FM-test: a fuzzy-set-theory-based approach to differential gene expression data analysis

Abstract Background Microarray techniques have revolutionized genomic research by making it possible to monitor the expression of thousands of genes in parallel. As the amount of microarray data being produced is increasing at an exponential rate, there is a great demand for efficient and effective expression data analysis tools. Comparison of gene expression profiles of patients against those of normal counterpart people will enhance our understanding of a disease and identify leads for therapeutic intervention. Results In this paper, we propose an innovative approach, fuzzy membership test (FM-test), based on fuzzy set theory to identify disease associated genes from microarray gene expression profiles. A new concept of FM d-value is defined to quantify the divergence of two sets of values. We further analyze the asymptotic property of FM-test, and then establish the relationship between FM d-value and p-value. We applied FM-test to a diabetes expression dataset and a lung cancer expression dataset, respectively. Within the 10 significant genes identified in diabetes dataset, six of them have been confirmed to be associated with diabetes in the literature and one has been suggested by other researchers. Within the 10 significantly overexpressed genes identified in lung cancer data, most (eight) of them have been confirmed by the literatures which are related to the lung cancer. Conclusion Our experiments on synthetic datasets show that FM-test is effective and robust. The results in diabetes and lung cancer datasets validated the effectiveness of FM-test. FM-test is implemented as a Web-based application and is available for free at http://database.cs.wayne.edu/bioinformatics

Mechanisms Underlying Adaptation to Life in Hydrogen Sulfide-Rich Environments

Hydrogen sulfide (H2S) is a potent toxicant interfering with oxidative phosphorylation in mitochondria and creating extreme environmental conditions in aquatic ecosystems. The mechanistic basis of adaptation to perpetual exposure to H2S remains poorly understood. We investigated evolutionarily independent lineages of livebearing fishes that have colonized and adapted to springs rich in H2S and compared their genome-wide gene expression patterns with closely related lineages from adjacent, nonsulfidic streams. Significant differences in gene expression were uncovered between all sulfidic and nonsulfidic population pairs. Variation in the number of differentially expressed genes among population pairs corresponded to differences in divergence times and rates of gene flow, which is consistent with neutral drift driving a substantial portion of gene expression variation among populations. Accordingly, there was little evidence for convergent evolution shaping large-scale gene expression patterns among independent sulfide spring populations. Nonetheless, we identified a small number of genes that was consistently differentially expressed in the same direction in all sulfidic and nonsulfidic population pairs. Functional annotation of shared differentially expressed genes indicated upregulation of genes associated with enzymatic H2S detoxification and transport of oxidized sulfur species, oxidative phosphorylation, energy metabolism, and pathways involved in responses to oxidative stress. Overall, our results suggest that modification of processes associated with H2S detoxification and toxicity likely complement each other to mediate elevated H2S tolerance in sulfide spring fishes. Our analyses allow for the development of novel hypotheses about biochemical and physiological mechanisms of adaptation to extreme environments

Mechanisms Underlying Adaptation to Life in Hydrogen Sulfide–Rich Environments

Hydrogen sulfide (H2S) is a potent toxicant interfering with oxidative phosphorylation in mitochondria and creating extreme environmental conditions in aquatic ecosystems. The mechanistic basis of adaptation to perpetual exposure to H2S remains poorly understood. We investigated evolutionarily independent lineages of livebearing fishes that have colonized and adapted to springs rich in H2S and compared their genome-wide gene expression patterns with closely related lineages from adjacent, nonsulfidic streams. Significant differences in gene expression were uncovered between all sulfidic and nonsulfidic population pairs. Variation in the number of differentially expressed genes among population pairs corresponded to differences in divergence times and rates of gene flow, which is consistent with neutral drift driving a substantial portion of gene expression variation among populations. Accordingly, there was little evidence for convergent evolution shaping large-scale gene expression patterns among independent sulfide spring populations. Nonetheless, we identified a small number of genes that was consistently differentially expressed in the same direction in all sulfidic and nonsulfidic population pairs. Functional annotation of shared differentially expressed genes indicated upregulation of genes associated with enzymatic H2S detoxification and transport of oxidized sulfur species, oxidative phosphorylation, energy metabolism, and pathways involved in responses to oxidative stress. Overall, our results suggest that modification of processes associated with H2S detoxification and toxicity likely complement each other to mediate elevated H2S tolerance in sulfide spring fishes. Our analyses allow for the development of novel hypotheses about biochemical and physiological mechanisms of adaptation to extreme environments

Exploring the variability in Behçet's disease prevalence:a meta-analytical approach

