfam domains Viral_helicase1 and RNA_dep_RNApol2 in all sequences, corresponding to HEL and RdRp domains, respectively. In addition Vmethyltransf and 2OG-FeII_Oxy, corresponding to MT and 2OG-FeII oxygenase (AlkB) domains, were identified in several sequences. However, for sequences from Flexiviridae and Tymoviridae there was no clear identification of any MT domain by Pfam, although they had been retrieved by PSI-Blast in a search for MT domains. Therefore HMMER was used to build a Pfam type profile for these sequences. A PSI-Blast search was initiated using residues 1–500 of Potato virus M (Carlavirus, NCBI gi9626090). Twelve representative sequences were selected from the search output, covering Carla, Fovea, Potex, Allexi, Capillo and Maculavirus. Subsequences representing the conserved region according to the PSI-Blast alignment, corresponding to residues 35–378 of the query sequence, were aligned using ClustalX, and a Pfam type profile was generated and calibrated using tools from the HMMER package. The resulting profile was able to identify putative methyltransferase domains in all Flexiviridae and Tymoviridae sequences in the data set.
Other Pfam domains – Peptidase_C21, C23, C33, C34, C35 and C41, A1pp and OTU – were also identified in subsets of sequences. A1pp is a member of the Appr-1-p processing enzyme family, and the domain is found in a number of otherwise unrelated proteins, including non-structural proteins of several types of ssRNA viruses. OTU is a member of a family of cysteine proteases that are homologous to the ovarian tumour (otu) gene in Drosophila. Members of this family are found in Eukaryotes, viruses and pathogenic bacteria.
Phylogenetic analysis
The MT, HEL and RdRp domains identified by Pfam as described above were extracted from the library sequences, aligned by ClustalX, and combined into a new alignment consisting of only these domain regions. This turned out to be necessary in order to get robust alignments. The intervening regions between the conserved domains are extremely variable in these sequences, and this tended to confuse alignment programs in the sense that conserved regions were not correctly aligned. The combined sequence alignment of domains from Closteroviridae, Flexiviridae and Tymoviridae was then used as input for building a phylogenetic tree with MEGA2. The final tree is shown in Figure 2, with polyproteins containing AlkB-like domains indicated.
Figure 2 Unrooted phylogenetic tree for Flexiviridae 1 and 2, Tymoviridae and Closteroviridae. Sequences are labelled with genus and NCBI gi accession number. Bootstrap values ≥ 80 are shown. Sequences with AlkB domains are indicated with black dots.
A second alignment was generated from all sequences with AlkB-like domains, using only the regions corresponding to MT, AlkB, HEL and RdRp Pfam domains. The domains were aligned individually, and the combined alignment was used as input for MEGA2. However, this data set did not give a reliable phylogeny (data not shown), and the separate domains of this alignment were therefore analysed individually and compared. This analysis is summarised in Table 2. For each domain a bootstrapped neighbour-joining (NJ) tree was generated with MEGA2. The average bootstrap support value over all branches was computed for each tree, and this value was clearly lower for the AlkB tree compared to the other trees. A maximum likelihood (ML) tree was generated for each domain with Tree-Puzzle. This showed the same trend, the likelihood values indicated that the AlkB tree was clearly inferior to the other trees. The individual trees were then compared using the quartet-based strict joint assertions (SJA) measure as implemented in the Component software package. Both the NJ and ML trees showed the same trend. The MT, HEL and RdRp domains gave similar tree structures, with SJA values between 0.053 and 0.161 for NJ trees and between 0.058 and 0.092 for ML trees when they were compared to each other. The AlkB domain gave a significantly different tree structure, with SJA values from 0.456 to 0.524 for NJ trees and from 0.258 to 0.317 for ML trees when compared to the MT, HEL and RdRp trees (the actual trees are given in Additional file 2). For comparison the SJA values for comparing the corresponding NJ and ML trees for MT, AlkB, HEL and RdRp were 0.054, 0.000, 0.040 and 0.003, respectively, showing that the NJ and ML procedures gave almost identical tree structures. Day has estimated expectation values and standard deviations for various distance measures (including SJA) for comparison of random trees [12]. The SJA values shown in Table 2 for comparisons between MT, HEL and RdRp NJ trees were 14.2 – 17.1 standard deviations from the expectation value of 0.665 for a tree with 22 nodes, whereas the corresponding values for the AlkB NJ tree were 4.4 – 5.4 standard deviations from the expectation value. Similar ranges were observed for the ML trees as well as for alternative distance measures, e.g. the Symmetric Difference (SD) measure (data not shown). Although this means that the SJA value for comparing AlkB trees to MT, HEL and RdRp trees were significantly better than for random trees, it also shows that the MT, HEL and RdRp trees were clearly more similar to each other than to the AlkB tree.
Table 2 Strict joint assertions distances for NJ and ML trees
ML\NJa MT AlkB HEL RdRp log Lb BS (%)c ID (%)d
MT - 0.488 0.161 0.053 -14068 85 27
AlkB 0.263 - 0.524 0.456 -4016 35 38
HEL 0.058 0.317 - 0.117 -10425 87 28
RdRp 0.062 0.258 0.092 - -14543 91 37
a Strict joint assertions (SJA) values based on quartets as computed by Component for ML trees (lower left) and NJ trees (upper right). SJA is defined as resolved and different quartets divided by all resolved quartets.
b The likelihood value from Tree-Puzzle.
c Average bootstrap value for all branches in each NJ tree.
d Average sequence identity for all pairs of sequences in each alignment.
The alignment of the AlkB domain seemed to be of comparable quality to the other alignments. In fact the AlkB domain had the highest average pairwise sequence identity, as seen in Table 2 (see Figure 3 for the actual alignment). In other words, these AlkB domains were as similar to each other as the other three domains with respect to sequence identity, but they did not represent a consistent evolutionary history when compared to the other domains of this polyprotein. This may indicate that the AlkB domains have evolved separately from the other domains, and possibly as several independent instances.
Figure 3 Multiple alignment of sequence regions corresponding to the AlkB domains. The alignment was generated with ClustalX. The residues involved in coordination of the essential Fe2+ ion are completely conserved, except in one of the Vitivirus sequences. These residues are the HxD motif, a single H, and the first R in the RxxxxxR motif. The function of the remaining conserved residues is unclear, but at least some of them may be involved in coordination of the substrate [10].
The degree of co-evolution was analysed by computing pairwise distances between sequence regions in the alignment of MT, AlkB, HEL and RdRp domains described above. In Figure 4 selected results are shown as scatter plots, where the Blosum 50 score value between e.g. the MT domains in a pair of sequences is plotted against the score value for AlkB domains in the same pair of sequences. Plots for the MT, HEL and RdRp domains show that they are strongly correlated for MT vs. RdRp (r2 = 0.95), MT vs. HEL (r2 = 0.87) and HEL vs. RdRp (r2 = 0.81). The plot of the AlkB domain vs. these three domains for the same set of sequences shows a very low degree of correlation for AlkB vs. RdRp (r2 = 0.10), AlkB vs. MT (r2 = 0.12) and AlkB vs. HEL (r2 = 0.16).
Figure 4 Pairwise distances between sequence regions corresponding to methyltransferase (MT), RdRp and AlkB domains. Each data point corresponds to e.g. RP-RP and MT-MT distances for the same pair of sequences, and sequences showing similar evolutionary distance in these two regions will fall on the diagonal. The pairwise distances were estimated from multiple alignments using the Blosum50 score matrix [47]. Trend lines were estimated with Excel. The trend line for AlkB vs. RdRp is heavily influenced by the point at (675, 670). It represents two Foveavirus sequences (NCBI gi3702789 and gi9630738), they are 98% identical over the full polyprotein sequence.
As mentioned above the genome organisation of these replicase polyprotein sequences seems to be very flexible. In order to analyse domain organisation the location of identified Pfam domains were plotted for a number of sequences, as shown in Figure 5.
Figure 5 Location of Pfam domains in the variable region of Flexiviridae 2 sequences. The regions have been extracted directly from Pfam output, and sequences and regions are drawn to scale. The black bar at each end of a motif indicates that a full-length motif has been found, for partial motifs the bar at the truncated end would be missing.
Similarity of viral AlkB domains to other AlkB sequences
The results described above may indicate that the AlkB domains have been integrated into the replicase polyprotein relatively recently (see Discussion). In order to test for potential sources selected AlkB domains were compared to non-viral sequences. PSI-Blast was used to search the NCBI nr database, removing all viral hits in the final search report. Most of the remaining top-scoring hits were from bacteria. This included two different strains of Xanthomonas, X. axonopodis pv citri and X. campestris pv campestris. Xanthomonas attacks plants such as citrus, beans, grapevine, rice and cotton [13]. The search also returned high-scoring hits from another plant pathogen, Xylella fastidiosa. This bacterium infects a great variety of plants, including grapevine, citrus, periwinkle, almond, oleander and coffee [14].
Potential similarities in variable regions
Pfam searches obviously will only identify known domain types in protein sequences. In order to identify potential similarities in regions that were not recognised by Pfam, systematic PSI-Blast searches were performed, using the polyprotein regions between the MT and HEL domains and searching against the NCBI database of reference sequences [15], excluding all viral entries. A maximum of 5 PSI-Blast iterations were allowed, with an inclusion threshold of 0.005. The expected homologues of the AlkB-domain were found with high confidence, as most of the E-values were < 1 × 10-50. Homologues of typical viral domains like the viral peptidases were obviously not found, as all viral database entries were excluded. Very few new similarities were found by these searches. Pepper ringspot virus (Tobravirus, NCBI gi20178599) showed significant similarity to site-specific DNA-methyltransferase from Nostoc sp (E = 1 × 10-74), as well as other cytosine 5C-specific DNA methylases. Bamboo mosaic virus (Potexvirus, NCBI gi9627984) showed similarity to aggregation substance Asa1 from Enterococcus faecalis (E = 6 × 10-34). A small number of additional similarities seemed to be caused by biased sequence properties (e.g. proline-rich regions), and were probably not significant. This included matches against mucin and cadherin-like proteins from Homo sapiens and multidomain presynaptic cytomatrix protein (piccolo) from Rattus norvegicus. In general the variable regions seemed to be truly variable, with very little similarity to other proteins, except for the Pfam domains already identified.
Loss of domains in related polyprotein sequences
As seen in Figures 2 and 5, some closely related sequences are lacking specific domains in the sense that HMMER does not find a significant similarity to the Pfam entries for these domains. In order to understand the degree of sequence variation associated with this domain loss, as well as the general sequence variation in conserved vs. non-conserved regions of typical polyproteins, several dot plots were generated. The dot plot for two Carlavirus sequences, Potato virus M (NCBI gi9626090) and Aconitum latent virus (NCBI gi14251191), is shown in Figure 6. The dot plot confirms that these two sequences are closely related in the MT, HEL and RdRp domains. However, there are significant differences in the region between MT and HEL. Potato virus M is lacking the AlkB domain whereas Aconitum latent virus is lacking the OTU domain. As seen from the dot plot, short regions of similarity close to the diagonal shows that both domains may have been present in an ancestral sequence. However, this region shows a high degree of sequence variation, and as indicated by the dot plot they are almost exclusively mutations. Non-essential or non-functional domains are probably rapidly lost. In this particular case, none of the typical AlkB motifs seem to be conserved in Potato virus M, indicating that this indeed is a non-functional AlkB domain.
Figure 6 Dot plots for Potato virus M (NCBI gi9626090) and Aconitum latent virus (NCBI gi14251191). To the left the full sequences are shown, using the program default for similarity threshold, and to the right the region with AlkB, OTU and peptidase integration, using a slightly lower (more sensitive) threshold for sequence similarity. The Pfam regions corresponding to MT (magenta), AlkB (red), OTU (green), peptidase (blue), HEL (yellow) and RdRp (cyan) domains are indicated.
Discussion
The N-terminal domains of Flexiviridae and Tymoviridae are methyltransferases
As described above the Pfam methyltransferase motif (Vmethyltransf) did not match any of the putative methyltransferase domains of Flexiviridae and Tymoviridae, despite the fact that they had been identified via PSI-Blast searches starting with known methyltransferases. Therefore an additional Pfam-type profile was generated. It is obviously a possibility that these domains in Flexiviridae and Tymoviridae are not methyltransferases, and that they are false positives from PSI-Blast. However, the essential residues of a typical viral methyltransferase motif are conserved in the alignment of these domains (data not shown) [16]. In Bamboo mosaic virus, which belongs to Flexiviridae, the residues H68, D122, R125 and Y213 have been identified as putative active site residues with similarity to the Sindbis virus-like methyltransferase [17], and it has been demonstrated that this region of the Bamboo mosaic virus has methyltransferase activity, as it catalyses the transfer of a methyl group from S-adenosylmethionine (AdoMet) to GTP or guanylylimidodiphosphate (GIDP). The corresponding sequence positions are almost completely conserved in the alignment of Flexiviridae and Tymoviridae N-terminal domains. This is most likely significant, as only 7 positions in total are completely conserved in this alignment, which means that the majority of the conserved positions are known to be essential for methyltransferase activity. Work e.g. by Hataya et al. seems to support the assumption that this sequence region is a methyltransferase domain [18]. It therefore seems likely that all the sequences with AlkB domains also contain functional MT, HEL and RdRp domains. The MT domains are probably involved in capping of genomic and subgenomic RNA [19].
The viral AlkB domains are most likely functional
Based on the bioinformatic evidence generated here, it seems reasonable to assume that the viral AlkB domains identified by Pfam are functional. All the essential residues found in 2-oxoglutarate- and Fe(II)-dependent oxygenases are conserved, in particular the putative Fe2+ coordinating H, D and H residues at alignment positions 19, 21 and 91 of Figure 3, and the 2-oxoglutarate coordinating R at position 100. The conserved R at position 106 is also very characteristic of AlkB homologues [10]. The fact that all AlkB-like domains identified in these viral genomes are full-length, compared to the Pfam profile, also seems to support the hypothesis that these domains are functional.
The AlkB domains are found in a subset of viral genomes
The Pfam searches show that AlkB domains are found only in a subset of the viral genomes. This subset is phylogenetically consistent (see Figure 2), as it is mainly restricted to the Flexiviridae, and in particular to a subset of the Flexiviridae consisting of Viti, Capillo, Tricho, Fovea and Carlavirus. This subset is well separated from the remaining Flexiviridae in the phylogenetic analysis. The split seems to be robust from bootstrap analysis, therefore this family will be discussed here as two subfamilies, Flexiviridae 1 and 2. The same split was observed by Adams et al. in their recent analysis of the Flexiviridae family [20]. Most of the AlkB domains (15) are found in Flexiviridae 2. The remaining AlkB domains are found in Flexiviridae 1 (5) and Closteroviridae (2). In general, all the Flexiviridae 2 sequences have at least one extra domain in addition to MT, HEL and RdRp: either AlkB, OTU-like cysteine protease or a peptidase. Most other plant viruses that are included in this survey do not have additional domains, except for Tymoviridae where a peptidase domain seems to be common. For the remaining plant virus families included here (excluding Tymoviridae and Flexiviridae 2), only 14% seem to have additional domains.
Introduction of AlkB domain in plant virus is probably a recent event
The observed distribution of AlkB domains could most easily be explained by assuming that an ancestral AlkB domain was integrated into the genome of the last common ancestor of the Flexiviridae 2 subfamily. Subsequent virus generations derived from this common ancestor would then also contain an AlkB domain, except in those cases where the domain was lost again. This scenario could also include subsequent transfer to a small number of other virus families e.g. by recombination.
If this scenario was correct, then one would expect the different domains of the polyprotein to have a similar evolutionary history. From the phylogenetic analysis (Table 2) this seems to be confirmed for the MT, HEL and RdRp domains, but not for the AlkB domain. This indicates that the AlkB domain may not have co-evolved with the other domains, at least until relatively recently. This seems to be confirmed by looking at the degree of co-evolution, which was analysed by computing pairwise distances between alignment regions representing the relevant domains (Figure 4). In the case of perfect co-evolution all points should fall on a diagonal. This seems to be the case for the MT, HEL and RdRp domains. However, the plot of the AlkB domain vs. these three domains for the same set of sequences does not show a similar correlation. Only some of the closely related sequence pairs in the upper right quadrant of the plot in Figure 4 show some degree of correlation for AlkB vs. RdRp. The most likely explanation seems to be that most of the AlkB domains have not co-evolved with the other domains for any significant period of time. This seems to rule out the possibility of ancient integration of the AlkB domain, except if we assume that an ancient viral AlkB domain has frequently recombined with other AlkB domains. However, it is difficult to distinguish a scenario with frequent recombination of AlkB domains from de novo integration, and the net effect on the properties observed here would be the same.
As seen in Figure 4, the range of score values is generally smaller for the AlkB domains than e.g. the RdRp domains, particularly if we exclude a couple of very high-scoring cases (see figure caption). On the other hand, the degree of sequence variation within the collection of AlkB domains is significant, average sequence identity for pairwise alignments is 38%, and only 10% of the positions are totally conserved. This can be consistent with a recent integration if we assume that several different AlkB-type vectors have been used for integration (see below for details). An increased mutation rate after integration could also have contributed to sequence diversity in this region. Moving the AlkB domain into a novel structural and functional context would have removed many of the original evolutionarily constraints, as well as introduced some new ones. This could have created a "punctuated equilibrium" type of situation, potentially leading to a very rapid evolution that could have introduced significant differences between the AlkB domains, independent of the evolution in the other domains. A high mutation rate seems to be the case for this region in general, as indicated in Figure 6. Although the MT, HEL and RdRp domains seem to be well conserved from the dot plot, there are very large sequence variations in the intervening region. One sequence in Figure 6 has a well conserved AlkB domain, the other an OTU domain. The fact that there are very weak sequence similarities in these two domains in the dot plot indicates that both sequences originally had both domains. However, the fact that this similarity now is very weak and without any of the typical AlkB active site motifs also indicates a high mutation rate where non-essential domains are rapidly lost. Therefore the conservation of AlkB domains is a strong indication that they are functional, as already mentioned.
The AlkB domains may represent several separate integrations
If we assume that AlkB domains have been integrated relatively recently, then either de novo integration or recombination (horizontal gene transfer) may have been the main driving force for spreading the AlkB domain to new genomes. In the first case a large number of individual integrations could have lead to the present situation. If horizontal gene transfer was the main driving force, the initial number of integrations might have been quite small. It is not easy to differentiate between these two situations.
The map of Pfam motifs in the variable region between the MT and HEL domains in Flexiviridae 2 polyproteins (Figure 5) shows that they have a very similar domain organisation, basically an AlkB domain followed by an OTU domain and a peptidase domain, located towards the C-terminal part of the sub-sequence. The relatively constant domain organisation seems to be consistent with a small number of initial integrations that were subsequently diffused to related genomes e.g. by homologous recombination. However, this is not fully consistent with the fact that the viruses with AlkB domains have been collected from hosts at very different locations, e.g. Canada, USA, Russia, Italy, Germany, France, India, Taiwan, China and Japan. Although import of virus-infected species or transmission by insects may transport viruses over significant distances, it is not obvious that this is enough to explain the observed distribution of AlkB-like domains. Therefore several independent integrations, mainly from closely related hosts, have to be considered as an alternative explanation. This explanation seems to be supported by the apparent lack of any consistent evolutionary relationships between the various AlkB domains, as seen in Table 2. It is not easy to see how this model can be consistent with the observed similarities in domain organisation in Flexiviridae. Assuming that this region has a high degree of variability, one would expect the variability to affect localisation of integrated domains as well. However, it is possible that conserved regions e.g. in the polyprotein play a significant role in integration of novel domains. It may be relevant in this context that preliminary simulations indicate that e.g. the AlkB domains tend to form independent folding domains in the folded RNA structure of the polyprotein RNA (F. Drabløs, unpublished data). This property may possibly facilitate the insertion of such domains into the viral genome.
The original AlkB integration may be of bacterial origin
There are many groups of organisms that can act as vectors and spread viruses, including bacteria, fungi, nematodes, arthropods and arachnids. The plant viruses may have acquired the AlkB domain either from the vector or from the host itself. As already mentioned, searching with viral AlkB domains in protein sequence databases resulted mainly in bacterial sequences, including the plant pathogens X. fastidiosa and campestris. It is therefore a reasonable possibility that AlkB domains in plant viruses have originated from bacterial mRNA. It is also possible that the mRNA originated from other vectors or from the host itself, but at the present time this is not easily verified or disproved because of the limited number of insect and plant genomes that have been sequenced.
The AlkB domain probably protects virus RNA against methylation
It has previously been suggested that the viral AlkB domain may be involved in protecting the virus against the post-transcriptional gene silencing (PTGS) system of the host [1]. PTGS is known as one of a plant's intrinsic defence mechanisms against viruses [21]. Gene silencing can occur either through repression of transcription (transcriptional gene silencing – TGS) or through mRNA degradation, PTGS. The PTGS-mechanism in plants shows similarities to RNA interference (RNAi) in animals [22]. This mechanism results in the specific degradation of RNA. Degradation can be activated by introduction of transgenes, RNA viruses or DNA sequences homologous to expressed genes [23]. Many viruses have developed mechanisms to counteract PTGS in order to successfully infect plants [24]. Two of these suppressors of PTGS have been identified as Hc-Protease and the 2b protein of Cucumber mosaic virus [25]. Although both proteins suppress PTGS, it is likely that they do so via different mechanisms. Could the AlkB-like domain found in some of the plant viruses also be a suppressor of PTGS? Previously reported research indicates that methylation of transcribed sequences is somehow connected with PTGS, and the methylation can be mediated by a direct RNA-DNA interaction [26]. This RNA-directed DNA methylation has been described in plants, and leads to de novo methylation of nearly all cytosine residues within the region of sequence identity between RNA and DNA [27]. Both RNA methylation and methylation of host proteins that are essential for viral replication would be detrimental to the virus. It has already been mentioned that AlkB repairs 1-methyladenine and 3-methylcytosine by oxidative demethylation. It is therefore possible that AlkB demethylates the nucleotides methylated by the PTGS mechanism, helping the virus to overcome one of the major defence mechanisms of the plant.
As shown here, only a subset of plant viruses have the AlkB domain. However, other viruses may be utilising naturally occurring AlkB proteins in the host. Viruses have to rely on a number of host proteins in order to replicate [28]. In some cases it is probably beneficial for the virus to integrate such genes into their own genome in order to ensure that they are accessible, although there will be a trade off between this advantage and the increased cost of maintaining a larger genome [29].
However, there is an alternative hypothesis with respect to the AlkB integration that also has to be considered. As discussed above, the AlkB domain seems to have been integrated relatively recently in viruses found at very different geographical locations, and the only obvious connection seems to be that most viruses belong to a subset of the Flexiviridae. However, the source of these viruses points at another common feature. As seen from the table given in Additional file 1, AlkB domains are often found in viruses associated with grapevine, apple, cherry, citrus and blueberry – crops where the usage of pesticides is common. It is known that several common pesticides (e.g. methyl bromide and some organophosphorus compounds) may cause methylation of DNA and RNA [30-33]. An integrated repair domain for methylation damage as part of the viral replication complex would therefore give the virus a competitive advantage in a highly methylating environment. The application of such pesticides would probably also stimulate AlkB production e.g. in co-infecting bacteria, giving these viruses easy access to AlkB mRNA for integration into their RNA genome.
It could be argued that a more active PTGS system in these plants would give a similar effect. However, in that case we would expect to see more ancient integrations of AlkB domains. It could also be argued that the presence of AlkB domains may be an artefact caused by promiscuous viral domains picking up available mRNA sequences during cultivation of viruses in the laboratory. However, given the large number of different laboratories involved, and the number of different hosts used (data not shown), this seems to be a very unlikely explanation.
The hypothesis that environmental compounds, in particular pesticides, may have provoked the integration of AlkB domains into the viral genomes depends upon a high mutation rate and frequent integrations of non-viral domains. The integrations have to be recent, not only in relative terms, compared to other domains in the same genome, but also in absolute terms, compared to the progress of modern agriculture. The integrations also have to be frequent, in the sense that it is likely that integration could have happened several times, in different biotopes.
It is difficult to estimate mutation rates in RNA viruses. They evolve very rapidly, and it is often difficult to assign reliable phylogenies. However, recent studies indicate that most ssRNA viruses have a mutation rate close to 10-3 substitutions per site per year [34], e.g. the SARS virus has 1.16–3.30 × 10-3 non-synonymous substitutions per site per year, which is considered to be a "moderate" ssRNA mutation rate [34]. If we assume that most ssRNA viruses have effective mutation rates within the same order of magnitude, a realistic mutation rate for the viruses included here might be something like 2.0 × 10-3. In that case, the MT, HEL and RdRp trees shown in Additional file 2 represent approximately between 325 and 750 years of evolution. In general the NJ trees estimate a slightly shorter evolutionary history (between 325 and 450 years) compared to the ML trees (between 550 and 750 years). In this estimate the Ampelovirus sequences have not been included, as they seem to have diverged from the remaining AlkB-containing viruses at a much earlier stage. If we believe that the AlkB integrations happened after the divergence of most sequence included here, as indicated by the lack of co-evolution in Figure 4, it does not seem unrealistic to assume that most of these integrations happened within the last 50 – 100 years or so. This estimate is of course very approximate, in particular since we do not know the true mutation rate of these viruses. However, it shows that a likely time span for AlkB integration is compatible with the evolution of modern agriculture. Unfortunately, because of the lack of any robust phylogeny for the viral AlkB sequences it does not make sense to do a similar estimate for that domain.
Although it is generally accepted that viruses frequently use recombination to acquire functionality [35], it is less well known how often this includes nonviral sequences. However, there are some well-documented examples, and in particular the properties of the ssRNA positive-strand Pestivirus may be relevant in this context. There are two biotopes of the pestiviruses, cytopathogenic (cp) and noncytopatogenic (noncp). The host is infected by the noncp form which is converted into the cp form by integration of a fragment of a cellular gene into the viral genome [36]. This introduces a protease cleavage site in the polyprotein. However, the important point here is that this happens as part of the normal infection process. It has been suggested that the integration is facilitated by the viral polymerase undergoing two subsequent template switches during minus-strand synthesis [37], although nonreplicative RNA recombination also may be a possibility [38]. Integration of cellular sequences have also been observed in other viruses, e.g. in influenza virus [39]. This shows that at least some viruses do have efficient mechanisms for recruitment of host genes into the viral genome. Therefore a recent and rapid integration of AlkB domains into selected plant virus genomes does not seem to be an unlikely scenario.
This study has focused on the AlkB domain, mainly as an attempt to get a better understanding of potential functions associated with this domain. However, it is likely that additional information about integration patterns and the relative importance of de novo integration vs. recombination can be achieved by a closer investigation of the other variable domains, e.g. by looking at how they correlate with the evolution of the AlkB domains.
Conclusions
We believe that the viral AlkB-like domains are conventional repair domains targeted towards the viral RNA. The integration of AlkB domains into viral genomes may have been provoked by environmental methylating agents, e.g. the introduction of DNA/RNA-methylating pesticides in farming. The hypothesis [1] that the domain interferes with the PTGS system of plants can not be excluded, but seems to be less consistent with observed features of the AlkB integration.
Methods
The NCBI nr protein sequence database was searched with PSI-Blast [40], with the output limited to viral sequences. Multiple alignments were made with ClustalX version 1.8 [41]. The phylogenetic tree in Figure 2 was made from ClustalX alignments by MEGA2 [42], using the neighbour-joining (NJ) approach with complete deletion of gap positions, Poisson correction of distances and 500 bootstrap steps. Phylogenetic trees for sequence regions from sequences with AlkB domains were made with the NJ approach as described above, but with 10.000 bootstrap steps. Corresponding trees were also made by the maximum likelihood approach (ML) by Tree-Puzzle version 5.2 [43], using an exact likelihood function, the VT matrix [44] and 10.000 puzzling steps. The trees from Tree-Puzzle were visualised with TreeView version 1.6.6 [45], and the NJ and ML trees were compared with Component version 2.0 [46]. Significance of pairwise tree distances were estimated using the data of Day [12]. Pairwise distances between sequences, for comparing evolution of AlkB domains to other viral domains, were computed directly from ClustalX alignments with local tools, using the Blosum50 mutation matrix [47], but without any correction for multiple substitutions. Motifs in protein sequences were identified using HMMER version 2.3.2 [48] with the Pfam library version 11.0 [49]. A Pfam-type profile for the methyltransferase domains of Flexiviridae and Tymoviridae was generated from a ClustalX alignment, using hmmbuild and hmmcalibrate from the HMMER package. Visualisation of motif positions in viral sequences was generated directly from the HMMER output files using a local tool as an interface to the GNU [50] groff software. Systematic large scale searches with polyprotein subsequences were done locally with PSI-Blast and the NCBI reference sequence database [15]. Dot plots for comparison of viral protein sequences were generated with Dotter version 3.0 [51].
List of abbreviations used
MT – Methyl transferase; HEL – Helicase; RdRp – RNA-dependent RNA polymerase; ssRNA – Single-stranded RNA; PTGS – Post-transcriptional gene silencing; 2OG – 2-oxoglutarate; (h)ABH – (human) AlkB homologue; OTU – Ovarian tumour-like protein; NJ – Neighbour-joining; ML – Maximum likelihood; SJA – Strict joint assertions.
Authors' contributions
MSB carried out all PSI-Blast searches, generated local (sub)sequence databases, and drafted the initial manuscript. FD conceived the study, carried out HMMER/Pfam searches, and estimated evolutionary distances. Both authors participated on sequence alignment, phylogenetic analysis and writing of the manuscript. Both authors have read and approved the final manuscript.
Supplementary Material
Additional File 1
Full listing (with GI numbers) of viral sequences and domains included in the analysis.
Click here for file
Additional File 2
Individual NJ and ML trees for relevant domains (MT, AlkB, HEL, RdRp).
Click here for file
Acknowledgements
This project has been supported by the Norwegian Research Council as part of the FUGE Bioinformatics platform project (NFR 151899/150).
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| 15627404 | PMC544882 | CC BY | 2021-01-04 16:39:33 | no | BMC Genomics. 2005 Jan 3; 6:1 | utf-8 | BMC Genomics | 2,005 | 10.1186/1471-2164-6-1 | oa_comm |
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BMC Evol BiolBMC Evolutionary Biology1471-2148BioMed Central London 1471-2148-5-11562906210.1186/1471-2148-5-1Research ArticleThe WRKY transcription factor superfamily: its origin in eukaryotes and expansion in plants Zhang Yuanji [email protected] Liangjiang [email protected] Plant Biology Division, The Samuel Roberts Noble Foundation, Ardmore, OK 73402, USA2005 3 1 2005 5 1 1 14 9 2004 3 1 2005 Copyright © 2005 Zhang and Wang; licensee BioMed Central Ltd.2005Zhang and Wang; 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
WRKY proteins are newly identified transcription factors involved in many plant processes including plant responses to biotic and abiotic stresses. To date, genes encoding WRKY proteins have been identified only from plants. Comprehensive search for WRKY genes in non-plant organisms and phylogenetic analysis would provide invaluable information about the origin and expansion of the WRKY family.
Results
We searched all publicly available sequence data for WRKY genes. A single copy of the WRKY gene encoding two WRKY domains was identified from Giardia lamblia, a primitive eukaryote, Dictyostelium discoideum, a slime mold closely related to the lineage of animals and fungi, and the green alga Chlamydomonas reinhardtii, an early branching of plants. This ancestral WRKY gene seems to have duplicated many times during the evolution of plants, resulting in a large family in evolutionarily advanced flowering plants. In rice, the WRKY gene family consists of over 100 members. Analyses suggest that the C-terminal domain of the two-WRKY-domain encoding gene appears to be the ancestor of the single-WRKY-domain encoding genes, and that the WRKY domains may be phylogenetically classified into five groups. We propose a model to explain the WRKY family's origin in eukaryotes and expansion in plants.
Conclusions
WRKY genes seem to have originated in early eukaryotes and greatly expanded in plants. The elucidation of the evolution and duplicative expansion of the WRKY genes should provide valuable information on their functions.
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Background
Transcriptional control is a major mechanism whereby a cell or organism regulates its gene expression. Sequence-specific DNA-binding transcription regulators, one class of transcription factors [1], play an essential role in modulating the rate of transcription of specific target genes. In this way, they direct the temporal and spatial expressions necessary for normal development and proper response to physiological or environmental stimuli. Comparative genome analysis reveals that genes for transcription regulators are abundantly present in plant and animal genomes, and the evolution and diversity of eukaryotes seem to be related to the expansion of lineage-specific transcription regulator families [2].
WRKY proteins are recently identified transcriptional regulators comprising a large gene family [3]. The first cDNA encoding a WRKY protein, SPF1, was cloned from sweet potato (Ipomoea batatas) [4]. Numerous genes for WRKY proteins have since been experimentally identified from more than 10 other plant species, including Arabidopsis thaliana [5,6], wild oats (Avena fatua) [7], orchardgrass (Dactylis glomerata) [8], barley (Hordeum vulgare) [9], tobacco (Nicotiana tabacum) [10-13], chamomile(Matricaria chamomilla) [14], rice (Oryza sativa) [9,15], parsley (Petroselinum crispum) [16,17], a desert legume (Retama raetam) [18], sugarcane (Saccharum hybrid cultivar) [19], bittersweet nightshade (Solanum dulcamara) [20], potato (Solanum tuberosum) [21,22], and wheat (Triticum aestivum) [9]. In addition, over 70 WRKY genes were identified in the Arabidopsis genome by sequence similarity comparisons [2,23]. To date, WRKY genes have not been cloned from species other than plants. The absence of WRKY homologues in the genomes of animals (Caenorhabditis elegans and Drosophila melanogaster) and yeast (Saccharomyces cerevisiae) [2] leads to the suggestion that WRKY transcription regulators are restricted to the plant kingdom [2,3]. As genome sequence data for species representing several major eukaryotic lineages are now available, we can re-examine whether WRKY genes are plant-specific or have ancestors predating the appearance of plants.
The WRKY family proteins contain one or two highly conserved WRKY domains characterized by the hallmark heptapeptide WRKYGQK and a zinc-finger structure distinct from other known zinc-finger motifs [3]. To regulate gene expression, the WRKY domain binds to the W box in the promoter of the target gene to modulate transcription [5,7,16,24]. In addition to the W box, a recent study indicates that the WRKY domain can also bind to SURE, a sugar responsive cis element, as a transcription activator [9].
In plants, many WRKY proteins are involved in the defense against attack from pathogenic bacteria [6,22,23,25-27], fungi [26], viruses [12,26,28], and oomycetes [21,26,29]. Further, WRKY genes are implicated in responses to the abiotic stresses of wounding [11,30], the combination of drought and heat [31], and cold [18,20]. It is also evident that some members of the family may play important regulatory roles in morphogenesis of trichomes [32] and embryos [8], senescence [26,33-35], dormancy [18], plant growth [27], and metabolic pathways [7,9,32,36].
Based on the number of WRKY domains and the pattern of the zinc-finger motif, Eulgem et al. [3] classified members of the WRKY superfamily from the Arabidopsis genome into three groups. Members of Group 1 typically contain two WRKY domains, while most proteins with one WRKY domain belong to Group 2. Group 3 proteins also have a single WRKY domain, but the pattern of the zinc-finger motif is unique. Eulgem et al. [3] further divided Group 2 into five subgroups, according to the phylogenetic analysis of the WRKY domains.
Given the large family of WRKY genes with divergent regulatory functions in important plant processes, it would be desirable to understand the evolutionary origin and gene duplications leading to the multi-member WRKY family. The clarification of the phylogenetic relationships among WRKY genes in model plants will also assist understanding of the functions of these genes in important crops. We have comprehensively searched all currently available sequence data for the existence of WRKY genes outside the plant kingdom. Homologues of WRKY genes are found from two eukaryotic species: Giardia lamblia, a primitive protozoan, and Dictyostelium discoideum, a slime mold. The data indicate an early origin of WRKY genes in eukaryota and tremendous gene amplifications in the plant lineage. We then cataloged the WRKY genes from the rice genome and compared them with Arabidopsis WRKY genes. We also identified WRKY genes from expressed sequence tags (ESTs) and EST-assembled sequence contigs from nineteen plant species. The result suggests that WRKY gene duplication events correlate with the increasing structural and functional complexities in land plants. We propose a model for the evolution of WRKY genes.
Results
WRKY genes in non-plant eukaryotes
We searched for WRKY genes in two comprehensive datasets, GenBank's non-redundant (nr) and dbEST of all species. Together these datasets contain over 13 million sequence records from more than 110,000 organisms [37]. Homologues of WRKY proteins are not found in the superkingdoms of archaea and eubacteria. In eukaryotes, no WRKY genes are identified from the lineages of fungi and animals.
Interestingly, two WRKY homologues were identified from non-plant eukaryotic species, and both have two WRKY domains [see Additional files 1 and 2]. The first protein (GenBank accession: EAA40901) is encoded by an intronless gene in the draft genome sequence of Giardia lamblia [38]. The unicellular protist Giardia is one of the most primitive organisms that represent the earliest branching among extant eukaryotes [39,40]. The second (accession AAO52331) is encoded by the genomic sequence of chromosome 2 of the slime mold Dictyostelium discoideum [41]. The genomic sequence for the WRKY domains were assembled from sequences generated from three libraries prepared by two groups [42], indicating that it is not from sequence contamination. The gene contains an intron, which interrupts the coding region between the two WRKY domains. For this species, about 150,000 EST sequences are currently available in GenBank. One EST (accession AU033476) aligns to the WRKY gene, indicating that the gene is expressed. D. discoideum belongs to the Mycetozoa, a lineage more closely related to animals and fungi than to green plants [41,43].
A WRKY gene in a green alga
Chlamydomonas reinhardtii is a unicellular green alga with a cell wall. It also has chloroplasts for photosynthesis. The evolutionary position of the species is located before the divergence of land plants [44,45]. The release 1.0 of its genome sequence has approximately 9 × whole genome shotgun coverage [46]. Since the gene annotation for the release is still at a preliminary stage, we predicted WRKY genes from the genome sequence (see Methods). The sequence similarity search between the genome sequence and Pfam's WRKY domain sequences indicated that the sequence 'Scaffold_1387' may encode WRKY domains. This sequence was then used for further WRKY domain and gene predictions. Despite minor differences in the gene structure prediction, both gene prediction programs FGENESH and GENSCAN agree on the major features of the protein, including the presence of two WRKY domains [see Additional files 1 and 2]. Moreover, the predicted peptide sequence of the WRKY domains is identical among all the gene and domain predictions. Sequence alignment by blastn indicates that six ESTs are from the predicted coding regions of the gene; the GenBank accessions for these ESTs are BI727288, AW772895, BM000804, BG846749, BE121978 and BQ821537.
A catalog of WRKY genes in rice
Rice, one of the most important crops for world agriculture, is recognized as a model monocot for the study of cereal crop genomes. A comprehensive catalog of rice WRKY genes would provide a basis for investigating the evolutionary patterns of the gene family and for transferring knowledge of the functions of these transcription factors from Arabidopsis to rice and from rice to other cereal crops.
We identified the members of the WRKY family in rice (Japonica variety) from its published genome sequence [47]. The WRKY gene identification procedure employed in this study (see Methods) was first tested with the Arabidopsis genome sequence. The procedure successfully identified all reported Arabidopsis WRKY genes [3,23]. The rice genome seems to encode 109 WRKY proteins, four of which have incomplete WRKY domains. The remaining 105 proteins with complete WRKY domains, listed in Additional file 3, were used for further analysis. The multiple sequence alignment of WRKY domains from rice, Arabidopsis, the green alga, the slime mold and Giardia lamblia, and the conserved WRKY domain patterns can be found in Additional file 2. Some rice genes encode identical WRKY domains. For example, OsWRKY34 and OsWRKY57 share identical amino acid sequences in the WRKY domains, but the nucleotide sequences for the domains are not identical and they are located in different chromosomes (1 and 4, respectively), indicating that they are distinct genes. Similarly, OsWRKY8 located in Chromosome 6 and OsWRKY76 located in Chromosome 2 also represent two genes. The following genes in parenthesis share the identical WRKY domains and have a high identity of the corresponding coding nucleotide sequences: (OsWRKY9, 101), (12, 98 and 99), (21, 97), (29, 96), (39, 105), (51, 103), (73, 104), (80, 102), and (82, 100). These highly similar genes may represent newly duplicated paralogues. The 105 genes are unevenly distributed in the 12 chromosomes, ranging from 25 genes (the highest number) in Chromosome 1 to two genes (the lowest) in Chromosome 10. Sequence alignment indicates that 60 WRKY genes have one or more matched rice ESTs from the dbEST database (data not shown). Out of the 105 proteins, 13 have two WRKY domains. We assigned the WRKY domains into subfamilies using phylogenetic analysis with already classified AtWRKY genes from A. thaliana [3] as the reference. Eleven proteins with two WRKY domains are assigned to Group 1 because their C-terminal domains belong to this group. Since the N- and C-terminal domains form distinct clusters, we designated the two domains as 1N and 1C, respectively. Six proteins with a single domain also belonged to Group 1. While OsWRKY15, 16, 73 and 104 have a single domain homologous to Group 1N, OsWRKY13 and 91 contain a single Group 1C domain. Interestingly, both N- and C-domains of the other two double-domain-containing proteins (OsWRKY66 and 67) are always clustered with Group 3 domains. Thirty-five single WRKY domain proteins are also assigned to this group. All together, there are 39 domains or 37 proteins in Group 3. We assigned 49 proteins to three new groups, Group 2_a + 2_b (13), Group 2_c (18), and Group 2_d + 2_e (18). These new groups are reorganized from the five subgroups IIa through IIe in Eulgem et al. [3] (see details of the classification in Discussion). Domains of OsWRKY 25 and 95 cannot be consistently classified and therefore remain unassigned [see Additional file 3].
