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PLoS One. 2016 Jul 28;11(7):e0160228. doi: 10.1371/journal.pone.0160228. eCollection 2016.

Prediction and Validation of Transcription Factors Modulating the Expression of Sestrin3 Gene Using an Integrated Computational and Experimental Approach.

Author information

1
Department of Biohealth Informatics, School of Informatics and Computing, Indiana University Purdue University, 719 Indiana Ave Ste 319, Walker Plaza Building, Indianapolis, Indiana, 46202, United States of America.
2
Department of Biochemistry and Molecular Biology, 635 Barnhill Drive, Indianapolis, Indiana, 46202, United States of America.
3
Department of Clinical Laboratory, Shandong Provincial Qianfoshan Hospital, 16766 Jingshi Road, Jinan, Shandong Province, 250014, China.
4
Division of Endocrinology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, Zhejiang Province, 325015, China.
5
Division of Gastroenterology and Hepatology, Department of Medicine, Indiana University, Indianapolis, Indiana, 46202, United States of America.
6
Roudebush Veterans Affairs Administration Hospital, Indianapolis, Indiana, 46202, United States of America.
7
Center for Computational Biology and Bioinformatics, Indiana University School of Medicine, 5021 Health Information and Translational Sciences (HITS), 410 West 10th Street, Indianapolis, Indiana, 46202, United States of America.
8
Department of Medical and Molecular Genetics, Indiana University School of Medicine, Medical Research and Library Building, 975 West Walnut Street, Indianapolis, Indiana, 46202, United States of America.

Abstract

SESN3 has been implicated in multiple biological processes including protection against oxidative stress, regulation of glucose and lipid metabolism. However, little is known about the factors and mechanisms controlling its gene expression at the transcriptional level. We performed in silico phylogenetic footprinting analysis of 5 kb upstream regions of a diverse set of human SESN3 orthologs for the identification of high confidence conserved binding motifs (BMo). We further analyzed the predicted BMo by a motif comparison tool to identify the TFs likely to bind these discovered motifs. Predicted TFs were then integrated with experimentally known protein-protein interactions and experimentally validated to delineate the important transcriptional regulators of SESN3. Our study revealed high confidence set of BMos (integrated with DNase I hypersensitivity sites) in the upstream regulatory regions of SESN3 that could be bound by transcription factors from multiple families including FOXOs, SMADs, SOXs, TCFs and HNF4A. TF-TF network analysis established hubs of interaction that include SMAD3, TCF3, SMAD2, HDAC2, SOX2, TAL1 and TCF12 as well as the likely protein complexes formed between them. We show using ChIP-PCR as well as over-expression and knock out studies that FOXO3 and SOX2 transcriptionally regulate the expression of SESN3 gene. Our findings provide an important roadmap to further our understanding on the regulation of SESN3.

PMID:
27466818
PMCID:
PMC4965051
DOI:
10.1371/journal.pone.0160228
[Indexed for MEDLINE]
Free PMC Article

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