KLF4 Recruits SWI/SNF to Increase Chromatin Accessibility and Reprogram the Endothelial Enhancer Landscape under Laminar Shear Stress

Moonen, Jan-Renier; Chappell, James; Shi, Minyi; Shinohara, Tsutomu; Li, Dan; Mumbach, Maxwell R.; Zhang, Fan; Nasser, Joseph; Daniel, H; Taylor, Shalina; Wang, Lingli; Metzger, Ross J.; Chang, Howard Y.; Engreitz, J; Snyder, M; Rabinovitch, Marlene

doi:10.1101/2020.07.10.195768

Cited by 11 publications

(16 citation statements)

References 82 publications

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“…The lack of this transcription factor in the vascular endothelium was shown to exacerbate hypoxia-induced PAH and increase the expression of ET-1 17 . Our results support this, and recent ChIP-seq data show that it binds to the EDN1 promoter in the position we studied 16 .…”

Section: Discussionsupporting

confidence: 93%

“…While silencing KLF4 was more effective in HeLa (31 %) than it did in PAECs (17 %), however, VDR only resulted in increased ET-1 production in PAECs (55 %). Altogether, in a static culture, PPARγ probably plays a bigger role in regulating ET-1 than KLF4, as its expression is triggered by shear stress 16 , or VDR do, as maybe their influence could be played indirectly.…”

Section: Discussionmentioning

confidence: 99%

“…Pulmonary Arterial Hypertension (PAH) is a rare and devastating disease that involves the thickening of the pulmonary arterioles, this leads to an increase in pulmonary vascular resistance and eventually to right heart failure 1 . The genetic basis of PAH has been slowly uncovered during the past decades, and there are now more than 16 genes involved with different degrees of evidence 2 . However, some genes are not causal but may modulate the development or the evolution of the disease.…”

Section: Introductionmentioning

confidence: 99%

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Common variation in EDN1 regulatory regions highlights the role of PPARγ as a key regulator of Endothelin in vitro

Lago-Docampo¹,

Solarat

Méndez-Martínez

et al. 2021

Preprint

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Pulmonary Arterial Hypertension (PAH) is a rare disease caused by the obliteration of the pulmonary arterioles, increasing pulmonary vascular resistance and eventually causing right heart failure. Endothelin-1 is a vasoconstrictor peptide whose levels are indicators of disease progression and its pathway is one of the commonest targeted by current treatments.We sequenced the EDN1 untranslated regions of a small subset of PAH patients, predicted the effect in silico, and used a luciferase assay with the different genotypes to analyze its influence on gene expression. Finally, we used siRNAs against the major transcription factors predicted for these regions (PPARγ, KLF4, and VDR) to assess Endothelin-1 expression in cell culture and validate the binding sites.First, we detected a SNP in the 5’UTR (rs397751713) and another in the 3’regulatory region (rs2859338) that increased luciferase activity in vitro depending on their genotype. We determined in silico that KLF4/PPARγ could be binding in the rs397751713 and VDR in rs2859338. By using siRNAs and luciferase, we determined that PPARγ binds differentially in rs397751713. PPARγ and VDR Knock-Down, increased EDN1 mRNA levels and Endothelin-1 production in PAECs, while PPARγ and KLF4 Knock-Down increased the Endothelin-1 production in HeLa.In conclusion, common variants in EDN1 regulatory regions could alter Endothelin-1 levels. We were able to validate that PPARγ binds in rs397751713 and is key to regulate Endothelin-1. Also, KLF4 and VDR regulate Endothelin-1 production in a cell-dependent manner, but for VDR this interaction does not happen by binding directly to the regions we studied.