Interestingly, several variant patterns of the WRKY domains exist in the rice WRKY proteins. Although the WRKYGQK peptide is highly conserved, nine variants with one or two amino acids substituted are observed in 19 domains, most of which belong to Groups 3 and 2_c (Table 1). While WRKYGEK and WRKYGKK are two common variants shared by seven (all in Group 3) and five (all in Group 2_c) domains, respectively, each of the other seven different heptapeptides occurs in only one protein. The WRKY domains also contain patterns of zinc-finger motifs that have not been reported in the literature (Table 1). No variants are found in domains of Groups 1C and 2_a + 2_b. The WRKY genes encoding the variant domain patterns might be functional, because 10 genes with a total of seven heptapeptide variants and two zinc-finger motif variants have sequenced ESTs, although the DNA binding capacity may be reduced [48]. Furthermore, ESTs have been sequenced from the gene regions for the variants of WRKYGEK, WRKYGKK, WKKYGQK and C_X6_C_X28_H_X1_C, indicating that these patterns are not artifacts of the gene prediction (Table 1).
Table 1 Variants of the conserved WRKYGQK peptide and zinc-finger motifs in rice WRKY domains
Pattern Domain Group Available ESTs
IDa Encoding the domain
Variants of WRKYGQK
WRKYGEK OsWRKY7 3
OsWRKY8 3 CA755335 Yes
OsWRKY65 3
OsWRKY72 3 CF282152, CF330819, CF303772, CF282153, CF330818, CF305084, CF328161 Yes
OsWRKY76 3 CA755335
OsWRKY77 3 Yes
OsWRKY94 3
WRKYGKK OsWRKY20 2_c
OsWRKY27 2_c
OsWRKY36 2_c D43156 No
OsWRKY46 2_c TC154521, AU093050 No
OsWRKY63 2_c TC143003, BE230596, BM419201 Yes
WRICGQK OsWRKY15 1N
WRMCGQK OsWRKY16 1N
WKKYGQK OsWRKY25 unassigned AU162739 Yes
WIKYGQK OsWRKY55 3
WKRYGQK OsWRKY66C 3 AW155482 No
WSKYEQK OsWRKY67N 3 CA760141 No
WRKYSEK OsWRKY92 3
Variants of zinc-finger motifs
C_X5_C_X25_H_X2_C OsWRKY6 2_d + 2_e
C_X8_C_X25_H_X1_C OsWRKY67N 3 CA760141 No
C_X6_C_X28_H_X1_C OsWRKY68 3 TC103502 Yes
aTIGR's TCs or GenBank's accessions.
Survey of WRKY genes in land plants
Since the genomes of rice and Arabidopsis have numerous WRKY genes whereas the green alga may have only a single copy, it would be interesting to investigate the gene duplication events of WRKY family during the course of evolution from unicellular plant organisms to flowering plants and the relationship between expansion of the WRKY family and the increased structural and functional complexities of the higher plants. Ideally, the complete set of WRKY genes should be identified from species representing different branches on the evolutionary tree of plants for further analysis. Unfortunately, genome sequence is currently not available for most plant species. However, a large number of EST sequences for many plants are publicly available and can be used to roughly estimate the minimum number of WRKY genes in these species.
We first surveyed GenBank's dbEST set and found that WRKY genes are widespread in land plants, as over 40 species have expressed WRKY genes (data not shown). We then estimated the number of unique WRKY genes for 17 species using their Gene Indices, which are assembled EST sequence contigs with the minimal redundancy, provided by The Institute for Genomic Research (TIGR) [49]. The analysis also included ESTs for the moss Physcomitrella patens and the fern Ceratopteris richardii whose Gene Indices are not available [see Additional file 4]. For the EST set, redundant ESTs for WRKY proteins were manually removed. Together these 19 species represent different branches on the evolutionary tree of the land plants. While the moss Physcomitrella is an early diverged land plant, the fern is an ancient vascular plant. The conifer Pinus represents the gymnosperm lineage, and the remaining are the evolutionarily more advanced flowering plants [50].
ESTs encoding WRKY proteins were identified in all the 19 species. Moreover, multiple WRKY genes are represented in the EST or contig sets for most plants including the moss and pine, with the most WRKY genes (109) from soybean [see Additional file 4]. Although the actual number of WRKY genes encoded in a plant genome can only be known using the genome sequence, EST datasets are useful to estimate the relative size of WRKY family in plant species whose genome sequences are not available, given sufficient large EST sets sampled from the genomes. If a set of ≥ 50,000 ESTs is considered a large sample, then pine, moss and 12 flowering plants listed in Additional file 4 have enough ESTs for the estimation. The comparison of the number of WRKY genes identified from EST sets with comparable size suggests that the genomes of moss and pine seem to encode much fewer WRKY genes than evolutionarily advanced flower plants. We also compared pine with Arabidopsis in another analysis using ESTs from GenBank's dbEST database (as of 10/28/2002). We identified ESTs for 46 Arabidopsis WRKY genes but only two pine WRKY genes, although Arabidopsis' EST set (176,915) is less than three times bigger than pine's (60,226).
The abundance of WRKY ESTs in the total EST set is lower for pine, fern and moss than for flowering plants, as the percentage of WRKY ESTs in the total EST set for the three non-flowering plants is among the lowest [see Additional file 4]. The WRKY EST abundance in an EST dataset may be affected by the number of WRKY genes in the species and by the expression levels of WRKY genes in the cells from which ESTs were obtained. For example, WRKY EST abundance for pine is much lower than that for tomato (0.0086% : 0.3546%, or ~ 1 : 40). The low WRKY EST abundance of pine may be partly due to fewer WRKY genes from pine than from tomato (4 : 51, or ~ 1 : 13) [see Additional file 4]. It is also possible that pine WRKY genes are lowly expressed. For example, for a tomato WRKY gene the average EST count is > 10, but for pine it is < 2.
The identified WRKY genes were phylogenetically classified into five groups [see Additional file 4]. In six WRKY genes identified from the moss ESTs, two are homologous to Group 2_c and three belong to Group 2_d + 2_e, indicating an early origin of these groups in land plants. In comparison, genes in Group 3 are only identified in the EST sets of flowering plants but not from EST data of more ancient plants, i.e., moss, fern and pine [see Additional file 4].
Phylogeny of the WRKY domains
To examine the evolutionary relationships among the WRKY domains, we estimated the phylogeny by using the neighbor-joining program from PHYLIP 3.57 for the amino acid sequences of WRKY domains from G. lamblia, the slime mold, the green alga, Arabidopsis and rice. The phylogenetic relationships were also inferred with the programs of the least squares and parsimony from PAUP 4.0 for the corresponding nucleotide sequences. We also did the same analysis for the rice dataset alone. The topology of trees obtained from these analyses is essentially the same, and the neighbor-joining tree is shown in Figure 1. Group 2 domains designated by Eulgem et al. [3] are not monophyletic, but form three distinct clades. These include: 2_a + 2_b, 2_c, and 2_d + 2_e. Moreover, Group 2_a + 2_b and Group 2_c are closely related to Group 1C domains, while Group 3 is clustered with Group 2_d + 2_e. In addition, the rice and Arabidopsis WRKY trees (not shown) consistently clustered WRKY1N domains as a monophyletic subtree and all other domains as a natural clade, supporting the suggestion that Groups 2 and 3 domains are more closely related to the C-terminal domains of Group 1 genes than to the N-terminal domains [3].
Figure 1 Unrooted phylogenetic tree of the WRKY domains. The tree was reconstructed from the amino acid sequences using the neighbor-joining program from Phylip 3.57. Clades of WRKY domains are labelled according to the classifications of AtWRKY domains by Eulgem et al [3] who proposed three groups and five subgroups in Group 2 (a, b, c, d and e). We suggest classifying WRKY domains into five groups modified from the old system. While Groups 1 and 3 are unchanged, the original subgroup 2_c is promoted to Group 2_c. Subgroups 2_a and 2_b, and subgroups 2_d and 2_e are combined to form two new groups, 2_a + 2_b, and 2_d + 2_e, respectively (see text for details). WRKY domains from G. lamblia are represented by thick and dark-green branches; the slime mold, thick and cyan; the green alga, thick and magenta; Arabidopsis, thin and blue; and rice, thin and red.
In flowering plants, genes encoding WRKY domains appear to have been duplicated independently in monocots and dicots. For Group 3 domains, three subsets each of which consists of five or more members only from rice can be distinguished from the phylogram shown in Figure 2. Similarly, six members of WRKY domains, all from Arabidopsis, are clustered together. Independent domain clusters of either species are also found in other WRKY subfamilies (data not shown). These results suggest that numerous duplications and diversifications for WRKY genes, particularly Group 3 genes, have occurred after the divergence of the monocots and dicots. Indeed, all rice WRKY domains with the sequence WRKYGEK (Table 1) are classified as a sub-cluster of the largest rice domain cluster in Group 3 (Figure 2), implying that multiple duplication events led to this large cluster in rice.
Figure 2 Phylogram of Group 3 WRKY domains from Arabidopsis (AtWRKY) and rice (OsWRKY). The amino acid sequences were analysed with the neighbor-joining and parsimony algorithms implemented in PHYLIP 3.57. Bootstrap values ≥ 50% are indicated above the nodes for distance analysis. The C-terminal domains, AtWRKY1C, was used as the outgroup. OsWRKY proteins with the variant WRKYGEK are marked by *.
Discussion
WRKY genes seem to be an innovation in eukaryota after the divergence of eubacteria – archaea – eukaryota. In eukaryotes, the WRKY genes are present in the green plants as well as in the ancient eukaryote G. lamblia and the mycetozoan D. discoideum, but not in fungi and animals. G. lamblia is a primitive unicellular eukaryote diverged ~ 1,500 million years ago (mya) [51]. Originally thought as plant-specific [2,3], the WRKY transcription factors therefore seem to have an early origin in eukaryotes. As the mycetozoa is closely related to the fungi-animal clade [41,43], the WRKY gene(s) may have been lost prior to the divergence of fungi and animals, but after the split of the slime mold and fungi-animal lineages.
Based on the current data, we propose a model for the origin and evolution of the WRKY factor family (Figure 3). First, the ancestor of the descendant WRKY genes found in G. lamblia, the slime mold and the green alga seems to be a Group 1 gene encoding two WRKY domains. The conservation of the C- and N-terminal domains suggests that they are derived from a single domain by domain duplication. Therefore we hypothesize that the earliest WRKY factor had one WRKY domain and the gene was innovated post the first appearance of eukaryotes ~ 2,500 mya [52] but prior to the divergence leading to Giardia protist, ~ 1,500 mya. Second, our data and the previous results by Eulgem et al. [3] suggest that the WRKY domains of groups 2_a + 2_b, 2_c, 2_d + 2_e and 3 are evolutionarily close to the WRKY1C domain. It seems that Group 1 genes which contain only the C-terminal WRKY domain are ancestors of the descendant WRKY genes in other groups. The N-terminal domain in Group 1 genes may have been lost prior to the gene duplication. As the green alga may have only one WRKY gene which belongs to Group 1, the duplications and diversifications leading to other groups in plants probably occurred some time after the divergence of chlorophytes and streptophytes, ~ 800 mya [53]. Third, the domain structure conservation [see Additional file 2] and the phylogenetic analysis (Figure 1) suggest that the three distinct subsets, Groups 2_a + 2_b, 2c, 2_d + 2_e, may be independently evolved from the Group 1 genes which have only the C-terminal domain. In addition, Group 3 genes appear to share a common ancestor with the clade 2_d + 2_e. The identification of 2_c and 2_d + 2_e genes in the moss EST data [see Additional file 4] suggests that the duplications of the genes in these groups predate the diversification of bryophytes, ~ 420 mya [50]. Although the WRKY genes in Group 2_a + 2_b and Group 3 are identified only from flowering plants in the current data, the origin of these genes seems to have occurred prior to the divergence of monocots and dicots, because the characteristic features of the WRKY domains in Group 3 are highly conserved in Arabidopsis and rice. In addition, multiple copies of Group 3 genes may exist in the common ancestor of monocots and dicots, since clusters with nested Arabidopsis and rice sequences are found in the group (Figure 2).
Figure 3 Model of the origin and duplications of WRKY gene family. The phylogenetic tree of eukaryotes using the archaea as the outgroup is modified from Baldauf and Doolittle [43] and Kenrick and Crane [50]. The solid lines correspond to branches where WRKY homologues are identified, while the thickness of the line represents the relative size of WRKY family for the branch, from the thinnest for one copy in Giardia, the slime mold and the green alga to the thickest for over 100 copies in rice. The broken lines represent branches where WRKY genes are not present or have not been identified. The WRKY gene is symbolized by the box for the WRKY domain and the lines for sequences around the domain. The text in the box indicates the group the WRKY domain belongs to (1, Group 1; 1N and 1C, N- and C-terminal domains of Group 1 proteins; a + b: Group 2_a + 2_b; c: Group 2_c; d + e, Group 2_d + 2_e; 3: Group 3). The major gene duplications and diversifications are shown above the branch. The number shown below the branch is the divergence time (million years ago) of its children branches. The branch length is not scaled to the evolutionary distance.
The classification of the WRKY family in Arabidopsis by Eulgem et al. [3] is not completely based on phylogenetic analysis and therefore does not necessarily reflect the evolutionary relationships among the groups. This is even apparent for the tree of AtWRKY genes built by the authors (see their Figure 3). For example, their Group 2 is not monophyletic, but seems to have several ancestors. Obviously it is necessary to implement a new classification scheme for the WRKY family to reflect the evolution of the WRKY domains. Based on phylogenetic analysis (Figure 1), conserved domain structures and intron positions of the WRKY domains [see Additional file 2, B], we suggest a new classification system modified from Eulgem et al. [3]. Instead of three groups and five subgroups under Group 2 in their classifications, genes are reorganized into five independent groups according to the phylogeny of their WRKY domains, i.e., Group 1, Group 2_a + 2_b, Group 2_c, Group 2_d + 2_e, and Group 3. The relationship between the modified system and the original of Eulgem et al. [3] is as follows. Groups 1 and 3 are unchanged, while Group 2_c corresponds to the subgroup c of the old Group 2. The original subgroups a and b, and d and e in the old Group 2 are combined to become two new groups, 2_a + 2_b, and 2_d + 2_e, respectively.
Our evolutionary analysis of WRKY transcription factors in this study may be important to the understanding of the overall mechanisms of biodiversity in the plant kingdom and the particular functions WRKY genes play in plant regulatory networks. First, the comparative analysis of WRKY factors in lower and higher plants indicates that the WRKY family expands as plants evolve from simpler, unicellular to more complex, multicellular forms. Since WRKY genes seem to play important regulatory roles in plants under abiotic and biotic stresses, and flowering plants which have the largest WRKY family are dominant over non-flowering plants in their distribution on the earth, WRKY genes might be essential for much of the enhanced adaptability of flowering plants to the environment. In comparison with pine, fern and moss, WRKY ESTs of flowering plants seem to be over-represented [see Additional file 4], suggesting that the normal functions of flowering plants might depend to a greater extent on the regulatory roles of these transcription factors. It would be interesting to analyze the functions of genes in Group 3, a greatly amplified group in monocots which are most advanced in evolution and most successful in adaptability. Second, the pairs of Arabidopsis WRKY genes, AtWRKY3 and 4, 8 and 28, 11 and 17, 14 and 35, 18 and 60, 24 and 56, and 38 and 62 share similar expression patterns in response to pathogen inoculation and salicylic acid treatment [23]. Phylogenetic analysis indicates that these pairs of genes are clustered together with high bootstrap value support (data not shown). Thus, the newly duplicated WRKY genes may overlap in functions to better protect the cell or organism from deleterious effects caused by gene mutation or deletion. Moreover, a number of WRKY genes from different phylogenetic groups may be activated by the same physiological or environmental stimulus, such as bacterial pathogen attack [6,25,27,54], viral pathogen attack [23], wounding [30], or senescence [33-35]. The WRKY genes are possibly involved in multiple pathways leading to an array of physiological responses. Nevertheless, the elucidation of the evolution and duplicative expansion of the WRKY genes should provide valuable information on their functions.
Conclusions
Originally believed to be plant-specific, WRKY transcription factor family has an early origin in eukaryotes and is also present in a slime mold which is more closely related to the lineage of fungi-animals than to plants. WRKY genes have been duplicated many times during evolution in plants, resulting in a large gene family for WRKY proteins in flowering plants. The elucidation of the evolutionary pathway of WRKY family and a new classification system we proposed based on phylogenetic analysis, conserved WRKY domain structures and intron positions should assist the functional characterization of WRKY genes.
Methods
Datasets
The annotated genome sequences of rice (Oryza sativa spp. japonica) (OSA1, released on 7/27/2003) and Arabidopsis (ATH1, released on 4/17/2003) were downloaded from TIGR [55]. OSA1 and ATH1 include nucleotide sequences of genes, mRNA and coding regions, peptide sequences, and the gene structure information such as the start and end of the exons in a gene. For the green alga Chlamydomonas reinhardtii, the genome sequence release 1.0 on 2/4/2003 was used [56]. We also downloaded Giardia lamblia genome sequence released on 1/1/2003 [57]. GenBank's Non-Redundant (nr), dbEST and taxonomy datasets were downloaded from National Center for Biotechnology Information (NCBI) [58]. TIGR's Gene Indices for plant species [see Additional file 4] and the slime mold Dictyostelium discoideum were downloaded from TIGR [59]. These Gene Indices represent non-redundant gene transcripts assembled from publicly available ESTs and annotated sequences [49]. Pfam's WRKY domain sequences (WRKY-seed) were also downloaded [60].
WRKY gene identification
We searched 'nr' and dbEST datasets for WRKY genes in species outside the plant phyla. The dbEST dataset was also used to survey the expressed WRKY genes in plant species. We aligned the sequences in the datasets with WRKY-seed using BLAST programs [61]. To determine the taxonomical distribution of WRKY genes from the BLAST output, we constructed a database where the BLAST results, the subject sequences and their associated taxonomy information from NCBI [58] were stored. The significant hits (E < 10-4) were parsed and manually checked for the presence of the characteristic features of the WRKY domain.
To systemically catalog the WRKY genes for rice and G. lamblia, we searched their genome sequences with blastp and PSI-BLAST [61] using WRKY-seed as the query. For PSI-BLAST, we used the default settings for three iterations. We also searched for WRKY genes with HMMER using the global profile of the WRKY domain [60]. HMMER, a sequence analysis tool based on profile Hidden Markov models [62], is available at [63]. The search results with the threshold of E < 10-4 for blastp and PSI-BLAST and E < 0.1 for HMMER were manually compared to remove non-WRKY hits. We also used the same strategy to identify the set of WRKY genes from the Arabidopsis genome.
To identify WRKY genes from the green alga, we first BLASTed its genome sequence against the WRKY-seed. The significantly aligned sequences (E < 10-4) were then subject to WRKY domain and gene predictions. The WRKY domain was predicted with the Pfam's DNA SEARCH [64], a web-interface backed by the GeneWise algorithm [65]. The WRKY gene was predicted by FGENESH using the profile for monocots [66,67] and GENSCAN using the profile for maize [68,69].
We also searched ESTs and EST-assembled contigs for the identified WRKY genes of rice, the green alga, G. lamblia and the slime mold, using blastn. An EST- or contig-hit was accepted if the identity of the alignment was > 96% for > 400 aligned nucleotides (nt), > 97% for 300 ~ 399 nt, > 98% for 200 ~ 299 nt, > 99% for 100 ~ 199 nt, and = 100% for 50 ~ 99 nt. The alignment with < 50 nt was discarded.
Analysis of WRKY genes
The WRKY domain boundary was defined as by Eulgem et al. [3]. The peptide sequences of the domains were aligned with ClustalX (v1.81, with default settings) [70] and the alignment was adjusted based on the conserved features of the WRKY domains. The results were then used to guide the alignment of the corresponding nucleotide sequences. The neighbor-joining algorithm implemented in PHYLIP 3.573c [71] for amino acid sequences with the pairwise distance computed under the PAM model, and the least square fit and most parsimony algorithms in PAUP* 4.0b10 [72] for nucleotide sequences were used for phylogenetic tree reconstruction.
Authors' contributions
LW initiated the study. YZ and LW carried out the analyses, and YZ drafted the manuscript.
Supplementary Material
Additional File 1
WRKY genes from Giardia lamblia, Dictyostelium discoideum and Chlamydomonas reinhadrtii
Click here for file
Additional File 2
Multiple alignments, domain classification and sequence conservation patterns of WRKY domains from rice (OsWRKY), Arabidopsis (AtWRKY), the green alga (ChrWRKY), the slime mold (DsWRKY) and Giardia lamblia (GlWRKY)
Click here for file
Additional File 3
Identified members of the WRKY superfamily in the rice genome
Click here for file
Additional File 4
Survey of WRKY genes from ESTs or their assembled gene indices for 19 plants and the phylogenetic classification of the genes
Click here for file
Acknowledgements
We thank Drs. Richard A. Dixon and Gregory D. May for critical reading of the manuscript. Financial support for this project was provided by the Samuel Roberts Noble Foundation.
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| 15629062 | PMC544883 | CC BY | 2021-01-04 16:37:16 | no | BMC Evol Biol. 2005 Jan 3; 5:1 | utf-8 | BMC Evol Biol | 2,005 | 10.1186/1471-2148-5-1 | oa_comm |
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BMC PediatrBMC Pediatrics1471-2431BioMed Central London 1471-2431-4-251560691210.1186/1471-2431-4-25Research ArticleHospitalisations for respiratory syncytial virus bronchiolitis in Akershus, Norway, 1993–2000: a population-based retrospective study Fjaerli Hans-Olav [email protected] Teresa [email protected] Dag [email protected] University of Oslo, Faculty Division Akershus University Hospital, 1474 Nordbyhagen, Norway2 Department of Paediatrics, Akershus University Hospital, 1474 Nordbyhagen, Norway3 Department of Laboratory Medicine, Children's and Women's Health, Norwegian University of Science and Technology; Department of Paediatrics, St. Olav University Hospital, Trondheim, Norway2004 17 12 2004 4 25 25 9 6 2004 17 12 2004 Copyright © 2004 Fjaerli et al; licensee BioMed Central Ltd.2004Fjaerli 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
RSV is recognized as the most important cause of serious lower respiratory tract illness in infants and young children worldwide leading to hospitalisation in a great number of cases, especially in certain high-risk groups. The aims of the present study were to identify risk groups, outcome and incidences of hospitalisation for RSV bronchiolitis in Norwegian children under two years of age and to compare the results with other studies.
Methods
We performed a population-based retrospective survey for the period 1993–2000 in children under two years of age hospitalised for RSV bronchiolitis.
Results
822 admissions from 764 patients were identified, 93% had one hospitalisation, while 7% had two or more hospitalisations. Mean annual hospitalisation incidences were 21.7 per 1.000 children under one year of age, 6.8 per 1.000 children at 1–2 years of age and 14.1 per 1.000 children under two years of age. 77 children (85 admissions) belonged to one or more high-risk groups such as preterm birth, trisomy 21 and congenital heart disease. For preterm children under one year of age, at 1–2 years of age and under two years of age hospitalisation incidences per 1.000 children were 23.5, 8.7 and 16.2 respectively. The incidence for children under two years of age with trisomy 21 was 153.8 per 1.000 children.
Conclusion
While the overall hospitalisation incidences and outcome of RSV bronchiolitis were in agreement with other studies, hospitalisation incidences for preterm children were lower than in many other studies. Age on admission for preterm children, when corrected for prematurity, was comparable to low-risk children. Length of hospitalisation and morbidity was high in both preterm children, children with a congenital heart disease and in children with trisomy 21, the last group being at particular high risk for severe disease.
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Background
Respiratory tract infections due to respiratory syncytial virus (RSV) are very common in young children worldwide. In temperate climates the infection occurs in yearly winter epidemics, and by two years of age most children have been infected [1]. Reinfections are common throughout life but the first infection is usually the most severe [2]. Symptoms vary from a mild upper respiratory tract infection to a severe bronchiolitis with hyperinflated lungs and hypoxemia [3]. Children in the first months of life, particularly those with preterm birth, underlying chronic lung disease (CLD), congenital heart disease (CHD), neuromuscular disease, airway malformations or impaired cellular immunity are at risk for severe disease [4-6].
Local epidemiological studies are important when considering new strategies for the prevention of RSV bronchiolitis. An example of such a strategy is the monthly injection of the humanised murine monoclonal antibody palivizumab to certain high-risk groups, which in many studies has shown a significant reduction in the incidence of hospitalisation for RSV bronchiolitis [7].
The aims of the present study are to identify risk groups, outcome and incidences of hospitalisation for RSV bronchiolitis in Norwegian children under two years of age, and to compare these results with other studies.
Methods
Background population
Akershus is a large suburban and countryside region located around the capital of Oslo with approximately 10% of the total population in Norway. The Paediatric Department at Akershus University Hospital (Ahus) is the only hospital for children under 15 years of age living in the 20 northern, southern and eastern communities of the region and serves all kinds of hospital services, except for paediatric surgery. Deliveries have been rather stable at approximately 3.500 per year in the service area during the last decade. Due to a local agreement infants from approximately 500 unselected term deliveries from our service area are born at Rikshospitalet in Oslo each year, but these infants are admitted to our hospital in case of disease during childhood. The number of children under two years of age living in the service area at any time during the follow-up period were identified from official Norwegian Population Statistics . The number of children under two years of age born before completed 37 weeks of gestation in the service area during the study period and still alive after the neonatal period was identified from the Norwegian Medical Birth Registry . All children with trisomy 21 have a program for follow-up from birth in our hospital and the number of children under two years of age with trisomy 21 was therefore identified from in-patient and out-patient hospital records. The number of children in the general population with a CHD was not known in the present study and hospitalisation incidences could therefore not be estimated. Overall mortality after the neonatal period as well as the number of children moving out of the service area during the study period were low and have not been corrected for.
Study population
The present study is a population-based retrospective survey on children under two years of age admitted to the hospital with a diagnosis of bronchiolitis (ICD 9 & 10) during the period February 5th 1993 to January 31th 2000 and diagnosed as positive with RSV in nasopharyngeal aspirate (NPA). RSV was diagnosed by an enzyme-linked immunosorbent assay (ELISA) for RSV (Abbott TestPack RSV). The test is simple and easy to perform giving a result within 30 minutes. The sensitivity, specificity, positive and negative predictive value is stated to be 74–92%, 86–100%, 81% and 93% respectively [8,9].
764 children who fulfilled the diagnostic criteria were identified. A total of 77 children belonged to one or more well known high-risk groups. Of these, 58 children had been born before completed 37 weeks of gestation, some with additional diseases such as CLD (12 children) and CHD (four children). 12 children had a diagnosis of CHD as the only additional disease and seven children had trisomy 21, four of these also diagnosed with CHD. These 77 children were regarded as a high-risk group for subgroup analysis. No children with other well-known risk-factors (neuromuscular disease, airway malformations, impaired cellular immunity or others) were identified. The remaining 687 children were born at term and healthy and regarded as a low-risk group for subgroup analysis.
Statistics
Data were analysed with the statistical programme SPSS, version 11. For analysis of gender we used the Chi square test and for comparisons between low-risk and high-risk groups we used the independent sample T-test. A p-value <0.05 was used as limit for statistical significance.
Results
Epidemiology
During this seven years follow-up period 764 children (822 admissions) were hospitalised for RSV bronchiolitis. 93% (707 children) had only one hospitalisation and 7% (57 children) had two or more hospitalisations for RSV bronchiolitis during the first two years of life. The highest number of admissions was recorded during the winter months December-April, but cases were also found in late spring and early autumn. Both the number of admissions and peak-time varied between seasons but with no specific pattern of variability (figure 1). A significant male predominance was observed, with 517 (63%) of all admissions being boys (P < 0.001)
Figure 1 Seasonal variations by month and year in hospitalisations for RSV bronchiolitis in children under two years of age in Akershus, Norway, February 1993 to January 2000
The majority of children (75% of all admissions) were hospitalised within the first year of life with children less than six months old being responsible for 45% of all admissions. Median age at hospitalisation was 6 months (range 0–23 months) and a median length of stay of 4 days (range 1–41 days) was observed (table 1).
Table 1 Age on admission and length of stay in children under two years of age hospitalised for RSV bronchiolitis in Akershus, Norway, February 1993 through January 2000
Risk groups Children (no.) Adm (no.) Median agea P-value (age) Median stayb P-value (stay)
All children 764 822 6.0 4.0
Prematurec 58 64 8.0 NSe 8.0 <0.001e
Corrected aged 58 64 5.4 NSe
Trisomy 21 7 8 9.0 NSe 7.5 <0.001e
CHD* 12 13 7.0 NSe 6.0 NSe
All low-risk 687 737 6.0 4.0
All high-risk 77 85 8.0 0.045e 8.0 <0.001e
Corrected aged 77 85 5.5 NSe
aAge in months
bDays including admission day
cBorn at <37 weeks of gestational age
dCorrected for weeks of prematurity
eVersus low-risk children
During the study period nine children (1.2%) developed severe respiratory distress and needed mechanical ventilation and two of them (0.3%) died. Four of these children had no known risk-factors for severe disease. However, the two children who died were both considered as high-risk patients, one of them was diagnosed with CHD and the other with trisomy 21 as well as CHD.
Of all RSV hospitalisations during the first two years of life 58 children (64 admissions) were born before completed 37 weeks of gestation with a median gestational age of 30 weeks (range 24–36 weeks). A significant male predominance of 68% was also observed in this group (P = 0.006). Median age on admission, after postnatal age, was 8 months (range 0–23 months). When corrected for prematurity (corrected age), median age on admission was 5.4 months (range -2.5–21.3 months). Also, a median length of hospitalisation of 8 days (range 2–27 days) was observed (table 1). Two of the preterm children (3.4%) needed mechanical ventilation and two were treated with inhaled nebulized ribavirin [10]. No preterm children in this study died from RSV bronchiolitis.
Seven children (eight admissions) diagnosed with trisomy 21 were hospitalised for RSV bronchiolitis during this follow-up study. Four of them also had a CHD. Two children needed mechanical ventilation during hospitalisation and one of them subsequently died.
A median age on admission of 9.0 months (range 1–20 months) and a median stay of 7.5 days (range 2–34 days) were recorded for children in this particular risk group (table 1).
A total of 20 children had a CHD, four combined with prematurity, four combined with trisomy 21, and 12 with no other additional risk-factor for severe disease. In the group of children with only CHD as risk factor median age on admission was 7.0 months (range 0–23 months) and median stay of 6.0 days (range 2–14 days) was observed (table 1). One child needed mechanical ventilation and subsequently died.
Hospitalisation incidences
Overall hospitalisation incidences were calculated from the known number by official Norwegian statistics of 58.179 children under two years of age living in the service area for the whole study period as well as for each year as given in table 2. As shown, the overall incidences for the whole study population was 21.7 admissions per 1.000 children under one year of age, 6.8 admissions per 1.000 children 1–2 years of age and 14.1 admissions per 1.000 children under two years of age, with some year to year variation (table 2).
Table 2 Mean annual hospitalisation incidences per 1.000 children by age and risk-groups in children under two years of age with RSV bronchiolitis in Akershus, Norway, 1993–2000
Children <1 year Children 1–2 years Children 0–2 years
Yeara Popd Adm Inc Popd Adm Inc Popd Adm Inc
All 93–94 4.027 70 17.4 4.195 16 3.8 8.222 86 10.5
All 94–95 3.999 53 13.3 4.192 25 6.0 8.191 78 9.5
All 95–96 3.994 112 28.0 4.132 30 7.3 8.126 142 17.5
All 96–97 4.055 80 19.7 4.143 22 5.3 8.198 102 12.4
All 97–98 4.154 115 27.7 4.242 57 13.4 8.396 172 20.5
All 98–99 4.178 70 16.8 4.350 15 3.4 8.528 85 10.0
All 99–00 4.127 120 29.1 4.391 37 8.4 8.518 157 18.4
All 93–2000 28.534 620 21.7 29.645 202 6.8 58.179 822 14.1
Prematureb 93–2000 1.999c 47 23.5 1.955c 17 8.7 3.954c 64 16.2
Trisomy 21 93–2000 52 8 153.8
aStudy period February 5th, 1993 to January 31th, 2000
bBorn at <37 weeks of gestational age
cNumber of children born at <37 weeks of gestation and corrected for a mean infant mortality rate of 2.4% during the neonatal period
dBackground population for selected risk groups
Data from the Norwegian Birth Registry identified that 1.999 infants born in the service area before completed 37 weeks of gestation were alive and below 1 year of age, and 1.955 infants were alive and between 1–2 years of age during the study period. As shown, the corresponding hospitalisation incidences for preterm children were 23.5, 8.7 and 16.2 per 1.000 children under one year of age, 1–2 years of age and <2 years of age respectively (table 2).
From the hospital records 52 children under two years of age were diagnosed with trisomy 21 during the follow-up period. Among these seven children (eight admissions) were hospitalised for RSV bronchiolitis, giving a hospitalisation incidence under two years of 153.8 per 1.000 children.
Discussion
In this retrospective study, as in many other comparable epidemiological studies, we found that RSV bronchiolitis appears as an annual winter epidemic with relatively high hospitalisation incidences, predominance of boys, young age, short length of stay, few complications and low mortality [1]. The rather long study period makes the results less vulnerable for yearly variations in magnitude of the annual epidemic and in virulence of the microbe. By using NPA for microbiological diagnosis in all children with symptoms of a lower respiratory tract infection like tachypnoe, wheezing or apnoic spells we believe that the great majority of cases are included. However, the enzyme-linked immunosorbent assay used in the present study could give both false positive and false negative results [9,11].
Several international studies have shown an increased incidence of RSV-related hospitalisations over the last two decades. A Norwegian study for the period 1972–78 showed a mean hospitalisation incidence of 9.5 per 1.000 children for children under one year of age compared to our much higher incidence of 21.7 per 1.000 children for the period 1993–2000 in the same age group [12]. Also, for children 1–2 years old the incidence in our study was much higher. A large study from USA for the period 1980–96 showed an increased incidence rising from 12.9 per 1.000 children in 1980 to 31.2 per 1.000 children in 1996 for children under one year of age [13]. However, comparing historical data is difficult, and for the period 1993–2000 we observed no increase in the mean annual hospitalisation incidences of RSV bronchiolitis. Factors such as increased microbial virulence, increased day-care attendance, improved microbiological diagnosis and more precise ICD-coding might all be important factors to explain these historical differences. Number of hospitalisations are also related to the severity of the clinical symptoms. Thus, changes in hospital admission policies over time as well as changes in availability of hospital services might also influence hospitalisation incidences. A recent study from USA for the period 1997–99 showed that RSV bronchiolitis was the leading cause of hospital admissions of infants younger than one year of age with an associated hospitalisation incidence of 25.2 per 1.000 infants. This is in accordance with our findings [14].
Our study showed that only 1.2% of the children in the study population needed mechanical ventilation. This result is lower than in many other studies [15]. On the other hand, when children were in need of mechanical ventilation, the risk of severe outcome was in our study very high, with two out of nine mechanically ventilated children subsequently dying. A Danish study showed that only 0.6% of their children needed mechanical ventilation, however, 20% of all children with severe respiratory failure were given ventilatory support with nasal continuous positive airway pressure (N-CPAP) and some infants possibly avoided mechanical ventilation for that reason [16]. More controlled studies on different treatment strategies are very much needed, especially when considering how to avoid respiratory failure and mechanical ventilation [17,18].
Another observation in our study was that among the mechanically ventilated children 44% had no underlying risk-factors for severe disease. Further research into why some otherwise healthy children have a severe course of RSV bronchiolitis is therefore very important, not least when trying to define risk groups even better [19]. Research for a better understanding of the host immune response to RSV bronchiolitis the later years might be one important piece in this puzzle [20-22].
For the population at large our study showed, as in other studies, a low mortality. A total of two children (0.3%) died, one had trisomy 21 with an atrioventricular septum defect (AVSD) and the other had an underlying CHD.
The number of high-risk children in our study is relatively small and the results should be interpreted with caution. The mean hospitalisation incidences for preterm children were lower than in many other studies, even though 20% of the preterm children suffered from CLD as well and had a low median weeks of gestational age (wGA) of 30. One possible explanation is that the population data for this particular risk group was not corrected for migration out of our service area and mortality after the neonatal period. However, it is unlikely that such corrections would significantly influence the results. Another explanation for the low incidence among preterm infants might be that most of them are hospitalised up to almost 40 wGA and are discharged only when the child has reached a weight of more than 2.000 g and have normal oral feedings. This means that they are more or less completely protected from RSV for a long period after birth. Our routines with focus on good parental information on how to protect the newborn infant from RSV after discharge from hospital might also be important. Median length of hospitalisation for preterm children was in our study significantly longer when compared to the low-risk children and is in accordance with results from many other larger studies.
Children with CHD are a wellknown risk-group for severe RSV disease. Children with trisomy 21 with or without a concomitant CHD should also be considered a high-risk group. Our study showed a mean hospitalisation incidence per 1.000 children under two years of age with trisomy 21 more than 11 times the whole study population. Even more important, out of a total of seven children with trisomy 21, two needed mechanical ventilation and one child died. We would, however, recommend a larger study to meet these observations for children with trisomy 21.
One of the findings in our study was the large proportion of otherwise healthy children needing mechanical ventilation. Our numbers are small, but when new policies to prevent severe RSV bronchiolitis are discussed this aspect should also be considered. The research for improved identification of risk-groups and development of an effective vaccine for immunization should therefore be given high priority.
Conclusions
While the overall hospitalisation incidences and outcome of RSV bronchiolitis were in agreement with other studies, hospitalisation incidences for preterm children were lower than in many other studies. Age on admission for preterm children, when corrected for prematurity, was comparable to low-risk children. Length of hospitalisation and morbidity was high in both preterm children, children with a congenital heart disease and in children with trisomy 21, the last group being at particular high risk for severe disease.
Competing interests
The author(s) declare that they have no competing interests.
Authors' contributions
Our individual contributions to the study have been as following: HOF had primary responsibility for protocol development, outcome assessment, data acquisition and analysis and writing of the manuscript. TF participated in the development of the protocol and analytic framework of the study, and contributed to the writing of the manuscript. DB participated in the data analyses and the writing 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:
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| 15606912 | PMC544884 | CC BY | 2021-01-04 16:31:00 | no | BMC Pediatr. 2004 Dec 17; 4:25 | utf-8 | BMC Pediatr | 2,004 | 10.1186/1471-2431-4-25 | oa_comm |
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BMC BioinformaticsBMC Bioinformatics1471-2105BioMed Central London 1471-2105-5-2091562634810.1186/1471-2105-5-209Research ArticleAnalysis of oligonucleotide array experiments with repeated measures using mixed models Li Hao [email protected] Constance L [email protected] Thomas V [email protected] Marilyn L [email protected] Arnold J [email protected] Department of Statistics, 815 Patterson Office Tower, University of Kentucky, Lexington, Kentucky 40506-0027, USA2 Department of Physiology, University of Kentucky, 800 Rose Street, Lexington, KY 40536-0298, USA3 Department of Anatomy and Neurobiology, College of Medicine, Univeristy of Kentucky, Lexington, KY 40536-0298, USA4 309 Sanders-Brown Center on Aging, 800 South Limestone Street, University of Kentucky College of Medicine, Lexington, KY 40536-0230, USA2004 30 12 2004 5 209 209 15 4 2004 30 12 2004 Copyright © 2004 Li et al; licensee BioMed Central Ltd.2004Li 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 or more factor mixed factorial experiments are becoming increasingly common in microarray data analysis. In this case study, the two factors are presence (Patients with Alzheimer's disease) or absence (Control) of the disease, and brain regions including olfactory bulb (OB) or cerebellum (CER). In the design considered in this manuscript, OB and CER are repeated measurements from the same subject and, hence, are correlated. It is critical to identify sources of variability in the analysis of oligonucleotide array experiments with repeated measures and correlations among data points have to be considered. In addition, multiple testing problems are more complicated in experiments with multi-level treatments or treatment combinations.
Results
In this study we adopted a linear mixed model to analyze oligonucleotide array experiments with repeated measures. We first construct a generalized F test to select differentially expressed genes. The Benjamini and Hochberg (BH) procedure of controlling false discovery rate (FDR) at 5% was applied to the P values of the generalized F test. For those genes with significant generalized F test, we then categorize them based on whether the interaction terms were significant or not at the α-level (αnew = 0.0033) determined by the FDR procedure. Since simple effects may be examined for the genes with significant interaction effect, we adopt the protected Fisher's least significant difference test (LSD) procedure at the level of αnew to control the family-wise error rate (FWER) for each gene examined.
Conclusions
A linear mixed model is appropriate for analysis of oligonucleotide array experiments with repeated measures. We constructed a generalized F test to select differentially expressed genes, and then applied a specific sequence of tests to identify factorial effects. This sequence of tests applied was designed to control for gene based FWER.
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Background
Experiments in which subjects are assigned randomly to levels of a treatment factor (or treatment combinations of more than one factor) and then are measured for trends at several sampling times, spaces or regions (within-subject factors) are increasingly common in clinical and medical research. The analysis of interaction, main effects and simple effects are appropriate for analyzing these types of experiments [1]. Main effects are average effects of a factor, and interaction effects measure differences between the effects of one factor at different levels of the other factor. As an example, this paper studies a 2 × 2 factorial treatment design, in which effects of two factors (treatment and region, for example) are studied and each factor has only two levels (with or without certain treatment, two different regions of studied subjects). The measurements from different regions of a subject are repeated measures on the individual and are correlated. In combination with microarray technology [2], this type of design allows one to investigate how treatments alter changes in gene expression in time or region simultaneously across a large number of genes. Two issues are crucial in the analysis of microarray experiments with repeated measures. Firstly, sources of variability must be identified, and the correlation structure among within-subject measurements needs to be taken into account; and secondly, multiple testing is also an immediate concern if tests of interaction, main effects, and/or simple effects are performed for each gene.
