Dynamic effects of genetic variation on gene expression revealed following hypoxic stress in cardiomyocytes

Ward, Michelle C.; Banovich, Nicholas E.; Sarkar, Abhishek; Stephens, Matthew; Gilad, Yoav

doi:10.1101/2020.03.28.012823

Cited by 13 publications

(17 citation statements)

References 108 publications

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“…To put these results into perspective, we reanalyzed eQTL summary statistics from a set of previous dynamic eQTL studies that come from a variety of different research contexts 12,42,43 ( Figure 6 ; Table S7 ). In each of these studies, hundreds to thousands of genes in each treatment condition have at least one eQTL which meets the standardized effect size threshold of 0.7 above.…”

Section: Resultsmentioning

confidence: 99%

“…To ascertain the power to detect eQTLs and dynamic eQTLs across a range of sample sizes and standardized effect sizes, we followed the example presented in Ward et al ., 2021 43 . In brief, for the power analysis we assumed a simple linear regression for eQTL mapping and a conservative Bonferroni correction for multiple testing (FWER=0.05).…”

Section: Methodsmentioning

confidence: 99%

“…We used summary statistics from eQTL mapping in three prior dynamic eQTL studies 12,42,43 to determine standardized effect sizes for eQTL association tests in each treatment condition. Briefly, p values from association tests were converted into Z-scores using the appropriate quantile function.…”

Section: Methodsmentioning

confidence: 99%

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Characterizing gene expression responses to biomechanical strain in anin vitromodel of osteoarthritis

Hung

Housman

Briscoe

et al. 2021

Preprint

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Osteoarthritis (OA) is a common chronic degenerative joint disease affecting articular cartilage and underlying bone. Both genetic and environmental factors appear to contribute to the development of this disease. Specifically, pathological levels of biomechanical stress on joints play a notable role in disease initiation and progression. Population-level gene expression studies of cartilage cells experiencing biomechanical stress may uncover gene-by-environment interactions relevant to OA and human joint health. To build a foundation for such studies, we applied differentiation protocols to develop an in vitro system of chondrogenic cell lines (iPSC-chondrocytes). We characterized gene regulatory responses of three human iPSC-chondrocyte lines to cyclic tensile strain treatment. We measured the contribution of biological and technical factors to gene expression variation in this system and, even in this small sample, found several genes that exhibit inter-individual expression differences in response to mechanical strain, including genes previously implicated in OA. Expanding this system to include iPSC-chondrocytes from a larger number of individuals will allow us to characterize and better understand gene-by-environment interactions related to OA and joint health.

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Section: Resultsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

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Characterizing gene expression responses to biomechanical strain in anin vitromodel of osteoarthritis

Hung

Housman

Briscoe

et al. 2021

Preprint

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“…In addition to recruiting epigenetic modifiers to act on chromatin, HIF-α is also directly methylated or acetylated itself by a number of these enzymes, and this may have important consequences for both its stability and its transcriptional activity (Figure 3). Specifically, p300 acetylates HIF1a Lys709, which stabilizes the protein in both normoxia and hypoxia [124] and can be reversed by the sirtuin 2 (SIRT2) deacetylase [125]. Similarly, p300/CBP associated factor (PCAF) is also able to acetylate HIF-1α at Lys674, again stabilizing the protein.…”

Section: Post-translational Modification Of Hif By Epigenetic Modifiersmentioning

confidence: 99%

Hypoxic Regulation of Gene Transcription and Chromatin: Cause and Effect

Kindrick

Mole

2020

IJMS

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Cellular responses to low oxygen (hypoxia) are fundamental to normal physiology and to the pathology of many common diseases. Hypoxia-inducible factor (HIF) is central to this by enhancing the transcriptional activity of many hundreds of genes. The cellular response to HIF is cell-type-specific and is largely governed by the pre-existing epigenetic landscape. Prior to activation, HIF-binding sites and the promoters of HIF-target genes are already accessible, in contact with each other through chromatin looping and display markers of activity. However, hypoxia also modulates the epigenetic environment, both in parallel to and as a consequence of HIF activation. This occurs through a combination of oxygen-sensitive changes in enzyme activity, transcriptional activation of epigenetic modifiers, and localized recruitment to chromatin by HIF and activated RNApol2. These hypoxic changes in the chromatin environment may both contribute to and occur as a consequence of transcriptional regulation. Nevertheless, they have the capacity to both modulate and extend the transcriptional response to hypoxia.

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“…Further work using computational cell type deconvolution has discovered approximately three thousand GTEx eQTLs whose effects are likely cell-type-specific [6]. Additional context-specific eQTL effects have been assayed in a variety of settings, including during immune stimulation [7,8], cell stress [9,10], cell differentiation [11], and drug or nutrient exposure [12][13][14].…”

Section: Introductionmentioning

confidence: 99%

Transcription factor regulation of eQTL activity across individuals and tissues

Flynn

Tsu

Kasela

et al. 2021

Preprint

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Tens of thousands of genetic variants associated with gene expression ( cis -eQTLs) have been discovered in the human population. These eQTLs are active in various tissues and contexts, but the molecular mechanisms of eQTL variability are poorly understood, hindering our understanding of genetic regulation across biological contexts. Since many eQTLs are believed to act by altering transcription factor (TF) binding affinity, we hypothesized that analyzing eQTL effect size as a function of TF level may allow discovery of mechanisms of eQTL variability. Using GTEx Consortium eQTL data from 49 tissues, we analyzed the interaction between eQTL effect size and TF level across tissues and across individuals within specific tissues and generated a list of 6,262 TF-eQTL interactions across 1,598 genes that are supported by at least two lines of evidence. These TF-eQTLs were enriched for various TF binding measures, supporting with orthogonal evidence that these eQTLs are regulated by the implicated TFs. We also found that our TF-eQTLs tend to overlap genes with gene-by-environment regulatory effects and to colocalize with GWAS loci, implying that our approach can help to elucidate mechanisms of context-specificity and trait associations. Finally, we highlight an interesting example of IKZF1 TF regulation of an APBB1IP gene eQTL that colocalizes with a GWAS signal for blood cell traits. Together, our findings provide candidate TF mechanisms for a large number of eQTLs and offer a generalizable approach for researchers to discover TF regulators of genetic variant effects in additional QTL datasets.

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Dynamic effects of genetic variation on gene expression revealed following hypoxic stress in cardiomyocytes

Cited by 13 publications

References 108 publications

Characterizing gene expression responses to biomechanical strain in anin vitromodel of osteoarthritis

Characterizing gene expression responses to biomechanical strain in anin vitromodel of osteoarthritis

Hypoxic Regulation of Gene Transcription and Chromatin: Cause and Effect

Transcription factor regulation of eQTL activity across individuals and tissues

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