Approaches to reduce false positives and false negatives in the analysis of microarray data: applications in type 1 diabetes research

<p>Abstract</p> <p>Background</p> <p>As studies of molecular biology system attempt to achieve a comprehensive understanding of a particular system, Type 1 errors may be a significant problem. However, few investigators are inclined to accept the increase in Type 2 errors (false positives) that may result when less stringent statistical cut-off values are used. To address this dilemma, we developed an analysis strategy that used a stringent statistical analysis to create a list of differentially expressed genes that served as "bait" to "fish out" other genes with similar patterns of expression.</p> <p>Results</p> <p>Comparing two strains of mice (NOD and C57Bl/6), we identified 93 genes with statistically significant differences in their patterns of expression. Hierarchical clustering identified an additional 39 genes with similar patterns of expression differences between the two strains. Pathway analysis was then employed: 1) identify the central genes and define biological processes that may be regulated by the genes identified, and 2) identify genes on the lists that could not be connected to each other in pathways (potential false positives). For networks created by both gene lists, the most connected (central) genes were interferon gamma (IFN-γ) and tumor necrosis factor alpha (TNF-α). These two cytokines are relevant to the biological differences between the two strains of mice. Furthermore, the network created by the list of 39 genes also suggested other biological differences between the strains.</p> <p>Conclusion</p> <p>Taken together, these data demonstrate how stringent statistical analysis, combined with hierarchical clustering and pathway analysis may offer deeper insight into the biological processes reflected from a set of expression array data. This approach allows us to 'recapture" false negative genes that otherwise would have been missed by the statistical analysis.</p

Molecular pathway alterations in CD4 T-cells of nonobese diabetic (NOD) mice in the preinsulitis phase of autoimmune diabetes

Type 1 diabetes (T1D) is a multigenic disease caused by T-cell mediated destruction of the insulin producing pancreatic islet β-cells. The earliest sign of islet autoimmunity in NOD mice, islet leukocytic infiltration or insulitis, is obvious at around 5 weeks of age. The molecular alterations that occur in T cells prior to insulitis and that may contribute to T1D development are poorly understood. Since CD4 T-cells are essential to T1D development, we tested the hypothesis that multiple genes/molecular pathways are altered in these cells prior to insulitis. We performed a genome-wide transcriptome and pathway analysis of whole, untreated CD4 T-cells from 2, 3, and 4 week-old NOD mice in comparison to two control strains (NOR and C57BL/6). We identified many differentially expressed genes in the NOD mice at each time point. Many of these genes (herein referred to as NOD altered genes) lie within known diabetes susceptibility (insulin-dependent diabetes, Idd) regions, e.g. two diabetes resistant loci, Idd27 (tripartite motif-containing family genes) and Idd13 (several genes), and the CD4 T-cell diabetogenic activity locus, Idd9/11 (2 genes, KH domain containing, RNA binding, signal transduction associated 1 and protein tyrosine phosphatase 4a2). The biological processes associated with these altered genes included, apoptosis/cell proliferation and metabolic pathways (predominant at 2 weeks); inflammation and cell signaling/activation (predominant at 3 weeks); and innate and adaptive immune responses (predominant at 4 weeks). Pathway analysis identified several factors that may regulate these abnormalities: eight, common to all 3 ages (interferon regulatory factor 1, hepatic nuclear factor 4, alpha, transformation related protein 53, BCL2-like 1 (lies within Idd13), interferon gamma, interleukin 4, interleukin 15, and prostaglandin E2); and two each, common to 2 and 4 weeks (androgen receptor and interleukin 6); and to 3 and 4 weeks (interferon alpha and interferon regulatory factor 7). Others were unique to the various ages, e.g. myelocytomatosis oncogene, jun oncogene, and amyloid beta (A4) to 2 weeks; tumor necrosis factor, transforming growth factor, beta 1, NFκB, ERK, and p38MAPK to 3 weeks; and interleukin 12 and signal transducer and activator of transcription 4 to 4 weeks. Thus, our study demonstrated that expression of many genes that lie within several Idds (e.g. Idd27, Idd13 and Idd9/11) was altered in CD4 T-cells in the early induction phase of autoimmune diabetes and identified their associated molecular pathways. These data offer the opportunity to test hypotheses on the roles played by the altered genes/molecular pathways, to understand better the mechanisms of CD4 T-cell diabetogenesis, and to develop new therapeutic strategies for T1D