It has been shown that replication is the key not only to increasing the precision of estimation but also to estimating errors associated with tests of significance [3]. Previously, a number of ways to identify and model various sources of errors were proposed for replicated microarray experiments, and corresponding methods of extracting differentially expressed genes were suggested [4-8]. Recently, a linear modelling approach [9] and analysis of microarray experiments using mixed models were also introduced [10-12], in which the dependency structure of repeated measurements at the probe level were discussed. Statistical methods to analyze more complicated experiments, where correlated measurements are taken on one or more factor levels have not yet been fully described. In this study, we modified the two-staged linear mixed models [10], and extended them to more complicated designs.
Attention to the multiplicity problem in gene expression analysis has been increasing. Numerous methods are available for controlling the family-wise type I error rate (FWER) [13-17]. Since microarray experiments are frequently exploratory in nature and the sample sizes are usually small, Benjamini and Hochberg [18]suggested a potentially more powerful procedure, the false discovery rate (FDR), to control the proportion of errors among the identified differentially expressed genes. A number of studies for controlling FDR have followed [17,19-25]. However, these approaches for dealing with the multiplicity problems in microarray experiments are largely focused on relatively simple one-way layout experimental designs, and the number of genes that are involved in an experiment was the major concern. More complicated designs, such factorial designs with two or more factors, intensify the multiplicity problem not only because thousands of genes are involved in an experiment, but also because tests for interactions, main effects, and, possibly, simple effects need to be performed to further characterize differences for each gene. It has not been suggested explicitly, however, how to deal with such multiple-testing problems for two factors (or more than two factors) factorial experiments in the microarray literature.
In this paper, we present a method for analyzing oligonuleotide array experiments with repeated measures using a linear mixed model, which allows us to model variance-covariance structures associated with such complicated experiments. Our method is also related to that of Wolfinger et al., 2001, Chu et al., 2002, Kerr et al., 2000, and Wernisch et al., 2002 [5,9-11]. In addition, we construct a generalized F test to test the null hypothesis that all the means for all Disease by Region combinations are equal. Benjamini and Hochberg (BH) procedure of controlling FDR at 5% is used for comparing P values of the generalized F tests. The test to determine whether the interaction term is significant is performed only for each gene with a significant generalized F test. In addition, simple effects are examined for the genes with significant interaction effect and main effects are tested for those differentially expressed genes which do no exhibit significant interaction. In the 2 × 2 factorial, this sequence of tests controls the maximum FWER and, hence the FWER for all genes. We also illustrate how to summarize and categorize the interactions using simple diagrams. We demonstrate our method on the analysis of microarray data from two regions of the brain, the olfactory bulb (OB) and the cerebellum (CER), from control subjects and patients with AD. Although a 2 × 2 experiment was used in this manuscript, our methods can be extended to designs with more than 2 factors or more than 2 levels in one or more factors. The OB was used because AD patients show pronounced decrements in their olfactory sensitivity early in the clinical course of the disease [26]. The cerebellum was selected as a control tissue because it is generally considered to be minimally affected in AD.
Results
Analysis of gene expression in OB and CER of controls and AD patients
Based on the statistical methods described (see Methods), 708 genes were considered to be significant by the procedure of controlling FDR at 5% for multiple testing across genes. The largest P-value considered to be significant was 0.0033 determined by the FDR procedure. Among the 708 genes, 137 show significant interaction at the level of 0.0033, 49 genes with significant disease effect (32 were up-regulated and 17 were down-regulated in AD patients) and 559 genes with significant regional effects (331 were up-regulated and 228 were down-regulated in the OB) (Table 1). There were 37 genes that appear on both lists of significant disease and regional effects (not shown). Further validation studies, such as real time RT-PCR, could be performed to examine which interpretation is more reasonable.
Table 1 Summary of genes with main effects
Main effects Disease Region
Direction I D I D
# of genes 32 17 331 228
Fold change 1.1~2.9 1.2~2.8 1.1~104.3 1.1~121.7
I: significant upregulation of gene expression;
D: significant downregulation of gene expression.
A significant interaction effect for a gene has to be explained so that the gene can be further categorized based on the nature of the possible alterations of their expression levels. The interaction patterns were identified based on the change directions and test results for the following simple effects: control vs AD for OB, control vs AD for CER, CER vs OB for control, and CER vs OB for AD. The interaction effects can also be illustrated using simple diagrams by plotting together the average log2 based intensities under control and AD conditions for both OB and CER. Nonparallel lines in a diagram often imply an interaction effect. An interaction effect can be either directional or magnitudinal. In this study, directional interactions refer to the situations when the changes (in gene expression) between AD and control in OB are in the opposite directions compared to the changes between AD and control in CER. In a magnitudinal interaction, the directions of the changes between AD and control are the same but the magnitudes of changes are significantly different. The gene LOC91614 (UniGene Cluster Hs.180545), which encodes novel 58.3 kDa protein, is an example with directional interaction effect. As shown in Figure 1 A1, it is significantly up-regulated 2.08 fold in the OBs of AD patients (Table 2) and significantly down-regulated 3 fold (1/0.33) in their CERs (Table 2). The function of this gene is unknown, but, based on the domains identified in its protein sequence, it is likely to be involved in intracellular signalling cascades. Given this divergence in the direction of regulation in these 2 brain regions, this gene would be of interest for further characterization. The gene encoding the proteolytic lysosomal enzyme cathepsin H (UniGene Cluster Hs.114931) has a different pattern of interaction effects as shown in Figure 1 A2. It was significantly up-regulated 3.51 fold in the OBs of AD patients (Table 2) and shows a slight non-significant trend toward up-regulation in their CERs (Table 2). This is consistent with the pronounced activation of lysosomal enzymes that occurs in regions of the AD brain vulnerable to neurodegeneration (Nixon et al., 2000), and with the slight increase in lysosomal density in the CER compared with the pronounced increase in sites of the AD brain with significant neuropathology (prefrontal cortex and hippocampus; [27]). The patterns for other genes with significant interaction effects were also determined by the similar method described above.
Figure 1 Simple diagrams to illustrate significant interaction, main effects of Disease and Region. The average log transformed intensities under control and AD conditions for both OB and CER were plotted together for each gene with either significant interaction or main effects. The two points from each region were connected using a straight line and the non-parallel lines imply interaction. Two examples genes with interaction effect were shown in A. A1, represents a directional interaction and A2 indicates an interaction in magnitude. Two genes with only main effect of disease were illustrated in B, one of which showed down-regulation in AD (B1), while the other genes were upregulated in AD for both OB and CER. In the bottom panel, two genes with only regional differences were shown. The gene in C1 has high expression level in CER, and the gene in C2 has an opposite situation. See also Table 4, 5.
Table 2 Example of genes with significant interaction effects
Gene LOC91614 Cathepsin H
OB CER OB CER
Con 7.02 6.61 6.58 6.78 7.09 6.83 10.53 10.63 10.49 9.46 9.71 9.42
6.74 ± 0.25 6.90 ± 0.17 10.55 ± 0.07 9.53 ± 0.16
AD 7.58 7.59 8.21 5.30 5.03 5.58 12.30 12.41 12.37 9.54 9.82 9.79
7.80 ± 0.36 5.30 ± 0.28 12.36 ± 0.06 9.72 ± 0.15
Overall P 0.00058 2.12e-06
Interaction P 0.00036 5.86e-06
ConOB vs ADOB Re + +
fold 2.08 3.51
dir I I
ConCER vs ADCER Re + +
fold 0.33 1.14
dir D N
ConOB vs ConCER Re - +
fold 0.90 2.03
dir N I
ADOB vs ADCER Re + +
fold 5.66 6.23
dir I I
ConOB_ADOB: control vs AD for OB; ConCER_ADCER: control vs AD for CER; ConOB_ConCER: OB vs CER for control subjects; ADOB_ADCER: OB vs CER for AD patients. Overall P: the P value of the generalized F test. Interaction P: the P value of the interaction term. Re: the results of the protected Fisher's LSD procedure, where "+" indicates a significant difference and "-" implies a non significant difference ; dir: the direction of alteration in gene expression levels; D for decrease, I for increase and N for no change in AD when comparing control vs AD, or in OB when comparing CER vs OB; fold: fold change of each pairwise comparison calculated from the inverse transformed log2 based data. The fold change of LOC91614 gene expression between AD and control in OB was calculated as: 2(7.58+7.59+8.21)/3/2(7.02+6.61+6.58)/3 = 2.08. The fold changes in other situations were calculated in a similar way. Similar notations were also used in Table 3. See also Figure 1.
In the absence of interaction effects, main effects are often meaningful. The genes that have either significant main effect of Disease or Region were also identified and characterized by examining the average difference between AD and controls or the average difference between OB and CER. Main effects can also be illustrated by the simple diagrams described above, in which the lines are often parallel. Four genes were used as examples to illustrate main effect of Disease and main effect of Region (Table 3, Figure 1).
Table 3 Example of genes with significant main effects
Gene Log2 based data Overall P-value Interaction P-value Main effect of Disease Main effect of Region
OB CER P value fold dir P value fold dir
Con 12.68 12.73 12.83 12.37 12.34 12.53
12.75 ± 0.08 12.41 ± 0.10
HMGN2 0.0003 0.6462 0.0009* 0.63 D 0.011† 1.24 N
AD 12.01 11.94 12.16 11.53 12.04 11.81
12.04 ± 0.11 11.79 ± 0.26
Con 10.44 10.22 10.26 10.09 10.16 9.80
10.31 ± 0.12 10.02 ± 0.19
TSG101 0.0002 0.0726 0.0021* 1.51 I 0.056 1.42 N
AD 11.20 11.01 10.96 10.30 10.59 10.34
11.06 ± 0.13 10.41 ± 0.16
Con 11.49 10.91 10.83 12.56 12.47 12.62
11.08 ± 0.36 12.55 ± 0.08
RELN 0.0025 0.8328 0.5004 0.92 N 0.0005* 0.37 D
AD 11.33 11.00 10.66 12.45 12.35 12.41
11.00 ± 0.34 12.41 ± 0.06
Con 14.01 14.54 13.89 13.21 13.34 12.74
14.15 ± 0.35 13.10 ± 0.32
B2M 0.0011 0.3767 0.5986 0.94 N 0.0002* 2.20 I
AD 14.03 13.84 14.47 12.70 12.88 13.06
14.11 ± 0.32 12.88 ± 0.18
* indicates the P values pass the FDR 5% criteria. † indicates the P values are smaller than 0.05 but larger than the critical value 0.0033 determined by the FDR procedure. Overall P-value and interaction P-value are the same with the Overall P and Interaction P in Table 4. P values of main effect of disease and region are the P values of Type III ANOVA test using proc mixed procedure in SAS. Fold of main effect of disease: the ratio of the average intensities of AD (average over OB and CER) over Control (average over OB and CER). Fold of main effect of region: the ratio of the average intensities of OB (average over Control and AD) over CER (average over Control and AD). See also Figure 1.
The genes HMGN2 and TSG101 both have significant effect of Disease (Table 3). HMGN2 (high mobility group nucleosomal binding protein 2) was significantly down-regulated (1.6 fold; p = 0.0009) in the OBs of AD patients compared to elderly non-demented controls; there was no significant difference in mean expression levels in the OB and CER as shown in Figure 1 B1 and B2. Its down-regulation in the OBs of AD patients is consistent with the generally reduced level of gene expression that has been described in the AD brain [28]. The gene TSG101, was up-regulated 1.51 fold (p = 0.0021), with no significant differences in expression levels in the OB and CER. The encoded protein is a member of the mammalian class E vps proteins, which mediate ubiquitination-dependent receptor sorting within the endosomal pathway. The up-regulation of TSG101 suggests a potential disruption of OB neurogenesis.
Two examples of genes with Regional effects are RELN and B2M (Table 3). RELN is expressed at lower levels in the OB than in the CER (2.7 fold, p <0.0005) as shown in Figure 1 C1 and C2. The encoded protein is a secreted extracellular matrix molecule that interacts with integrin signalling to generate a signal for migratory developing neurons to stop and form layers; thus, a defect in this gene results in improper development of the cerebellum as well as other brain regions [29]. B2M, the gene encoding β2 microglobulin, is expressed at 2.2-fold higher levels in the OB than in the CER (p < 0.0002). One potential explanation for the higher levels of B2M expression in the OB than the CER is that antigens can enter the brain directly along the pathway provided by the axon of the olfactory receptor neuron or within the sheath of the olfactory nerve; numerous proteins and pathogens enter the brain via this route (e.g., [30,31]. The potentially higher level of antigenic stimulation in the OB may result in the up-regulation of B2M expression, which would not occur in the CER due to the lack of such a direct connection with the external environment.
The remaining genes with either significant effects of Disease or Region were also identified and categorized in a similar way and summarized in Table 1.
Discussion
In this study, we adopted a linear mixed model to analyze oligonuleotide array experiments with repeated measures. We constructed a generalized F test to select differentially expressed genes and compared our method to another frequently used approach. Using the method described above, we identified 708 differentially expressed genes, 137 of which have significant interaction, and 571 genes have main effect of either Disease or Region. Using simple diagrams, we can illustrate and further categorize the interactions and main effects.
This linear mixed model approach allows us to identify various sources of variability, including experimental effects, random effects of subjects and random error. The performance of the generalized F statistic depends on the validity of the assumed covariance structures and the degree of replication. We assumed homoscedastic variances for each gene. This may not be true for all genes in reality. With small sample sizes, which are common in microarray studies, simpler covariance structures which require the estimation of fewer variance components are preferred. Simulation studies showed that, with sample size of 3, the generalized F test performs reasonably well in cases with homoscedastic variances.
We also tested the factorial effects on the 708 genes which were identified by BH procedure using the more conservative Bonferroni adjustment to the α-level in order to simultaneously control FDR and the possibility of performing multiple tests for the factorial effects. For example, controlling FDR at 0.05/3 = 1.67%, produced a list of 77 genes. The method we developed is more powerful. In addition, only regional effects were identified without significant interaction and main effect of disease by the alternative method.
In this manuscript, we adopt BH procedure to control FDR at 5% based on the generalized F tests. Any other standard multiple testing procedures may also be applied. A specific sequence of tests was used to identify factorial effects and control the gene-based FWER in our study. For researchers who are interested in all pairwise comparisons among treatment groups, Hayter's modification of the LSD method [32] controls the FWER for all genes.
We also assumed independence of the significant tests among genes. This assumption, which is also adopted in the majority of the microarray literature, may not be completely valid since gene expression is tightly regulated. The correlation among the genes varies from developmental stages, tissue to tissue, etc., and we may never be able to quantify it precisely. The assumption that genes are correlated in small clusters has been adopted by Benjamini and Yekutieli [21] in their FDR control study. This assumption, however, has not been completely verified.
Conclusions
A linear mixed model is appropriate for analysis of oligonucleotide array experiments with repeated measures, allowing us to quantify various sources of error. We constructed a generalized F test to select differentially expressed genes, and then applied a specific sequence of tests to identify factorial effects. This sequence of tests applied was designed to control for gene based FWER. Our methods can be extended to designs with more than 2 factors or more than 2 levels in one or more factors. The generalized F test can be constructed for any number of factors or levels of factors.
Methods
Sources and processing of tissue
OBs were obtained with appropriate informed consent from patients with Alzheimer's disease (AD) and control subjects enrolled in the Biologically Resilient Adults in Neurological Studies (BRAiNS) project of the Sanders-Brown Center on Aging. At autopsy, OBs and pieces of the lateral tip of the cerebellums were removed from 6 females, 3 with AD (mean age, 79.0 years; mean postmortem interval 3.8 h) and 3 controls (mean age, 78.6 years; mean postmortem interval 2.9 h), and immediately placed in liquid nitrogen. BRAiNS control subjects had no clinical evidence of dementia or other neurological problems and scored within the normal range on yearly mental status tests; on neuropathological examination, their brains exhibited age-related but not disease-related changes. AD patients received a diagnosis of probable AD in the Memory Disorders Clinic; on neuropathological examination, their brains met multiple criteria for definite AD and exhibited no indications of complications from cerebrovascular disease [33].
OBs and cerebellum were homogenized in TRI-Reagent (Molecular Research, Inc., Cincinnati, OH), and total RNA was extracted according to the manufacturer's protocol. RNA concentration was determined spectrophotometrically; its integrity and quality were assessed by spectrophotometry, agarose gel electrophoresis, and Bioanalyzer (Agilent, Technologies, Wilmington, DE) virtual gels. Following target preparation, the samples were hybridized onto the Affymetrix Human Genome U133_A and _B GeneChips at the University of Kentucky Microarray Core Facility according to Affymetrix protocols.
Experimental design
OBs and pieces of the lateral tip of the cerebellums were previously removed from each of 3 control subjects and 3 patients with AD (all female with similar ages). Total RNA was extracted from OB and CER tissues for each subject. Five μg RNA from the OB and CER of each individual were hybridized with Affymetrix Human Genome U133_A and _B chips (2 GeneChips/tissue/individual = 24 GeneChips). Data from U133_A and _B chips for each RNA sample were combined to give 12 data sets with signal intensities for 44828 targets. Under the assumption of independence among genes, we have a 2 × 2 factorial design for each gene with one factor being either control or AD and with repeated measures (regions, OB or CER) on each subject. The arrangement for the 2 × 2 mixed factorial design in this experiment is shown as in Table 4, where μ11, μ12, μ21, and μ22 denotes the average log 2 based expression levels measured in OB of controls, CER of controls, OB of AD patients and CER of AD patients respectively. Corresponding measurements from the same subject are correlated and they are marked as same color. Our primary interests are to identify various sources of variability and differentially expressed genes.
Table 4 The arrangement for the 2 × 2 factorial design with repeated measures
OB CER
Control μ11 μ12
AD μ21 μ22
μ11, μ12, μ21, and μ22 are the true means of measurements in OB of controls, CER of controls, OB of AD patients and CER of AD patients respectively.
Data preparation
Normalization
Background correction and initial total intensity normalization were first performed for the microarray raw data using Affymetrix Version 5 software [34], resulting in gene intensities for each gene-chip combination. The log intensities values were used in later processing. We chose the local regression method (loess) [35-37] to normalize the chips within each of the four treatment combinations. The total intensity method was performed to normalize array across treatment combinations.
Data Filtering
In our study, all positive control genes and genes that resulted in an "absent" call for all chips were removed from further analysis. If there was no evidence that these genes were expressed in any of the samples, then these genes can be removed to reduce problems associated with multiple comparisons. Other methods of removing low intensity points were also suggested by Bolstad et al., 2003 [37]. All ESTs were also removed from the analysis. Since the primary interest of these experiments is to identify known genes that are differentially regulated, eliminating ESTs will further reduce problems with multiple comparisons. After data filtering steps, 10,590 genes remained, and the base-2 logarithms of background-corrected and normalized intensities of these genes were subject to further statistical analyses.
Algorithm and analysis
Analysis of variance components
We use a linear mixed model to describe the experiment.
Let Ygijk be the base-2 logarithm of background-corrected and normalized intensity of the gth gene, g = 1, ..., 10590, in the ith Treatment group i = 1, 2, from the jth Region, j = 1, 2, on the kth subject k = 1, 2, 3. "Treatment" here signifies the health condition of the subjects (controls or AD patients). A complete linear mixed model for this experiment:
Ygijk = μ + Di + Sik + Rj + (DR)ij + Aijk + Gg + (GD)gi + (GR)gj + (GDR)gij + εgijk, (1)
where μ is the grand mean, Di and Rj and are the main effects of treatments, regions respectively, and (DR)ij are the treatment-region interaction effects. Here Sik are the random effects of subjects within disease group and Aijk are the random effects of chips. The symbols Gg, (GD)gi, (GR)gj and (GDR)gij represent the main effect of gene, gene-treatment interaction effects, gene-region interaction effects, gene-treatment-region interaction effects, while εgijk are the additive stochastic errors
In general, it is impractical, using currently available software, to fit linear models such as (1) with microarray data involving manipulation of the full covariance matrix of observation variables that usually contains thousands of levels. To be conceptually and computationally more efficient, Wolfinger et al., 2001 [10] suggested a two-step model to separate experimental-wise systematic effects (normalization sub-model) and the remaining effects for each gene (gene sub-model). In our case, however, the design matrix for the fixed effects of Di, Rj and (DR)ij is orthogonal to the design matrix for the fixed effects involving each gene, including Gg, (GD)gi, (GR)gj and (GDR)gij. Therefore, the normalization model has no effect on the inference for each gene under the assumption in (1). A simpler model can be adopted for each gene, and the random effect Sik is absorbed by Sgik terms and Aijk is absorbed by εgijk terms. We make standard stochastic assumptions that the random effects Sgik, and εgijk are normally distributed with zero means with variances σgs2, and σg2 respectively. These random effects are assumed to be independent both across their indices. The model equation then becomes
Ygijk = μg + Dgi + Rgj + (DR)gij + Sgik + εgijk. (2)
In matrix notation, the model equation for each gene can be written
Y = Xβ + Zu + ε, (3)
where Y is a vector of observations, X and Z are matrices of known constants for the fixed effects and random effects, respectively, β is a vector containing fixed effect parameters Dgi, Rgj, and (DR)gij, u is a vector of random effects, and ε is the error or residual vector. Therefore, Y ~ MVN (Xβ,V) where V = ZDZ' + Σ. The covariance matrices D = var(u) and Σ = var(ε) can have any valid variance-covariance matrix form. The variances of gene specific subject effects S can vary for different treatments and different genes, while ε effects can have different variances for different treatments, regions and different genes. The remaining terms are fixed effects. All effects and variance components in the model can be estimated using the method of restricted maximum likelihood (REML) [38].
In the homogeneous variance case assumed here, since observations across subjects are independent, the variance-covariance matrix for gene g, Vg, is block diagonal where
Vg = diag (Σg)
and
If the assumption of homoscedasticity is not viable, the variance-covariance for gene g can easily be accommodated by allowing Σg to vary across disease groups.
Estimation of model parameters
The estimate of primary interest is β, which containing treatment, region effects and treatment-region interaction for each gene. For each gene, β is estimated by
The estimated has covariance
where in practice components of V are replaced by their REML estimates. See Verbeke and Molenberghs (2000) [1] for methods to derive equations (3)–(5) and REML estimates of the random components in details.
Construct a generalized F test
Genes showing significant interaction effects are defined as those in which the difference in expression levels between control and AD is not the same with the difference between OB and CER. Main effects are meaningful in the absence of interaction effect. Genes showing a significant disease-related effect or main effect of disease are defined as those either under- or over-expressed by AD patients compared to controls at the same extent in both OB and CER, while genes with significant main effect of region are those either under- or over-expressed in OB compared to CER at the same extent by both AD and controls. If the expression levels for a gene are the same across all treatment-region combinations, then there will be neither significant interaction nor main effects; therefore this gene should be excluded from further analysis. The expression of other genes may be altered by treatment or/and region effects, and further analysis of these genes is needed to characterize the experimental effects. Therefore the first step to select differentially expressed genes in factorial designs is to choose those for each of which the hypothesis of equality of all cell means, μ11 = μ12 = μ21 = μ22, is rejected. Because of the specific variance-covariance structure for a repeated measures experiment with two levels of the within subject factor, it is convenient to test the equivalent composite hypothesis for each gene g which is stated in terms of the main effects and the interaction. Specifically, we consider
We can test this composite null hypothesis of no interaction and main effects simultaneously by setting up 3 corresponding linear contrasts listed in Table 5. A contrast is a linear combination of parameters, for which the coefficients sum to zero [39]. Let L be the 3 × 8 matrix containing the coefficients of the 3 contrasts, then Ho is simplified as Lβ = 0, where Lβ is estimable, and can be tested using the generalized F test
Table 5 Set up hypothesis using linear contrasts
Ho L
Effects Mean Dg1 Dg2 Rg1 Rg2 (DR)g11 (DR)g12 (DR)g21 (DR)g22
(DR)gij = 0 (μ21 - μ11) - (μ22 - μ12) = 0 0 0 0 0 -1 1 1 -1
Dgi = 0 (μ11 + μ12) - (μ21 + μ22) = 0 2 -2 0 0 1 1 -1 -1
Rgj = 0 (μ11 + μ21) - (μ12 + μ22) = 0 0 0 2 -2 1 -1 1 -1
The hypothesis in terms of model parameters and means were listed. The coefficients for the model parameters of the linear contrasts were determined for the corresponding hypotheses.
Under Ho, the generalized F is distributed approximately as Snedecor's F with degrees of freedom rank(L) and ν (F[rank(L), ν]). Since the variance-covariance matrix V satisfies a compound symmetry condition, in our example this statistic is distributed as F[3, 4]. Under other assumptions of the variance-covariance structures, the denominator degrees of freedom ν can be approximated by the degrees of freedom to estimate L(X'V-1 X)-1 L' using Satterthwaite's procedure [38,40]. Details about how to select appropriate covariance structures were discussed by Littell et al. (1996) [38] and Keselman et al. (1998) [41].
Adjustment for multiple tests
Multiple testing problems in microarray experiments with factorial designs are at least two-fold. Usually, hypothesis tests are performed for each of thousands of genes involved, and tests of main effects and interactions may also be needed for each gene. Based on the generalized F test we constructed above, we now suggest a method for adjusting multiple tests.
The most commonly used methods to adjust multiple tests are of controlling either FWER or FDR. These methods are first applied to the P-values from the generalized F tests, providing a list of genes that exhibit significant difference among the four cell means of Disease by Region combination. Some of these genes may have significant interactions, or only the main effects of treatment and/or region are significant. Further characterizing the significant interactions are one of the major interests for researchers, and methods for investigate interaction contrasts are available [42-44]. In our study, simple effects were examined for the genes the have a significant interaction to detect the difference between specific comparisons. Protected by the generalized F test, Fisher's least significant difference test (LSD) method can be used to test the necessary simple effects. Here the appropriate error terms for these simple effects depend on whether the comparisons involve measurements from same Disease groups or not. This sequence of tests proposed in this paper are more powerful, while still allowing for the control of FWER or FDR, compared to directly adjusting P-values using BH procedure with Bonferroni correction. In the latter method, if we control overall FDR at 5%, we would perform BH procedure at level of 1.67% or 0.05/3 for each test of interaction, main effect of disease or region.
Recipe of the analysis
A short summary of the statistical methods used in this study follows:
1. Linear mixed models were used to describe the data based on the experimental design and some common assumptions, and the variance components were specified.
2. For each gene, a generalized F test was performed based on the described model, and the corresponding P-value was obtained.
3. To adjust the multiple tests for numbers of genes, the BH method of controlling FDR [18] at 5% was applied to the P-values obtained above, providing a list of genes (list I) that exhibit significant differences among the means of the Disease*Region combinations.
4. Using αnew, which equals to the largest P-value considered to be significant in step3 as the cut-off point, we choose genes with significant interactions (list II) from list I and, for genes in list II, to test the simple effects. By complete enumerating of all possible combinations of main effects and interaction effect, one can prove that αnew is an appropriate choice to control the FWERs while selecting genes with either significant interaction or main effects in 2 × 2 factorial experiments. From the remaining genes, significant main effects of either disease or region (list III) were selected. In the example used in this study, αnew = 0.0033.
Statistical software
Data normalization and generation of simulated data were performed using S-plus version 6.1. We used SAS (version 9.0) proc mixed procedure to do Model fitting and significance analysis. The SAS program implementing linear mixed models for the AD data is available on request from the first author.
Simulation studies
We constructed a generalized F test to select differentially expressed genes (see method). To assess the performance of the constructed generalized F test with small sample sizes, we performed simulation studies. Since expression levels of genes in the OB or CER from an individual (either a control or patient with AD) are considered to be repeated measures, correlated data should be generated for the simulations. First, we studied the case (case I) with equal variance and covariance structure for each individual subject (control or AD patient). We generated 10,000 sets of correlated data; each set has 6 bivariate observations, with mean 20 and the following covariance structure for each subject under either Disease condition (i = 1 for control, and i = 2 for AD; j or j' = 1 for OB, and j or j' = 2 for CER, k = 1, 2, 3):
where Ygijk and Ygij'k are measurements from the jth and j'th levels of Region for the kth subject in the ith level of Disease for gene g. The generalized F-statistics were computed for each of the 10,000 data sets and the histogram of the generalized F-statistics was compared with that of randomly generated F values from a F[3, 4] distribution as shown in figure 2A. The histogram of the generalized F-statistics has a slightly larger tail. The proportion of the generalized F-statistics that were no larger than the critical value, F[3, 4, α = 0.05] = 6.59 was 5.12%, instead of the nominal 5%, 4.97% random generated F values were smaller than 6.59.
Figure 2 Histograms of Simulated F statistics. The histograms of the F statistics from F[3, 4] (grey in A, B), simulated data with same covariance structure among individuals (cyan, case I in A) or unequal variance for subjects from controls and AD patients (blue, case II in B). Case I has slightly larger tail than random generated F values, and the right tail of case II were thicker than both of cases above.
More complicated variance and covariance structure can also be assumed. For example, the controls and AD patients may have a different covariance matrix. We then generated simulated data to study cases like above (case II). Using the same covariance structure as above, we generate 10,000 sets of data for controls. For AD, we generated 10,000 sets of data using a different covariance structure
Then we computed the generalized F-statistics and compared them with randomly generated F values described above (Figure 2B). The histogram of the generalized F-statistics has a slightly larger tail than those of both random generated F values and case II. There was 5.42% of the generalized F-statistics in case II were larger than the critical value 6.59. With small sample size in both cases (n = 3), the constructed generalized F-statistics behave reasonably well.
Authors' contributions
HL carried out the study. AJS, and CLW supervised the study. TVG and MLG carried out the molecular genetics studies. All authors contributed to the writing of this manuscript. All authors read and approved the final manuscript.
Acknowledgements
This work was supported by NIH AG-16345 (MLG), NIH-P20RR16481 and NSF-EPS-0132295 (AJS), NIH AG-016824-23 (TVG). We also wish to thank Donna Wall, Microarray Facility, and Radhika Vaishnav, Department of Anatomy and Neurobiology, for their expertise.
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BMC UrolBMC Urology1471-2490BioMed Central London 1471-2490-5-11564212410.1186/1471-2490-5-1Research ArticleIs DRE essential for the follow up of prostate cancer patients? A prospective audit of 194 patients Ragavan Narasimhan [email protected] Vijay K [email protected] Sujoy [email protected] Jennifer [email protected] Shyam S [email protected] Michael E [email protected] Rosemary A [email protected] Department of Urology, Lancashire Teaching Hospitals NHS Trust, Preston, Lancashire, United Kingdom2005 10 1 2005 5 1 1 27 9 2004 10 1 2005 Copyright © 2005 Ragavan et al; licensee BioMed Central Ltd.2005Ragavan 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
Prostate cancer follow up forms a substantial part of the urology outpatient workload. Nurse led prostate cancer follow up clinics are becoming more common. Routine follow-up may involve performing DRE, which may require training.
Objectives
The aim of this audit was to assess the factors that influenced the change in the management of prostate cancer patients during follow up. This would allow us to pave the way towards a protocol driven follow up clinic led by nurse specialists without formal training in DRE.
Results
194 prostate cancer patients were seen over a period of two months and all the patients had DRE performed on at least one occasion. The management was changed in 47 patients. The most common factor influencing this change was PSA trend. A change in DRE findings influenced advancement of the clinic visit in 2 patients.
Conclusions
PSA is the most common factor influencing change in the management of these patients. Nurse specialists can run prostate cancer follow-up clinics in parallel to existing consultant clinics and reserve DRE only for those patients who have a PSA change or have onset of new symptoms. However larger studies are required involving all the subgroups of patients to identify the subgroups of patients who will require DRE routinely.
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Introduction
Prostate cancer ranks first amongst all male urological cancers [1]. In the UK, 26027 new patients were diagnosed with prostate cancer during 2001 [1]. The evidence suggests an increasing trend in the incidence in the recent years, being 18201 in 1997 [2]. Nonetheless, better treatment modalities and earlier detection has resulted in a decrease in cancer related mortality [3]. This is shown in the age-standardized death rate per million population for prostate cancer, being 302 and 274 in 1991 and 2001 respectively.
Widespread PSA testing and increased awareness has led to the detection of early prostate cancer in many patients [4]. This has probably resulted in more patients requiring long periods of follow up. Nurse Specialists in UK health care system have evolved to share the increasing demand on the clinicians to meet the targets and waiting times in all the specialties. In urology, Nurse Specialists have assumed various roles including prostate assessment clinics, urodynamics and flexible cystoscopy [5]. In some health care trusts, Nurse Specialists are involved in the follow up of treated prostate cancer patients.
Faithfull et al studied the use of telephone follow up of prostate cancer patients by nurse specialists. They found that this method of follow-up at 3, 6 and 12 weeks post radiotherapy was effective and economical [6]. In addition a study on the follow-up of prostate cancer patients by on-demand contact with a nurse specialist was found to be as effective as traditional outpatient follow up by urologists [7].
The EAU guidelines [8] suggest that prostate cancer patients should be followed at regular intervals with a disease specific history and PSA estimation supplemented by digital rectal examination. This would suggest that all Nurse Specialists undertaking the role of follow-up of such patients should be trained in DRE. Data on the role of DRE in the follow up of prostate cancer patients is available only for the subgroup of patients who have had treatment with curative intent (radical prostatectomy or radical radiotherapy) and these studies show that PSA trend plays a more important role than DRE. However there is limited data available on the role of DRE and other factors (e.g. LUTS, Bone pain etc) in the follow up of diagnosed prostate cancer patients in the general setting involving all treatment varieties which is likely to be encountered in a nurse led follow up clinic.
The aim of this audit was to prospectively assess the various factors that influence a change in the management of the prostate cancer patients on follow up and to highlight the feasibility of nurse led clinics for the follow up of prostate cancer patients.
Methods
Over a two-month period (Dec 2002–Jan 2003) all the prostate cancer patients being followed up in the Urology outpatient clinics at our institution were audited prospectively. The patients were seen by a Consultant, Specialist Registrar or Senior House Officer. The period of follow-up, initial stage of the disease, management modality, consecutive PSA values and consecutive DRE findings (if available) were recorded on specifically designed data collection forms. All the patients had DRE done on at least one occasion. The change in the management was defined as any alteration in the follow-up pattern; either as an advancement or postponement of a future appointment, the need for further investigation or treatment, the admission of a patient and the referral to a different specialist, for example an Oncologist or Palliative Care specialist
The attending physicians were requested to record whether there was any change in the management and which factors influenced the change. They were specifically requested to record whether DRE influenced a change.
Results
During the period studied 194 patients being followed up for treated prostate cancer were included. The mean age was 74.8 years and the stages at initial diagnosis were: T1 (n = 73), T2 (n = 63), T3 (n = 44), T4 (n = 14). Ten patients had metastatic disease. The management modalities that these patients had undergone included: hormonal manipulation (68), orchidectomy (8), radical radiotherapy with hormonal manipulation (15), radical radiotherapy (48), radical prostatectomy (21), brachytherapy (1) and active surveillance (33) (Table 1). The management changed in 47 of 194 (24%) patients. The factors that influenced the changes included PSA trend (n = 27), LUTS (n = 10), bone pain (n = 4), change in DRE findings (n = 2) and other factors namely abnormal renal functions (n = 1), hematochezia (n = 1), pruritis (n = 1) and erectile dysfunction (n = 1) (Table 2).
Table 1 Management categories of the follow up prostate cancer patients
Management Number of patients Percentage of the total number (n = 194)
Active surveillance 33 17
Radical prostatectomy 21 10.8
Radical radiotherapy 48 24.8
Radical radiotherapy With hormones 15 7.8
Brachytherapy 1 0.5
Hormone therapy 68 35
Orchiectomy 8 4.1
Table 2 Factors that influenced a change in management
Factors Number of patients Percentage of the total number of changes (n = 47)
PSA trend 27 57.5
Lower urinary tract symptoms 10 21.3
Bone pains 4 8.5
DRE findings 2 4.3
Pruritis 1 2.1
Altered renal functions 1 2.1
Erectile dysfunction 1 2.1
Bleeding per rectum 1 2.1
In this audit PSA trend was the most common factor that resulted in a management change. In the two patients there was a change in DRE findings (progression from T2b disease to T3 disease as observed by the assessor). This only resulted in the subsequent visit being sooner than planned.
Discussion
The follow up of patients with prostate cancer has traditionally included a disease specific history, serial PSA estimations and a DRE. The roles of PSA and DRE have been extensively evaluated in the diagnosis of prostate cancer patients [9,10]. There have only been a few studies questioning the importance of DRE in the follow up of patients treated with a curative intent [[11-13] and [14]]. These have been based on groups of patients undergoing specific treatments. These studies concluded that DRE is unnecessary in the follow up of patients if PSA is undetectable. However there have been rare case reports describing local or systemic recurrence in the absence of detectable PSA [15,16].
There are no reported studies in the English language assessing the role of routine DRE in the follow up of all treated prostate cancer patients in a general urology outpatient setting. In addition, studies assessing the various factors (e.g LUTS, bone pains etc) that influence a change in the management of these patients have not been reported.
The present audit shows that PSA trend is the most common factor influencing a change in management whilst DRE plays a very limited role. Further, there are other factors that influence a change in the management of these patients' e.g. Bone pain and LUTS.
Although the numbers of patients involved in this audit are moderate it would suggest that Nurse Specialists could deliver the optimum care in following up treated prostate cancer patients. Such Nurse led clinics could be carried out in parallel to the existing Consultant clinics thereby allowing the availability of medical personnel to perform DRE where deemed necessary. A protocol to perform DRE when there is an increase in PSA, onset of new symptoms or worsening of existing symptoms would be suitable for such a clinic. This audit suggests that Nurse Specialists need not be trained to perform DRE before the establishment of such clinics. However larger studies are required to identify subgroups of treated prostate cancer patients who may require a DRE on a regular basis. Alternatively nurses could be taught to undertake DRE thereby further reducing clinician workload. This would require a standardised and validated teaching method, which currently does not exist. In our hospital this audit has influenced the initiation of Nurse led prostate cancer follow up clinics conducted in parallel to the consultant clinics.
Competing interests
The author(s) declare that they have no competing interests.
Authors' contributions
NR – Along with VKS conceived the study, collected the data and jointly prepared the text with VKS – Along with NR conceived the study, assessed the data and prepared the text, SG – Participated in collecting patients details and in the preparation of the text, JH – helped in approaching the patients and data collection, SSM – Advised regarding the design of the study and contributed to the text, MEW – Advised regarding the design of the study and contributed to the text, RAB – Overall supervision of the project with periodic assessment on progress and preparation of text
All authors have read and approved the final manuscript.
Pre-publication history
The pre-publication history for this paper can be accessed here:
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Crawford ED Epidemiology of prostate cancer Urology 2003 62 3 12 14706503 10.1016/j.urology.2003.10.013
Taylor JM Pearce I O'Flynn KJ Nurse-led cystoscopy: the next step BJU Int 2002 90 45 6 12081768 10.1046/j.1464-410X.2002.02831.x
Faithfull S Corner J Meyer L Huddart R Dearnaley D Evaluation of nurse-led follow up for patients undergoing pelvic radiotherapy Br J Cancer 2001 85 1853 64 2001 Dec 14 11747326 10.1054/bjoc.2001.2173
Helgesen F Andersson SO Gustafsson O Varenhorst E Goben B Carnock S Sehlstedt L Carlsson P Holmberg L Johansson JE Follow-up of prostate cancer patients by on-demand contacts with a Specialist nurse: a randomized study Scand J Urol Nephrol 2000 34 55 61 10757272 10.1080/003655900750016904
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Johnstone PA Mc Farland JT Riffenburgh RH Amling CL Efficacy of digital rectal examination after radiotherapy for prostate cancer J Urol 2001 166 1684 7 11586202 10.1097/00005392-200111000-00016
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BMC Med EducBMC Medical Education1472-6920BioMed Central London 1472-6920-5-11563435910.1186/1472-6920-5-1DebateSicily statement on evidence-based practice Dawes Martin [email protected] William [email protected] Paul [email protected] Antonino [email protected] Janet [email protected] Kevork [email protected] Franz [email protected] Amanda [email protected] James [email protected] Department of Family Medicine. McGill University, Montreal, Canada2 The Lancet, Jamestown Road, London, UK3 Department of Primary Health Care, Centre for Evidence-Based Practice, Oxford University, Oxford, UK4 Gruppo Italiano per la Medicina Basata sulle Evidenze (GIMBE), Passaggio L. da Vinci, 16 – 90145 Palermo, Italy5 London Health Sciences Centre, Department of Physiology & Pharmacology, University of Western Ontario, London, Ontario, Canada6 School of Medicine, Health Policy and Practice, University of East Anglia Norwich, UK7 University Hospital Ulm, Clinical Economics, Ulm, Germany8 Department Public Health and Epidemiology, University of Birmingham, Birmingham UK9 United Bristol Healthcare Trust, Bristol, UK2005 5 1 2005 5 1 1 3 10 2004 5 1 2005 Copyright © 2005 Dawes et al; licensee BioMed Central Ltd.2005Dawes 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 variety of definitions of evidence-based practice (EBP) exist. However, definitions are in themselves insufficient to explain the underlying processes of EBP and to differentiate between an evidence-based process and evidence-based outcome. There is a need for a clear statement of what Evidence-Based Practice (EBP) means, a description of the skills required to practise in an evidence-based manner and a curriculum that outlines the minimum requirements for training health professionals in EBP. This consensus statement is based on current literature and incorporating the experience of delegates attending the 2003 Conference of Evidence-Based Health Care Teachers and Developers ("Signposting the future of EBHC").
