C/EBP␤ Reprograms White 3T3-L1 Preadipocytes to a Brown Adipocyte Pattern of Gene Expression *

cAMP-dependent protein kinase induction of PPAR␥ coactivator-1␣ (PGC-1␣) and uncoupling protein 1 (UCP1) expression is an essential step in the commitment of preadipocytes to the brown adipose tissue (BAT) lineage. We studied the molecular mechanisms responsible for differential expression of PGC-1␣ in HIB1B (BAT) and 3T3-L1 white adipose tissue (WAT) precursor cell lines. In HIB1B cells PGC-1␣ and UCP1 expression is cAMP-inducible, but in 3T3-L1 cells, expression is reduced and is cAMP-insensitive. A proximal 264-bp PGC-1␣ reporter construct was cAMP-inducible only in HIB1B cells and was suppressed by site-directed mutagenesis of the proximal cAMP response element (CRE). In electrophoretic mobility shift assays, the transcription factors CREB and C/EBP␤, but not C/EBP␣ and C/EBP␦, bound to the CRE on the PGC-1␣ promoter region in HIB1B and 3T3-L1 cells. Chromatin immunoprecipitation studies demonstrated that C/EBP␤ and CREB bound to the CRE region in HIB1B and 3T3-L1 cell lysates. C/EBP␤ expression was induced by cAMP only in HIB1B cells, and overexpression of C/EBP␤ rescued cAMP-inducible PGC-1␣ and UCP1 expression in 3T3-L1 cells. These data demonstrate that differentiation of preadipocytes toward the BAT rather than the WAT phenotype is controlled in part by the action of C/EBP␤ on the CRE in PGC-1␣ proximal promoter

Revealing Pathway Dynamics in Heart Diseases by Analyzing Multiple Differential Networks

<div><p>Development of heart diseases is driven by dynamic changes in both the activity and connectivity of gene pathways. Understanding these dynamic events is critical for understanding pathogenic mechanisms and development of effective treatment. Currently, there is a lack of computational methods that enable analysis of multiple gene networks, each of which exhibits differential activity compared to the network of the baseline/healthy condition. We describe the <i>i</i>MDM algorithm to identify both unique and shared gene modules across multiple differential co-expression networks, termed M-DMs (<u>m</u>ultiple <u>d</u>ifferential <u>m</u>odules). We applied <i>i</i>MDM to a time-course RNA-Seq dataset generated using a murine heart failure model generated on two genotypes. We showed that <i>i</i>MDM achieves higher accuracy in inferring gene modules compared to using single or multiple co-expression networks. We found that condition-specific M-DMs exhibit differential activities, mediate different biological processes, and are enriched for genes with known cardiovascular phenotypes. By analyzing M-DMs that are present in multiple conditions, we revealed dynamic changes in pathway activity and connectivity across heart failure conditions. We further showed that module dynamics were correlated with the dynamics of disease phenotypes during the development of heart failure. Thus, pathway dynamics is a powerful measure for understanding pathogenesis. <i>i</i>MDM provides a principled way to dissect the dynamics of gene pathways and its relationship to the dynamics of disease phenotype. With the exponential growth of omics data, our method can aid in generating systems-level insights into disease progression.</p></div

M-DMs identified from multiple differential co-expression networks.

<p>A, An example 2-DM identified in WTTAC and KOTAC DCNs. It was enriched for genes involved in oxidation reduction. Node color is proportional to the average p-value of differential gene expression between the two disease conditions and baseline (WTSH) condition. Octagon with red border, genes whose mutations lead to cardiovascular phenotypes. Left panel, Rewiring of the 2-DM. Only edges that exhibit significant changes in edge weights between the two DCNs are shown. Edge thickness is proportional to the absolute value of difference. Difference was calculated as “KOTAC—WTTAC”. Red, increase; green, decrease. Unconnected nodes indicate there was no edge connected to the nodes that exhibit significance change in weight between the two conditions. Middle panel, expression profiles of module genes in four conditions. Each condition has four time points. Expression levels of each gene across all samples were normalized by Z-score transformation. P-values for gene expression level difference were based on t-test. Right panel, histogram for edge weights of the 2-DM in the respective networks. B, Correlation between module activity and phenotypic measures. Row, phenotypic measures; column, M-DMs. All, 3-DMs (WTTAC+KOTAC+KOSH). Module activity is the average normalized gene expression level of all member genes in a module. FS%, left ventricular fractional shortening; HW/TL, heart weight normalized by tibia length; LVID(d), left ventricular internal diameter in diastole. C, Histograms of the module activity and phenotype correlations for dynamic and static 2-DMs. P-values were based on one-sided t-test.</p

Example 1-DMs uniquely identified in WTTAC and KOTAC DCNs.

<p>A, 1-DM unique to the WTTAC DCN and was enriched for genes involved in regulation of cell adhesion. B, 1-DM unique to the KOTAC DCN and was enriched for genes involved in fatty acid metabolism. Top panel, visualization of the module using Cytoscape [<a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1004332#pcbi.1004332.ref021" target="_blank">21</a>]. Node color is proportional to the p-value of differential gene expression between disease and baseline (WTSH) conditions. Octagon with red border, genes whose mutations lead to cardiovascular phenotypes. Middle panel, expression profiles of module genes in four conditions. Each condition has four time points. Expression levels of each gene across all samples were normalized by Z-score transformation. P-values for the difference in gene expression level were based on t-test. Bottom panel, histogram for edge weights of discovered modules in the WTTAC and KOTAC DCNs.</p