Additional file 4: Table S3. of Inter-tissue coexpression network analysis reveals DPP4 as an important gene in heart to blood communication

Number of significant gene-modules identified for each tissue pair. (PDF 42 kb

Additional file 2: of Inter-tissue coexpression network analysis reveals DPP4 as an important gene in heart to blood communication

Supporting notes. Figure S1. The optimal numbers of principal components (PCs) to correct in each tissue. Figure S2. Histograms of correlation coefficients between sample ischemic time and RINs with gene expression profiles in nine tissues. Red lines are for correlation with RINs, and blue lines are for correlation with sample ischemic time. Solid lines are for empirical gene expression profiles in the study, dashed lines are for permuted data. (DOCX 500 kb

Additional file 1: Tables S1-S6. of Nur77-deficiency in bone marrow-derived macrophages modulates inflammatory responses, extracellular matrix homeostasis, phagocytosis and tolerance

Table S1. Top 25 up- and downregulated genes in Nur77-KO vs WT BMM. Table S2. Top 25 canonical pathways associated with differentially expressed genes in Nur77-KO vs WT BMM. Table S3. Top 20 gene sets identified with GSEA that are upregulated in Nur77-KO vs WT BMM. Table S4. Upstream Regulators in Nur77-KO vs WT BMM. Table S5. Top 25 up- and downregulated genes in LPS-stimulated Nur77-KO vs WT BMM. Table S6. Top 25 canonical pathways associated with differentially expressed genes in LPS-stimulated Nur77-KO vs WT BMM. (PDF 140 kb

Integrative Analysis of DNA Methylation and Gene Expression Data Identifies <i>EPAS1</i> as a Key Regulator of COPD

<div><p>Chronic Obstructive Pulmonary Disease (COPD) is a complex disease. Genetic, epigenetic, and environmental factors are known to contribute to COPD risk and disease progression. Therefore we developed a systematic approach to identify key regulators of COPD that integrates genome-wide DNA methylation, gene expression, and phenotype data in lung tissue from COPD and control samples. Our integrative analysis identified 126 key regulators of COPD. We identified <i>EPAS1</i> as the only key regulator whose downstream genes significantly overlapped with multiple genes sets associated with COPD disease severity. <i>EPAS1</i> is distinct in comparison with other key regulators in terms of methylation profile and downstream target genes. Genes predicted to be regulated by <i>EPAS1</i> were enriched for biological processes including signaling, cell communications, and system development. We confirmed that EPAS1 protein levels are lower in human COPD lung tissue compared to non-disease controls and that <i>Epas1</i> gene expression is reduced in mice chronically exposed to cigarette smoke. As <i>EPAS1</i> downstream genes were significantly enriched for hypoxia responsive genes in endothelial cells, we tested <i>EPAS1</i> function in human endothelial cells. <i>EPAS1</i> knockdown by siRNA in endothelial cells impacted genes that significantly overlapped with <i>EPAS1</i> downstream genes in lung tissue including hypoxia responsive genes, and genes associated with emphysema severity. Our first integrative analysis of genome-wide DNA methylation and gene expression profiles illustrates that not only does DNA methylation play a ‘causal’ role in the molecular pathophysiology of COPD, but it can be leveraged to directly identify novel key mediators of this pathophysiology.</p></div

Gene expression levels of <i>Epas1</i> and <i>Vegfa</i> were lower in chronic smoking mice than non-smoking age-matched mice at the time when COPD develops in different mouse models.

<p><b>A</b>) Gene expression levels of <i>Epas1</i> and <i>Vegfa</i> in C57BL/6J mice that develop COPD after 6 months chronic exposure to cigarette smoke. <b>B</b>) Gene expression levels of <i>Epas1</i> and <i>Vegfa</i> in A/J mice that develop COPD after 2 months chronic exposure to cigarette smoke. The t-test was used to compare <i>Epas1</i> or <i>Vegfa</i> expression levels in mice with or without chronic smoke exposure.</p

Relationships between DNA methylation and gene expression.

<p><b>A</b>) <i>Cis</i> regulation was defined by the correlation of the methylation level at the promoter region of a gene with expression level of the gene. <b>B</b>) <i>Trans</i> regulation was defined by the correlation of a methylation level at the promoter region of a gene with expression level of other genes. <b>C</b>) Potential relationships between <i>cis</i> and <i>trans</i> regulations. There are two potential causal mechanisms of <i>cis</i> and <i>trans</i> connections: Model I, where the methylation level regulates <i>trans</i> gene expression via the <i>cis</i> gene expression, and Model II, where <i>Trans</i> gene expression regulates the <i>cis</i> gene via controlling its methylation level. It is also possible that <i>cis</i> and <i>trans</i> connections are independently regulated by a factor X.</p

The causality test of <i>trans</i> methyl-mRNA pairs.

<p><b>A</b>) and <b>B</b>) are causality test results for the causal model whereby methylation regulates <i>trans</i> gene expression (methylation → <i>cis</i> gene expression →<i>trans</i> gene expression) in control and COPD data sets, respectively. The Y-axis is the –log10 of the p-values for the Spearman correlation between and and the X-axis is –log10 of the p-values for the Spearman correlation between and . A causal relationship (methylation → <i>cis</i> gene expression →<i>trans</i> gene expression) was defined if the p-value of was <0.0001 and the p-value of was>0.01 (see <a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1004898#s4" target="_blank">Methods</a> for details). A total of 30,177 and 362,095 causal pairs were inferred in control and COPD samples, respectively. <b>C</b>) and <b>D</b>) are the causality test results for the causal model whereby <i>trans</i> gene expression regulates methylation variation (<i>trans</i> gene expression→methylation → <i>cis</i> gene expression) in control and COPD data sets, respectively. The Y-axis is the –log10 of the p-values for the Spearman correlation and the X-axis is –log10 of the p-values for the Spearman correlation . A causal relationship (<i>trans</i> gene expression→methylation→ <i>cis</i> gene expression) was defined if the p-value of was <0.0001 and the p-value of was>0.01 (see <a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1004898#s4" target="_blank">Methods</a> for details). A total of 1,241 and 19,173 causal pairs were inferred in control and COPD, respectively.</p

The numbers of downstream genes regulated by DNA methylation level variation follow a scale-free distribution (a linear relationship in log-log plots).

<p><b>A</b>) The numbers in control; <b>B</b>) The numbers in COPD.</p

<i>EPAS1</i> siRNA signatures in human and mouse endothelial cells overlap with multiple COPD disease severity related signature sets.

<p><i>EPAS1</i> siRNA signatures in human and mouse endothelial cells overlap with multiple COPD disease severity related signature sets.</p

Comparing characteristics of key regulators with 5 COPD severity related traits in LGRC.

<p><b>A</b>) Comparing lung DNA methylation profiles of key regulators with 5 COPD severity related traits by Spearman correlation. At the Fisher's exact test p-value <0.05, the DNA methylation level variations of 3 key regulators, <i>ACSF3</i>, <i>SELO</i>, and <i>EPAS1</i>, were correlated with all 5 COPD severity related traits. <b>B</b>) Comparing downstream genes of key regulators with gene signature sets for 5 COPD severity related traits by the hypergeometric test. At the Fisher’s exact test p-value<0.05, only the key regulator <i>EPAS1</i>'s downstream genes significantly overlapped with gene signature sets for all 5 COPD severity related traits.</p