Background: Surveys of Behçet’s disease (BD) have shown substantial geographic variations in prevalence, but some of these differences may result from methodological inconsistencies. This meta-analysis explored the effect of geographic location and study methodology on the prevalence of BD. Methods: We systematically searched the literature in electronic databases and by handsearching to identify population-based prevalence surveys of BD. Studies were eligible if they provided an original population-based prevalence estimate for BD with the number of prevalent cases identified in the study area. Pooled prevalence proportions across all studies were computed by using random effects models based on a Poisson normal distribution. Pre-defined subgroup analyses and meta-regression were used to investigate the effect of covariates on the prevalence proportions. Results: We included 45 reports published from 1974 to 2015 and covering worldwide areas. The pooled estimates of prevalence proportions (expressed as cases/100 000 inhabitants) were 10.3 (95% CI 6.1, 17.7) for all studies and 119.8 (59.8, 239.9) for Turkey, 31.8 (12.9, 78.4) for the Middle East, 4.5 (2.2, 9.4) for Asia and 3.3 (2.1, 5.2) for Europe. Subgroup analyses showed a strikingly greater prevalence for studies with a sample survey design than a census design [82.5 (95% CI 47.3, 143.9) vs 3.6 (2.6, 5.1)]. Metaregression identified study design as an independent covariate significantly affecting BD prevalence proportions. Conclusions: Differences in BD prevalence proportions likely reflect a combination of true geographic variation and methodological artefacts. In particular, use of a sample or census study design may strongly affect the estimated prevalence

Identification of STAT5A and STAT5B Target Genes in Human T Cells

<div><p>Signal transducer and activator of transcription (STAT) comprises a family of universal transcription factors that help cells sense and respond to environmental signals. STAT5 refers to two highly related proteins, STAT5A and STAT5B, with critical function: their complete deficiency is lethal in mice; in humans, STAT5B deficiency alone leads to endocrine and immunological problems, while STAT5A deficiency has not been reported. STAT5A and STAT5B show peptide sequence similarities greater than 90%, but subtle structural differences suggest possible non-redundant roles in gene regulation. However, these roles remain unclear in humans. We applied chromatin immunoprecipitation followed by DNA sequencing using human CD4⁺ T cells to detect candidate genes regulated by STAT5A and/or STAT5B, and quantitative-PCR in <i>STAT5A</i> or <i>STAT5B</i> knock-down (KD) human CD4⁺ T cells to validate the findings. Our data show STAT5A and STAT5B play redundant roles in cell proliferation and apoptosis via <i>SGK1</i> interaction. Interestingly, we found a novel, unique role for STAT5A in binding to genes involved in neural development and function (<i>NDRG1</i>, <i>DNAJC6</i>, and <i>SSH2</i>), while STAT5B appears to play a distinct role in T cell development and function via <i>DOCK8</i>, <i>SNX9</i>, <i>FOXP3</i> and <i>IL2RA</i> binding. Our results also suggest that one or more co-activators for STAT5A and/or STAT5B may play important roles in establishing different binding abilities and gene regulation behaviors. The new identification of these genes regulated by STAT5A and/or STAT5B has major implications for understanding the pathophysiology of cancer progression, neural disorders, and immune abnormalities.</p></div

Localization of STAT5A and STAT5B, and monomers and dimers of STAT5A and STAT5B.

<p><b>A</b> demonstrates translocation of STAT5A and STAT5B into cell nuclei after 30-2 (40× confocal). Yellow, STAT5A; purple, STAT5B; blue, nucleus. <b>B</b> and <b>C</b>. Detection of STAT5A and STAT5B proteins in cytoplasmic or nuclear proteins fractionated from CD4⁺ T cells after PHA-P stimulation for 3 days followed by incubation with rhIL-2 for 30 min. <b>B</b>. STAT5A monomer (91 kDa, arrow 1) and STAT5A dimer (arrow 2) in native cytoplasmic or nuclear proteins, detected with anti-STAT5A Ab. <b>C</b>. STAT5B monomer (90 kDa, arrow 3) and STAT5B dimer (arrow 4) in native cytoplasmic or nuclear proteins, detected using anti-STAT5B Ab. <b>D</b>. Detection of phosphorylated STAT5 proteins in STAT5A- or STAT5B- immunoprecipitated nuclear proteins fractionated from CD4⁺ T cells after PHA-P stimulation for 3 days followed by incubation with rh-IL-2 for 0 min, 30 min or 3 days. <b>E</b>. Detection of phosphorylated STAT5 proteins in cytoplasmic or nuclear proteins fractionated from CD4⁺ T cells after PHA-P stimulation for 3 days followed by incubation with rhIL-2 for 3 days, and control (unstimulated condition).</p

Binding ability, motif sequences and binding sites.