Discussion
Evidence-Based Practice has evolved in both scope and definition. Evidence-Based Practice (EBP) requires that decisions about health care are based on the best available, current, valid and relevant evidence. These decisions should be made by those receiving care, informed by the tacit and explicit knowledge of those providing care, within the context of available resources.
Health care professionals must be able to gain, assess, apply and integrate new knowledge and have the ability to adapt to changing circumstances throughout their professional life. Curricula to deliver these aptitudes need to be grounded in the five-step model of EBP, and informed by ongoing research. Core assessment tools for each of the steps should continue to be developed, validated, and made freely available.
Summary
All health care professionals need to understand the principles of EBP, recognise EBP in action, implement evidence-based policies, and have a critical attitude to their own practice and to evidence. Without these skills, professionals and organisations will find it difficult to provide 'best practice'.
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Background
The Sicily statement on evidence-based practice
"Knowing is not enough; we must apply. Willing is not enough, we must do" [1]
Health care delivered in ignorance of available research evidence, misses important opportunities to benefit patients and may cause significant harm [2-4]. Providing evidence-based care is recognised as a key skill for health care workers from diverse professions and cultures [5-10]. The ability to deliver evidence-based practice promotes individualisation of care and assures the quality of health care for patients today as well as those of tomorrow [11].
A variety of definitions of evidence-based practice (EBP) have been proposed. However, definitions are in themselves insufficient to explain the underlying processes of EBP and to differentiate between an evidence-based process and evidence-based outcome.
Towards this goal, we propose three points to clarify and promote the realisation of EBP:
1) A clear statement of what EBP means.
2) A description of the minimum skill set required to practise in an evidence-based way.
3) A curriculum that outlines the minimum standard educational requirements for training health professionals in EBP.
This statement was conceived by the delegates of the second international conference of Evidence-Based Health Care Teachers and Developers held in Sicily in September 2003 ("Signposting the future of EBHC", [12]). In response to a request from the delegates at this conference's final plenary session the steering committee prepared the first draft. The proposed statement and a topic questionnaire were then circulated to all 86 attendees of the Sicily conference for suggestions and clarifications. Eighteen professions allied to health from 18 countries were represented. Suggestions were incorporated and a final paper approved by consensus.
Discussion
Increase in medical information
During the last century there has been an exponential growth of research and knowledge [13,14]. The growth of health care information has been particularly rapid in diagnostic and therapeutic technologies. The volume of medical papers published doubles every 10 to 15 years [15]. Electronic searching of this expanding evidence base was initiated by the National Library of Medicine in 1966 [16]. Electronic access to full text articles and journals started to become available in 1998 [17]. Increasingly, specialist databases of utility for health professionals are being developed, such as the Physiotherapy Evidence Database [18] and the C2-SPECTR [19]. Regular use of these resources is identified as one marker for lifelong learning among physicians [20], but the process is not easy [21]. Identification of the best methods to understand and integrate patient values, such as decision aids or patient-centred consultations, is still at the early stages of development [22].
With this expansion of information, our knowledge should be greater and our practice should be more effective. Unfortunately this is too often not the case [23]. This recognised gap between best evidence and practice is one of the driving forces behind the development of EBP.
Clinical decision making
Good practice including effective clinical decision making – step 4 of the EBP process – requires the explicit research evidence and non-research knowledge (tacit knowledge or accumulated wisdom). Clinical decision making is the end point of a process that includes clinical reasoning, problem solving, and awareness of patient and health care context [24]. This process is uncertain and frequently no "correct" decision exists. EBP can help with some of the uncertainties in this decision process by using the explicit knowledge obtainable from research information. But to do so the research information must be transformed into clinicians' knowledge. Information can be defined as data that has been sorted, analysed, & displayed and communicated through language, graphic displays, or numeric tables. Explicit knowledge is then the meaning people create using this information and its application through action in specific settings [25]. For example clinician's knowledge should include the need to evaluate quickly the patient with chest pain to take advantage of the research proven window of opportunity for treatment of acute coronary syndrome. Step 4 also requires the tacit knowledge which comes from the wisdom of experience, informed by evidence and outcomes, and which is consequently harder to share. An example is the recognition of a sick child. Research may develop a list of clinical features that, when present, denote severe illness in a child. While this list will help the inexperienced junior doctor, nurse, or midwife, the experienced health practitioner has a tacit knowledge of "sickness" in a child that comes from both knowledge of the features list and assimilation with experience, thereby speeding up the recognition of "sickness" in a child.
Principles & development of evidence-based practice
The term "Evidence-based medicine" was introduced in the medical literature in 1991 [26]. An original definition suggested the process was "an ability to assess the validity and importance of evidence before applying it to day-to-day clinical problems" [27,28]. The initial definition of evidence-based practice was within the context of medicine, where it is well recognised that many treatments do not work as hoped [29]. Since then, many professions allied to health and social care have embraced the advantages of an evidence-based approach to practice and learning [5-8,30]. Therefore we propose that the concept of evidence-based medicine be broadened to evidence-based practice to reflect the benefits of entire health care teams and organisations adopting a shared evidence-based approach. This emphasises the fact that evidence-based practitioners may share more attitudes in common with other evidence-based practitioners than with non evidence-based colleagues from their own profession who do not embrace an evidence-based paradigm.
EBP evolved from the application of clinical epidemiology and critical appraisal to explicit decision making within the clinician's daily practice, but this was only one part of the larger process of integration of evidence into practice. Initially there was a paucity of tools and programmes to help health professionals learn evidence-based practice. In response to this need, workshops based on those founded at McMaster by Sackett, Haynes, Guyatt and colleagues were set up around the world. During this period several textbooks on EBP were published accompanied by the development of on-line supportive materials.
The initial focus on critical appraisal led to debate on the practicality of the use of evidence within patient care. In particular, the unrealistic expectation that evidence should be tracked down and critically appraised for all knowledge gaps led to early recognition of practical limitations and disenfranchisement amongst some practitioners [31]. The growing awareness of the need for good evidence also led to awareness of the possible traps of rapid critical appraisal. For example problems, such as inadequate randomisation or publication bias, may cause a dramatic overestimation of therapeutic effectiveness [32]. In response, pre-searched, pre-appraised resources, such as the systematic reviews of the Cochrane Collaboration [33], the evidence synopses of Clinical Evidence [34] and secondary publications such as Evidence Based Medicine [35] have been developed [36], though these currently only cover a small proportion of clinical questions.
Process of Evidence Based Practice
The five steps of EBP were first described in 1992 [37] and most steps have now been subjected to trials of teaching effectiveness (indicated by references)
1. Translation of uncertainty to an answerable question [38]
2. Systematic retrieval of best evidence available [39]
3. Critical appraisal of evidence for validity, clinical relevance, and applicability [40]
4. Application of results in practice [41]
5. Evaluation of performance [42]
This five-step model forms the basis for both clinical practice and teaching EBP, for as Rosenberg and Donald observed, "an immediate attraction of evidence-based medicine is that it integrates medical education with clinical practice" [43].
Curricula outline of minimum standard educational requirements
Different practitioners at different levels of responsibility within evidence-based organisations will require different skills for EBP and different types of evidence. It is a minimum requirement that all practitioners understand the principles of EBP, implement evidence-based policies, and have a critical attitude to their own practice and to evidence. Without these skills and attitidues, health care professionals will find it difficult to provide 'best practice'. Teachers, commissioners, and those in positions of leadership will require appraisal skills that come with higher training and continued use [44].
The wider knowledge and use of these skills will help health professionals meet some of Hurd's list of desired educational outcomes [45] in being able to:
• distinguish evidence from propaganda (advertisement)
• probability from certainty
• data from assertions
• rational belief from superstitions
• science from folklore
Curricula that outline the minimum standard educational requirements for practitioners
Evidence-based practitioners need additional skills to supplement traditional knowledge. Health care graduates should "be able to gain, assess, apply and integrate new knowledge and have the ability to adapt to changing circumstances throughout their professional life" [46]. Observational studies suggest that one way to 'future-proof' health care graduates, is to train them in the necessary skills to support life-long learning through the five-step model of EBM [47].
Learning has three components: knowledge, skills and attitudes. It is said that "attitudes are caught, not taught" [48]. Attitudes, such as comfort with managing uncertainty and reflective learning, provide the psychological framework in which evidence is appraised and applied, described by Sackett as "the conscientious, explicit and judicious use of current best evidence in making decisions about the care of individual patients" [49]. This presents a challenge, as EBP is rarely taught well [50] and is applied (and observed) irregularly at the point of patient contact [51] where professional attitudes are formed, and students learn to incorporate theory into practical skills for patient care. Patient involvement in decision making is part of the process of being an effective practitioner. The degree of involvement and the methods by which this is achieved will depend on the setting, the patients and the practitioner.
The curriculum framework for EBP should consider the importance of all steps shown in Table 1. Often courses focus on one of these elements, most commonly critical appraisal, but a balance of skills in each of the steps is needed to take a student from question through to application. Indeed, the most difficult step (sometimes dubbed "step 0") is to get students and colleagues to recognise and admit uncertainties. As Table 1 suggests, learning should be focused on educational outcome, which in turn needs to reflect the clinical setting. This practical orientation means that EBP teaching and assessment needs to consider the real-time setting of practice, and hence searching and appraisal need to be done in minutes rather than hours or days. Table 1 provides examples of established methods of teaching and assessment for each step, but further compilation, innovation, development, and testing are needed. Future research should be informed by the movement in best evidence medical education (BEME) [52].
Table 1 Description of evidence for aspects of Evidence-Based Practice teaching and assessment
Educational outcome Examples of methods of teaching Examples of methods of assessment
Translation of uncertainty into an answerable question. The student identifies knowledge gaps during the course of practice and asks foreground questions to fill these gaps, The student should ask focused questions that lead to effective search and appraisal strategies. Presenting clinical scenarios or asking for students to share a problem encountered in clinical practice. Framing a focussed, answerable question in a structured format [38]. Several formats are taught: 3 part (patient-intervention-outcome), 4 part (patient-intervention/exposure-comparator-outcome), or 5 part (patient-intervention/exposure-comparator-outcome-time) questions. The skills can be assessed by presenting a clinical scenario and asking the student to form a focussed, answerable question (included in the Fresno test) [53].
Search for and retrieval of evidence. The student can design and conduct a search strategy to answer questions. The strategy should be effective and comprehensive: likely to retrieve all relevant evidence. The student understands the strengths and weaknesses of the different sources of evidence. Theoretical instruction backed by a supervised practical session with online connection [39]. A variety of databases should be shown such as Cochrane, MEDLINE, CINAHL, Evidence-Based Medicine, SumSearch, tripdatabase.com with the relative benefits discussed. Computer based OSCE has been used to test the abilities of framing questions, searching, and retrieving appropriate evidence [54].
Critical appraisal of evidence for validity and clinical importance. The student can appraise the validity of a study. The appraisal will include: the suitability of the type of study to the type of question asked, the design of the study and sources of bias, the reliability of outcome measures chosen, and the suitability and robustness of the analysis employed. The student can appraise the importance of the outcomes and translate them into clinically meaningful summary statistics, such as number needed to treat (NNT). This is probably the most widely taught skill [55]Examples include the Critical Appraisals Skills Program [56]. Tests for critical appraisal of validity include the Berlin Questionnaire [57] and the Fresno test.
Application of appraised evidence to practice The student can assess the relevance of the appraised evidence to the need that prompted the question. The student can explore the patient's values and the acceptability of the answer. Examples include applying the identified evidence to the specific context that led to the quest for evidence. This requires exploration of the generalisability of the evidence to the specific scenario, and 'particularising' outcomes by adjusting for patient-specific risks[58]. Objective structured clinical examination involving clinical application and interaction with patient after reading supplied evidence [59].
Evaluation of performance. The student asks focussed questions, searches sources of evidence, appraises or uses pre-appraised evidence and applies these in practice. The student reflects on how well these activities are performed. Role modelling by EBP teachers. The encouragement of adult learning styles. Journal clubs [60]. Use of a questionnaire to assess knowledge, attitude and behaviour [61].
Recommendations
The term 'EBM' has evolved into a larger phenomenon, as increasing numbers of practitioners in various disciplines recognise the importance of evidence to inform all types of health care decisions. Furthermore, greater patient choice and complexity of care mean that many professionals practise as a team. In recognition of the importance of a united commitment to the principles of 'best practice', we propose that the term 'evidence-based practice' (EBP) be used to describe all aspects of this discipline.
To ensure that future health care users can be assured of receiving 'best practice' regardless of the type or location of the care received, we make the following recommendations for education:
1. The professions and their colleges should incorporate the necessary knowledge, skills and attitudes of EBP into their training and registration requirements.
2. Curricula to deliver these competencies should be grounded in the "five-step model" (Table 1).
3. Further research into the most effective and efficient methods for teaching each step should be fostered, and linked with ongoing systematic reviews on each step.
4. Core assessment tools for each of the steps should be developed, validated, and made freely available internationally.
5. Courses that claim to teach EBP should have effective methods for teaching and evaluating all components.
Evidence-Based Practice (EBP) requires that decisions about health care are based on the best available, current, valid and relevant evidence. These decisions should be made by those receiving care, informed by the tacit and explicit knowledge of those providing care, within the context of available resources.
Finally, EBP requires a health care infrastructure committed to best practice, and able to provide full and rapid access to electronic databases at the point of care delivery. We believe that without the skills and resources for all the relevant components of this framework, the practice of a health care professional, or a health care organisation, cannot be said to provide their users with evidence-based care.
Summary
1. This consensus statement is from an international working group representing both organisations and individual teachers and developers of evidence-based practice.
2. Evidence-Based Practice (EBP) requires that decisions about health care are based on the best available, current, valid and relevant evidence. These decisions should be made by those receiving care, informed by the tacit and explicit knowledge of those providing care, within the context of available resources.
3. All health care professionals need to understand the principles of EBP, recognise it in action, implement evidence-based policies, and have a critical attitude to their own practice and to evidence. Without these skills professionals will find it difficult to provide 'best practice'.
4. The teaching of EBP should, as far as possible, be integrated into the clinical setting and routine care so that students not only learn the principles and skills, but learn how to incorporate these skills with their own life-long learning and patient care.
Competing interests
All of the authors have received honoraria for teaching EBP FP is a consultant of Lilly Deutschland GmbH. The International Conferences of EBHC Teachers and Developers do not accept sponsorship from health technologies (including pharmaceutical) manufacturers.
Authors contributions
MD, WS & PG wrote the original draft. AC, JM, KH, FP, AB & JO contributed to the concept and all revised drafts of the statement.
Pre-publication history
The pre-publication history for this paper can be accessed here:
Acknowledgements
Contributions by the following conference delegates are greatly valued and helped to make this statement representative of different health professions and healthcare systems: Andrew Booth, Rosalie Bennett, Thierry Christiaens, Mark Cooke, Madelon Finkel, Simon French, Frances Gardner, Amit Ghosh, Michel Labrecque, Elizabeth Meerabeau, Felice Musicco, Claire Parkin, Nancy Spector, Mel Stewart, Katharine Wylie. Dr. Badri Badrinath helped to search the literature
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BMC BioinformaticsBMC Bioinformatics1471-2105BioMed Central London 1471-2105-5-1991560359010.1186/1471-2105-5-199Research ArticleHierarchical structure and modules in the Escherichia coli transcriptional regulatory network revealed by a new top-down approach Ma Hong-Wu [email protected] Jan [email protected] An-Ping [email protected] Department of Genome Analysis, GBF – German Research Center for Biotechnology, Mascheroder Weg 1, 38124 Braunschweig, Germany2 Department of Mucosal Immunity, GBF – German Research Center for Biotechnology, Mascheroder Weg 1, 38124 Braunschweig, Germany3 Medical Microbiology and Hospital Hygiene, Medical School Hannover, Carl-Neuberg-Str. 1, 30625 Hannover, Germany2004 16 12 2004 5 199 199 28 7 2004 16 12 2004 Copyright © 2004 Ma et al; licensee BioMed Central Ltd.2004Ma 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
Cellular functions are coordinately carried out by groups of genes forming functional modules. Identifying such modules in the transcriptional regulatory network (TRN) of organisms is important for understanding the structure and function of these fundamental cellular networks and essential for the emerging modular biology. So far, the global connectivity structure of TRN has not been well studied and consequently not applied for the identification of functional modules. Moreover, network motifs such as feed forward loop are recently proposed to be basic building blocks of TRN. However, their relationship to functional modules is not clear.
Results
In this work we proposed a top-down approach to identify modules in the TRN of E. coli. By studying the global connectivity structure of the regulatory network, we first revealed a five-layer hierarchical structure in which all the regulatory relationships are downward. Based on this regulatory hierarchy, we developed a new method to decompose the regulatory network into functional modules and to identify global regulators governing multiple modules. As a result, 10 global regulators and 39 modules were identified and shown to have well defined functions. We then investigated the distribution and composition of the two basic network motifs (feed forward loop and bi-fan motif) in the hierarchical structure of TRN. We found that most of these network motifs include global regulators, indicating that these motifs are not basic building blocks of modules since modules should not contain global regulators.
Conclusion
The transcriptional regulatory network of E. coli possesses a multi-layer hierarchical modular structure without feedback regulation at transcription level. This hierarchical structure builds the basis for a new and simple decomposition method which is suitable for the identification of functional modules and global regulators in the transcriptional regulatory network of E. coli. Analysis of the distribution of feed forward loops and bi-fan motifs in the hierarchical structure suggests that these network motifs are not elementary building blocks of functional modules in the transcriptional regulatory network of E. coli.
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Background
Genome sequencing and high-throughput technologies of functional genomics generate a huge amount of information about cellular components and their functions in an unprecedented pace. These advances make it possible to reconstruct large scale biological networks (metabolism, gene regulation, signal transduction, protein-protein interaction etc.) at a whole cell level [1-4]. One of the key issues in the contemporary genomic biology is to understand the structure and function of these cellular networks at different molecular levels. Among them, the transcriptional regulatory network (TRN) plays a central role in cellular function because it regulates gene expression and metabolism and is often the final step of signal transduction [5,6]. Genome scale TRNs have been reconstructed for well studied organisms such as Escherichia coli and Saccharomyces cerevisiae [4,5,7,8]. Recent studies of TRNs have been concentrated on the topological structure and its correlation with gene expression data from microarray experiments, the evolutionary relationship between regulators, the network motifs and the global regulators in the network etc [7-15]. Network motifs are regarded as the basic building blocks of complex networks [16,17]. Feed forward loop (FF loop) and Bi-fan motif were found to be the two most important network motifs in TRN [7]. In a recent study, Dobrin et al. [9] reported that the motifs in E. coli TRN aggregated into homologous motif clusters that largely overlapped with known biological functions and further formed a giant motif supercluster which comprised about half of the nodes in the giant component of the whole network. This study provided interesting information for understanding the organization principle of regulatory networks. A different approach for studying network organization is the so called "top-down view"[18]. It starts from the whole network structure and identifies subsystems or modules by network decomposition. It is generally recognized that most cellular functions are coordinately carried out by groups of genes forming functional modules [19-25]. The identification of modules is thus an essential step for obtaining any testable biological hypotheses from the network structure. Several methods have been proposed to detect modules in metabolic networks and protein-protein interaction networks based on the topology of the network [21,26-30]. As shown in our recent work [27] the global connectivity structure of metabolic network was useful for a more reasonable decomposition of it into functional modules. However, the global structure of TRN has been so far not taken into account in its decomposition. In fact, little is known about the global connectivity structure of TRN.
In this work we demonstrated the applicability of a top-down approach for the identification of functional modules in TRN with the well established transcriptional regulatory network of E. coli as an example. For this purpose we first showed an uncovered global hierarchical structure. Global regulators and modules with clearly defined functions were then identified by a new network decomposition method based on the hierarchical structure. We further investigated the distribution of the two basic network motifs, feed forward loop and bi-fan motif, in the network hierarchical structure and examined their relationship to functional modules.
Results and discussion
The hierarchical structure of regulatory network
The transcriptional regulatory network of E. coli considered in this work is based on RegulonDB [5] and complemented by Shen-Orr et al [7]. It consists of 413 nodes and 576 links as shown in Fig. 1A. To investigate the network global connectivity, we calculated the weakly connected components and the strongly connected components in the network using the software Pajek [31]. A subset of nodes in a network is called a weakly connected component (WCC) if from every node of the subset all the other nodes belonging to the same subset can be reached when ignoring the direction of the links. If the direction is considered such a fully connected subset is then called a strongly connected component (SCC). We found 28 WCCs in the network. The largest one (the so-called giant component) consists of 325 operons which accounts for more than three quarters of the whole network. Among the other WCCs 20 of them contain only two operons and only 4 WCCs contain more than five operons. The existence of the giant component in TRN is similar to that found previously in metabolic networks[32]. However, different from metabolic networks we find that there is no SCC in the TRN of E. coli. This means that there are no regulatory cycles (e.g. gene A regulates gene B and gene B also regulates gene A through another path) in the TRN of E. coli. This result implies an acyclic structure of the E. coli TRN in which the nodes can be placed in different layers according to their depth. To identify such a hierachical structure we rearranged the operons in the following way: (1) operons which do not code for transcription factors (TFs) or code for a TF which only regulates its own expression (auto-regulatory loop) were assigned to layer 1 (the lowest layer); (2) then we removed all the operons in layer 1 and from the remaining network identified TFs which do not regulate other operons and assigned the corresponding operons in layer 2; (3) we repeated step 2 to remove nodes which have been assigned to a layer and identified a new layer until all the operons were assigned to different layers. As a result, a five layer hierarchical structure was uncovered as shown in Fig. 1B. All the regulatory links in this graph are downward and there is no link between operons in the same layer (except the auto-regulatory loops).
Figure 1 Hierarchical structure of E. coli transcriptional regulatory network. A: The original unorganized network. B: the hierarchical regulation structure in which all the regulatory links are downward. Nodes in the graph are operons. Links show the transcriptional regulatory relationships. The global regulators found in this work are shown in red. The yellow marked nodes are operons in the longest regulatory pathway related with flagella motility.
The multi-layer hierarchical structure of the E. coli TRN implies that no feed back regulation exists at transcription level. We noticed that Shen-Orr et al have also reported that there was no feed back loop in the E. coli regulatory network [7]. We further examined the yeast regulatory network proposed by Guelzim et al [8] and found it also has a similar hierarchical structure (result not shown). This gives rise to the question why the transcriptional regulatory networks of these organisms possess such an acyclic hierarchical structure. A possible explanation is that the interactions in TRN are interactions between proteins and DNAs. Therefore, a regulated gene must has been transcribed and translated into its protein product (which is eventually further modified by cofactor binding) to make a feedback interaction between it and its regulator gene possible. The well studied lac operon may be used as an example to further illustrate this point. Lac operon is not expressed unless lactose is available for the cell because it is repressed by the lac repressor. Lac repressor (the protein but not the gene) is the control element of the system. Its existence (expression) is necessary for the cell to properly response to environmental changes (i.e. the presence and absence of lactose). Therefore, for cells to quickly and properly response to changes of environmental conditions it is of advantage to keep a set of proteins expressed in all conditions and through them to regulate the expression of other genes in a hierarchical way. Feedback control of gene expression may be mainly through other interactions (e.g. metabolite and protein interaction) rather than through transcriptional interactions between proteins and genes. In fact, many transcription factors can bind small molecules to gain or loss their ability to bind DNA.
The five-layer hierarchy shown in Fig. 1B does not necessarily mean that TFs at the top layer require 4 steps to regulate operons at the bottom layer. In fact, many operons at the bottom layer are directly regulated by top layer TFs. Among the 717 linked pairs of operons, 516 are directly connected. The average path length of the network is only 1.36, suggesting a fast and efficient response of cells to environment perturbations in general. The longest regulation path in the network is IHF → OmpR → FlhDC → FliA and further to seven operons (marked yellow in Fig. 1B) related to flagella motility. The finding that there is no short-cut between these regulators and the regulated operons is unexpected. Regulatory relationships may exist between them but are not yet identified. Actually five operons that are regulated by FliA are also directly regulated by FlhDC, resulting in a shorter path between the upper layer regulators (FlhDC) and these operons.
Network decomposition, global regulators and modules
Based on the uncovered hierarchical organization structure we propose a new method to identify functional modules in TRN. As discussed above, there is a giant weakly connected component in the whole TRN of E. coli. We find that the giant component preserves the five layer hierarchical structure of the whole network. It also includes the single large motif super cluster found by Dorbin et al. [9] and thus preserves most of the network motifs in the whole network. Therefore, in the subsequent steps we focus on this giant component to present a new method to identify global regulators and modules in the network.
First we removed all the operons in the top three layers and the operons which are regulated only by them, resulting in a network with 221 nodes and 186 links as shown in Fig. 2. We found 41 weakly connected components in this reduced network (shown in different colors in Fig. 2). In contrast to the whole network, there is no giant component in the reduced network. The three largest components contain 38, 36 and 21 operons respectively (the nodes in blue, yellow and red respectively). It can be seen from Fig. 2 that these three components are connected by only one or two nodes. Therefore we can decompose them into two relatively independent parts by cutting through these nodes. For example, the largest component was separated by cutting gcvTHP which codes enzymes for glycine cleavage. We checked the function of the operons in the two separated parts by using EcoGene database [33]. The left part of this WCC is mainly for purine synthesis and the right part for amino acid uptake. All the other WCCs are very small with less than 12 operons. Most of them are regulated by only one regulator and thus they are in the same regulon. The functions of the operons in these WCCs are closely related. Therefore, we considered the WCCs (the two split parts for the three largest WCCs) which contain at least three nodes as preliminary modules in the network. Altogether 24 preliminary modules were obtained. The 20 small WCCs which contain only two nodes may be regulated by the same regulators at upper layers and thus can be grouped in the next organization level. In the next step, we extended the 24 preliminary modules by moving upward to include the regulators at the third, fourth and fifth layers consecutively and their regulated operons. Each of the regulator was investigated to find its linked preliminary modules and the number of links between them. The regulator was then classified into the module with the most connections. If a regulator has more links with operons which have not been assigned to any preliminary module than with any preliminary module, it formed a new module together with its regulated operons. In this way, the many small two-node components in the low hierarchy level can be grouped to form new modules. On the other hand, the regulators that regulate operons in three or more preliminary modules were regarded as global regulators and not assigned to any module. Using this method, 10 global regulators (Table 1 and Fig. 1B) and 33 modules (Table 2 and Fig. 3) are identified. In addition, 6 modules found from the small WCCs of the whole network which contain at least three operons are also included in Table 2. In Fig. 3 we place the ten global regulators in the central part, whereas the 33 modules are in the periphery part around them. We can see that the periphery modules are connected mainly through the global regulators.
Figure 2 Preliminary modules in the reduced transcriptional regulatory network of E. coli. All the operons in the top three layers (Fig. 1b) and operons which are regulated only by them were removed to reduce the network. The weakly connected components of the reduced network were calculated and shown in different colors. Only WCCs which contain at least three nodes were considered as preliminary modules. The small WCCs which contain only two nodes were grouped at upper regulation level. The three largest WCCs were split into two preliminary modules by investigating their connectivity.
Figure 3 Functional modules in the transcriptional regulatory network of E. coli. Operons in different modules are shown in different colors. The ten global regulators form the core part of the network. The periphery modules are connected mainly through the global regulators. Depending on the connectivity between the modules and their connectivity to the global regulators, these modules can be further grouped to larger modules at a higher level.
Table 1 Global regulators and their regulated operons and functions in the regulatory network of E. coli.
Global regulator directly regulated Operons Total regulated operons Modules regulated Function
IHF 21 39 15 integration host factor
CspA 2 24 5 Cold shock protein
CRP 72 112 21 cAMP receptor protein
FNR 22 38 16 anaerobic regulator, regulatory gene for nitrite and nitrate reductases, fumarate reductase
HNS 7 22 5 DNA-binding global regulator; involved in chromosome organization; preferentially binds bent DNA
OmpR 6 20 3 Response regulator for osmoregulation; regulates production of membrane proteins
RpoN 12 17 4 RNA polymerase sigma 54 subunit
RpoS 14 24 8 stationary phase sigma factor
ArcA 20 21 6 Response regulator protein represses aerobic genes under anaerobic growth conditions and activates some anaerobic genes
NarL 13 15 5 Two-component regulator protein for nitrate/nitrite response
Table 2 Functional investigation of modules identified.
index Operons included Biological function description
1 aceBAK, acs, adhE, fruBKA, fruR, icdA, iclMR, mlc, ppsA, ptsG, ptsHI_crr, pykF Hexose PTS transport system, PEP generation, Acetate usage, glyoxylate shunt
2 acnA, fpr, fumC, marRAB, nfo, sodA, soxR, soxS, zwf Oxidative stress response
3 ada_alkB, aidB, alkA, ahpCF, dps, gorA, katG, oxyR Oxidative stress response, Alkylation
4 alaWX, aldB, argU, argW, argX_hisR_leuT_proM, aspV, dnaA, leuQPV, leuX, lysT_valT_lysW, metT_leuW_glnUW_metU_glnVX, metY_yhbC_nusA_infB, nrdAB, pdhR_aceEF_lpdA, pheU, pheV, proK, proL, proP, sdhCDAB_b0725_sucABCD, serT, serX, thrU_tyrU_glyT_thrT, thrW, tyrTV, valUXY_lysV, yhdG_fis rRNA, tRNA genes, DNA synthesis system, pyruvate dehydrogenase and ketoglutarate dehydrogenase system
5 araBAD, araC, araE, araFGH, araJ Arabinose uptake and usage
6 argCBH, argD, argE, argF, argI, argR, carAB Arginine usage, urea cycle
7 caiF, caiTABCDE, fixABCX Carnitine usage
8 clpP, dnaKJ, grpE, hflB, htpG, htpY, ibpAB, lon, mopA, mopB, rpoH Heat shock response
9 codBA, cvpA_purF_ubiX, glnB, glyA, guaBA, metA, metH, metR, prsA, purC, purEK, purHD, purL, purMN, purR, pyrC, pyrD, speA, ycfC_purB, metC, metF, metJ Purine synthesis, purine and pyrimidine salvage pathway, methionine synthesis
10 cpxAR, cpxP, dsbA, ecfI, htrA, motABcheAW, ppiA, skp_lpxDA_fabZ, tsr, xprB_dsbC_recJ Stress response, Conjugative plasmid expression, cell motility and Chemotaxis
11 dctA, dcuB_fumB, frdABCD, yjdHG C4 dicarboxylate uptake
12 edd_eda, gntKU, gntR, gntT Gluconate usage, ED pathway
13 csgBA, csgDEFG, envY_ompT, evgA, gcvA, gcvR, gcvTHP, gltBDF, ilvIH, kbl_tdh, livJ, livKHMGF, lrp, lysU, ompC, ompF, oppABCDF, osmC, sdaA, serA, stpA Amino acid uptake and usage
14 fdhF, fhlA, hycABCDEFGH, hypABCDE Formate hydrogenlyase system
15 flgAMN, flgBCDEFGHIJ, flgKL, flgMN, flhBAE, flhDC, fliAZY, fliC, fliDST, fliE, fliFGHIJK, fliLMNOPQR, tarTapcheRBYZ Flagella motility system
16 ftsQAZ, rcsAB, wza_wzb_b2060_wcaA_wcaB Capsule synthesis, cell division
17 gdhA, glnALG, glnHPQ, nac, putAP Glutamine and proline utilization
18 glmUS, manXYZ, nagBACD, nagE Glucosamine, mannose utilization
19 glpACB, glpD, glpFK, glpR, glpTQ Glycerol phosphate utilization
20 lysA, lysR, tdcABCDEFG, tdcR Serine, threonine usage
21 malEFG, malK_lamB_malM, malPQ, malS, malT, malZ Maltose utilization
22 rhaBAD, rhaSR, rhaT Rhamnose utilization
23 appCBA, appY, betIBA, betT, cydAB, cyoABCDE, fadBA, focA_pflB, fumA, glcC, glcDEFGB, gltA, lctPRD, mdh, nuoABCEFGHIJKLMN, fabA, fadL, fadR, uspA Oxidative phosphorylation, Glycolate, lactose utilization, fatty acid degradation
24 cytR, deoCABD, deoR, nupC, nupG, tsx, udp Nucleosides uptake and usage
25 cirA, entCEBA, fecABCDE, fecIR, fepA_entD, fepB, fepDGC, fhuACDB, fur, tonB Iron uptake system
26 galETKM, galR, galS, mglBAC Galactose uptake and usage
27 dmsABC, fdnGHI, narGHJI, narK, nirBDC_cysG, nrfABCDEFG, torCAD, torR Nitrogen metabolism, Nitrate and nitrite reductase,
28 narZYWV, nhaA, nhaR, osmY intracellular pH regulation
29 aslB, inaA, mdlA, rob, ybaO, ybiS, yfhD Stress response
30 cutC, dapA_nlpB_purA, ecfABC, ecfD, ecfF, ecfG, ecfH, ecfJ, ecfK, ecfLM, fkpA, ksgA_epaG_epaH, lpxDA_fabZ, mdoGH, nlpB_purA, ostA_surA_pdxA, rfaDFCL, rfbO, rpoE_rseABC, uppS_cdsA_ecfE RpoE regulated stress response, lipopolysaccharide synthesis
31 ansB, cpdB, cyaA, dadAX, epd_pgk, glgCAP, glgS, ivbL_ilvBN, ompA, speC, srlAEBD_gutM_srlR_gutQ, tnaLAB, ubiG, yhfA Sorbitol and Glycogen metabolism
32 atoC, atoB, hydHG, hypA, pspABCDE, pspF, rtcAB, rtcR, zraP Phage shock protein, Zn-resistence system, Acetoacetate metabolism
33 dsdC, dsdXA, ebgAC, ebgR, fucAO, fucPIKUR, lacI, lacZYA, malI, malXY, melAB, melR, uhpA, uhpT, yiaJ, yiaKLMNOPQRS Lactose, maltose, fucose, dehydroascorbate, xylulose, melibiose transport and metabolism
34 aroF_tyrA, aroG, aroH, aroL_yaiA_aroM, aroP, mtr, trpLEDCBA, trpR, tyrP, tyrR Aromatic amino acid synthesis
35 bioA, bioBFCD, birA_murA Biotin synthesis
36 cbl, cysB, cysDNC, cysJIH, cysK, cysPUWAM, ssuEADCB, tauABCD Sulfur metabolism, cysteine synthesis, Taurine utilization
37 exuR, exuT, uidR, uidABC, uxaCA, uxuABR Utilization of hexUronide
38 lexA_dinF, polB, recA, recN, rpsU_dnaG_rpoD, ssb, sulA, umuDC, uvrA, uvrB, uvrC, uvrD DNA recombination and repair, UV resistent
39 phnCDE_f73_phnFGHIJKLMNOP, phoA, phoBR, phoE, pstSCAB_phoU Phosphate metabolism
To investigate if the modules identified from anaylisis of the network structure are really functionally related, we checked the functions of the genes in the individual modules by using database EcoGene [33]. Most genes in the same module turned out to have closely related biological function. Thus, we can assign clearly defined functions for most of the modules. However, there are also several modules which include operons that are seemingly functionally not closely related. For example, there are also several operons for acetate usage in module 1 (Table 2) besides the operons for the PTS sugar transport system. One of the acetate usage operons aceBAK is also repressed by FruR, the regulator for fructose uptake. This makes the cell not to use acetate as a substrate in the presence of fructose. Therefore, the two different pathways are actually functionally related from a regulation viewpoint. The other three modules (module 4, 23 and 32) which include operons with different functions are actually linked by certain global regulators (fis, arcA and rpoN respectively). They are not connected by any local regulators with specific functions. Thus, it is not strange that they are not closely functionally related. One reason for this problem is probably the information incompleteness of the network. The regulatory network considered in this work contains only about twenty percent of the genes in the E. coli genome. With more and more information available we can include more interactions and genes in the network to obtain more reasonable modules by structural analysis. Identifying these functional modules can help us to gain a general view of the function (or ability) of organisms. Furthermore, we can compare these structure based modules with modules from hierarchical classification results of microarray experiments to find unknown regulatory relationships.
We compared the ten global regulators with those found in three previous studies by considering the number of directly or indirectly regulated genes (operons) and their structure and function diversity [7,10,11]. Five of them (CRP, IHF, FNR, HNS, ArcA) have been identified in all the three studies. The other five regulators have also been recognized as global in either one or two of the three studies. Our definition of global regulators is directly linked to the identification of functional modules. Modules are sets of genes with closely related function. An important criterion for a regulator to be regarded as global is that it regulates genes with diverse but concerted functions. Therefore, determination of global regulators by the number of regulated modules is more reasonable than that solely by the number of genes or operons. From Fig. 3 we can see that the number of links among the modules is far less than that between the global regulators and the modules. This indicates that the global regulators introduce the major cross-talks between modules and link them together to form the whole network. Therefore, breaking the links through the global regulators can help to identify the true modules as shown in this work.
Network motifs and motif clusters
To investigate if network motifs, which are considered to be the elementary building blocks of the whole network [17], are basic building blocks of modules and if motif clusters are generally equivalent to functional modules, we calculated the feed forward loops (FF loops) in the TRN of E. coli. In agreement with the results of Dobrin et al [9], the 42 FF loops in the network aggregate to seven homologous motif clusters (see Additional file Additional file 1). Four of the motif clusters are generally in consistence with the modules identified in this study (Table 2), including the flagellar-motor module (module 15), the osmoregulated porin gene module (13), the oxidative stress response module (2) and the methionine biosynthesis module (9). The third feed forward cluster found by Dorbin et al. [9] comprises genes of nitrogen regulation and formate regulon. They are found in two separate modules (14 and 17 respectively) in this work. In contrast to the good agreement for the five motif clusters, the other two clusters include genes belonging to many different modules. For example, the CRP cluster (see Additional file 1) consists of genes for usage of different carbon sources such as arabinose (module 5), carnitine (7), fucose (33), maltose (21), galactose (26) and mannose (18). The reason for this discrepancy is that each of the two clusters contains a global regulator (FNR and CRP respectively) which regulates genes with various functions. We further investigated the distribution of the 42 FF loops in the hierarchical structure and find that 32 of them contain one of the ten global regulators. Because modules are defined as subsets of genes with closely related functions, while global regulators tend to regulate functionally far related genes, clusters formed from network motifs which contain global regulators are not proper candidates for modules. For the four consistent motif clusters, three of them are formed from the ten FF loops that do not contain global regulators. Cluster four (osmoregulated porin gene) contains the global regulators IHF and OmpR. As shown in Fig. 1B, these two global regulators also regulate genes with flagellar motility function (module 15) and many other genes with different functions. Therefore, these two regulators cannot be properly placed in one module though most of the other genes in the cluster are functionally related. We also calculated the bi-fan motifs and find that 180 of the 209 bi-fan motifs contain global regulators. Among them 130 bi-fan motifs contain two global regulators. This means that two target operons would be coregulated by two global regulators. The fact that most network motifs contain global regulators which regulate functionally far related operons indicates that motifs cannot be regarded as elementary building blocks of functional modules because global regulators should not belong to any module with specific functions.
Conclusions
The E. coli transcriptional regulatory network presently known possesses a multi-layer hierarchical structure with no feedback regulation at transcription level. Regulators in the top layers of the hierarchical structure can be considered as global regulators that often act together with local regulators to regulate genes in the bottom layer. Based on the hierarchical structure a new decomposition method is proposed which can be used to identify functional modules in the network. Analysis of operon composition of the two well-known network motifs (feed forward loop and bi-fan motif) and their distribution in the hierarchical structure suggests that they are not elementary building blocks of functional modules in the transcriptional regulatory network of E. coli.
Methods
Network reconstruction and representation
The original transcriptional regulatory database of E. coli was obtained from the website of Alon's research group[34]. This database is mainly based on the RegulonDB [5] and complemented by Shen-Orr et al [7]. We removed three operons (gatR_1, rcsA and rotA) because they are either the same with another operon or inside another operon. GatR_1 has been merged with gatR_2 in the updated annotation of E. coli genome in the database EcoGene [33]. RcsA is part of the rcsAB operon, while rotA is the same with ppiA. Another operon, nycA, was not found in any E. coli genome database. We searched the original literature [35] for this gene from the database obtained from Shen-Orr et al [7] and could still not find it. Therefore, we removed the nycA operon from the network. There are also six operons (emrRAB, gatYZABCDR, hipBA, idnDOTR, moaABCDE and mtlADR) in the network that are only autoregulated and hence do not connected with other operons. Therefore, we ignored these operons as well when analyzing the network connectivity structure. The resulting network consists of 413 nodes (operons) and 576 directed links (regulatory relationships). The 54 autoregulatory relationships in the network are represented as loops in the graph.
Network structure analysis
Calculations for the network structure analysis were carried out by using the software Pajek [31]. The number of directly regulated operons of a regulator gene equals to its output degree, while the total number of directly and indirectly regulated operons equals to its output domain. The connected components were found by calculating the weakly connected components (the direction ignored because the regulatory network is an acyclic directed graph).
Network motif calculation
From the hierarchical structure, feed forward loops are easily found by searching for all the fully connected triads which are located in different regulatory layers (not necessary to be three nearby layers). Bi-fan motifs are searched by using the subgraph searching algorithm in Pajek [31].
Authors' contributions
HWM performed the analyses and drafted the manuscript. JB contributed to the concept and promoted the work. APZ is the project leader. He contributed to the concept, supervised the study and was involved in writing the manuscript. All authors have read and approved the final manuscript.
