Cited by 256Genetic Associations and EpidemiologyStress Responses and CortisolTryptophan and brain disorders

Computational identification of gene–social environment interaction at the human IL6 locus

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Cole, Steven W., Arevalo, Jesusa M. G., Takahashi, Rie, Sloan, Erica K., Lutgendorf, Susan K., Sood, Anil K., Sheridan, John F., Seeman, Teresa E.·2010·Proceedings of the National Academy of Sciences

Abstract

To identify genetic factors that interact with social environments to impact human health, we used a bioinformatic strategy that couples expression array–based detection of environmentally responsive transcription factors with in silico discovery of regulatory polymorphisms to predict genetic loci that modulate transcriptional responses to stressful environments. Tests of one predicted interaction locus in the human IL6 promoter (SNP rs1800795) verified that it modulates transcriptional response to β-adrenergic activation of the GATA1 transcription factor in vitro. In vivo validation studies confirmed links between adverse social conditions and increased transcription of GATA1 target genes in primary neural, immune, and cancer cells. Epidemiologic analyses verified the health significance of those molecular interactions by documenting increased 10-year mortality risk associated with late-life depressive symptoms that occurred solely for homozygous carriers of the GATA1-sensitive G allele of rs1800795. Gating of depression-related mortality risk by IL6 genotype pertained only to inflammation-related causes of death and was associated with increased chronic inflammation as indexed by plasma C-reactive protein. Computational modeling of molecular interactions, in vitro biochemical analyses, in vivo animal modeling, and human molecular epidemiologic analyses thus converge in identifying β-adrenergic activation of GATA1 as a molecular pathway by which social adversity can alter human health risk selectively depending on individual genetic status at the IL6 locus.

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Crossref · 10.1073/pnas.0911515107

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Authors
Cole, S. W., Arevalo, J. M. G., Takahashi, R., Sloan, E. K., Lutgendorf, S. K., Sood, A. K., Sheridan, J. F., & Seeman, T. E.
Affiliations
  • Department of Medicine and
  • University of California, Los Angeles (US)
  • University of Iowa (US)
  • The University of Texas MD Anderson Cancer Center (US)
  • The Ohio State University (US)
Venue
Proceedings of the National Academy of Sciences
Published
2010-02-22
Volume
107
Issue
12
Pages
5681-5686
ISSN
  • 0027-8424 (ISSN-L)
  • 1091-6490
OpenAlex
W2133353372
Cited by
256
References
66
Citations by year
2026: 11 · 2025: 13 · 2024: 15 · 2023: 8 · 2022: 13 · 2021: 6 · 2020: 15 · 2019: 11 · 2018: 14 · 2017: 13 · 2016: 14 · 2015: 19 · 2014: 36 · 2013: 20 · 2012: 23
FWCI
12.24
Type
journal article
Language
en
Open access
Locations
  • Proceedings of the National Academy of Sciences (publishedVersion)
  • PubMed Central (publishedVersion)
  • PubMed (publishedVersion)
Funders
  • National Institutes of Health
  • National Institute on Aging (AG028748, AG107265, P60AG010415, AG010415, P30AG028748)
  • National Institute of Mental Health (MH046801, R01MH046801)
  • National Cancer Institute (CA 109298, R01CA110793, CA 110793, R01CA109298, R01CA116778, R01CA104825, CA-116778)
  • National Institute of Allergy and Infectious Diseases (R01AI052737, AI052737)
Topics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic Associations and Epidemiology
  • Life Sciences › Neuroscience › Behavioral Neuroscience › Stress Responses and Cortisol
  • Life Sciences › Neuroscience › Biological Psychiatry › Tryptophan and brain disorders
Keywords
gene-environment interaction, rs1800795, beta-adrenergic signaling, social adversity, depression, mortality risk, chronic inflammation, C-reactive protein, regulatory polymorphisms
MeSH terms
Base Sequence, DNA, Humans, Models, Genetic, Promoter Regions, Genetic, Social Environment, Stress, Psychological, Sympathetic Nervous System, Transcription Factors, Transcriptional Activation, Interleukin-6, Molecular Epidemiology, Computational Biology, Oligonucleotide Array Sequence Analysis, Polymorphism, Single Nucleotide, RNA, Small Interfering, GATA1 Transcription Factor

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