The dynamic effect of genetic variation on the in vivo ER stress transcriptional response in different tissues.
The dynamic effect of genetic variation on the in vivo ER stress transcriptional response in different tissues.
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DOI:
10.1093/g3journal/jkac104
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发表时间:
2022-05-30
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影响因子:
--
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--
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The genetic regulation of gene expression varies greatly across tissue-type and individuals and can be strongly influenced by the environment. Many variants, under healthy control conditions, may be silent or even have the opposite effect under diseased stress conditions. This study uses an in vivo mouse model to investigate how the effect of genetic variation changes with cellular stress across different tissues. Endoplasmic reticulum stress occurs when misfolded proteins accumulate in the endoplasmic reticulum. This triggers the unfolded protein response, a large transcriptional response which attempts to restore homeostasis. This transcriptional response, despite being a conserved, basic cellular process, is highly variable across different genetic backgrounds, making it an ideal system to study the dynamic effects of genetic variation. In this study, we sought to better understand how genetic variation alters expression across tissues, in the presence and absence of endoplasmic reticulum stress. The use of different mouse strains and their F1s allow us to also identify context-specific cis- and trans- regulatory variation underlying variable transcriptional responses. We found hundreds of genes that respond to endoplasmic reticulum stress in a tissue- and/or genotype-dependent manner. The majority of the regulatory effects we identified were acting in cis-, which in turn, contribute to the variable endoplasmic reticulum stress- and tissue-specific transcriptional response. This study demonstrates the need for incorporating environmental stressors across multiple different tissues in future studies to better elucidate the effect of any particular genetic factor in basic biological pathways, like the endoplasmic reticulum stress response.
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影响因子:
64.8
作者:
GTEx Consortium;Laboratory, Data Analysis &Coordinating Center (LDACC)—Analysis Working Group;Statistical Methods groups—Analysis Working Group;Enhancing GTEx (eGTEx) groups;NIH Common Fund;NIH/NCI;NIH/NHGRI;NIH/NIMH;NIH/NIDA;Biospecimen Collection Source Site—NDRI;Biospecimen Collection Source Site—RPCI;Biospecimen Core Resource—VARI;Brain Bank Repository—University of Miami Brain Endowment Bank;Leidos Biomedical—Project Management;ELSI Study;Genome Browser Data Integration &Visualization—EBI;Genome Browser Data Integration &Visualization—UCSC Genomics Institute, University of California Santa Cruz;Lead analysts:;Laboratory, Data Analysis &Coordinating Center (LDACC):;NIH program management:;Biospecimen collection:;Pathology:;eQTL manuscript working group:;Battle A;Brown CD;Engelhardt BE;Montgomery SB
通讯作者:
Montgomery SB
影响因子:
9.9
作者:
Grishkevich, Vladislav;Ben-Elazar, Shay;Hashimshony, Tamar;Schott, Daniel H.;Hunter, Craig P.;Yanai, Itai
通讯作者:
Yanai, Itai
影响因子:
9.8
作者:
Dombroski, Beth A.;Nayak, Renuka R.;Spielman, Richard S.
通讯作者:
Spielman, Richard S.
影响因子:
5.4
作者:
De Strooper, Bart;Iwatsubo, Takeshi;Wolfe, Michael S.
通讯作者:
Wolfe, Michael S.
DOI:
10.1073/pnas.0914072107
发表时间:
2010-08-31
影响因子:
11.1
作者:
Auf, Gregor;Jabouille, Arnaud;Moenner, Michel
通讯作者:
Moenner, Michel