Supplementary Material
Additional File 1
Supplenmentary table 1: Network motifs and motif clusters in the E. coli transcriptional regulatory network. Supplenmentary fig 1: Modules in the hierarchical structure of the E. coli transcriptional regulatory network.
Click here for file
Acknowledgements
This work was financially supported through the Braunschweig Bioinformatic Competence Center project "Intergenomics" of the Ministry for Education and Research (BMBF), Germany (Grant No. 031U110A) and by the project B6 in the Sonderforschungsbereich 578 der Deutschen Forschungsgemeinschaft (DFG).
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| 15603590 | PMC544888 | CC BY | 2021-01-04 16:36:38 | no | BMC Bioinformatics. 2004 Dec 16; 5:199 | utf-8 | BMC Bioinformatics | 2,004 | 10.1186/1471-2105-5-199 | oa_comm |
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BMC GenomicsBMC Genomics1471-2164BioMed Central London 1471-2164-5-941559835410.1186/1471-2164-5-94Research ArticlePrognostic meta-signature of breast cancer developed by two-stage mixture modeling of microarray data Shen Ronglai [email protected] Debashis [email protected] Arul M [email protected] Department of Biostatistics, University of Michigan, Ann Arbor, MI 48109, USA2 Department of Pathology, University of Michigan, Ann Arbor, MI 48109, USA3 Department of Urology, University of Michigan, Ann Arbor, MI 48109, USA4 The Comprehensive Cancer Center, University of Michigan, Ann Arbor, MI 48109, USA2004 14 12 2004 5 94 94 30 6 2004 14 12 2004 Copyright © 2004 Shen et al; licensee BioMed Central Ltd.2004Shen 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
An increasing number of studies have profiled tumor specimens using distinct microarray platforms and analysis techniques. With the accumulating amount of microarray data, one of the most intriguing yet challenging tasks is to develop robust statistical models to integrate the findings.
Results
By applying a two-stage Bayesian mixture modeling strategy, we were able to assimilate and analyze four independent microarray studies to derive an inter-study validated "meta-signature" associated with breast cancer prognosis. Combining multiple studies (n = 305 samples) on a common probability scale, we developed a 90-gene meta-signature, which strongly associated with survival in breast cancer patients. Given the set of independent studies using different microarray platforms which included spotted cDNAs, Affymetrix GeneChip, and inkjet oligonucleotides, the individually identified classifiers yielded gene sets predictive of survival in each study cohort. The study-specific gene signatures, however, had minimal overlap with each other, and performed poorly in pairwise cross-validation. The meta-signature, on the other hand, accommodated such heterogeneity and achieved comparable or better prognostic performance when compared with the individual signatures. Further by comparing to a global standardization method, the mixture model based data transformation demonstrated superior properties for data integration and provided solid basis for building classifiers at the second stage. Functional annotation revealed that genes involved in cell cycle and signal transduction activities were over-represented in the meta-signature.
Conclusion
The mixture modeling approach unifies disparate gene expression data on a common probability scale allowing for robust, inter-study validated prognostic signatures to be obtained. With the emerging utility of microarrays for cancer prognosis, it will be important to establish paradigms to meta-analyze disparate gene expression data for prognostic signatures of potential clinical use.
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Introduction
DNA microarray analysis has been shown to be a powerful tool in various aspects of cancer research [1]. With the increasing availability of published microarray data sets, there is a tremendous need to develop approaches for validating and integrating results across multiple studies. A major concern in the meta-analysis of DNA microarrays is the lack of a single standard experimental platform for data generation. Expression profiling data based on different technologies can vary significantly in measurement scale and variation structure. It poses a great challenge to compare and integrate results across independent microarray studies. In a recent study of diffuse large B cell lymphoma (DLBCL), Wright et al. [2] sought to bridge two different microarray platforms by validating findings from a cDNA lymphochip microarray using an independent dataset generated using Affymetrix oligonucleotide arrays. Although the idea of training and testing classifiers is frequently used for discriminant analysis, this application to distinct expression array platforms is less common.
More systematic approaches have been proposed for integration of findings from multiple studies using different array technologies. Rhodes et al. [3] have proposed methods to summarize significance levels of a gene in discriminating cancer versus normal samples across multiple gene profiling studies. By ranking the q-values [4] from sets of combinations, a cohort of genes from the four studies was identified to be abnormally expressed in prostate cancer. Choi et al. [5] suggested combining effect size using a hierarchical model, where the estimated effect size in individual studies follows a normal distribution with mean zero and between study variance τ2. The effect size was defined to be the difference between the tumor and normal sample means divided by pooled standard deviation. From a Bayesian perspective, Wang et al. [6] used data from one study to generate a prior distribution of the differences in logarithm of gene expression between diseased and normal groups, and subsequent microarray studies updated the parameter values of the prior. Assuming a normal error distribution, the differences were then combined to form a posterior mean. Although phrased using different model frameworks, these methods are similar in the spirit of combining the standardized differences between two sample means across multiple studies. It has been shown, however, that the overlap between significant gene detection on different array platforms is only moderate due to low comparability of independent data sets [7]. The large variability brought in by microarray datasets using different platforms is expected to affect the sensitivity and specificity of summary statistics constructed in various ways across studies. Given the inherent differences of the microarray techniques, heterogeneity of the sample populations, and low comparability of the independently generated data sets, meta-analysis of microarrays remains a difficult task.
A recent study proposed a Bayesian mixture model based transformation of DNA mi-croarray data with potential features applicable to meta-analysis of microarray studies [8]. The basic idea is to estimate the probability of over-, under- or baseline expression for gene sample combinations given the observed expression measurements. With data-driven estimation of these quantities, one can translate the raw expression measurement into a probability of differential expression. As a result, poe (i.e., probability of expression) was introduced as a new scale and used in the context of molecular classification [8]. The platform-free property of this scale, however, motivated us to incorporate poe in a framework to meta-analyze microarray data. Several desirable features of using poe as a new expression scale include the following: 1. poe provides a scaleless measure and thereby facilitates data integration across microarray platforms; 2. poe is a model-based transformation with direct biological implications in the context of gene expression data, as it is estimated based on a method that adopts an underlying mixture distribution that accommodates over-, under-, and unchanged expression categories; 3. poe unmasks differential expression patterns in microarray data by offsetting the influence of extreme expression values [9]; 4. Data integration based on poe allows merging of samples on the unified scale rather than using gene-specific summaries.
In recent publications of breast cancer microarray studies, several groups have explored the hypothesis that the capacity to metastasize is intrinsic to the tumor and therefore can be revealed by gene expression pattern. Four independent studies have correlated gene expression profiles generated from distinct DNA microarray platforms to breast cancer prognosis [10-13]. Among the four, Sorlie et al. [10] and Sotiriou et al. [12], both cDNA microarray studies, applied unsupervised clustering and identified several breast cancer subtypes characterized by differential expression of a cohort of genes. Further, they correlated the tumor subtypes derived from the expression profile with survival outcome and in both cases found that, as expected, the ERBB2+ subtype correlated with shorter survival times. On the other hand, van't Veer et al. [11], an inkjet oligonucleotide array study, and Huang et al. [13], an Affymetrix GeneChip study, have built classification models based on gene expression profiles to predict 5-year or 3-year recurrence status. In all four studies, however, the authors explored a common hypothesis that molecular profiles were able to provide a more accurate prediction of patient survival compared with clinical/pathological parameters. These studies therefore provided an excellent basis for developing a meta-analysis of microarrays with regard to disease prognosis.
In this proof-of-concept study, we propose a two-stage meta-analysis of microarrays based on poe. We applied our method to the aforementioned breast cancer DNA microarray data sets. With the strength of the poe transformation and data integration, our goal was to develop an inter-study validated meta-signature that predicts relapse-free survival in breast cancer patients with improved statistical power and reliability.
Results
Development of the two-stage Bayesian mixture modeling approach for the meta-analysis of microarray data
Figure 1 outlines the two-stage Bayesian mixture modeling strategy. The idea is to build a scale that can be combined across different microarray platforms, and therefore allows simultaneous examination of independent data sets. The stage 1 of the analysis involves data-driven estimation of posterior probability of differential expression, namely poe. The Bayesian hierarchical model employed for estimation borrows strength across genes by assuming further distributions for the gene-specific parameters (see Methods). For data integration purposes, we focused on a common set of 2,555 genes that were profiled in each of the four studies. Although the cost for compiling common genes is a loss of potential predictive features, it is not unreasonable to assume, given the analogous hypothesis explored in each study, that the common set represents the most relevant genes of interest for breast cancer prognosis. The resulting values of poe represent signed probability of differential expression for gene j in sample i, and thus provide a unified measure across studies. Further, the transformation improves contrast in each data set by removing the influence of extreme expression values. In stage 2, the expression profile of tumor samples from multiple studies were combined on the poe scale to generate a meta-cohort. The benefit of data integration using poe is twofold. First, it improves power of statistical analysis by increasing the sample size. Such integration of independent data sets renders sensitivity to those small yet consistent expression changes for certain genes. Second, it reduces the chance of false positive features due to artifacts from a single study, and allows reliable findings across studies. In this paper, we integrated four breast cancer microarray data sets of distinct platforms (Table 1), and developed a prognostic meta-signature for disease recurrence.
Figure 1 Meta-analysis of microarray data using a two-stage mixture model approach.
Table 1 Breast cancer gene expression data sets used in the prognostic meta-analysis. Bad outcome (Y = 1) is defined as recurrence during follow-up, and good outcome (Y = 0) is defined as remaining recurrence-free for at least three years.
Authors Array platform Number of array elements Sample size (n) Good outcome (n0) Bad outcome (n1)
Sorlie et al. Spotted cDNA 8102 58 23 35
van't Veer et al. Inkjet oligonucleotide 25000 78 44 34
Sotiriou et al. Spotted cDNA 7650 98 53 45
Huang et al. Affymetrix chip 12625 71 36 35
Building a gene expression meta-signature for breast cancer prognosis
In the second stage of the analysis, We assessed the performance of the genes found using the meta-analysis methods based on classification accuracy. A complication is that while most methods of classification deal with data from two populations, the response with which we wish to build classifiers to predict is time to breast cancer recurrence. While the ideal data would have information on time to recurrence on all subjects (potentially censored), not all studies have the time to recurrence information available and instead provide data on recurrence within a certain time interval (e.g., recurrence within five years versus no recurrence within five years). To deal with this issue, we utilized a dichotomization where a bad outcome is recurrence during followup and a good outcome is remaining recurrence-free for at least three years. The additional constraint for the good outcome group is to reduce potential bias introduced by short censoring due to insufficient length of follow-up. This is particularly relevant in cross-study analysis, given the heterogeneity in patient recruitment criteria and study designs. Accordingly, of the combined meta-cohort (n = 305) of breast cancer patients, 48.9% were in the poor outcome group, whereas 51.1% in the good outcome group. The sample sizes for each study are shown in Table 1.
Each gene was then associated with the recurrence status by a logistic regression within a leave-one-out cross validation scheme, and rank-ordered by the significance level of the coefficient. As a result, 23 genes held up as significant predictor of recurrence (P ≤ 0.001) in all cross-validation steps, representing a cohort of essential genes strongly associated with breast cancer recurrence. By random chance, there would be on average 2.5 genes to be found significant at P ≤ 0.001 in a set of 2,555. By finding 23 genes with a P ≤ 0.001, it is clear that there are much more predictive features than would be expected by random chance.
To identify a prognostic meta-signature, we define a risk index (RI) as a linear combination of the poe profile and the coefficient estimates from the univariate logistic regression for each gene j. Large positive values of RI indicate high risk of failure, whereas large negative values of RI indicate low risk of failure. Classification of sample i to the risk groups is then based on the ith leave-one-out risk index. The classifier is = I{RIi >c}, with c being the empirical quantiles of the risk indices. The number of genes in a classifier is treated as a parameter and optimized to minimize the prediction error rates. More details on building a classifier at the second stage are described in the Methods section.
The 90-gene expression meta-signature predicts clinical outcome in breast cancer patients
By minimizing the misclassification error, we obtained a 90 gene meta-signature that reliably predicts outcome in the meta-cohort. This meta-signature classified 122 patients into a high risk group, where 84 (69%) of them had a recurrence. On the other hand, the signature classified 183 patients into a low risk group, where 118 (64%) of them did not recur by the end of the followup. By cross-tabulating the risk groups predicted by the meta-signature and the actual recurrence status, we obtained an estimated odds ratio of 4.0 (95% CI: 2.5–6.5, P < 0.0001). In spite of the heterogeneity of the combined patient population, the meta-signature predicted the odds of recurrence for a patient showing a high risk signature as four times of the odds of recurrence for a patient showing a low risk signature. Several studies have implicated that the lymph node status is one of the principal clinical factors to classify patients in relation to the risk of relapse of breast cancer [14-16]. Although there have been controversial findings with regard to its predictive values in breast cancer survival outcome, we have shown in the meta-cohort that the nodal status is a significant risk factor of recurrence. The estimated odds of recurrence for node-positive patients is two times higher than the odds of recurrence for node-negative patients (95% CI:1.3–3.2, P = 0.002) in the combined samples.
Kaplan-Meier analysis provides further evidence that the meta-signature was a significant prognostic index of breast cancer recurrence in the meta-cohort (Figure 2). The estimated three-year survival rate was 76.0%(± 3.2%) for low risk signature and 45.9%(± 4.5%) for high risk signature. Nodal status, on the other hand, was less discriminative at the three-year time point with an estimated survival rate of 71.7%(± 3.7%) for lymph node negative patients and 56.2%(± 4.0) for lymph node positive patients. Node-negative patients, although generally considered to be at low risk of recurrence, are heterogeneous in disease progression. About one third of node-negative patients develop local recurrence [17]. Many studies have therefore explored the potential of using molecular biomarkers to further differentiate patient survival outcome in nodal negative cohort [18-21]. As shown in Figure 2C and 2D, the meta-signature further differentiated 48 (31.6%) of the LN- patients to be at higher risk of recurrence during followup (P < 0.0001). Similarly for nodal positive patients, a cohort thought to be at high risk of recurrence, the meta-signature identified 79 (51.6%) of the LN+ patients to have, in fact, lower recurrence risk over time (P < 0.0001, Figure 2D). In contrast, nodal status failed to maintain its predictive power after controlling for the meta-signature risk groups (P = 0.05 and 0.12 in low risk signature and high risk signature group respectively). A multivariate logistic regression model suggested that the meta-signature is an independent predictor of the recurrent status with respect to nodal status in the meta-cohort (OR = 3.7(2.3–6.1), P < 0.0001).
Figure 2 The 90-gene meta-signature displayed greater performance than nodal status in predicting relapse-free survival in breast cancer, and it further predicts survival outcome in nodal status sub-cohorts. (A) Lymph node status correlates with survival outcome (P = 0.0004). (B) The meta-signature correlates with survival outcome (P = 2 × 10-10). (C) The meta-signature differentiates risk groups in nodal negative patients (P = 2.6 × 10-5). (D) The meta-signature predicts risk groups in nodal positive patients (P = 7.0 × 10-5).
Comparison of the meta-signature to the study-specific signatures
To comprehend the potential gains of such two-stage meta analysis over individual analysis in each single study cohort, we constructed study-wise gene expression signatures using the same method. By minimizing the misclassification errors, we obtained a signature consisting 10, 60, 100, and 130 genes for Sorlie, van't Veer, Sotiriou, and Huang study cohort respectively (Additional file 5). The results of the classifiers are summarized in Table 2. In fact, not only did the size of the study-specific signatures vary significantly, but the elements of the signatures had very little overlap. At most two genes appeared in more than one signature among the four. In addition, signature identified in one study tended to have poor performance in other studies. Table 3 lists the estimated odds ratios for disease outcome and risk groups predicted by a gene expression signature. An individual signature identified in one study cohort demonstrated considerable shrinkage in the odds ratio estimates and non-significant 95% confidence intervals in the validation studies, indicating significantly reduced discriminative power in the testing cohorts. Kaplan-Meier analysis provided further evidence that the study-specific signatures performed poorly in pairwise cross-validations (Additional file 6).
Table 2 Comparisons of the number of genes (Size), the number of elements overlap with the meta-signature (overlap), and the prediction error rates for the signatures identified in individual study cohort and in the meta-cohort.
Sorlie van't Veer Sotiriou Huang Meta-cohort
Size 10 60 90 140 90
Overlap 4 14 19 6 --
Prediction error rate 0.28 0.29 0.35 0.18 0.33
Table 3 Comparison of the performances of the individual signatures and the meta-signature in each single study cohort. Table lists odds ratios (95% confidence interval) comparing the odds of actual recurrence for those being classified as high risk to the odds of recurrence for those being classified as low risk of recurrence by each signature.
Cohort
Signature Sorlie (n = 58) van't Veer (n = 78) Sotiriou (n = 98) Huang (n = 71)
Sorlie (D = 10) 18.6 (5.0, 69.5) 2.1 (0.8, 5.4) 2.3 (1.0, 5.3) 10.87 (3.5, 33.8)
van't Veer (D = 60) 3.1 (1.1, 9.2) 10.6 (3.3, 33.9) 4.1 (1.7, 9.7) 1.3 (0.5, 3.4)
Sotiriou (D = 100) 1.7 (0.6, 5.0) 3.5 (1.4, 8.9) 7.8 (3.0, 20.1) 1.5 (0.6, 3.7)
Huang (D = 130) 5.1 (1.6, 15.7) 2.3 (0.9, 5.6) 0.9 (0.4, 2.0) 184.9 (30.1, 1137.2)
Meta (D = 90) 25.0 (4.2, 149.0) 4.1 (1.6, 10.6) 6.0 (2.5, 14.5) 5.8 (2.1, 16.5)
D is the number of genes in a signature. n is the sample size for each cohort.
Meta-analysis accounts for such heterogeneity of the individual signatures in two ways. First its overlap with the study-specific signatures ranged from 3–40%. The excluded genes are likely to be cohort-specific findings that can not be replicated. Second, the meta-signature recruited 41 genes not previously picked by any of the single cohort signature, likely representing predictive features with small but consistent effects previously masked in single studies. When examining the performances of the gene signatures, the meta-signature showed a comparable or better performance compared with the individually optimized signatures both in the odds ratio estimates (Bottom row of Table 3) and in Kaplan-Meier analysis (Figure 3). This shows that the meta-signature can serve as a common breast cancer recurrence index that is able to predict patient survival in heterogeneous sample populations. When a gene signature built in one study cohort performs differently in another, such meta analysis provides a solution to identify a cross-study validated expression signature that holds across independent samples.
Figure 3 The 90-gene meta-signature achieves similar or better performance than the individually optimized signatures. A and E compare the Kaplan-Meier curves stratified by high versus low risk group predicted by the study-specific signature and by the meta-signature respectively in the Sorlie study cohort; B and F show similar comparison in the van't Veer study cohort; C and G show similar comparison in the Sotiriou study cohort; and D and H show comparison in the Huang study cohort.
Comparison of data integration based on poe transformation and simple linear rescaling
An alternative approach to integrating data across multiple datasets is to perform a study-wise global normalization. For one study, let be the globally scaled expression value for gene j in sample i. Each study dataset is then standardized to have zero mean and unit standard deviation. The linearly rescaled values can also be used for data integration purposes in that expression values generated from different array platforms are standardized to a common scale.
Such an approach is less computationally challenging compared to the mixture model-based rescaling described in the previous sections. However, there are several advantages to the mixture model-based transformation. First, the method incorporates biological information into estimating the posterior probabilities of expression. The transformed values carry meaningful interpretations as signed probabilities of differential expression of a gene in a particular sample. Second, the underlying normal and uniform mixture distributions give equal density in the tails and is effective in reducing the influence of extreme expression values. And third, the Bayesian hierarchical modeling approach borrows strength across genes resulting in shrinkage-type estimators for a large correlated gene-specific parameter vector. This is a method in which the high dimensional gene expression data are denoised.
To study the benefit of data integration based on poe compared to that based on the linearly rescaled values, we compared the model performances based on data integration by these two methods. Figure 4A shows that with the poe transformation, misclassification rates steadily decreases as more genes are used in the classifier. Performance based on linearly rescaled data (Figure 4B), however, is unpredictable. Figure 4C and 4D uses a 90-gene meta-signature based on poe and based on the global standardization respectively in predicting survival. The signature based on poe is noticeably better than the signature based on global standardization in differentiating patients at low risk of recurrence from those at high risk of recurrence. Taken together, the poe transformation outperforms the linear rescaling method in combining multiple microarray data sets. The meta-signature identified based on poe values therefore offers more reliable prediction of recurrence-free survival in the meta-cohort.
Figure 4 Comparison of model performances based on data integrated by poe transformation (A and C) and global standardization (B and D). A set of top 10 to 200 genes were used in a classifier to construct risk index and 40th to 70th percentiles of the cross-validated RIs were then used to dichotomize samples into a high risk or a low risk group. A. Misclassification rates based on poe transformation and B. based on global standardization. C. Performance of the 90-gene signature built on poe and D. built on global standardized data in differentiating patients at low risk of recurrence from those at high risk of recurrence.
The meta-signature displays two distinct expression patterns
A heat map representation of the poe profile for the 90 gene meta-signature revealed two distinct patterns of differential expression (Figure 5A). Genes in the top half of the matrix displayed consistently high probability of over-expression (yellow) in the recurrent samples (R). On the other hand, genes in the bottom half displayed great probability of under-expression (blue) in the recurrent group. Individually generated heat maps of the raw data confirmed such distinct patterns at raw measurement levels (Figure 5B). Functional annotation revealed genes involved in many important biological processes such as cell cycle regulation (e.g., CDC28 protein kinase regulator subunit 2), cell adhesion (e.g., chemokine C-X3-C motif receptor 1), and apoptosis (e.g., secreted frizzled-related protein 4). A complete list of the meta-signature genes can be found in the Additional file 7. Some of the genes in the meta-signature were previously shown to correlated with breast cancer survival outcome. For example, Keyomarsi et al. [22] demonstrated the association of the cell cycle regulator cyclin E and death due to breast cancer.
Figure 5 The 90 gene meta-signature displayed two distinct patterns of expression in breast cancer groups. (A) Heat map representation of differential expression probabilities for the 90 gene meta-signature across the combined samples. The top set of genes showed consistently high probability of over-expression (yellow) in the poor outcome group, and the bottom set of genes showed consistently high probability of down-regulation (blue) in the poor outcome group. (B) Heat map of log-transformed raw data. Individually generated heat maps of the raw measurements of gene expression confirmed the distinct expression patterns of the meta-signature from independent studies. Red represents up-regulation while green represents down-regulation. R (recurred) – poor outcome group; RF (recurrence-free) – good outcome group.
Enriched functional classes in the meta-signature
To gain a better understanding of the processes related to disease recurrence, we examined whether a particular functionally defined biological process is enriched in the recurrence signature. Each of the ninety genes were mapped to Gene ontology (GO) terms and then grouped by functional classes. Based on the hypergeometric distribution, we calculated the significance of over-representation of a particular process in the signature. Figure 6 demonstrated the top seven enriched functional groups in the meta-signature, comparing the total proportion (out of 2310 annotated) and the signature proportion (out of 85 annotated) of genes in each group. Cell cycle regulation is the most highly over-represented category (P = 0.001). All genes under this category except BCL2 displayed increased expression level, reflecting elevated cell cycle activities. Signal transduction represents the largest functional class over-represented in the meta-signature. Genes involved in signalling pathways that regulate cell growth (VEGF, PPP2R5C), immune response (TRAF3), apoptosis (SFRP4), and other processes are found to constitute 15.7% of the meta-signature compared to the 9.7% in the entire gene set (the common set).
Figure 6 Top seven over-represented functional classes in the meta-signature. Black bars represent proportion of genes associated with each of the GO terms among the meta-signature, and white bars represent the corresponding proportion among the total study population of 2555 genes. P-value represents the significance of over-representation based on a hypergeometric distribution, and is calculated as the probability of observing larger proportion of a particular functional group genes in the meta-signature than in the entire gene set. The meta-signature genes are listed under each functional class.
Discussion
Several important issues to consider when integrating microarray studies include use of different gene expression measurement scales, varying analytical power and reliability of the results for individual studies. To account for these issues, we proposed a two-stage mixture modeling strategy, the strength of which was built on the mixture model based transformation and the subsequent data integration on the poe scale. In particular, poe provides a unified platform-free scale, and simultaneously enhances the intrinsic contrast in the expression data. Furthermore, combining sample pools on the poe scale mitigates the influence of potential artifacts from a single study. The benefit of such data integration is reflected on two counts. One, integrated sample cohorts improve the reliability of the findings by guarding against false positive results from a single study. Two, it increases the statistical power to detect small consistent effects that can be otherwise masked by inadequacy of the sample size of an individual data set. By implementing this modeling approach, we were able to combine information from four microarray studies to build an inter-study validated meta-signature for predicting survival in breast cancer patients.
As described earlier, a common set of 2555 genes was used in this meta-analysis, as it is important to provide the same context for data-driven estimation of the posterior probabilities. Although we assume the common set comprises the most biologically relevant genes, the loss of potential predictive genes, however, may offset the statistical power of the analysis. For example, one of our recent studies has established the polycomb protein EZH2 to be an independent predictor of breast cancer survival outcome[23]. This gene was filtered out of the meta-analysis as one of the studies [12] did not profile EZH2. However, in each of the other three studies where EZH2 was profiled on the array, its expression level was found to correlate with survival (data not shown), which confirmed its role as a prognostic marker. Alternative approaches to allow genes profiled in some studies but not others is a topic for future research.
Functional annotation of the meta-signature revealed genes such as Cyclin E and BCL2, which were previously shown to be correlated with survival outcome in breast cancer [22,24]. A strength of the inter-study validated signature is the capability of recruiting genes which may not be significant in one study due to limiting sample size or artifacts of the experiments. In this sense, the meta-signature will be more stable and less subjective to variations in subsets of the samples. As a result, the predictive genes in a meta-signature may carry more reliable information about tumor progression and patient survival.
In conclusion, a distinction of the analysis presented here relative to those by other authors [3,6] is that we sought to find genes that were predictive of recurrence rather than predictive of diseased versus nondiseased status. Given the heterogeneity of the tumors with respect to treatment response and survival outcome, a prognostic prediction analysis is generally more difficult because it is a more complicated phenotype. Further, a prognostic signature (classifier) of failure risk trained in one cohort is often times difficult to validate in independent cohorts. The meta-analysis method presented here may potentially provide more powerful gene signatures that are predictive of prognosis because they are validated across multiple studies.
Methods
Data collection and preparation
The breast cancer microarray data sets were obtained at the author's websites from four recently published studies [10-13]. Each data were preprocessed, either by a lowess normalization for two-channel microarray data [25] or a robust analysis for Affymetrix data [26]. We filtered for a common set of 2,555 genes from these four studies by Unigene Cluster IDs. Each data matrix of the 2,555 genes was then normalized by median centering and dividing by the standard deviation for each gene. Missing data were imputed by the k-nearest neighbors imputation algorithm [27].
Mixture modeling of microarray data
Each of the four raw data sets was treated as an expression matrix X with elements xij, where i = 1, ..., mk, j = 1, ..., n (k = 1, .., 4 and n = 2,555). The expression measurement xij can be the ratio of the two fluorescent dye hybridization intensities for the spotted cDNA arrays[10,12] and the Intjek oligonucleotide array [11], or averaged difference between the perfect match and mismatch probe hybridizations for the Affymetrix gene chip [13]. Let E be a latent class variable, and eij indicates over-, under- or normal expression for each entry of the R matrices. We have:
The values of eij are latent and not directly observed from the data. We were interested in estimating the probabilities of eij being 1 or -1 given the observed raw expression xij, which were denoted as and . Estimates of these latent quantities were obtained under a Bayesian mixture model setting. In particular, we assume the raw expression xij falls into one of the three expression categories. For each gene j, the expression then arises from a mixture of three distributions:
(xij|eij = 1) ~ f1,j(·), (xij|eij = 0) ~ f0,j(·), and (xij|eij = -1) ~ f-1,j(·).
In the mixture, f1,j, f0,j and f-1,j are the density functions of the following distributions:
respectively. Here, U refers to a uniform distribution and N refers to a normal distribution. αi + μj is both the mean of the normal distribution and the threshold point in the uniform distribution. μj is the gene effect and αi is the sample effect. The and provide limits to the uniform distribution in the mixture, and are set to be at least 3σj. = P(eij = 1) and = Peij = -1) are the multinomial probabilities for eij. With the specifications of models, we can calculate the latent quantities by Bayes' rule:
By noting that the supports for the two uniform distributions are disjoint, the probabilities of differential expression are mutually exclusive with the forms:
A one dimension measure can thus be constructed as poe = p+ - p-. As a result, poe ranges from -1 to 1, and can be interpreted as the signed conditional probability of differential expression.
To borrow strength across genes, the estimation of the gene-specific parameters was formulated under a Bayesian hierarchical model setting. Given the large amount of parameters, prior distributions were specified to model the variation of the gene-specific parameter estimates, in particular,
We followed the recommendations of Parmigiani et al. [8] in terms of the prior choices. A Metropolis-Hastings MCMC sampling algorithm was then implemented to approximate the posterior distributions of the parameters. Data augmentation started at a set of data-driven initiating parameter values. For example, trimmed means and variances across samples were used as the initial values for the parameters in the normal distribution of the mixture. Further details of the Bayesian hierarchical mixture model used here can be found in Parmigiani et al. [8]. Matrices of were obtained for each of the five data sets (Additional files 1, 2, 3, 4).
Leave-one-out cross validation and risk index computation
For the combined sample pool of the breast cancer patients (the meta-cohort), we defined outcome groups as recurred during followup and remained relapse-free for at least 3 years. In particular, Let Ti be the event time for subject i, Ci be the censoring time for subject i, and δi = 1{Ti <Ci} be the censoring indicator. Define a new outcome variable,
where t* can be specified with clinical knowledge. We chose t* = 3 years in this study. We then consider constructing classifiers using y; note that y = 1 corresponds to the poor outcome group and y = 0 to the good outcome group. The sample sizes for each study are shown in Table 1.
Logistic regression was used to build a classifier for prognosis. For each gene j, we fit the following univariate logistic regression model using data from all studies:
where x* is the rescaled value that allows data integration across multiple studies. The esti-mated values of βj, , are then used to form a risk score using a variation of the compound covariate predictor method [28,29]; for a given set of covariate values x1, ..., xD, the risk index is given as .
If we want to assess the performance of the classifier, we must deal with the issue of training and testing the model using the same data. An "honest" estimate of the prediction error rate is obtained using leave-one-out cross-validation. Define a risk index , where , and is the effect estimate for gene j in the combined meta-cohort without the ith sample. The risk index for sample i is a weighted linear combination of the expression profiles of the top D genes, where the ranking of the genes is based on their corresponding significance in the univariate logistic model fit. Classification of sample i to the risk groups is then based on the ith leave-one-out risk index, i.e., = I{RIi >c} with c being the empirical quantiles (40th - 70th) of the RI's. The number of genes D in a classifier is treated as a parameter and optimized to minimize the prediction error rates.
The list of the top cumulative genes in the meta-signature was obtained by ranking all 2,555 genes by the number of times in the leave-one-out cross-validation steps that each one had a P-value from the univariate logistic regression less than 0.001.
Heat map display
We used the treeview software [30] to generate a heat map representation of the poe pro-files of the meta-signature. Yellow represents high probability of over-expression and blue represents high probability of under-expression. For heat maps of raw data matrices, we preprocessed the data by mean centering and then dividing by the standard deviation for each row. The means and the standard deviations used in the normalization were the relapse-free (RF) sample means and variances for each study data. The values for the recurrence (R) samples after standardizing then represented the number of standard deviations over or under the mean RF sample expression.
Authors' contributions
RS, DG, and AC designed the study. RS carried out the statistical analysis and prepared the manuscript. DG and AC supervised the analysis and participated in the manuscript preparation. All authors read and approved the final manuscript.
Supplementary Material
Additional File 5
Plots of misclassification rates. The PDF file lists plots of misclassification error rates for classifiers identified in each individual study cohort and the meta-cohort.
Click here for file
Additional File 6
Plots of Kaplan-Meier curves. The PDF file lists Kaplan-Meier plots for study-wise cross-validation of the individually identified signatures. A gene signature was trained in one study cohort and used to validate in each of the other study cohorts as testing sets.
Click here for file
Additional File 7
Meta-signature gene list. The excel file contains a list of Unigene ID, gene symbol, and full name of the 90 genes in the meta-signature.
Click here for file
Additional File 1
POE imputation of the Sorlie data. The excel file contains a table of imputed signed probability matrix transformed from the Sorlie et al. study data (2,555 times 58 in dimension).
Click here for file
Additional File 2
POE imputation of the van't Veer data. The excel file contains a table of imputed signed probability matrix transformed from the van't Veer et al. study data (2,555 times 78 in dimension).
Click here for file
Additional File 3
POE imputation of the Sotiriou data. The excel file contains a table of imputed signed probability matrix transformed from the Sotiriou et al. study data (2,555 times 98 in dimension).
Click here for file
Additional File 4
POE imputation of the Huang data. The excel file contains a table of imputed signed probability matrix transformed from the Huang et al. study data (2,555 times 71 in dimension).
Click here for file
Acknowledgements
We thank the authors of the breast cancer gene expression studies used in this meta-analysis for making their data publicly available. We also thank T.R. Barrette, D.R. Rhodes, J. Yu, and R. Lnu for bioinformatics support on this project. This work was supported in part by grants from the National Institutes of Health and National Science Foundation grant Nos. R01 GM72007, R01 CA97063, and P50 CA69568; V Foundation; the Mary Kay Ash Foundation; and American Cancer Society grant No. RSG-02-179-01-(MGO). A.M.C. is a Biomedical scholar of the Pew Foundation. Finally, we thank the anonymous reviewers for their valuable comments and suggestions for the improvement of the manuscript.
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| 15598354 | PMC544889 | CC BY | 2021-01-04 16:39:23 | no | BMC Genomics. 2004 Dec 14; 5:94 | utf-8 | BMC Genomics | 2,004 | 10.1186/1471-2164-5-94 | oa_comm |
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BMC NeurolBMC Neurology1471-2377BioMed Central London 1471-2377-4-241560147910.1186/1471-2377-4-24Research ArticleControversial significance of early S100B levels after cardiac surgery Jönsson Henrik [email protected] Per [email protected]äckström Martin [email protected] Christer [email protected] Cecilia [email protected] Sten [email protected] Department of Heart-Lung Diseases, Lund University Hospital, Lund, Sweden2 Departments of Psychology, Lund University, Lund, Sweden3 Department of Medical Neurochemistry, Lund University Hospital, Lund, Sweden2004 16 12 2004 4 24 24 23 6 2004 16 12 2004 Copyright © 2004 Jönsson et al; licensee BioMed Central Ltd.2004Jönsson 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 brain-derived protein S100B has been shown to be a useful marker of brain injury of different etiologies. Cognitive dysfunction after cardiac surgery using cardiopulmonary bypass has been reported to occur in up to 70% of patients. In this study we tried to evaluate S100B as a marker for cognitive dysfunction after coronary bypass surgery with cardiopulmonary bypass in a model where the inflow of S100B from shed mediastinal blood was corrected for.
Methods
56 patients scheduled for coronary artery bypass grafting underwent prospective neuropsychological testing. The test scores were standardized and an impairment index was constructed. S100B was sampled at the end of surgery, hourly for the first 6 hours, and then 8, 10, 15, 24 and 48 hours after surgery. None of the patients received autotransfusion.
Results
In simple linear analysis, no significant relation was found between S100B levels and neuropsychological outcome. In a backwards stepwise regression analysis the three variables, S100B levels at the end of cardiopulmonary bypass, S100B levels 1 hour later and the age of the patients were found to explain part of the neuropsychological deterioration (r = 0.49, p < 0.005).
Conclusions
In this study we found that S100B levels 1 hour after surgery seem to be the most informative. Our attempt to control the increased levels of S100B caused by contamination from the surgical field did not yield different results. We conclude that the clinical value of S100B as a predictive measurement of postoperative cognitive dysfunction after cardiac surgery is limited.
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Background
Despite the fact that incidence figures between 4–79% have been reported for cognitive dysfunction after cardiac surgery [1], diagnostic steps are seldom taken to diagnose this impairment. The golden standard for detecting cognitive dysfunction is neuropsychological tests, which are complex and difficult to use as a routine procedure. Lately the brain derived protein S100B has been proposed as a simple method for detecting brain dysfunction after cardiac surgery [2-5]. The protein is a member of the larger S100 family, where S100B is one isoform, and considered to be brain specific, and the other is the S100A isoform [6]. The S100B chain is uniform in contrast to the S100A chain which exhibits several subgroups. The dimers of interest in studies concerning cerebral events are those containing S100B (S100BB and S100A1B). The physiological roles of S100B are pleiotropic including neurotrophic and neuroprotective functions, mediated by calcium dependent regulation of phosporylation, enzyme activation and proliferation [6]. On the other hand, high concentrations of S100B have been shown to be toxic and induce apoptosis in neuronal cell cultures [7,8]. At least five experimental studies have indicated a possible role of S100B in learning and memory function, three of which reported impaired memory and learning effects in transgenic S100B mice and two reported memory deficits after injection of S100B antiserum [9-13].
Several studies in humans suffering from stroke of different ethiologies, have shown a rather strong correlation between serum levels of S100B and size of lesion(s) as well as outcome [14-17].
Lately a number of studies have addressed the question whether S100B can be considered as a marker for cognitive dysfunction after cardiac surgery; however the conclusions presented are disparate. One major reason for this could be the fact that S100B is present in high concentrations in shed mediastinal blood that is retransfused to the patient by cardiotomy suction and autotransfusion, thus obscuring the measured levels of S100B early after surgery. To date, only one study have been published where this contamination was taken into account [18]. We recently reported the half life of S100B in serum to be 25 min [19]. With the present study we wanted to use our knowledge of inflow and elimination to obtain a more reliable measurement of cerebral release of S100B after cardiac surgery and correlate it with neuropsychological outcome.
Methods
Study design
The study group comprised 56 patients who underwent coronary artery bypass graft (CABG) surgery at the Division of Cardiac Surgery, University Hospital MAS, Malmoe, Sweden and Department of Cardiothoracic Surgery, Lund University Hospital, Lund, Sweden. Only patients planned for elective CABG with cardiopulmonary bypass (CPB) as their sole procedure were included. The study protocol was approved by the local ethics committee, and patients gave a written informed consent before the study protocol was initiated. Patients with a history of stroke, transient ischemic attack (TIA), reversible neurological disorder (RIND), known carotid artery disease or other brain diseases were excluded. In order to avoid possible influence of renal disorder on the elimination of S100B, patients with known renal failure (Creatinin > 160 μmol/L) were excluded.
The patients were examined for signs of neurological dysfunctions daily during the hospital stay by either experienced cardiac anesthesiologists or by experienced cardiac surgeons.
Perioperative management
Anesthesia was induced with midazolam 3–5 mg iv (Dormicum®, Roche, Basel, Switzerland) or propofol (Diprivan®, Zeneca Ltd, Cheshire, England) 10 mg/kg. It was subsequently maintained with fentanyl 10 μg/kg (Leptanal®, Janssen Pharmaceutica, Beerse, Belgien), a continuous infusion of propofol 3–6 mg/kg/h or inhalation of isoflurane 0,5–1% (Forene®, Abbott Laboratories). Nitrous oxide (Aga Industries, Stockholm, Sweden) was used before CPB but not during or after CPB.
CABG surgery was performed during aortic cross clamping with the distal anastomosis preceding the proximal anastomosis. A tangential occluder replaced the cross-clamp during the proximal anastomosis. Antegrade cold S:t Thomas crystalloid cardioplegia was used (Cardioplegi®, Pharmacia-Upjohn, Uppsala, Sweden) and administered in the ascending aorta and the anastomosed vein-grafts intermittently.
Perfusion was performed with a roller pump (Cobe Industries, Denver, Colorado, USA). The perfusion catheters and circuit were made of polyvinylchloride in the line and silicon in the pumphead. The arterial cannulation was made in the ascending aorta and venous cannulation in the right atrium by a two-stage venous cannula. All circuits contained a heparin-coated 40 μm arterial filter (Cobe Sence, Cobe Industries) and a membrane oxygenator (Cobe Duo oxygenator, Cobe Industries). The circuit was primed with approximately 1000 ml of Ringer's lactate (Pharmacia-Upjohn), 250 ml 15% Mannitol (Pharmacia-Upjohn) and 75 mmol Addex tromethamine (Pharmacia-Upjohn). Perfusion flow was non-pulsatile with a flow rate of 2.4 l/min/m2 at normothermia. The perfusate was cooled to approximately 32°C. Heparin (400 U/kg bodyweight) was given prior to cannulation and reversed with equal doses of protamine sulphate at decannulation.
After surgery, the patients patient were transferred to the ICU for recovery and enrolled in the sampling scheme for S100B analysis. None of the patients received autotransfusion.
S100-protein analysis and calculations
Serum for S100B analysis was sampled before surgery, at the end of CPB, and then 1, 2, 3, 4, 5, 6, 8, 10, 15, 24 and 48 hours after surgery. The S100B levels at these time points will be referred to as T0, T1, T2....T48. Blood samples, both arterial and venous samples, were cooled and centrifuged within 5 hours. All samples were measured by a monoclonal two-site immunoluminometric assay (Sangtec 100, AB Sangtec Medical, Bromma, Sweden).