Molecular Phenotyping of Immune Cells from Young NOD Mice Reveals Abnormal Metabolic Pathways in the Early Induction Phase of Autoimmune Diabetes

<div><p>Islet leukocytic infiltration (insulitis) is first obvious at around 4 weeks of age in the NOD mouse – a model for human type 1 diabetes (T1D). The molecular events that lead to insulitis and initiate autoimmune diabetes are poorly understood. Since TID is caused by numerous genes, we hypothesized that multiple molecular pathways are altered and interact to initiate this disease. We evaluated the molecular phenotype (mRNA and protein expression) and molecular networks of <em>ex vivo</em> unfractionated spleen leukocytes from 2 and 4 week-old NOD mice in comparison to two control strains. Analysis of the global gene expression profiles and hierarchical clustering revealed that the majority (∼90%) of the differentially expressed genes in NOD mice were repressed. Furthermore, analysis using a modern suite of multiple bioinformatics approaches identified abnormal molecular pathways that can be divided broadly into 2 categories: metabolic pathways, which were predominant at 2 weeks, and immune response pathways, which were predominant at 4 weeks. Network analysis by Ingenuity pathway analysis identified key genes/molecules that may play a role in regulating these pathways. These included five that were common to both ages (TNF, HNF4A, IL15, Progesterone, and YWHAZ), and others that were unique to 2 weeks (e.g. MYC/MYCN, TGFB1, and IL2) and to 4 weeks (e.g. IFNG, beta-estradiol, p53, NFKB, AKT, PRKCA, IL12, and HLA-C). Based on the literature, genes that may play a role in regulating metabolic pathways at 2 weeks include Myc and HNF4A, and at 4 weeks, beta-estradiol, p53, Akt, HNF4A and AR. Our data suggest that abnormalities in regulation of metabolic pathways in the immune cells of young NOD mice lead to abnormalities in the immune response pathways and as such may play a role in the initiation of autoimmune diabetes. Thus, targeting metabolism may provide novel approaches to preventing and/or treating autoimmune diabetes.</p> </div

Enriched molecular function/cellular component Gene Ontology (GO) categories for the lists of differentially expressed transcripts in spleen leukocytes from 2 week and 4 week old NOD mice compared to two control strains.

<p>One-way ANOVA (p<0.05, Benjamini-Hochberg) of mRNA expression data followed by hierarchical clustering identified 187 and 327 probe sets whose expression was altered in spleen leukocytes from NOD mice compared to two control strains (NON and C57BL/6) at 2 weeks and 4 weeks, respectively. The lists of differentially expressed genes were analyzed in WebGestalt (<a href="http://bioinfo.vanderbilt.edu/webgestalt" target="_blank">http://bioinfo.vanderbilt.edu/webgestalt</a>) for enriched GO categories using hypergeometric test (p<0.01, Benjamini-Hochberg); dashes (-) indicate that the corresponding categories were not significantly enriched at the respective ages. Categories represented by ≥5 genes are shown. <b>Bold text</b> indicates categories that were common to both age groups.</p

Transcripts with highly significant expression differences in spleen leukocytes of 4 week old NOD mice compared to two control strains.

<p>Statistical analysis of mRNA expression data by 1-way-ANOVA (Benjamini-Hochberg, p<0.005) followed by hierarchical clustering identified 76 probe sets (representing 72 different genes) that were differentially expressed in spleen leukocytes of 4 week old NOD mice compared to two control strains (NON and C57BL/6); H, indicates 8 genes (∼11%) with significantly higher expression in NOD relative to controls; the rest, 68 (∼89%) had significantly lower expression in NOD. Fold change (FC) was calculated by ratio of means of expression in NOD mice versus controls; FC of genes of lower expression in NOD are indicated by negative values. We assigned the genes to selected functional categories based on information obtained by searches of public databases. Other genes under “Other” include: Fcrla, Otu1, Ankrd16, Treml2, Plekha2, Fam3c, 0610039P13Rik, Fam65b, Pyhin1, <b>5031439G07Rik, C9orf21, LOC434484 (H)</b>; Unknown genes include: <b>D17H6S56E-5, A630001G21Rik</b>, A530030E21Rik. Genes highlighted with <b>bold font</b> were differentially expressed at both 2 and 4 weeks.</p