Dataset Information

Lineage-Specific and Non-specific Cytokine-Sensing Genes Respond Differentially to the Master Regulator STAT5.

ABSTRACT: STAT5, a member of the family of signal transducers and activators of transcription, senses cytokines and controls the biology of cell lineages, including mammary, liver, and T cells. Here, we show that STAT5 activates lineage-specific and widely expressed genes through different mechanisms. STAT5 preferentially binds to promoter sequences of cytokine-responsive genes expressed across cell types and to putative enhancers of lineage-specific genes. While chromatin accessibility of STAT5-based enhancers was dependent on cytokine exposure, STAT5-responsive promoters of widely expressed target genes were generally constitutively accessible. While the contribution of STAT5 to enhancers is well established, its role on promoters is poorly understood. To address this, we focused on Socs2, a widely expressed cytokine-sensing gene. Upon deletion of the STAT5 response elements from the Socs2 promoter in mice, cytokine induction was abrogated, while basal activity remained intact. Our data suggest that promoter-bound STAT5 modulates cytokine responses and enhancer-bound STAT5 is mandatory for gene activation.

SUBMITTER: Zeng X

PROVIDER: S-EPMC5217531 | biostudies-literature | 2016 Dec

REPOSITORIES: biostudies-literature

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Lineage-Specific and Non-specific Cytokine-Sensing Genes Respond Differentially to the Master Regulator STAT5.

Zeng Xianke X Willi Michaela M Shin Ha Youn HY Hennighausen Lothar L Wang Chaochen C

Cell reports 20161201 12

STAT5, a member of the family of signal transducers and activators of transcription, senses cytokines and controls the biology of cell lineages, including mammary, liver, and T cells. Here, we show that STAT5 activates lineage-specific and widely expressed genes through different mechanisms. STAT5 preferentially binds to promoter sequences of cytokine-responsive genes expressed across cell types and to putative enhancers of lineage-specific genes. While chromatin accessibility of STAT5-based enh ...[more]

PMID: 28009300

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Project description:Analytic approaches confined to fold-change comparisons of gene expression patterns between states of health and disease are unable to distinguish between primary causal disease drivers and secondary noncausal events. Genome-wide reverse engineering approaches can facilitate the identification of candidate genes that may distinguish between causal and associative interactions and may account for the emergence or maintenance of pathologic phenotypes. In this work, we used the algorithm for the reconstruction of accurate cellular networks (ARACNE) to analyze a large gene expression profile data set (313 gingival tissue samples from a cross-sectional study of 120 periodontitis patients) obtained from clinically healthy (n = 70) or periodontitis-affected (n = 243) gingival sites. The generated transcriptional regulatory network of the gingival interactome was subsequently interrogated with the master regulator inference algorithm (MARINA) and gene expression signature data from healthy and periodontitis-affected gingiva. Our analyses identified 41 consensus master regulator genes (MRs), the regulons of which comprised between 25 and 833 genes. Regulons of 7 MRs (HCLS1, ZNF823, XBP1, ZNF750, RORA, TFAP2C, and ZNF57) included >500 genes each. Gene set enrichment analysis indicated differential expression of these regulons in gingival health versus disease with a type 1 error between 2% and 0.5% and with >80% of the regulon genes in the leading edge. Ingenuity pathway analysis showed significant enrichment of 36 regulons for several pathways, while 6 regulons (those of MRs HCLS1, IKZF3, ETS1, NHLH2, POU2F2, and VAV1) were enriched for >10 pathways. Pathways related to immune system signaling and development were the ones most frequently enriched across all regulons. The unbiased analysis of genome-wide regulatory networks can enhance our understanding of the pathobiology of human periodontitis and, after appropriate validation, ultimately identify target molecules of diagnostic, prognostic, or therapeutic value.

Dataset Information

Lineage-Specific and Non-specific Cytokine-Sensing Genes Respond Differentially to the Master Regulator STAT5.

Publications

Lineage-Specific and Non-specific Cytokine-Sensing Genes Respond Differentially to the Master Regulator STAT5.

Similar Datasets

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