S100B kinetic calculations
Since early levels of S100B are contaminated by S100B from cardiotomy suction, an attempt was made to exclude this S100B from the levels measured one and two hours after surgery. Assuming that all of the measured S100B at the termination of CPB was a contamination, this non-cerebral S100B was eliminated with a half-life of 25 minutes, as illustrated in figure 1a and 1b. The estimated true levels cleansed from the contamination 1 and 2 hours after the end of surgery were thereby calculated by the formula:
Figure 1 1a – Measured S100B release pattern in one patient and the calculated residual levels from the S100B from cardiotomy suction during surgery, a half-life of 25 minutes was used. 1b – Estimated true release, calculated by subtracting the residual levels (from 1a) from measured levels. 1c – Measured levels and estimated true release from one patient with high S100B at T0 and low estimated true release at T1. This patient also had a good neuropsychological outcome. 1d – Measured levels and estimated true release from one patient with low S100B at T0 and high estimated true release at T1. This patient had a bad neuropsychological outcome.
where Ce is the estimated true levels of S100B at time t, Ct = the measured concentration of S100B at T1 or T2, C0 the concentration at the end of CPB, t the time after T0 and t1/2 the half-life of S100B.
We have earlier suggested that the elimination rate could be used as another measure of cerebral release [5], and the elimination rates of S100B between the end of CPB (T0) and 1 hour later, between T1 and T2, and between T2 and T3 were calculated accordingly. The differences between measured S100B levels at T0 and T1, T0 and T2 were also calculated.
Neuropsychological method
The patients underwent neuropsychological testing by the same trained neuropsychologist 1–2 days before and 5–7 days after surgery. The tests used were: Mental Control, Figural Memory, Logical Memory (A/B), Visual Reproduction, Rey Auditory/Verbal Learning Test (RAVLT), Trail Making A, Trail Making B, Digit Symbol, Digit Span, Visual Memory Span, Visual Paired Associates II or Verbal Paired Associates I and RAVLT, Delayed Retention. The tests were chosen from the Wechsler Memory Scale-Revised (WMS-R), the Wechsler Adult Intelligence Scale (WAIS – R) and the Halsted-Reitan Neuropsychological Battery [20,21].
Differences for each sub-test were first calculated and then standardized to z-values. All sub-tests were then aggregated to create an impairment index [4]. The impairment index was a continuous variable, where a positive value reflected an improvement in neuropsychological test and a negative value reflected deterioration. The incidence of neuropsychological impairment in the study group was calculated according to the 1 standard deviation criterion (SD) as defined by Newman and the 20% criterion as defined by Stump [22-24].
Statistical analysis
All results were analyzed with the Statistica version 5.0 for PC. Regression analysis was performed with the least square method with a casewise deletion of missing data. For the multiple regression a backwards stepwise regression was performed to determine which variables to include in the final analysis. The initial variables included in this analysis were age, gender, perfusion time, years of education, and S100B levels at all sampling times. A p-value < 0,05 was considered significant. Unless otherwise stated numerical values are presented as mean ± 1 standard deviation.
Results
Patient demographics are presented in table 1. None of the patients suffered a clinically detectable stroke. The incidence of neuropsychological impairment was 37,5% (n = 21, 95% C.I 26,0–50,6%) according to the 1 standard deviation criterion, and 80,4% (n = 45, 95% C.I. 68,1–88,6%) according to the 20% criterion.
Table 1 Demographics for the study group.
Mean St. dev.
Number 56
Sex (M/F) 47/9
Age (years) 60.4 9.0
Education (years) 9.6 3.1
Perfusion time (Minutes) 82.8 31.5
X-clamp (minutes) 53.9 23.5
The appearance of S100B protein followed the same pattern in all patients with high levels at the end of CPB and a decrease or slight increase the first hour (figure 2). Thereafter a subsequent decrease was observed during the rest of the study period, except in one patient in whom the concentration of S100B increased to 2,0 μg/L 48 hours after surgery. Mean estimated true release of S100B, calculated according to equation [1], was lower at T1 and T2 compared to measured S100B levels (1.43 ± 1.37 μg/L vs. 2.11 ± 1.81 μg/L and 1.22 ± 1.10 μg/L vs. 1.32 ± 1.17 μg/L). Three representative examples of this calculation are shown in figure 1b, 1c and 1d.
Figure 2 S100B release pattern after cardiac surgery with cardiopulmonary bypass shown as a boxplot.
We found a correlation between patient age and S100B levels measured up to 24 hours after bypass (Table 2). No significant correlation was found between measured S100B levels at the sampling times and neuropsychological impairment in simple regression analysis (Table 3). Neither did age, years of education and duration of perfusion correlate with neuropsychological impairment when tested in simple regression analysis (Table 3).
Table 2 The relation between S100B levels and patient age tested in univariate linear regression analysis.
r-value p-level
S100B at T0 0.19 n. s
S100B at T1 0.30 <0.05
S100B at T2 0.30 <0.05
S100B at T3 0.34 <0.05
S100B at T4 0.37 <0.05
S100B at T5 0.32 <0.05
S100B at T6 0.37 <0.05
S100B at T8 0.38 <0.01
S100B at T10 0.42 <0.005
S100B at T15 0.36 <0.05
S100B at T24 0.46 <0.005
S100B at T48 0.004 n.s
Table 3 The relation between S100B levels, demographic variables and neuropsychological impairment index, expressed as r-value from linear regression analysis (* = p < 0.05). Mean (± SD) S100B levels are also presented. (Est. true release = estimated true release when residual levels from contamination have been excluded)
Mean ± St.dev. r-value
S100B at T0 3.63 ± 2.84 0.08
S100B at T1 2.11 ± 1.81 -0.16
S100B at T2 1.32 ± 1.17 -0.11
S100B at T3 0.87 ± 0.73 -0.12
S100B at T4 0.60 ± 0.42 -0.06
S100B at T5 0.49 ± 0.38 -0.12
S100B at T6 0.42 ± 0.33 -0.15
S100B at T8 0.38 ± 0.25 -0.12
S100B at T10 0.31 ± 0.21 -0.06
S100B at T15 0.25 ± 0.16 -0.09
S100B at T24 0.21 ± 0.11 -0.10
S100B at T48 0.21 ± 0.27 -0.09
Est. true release at T1 1.43 ± 1.37 -0.22
Est. true residual at T2 1.22 ± 1.10 -0.12
Elimination rate T0-T1 0.50 ± 0.46 0.26
Elimination rate T1-T2 0.48 ± 0.25 -0.05
Elimination rate T2-T3 0.68 ± 0.32 -0.12
Difference T0-T1 1.53 ± 1.66 0,30*
Difference T0-T2 2.31 ± 2.01 0,18
Age 0.16
Education -0.06
Perfusion time 0.12
In multiple regression analysis, measured S100B levels at the end of CPB (T0), one hour later (T1) and age were found to explain part of the neuropsychological impairment (r = 0.49, p < 0.005, Table 4). It is worth noting that the correlation was positive at T0 and negative at T1, implicating a better outcome if S100B was high at T0 and worse if the levels were high at T1. Age had a negative correlation. Furthermore the combination of age, S100B at T0 and S100B at either T3 or T6 gave significant correlations in the same manner (r = 0.41, p < 0.05 and r = 0.42, p < 0.05 respectively). The difference between S100B levels at T0 and T1 was found to correlate positively with neuropsychological outcome (Table 3). No correlation was found when the estimated true release of S100B was tested against neuropsychological outcome, neither did the elimination rate fall out significantly in simple regression analysis (Table 3).
Table 4 The results of the backwards stepwise multiple regression analysis model to explain the neuropsychological impairment index (r = 0.49, p < 0.005).
Variable Partial correlation Beta p-value
S100 at T0 0.45 0.845 <0.005
S100 at T1 -0.47 -0.939 <0.001
Age 0.31 0.307 <0.05
Regression <0.005
Discussion
This study presents some interesting findings that may be relevant in clinical practice. There is a clear relation between patient age and release of S100B up to 24 hours after cardiac surgery. This correlation is strong and not influenced by the perioperative contamination of S100B caused by the use of coronary suction [4].
The univariate analysis did not provide a clear answer to the question whether there is a relationship between neuropsychological outcome and S100B release. However, from table 3 it is worth noting that the r-values in univariate regression analysis are negative at all sampling points except at T0 (immediately after bypass). The consistent finding of a negative correlation 1 hour and onwards after bypass is intriguing and warrants further exploration.
The multiple regression analysis resulted in a significant correlation between neuropsychological deterioration and the three variables: S100B at T0, S100B at T1 and patient age. The r-value in the multiple regression analysis was 0.49, which evokes the question why the multiple regression is stronger than the univariate regression. One possible explanation could be that by including the S100B level at the end of CPB we compensate for some part of the contamination in the S100B levels one hour after surgery.
Age correlated also with outcome. As expected, the older the patient the higher the risk for neuropsychological deterioration, which is in accordance to the fact that age is a risk factor for decline in neuropsychological tests after surgery [23].
Our results are to some extent in concordance with other reports in this field. Two groups have reported no relation between S100B and neuropsychological test results, [2,18] and two groups have found a relation using a composite S100B end-point [3,4]. Only one of these studies was designed to exclude the extracerebral inflow of S100B [18]. These contradicting findings support our notion that S100B kinetics after cardiac surgery are complicated.
Interestingly, in the multiple regression analysis there was a positive correlation between S100B levels at T0 and outcome, a finding that stands in contrast to the hypothesis that S100B could be used as serum marker for cognitive dysfunction. This finding is especially interesting since most of the measure S100B at T0 is contamination. In an earlier study we found that approximately 80% of measured S100B at this point is from extracerebral sources in CABG patients [4]. We offer no explanation for this unexpected finding, but it is intriguing and calls for further investigations.
When interpreting the results of our study, as well as comparing them with those of other reports, several issues need to be addressed. To begin with the origin of S100B measured after CPB is not exclusively cerebral. From our previous study as well as from reports by Anderson et al [25], it is clear that shed blood in the mediastinum contains very high levels of S100B. When this blood is retransfused either directly by cardiotomy suction or later in the course by autotransfusion during the postoperative care, the systemic levels of S100B are affected. The source of this S100B is non-cerebral, probably from S100B containing tissue such as fat, skin and bone marrow. No autotransfusion was used in this study, however reabsorbtion of S100B from injured fat tissue may contribute to the measured serum levels of S100B.
Since we previously have been able to determine the half life of S100B in blood to be 25 min, it was possible to control for the peri-operative contribution from cardiotomy suctions by using the equation mentioned to obtain the estimated true release at T1 and T2. When these values were entered in simple regression analysis, no correlation was found to neuropsychological outcome. However, if multiple regression analysis was used, a positive correlation was found, these results were identical to those obtained with the measured S100B values, and therefore we can conclude that the use of the correction procedure represented by the equation does not contribute to the evaluation of the results. Moreover, we should be careful to overinterpret the significance of correlations in a study of relatively few patients and several end-points, i.e sampling times for S100B. There is always a risk of accepting a false hypothesis. Preferably, a study of this sort should contain a large number of patients and only one or few end-points. However, the aim of this study was to clarify the complex kinetics of early S100B release and its possible connection to a neuropsychological decline, not to determine the perfect use of S100b for detecting neuropsychological outcome after cardiac surgery.
In the mathematical correction model suggested here, we assume that all S100B measured at T0 is of extrecerebral origin. This is of course not the case, but we do not know the relation between cerebral and extracerebral S100B at this time-point in each individual. By assuming that none of the S100B measured at T0 is of cerebral origin, we can be sure that our mathematical model does not include non-cerebral S100B at T1 and later. However, these levels might be lower than the true levels of cerebral S100B since we could have excluded cerebral S100B present at T0. This could also be an explanation that the multiple regression analysis showed stronger correlations.
One important issue, which must be considered in this type of study, is the strength that can be expected in the correlation between two different measures of brain function. The functional domains in the brain covered by neuropsychological tests vary depending on the tests used and are by no means complete in any test battery. By the same reasoning, the magnitude of a possible cerebral release of S100B after an injury may vary according to the severity of the insult as well as the location of the insult, since glia dense areas express more S100B than others [26].
Conclusions
In conclusion, with the present knowledge, a single S100B sampled in the postoperative course after cardiac surgery can not be of use in clinical practice in order to predict neuropsychological outcome with an acceptable sensitivity.
However, utilizing a statistic model, an association between S100B levels the first hours after surgery and neuropsychological outcome was found, where the most informative time point seems to be 1 hour after the termination of CPB.
The controversial significance of an increased S100B immediately after surgery is indeed intriguing and it inspires further studies of the mechanisms of S100B release after cardiac surgery as well as other fields of brain damage.
Competing interests
The author(s) declare that they have no competing interests.
Authors' contributions
HJ: Principal investigator recruited, enrolled patients analyzed and wrote the paper
PJ: Had an integral role in the planning and the analysis, also help with writing
MB: Designed neuropsychological test battery. Analyzed neuropsychological data
CB: Performed all neuropsychological testing with patients
CA: Did the S100B-analyzes
SB: Mentor an principal leader of the project, who also helped with the writing.
Pre-publication history
The pre-publication history for this paper can be accessed here:
Acknowledgement
We would like to acknowledge Sangtec-Diasorin AB, Bromma, Sweden for supporting us with kits for S100B analysis.
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| 15601479 | PMC544890 | CC BY | 2021-01-04 16:28:50 | no | BMC Neurol. 2004 Dec 16; 4:24 | utf-8 | BMC Neurol | 2,004 | 10.1186/1471-2377-4-24 | oa_comm |
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Lipids Health DisLipids in Health and Disease1476-511XBioMed Central London 1476-511X-3-291560147810.1186/1476-511X-3-29ResearchCorrelates of serum lipoprotein (A) in children and adolescents in the United States. The third National Health Nutrition and Examination Survey (NHANES-III) Obisesan Thomas O [email protected] Muktar H [email protected] Abayomi S [email protected] Vernon [email protected] Celia J [email protected] Charles N [email protected] Section of Geriatrics, Department of Medicine, Howard University Hospital, Washington, USA2 Department of Epidemiology, University of Alabama at Birmingham, USA3 Department of Human Health & Leisure Studies, Howard University, Washington, USA4 Institute for Women's Health, Howard University Hospital, Washington, USA5 National Human Genome Center Genetic Epidemiology Unit, Department of Microbiology, Howard University, Washington, USA2004 16 12 2004 3 29 29 11 11 2004 16 12 2004 Copyright © 2004 Obisesan et al; licensee BioMed Central Ltd.2004Obisesan et al; licensee BioMed Central Ltd.This is an Open Access article distributed under the terms of the Creative Commons Attribution License (), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Objective
To determine the correlates of serum lipoprotein (a) (Lp(a)) in children and adolescents in the United States.
Methods
Cross-sectional study using representative data from a US national sample for persons aged 4–19 years participating in The Third National Health Nutrition and Examination Survey (NHANES-III).
Results
We observed ethnicity-related differences in levels of Lp(a) > 30 mg/dl, with values being markedly higher in African American (black) than nonhispanic white (white) and Mexican American children in multivariate model (P < 0.001). Higher levels of Lp(a) > 30 mg/dl associated with parental history of body mass index and residence in metro compared to nonmetro in Blacks, and high birth weight in Mexican American children in the NHANES-III. In the entire group, total cholesterol (which included Lp(a)) and parental history of premature heart attack/angina before age 50 (P < 0.02) showed consistent, independent, positive association with Lp(a). In subgroup analysis, this association was only evident in white (P = 0.04) and black (P = 0.05) children. However, no such collective consistent associations of Lp(a) were found with age, gender, or birth weight.
Conclusion
Ethnicity-related differences in mean Lp(a) exist among children and adolescents in the United States and parental history of premature heart attack/angina significantly associated with levels of Lp(a) in children. Further research on the associations of Lp(a) levels in childhood with subsequent risk of atherosclerosis is needed.
Lipoprotein(a)AdolescenceGenderEthnicityParental History
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Introduction
Levels of serum or plasma Lp(a) above 30 mg/dL are associated with increased risk of coronary artery disease and stroke in adults of European descent [1-3]. Given the high degree of structural homology of one of the domains of its apolipoprotein(a) component with plasminogen, one proposed mechanism is interference with thrombolysis [1,3]. Adults of African descent have mean levels of Lp(a) approximately twice those of Europeans but do not have commensurately increased risk of atherosclerotic disease; nor has Lp(a) been shown to be a coronary artery disease risk factor in Blacks [4,5]. The explanations for the differential effects of Lp(a) on CVD risk among different populations are poorly understood. Because birth weight has been shown to influence levels of Lp(a) [6], and adverse patterns of blood lipids and atherosclerosis itself begin in childhood, studies of population and individual differences in the early onset and progression of risk factors through adolescence are important [7]. Given the reported contribution of intrinsic factors, family history, and environmental factors to the CVD risk in adults [8-10], the identification of inherited risk markers and environmental variables that may interact with levels of Lp(a) > 30 mg/dl to modify its influence on the development of atherosclerosis at an early age, is therefore imperative.
Few studies have examined the epidemiology of Lp(a) in representative samples of total populations of children and adolescents[11,12]. However, no study has examined whether the effects of inherited and acquired or environmental factors interact with Lp(a) > 30 mg/dl, to cause differential attributable risk in different populations using data from a nationally representative sample of children in the US. We utilized data from a national survey of over 30,000 persons age 1 year and older with extensive blood lipid data to examine correlates of Lp(a) in children and adolescents and specifically to determine whether: [1] ethnic differences in shape of Lp(a) distributions seen in adults are also seen as early as age 4 in children; [2] family history of cardiovascular disease is associated with higher levels of Lp(a); [3] the effects of ethnicity and family history of CVD on the levels of Lp(a) are influenced by low birth weight, other personal, behavioral or environmental variables.
Methods
Data for this analysis was obtained from The Third National Health and Nutrition Examination Survey (NHANES-III) conducted on a nationwide multi-stage probability sample of about 40,000 persons from the civilian, non-institutionalized population aged 2 months and over of the United States excluding reservation lands of American Indians. Of these, 31,311 were examined. Our analysis was restricted to children aged 4–11 years (518 whites, 877 blacks, and 685 Mexican Americans) and adolescents aged 4–19 years (336 Whites, 665 Blacks and 504 Mexican Americans) with valid Lp(a) measurements in Phase II of the survey conducted in 1991–1994. Details of the planning, sampling, operation, informed consent procedures, and measures taken to maintain confidentiality of information have been previously detailed [13].
Demographic, medical history and behavioral information were collected prior to the examination by household interview of the parents or guardians of children and of adolescents aged 12 and over. Parents of children aged 2 months-11 years were asked "How much did the child weigh at birth?". Parents responding "don't know" were asked "Did the child weigh more than 5 1/2 pounds (2500 grams) or less? Responders were then asked, "Did the child weigh more than 9 pounds (4100 grams) or less?" An approximate category of weight at birth was created by combined responses to exact birth weights and the latter two questions. Participants' parent or guardian was also asked, "Has either of the biological parents ever been told by a doctor that he or she had a) high blood pressure or stroke before age 50 b) heart attack or angina before the age of 50? c) high blood cholesterol at any age? d) diabetes at any age? All "Yes" responses were followed by "Which, father, mother, or both?" Other interview variables are described elsewhere [13].
Blood samples were obtained at the examination centers [14]. A subsample of persons 12 years and over was asked to fast overnight for the examination of lipids in the morning. Lp(a) in serum was measured immunochemically by using an enzyme-lined immunosorbant assay (ELISA) (Strategic Diagnostics, Newark, DE) [14], which does not have cross reactivity with plasminogen or LDL non sensitive to apo(a) size heterogeneity. The normal range was set at 0 to 30 mg/dL because concentrations above 30 mg/dL have been associated with increase risk for coronary heart disease and stroke [1,3] (Plasma concentrations were 3 % lower than serum concentrations). The quality control of the Lp(a) assay has been described in detail elsewhere [14]. Serum samples with Lp(a) > 80 mg/dL were diluted into the assay range with sample diluent.
Serum total cholesterol were determined at the Centers for Disease Control using a modified ferric chloride technique (GFAA/Perkin-Elmer Model 3030 and 5100) [14]. High-density lipoprotein (HDL) was measured in serum following the precipitation of other lipoproteins with a polyanion/divalent cation mixture and triglycerides were measured enzymatically by Hitachi 704 Analyzer autoanalyzer (Boehringer-Mannheim Diagnostics). LDL cholesterol level was calculated by the Friedewald equation for individuals 12 years and older who were examined in the morning and fasted 9 hours or more, and whose triglyceride concentration was less than or equal to 400 mg/dL. Because fasting was not required in children, LDL could be calculated on only 15% of this sample. In selected analyses, serum total cholesterol was corrected for Lp(a) cholesterol as follows: TCc = TC - Lp(a) × 0.30 [11]. Standing height was measured to the nearest 0.1 centimeter, weight to the nearest 0.01 kg, triceps, subscapular, suprailiac and mid-thigh skinfold thickness to the nearest 0.1 millimeter and waist and buttocks circumference to the nearest 0.1 centimeter [15,16].
Statistical analysis
Population estimates for many of the variables other than Lp(a) have been published by the National Center for Health Statistics [14,17]. Because, body weight, family history, socio-economic factors including income, gender, ethnicity, birth weight and regional diversity have been shown to influence levels of Lp(a) and or CVD risk in general, [18,6,21] our analyses of the population estimate and correlates of Lp(a) were mindful of these factors. In order to ensure adequate weight for a given age group, and to examine pre- peri- and post pubertal effects on levels of Lp(a), quintile distribution of age was used as a categorical variable. Detailed descriptive statistics and measures of association were computed initially using unweighted data. Kendall's nonparametric rank correlation was used to assess the association of Lp(a) with other variables and compared to Pearson correlation [22]. To determine the influence of gender and ethnicity on the distribution of Lp(a), analysis of covariance was used to compute adjusted means for subjects within sex and ethnic categories, and to assess the statistical significance of differences of means among groups. Stepwise logistic multiple regression analysis was used to develop models for predicting Lp(a) >30 mg/dL for each sex, and ethnic group [22]. Only variables with pre-specified hypotheses and with statistically significant univariate correlation coefficients were eligible to enter the regression models. Following these preliminary analyses, preplanned hypotheses and major findings of the unweighted analyses were confirmed using techniques that incorporated sampling weights and design features of the survey [14]. Population estimates for mean Lp(a) and percentiles and statistical tests of weighted proportions were produced using Statistical Analysis System (SAS) callable SUDAAN [23]. Chi-Square analysis was used for the comparisons of distributions of Lp(a) categorized into 10 mg/dL strata between sex, ethnicity and age groups. Associations of Lp(a) with other variables were confirmed in final weighted analysis, using PROC LOGIST procedure in SUDAAN [23] with alpha set at <0.05. Since substantial proportions of white and Mexican American children had undetectable Lp(a), log or other transformations could not produce an approximately normal distribution of Lp(a) for parametric analyses. Therefore analytic results presented are primarily those using Lp(a) > 30 mg/dL as a categorized variable.
Results
Univariate Analyses
Ethnicity
Blacks had higher median Lp(a) than whites, who had higher levels than Mexican Americans (Table 1). The difference was already apparent at ages 4–5 years. Further, the shape of the Lp(a) distributions differed markedly for BLACKS compared to other ethnic groups at each age and overall. Blacks had a bimodal distribution that was less skewed than whites or Mexican Americans (Figure 1). The percentage of children aged 4–19 with Lp(a) > 30 mg/dL, was higher in Blacks (54.3, SE 1.8) than in Whites (20.3, SE 2.4) or Mexican Americans (16.8, SE 2.3), both overall (Chi square 47.4, p < 0.001) and in each age group (Table 1).
Table 1 Selected percentiles of lipoprotein(a) distributions and prevalence of concentrations > 30 mg/dL in children and young adults aged 4–19 years by ethnic group and age: NHANES-III, 1988–1994.
Ethnic group Age (yrs) Lipoprotein(a) (mg/dL) N
Percentile Percent > 30 (mg/dL)
5 10 50 90 95
Nonhispanic white
4–5 0 0 7 38 62 15.0 214
6–11 0 0 12 48 65 18.8 304
12–15 0 0 10 48 56 20.2 187
16–19 0 0 9 53 62 25.8 149
Nonhispanic black
4–5 2 6 31 75 94 52.5* 303
6–11 1 5 32 76 100 53.3* 574
12–15 0 5 33 77 95 56.4* 358
16–19 1 6 31 69 76 54.6* 307
Mexican American
4–5 0 0 5 30 48 8.2 309
6–11 0 0 9 45 62 21.0 376
12–15 0 0 9 48 58 20.1 272
16–19 0 0 8 36 52 11.4 232
* Indicates unadjusted statistically significant difference in levels of Lp(a) > 30 mg/dl between black and white children, and between black and Mexican American children in the same age group. Significance level was set at < 0.05.
Figure 1 Percent Frequency of Lp(a) mg/dl in children aged 4–16 years, by ethnic group in the Third National Health and Nutrition Examination Survey, 1988–1994.
Age
Table 3 shows percentiles by sex, age and ethnic group. Among boys of all sex-ethnicity groups, median Lp(a) was higher at age 6–11 than at age 4–5, then tended to decline slightly through age 16–19. Among girls, there was no consistent pattern for median Lp(a), being highest at age 16–19 in whites, at age 12–15 in blacks, and at 6–11 in Mexican Americans. The percentage with Lp(a) > 30 mg/dL varied significantly by age group only in Mexican Americans (Chi square = 10.6, P = 0.02) (Table 1).
Table 3 Selected percentiles of lipoprotein(a) distributions and prevalence of concentrations > 30 mg/dL in children and young adults aged 4–19 years by ethnic group and age: NHANES-III, 1988–1994.
Ethnicity Age (Yrs) Lipoprotein(a) mg/dL N
Percentile
5 10 50 90 95
Girls
Nonhispanic white
4–5 0 0 7 35 62 113
6–11 0 0 9.5 34 60 146
12–15 0 0 8.5 47 55 106
16–19 0 0 13 54 61 84
Nonhispanic black
4–5 1 7 32.5 74 100 146
6–11 0 3 31 72 98 288
12–15 2 9 34 78 102 191
16–19 1 5 30 71 80 166
Mexican American
4–5 0 0 6 28 36 152
6–11 0 0 11 46 59 177
12–15 0 0 9 47 56.5 140
16–19 0 0 10 34 52 119
Boys
Nonhispanic white
4–5 0 0 7 38 62 101
6–11 0 0 17 63 75 158
12–15 0 0 11 49 56 81
16–19 0 0 7 38 65 65
Nonhispanic black
4–5 2 5 31 75 94 157
6–11 2 6 34 78 105 286
12–15 0 2 32 75 78 167
16–19 0 9 32 66 75 141
Mexican American
4–5 0 0 5 34 57 157
6–11 0 0 8 45 67 199
12–15 0 0 8.5 49 60 132
16–19 0 0 7 37 55 113
Gender
Median Lp(a) did not differ consistently by gender across age or ethnic groups (Table 3). Similarly, the percentage with Lp(a) > 30 mg/dL did not differ significantly by gender within ethnic groups (Chi Square = 0.003, P = 0.96).
Birth Weight
Birth weight by parental recall was available for children aged 4–11 years (Table 4). Birth weight did not vary with Lp(a) in any ethnic group. Further, in blacks, the percentage with Lp(a) >30 mg/dL did not differ by birth weight category (52.5, 53.3, 56.4, respectively). Small numbers of cases with both abnormal birth weight and elevated Lp(a) >30 mg/dL among whites and Mexican Americans precluded meaningful analysis.
Table 4 Selected percentiles of lipoprotein(a) distributions and prevalence of concentrations > 30 mg/dL in children aged 4–11 years by ethnic group and birth-weight: NHANES-III, 1988–1994.
Ethnic group Age (yrs) Lipoprotein(a) mg/dL N
Birth Weight Median 50 Percent > 30 mg/dL
Nonhispanic white
<2500 gm 12 15.0 32
2500–4100 gm 10 18.8 441
>4100 gm 7 20.2 43
Nonhispanic black
<2500 gm 32 52.5 115
2500–4100 gm 32 53.3 702
>4100 gm 29 56.4 44
Mexican American
<2500 gm 3 8.2 45
2500–4100 gm 8 21.0* 573
>4100 gm 5 20.1* 57
* Indicates within group statistically significant association of Lp(a) > 30 mg/dl with birth weight using birth weight < 2500 gm as the reference value. Significance level was set at < 0.05.
Family history
In all the groups combined (age range 4–16 years), the percentage with Lp(a) > 30 mg/dL was significantly higher among those with parental history of heart attack/angina before age 50 years compared to those without (50.0 percent versus 30.2 percent), Chi square 2.72, P = 0.011), whereas the percentage (Lp(a) > 30 mg/dL) was similar among those with parental history of diabetes and high cholesterol vs. those without (Figure 2). Due to small numbers of children with a history of heart attack within ethnic groups, the difference in percentage of persons with Lp(a) did not attain significance in within-groups analysis: white children, 42.56% versus 18.36%, P = 0.23, black children 72.37% versus 54.06%, P = 0.12, Mexican American children 34.46% versus 18.26%, P = 0.33 (Table 2). In white children, the percentage was higher in children with a parent with high blood cholesterol compared to children without (26.74% versus 16.12%; P = 0.02). However, no significant differences were seen in other groups.
Figure 2 Prevalence of lipoprotein(a) concentration > 30 mg/dl in children aged 4–16 years, by combined ethnic group, and parental history of heart disease or angina, high cholesterol, or diabetes below age 50 in the Third National Health and Nutrition Examination Survey, 1988–1994
Table 2 Median lipoprotein(a) and prevalence of concentrations >30 mg/dL in children aged 4–16 years by ethnic group and parental history of heart or an angina below age 50, high cholesterol, or diabetes: NHANES-III, 1988–1994
Ethnic group Lipoprotein(a) mg/dL N
Median (mg/dL) Percent > 30 (mg/dL)
Heart Attack High Cholesterol Diabetes Heart Attack High Cholesterol Diabetes Heart Attack High Cholesterol Diabetes
Nonhispanic white
Yes 25 13 12.5 42.6* 26.7 32.8 15 138.0 34
No 10 8 8 18.4 16.1 18.0 722 586.0 701
Nonhispanic black
Yes 45 28 33.5 72.4* 48.9 56.6 53 111.0 86
No 32 33 32 54.1 55.5 54.8 1242 1176.0 1206
Mexican American
Yes 12 9.5 7 34.5* 19.6 19.2 21 134.0 45
No 7.5 7 8 18.3 18.7 18.7 990 870.0 966
* Indicates statistically significant association of parental history of heart attack before age 50 with levels of Lp(a) > 30 mg/dl nonhispanic black, nonhispanic white and Mexican American children. Significance level was set at < 0.05.
Region
Among white children, median Lp(a) was lower in the Midwest (8.2 mg/dL) and South (8.2) than in the Northeast (16.4), or West (10.1). No differences were noted for Blacks and too few Mexican Americans lived in the Northeast and Midwest for evaluation. In white children, the percentage with Lp(a) > 30 mg/dL were 15.1 in the Midwest, Northeast 25.1, South 21.2, West 21.4 (P = 0.56). Among blacks, a greater percent of metropolitan, compared to non-metropolitan (57% Vs 49.7%; P = 0.04) had Lp(a) > 30 mg/dL. No significant differences were seen in other groups.
Income
Family income < $20,000 was not associated with Lp(a) > 30 mg/dL in whites or Mexicans, but Blacks with low income tended to have a higher percentage of individuals with Lp(a) >30 mg/dL (56.9% versus 50.5%, P = 0.10). Poverty income ratio was not significantly correlated with Lp(a).
Multivariate Analyses
The following additional variables known to influence CVD risk were assessed as correlates of Lp(a) by ethnic group: age, total serum cholesterol, HDL cholesterol, casual triglycerides, hours of fasting, weight, height, body mass index, waist circumference, waist-to-hip ratio, subscapular skinfold thickness, suprailiac skinfold thickness, pulse rate, systolic and diastolic blood pressure, heavy activity frequency or TV hours. Logistic regression analysis with Lp(a) > 30 as dichotomous dependent variable and age in months as independent variable revealed a significant linear association in whites (beta = 0.004, SE = 0.002, P = 0.03) and a quadratic association in Mexican Americans (age beta 0.068, SE = 0.013, P < 0.001, age squared beta -0.000, SE beta 0.000, P < 0.001), indicating a lower prevalence of Lp(a) > 30 mg/dL at both ages 4–5 and 16–19 than at 6–15 years. Age and age squared, and sex were entered first in all analyses described below. Controlling for age, sex was not significantly associated with high Lp(a) in any group. Compared to whites, non-hispanic black ethnicity was significantly associated with high Lp(a) after controlling for age and sex (P < 0.001). Mexican American ethnicity was not significantly associated with lower prevalence of high Lp(a) (P = 0.35). Within ethnic groups at ages 4–11 years, low birth weight (<2500 g) was not significantly associated with high Lp(a) after controlling for age and sex. High birth weight (>4100 g) was associated with high Lp(a) (beta 1.87, P = 0.02) only in Mexican Americans.
Parental history of heart attack/angina before age 50 was significantly associated with Lp(a) >30 after controlling for age and sex both in white children (beta -1.14, SE 0.55, P = 0.04) and in blacks (beta -0.80, SE 0.39, P = 0.05). Parental history of heart attack/angina was also significantly associated with high Lp(a) in all children (P = 0.02) after controlling for age, sex and ethnicity. In white children, parental history of high blood cholesterol (P = 0.07) and diabetes mellitus (P = 0.16) were not significantly associated with high Lp(a) after adjustment for age and sex.
Residence in central cities/fringe areas remained significantly associated with high Lp(a) in black children after controlling for age, sex, region, season, and time of the day (beta = -0.33, SE = 0.13, P = 0.02). Region, rural/urban code, family income < 20,000 or higher poverty income ratio were not significantly associated with high Lp(a) in white or Mexican American children (all P > 0.05).
Body mass index or weight were significant predictors of Lp(a) > 30 mg/dL only among black children after controlling for age, sex, region, rural/urban code, season and poverty income ratio, e.g. weight(kg) P = 0.02. Neither HDL cholesterol nor casual triglyceride concentration was significantly associated with Lp(a) after controlling for multiple variables. Total serum cholesterol was significantly associated with Lp(a) after controlling for multiple variables in all three ethnic groups as expected.
Discussion
The most important findings of this study are that ethnicity significantly associated with Lp(a), and that parental history of heart attack significantly associated with Lp(a) levels >= 30 mg/dl. While non-hispanic black children had significantly higher total Lp(a) level, compared to white and Mexican American children, no consistent associations of age or gender with Lp(a) were found in NHANES-III below age 20. Low birth weight (<2500 g) was not significantly associated with high Lp(a) after controlling for age and sex in the entire group. Higher levels of Lp(a) >30 mg/dl was evident in metropolitan compared to non-metropolitan non-hispanic black children.
Mechanisms
Lp(a) is a circulating particle that consists of phospholipids, cholesterol, and apolipoprotein B-100 (i.e. a LDL particle), with apolipoprotein(a) attached to the latter at a single point [5,24]. Like LDL, Lp(a) when oxidized may promote atherosclerosis by promoting formation of foam cells which release growth factors. Lp(a) acquires a pathogenic profile on entering the arterial cell wall as a result of the influence of factors operating in the inflammatory environment of the atheromatous vessel, such as proteolytic enzymes of the metalloproteinase family [25]. About 80% of the amino acids in apo(a) are homologous with those of plasminogen, suggesting a possibly thrombolytic effect which might both promote atherosclerosis and trigger acute thrombotic occlusions [1,3,26].
Whereas levels of Lp(a) above 30 mg/dL was shown to increase risk of coronary heart disease in European samples [3,24], no such association has been found in black populations, in whom the concentration is twice that in Europeans [24,27]. Study of serum concentrations of this particle in children is especially important since, unlike LDL, its concentration is postulated to be remarkably stable throughout the life of an individual. Thus, identification of persons at increased risk early in life would permit more effective intervention to lower levels of modifiable risk factors such as LDL cholesterol.
Environment, Ethnicity, Age and Gender
A number of studies of adults have compared Lp(a) levels in Whites and Blacks, and have uniformly reported two-fold higher levels in Blacks [4,5,11]. In Texas, Mexican American adults were found to have lower Lp(a) than whites. Conversely, Kambor et al observed higher mean and median plasma Lp(a) concentrations in hispanic men than white men in Colorado with lesser difference seen in women[28]. Although the explanations for these findings remain unclear, environmental factors, genetic admixture [29] or a combination of both should be considered.
No previous reports of studies comparing Lp(a) in whites and blacks and Mexican American children in the same study were found prior to NHANES-III. In fact, few comparisons of plasma Lp(a) concentrations were found for hispanic children or other children below age 8 years prior to NHANES-III [30,31]. The present findings extend the published report by examining children in greater detail and examining the relationship of birth weight and family history of cardiovascular disease with Lp(a) in children.
Perhaps one of the most noteworthy observation from this study is the significantly higher levels of Lp(a) >30 mg/dl in black, compared to white and Mexican American children (Table 1). Ethnic-related differences in Lp(a) similar to those in adults were found in children as young as 4–5 years of age, supporting the presence of higher levels of Lp(a) in black children compared to other ethnic groups (Table 1). This observation is consistent with findings of the Bogalusa Heart Study of white and black children, and the NHLBI Growth and Health Study of girls that showed higher Lp(a) levels in black than white children [30,32]. Findings in Mexican Americans in the NHANES-III study is analogous to reports from the Colorado study showing a greater percent of Hispanics than whites (19% versus 12%) to have Lp(a) > 25 mg/dL.
The explanation for ethnic-related differences in levels of Lp(a) in the US remains unclear. Other than total cholesterol, no single environmental or biological variable consistently associated with levels of Lp(a) in the NHANES-III sample. While levels of Lp(a) > 30 mg/dl received significant contributions from BMI and residence in metro compared to non-metro in black children, only higher birth weight significantly contributed to levels of Lp(a) in Mexican American children. Contrary to a previous report of association of low birth weight with elevated Lp(a) concentration in black children, [6] we found no consistent association of low birth weight with levels of Lp(a) in black or white children in the present study (Table 4).
At the genetic level, heritability estimates were reportedly higher for Whites than for Blacks [33,34] despite the disproportionately higher levels of Lp(a) in Blacks. This observation raises an important question about the genetic determinants of differential levels of Lp(a) in non-hispanic Blacks compared to Whites. A recent study on genetic linkage analysis by Barkley et al found no linkage evidence to support the presence of a single but separate gene with large effects specifically segregating in non-hispanic Blacks that may account for elevated Lp(a) levels[35] Conversely, high levels of Lp(a) levels have been suggested to be an old African trait that is associated with mutations in the coding sequences of apo(a) [36]. Collectively, these disagreements among studies suggest that higher levels of Lp(a) in non-hispanic Blacks compared to other ethnic groups, may result from a complex interaction of genes with environmental and metabolic factors, [37]. Future identification of the presence and nature of this interaction is imperative.
Studies of adults found an association of higher age and female gender with higher Lp(a) levels [38]. The Bogalusa Heart Study found a small but significant gender difference and a weak positive correlation with age (p < 0.001) in white girls 11–17 years of age [30]. However, we found no consistent associations of age or gender with Lp(a) in NHANES-III below age 20 (Table 3). Despite the levels of Lp(a) that tended to be highest between age 6 – 11 years in boys, the lack of similar trend in girls, and the absence of age-related difference in the levels of Lp(a) in the combined group, suggests that pre-pubertal or pubertal status may not significantly influence levels of Lp(a) in children.
Altogether, our observation from the present NHANES-III study, together with the work of others [30,38] found very few significant associations of Lp(a) with personal, behavioral, or environmental variables. It therefore appears likely, that multiple factors at the environment and or genetic levels may act together to differentially influence levels of Lp(a) in children and adolescents in the US.
Family History
Few studies of the association of Lp(a) with family history have been reported in children [30,39]. Our observations of significant association of parental history of heart attack/angina before age 50, with levels of Lp(a) >30 mg/dl in NHANES-III (Figure 2) are consistent with results from the Bogalusa study that found an association of parental history of premature heart attack with higher levels of Lp(a) [30]. However, contrary to Bogalusa study showing an association of Lp(a) with parental history of hypercholesterolemia, trends for family history in black children in NHANES-III were concordant with those in whites, although not attaining statistical significance. More recently, Dirisamer and colleagues provided additional support for higher levels of Lp(a) levels in children and adolescents from families with premature coronary heart disease compared to those without familial coronary heart disease [40]. In young adults aged 23–35 years in the CARDIA study, a non-significant trend toward higher Lp(a) levels in those with a family history of myocardial infarction was observed in whites, but no association was seen in blacks [33]. Collectively, the association of parental history of premature heart attack appears associated with levels of Lp(a) > 30 mg/dl, may lend support to the theory of genetic underpinning for the higher levels of Lp(a) observed in black children.
In the NHANES-III study, no association of Lp(a) were seen with family history of stroke, hypertension, or diabetes. Similarly, family history of high cholesterol and diabetes were not significantly associated with levels of Lp(a) >30 mg/dl in the entire sample (Figure 2), except in non-hispanic white children (Table 2). Cross-sectional studies of adults have not consistently shown a relationship of Lp(a) with NIDDM. Conversely, several reports indicate an association between IDDM with Lp(a) [29,41]. However, NIDDM is thought to have a stronger genetic component in its etiology than IDDM [42]. Despite the inconsistencies in the literature, there is strong evidence to suggest that Lp(a) is a risk factor for vascular disease in diabetics [43]. Further research on clinical and subclinical diabetes and Lp(a) is needed.