<p><b>A</b> shows binding ability of STAT5A after 3 days of exposure to rhIL-2 versus binding ability of STAT5A after 30 min of exposure to rhIL-2. It shows results of STAT5A ChIP-seq on chromosome 18 performed in CD4⁺ T cells incubated with rhIL-2 for 3 days (top); in CD4⁺ cells incubated with rhIL-2 for 30 min (middle); compared with control ChIP-seq in CD4⁺ T cells (bottom). <b>B</b> shows consensus motif sequences for STAT5A and/or STAT5B. <b>C</b> shows binding sites for <i>SGK1</i>, detected by both STAT5A and STAT5B ChIP-seq. <b>D</b> and <b>E</b> show the detection of the sequence “TTCCTAGAA” by STAT5A ChIP-seq in <i>DNAJC6</i>, and by STAT5B ChIP-seq in <i>DOCK8</i>. <b>F</b> shows that <i>FOXP3</i> and <i>PPP1R3F</i> are located within 10,000 bp of the two binding sites. <b>G</b> shows the five binding sites for <i>IL2RA</i>.</p

Validation of candidate genes from ChIP-seq via QT-PCR in <i>STAT5A</i> and <i>STAT5B</i> knock-down human CD4⁺ T cells.

<p>The expression levels were compared <i>STAT5A</i> KD or <i>STAT5B</i> KD CD4⁺ T cells with control CD4⁺ T cells. Genes regulated similarly by both STAT5A and STAT5B are <i>SGK1</i>, <i>GTF2H5</i> and <i>SLC22A5</i>. Genes regulated specifically by STAT5A are <i>NDRG1</i>, <i>DNAJC6</i>, <i>ST3GAL1</i>, <i>SAMD4A</i>, <i>SSH2</i>, <i>MAP3K5</i> and <i>BCL2L1</i>. Genes regulated specifically by STAT5B are <i>DOCK8</i>, <i>SNX9</i>, <i>SKAP1</i>, <i>TNFSF10</i>, <i>FOXP3</i>, <i>IL2RA</i> and <i>UGCG</i>. Data is presented as mean ± SEM. *, P<0.01; **, P<0.001. There were no statistical differences if not annotated. <b>Abbreviations</b>: <i>SGK1</i>, serum/glucocorticoid regulated kinase 1; <i>GTF2H5</i>, general transcription factor IIH, polypeptide 5; <i>BCL2L1</i>, BCL2-like 1; <i>SLC22A5</i>, solute carrier family 22 (organic cation/carnitine transporter), member 5; <i>CDKAL1</i>, CDK5 regulatory subunit associated protein 1-like 1; <i>DNM2</i>, dynamin 2; <i>DUSP5</i>, dual specificity phosphatase 5; <i>MBP</i>, myelin basic protein; <i>ARL4C</i>, ADP-ribosylation factor-like 4C; <i>NDRG1</i>, N-myc downstream regulated 1; <i>DNAJC6</i>, DnaJ (Hsp40) homolog, subfamily C, member 6; <i>ST3GAL1</i>, ST3 beta-galactoside alpha-2,3-sialyltransferase 1; <i>SAMD4A</i>, sterile alpha motif domain containing 4A; <i>SSH2</i>, slingshot protein phosphatase 2; <i>MAP3K5</i>, mitogen-activated protein kinase kinase kinase 5; <i>CBS</i>, cystathionine-beta-synthase; <i>PPP2R2B</i>, protein phosphatase 2, regulatory subunit B, beta; <i>DOCK8</i>, dedicator of cytokinesis 8; <i>SNX9</i>, sorting nexin 9; <i>SKAP1</i>, src kinase associated phosphoprotein 1; <i>PTGER1</i>, prostaglandin E receptor 1 (subtype EP1), 42 kDa; <i>DIDO1</i>, death inducer-obliterator 1; <i>TNFSF10</i>, tumor necrosis factor (ligand) superfamily, member 10; <i>FOXP3</i>, forkhead box P3; <i>IL2RA</i>, interleukin 2 receptor, alpha; <i>UGCG</i>, UDP-glucose ceramide glucosyltransferase; <i>LNPEP</i>, leucyl/cystinyl aminopeptidase; <i>PPP1R3F</i>, protein phosphatase 1, regulatory subunit 3F.</p

Binding site q-scores for STAT5A and STAT5B ChIP-seq.

<p>E indicates a group of the candidate genes detected equally by STAT5A and STAT5B ChIP-seq. 5A indicates a group of the candidate genes detected specifically or dominantly by STAT5A ChIP-seq. 5B indicates a group of the candidate genes detected specifically or dominantly by STAT5B ChIP-seq. (-) indicates no significant detection.</p

Detected binding site sequences on candidate genes detected specifically or dominantly with STAT5B ChIP-seq.

<p><b>Abbreviation</b>: sp, specifically detected; dom, dominantly detected.</p