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Section: Discussionsupporting

confidence: 93%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Common variation in EDN1 regulatory regions highlights the role of PPARγ as a key regulator of Endothelin in vitro

Lago-Docampo¹,

Solarat

Méndez-Martínez

et al. 2021

Preprint

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“…A considerable number of genes (~350) have been described to be differentially expressed upon changes of hemodynamic forces (in particular by stretching forces and shear stress) [60][61][62][63]. Recently, a KLF4/SWI/SNF complex has been identified as a master regulator of chromatin remodeling upon endothelial shear stress [64]. Therefore, adaptive gene expression enables ECs to adjust the vascular system to hemodynamic changes by regulating angiogenesis, vascular tone, migration and differentiation [60].…”

Section: Endothelial Heterogeneity Along the Vascular Treementioning

confidence: 99%

Vascular Endothelial Cells: Heterogeneity and Targeting Approaches

Hennigs

Matuszcak

Trepel³

et al. 2021

Cells

106

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Forming the inner layer of the vascular system, endothelial cells (ECs) facilitate a multitude of crucial physiological processes throughout the body. Vascular ECs enable the vessel wall passage of nutrients and diffusion of oxygen from the blood into adjacent cellular structures. ECs regulate vascular tone and blood coagulation as well as adhesion and transmigration of circulating cells. The multitude of EC functions is reflected by tremendous cellular diversity. Vascular ECs can form extremely tight barriers, thereby restricting the passage of xenobiotics or immune cell invasion, whereas, in other organ systems, the endothelial layer is fenestrated (e.g., glomeruli in the kidney), or discontinuous (e.g., liver sinusoids) and less dense to allow for rapid molecular exchange. ECs not only differ between organs or vascular systems, they also change along the vascular tree and specialized subpopulations of ECs can be found within the capillaries of a single organ. Molecular tools that enable selective vascular targeting are helpful to experimentally dissect the role of distinct EC populations, to improve molecular imaging and pave the way for novel treatment options for vascular diseases. This review provides an overview of endothelial diversity and highlights the most successful methods for selective targeting of distinct EC subpopulations.

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“…This Hi-C matrix (5 Kb) resolution is available here: ftp: //ftp.broadinstitute.org/outgoing/lincRNA/average_hic/average_hic.v2.191020. tar.gz 23,69 . For each cell type we performed the following steps.…”

Section: Predicting Gene Expression Across Individuals Using Imperiomentioning

confidence: 99%

Integrative approaches to improve the informativeness of deep learning models for human complex diseases

Dey

Kim

Gazal

et al. 2020

Preprint

Self Cite

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Deep learning models have achieved great success in predicting genome-wide regulatory effects from DNA sequence, but recent work has reported that SNP annotations derived from these predictions contribute limited unique information for human complex disease. Here, we explore three integrative approaches to improve the disease informativeness of allelic-effect annotations (predicted difference between reference and variant alleles) constructed using two previously trained deep learning models, DeepSEA and Basenji. First, we employ gradient boosting to learn optimal combinations of deep learning annotations, using (off-chromosome) fine-mapped SNPs and matched control SNPs for training. Second, we improve the specificity of these annotations by restricting them to SNPs implicated by (proximal and distal) SNP-to-gene (S2G) linking strategies, e.g. prioritizing SNPs involved in gene regulation. Third, we predict gene expression (and derive allelic-effect annotations) from deep learning annotations at SNPs implicated by S2G linking strategies — generalizing the previously proposed ExPecto approach, which incorporates deep learning annotations based on distance to TSS. We evaluated these approaches using stratified LD score regression, using functional data in blood and focusing on 11 autoimmune diseases and blood-related traits (average N=306K). We determined that the three approaches produced SNP annotations that were uniquely informative for these diseases/traits, despite the fact that linear combinations of the underlying DeepSEA and Basenji blood annotations were not uniquely informative for these diseases/traits. Our results highlight the benefits of integrating SNP annotations produced by deep learning models with other types of data, including data linking SNPs to genes.

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KLF4 Recruits SWI/SNF to Increase Chromatin Accessibility and Reprogram the Endothelial Enhancer Landscape under Laminar Shear Stress

Cited by 11 publications

References 82 publications

Common variation in EDN1 regulatory regions highlights the role of PPARγ as a key regulator of Endothelin in vitro

Common variation in EDN1 regulatory regions highlights the role of PPARγ as a key regulator of Endothelin in vitro

Vascular Endothelial Cells: Heterogeneity and Targeting Approaches

Integrative approaches to improve the informativeness of deep learning models for human complex diseases

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