Limitations
Limitations of the present study include possible bias from survey non-response, missing values for some variables, and confounding by variables not measured. Fortunately, several special studies of earlier NHANES-III data have indicated little bias due to non-response [44]. Although, adequate reliability has been demonstrated for Lp(a) measurement [14], the lack of a single, generally accepted laboratory method and national standardization program remains a problem, perhaps explaining in part the inconsistencies among studies [3]. The relatively large sample size provided good statistical power and the conservative criteria for statistical significance reduced the possibility of chance findings attaining significance despite a large number of tests. Overall, the representativeness of the sample and the use of sample weights provided wide generalizability of the results to United States black and white and Mexican American children and adolescents of the same ages.
In conclusion, ethnicity significantly associated with levels of Lp(a). Parental history of heart attack/angina before age 50 years associated with levels of Lp(a) > 30 mg/dl in offspring. Collectively, different pathological thresholds may have to be established for elevated serum Lp(a) levels, to be used as a risk marker for coronary heart disease in different populations. Future research should include longitudinal studies of Lp(a) in white, black and hispanic children followed to adulthood. Racial admixture as well as environment and behavioral variables associated with acculturation and urban residence should be studied, especially in Mexican American and black populations. Standardization of methods will facilitate inter-study and longitudinal comparisons.
Acknowledgements
We acknowledge Thomas Socey, for computer programming; and the staff and contractors of the Division of Health Examination Statistics of the National Center for Health Statistics, who conducted the survey and prepared the data for analysis.
The project was supported by grant #AG00980 (NIA) to Obisesan TO, and RR10284 (HU).
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| 15601478 | PMC544891 | CC BY | 2021-01-04 16:39:18 | no | Lipids Health Dis. 2004 Dec 16; 3:29 | utf-8 | Lipids Health Dis | 2,004 | 10.1186/1476-511X-3-29 | oa_comm |
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BMC Public HealthBMC Public Health1471-2458BioMed Central London 1471-2458-5-31563893710.1186/1471-2458-5-3Research ArticleStrategies to prevent HIV transmission among heterosexual African-American men Essien Ekere J [email protected] Angela F [email protected] Ronald J [email protected] Gbadebo O [email protected] Nora I [email protected] The HIV Prevention Research Group. College of Pharmacy, University of Houston, 1441 Moursund Street, Houston, Texas 77030, USA2 Center for Health Promotion and Prevention Research. University of Texas School of Public Health, Houston, Texas 77030, USA3 College of Pharmacy, Texas Southern University, Houston. Texas 77004, USA2005 7 1 2005 5 3 3 27 7 2004 7 1 2005 Copyright © 2005 Essien et al; licensee BioMed Central Ltd.2005Essien 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
As part of qualitative research for developing a culturally sensitive and developmentally appropriate videotape-based HIV prevention intervention for heterosexual African- American men, six focus groups were conducted with thirty African-American men to determine their perceptions of AIDS as a threat to the African-American community, characteristics of past situations that have placed African Americans at risk for HIV infection, their personal high risk behaviors, and suggestions on how HIV intervention videotapes could be produced to achieve maximum levels of interest among African-American men in HIV training programs.
Methods
The groups took place at a low-income housing project in Houston, Texas, a major epicenter for HIV/AIDS. Each group was audiotaped, transcribed, and analyzed using theme and domain analysis.
Results
The results revealed that low-income African-American men perceive HIV/AIDS as a threat to their community and they have placed themselves at risk of HIV infection based on unsafe sex practices, substance abuse, and lack of knowledge. They also cite lack of income to purchase condoms as a barrier to safe sex practice. They believe that HIV training programs should address these risk factors and that videotapes developed for prevention should offer a sensationalized look at the effects of HIV/AIDS on affected persons. They further believe that programs should be held in African-American communities and should include condoms to facilitate reduction of risk behaviors.
Conclusions
The results indicate that the respondents taking part in this study believe that HIV and AIDS are continued threats to the African-American community because of sexual risk taking behavior, that is, failure to use condoms. Further, African-American men are having sex without condoms when having sex with women often when they are under the influence of alcohol or other mind-altering substances and they are having sex with men while incarcerated and become infected and once released resume unprotected sexual relations with women. According to the men, substance abuse is an important part of the problem of HIV in the African-American community. This is in keeping with research that shows that drug use, especially crack cocaine, is linked to sexual risk taking among African Americans and to increased likelihood of becoming infected with other sexually transmitted diseases (STDs) including HIV. Thus, interventions for men should address condom use, condom availability, skills for using condoms, eroticizing condoms and substance abuse prevention. Men in the present study also strongly recommended that HIV/AIDS videotaped messages should include footage of the sensational effects of the disease.
African AmericansmenHIVAIDSrisk behaviors
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Background
The HIV/AIDS epidemic continues to be a major public health challenge in the African-American community. Although African Americans constitute only 13% of the United States population, they have accounted for 39% of all the 886,000 estimated AIDS cases that have been diagnosed since the epidemic began in 1981 [1]. In 2002, African Americans comprised about 50% of the 42,000 AIDS cases diagnosed among adults in the United States, and also accounted for more than half of the HIV diagnoses that were reported to the Centers for Disease Control and Prevention (CDC) [1]. Amongst these, the rate of diagnoses among African-American men was almost nine times greater than the rate for white men [1]. The CDC has identified sexual contact between African-American men who have sex with men, injection drug use, and heterosexual contact respectively, as the leading causes of HIV infection among this group [1].
While several successful intervention programs have been developed, evaluated, and implemented for African-American men who have sex with men, African-American women, and African-American adolescents [2], only a limited number of prevention programs have been implemented for heterosexual African-American men [2]. This is unusual given that heterosexual transmission has been identified as the leading cause of HIV infection among African-American women [1].
Several factors other than those reported by the CDC have placed African-American men at an increased risk of HIV infection. Essien et al. [3] explored the role of misperceptions about HIV transmission among African-American men and women by conducting eleven focus groups with sixty-nine men and women. They reported myths and misperceptions about HIV transmission such as denial of personal risk, perceptions that HIV was a disease that happened to outsiders and "others" and the perceived role of the government in the development of HIV as contributing factors to the efficiency of HIV transmission in this population. In a follow-up study to assess the range of discrepancy in self-reported sexual identity and sexual behavior in men and women of four racial/ethnic groups, Ross et al. [4] found that 43% of the African-American men in their sample who self-identified as heterosexuals were also having sex with other men. This finding is consistent with a recent report by Montgomery et al. [5] which showed that, among African-American men who had sex with men, 34% had also been engaged in a bisexual relationship. Research has shown that bisexual African-American men may not relate to HIV prevention messages that have been developed for gay men [1]. Thus, intervention programs for African-American men must be innovative and firmly grounded in the socio-cultural contexts of sexual risk taking in this population.
An array of socioeconomic and cultural factors exacerbates high-risk behaviors that place African-American men at risk for HIV. First, nearly 25% of African Americans live in poverty [6], and several studies have shown a direct relationship between poverty and increased prevalence of HIV/AIDS [7-10]. This may be due to high rates of unemployment, associated drug related economic activities, money-for-sex and drugs-for-sex exchanges, and inadequate access to health care and HIV prevention programs [1,11,12]. Secondly, injection drug use has been identified as the next leading cause of HIV infection among African-American men [1]. Aside from sharing injection paraphernalia, African Americans who are substance abusers often engage in high risk behaviors such as unprotected sex when they are under the influence of drugs and alcohol [13]. Thirdly, cultural and social norms in African-American communities are not supportive of homosexual behavior. It has been argued that negative attitudes associated with homophobia may lead to psychological distress and sexual risk taking among African-American men leading them to having relationships with both women and men [14].
A major challenge facing HIV prevention interventionists is the issue of developing HIV prevention programs that are firmly grounded in the cultural nuances of African Americans and the translation of these programs into effective HIV prevention practice. To date, several HIV intervention programs have been developed for African Americans [15-18], and the most promising interventions have been programs that are based on social cognitive principles. However, Kalichman et al. [19] have noted that HIV prevention programs that are based on social cognitive principles and proven to be effective in the scientific literature have not been widely utilized because of their reliance on expert interventionists for implementation in face-to-face formats, making them difficult to transfer to community-based organizations [19]. In contrast, social cognitive theory principles applied to HIV prevention can be delivered effectively by videotape and community-based organization personnel with minimal training in skills building techniques [19]. The rationale for using videotapes as part of an HIV intervention delivery system is provided by the emerging literature which demonstrates the feasibility of this medium in changing high risk sexual behaviors [20-23]. The research presented here results from qualitative studies conducted in Houston, Texas, to examine the sociocultural contexts of sexual risk taking among African-American men and to determine how a videotape-based HIV prevention intervention could be tailored so that it is effective in preventing HIV transmission among African-American men.
Methods
Design
This study used a qualitative exploratory research design to elicit information about the strategies for preventing HIV transmission among African-American men, ages 18–29. Thirty low-income African-American men participated in five focus groups that were conducted at a housing project targeted for convenience sampling and because of its location in close proximity to the research institution in Houston, Texas, a leading HIV/AIDS epicenter in the United States [1]. Approval for the study was obtained from the relevant university Committee for the Protection of Human Subjects.
Procedure
The investigative team recruited focus group participants by displaying posters and flyers at strategic locations in the housing project identified by the manager. The flyer listed the study inclusion criteria: African-American heterosexual male, aged 18 – 29, self-reported unprotected vaginal sex in the last six months or having been diagnosed or treated for a sexually transmitted disease in the past year. The flyer also listed a university telephone number that prospective participants could call to obtain additional information and/or schedule their participation in a group. When calls were received, a trained research assistant confirmed eligibility. Those selected for participation were then encouraged to invite their friends to participate. Of the 97 prospective participants who contacted the university, 54 agreed to participate. Of that number, 30 appeared on the scheduled day and formed the study sample. The focus groups were conducted at the housing project's clubhouse by a facilitator with approximately 15 years experience conducting qualitative research studies with African-American men. Assistance was provided by a trained research assistant who took notes to ensure that pertinent information not captured by the audiotape was obtained and to record major points discussed.
Participants were informed of the benefits of their involvement, that involvement was voluntary, that they could refuse to answer any question, and that they could cease participation at any time without penalty. Prior to the start of each group, written informed consent was obtained and agreement obtained to record the session. The participants were also told about the confidentiality of the information discussed at the meeting, especially important given that some of the men were acquaintances as a result of residency in or near the housing complex. They were advised that they would receive a $25 mall gift certificate and condoms as incentives for their participation. Respondents were also advised that the tapes, which were anonymous, would be destroyed following transcription and checking. All questions as well as the informed consent were provided in English.
Data collection
The investigative team utilized semi-structured and open-ended questions to elicit information from participants based on our research interest in determining perceptions of AIDS as a threat to African-American communities, characteristics of past situations which placed African Americans at risk for HIV infection, suggestions on ways to recruit African Americans into HIV/AIDS prevention programs and on how HIV intervention videotapes could be produced to achieve maximum levels of interest.
A three-step staging process was used to generate the questions used in the focus groups: (1) generation of working hypotheses on facilitators and barriers to HIV prevention and program messages and methods by conducting interviews with six key informants experienced in HIV/AIDS prevention among African Americans; (2) reshaping of hypotheses until no new information was received by testing them in interviews with men similar to the target population; and (3) using the resulting hypotheses to generate questions and field testing them with members of the target population after which final changes were made.
A guide consisting of a written list of questions and probes was used to conduct the interviews. According to McCracken [24], the advantages of using such a guide include the assurance that all areas of interest are covered and allowing the researcher to focus his attention on listening to the informants, thereby enabling a better understanding of their lines of thought and possibly unanticipated explanations of the concepts. The duration of each group was about two hours.
Data analysis
The standard grounded theory approach of Glaser and Strauss was performed to conduct data analysis [25]. Grounded theory procedure and techniques were utilized for data analysis by doing a line-by-line analysis of the focus group transcripts and identifying the emerging themes [26]. The thematic concepts representing ideas expressed by a majority of the members of three or more focus groups were characterized as a domain and are reported below.
Results
Study participants
The study sample was comprised of 30 men between the ages of 18 and 29. Educational attainment ranged from tenth grade to college graduate with the greatest number of men stating that they had at least one year of post-secondary education. Most men identified themselves as day laborers attempting to earn income by doing various odd jobs as they become available. The remainder was unemployed. No men identified themselves as HIV positive, and none of the respondents had health insurance.
Perception regarding AIDS and African Americans
The situational determinants of HIV risk taking and their impact on HIV/AIIDS prevention behaviors and education programs were examined. The discussion began by asking the men for their overall view regarding HIV and its impact on the African-American community. Specifically, they were asked if they believe that AIDS is a threat to the African-American community and to what they attribute its rapid spread. About one-half of men stated that it is due to lack of unsafe sex practices including having sex with multiple partners and failure to use condoms. A large number of men believed that African Americans have higher rates of HIV and AIDS due to their use of alcohol and drugs while a smaller number stated that AIDS is not a threat to the African-American community, but rather it is a universal problem or that African Americans have feelings of invincibility because they think that HIV happens to others and that God will protect them. A lesser number attribute the rapid spread of HIV and AIDS among African Americans to incarcerated men having sex with men while behind bars and returning to their female partners when they are released.
Clarence, a 26-year old forklift operator, believes that AIDS is a threat to the African-American community due to failure to use protection from lack of understanding about the disease:
"A lot don't understand the disease or what the effect of it may be so they have unprotected sex because they don't understand the disease really well as they supposed to."
Ben, a 22 year old promotional worker, expressed his concerns about men having sex with men and also with women. He states:
"It is not a problem on the outside, but a lot of men have sex without condoms in the jails and they are bringing it right back home, and they are not telling anybody that they had sex in jail, and they are bringing it and sharing it among the girls."
Kenny, a 27 year old temporary worker, adds:
"I was recently locked up a couple of years ago. I met some guys you'd never think would be gay, undercover homosexuals, they pump weights with you, run track, and play basketball with you, but as soon as they are behind closed doors, their cellmate might be gay and they are banging in there. Even black gangs, sometimes when they initiate them, they do that, and they bring the disease back home."
Thus, incarceration and gang membership may facilitate HIV transmission and may be partially responsible for the high rates of bisexual activities in the African-American community.
Jason, a 20 year old laborer explained that AIDS is a universal threat but that it is an epidemic among African Americans due to associated substance abuse and failure to use protection:
"It's a threat to everyone but we have a tendency in the black race, especially now with another epidemic that we have in our neighborhood called crack. People have a tendency now to be even careless with this drug. So they do things that they would not naturally do. Just having sex. The trust thing – we feel that we won't use condoms because we feel like they're uncomfortable, yeah, they kill the sensation and so then we're gonna jump in and do the do and so it has a stronger effect on us now than a lot of races."
Terry, a 28-year old home improver, asserts that African Americans do not believe that God will inflict added suffering (e.g., AIDS) upon them because they have already experienced hardship, God would not cause something else to make life difficult. He said:
"It's because of the color of our skin. People believe that, seems to believe that, oh, God is going to let them know or it's not going to happen to me or something like this. We done had enough bad happen to us."
Karl, a 29 year old college graduate did not believe that AIDS is a threat to the African-American community, but rather that statistics are inflated for this group due to the places where they obtain medical treatment. He believes the government keeps data that private physicians do not which results in inflated numbers for African Americans. Mistrust of healthcare system reporting about African Americans leads him to believe the situation is not as bleak as reports indicate. He stated:
"Most African Americans go to clinics which are state or federally funded and they keep that data. And most people with private health insurance go to their doctors and those private doctors don't submit that same data. So when you look at the quote-unquote per capita, it always looks like it is higher in the African-American community."
Risk situations for HIV transmission
When asked to describe the means by which they had personally placed themselves at risk for HIV infection, almost half of participants cited unprotected sex or not taking the time to use protection "in the heat of the moment." These men did not have condoms in their possession, did not take the time to use a condom, or simply disregarded condom use because of dislike for them. Similarly, about one-third of men described specific situations with risks known to them at the time they engaged in unprotected intercourse. They included having sex with an HIV infected person, having sex with a stranger, sex with a drug user, and sex in exchange for money or drugs. The remainder of men stated that their drinking and drug use had created HIV risk situations.
Austin, a 25 year old pipe fitter, explained that when he went to clubs and took women home, the result was being irresponsible and having unprotected sex:
"I've been drinking, just being in the room... the heat of the moment ... the heat of passion, just the heat of the moment taking away all the common sense."
Clarence is living with the consequences of having sex with strangers in a mood altered state. Although he did not contract HIV, he expressed that alcohol and unprotected sex resulted in him contracting hepatitis A.:
"Alcohol did that to me. I had sex with many women that I didn't know had the disease or nothing and I caught hepatitis A behind it once."
Like Austin and Clarence, Jason attributed his involvement in HIV risk situations as resulting from substance abuse. He stated:
"You don't mean to do it, but any mind-altering drug will lead you into a situation where you will have unprotected sex."
Joe, a 24 year old truck driver explained that he has unprotected sex in the heat of the moment and once he becomes involved in a sexual act, contact will occur without condom use.
"Once you get in a certain mood and you get stuck, I'm in that freak mode; there ain't no protection."
Harry, a 23 year old landscaper, described how he was told that his partner has AIDS but he continues to have sex with her but gets himself tested for HIV every 6 months. He, in effect, continues to take a chance and uses the test as a way to keep himself informed.
"In fact, I had two girls and they telling me that you know your partner tell you where they got AIDS from her, but yet and still you get yourself checked and you don't come up with AIDS. You know there's a doubt there somewhere. Every six months I have myself checked for almost a year because I supposed to get it from her. How did I miss it? I don't know."
Barriers to practicing safe sex
Like their perceptions about the reasons for the rapid spread of HIV and the situations that have placed them at risk for infection, one-third of men stated that substance abuse is a barrier to safe sex practices. Other reasons stated as barriers were lack of money for condoms and judging a partner on appearance alone. About one-fifth of participants said they had no barriers to practicing safe sex.
Karl believes that alcohol and drug use, specifically crack cocaine, creates a feeling of power over women which results in the women failing to negotiate safe sex practices. He said:
"I think alcohol and drugs, mood altering, mind altering, especially with that crack cocaine. When you get that crack cocaine, the fist thing you get, and you take you a hit, you automatically assume power. You know with that crack, you have power over women. You know you can make them do what you want them to do."
Freddy, a 28-year old laborer, explained that when a choice between drugs and condom has to be made, he will choose drugs. He stated that he did not have money to buy both condoms and drugs:
"I don't have enough money to go out and buy condoms that everybody wants. I need to spend money on condoms or I'm a spend it on getting the next hit."
Although infrequently stated, Jason said that he had, in the past, judged a woman's HIV/AIDS status based on her appearance:
"What prevented me from practicing safe sex in the past was, like I said, the way she looked. You feel that you know her, but now I see that you can't go on that."
Barry, a 26-year old construction worker, stated that he always uses protection. He said:
"Like I said earlier, if I can't find a plastic baggie or the plastic that you break on in, I wear a sandwich bag, hey, if I can't use that, I won't mess around. I won't do it!"
Facilitators to safe sex practices
Close to one-half of men were motivated to practice safe sex because they were personally acquainted with someone who had been affected by AIDS and had seen its effects firsthand. A quarter of men was adamant about remaining disease free or had previously acquired sexually transmitted infections. About 15% simply stated that life was their motivation.
Barry has seen the effects of AIDS firsthand and the images have caused him to use condoms. He said:
"I was about to do it with no condom and that seriously woke me up, put a stop and then God stepped in and then about a year later, she passed. The loss of friends and hearing people over the news. Their body just shrivel up cause the body can't hold their bladder, their bowels I mean it's the ugliest sight to see. You know and that's enough to wake you up."
Austin, who in the past had taken strangers home and engaged in unprotected sex, explains that his concern for his future facilitates his use of safe sex practices:
"My motivation is AIDS, not contracting AIDS. The fact that I don't have any kids or I'm not married ... something that will affect my future. I mean I want kids. I wanna be married. I mean if I have AIDS or what have you, don't none of that happen."
Jason reiterates Austin's comments quite simply when he states:
"I want to live. That's the bottom line."
Motivation for safe sex practices was not always associated with action even in the face of prior sexually transmitted infection.
Joe adds:
"One thing that motivates me is, I don't necessarily take heed to it though, but the only venereal disease that I have had in the past is them crabs or whatever you want to call it. One thing that motivates me is that I had got crabs and I felt dirty, I mean I felt real bad."
Strategies for preventing HIV infection
In light of their experiences, participants were asked to describe what they would recommend to curtail the spread of AIDS in the African-American community including what specific governmental, media and community interventions they would consider effective. A variety of recommendations were offered, but the majority believed that more education is needed. Other men suggested that condoms are distributed without charge and consistent condom use is urged. To recruit African Americans into HIV training programs, financial and social incentives were highly recommended as were the use of a community-based, community-friendly approach to program recruitment and implementation.
Trevor, a 27-year old who is unemployed said:
'The way that AIDS can be prevented is just through education, you know, education and that's basically the only tool that we have is education. Uh, I think the parents play a good role in it [education] too."
Wally, a 20-year old bricklayer adds:
"There's only one way and it's through education and I mean education will sum it up. We have to educate on drugs, we have to educate on protective sex. I mean, education is it."
Freddy echoes the sentiments of Trevor and Wally regarding education but also discussed the need to provide condoms:
"We need to get more condoms out on the streets. All STDs have to be prevented so we have to get more education on the streets, not just about AIDS because they [AIDS and other STDs] work hand in hand."
Incentives such as gifts and social events were recommended as a means to get African-American participation in HIV prevention and risk reduction interventions. Wally stated the need to provide incentives to increase program participation:
"You can give and receive at the same time. What I'm saying is you may have to start having a little gift or something to get them to come in the first few times, or a meal."
Barry adds:
"You gotta give something to get something. Another thing, have you a little barbecue, have it sitting out or something and you can get a whole lot of people. And believe it or not, a crowd of people will open up to a conversation, too."
Karl stressed incentives and programs for adolescents:
"Sometimes especially with the younger generation, you have to give them something that they want. Give them caps, give them T-shirts, but at the same time, push the condoms."
Outreach workers were advised to eliminate formality and go into the community to provide their programs. They should make an effort to ensure the target community members are comfortable with their presence. Jason said:
"And go into the neighborhoods and walk around and talk to folks. People have a tendency to stay away from people in a suit and tie in a neighborhood."
Gene, a 20-year old laborer added:
"Instead of making it formal, just come in off the streets. Go to the people and give them that incentive and sit down and talk to them."
It is believed among two-thirds of men participating in these discussion groups that the government can assist AIDS prevention by firstly, providing funding for community-based education programs, secondly, providing convenient and free testing, and thirdly, free condom distribution. Other suggestions included quarantining or having a salient means of identifying those affected by HIV and AIDS, providing more medication and treatment for those affected, and having stiffer penalties for those who knowingly transmit the virus.
Jason believes in the efficacy of outreach programs that provide free testing and incentives:
"Just have three of four vans, just go to different neighborhood, have free testing, give T-shirts."
Terry believes in the community approach to governmental intervention:
"I think the greatest weapon we have against AIDS is knowledge and we must bring some type of community-based program."
Roger asserted his belief that the entire US population should be tested and those with HIV and AIDS identified and placed in isolation:
"Either quarantine, mandatory testing for HIV for the whole US population. You go to get tested. You're going to a quarantine island for the rest of your life."
Terry believes that the government should impose stringent punishment against persons who knowingly infect others with HIV:
"I think we can make stiffer penalties for people with AIDS that are willingly passing the disease on. Probably the government can open some more clinics or help the doctors find a cure."
To create a video that addresses HIV prevention, close to one-half of participants recommended using a sensational approach that includes those morbidly affected by HIV and AIDS. They believe that the video should include footage of people in pain, being shunned by friends and family, and suffering tremendous physical pain. They also recommended testimonials from infected persons. A quarter of respondents believed that the video should be educational providing information about transmission and prevention. The remainder suggested the use of rappers to promote the message of prevention and acknowledgement that HIV happens to African Americans.
Karl who has a college degree stated that a video developed for AIDS prevention should include the disease's effects on the person:
"They need to see those people suffering to let them know that this is how you're going to end up if you don't begin to practice safe sex. But also how people are being discriminatory to you, treating you like crap like you're this or that or you're contagious or something."
Roger has similar views but adds that education about high risk behaviors should be included:
"Stay away from the dope and don't go tricking and also go to the hospitals. A lot of African Americans are not educated on the full blown AIDS and how it destroys the major organs in your body. And let them see first hand on what you're dealing with here."
Jason would like a video that includes the various modes of transmission as well as prevention information:
"Just about everything that you can get on it from drug use to safe sex to having sex with the same partner. You know it's different ways you can catch it and a lot of people don't know that."
When asked what can be done to recruit African Americans to HIV training programs, most men suggested the use of participation incentives, especially those that are financial and preventive (condoms). The men also recommended recruiting participants from and having the programs take place within the communities in which the target population resides.
Wally said:
"You may have to start with a few passes; they can be little small things. Then everyone would gradually grab a hold and then their minds would be off on what you're giving personally and they could see what they can receive. You can give and receive at the same time. What I'm saying is you may have to start having a little gift or something to get them to come in the first few times, or a meal."
Jason suggests community-based recruitment:
"Stand at every corner. AIDS is gonna get too bad in a couple of years. We don't want it to be too late before we say, "Hey, let's put education groups here and people just stop here and get rubbers."
Conclusions
The results indicate that the respondents taking part in this study believe that HIV and AIDS are continued threats to the African-American community because of sexual risk taking behavior, that is, failure to use condoms. Further, African-American men are having sex without condoms when having sex with women often when they are under the influence of alcohol or other mind-altering substances and they are having sex with men while incarcerated and become infected and once released resume unprotected sexual relations with women. According to the men, substance abuse is an important part of the problem of HIV in the African-American community. This is in keeping with research that shows that drug use, especially crack cocaine, is linked to sexual risk taking among African Americans and to increased likelihood of becoming infected with other sexually transmitted diseases (STDs) including HIV [27,28]. African Americans are disproportionately affected by STDs [1] and African-American crack users are more likely to have multiple partners and to participate in drug for sex exchanges [29,30]. According to the men interviewed, they were less likely to use a condom when under the influence. Research shows that crack cocaine users may not perceive sexual risk taking as an important self-threat compared to other social and health issues they confront on a daily basis [27].
Although most respondents indicated they have knowledge of behaviors that place one at risk for HIV and they are motivated by reasons such as life and not wanting to contract an STD, they still fail to consistently use condoms and may even have sex with someone they know is HIV positive and they continue to suggest that more educational programs are needed. This shows that knowledge and motivation are not enough for behavior change to occur. These men need education but education that is provided in a way that has been different from what has been offered in the past because it seems not to change their behaviors. Interventions for men should address condom use, condom availability, skills for using condoms, and eroticizing condoms. Substance use programs should incorporate sexual risks. Men in the present study strongly recommended that HIV/AIDS videotaped messages should include footage of the sensational effects of the disease. There is a trend toward reality broadcasting in the US and development and field testing of such a videotape for AIDS prevention might prove worthwhile [31]. Low-income women taking part in a similar focused group discussion also recommended the use of sensationalism. Contrary to research indicating fear or sensationalism does not work [32-34], based on these results it may be worthwhile to field test such a videotape with groups of African Americans to evaluate the utility of such a teaching tool. If the tide of HIV and AIDS infection among African Americans is to be reduced, programs must incorporate culturally relevant contextual information presented to the target audience in a setting and in a manner that addresses their norms and beliefs and provides them the knowledge and skills needed to make correct decisions. The message could be presented using behavioral journalism, an approach espoused by McAlister [35] that offers a balance between the message source and the audience constructing messages within a theoretic framework and tailoring them to specific audiences. It may well be that this approach works with low-income African Americans who are engaged in high risk behaviors. Such a program could also include information about transmission routes, a subject identified as important to discuss when developing training programs.
Prior research has identified the leading causes of HIV infection among African-American men that includes sexual contact between African-American men who have sex with men, injection drug use and heterosexual contact. These high-risk behaviors are exacerbated by the high rate of poverty among African Americans that may be due to high rates of unemployment and associated drug related economic activities, injection drug, and substance abusers' engagement in unprotected sex when they are under the influence of drugs and alcohol [1,13]. The present study sought to identify the sociocultural contexts of sexual risk taking among African-American men and to gather information that could be used to develop and disseminate a videotape-based HIV prevention intervention for African-American men. Although the CDC identifies the leading cause of HIV transmission among African-American men as homosexual contact, none of the participants in the present study acknowledged this cause [1]. Men of color who have sex with other men often do so without disclosure and may consider themselves heterosexual [36]. Further research is needed in this area to determine how best to address this issue within the cultural context of African Americans and non-disclosing men. Lack of money for condoms was identified as a barrier to condom use. Many of the men believed that condoms should be a part of any HIV training program and that they could also be given as participation incentives for coming to a training program. Prior research has indicated that a combined approach to HIV prevention that includes HIV testing and counseling, educational and behavioral interventions delivered through community outreach, condom distribution and substance abuse treatment are effective means for reducing transmission [37]. However, such interventions have not yet been widely implemented in a sustained and integrated fashion.
Although most of the men had completed high school or beyond, they were mainly homeless or living in substandard housing. Their conditions of poverty may exacerbate their risk for infection because they may engage in illegal or high-risk behaviors. The barriers these men face related to poverty should also be addressed when developing training programs. This may call for the involvement of social service workers. Like programs developed for African-American women, they must be presented in settings that are familiar, comfortable and easily accessible to the target audience and incorporate culturally relevant contextual information [38].
Before developing programs, health educators should become familiar with the facilitators and barriers to HIV risk reduction behaviors among this population. They should also recognize that African Americans are a heterogeneous group and that not all messages will work with all audiences. The messages must be tailored and celebrate the diversity that exists within this population. Interventions such as videotapes should have mass appeal yet contain contextual, cultural, and gender specific messages.
There are several limitations to this study. Qualitative data collection methodology was employed and it is therefore not possible to make assumptions or draw inferences. Next, the data cannot be generalized to other African American men. The participants were homeless and low-income and selected using a convenience sampling approach. The goal of this study, however, was to recruit low-income African-American men because by virtue of their income status they may be at increased risk for HIV infection. Although the findings from this research are limited, they may provide a foundation for conducting future research among and developing a videotape-based HIV intervention for low-income African-American men.
Competing Interests
The author(s) declare that they have no competing interests.
Authors' contributions
EJE, AFM and GOO conceived and designed the study. EJE, AFM, RJP jointly planned and executed the data analyses. EJE and AFM wrote the paper with assistance from RJP, GOO and NIO.
Table 1 Focus group guide questions
1. Do you perceive AIDS as a threat to the African American community and why?
2. What are the perceived roles of men in heterosexual relationships in the African American community?
3. What are the expectations for personal and sexual responsibilities for contraception and sexually transmitted diseases prevention among African American men?
4. What situations have placed you at risk for HIV infection in the past?
5 How have alcohol and drug use placed you at risk of HIV infection?
6. What are the things that motivate you to practice safe sex?
7. What are the things or barriers that prevent you from practicing safe sex
8. Why do you think that AIDS is spreading so rapidly in the African American community?
9. What information do you think we need to include in a videotape developed to train African Americans about HIV prevention that will encourage them to watch the videotapes?
10. Do you have any other suggestions on how AIDS can be prevented in the African American community?
11. What can we do to get people to sign up for focus groups such as this one and also get them to participate in HIV/AIDS training programs?
12. What can we do to make these training programs most useful to you?
Pre-publication history
The pre-publication history for this paper can be accessed here:
Acknowledgement
National Institute of Mental Health (NIMH) grant number R01 MH 062960-03 supported this research.
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| 15638937 | PMC544892 | CC BY | 2021-01-04 16:28:55 | no | BMC Public Health. 2005 Jan 7; 5:3 | utf-8 | BMC Public Health | 2,005 | 10.1186/1471-2458-5-3 | oa_comm |
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Mol CancerMolecular Cancer1476-4598BioMed Central London 1476-4598-3-381561323610.1186/1476-4598-3-38ResearchHypoxia-mediated apoptosis in oral carcinoma cells occurs via two independent pathways Nagaraj Nagathihalli S [email protected] Nadarajah [email protected] Wolfgang [email protected] Department of Medicine, James Graham Brown Cancer Center, University of Louisville, Louisville, Kentucky 40202, USA2 Department of Pharmacology & Toxicology, University of Louisville, Louisville, Kentucky 40202, USA3 Department of Diagnostic Sciences, The University of Texas Health Science Center at Houston, Dental Branch, Houston, Texas 77030, USA2004 21 12 2004 3 38 38 6 9 2004 21 12 2004 Copyright © 2004 Nagaraj et al; licensee BioMed Central Ltd.2004Nagaraj 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 are attempting to elucidate the mechanism of apoptotic cell death induced by hypoxia in oral cancer cells. Since hypoxia can render solid tumors more resistant to radiation and chemotherapy, understanding the pathways involved in hypoxia-induced apoptosis of oral cancer cells would be of significant therapeutic value.
Results
Here we showed that oral cancer cells from primary tumor and lymph node metastasis undergo apoptosis after 24 to 48 h of hypoxia. During hypoxic growth, an increase in caspase-3 proteolytic activity was observed, accompanied by the cleavage of PARP (poly (ADP-ribose) polymerase) indicative of caspase activity. In addition, hypoxic stress also lead to activation of caspase-8, -9, and -10 but not -1, elicited the release of cytochrome C into the cytosol, and resulted in internucleosomal DNA fragmentation.
Conclusion
These results show that hypoxia-induced apoptosis in oral carcinoma cell lines relies on both intrinsic (mitochondrial) and extrinsic (cell death receptor mediated) pathways. This novel evidence will assist in designing more efficient combination chemotherapy approaches as promising strategy for the treatment of oral cancers.
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Background
Oral cancer is one of the 10 most frequently occurring cancers worldwide, and its incidence in Europe and the United States ranges from 2% to 6% among all cancer patients [1,2]. The 5-year survival rate of less than 50% has not substantially improved over the past several decades, since many oral carcinomas respond poorly to chemotherapy approaches and their responses to radiation therapy have been highly variable.
Hypoxia, a reduction in the level of tissue oxygen tension, occurs during acute and chronic vascular disease, pulmonary disease and cancer, and can lead to apoptotic or necrotic cell death [3,4]. Fast growing tumors become hypoxic because newly developed blood vessels are inefficient and have poor blood flow. Although hypoxia is toxic to both cancer cells and normal cells, tumor cells can undergo genetic and adaptive changes in response to hypoxia that allow them to survive and proliferate [5]. Thus, hypoxic growth can result in a tumor with more aggressive growth characteristics and more malignant phenotype [3]. Although micro-environmental irregularities in solid tumors have been well documented, little is known about how different types of tumor cell phenotypes tolerate and respond to these conditions.
Apoptotic cell death is controlled by pro-apoptotic caspases, proteases that are synthesized as inactive precursors and activated by proteolytic processing [6]. The apoptotic cascade can be initiated via two major pathways, involving either the release of cytochrome C from the mitochondria (mitochondrial pathway) [7] or activation of death receptors in response to ligand binding (death receptor pathway) [8]. Upon triggering of either pathway, caspases, the final executioners of apoptosis, are activated, causing degradation of cellular proteins and leading to typical morphological changes such as chromatin condensation, nuclear shrinkage, and the formation of apoptotic bodies [9]. Both pathways are differentially involved in the cellular response to diverse apoptotic stimuli [10,11]. The majority of chemotherapeutic agents trigger the mitochondrial pathway, but the death receptors have also been reported to be involved in chemotherapy-induced apoptosis [12].
Death ligands such as TNF-α or CD95L recruit, via the adapter molecule FADD, cytoplasmic monomeric initiator caspase-8 to their surface receptors, resulting in dimerization and activation of caspase-8 [13,14]. Active caspase-8 cleaves and activates downstream effector caspases including caspase-3, -6 or -7, which degrade a broad range of cellular proteins and trigger the appearance of the apoptotic morphology [6,15]. On the other hand, mitochondria are important regulatory sites of the apoptotic process [16]. Defects in mitochondrial function result in release of cytochrome C, which can associate with Apaf-1 (apoptosis protease activating factor) and pro-caspase-9. The observation that chemical inhibition of caspase-9 blocks hypoxia-induced apoptosis points to a role of the complex in hypoxia-induced apoptosis [17,18]. This activation complex results in auto-processing of caspase-9 and further activation of downstream caspases, such as caspase-3 [19,20]. Activation of caspase-3 has been linked to the proteolytic cleavage of cellular substrates including poly-ADP-ribose-polymerase (PARP) [21], and is also necessary for the nuclear changes and chromatin condensation associated with apoptosis [22].
The low oxygen tension in hypoxic tumors is known to interfere with the efficacy of chemotherapy or radiotherapy. Also, hypoxia-induced apoptosis may impose a selection pressure favoring growth of more resistant tumor cells. However, the factors leading to hypoxia-induced apoptosis and their relative contribution to intrinsic and extrinsic apoptotic pathways are not well characterized. In the present study, we determined which factors in the mitochondria-dependent and -independent apoptosis pathways are activated in oral cancer cells. We observed that hypoxia-induced apoptotic cell death occurs through activation of caspase 8, but also cytochrome C release, caspase-9 activation, and results in caspase 3 processing, PARP cleavage, and DNA fragmentation. These results suggest that hypoxia-induced apoptosis in oral carcinomas cells relies on both intrinsic (mitochondrial) and also extrinsic (cell death receptor mediated) pathways.
Results
Hypoxia condition
Direct measurements of oxygen tensions in human tumors show a range of median oxygen tensions from 1.3 to 3.9 % (10–30 mm Hg), with readings recorded as low as 0.01 % (0.08 mm Hg) under severe hypoxia, whereas in normal tissues O2 levels can range from 3.1 to 8.7 % (24–66 mm Hg) [25]. Severe hypoxia stimulates cells to undergo apoptosis, whereas oxygen levels above 0.5 % prevent cell death, indicating tight regulation of cellular responses to the microenvironment. Critical O2 levels (hypoxic thresholds) characterize the upper limit of the hypoxic range below which activities and functions progressively become restricted. These levels can encompass O2 partial pressures from 35 mm Hg (start of reduced cell death in conventional photodynamic therapy or restricted efficacy of some immunotherapy) to 0.02 mm Hg; below this level, cytochromes aa3 and c are no longer fully oxidized [25]. During severe hypoxia or anoxia, a cascade of events is initiated that leads to global apoptotic cell death, thereby preventing the accumulation of cells with hypoxia-induced regulatory responses or mutations [26]. Thus, a condition of less severe hypoxia (1 %) was chosen to be able to monitor the response of viable cells under this condition.
Morphological changes and DNA fragmentation during hypoxia
Trypan Blue dye exclusion was used to quantitate the number of viable cells after 24 and 48 h of hypoxia. TUNEL assays were used to visualize apoptotic cells, and DNA fragmentation into oligonucleosome-sized fragments, indicative of apoptotic cellular death, was monitored by gel electrophoresis [27]. The percentage of viable cells was steadily reduced in hypoxia-treated cells compared to normoxic control cells (Figure 1). Whereas approximately 5 – 15 % reduction of viability was seen at 24 h, all hypoxic cell lines declined further at 48 h to 85 – 75 % viability compared to normoxic growth. Hypoxic growth induced morphological alterations typical for apoptotic cell death, as determined by TUNEL assays (Figure 2). The hypoxia-treated cells showed extensive nuclear dye staining indicative for DNA breakage and cell death. This effect was detectable at 24 h (data not shown) and very apparent after 48 h of hypoxia, when most of the treated cells showed the typical morphology of apoptotic nuclear condensation. In contrast, nuclear staining was much weaker and less pronounced for normoxic cells up to 48 h incubation. Hypoxic conditions induced the chromatin alterations typical for apoptotic cell death, as demonstrated by fragmentation of chromosomal DNA into nucleosomal DNA ladders (Figure 3). Exposure to hypoxia for 24 or 48 h resulted in a time-dependent increase in DNA fragmentation in all four oral cancer cells, whereas no internucleosomal DNA fragmentation was observed in the normoxic control cultures (Figure 3). It is also apparent that the two metastatic lines 686Ln and 1386Ln had less extensive DNA fragmentation compared to their respective primary tumor lines 686Tu and 1386Tu.
Figure 1 Viability assay for oral carcinoma cells under hypoxia. The numbers of viable (Trypan blue-excluding) cells were determined after 24 or 48 hours, and the percent of viability of hypoxic cells plotted relative to normoxic control cells; viabilities under normoxia were 100% for all cells. The results show the mean (± SD) of three independent experiments. 686Tu = open square; 686Ln = closed square; 1386Tu = open circle; 1386Ln = closed circle.
Figure 2 Hypoxia-induced nuclear TUNEL staining in oral carcinoma cells. The cells were incubated for 48 h in hypoxic or normoxic conditions, and photographs were taken after TUNEL staining of cells with DAB. Few apoptotic nuclei were observed in normoxic cells, but exposure to hypoxia for 48 h induced nuclear DNA condensation and fragmentation. Cells with nuclei showing strong chromatin condensation and nuclear fragmentation were considered apoptotic.
Figure 3 Effect of hypoxia on apoptosis induction as determined by DNA fragmentation. Nucleosomal DNA fragments in 686 (A) and 1386 (B) cells were analyzed by gel electrophoresis after 24 or 48 h hypoxia. Apoptosis was confirmed by the appearance of a ladder of oligonucleosomal DNA. M, molecular standard; P, positive DNA ladder control.
