Literature DB >> 26908612

The effect of non-coding DNA variations on P53 and cMYC competitive inhibition at cis-overlapping motifs.

Katherine Kin1, Xi Chen1, Manuel Gonzalez-Garay2, Walid D Fakhouri3.   

Abstract

Non-coding DNA variations play a critical role in increasing the risk for development of common complex diseases, and account for the majority of SNPs highly associated with cancer. However, it remains a challenge to identify etiologic variants and to predict their pathological effects on target gene expression for clinical purposes. Cis-overlapping motifs (COMs) are elements of enhancer regions that impact gene expression by enabling competitive binding and switching between transcription factors. Mutations within COMs are especially important when the involved transcription factors have opposing effects on gene regulation, like P53 tumor suppressor and cMYC proto-oncogene. In this study, genome-wide analysis of ChIP-seq data from human cancer and mouse embryonic cells identified a significant number of putative regulatory elements with signals for both P53 and cMYC. Each co-occupied element contains, on average, two COMs, and one common SNP every two COMs. Gene ontology of predicted target genes for COMs showed that the majority are involved in DNA damage, apoptosis, cell cycle regulation, and RNA processing. EMSA results showed that both cMYC and P53 bind to cis-overlapping motifs within a ChIP-seq co-occupied region in Chr12. In vitro functional analysis of selected co-occupied elements verified enhancer activity, and also showed that the occurrence of SNPs within three COMs significantly altered enhancer activity. We identified a list of COM-associated functional SNPs that are in close proximity to SNPs associated with common diseases in large population studies. These results suggest a potential molecular mechanism to identify etiologic regulatory mutations associated with common diseases.
© The Author 2016. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

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Year:  2016        PMID: 26908612      PMCID: PMC6281388          DOI: 10.1093/hmg/ddw030

Source DB:  PubMed          Journal:  Hum Mol Genet        ISSN: 0964-6906            Impact factor:   6.150


  47 in total

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  3 in total

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Authors:  Howard B Lieberman; Sunil K Panigrahi; Kevin M Hopkins; Li Wang; Constantinos G Broustas
Journal:  Radiat Res       Date:  2017-01-31       Impact factor: 2.841

2.  Allele-specific epigenome maps reveal sequence-dependent stochastic switching at regulatory loci.

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Journal:  Science       Date:  2018-08-23       Impact factor: 47.728

3.  Identification of Disease Risk DNA Variations is Shaping the Future of Precision Health.

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  3 in total

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