Processing of caspases, PARP cleavage and cytochrome C release
The effects of hypoxia treatment on activation of key caspases and PARP in the four cell lines 686Tu/Ln and 1386Tu/Ln was determined by Western blotting using antibodies that recognize both full-length and cleaved proteins (Figures 4 and 5). Growth of cells under hypoxia caused a time-dependent processing of caspase-3, -8 and -9. For all four cell lines, hypoxia resulted in enhancement of procaspase-3 (32 kD) cleavage into the two immunoreactive fragments of ~20 and ~11 kD at the 24 and 48 h time points (Figures 4A and 5A). This treatment also resulted in cleavage of the 47 kD procaspase-9 to yield fragments of ~37 and ~20 kD, in parallel to caspase-3 cleavage (Figures 4C and 5C). Furthermore, caspase 8 was present primarily as ~55 kD pro-form in normoxic cells, whereas exposure to hypoxia resulted in its time-dependent processing to the ~32 kD active form (Figures 4B and 5B). These observations point towards involvement of both caspase-9 and caspase-8 in hypoxia-mediated cleavage of caspase-3 in all four cell lines. They suggest that a cascade of caspase activation occurs through the mitochondrial and also through the cell death receptor pathway in these cells in response to hypoxia.
Figure 4 Response of apoptosis-related proteins to hypoxia. Western blot analysis of 686Tu (Tu) or 686Ln (Ln) cell extracts (30 μg each lane) after 24 or 48 hours of hypoxia (H) or normoxia (N) treatment. A: Cleavage of procaspase-3 (32 kD) into a 20 and 11 kD species; B: Cleavage of procaspase-8 (55 kD) into the 32 kD product (23 kD product not shown); C: Cleavage of procaspase-9 (47 kD) into lower mol. weight products; D: Processing of PARP (113 kD) into the typical 89 kD protein and a lower molecular weight product (not shown); E: cytochrome C (15 kD) release into cytosolic fraction; F: Re-probing for β-actin as an internal loading control. Data are representative of at least two independent experiments with similar results.
Figure 5 Response of apoptosis-related proteins to hypoxia. Western blot analysis of 1386Tu (Tu) or 1386Ln (Ln) cell extracts (30 μg each lane) after 24 or 48 hours of hypoxia (H) or normoxia (N) treatment. Other legend details are as for Figure 4.
To gain further insight into the role of mitochondria in this process, the extent of cytochrome C release under hypoxia was analyzed. Translocation of cytochrome C from the mitochondria to the cytosol was detected in all four cell lines after 24 h, and more pronounced after 48 h, of hypoxia (Figures 4E and 5E). This release of cytochrome C was a controlled event and not due to physical disruption of mitochondria, since no signal for intra-mitochondrial cytochrome oxidase could be detected in the same cytosolic fractions under these conditions (data not shown). Thus, these results demonstrate that hypoxia induced the release of cytochrome C from intact mitochondria.
Since we observed that hypoxia activated caspase-3 in the oral carcinoma cells, we investigated the cleavage of the caspase-3 substrate PARP under hypoxic versus normal growth. Clearly, cleavage of PARP, as indicated by a decrease in the full-length 113 kD protein and appearance of the 85 kD cleaved PARP product, was prominent in hypoxic cells, whereas it was almost completely absent in normoxia cells (Figures 4D and 5D). A small amount of cleaved PARP was already found after 24 h hypoxic conditions, and this effect was much more pronounced at 48 h. Only very small amounts of PARP cleavage product could be detected in the normoxic 1386 cell line pair, whereas for the 686 pair PARP cleavage appeared undetectable.
Caspase activities during hypoxia-mediated apoptosis
As caspases are early effectors for triggering apoptosis, assays to determine caspase enzymatic activities further substantiated our findings that hypoxia-induced apoptosis occurs through both intrinsic (mitochondrial) and also extrinsic (cell death receptor mediated) pathways. We examined caspase proteolytic activities in cell extracts using fluorogenic peptide substrates specific for individual caspases. These substrates are conjugated with AFC or AMC and have aspartic acid residues at P1 positions, a requirement for caspase proteolysis. Cleavage of these substrates after the aspartic acid residue results in release of unbound AFC or AMC which can be monitored fluorometrically.
Detergent extracts prepared from cells after exposure to hypoxia or normoxia for either 24 or 48 h were tested for caspase cleavage activities, and specific inhibitors for control measurements were used as described in Materials and Methods. N-Ac-DEVD-AFC is cleaved by caspase-3 and -7, but may also be cleaved by other caspases, N-Ac-YVAD-AFC is cleaved by caspase-1, N-Ac-IETD-AMC by caspase-8, N-Ac-LEHD-AFC by caspase-9, and N-Ac-AEVD-AFC by caspase-10. Activities of caspase-3, -8, -9 and -10 were clearly and consistently elevated during hypoxia treatment for up to 48 h compared to normoxic growth for the 686 (Figure 6) and 1386 (Figure 7) cell line pairs. In contrast, only low caspase-1 activities were found for all four cell lines in normoxic condition, and these were not substantially altered after hypoxia challenge at any of the times examined (Figures 6A and 7A).
Figure 6 Hypoxia-stimulated caspase activities in 686 oral cancer cells. The 686Tu (open bars) and 686Ln (closed bars) cells were exposed to hypoxia or normoxic control growth for 24 or 48 hours, and induction of caspase activities were assayed as described in Materials & Methods. A: caspase-1; B: caspase-3; C: caspase-8; D: caspase-9; E: caspase-10. The means ± S. D. of three independent experiments are shown.
Figure 7 Hypoxia-stimulated caspase activation in 1386 oral cancer cells. The 1386Tu (open bars) and 1386Ln (closed bars) were analyzed; other legend details are as for Figure 6.
Overall, significant induction of hypoxia-mediated cleavage activities for the N-Ac-DEVD-AFC, N-Ac-IETD-AMC and N-Ac-LEHD-AFC substrates was detected in all four cell extracts, and induction of these activities correlated well with the levels observed for caspase-3, caspase-8 and caspase-9 protein expression and processing. Thus, induction of apoptosis was preceded by the activation of activator caspase-8, initiating receptor-mediated apoptosis, and caspase-9, initiating mitochondrial apoptosis, as well as the effector caspase-3.
Effects of caspase inhibitors on caspase activity profile
We investigated the effects of individual cell-permeable caspase inhibitors on caspase-3, -8 and -9 activities during hypoxic growth for 48 hours (Figure 8). These inhibitors can enter viable cells and are covalently and irreversibly bound to their target caspases. Z-VAD-fmk was a pan-caspase inhibitor for all caspases analyzed; z-DEVD-fmk was inhibitor for caspase-3, z-LEHD-fmk for caspase-9, and z-IETD-fmk for caspase-8 activity. The presence of Z-DEVD-fmk clearly inhibited activity of its target protease caspase-3, but not caspases 8 or 9. The pan-caspase inhibitor z-VAD-fmk, as expected, diminished activities of all three tested caspases. Z-LEHD-fmk as inhibitor for caspase 9 did not affect activity of caspase-8, but did partially decrease caspase-3 activity. Finally, the z-IETD-fmk caspase-8 inhibitor also clearly decreased caspase-3 activity in addition to the target caspase. These data showed that both caspase-8 and caspase-9 contribute to the overall caspase-3 activity during hypoxic cell growth, and that those are the main caspases involved in hypoxia-mediated apoptosis activation pathways of these oral cancer cells. They suggest that both caspase-8 and caspase-9 activation pathways contributed to the activation of the major executioner caspases, such as caspases-3 and possibly caspase-7.
Figure 8 Prevention of hypoxia-stimulated caspase activities by intracellular caspase inhibitors. The 686Tu (open bars) and 686Ln (closed bars) cells were grown under hypoxia for 48 hours in the presence of different cell-permeable caspase inhibitors, and caspase activities were assayed as described in Materials & Methods. A: caspase-3 activity; B: caspase-8 activity; C: caspase-9 activity. The vertical dashed columns represent cell growth in the presence of the following caspase inhibitors: z-DEVD-fmk, caspase-3; z-VAD-fmk, pan-caspase; z-LEHD-fmk, caspase-9; z-IETD-fmk, caspase-8. The means ± S. D. of three independent experiments are shown.
Discussion
The aim of this study was to identify factors which contribute to hypoxia-induced cell death in human oral cancer cells. The involvement of caspase pathways in induction of apoptosis of oral cancer cells during hypoxia was not previously determined. In the present study, we provide novel evidence for the participation of both initiator and effector caspases in this process (Figure 9). We showed that exposure to hypoxia elicits apoptotic cell death, and that this process relies on both intrinsic (mitochondrial) and also extrinsic (cell death receptor mediated) pathways. Our data showed that caspase-3, caspase-8, caspase-9, and caspase-10, but not the pro-inflammatory caspase-1, are activated during hypoxic growth. Activation of the executioner caspase-3 can be blocked in hypoxic cells by inhibitors of upstream caspases 8 or 9 during cell growth. We also observed that hypoxia-mediated apoptosis of oral cancer cells is associated with controlled cytochrome C release from mitochondria, proteolytic cleavage of PARP, and DNA fragmentation. Our results are in agreement with data on hypoxia-induced apoptosis in other cells [28-30]. In line with our data, others have observed activation of both caspase-9 and caspase-8 following hypoxic stress in animal models of brain ischemia [31,32]. Studies with caspase-9 knock-out mice demonstrated that caspase-9 is a critical upstream activator of the caspase cascade in vivo and may be essential for the processing of caspase-3 [33,34]. Also, earlier reports showed that chemical inhibition of caspase-9 protects against hypoxia-mediated effects [17,18].
Figure 9 Potential pathways leading to apoptosis induction during hypoxia treatment. Hypoxia-induced apoptosis in oral carcinoma cell lines relies on both intrinsic (mitochondrial pathways) and also extrinsic (cell death receptor mediated) pathways. Key steps are activation of procaspase-8 or procaspase-9, then procaspase-3, and the subsequent cleavage of PARP by activated caspase-3, resulting in the induction of apoptosis.
On the other hand, it was suggested that key elements of the death receptor pathway are essential for hypoxia-induced apoptosis. The extrinsic pathway of apoptosis is initiated by death ligands, such as the Fas ligand or TRAIL (TNF-α related apoptosis inducing ligand), leading to the activation of caspase-8 and caspase-3 [35,36]. Recent studies indicate that DISC (Death Inducing Signaling Complex) formation precedes formation of Fas surface clusters, and that such clustering is dependent on DISC-generated active caspase-8 [37]. Also, TRAIL can induce receptor-mediated cell death selectively in tumor cells and is not active in non-malignant cells [38,39]. It was shown previously that in some tumor cells, only the receptor-independent mitochondrial pathway is activated during hypoxia without caspase-8 involvement [4]. On the other hand, there is recent evidence that TRAIL receptor-mediated apoptosis induction can be maintained and functional during hypoxic growth of tumor cells [39]. In view of the equal contribution of the caspase-8 and caspase-9 pathways established here, future work needs to examine the detailed mechanisms of receptor-mediated caspase activation with respect to TRAIL and death receptor-DISC-caspase-8 cascade, as well as the mitochondria-cytochrome C-caspase-9 cascade, and the possible involvement of HIF-1α as activator of caspases in OSCC cells. It was reported that hypoxia can induce upregulation of cell death receptors or death receptor ligands [40], and that inhibition of caspase-8 or FADD may interfere with hypoxia-induced apoptosis [18,41]. Our data also suggest that the activator caspase-8 is an integral component of the cell death-inducing mechanism in oral cancer cells, in agreement with other studies [32]. In receptor-mediated apoptosis, activation of caspase-8 represents a point of commitment to cell death. Thus, our data clearly show that in oral carcinoma cells two types of pathways are activated (Figure 9).
Hypoxia-induced caspase-3 activation and DNA fragmentation have been described by others recently [42,43], as well as caspase activation accompanying cytochrome C release from mitochondria [28-30]. Such findings correlate well with our studies showing that caspases-3, -8, and -9 activity and expression was significantly higher in hypoxic than in normoxic cells, and similar caspase activation was observed in the hypoxic cerebral cortex of newborn piglets [34]. In our cell system, PARP cleavage was observed within 24 h of hypoxia treatment and was accompanied by the appearance of a ~11 kD procaspase-3 cleavage product, suggesting activation of caspase-3. Caspase-3 is an executioner caspase that can be activated by a mitochondrial pathway involving release of cytochrome C [44]; alternatively, caspase-3 can also be activated by caspase-8 [45,46]. The results of the present study indicate that hypoxia-induced cleavage of procaspase-3 appears to be mediated by both caspase-9 and caspase-8 pathways.
Although cleavage of procaspase-9 was evident as early as 20 h into hypoxia treatment, it is possible that its activation is mediated by other caspases at earlier time points. Currently, possible involvement of other Bcl-2 family of apoptosis regulating proteins (e.g. Bad, Bag, Bak, Bik, etc.) in hypoxia-induced activation of the mitochondrial caspase cascade cannot be ruled out. The key regulator of hypoxia-induced cellular response is believed to be hypoxia inducible factor 1 (HIF-1). For all cell lines used here, we observed recently that there were several-fold increases in HIF-1α expression during hypoxia compared to normoxia (Wickramasinghe N, Banerjee K, Nagaraj N, Vigneswaran N and Zacharias W, manuscript submitted). HIF-1 can initiate apoptosis by inducing pro-apoptotic proteins such as BNIP3 or NIX, which will inhibit Bcl anti-apoptotic activity. It can also cause stabilization of wild-type p53 tumor suppressor, an effect that is lost in cells with pre-existing p53 mutations [26,47]. On the other hand, anti-apoptotic proteins, such as IAP-2, can be induced during hypoxia, whereas the pro-apoptotic protein Bax can be downregulated, leading to decreased accumulation of Bax in the mitochondria and thus decreased mitochondrial leakage and cytochrome C release [26,48].
It is apparent that during hypoxia, an intricate balance exists between factors that induce or counteract apoptosis, or even stimulate proliferation. More detailed studies are needed to define the precise mechanism for hypoxia-induced cleavage of procaspase-9 and procaspase-8; however, our results clearly demonstrate involvement of both caspase-8 and caspase-9 in hypoxia-mediated cleavage of caspase-3 and PARP. Because caspase-3 is a critical mediator of apoptosis [49] and correlates with the onset of apoptosis in oral cancer cells, it may be a potential marker for predicting response or resistance to chemotherapeutic agents in oral cancer.
The detailed temporal and spatial relationship of these events to other components of the apoptotic pathway including downstream caspases remain to be determined. Recently, the targeted elimination of oral squamous cell carcinoma cells by inducing apoptosis has emerged as a valued strategy to combat oral cancer [50]. Increased mitochondrial permeability is a crucial event in many types of chemotherapy-induced apoptosis and leads to release of cytochrome C from the mitochondrial intermembrane space. Our study confirmed that the release of cytochrome C was actually augmented during hypoxic growth, indicating a possible role of cytochrome C in hypoxia-mediated apoptosis. However, it also has been reported that certain anticancer drugs induce apoptosis in oral cancer cells but do not trigger cytochrome C release, thereby suggesting that cytochrome C can be an inducer-dependent phenomenon [51].
In some of the caspase cleavage assays, slightly lower activities were found for the metastatic Ln cells compared to the corresponding primary Tu cells. Also, the final appearance of nucleosomal DNA ladders is much more pronounced in both Tu cells than in their Ln counterparts. Although some of those differences were only minor, they are in line with previous studies which demonstrated much higher resistance of metastatic OSCC lines to TRAIL-induced cell death [38] and also to TNF-α-induced apoptosis [52] than their corresponding primary tumor lines. Such differential apoptosis sensitivity has also been observed recently in a different matched cell line pair form head & neck primary and metastatic carcinoma (UMSCC101A versus UMSCC101B; unpublished observations from this lab). On the other hand, a very apparent difference among the four cells is that caspase-8 activity is in general several-fold higher in the 686Tu/Ln pair than in the 1386Tu/Ln pair, which presumably is a reflection of the different pathologic histories of the two patients from which the respective tumor tissues were derived.
Conclusions
In summary, we have reported that hypoxia directs apoptosis through mitochondria and cell death receptor mediated signaling pathways in oral cancer cells. We believe that this is the first report on caspase-dependent mechanisms during hypoxia in human oral cancer cells. Exposure to hypoxia lead to the activation of procaspase-9, -8, -3, and -10, cytochrome C release from mitochondria, with subsequent cleavage of PARP by activated caspase 3, finally resulting in the induction of apoptosis. The detailed molecular and sequential mechanisms of such hypoxia-induced caspase activation leading to apoptosis need further investigation. However, the knowledge of the relevant signaling cascades participating in this process can provide important insights in the mechanisms of acquired apoptotic deficiencies during malignant progression in poorly oxygenated oral carcinomas. It is well established that poor oxygenation of solid tumors is associated with poor prognosis. This may not only be due to direct effects of hypoxia on the efficacy on certain tumor treatment modalities, but also due to the evolvement of resistant tumor cells during the ontogenesis of a tumor under hypoxic conditions [39]. Our novel evidence, showing that hypoxia can induce apoptosis through both pathways, will assist in designing more efficient combination chemotherapy approaches as promising strategy for the treatment of oral cancers.
Methods
Cell lines
MDA-686Tu (686Tu) and MDA-686Ln (686Ln) cell lines were derived concurrently from the primary tumor and lymph node metastasis, respectively, of OSCC involving the left tonsillar fossa and posterior portion of the tongue in a 49 year old man (tumor stage T3N3B). MDA-1386Tu (1386Tu) and MDA-1386Ln (1386Ln) cell lines were obtained from the primary tumor and lymph node metastasis, respectively, of a 71 year old male patient with primary hypopharynx tumor (tumor stage T4N3B). All cell lines were generous gifts from Dr Peter Sacks, New York University, New York [23]. The cell lines were routinely maintained in DMEM/F12 50/50 mix (Cambrex BioScience, Walkersville, MD) containing 10% fetal bovine serum and 0.4 μg/ml hydrocortisone at 37°C with 5% CO2. All protocols for the use of human cell lines in this work were approved by the Institutional Review Boards of The University of Louisville and the University of Texas at Houston.
Hypoxia exposure
Hypoxic conditions were produced by placing logarithmic phase subconfluent monolayer cultures, grown on 100 mm dishes, in a modular incubator chamber and equilibrating for 30 minutes with humidified gas containing 1 % oxygen, 5 % CO2 and 94 % nitrogen. The cell lines were maintained under hypoxic conditions for periods of 24 or 48 hours. Control cells were grown in normal oxygen conditions for the same duration. After incubation, media collection and cell harvesting were done immediately within 2–3 minutes to avoid adaptation of the cells to re-oxygenation.
Cell viability assays
Determination of cell viability was done by Trypan Blue dye exclusion assay. Cells were grown in six-well plates (2 × 104 cells/well) in 3 ml medium to 70 % confluence, then washed and treated for hypoxia in DMEM/F12 medium with 10 % fetal bovine serum. For viability counting, cultures containing both dead and live cells from each well were collected, centrifuged, and resuspended in 0.5 ml FBS-free DMEM/F12. An aliquot of 0.1 ml was taken and incubated with 0.1 ml of Trypan Blue dye (0.4 %) for 5 min. Both live (unstained) and dead (blue) cells were counted in triplicate measurements from randomly selected fields in a hemocytometer.
Protein extractions and Western blotting
Cultured cells were rinsed with PBS, gently scraped into 1 ml of PBS, and centrifuged at 4,000 rpm for 3 min. The pellets were resuspended into RIPA buffer (10 mM Tris-HCl, pH 7.4, 150 mM NaCl, 1 % Triton X-100, 0.1 % SDS, and 1 mM EDTA) containing fresh protease inhibitors (0.5 mM phenylmethylsulfonyl fluoride, 10 μg/ml aprotinin, and 2 μg/ml of both leupeptin and pepstatin) (all from Sigma, St. Louis, MO). Then, cell extracts were sonicated (Model 550 Sonic Dismembrator, Fisher Scientific, Pittsburgh, PA) for 1 min (1.0 sec on/0.5 sec off pulses) and cell debris was removed by centrifugation. Proteins were quantified using the Bradford protein assay kit (Bio-Rad, Hercules, CA) and compared with a γ-globulin standard curve. Equal amounts of total proteins were separated on a SDS-polyacrylamide gel and transferred onto a nitrocellulose membrane by electroblotting overnight at 20 V. Membranes were blocked in TBS-T (10 mM Tris-HCL, 150 mM NaCl, 0.25 % Tween 20, pH 7.5) with 5 % fat-free powdered milk at room temperature for 1 h. After rinsing membranes in TBS-T, the following primary antibodies were used: rabbit polyclonal IgGs for caspase-3 (H-277), poly(ADP-ribose) polymerase (PARP) (H-250), caspase-8 (H-134), caspase-9 (H-170) (all from Santa Cruz Biotechnology, St. Cruz, CA), or mouse monoclonal β-actin antibody (Sigma, St. Louis, MO). After incubation overnight at 4°C or 1 h at room temperature, the membranes were washed four times, 10 min each, in TBS-T. Secondary antibodies used were either horseradish peroxidase-conjugated goat anti-rabbit IgG or goat anti-mouse IgG (ICN, Costa Mesa, CA), followed by five washes with TBS-T. Bands were detected using the enhanced chemiluminescence ECL substrate (Amersham Biosciences, Piscataway, NJ). For β-actin detection, previously probed membranes were soaked in stripping buffer (70 mM Tris-HCl, pH 6.8, 2 % SDS, 0.1 % β-mercaptoethanol) at 60°C for 30 min and incubation as above.
Caspase assays
After hypoxia treatment for 24 or 48-hours, treated and control cell cultures were rinsed once in cold PBS and collected in cold PBS by scraping. After centrifugation and removal of PBS, cell pellets were kept at -80°C until caspase assays were performed. The frozen pellets were resuspended in caspase lysis buffer (10 mM HEPES, pH 7.4, 2 mM EDTA, 0.1 % CHAPS) supplemented with protease inhibitors (5 mM dithiothreitol, 1 mM phenylmethylsulfonyl fluoride, 10 μg/ml pepstatin A, 10 μg/ml aprotinin, and 20 μg/ml leupeptin). Freeze-thaw cell lysis cycles were performed by alternatively transferring the samples from an ethanol/dry ice bath to a 37°C water bath five times. The supernatant was collected after 20 min of centrifugation at 12,000 rpm in a cold microcentrifuge. Assays were performed in caspase buffer (10 mM PIPES, pH 7.4, 2 mM EDTA, 0.1 % CHAPS, 5 mM dithiothreitol), to which 50 μM of substrate and 5 μl of protein extract were added to yield a final volume of 100 μl. Peptide substrates for caspase-3, N-acetyl-Asp-Glu-Val-Asp-AFC (DEVD-AFC), caspase-1, N-acetyl-Tyr-Val-Ala-Asp-AFC (YVAD-AFC), caspase-8, N-acetyl-Ile-Glu-Thr-Asp-AMC (IETD-AMC), caspase-9, N-acetyl-Leu-Glu-His-Asp-AFC (LEHD-AFC) (Biomol, Plymouth Meeting, PA) and caspase-10, N-acetyl-Ala-Glu-Val-Asp-AFC (AEVD-AFC) (Alexis, San Diego, CA) were dissolved in dimethyl sulfoxide. The respective specific inhibitors N-acetyl-Asp-Glu-Val-Asp-CHO (DEVD-CHO), N-acetyl-Tyr-Val-Ala-Asp-CHO (YVAD-CHO), N-acetyl-Ile-Glu-Thr-Asp-CHO (IETD-CHO), N-acetyl-Leu-Glu-His-Asp-CHO (LEHD-CHO) (Biomol), and N-acetyl-Ala-Glu-Val-Asp-CHO (AEVD-CHO) (Alexis, San Diego, CA) were used in control assay reactions. Assays were performed in black-wall, clear bottom plates using a Spectramax Gemini XS Microplate Spectrofluorometer (Molecular Devices); reading was at 500 nm after excitation at 405 nm for 7-amino-4-trifluoromethylcoumarin (AFC) and at 380 nm after excitation at 460 nm for 7-amino-4-methylcoumarin (AMC). The results were compared against AFC and AMC standard curves generated in parallel. Specific activity was expressed as units, with 1 unit defined as AFC or AMC release of 1 nMol/hour/μg protein.
Cytochrome C release assays
Cells were collected at the indicated times and washed once in ice cold PBS. Cell pellets were resuspended in cytosol extraction buffer, and cytosolic extracts were prepared by the method described previously [24]. Western blotting for cytochrome C was done with mouse monoclonal anti-cytochrome C IgG (BD Biosciences-Pharmingen, San Diego, CA) as described above; the absence of intra-mitochondrial proteins was verified by blotting for mitochondrial cytochrome oxidase with mouse monoclonal anti-cytochrome oxidase IgG (BD Biosciences-Pharmingen, San Diego, CA).
Hypoxic growth in presence of caspase inhibitors
Oral cancer cells 1386 and 686 were exposed to hypoxia for 48 hours in the presence or absence of individual cell-permeable inhibitors for caspase-3 (z-DEVD-fmk; 10 μM), caspase-8 (z-IETD-fmk; 20 μM), caspase-9 (z-LEHD-fmk; 20 μM), or pan-caspase (z-VAD-fmk; 10 μM) (all Santa Cruz Biotechnology, Santa Cruz, CA), and processed for caspase activity assays as above.
Analysis of DNA fragmentation
Apoptotic cells were detected by in situ TdT-mediated dUTP nick end labeling (TUNEL) assays using the In Situ Cell Death Detection Kit POD, and nucleosomal DNA fragments detected with the Apoptotic DNA Ladder Kit (both from Roche, Indianapolis, IN). DNA fragments were resolved on 2 % agarose gels for visualizations of apoptosis-indicative DNA ladders.
List of Abbreviations
AFC, 7-amino-4-trifluoromethylcoumarin; AMC, 7-amino 4-methyl coumarin; DAB, diaminobenzidine; DISC, death-inducing signaling complex; ECL, enhanced chemiluminescence; FADD, Fas-associated death domain protein; fmk, fluoromethylketone; PARP, poly (ADP-ribose) polymerase; TRAIL, TNF-α related apoptosis inducing ligand; TUNEL, TdT-mediated dUTP nick end labeling; z, benzyloxycarbonyl.
Authors' contributions
NSN carried out the molecular and enzymatic studies and drafted the manuscript. NV participated in the design of the study, interpretation of collected data, and contributed to the manuscript preparation. WZ conceived and directed the study, contributed its design and coordination, and participated in the interpretation and final manuscript preparation. All authors read and approved the final manuscript.
Acknowledgements
The work was supported by NIH grant DE13150 and by Philip Morris USA Inc. and Philip Morris International (W.Z.), and by a postdoctoral fellowship award from the Univ. of Louisville Brown Cancer Center (N.N.). The cell lines 686Tu, 686Ln, 1386Tu, and 1386Ln were kind gifts from Dr. P. Sacks, New York University, New York, NY.
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| 15613236 | PMC544893 | CC BY | 2021-01-04 16:36:34 | no | Mol Cancer. 2004 Dec 21; 3:38 | utf-8 | Mol Cancer | 2,004 | 10.1186/1476-4598-3-38 | oa_comm |
==== Front
BMC GenomicsBMC Genomics1471-2164BioMed Central London 1471-2164-5-931558832810.1186/1471-2164-5-93DatabaseGOLD.db: genomics of lipid-associated disorders database Hackl Hubert [email protected] Michael [email protected] Bernhard [email protected] Jürgen [email protected] Gernot [email protected] Diego [email protected] Zlatko [email protected] Institute for Genomics and Bioinformatics and Christian-Doppler-Laboratory for Genomics and Bioinformatics, Graz University of Technology, Petersgasse 14, 8010 Graz, Austria2004 10 12 2004 5 93 93 6 9 2004 10 12 2004 Copyright © 2004 Hackl et al; licensee BioMed Central Ltd.2004Hackl 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 GOLD.db (Genomics of Lipid-Associated Disorders Database) was developed to address the need for integrating disparate information on the function and properties of genes and their products that are particularly relevant to the biology, diagnosis management, treatment, and prevention of lipid-associated disorders.
Description
The GOLD.db provides a reference for pathways and information about the relevant genes and proteins in an efficiently organized way. The main focus was to provide biological pathways with image maps and visual pathway information for lipid metabolism and obesity-related research. This database provides also the possibility to map gene expression data individually to each pathway. Gene expression at different experimental conditions can be viewed sequentially in context of the pathway. Related large scale gene expression data sets were provided and can be searched for specific genes to integrate information regarding their expression levels in different studies and conditions. Analytic and data mining tools, reagents, protocols, references, and links to relevant genomic resources were included in the database. Finally, the usability of the database was demonstrated using an example about the regulation of Pten mRNA during adipocyte differentiation in the context of relevant pathways.
Conclusions
The GOLD.db will be a valuable tool that allow researchers to efficiently analyze patterns of gene expression and to display them in a variety of useful and informative ways, allowing outside researchers to perform queries pertaining to gene expression results in the context of biological processes and pathways.
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Background
The excessive consumption of high calorie, high fat diets and the adoption of a sedentary life style have made obesity and atherosclerosis major health problems in Western societies. In the USA, over 50% of the population are over-weight (BMI > 25) and close to 25% are considered obese (BMI > 30) [1,2]. As a consequence, a large fraction of the population is at risk to develop a broad range of common, life-threatening diseases including non-insulin dependent diabetes, various hyperlipidemias, high blood pressure and atherosclerosis. Vascular disease including coronary heart disease and stroke is currently the major cause of death in the United States and in other industrialized nations.
At the root of obesity and atherosclerosis is an excessive deposition of neutral lipids. Adipose tissue accumulates predominantly triglycerides, whereas macrophages along the blood vessel wall mainly accumulate cholesterol and cholesteryl esters. Accordingly, a detailed understanding of the molecular mechanisms that govern the balance between lipid deposition and mobilization is fundamentally important for the prevention and improved treatment of disease. In addition to the apparent environmental components involved in the pathogenesis of disorders related to lipid and energy metabolism, a large number of studies have provided undisputed evidence that susceptibility genes contribute around 50% of the phenotype. These genes encode products involved in the cellular uptake, synthesis, deposition and/or mobilization of lipids. However, characterization of many if not most of these genes and their products remains rudimentary. Deficiencies in the current level of understanding extend to key enzymes such as important triglyceride hydrolases in adipose tissue [3] or cholesteryl ester hydrolases in macrophages, hormones, signal transduction pathways, and the regulation of the transcription of relevant genes.
While medical molecular biology traditionally associates single genes and gene products with diseases, a growing body of evidence suggests that several common disease phenotypes arise from the delicate interaction of many genes as well as gene-environment interactions. To elucidate the development of obesity and atherosclerosis, it will be necessary to analyze patterns of gene expression and relate them to various metabolic states. To discover novel genes, processes and pathways that regulate lipid deposition and mobilization, a departure from hypothesis-driven research and turn to a discovery-driven approach is necessary. The application of high-throughput technologies and genome-based analysis will provide the tools for the analysis of gene-gene and gene-environment interactions in a systematic and comprehensive manner.
To facilitate genomic research we have initiated the development of a system for storing, integrating, and analyzing relevant data needed to decipher the molecular anatomy of lipid associated disorders. In order to provide a reference for pathways and information of the relevant genes and proteins in an efficiently organized way, we have created the Genomics Of Lipid-Associated Disorders database (GOLD.db). The GOLD.db integrates disparate information on the function and properties of genes and their protein products that are particularly relevant to the biology, diagnosis management, treatment, and prevention of lipid-associated disorders.
Construction and content
The main goal of the GOLD.db was to provide biological pathways with image maps and visual pathway information. For each element in the pathway, specific information exists including structured information about a gene, protein, function, literature, and links. The GOLD.db provides also the possibility to map gene expression data individually to each pathway. Additionally, analytic and data mining tools, reagents, protocols, references, and links to relevant genomic resources were included in the database.
The GOLD.db was implemented in Java technology [4]. Hence, the pathway editor, as well the web application are platform independent. The web application of GOLD.db is build in Java Servlets and JavaServer Pages technology based on the Model-View-Controller Architecture [5]. For the implementation, the freely available struts framework [6] was used. This code can be easily deployed in any Servlet Container. We used the Servlet Container Tomcat (also freely available at [7]) which is accessible from all web browsers. Oracle 9i was used as database management system. The interface between the Java and the Database management system was established using Java database connectivity (JDBC) 2.0. Therefore, migration to other freely available DBMSs like mySQL can be easily done. For additional storage and communication between the pathway-editor components, the markup language XML containing structured, human readable information, was used. The provided pathways can be downloaded as Scalable Vector Graphics (SVG) [8], a standard for describing two-dimensional graphics in XML, and can be visualized in this format on the client side with the web browser using a plug-in for SVG.
For tracking the repository of the reagents like clone resources and libraries which can be used for microarray studies, we have developed a relational database. Information about the vector, the sequence and length of the clone insert, primers for the PCR amplification, tissue, organism, accession number, library, container, storage information, date and person and access to other clone bases (e.g. IMAGE Consortium) can be stored. Users of the GOLD.db can list these clones and get all the information about each available clone. With restricted access, clone information or even clone lists can be uploaded and selection lists can be created and deleted. The input mask is designed in such way that the user can choose one of the elements of the created selection lists.
In order to deal with the huge amount of data associated with large scale studies and to perform sequence based and microarray analysis, several bioinformatic tools were integrated or can be downloaded. Sequence similarity search against databases can be performed with BLAST (Basic Local Alignment Search Tool) [9], FASTA [10] or HMM (Hiden Markov Models) [11] on a 50 CPU Myrinet Cluster. The sequence retrieval system SRS (LION Bioscience AG, Heidelberg, Germany) was included to enable rapid, easy and user friendly access to the large volumes of diverse and heterogeneous data [12]. The latest version of the PathwayEditor for the construction of biological pathway diagrams can be downloaded. For microarray analysis the platform independent JAVA tools ArrayNorm [13] for normalization of microarray data and Genesis [14] for clustering and analysis of large scale gene expression datasets were made available.
Utility and discussion
Pathways
In order to construct the biological pathways of interest, we have developed a pathway editor [15] and an extended version to map gene expression data (pathway mapper). This drawing tool provides the possibility to draw elements – typically representing a gene as part of the pathway – and the connection between those elements. The benefit of this tool is that information can be appended to each element via an input mask. This information can be accessed by clicking on the corresponding element in the image map within the pathway mapper or when saved and uploaded via the web interface to the GOLD.db. To design this pathway service as flexible as possible, features are provided for the remove, up- and download of relevant pathways (image maps) including the underlying additional information of the elements. However, this service is on a restricted basis to prohibit unauthorized access. Since some pathways tend to become very detailed an option to search for genes or gene accession number, respectively, within the pathways was built in. The pathway editor is executable as a standalone application and is available from [16]. Currently annotated pathways are the insulin signaling pathway, the IGF-1 pathway and the adipogenesis regulatory network. Other pathways of lipid metabolism will follow in the near future. Available KEGG pathways can also be adapted with the pathway editor based on the provided XML files [17] and uploaded in the same way. All relevant KEGG pathways for different organisms are provided. Moreover, pathways from BioCarta were made available within the GOLD.db and HTML files [18] were parsed to provide additional meta-information of the pathway elements.
For each element in the pathways a specific information field exists. The field includes structured information about a gene, protein, function, literature, and links to well-curated and annotated databases. Besides the gene name and the symbol name – for human the HUGO symbols and gene names and for mouse the MGI nomenclature were used – RefSeq numbers for the transcript and the protein as well as a link to SwissProt/UniProt and LocusLink is available. For the elements of the KEGG pathways a link to the provided enzyme or product information was given. The description, localization and classification of the factors are entered by the annotator in plain text and are accessed in the same format. The references used to generate the content of the database entries can be appended, including a link to the PubMed entry. There is also the possibility to create a list of reference entries for the pathway. If a clone for a specific gene is available in the clone resources, the clone name will be displayed automatically and a link with optional information about this clone is provided.
Mapping of gene expression data sets to pathways
Through the integration of several types of biological information deeper insights into the molecular mechanisms and biological processes can be gained than just by the analysis of one type of experimental results. In the GOLD.db it is possible to map gene expression data (for instance results of microarray studies) to the corresponding elements of the available pathways similar to previous efforts [19]. Either an individual or a provided gene expression data set can be used to visualize the gene expression at different experimental conditions sequentially or all at once in the context of a pathway. If an element (gene) of the pathway is included in the data set, the related symbol in the image map is color coded according to the relative gene expression or the log ratio in two color microarray experiments, respectively.
As key for the mapped relation the RefSeq number [20] is used. Hence, only those elements in the data set file are mapped, where the RefSeq number in the data set is specified. For the KEGG pathways each element classified by the enzyme classification number (EC) is virtually subdivided into different corresponding RefSeq entries, since one EC is represented by one or more RefSeq entries.
Curated gene expression data sets
Analysis of gene expression patterns in animal and cell models for lipid-associated disorders will help to understand the fundamental gene relations and regulatory mechanisms responsible for the development of obesity related diseases. The huge amount of data associated with the analysis of large scale gene expression analysis raises the demand of tools for storing, processing and retrieving complex information. Although a number of studies have been published and despite the requirements of some journals to deposit microarray data in public databases like GEO or ArrayExpress , it is still very difficult for researchers to obtain the original data. Web sites with Supplementary information are not maintained and/or not further developed. Hence, a database with a large collection of curated datasets will be enormously valuable for the community. Approaches to upload and retrieve gene expression data were pursued within the GOLD.db. Large scale gene expression data sets can be uploaded in form of tab delimited text files (Stanford file format) [21] as used for cluster analysis programs together with additional information about the experimental conditions and the citation for already published data sets. Within those data sets the search for specific genes is possible to provide integrated visualization of gene expression levels in different studies and experimental conditions.
Example for using GOLD.db: regulation of Pten during adipocyte differentiation
Recently, it was shown that insulin sensitivity, energy expenditure, and thermogenesis were enhanced in adipose-specific Pten-deficient (AdipoPten-KO) mice. Body and adipose tissues weight in these mice were significantly lower than those of control mice in spite of a larger food intake [22]. We addressed the question how is the expression of the Pten gene regulated during adipocyte differentiation in different models and experimental setups and in which pathways is PTEN involved. The workflow for the analysis is described in Figure 1. Pten (phosphatase and tensin homolog deleted on chromosome 10) is known as tumor suppressor gene and is a protein and lipid phosphatase with the major substrate phosphatiylinositol 3,4,5-triphosphate (PIP3), as indicated in the annotated insulin signaling pathway within the GOLD.db. In fact, Pten regulates negatively the insulin signaling pathway in 3T3-L1 adipocytes [23].
Figure 1 Various result tables from using GOLD.db to address the question
how is PTEN regulated during adipocyte differentiation (top left: result of search in SRS for phosphatase and tensin homolog; top right: pathways, in which PTEN is involved; bottom left: relative
gene expression levels of PTEN in different datasets; bottom right: PTEN dependent cell cyle pathway with mapped gene expression levels)
During adipocyte differentiation cyclin dependent kinase inhibitors, like p21 leads to a hypophosphorylation of the Retinoblastoma protein (Rb) which allows binding to the E2F transcription factor, causing cells to permanently exit the cell cycle – a required step in adipocyte differentiation called mitotic clonal expansion – before entering the terminal differentiation state. pRb interacts physically with adipogenic CCAAT/enhancer-binding proteins and positively regulates transactivation by C/EBPβ and therefore plays a pivotal role in adipocyte differentiation [24,25]. Hence, since a) PTEN is expressed during adipogenesis (Figure 1), b) is involved in the regulation of Rb [22], a major player in adipogenesis, and c) is an important component in cell cycle arrest and apoptosis (Figure 1), it can be postulated that PTEN plays an important role in fat cell development.
Thus, using recently identified key player for food intake and weight control and using the GOLD.db, it is possible to address relevant questions and generate testable hypotheses on the molecular mechanisms of fat cell development.
Conclusions
The vast quantity of gene expression data generated in genomic studies presents a number of challenges for their effective analysis and interpretation. In order to fully understand the changes in expression that will be observed, we must correlate these data with phenotype, genotype, metabolism and other information including the tissue distribution and time course expression data gleaned from previous studies. The goal of our work was the development of a specialized database and tools that allow researchers to efficiently analyze patterns of gene expression and to display them in a variety of useful and informative ways, allowing outside researchers to perform queries pertaining to gene expression results in the context of biological processes and pathways. The uniqueness of the GOLDdb database we have developed is threefold: 1) the inclusion of annotated pathways, 2) the availability of curated datasets and 3) the possibility to map experimental data on biological pathways. The upcoming challenges will be to include data from functional analysis and proteomics data, which will give us new opportunities in understanding mechanisms of different applications and lipid-associated disorders in particular.
Availability and requirements
The GOLD.db database should be cited with the present publication as a reference. Access to GOLD.db is possible through the world wide web at . The pathway editor and the clone tracker are available free of charge to academic, government, and other nonprofit institutions.
Author's contributions
HH was responsible for the content, the annotation process, webdesign, and processing of data sets. MM was responsible for the implementation of the database and web application as well as the relational database for the clone tracker. BM and JH had implemented the mapping of expression data to pathways. GS is involved in providing of sequence analysis tools and server software. DMS has annotated the insulin signaling pathway. ZT was responsible for the design of the study and for overall project coordination.
Acknowledgements
This work was supported by the Austrian Science Fund, Project SFB Biomembranes F718, the GEN-AU projects Bioinformatics Integration Network (BIN) and Genomics of Lipid-Associated Disorders (GOLD). Diego Miranda-Saavedra was supported by an EU Marie Curie Training Site program "Genomics of Lipid Metabolism". Michael Maurer was supported by a grant from the Austrian Academy of Sciences.
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| 15588328 | PMC544894 | CC BY | 2021-01-04 16:39:23 | no | BMC Genomics. 2004 Dec 10; 5:93 | utf-8 | BMC Genomics | 2,004 | 10.1186/1471-2164-5-93 | oa_comm |
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