Detailed Regulatory Interaction Map of the Human Heart Facilitates Gene Discovery for Cardiovascular Disease

Bianchi, Valerio; Geeven, Geert; Tucker, Nathan R.; Hilvering, Catharina R.E.; Hall, Amelia Weber; Roselli, Carolina; Hill, Matthew C.; Martin, James F.; Margulies, Kenneth B.; Ellinor, Patrick T.

doi:10.1101/705715

Cited by 8 publications

(4 citation statements)

References 111 publications

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“…To identify locations that are looped to each location, we used K562 Hi-C data that was previously generated and processed 59 . We then used the peakHiC tool 60 to call loops for each landing pad with the following parameters: window size = 80, alphaFDR = 0.5, minimum distance = 10kb, qWr = 1. The regions identified by peakHiC were overlapped with chromHMM annotations using the GenomicRanges R package 58 to identify putatively interacting enhancers with each landing pad.…”

Section: Methodsmentioning

confidence: 99%

Massively parallel characterization of insulator activity across the genome

Hong

Erickson

et al. 2022

Preprint

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Insulators are cis-regulatory sequences (CRSs) that can block enhancers from activating target promoters or act as barriers to block the spread of heterochromatin. Their name derives from their ability to 'insulate' transgenes from genomic position effects, an important function in gene therapy and biotechnology applications that require high levels of sustained transgene expression. In theory, flanking transgenes with insulators protects them from position effects, but in practice, efforts to insulate transgenes meet with mixed success because the contextual requirements for insulator function in the genome are not well understood. A key question is whether insulators are modular elements that can function anywhere in the genome or whether they are adapted to function only in certain genomic locations. To distinguish between these two possibilities we developed MPIRE (Massively Parallel Integrated Regulatory Elements) and used it to measure the effects of three insulators (A2, cHS4, ALOXE3) and their mutants at thousands of locations across the genome. Our results show that each insulator functions in only a small number of genomic locations, and that insulator function depends on the sequence motifs that comprise each insulator. All three insulators can block enhancers in the genome, but specificity arises because each insulator blocks enhancers that are bound by different sets of transcription factors. All three insulators can block enhancers in the genome, but only ALOXE3 can act as a heterochromatin barrier. We conclude that insulator function is highly context dependent and that MPIRE is a robust and systematic method for revealing the context dependencies of insulators and other cis-regulatory elements across the genome.

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Section: Methodsmentioning

confidence: 99%

Massively parallel characterization of insulator activity across the genome

Hong

Erickson

et al. 2022

Preprint

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“…We assessed for potential long-range chromatin interactions using Hi-C analysis in adult heart tissues obtained from the Myocardial Applied Genomics Network (MAGNet, www.med.upenn. edu/magnet) at the University of Pennsylvania 60 .…”

Section: Bioinformatics and In Silico Functional Analysesmentioning

confidence: 99%

Clinical and genetic associations of deep learning-derived cardiac magnetic resonance-based left ventricular mass

et al. 2023

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Left ventricular mass is a risk marker for cardiovascular events, and may indicate an underlying cardiomyopathy. Cardiac magnetic resonance is the gold-standard for left ventricular mass estimation, but is challenging to obtain at scale. Here, we use deep learning to enable genome-wide association study of cardiac magnetic resonance-derived left ventricular mass indexed to body surface area within 43,230 UK Biobank participants. We identify 12 genome-wide associations (1 known at TTN and 11 novel for left ventricular mass), implicating genes previously associated with cardiac contractility and cardiomyopathy. Cardiac magnetic resonance-derived indexed left ventricular mass is associated with incident dilated and hypertrophic cardiomyopathies, and implantable cardioverter-defibrillator implant. An indexed left ventricular mass polygenic risk score ≥90th percentile is also associated with incident implantable cardioverter-defibrillator implant in separate UK Biobank (hazard ratio 1.22, 95% CI 1.05-1.44) and Mass General Brigham (hazard ratio 1.75, 95% CI 1.12-2.74) samples. Here, we perform a genome-wide association study of cardiac magnetic resonance-derived indexed left ventricular mass to identify 11 novel variants and demonstrate that cardiac magnetic resonance-derived and genetically predicted indexed left ventricular mass are associated with incident cardiomyopathy.

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“…The reads were then processed with the Juicer pipeline 56 to generate HiC contact files for each replicate. We then used the peakHiC tool 57 to call loops from each IR with the following parameters: window size = 80, alphaFDR = 0.5, minimum distance = 10kb, qWr = 1. Using these parameters each IR was looped to a median of 3 regions (range 0-7).…”

Section: Methodsmentioning

confidence: 99%

Effect of genomic and cellular environments on gene expression noise

Hong

Ramu

Zhao

et al. 2022

Preprint

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Individual cells from isogenic populations often display large cell-to-cell differences in gene expression. This 'noise' in expression derives from several sources, including the genomic and cellular environment in which a gene resides. Large-scale maps of genomic environments have revealed the effects of epigenetic modifications and transcription factor occupancy on mean expression levels, but leveraging such maps to explain expression noise will require new methods to assay how expression noise changes at locations across the genome. To address this gap, we present Single-cell Analysis of Reporter Gene Expression Noise and Transcriptome (SARGENT), a method that simultaneously measures the noisiness of reporter genes integrated throughout the genome and the global mRNA profiles of individual reporter-gene-containing cells. Using SARGENT, we performed the first comprehensive genome-wide survey of how genomic locations impact gene expression noise. We found that the mean and noise of expression correlate with different histone modifications. We quantified the intrinsic and extrinsic components of reporter gene noise and, using the associated mRNA profiles, assigned the extrinsic component to differences between the CD24+ 'stem-like' sub-state and the more 'differentiated' sub-state. SARGENT also reveals the effects of transgene integrations on endogenous gene expression, which will help guide the search for 'safe-harbor' loci. Taken together, we show that SARGENT is a powerful tool to measure both the mean and noise of gene expression at locations across the genome, and that the data generated by SARGENT reveals important insights into the regulation of gene expression noise genome-wide.

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Detailed Regulatory Interaction Map of the Human Heart Facilitates Gene Discovery for Cardiovascular Disease

Cited by 8 publications

References 111 publications

Massively parallel characterization of insulator activity across the genome

Massively parallel characterization of insulator activity across the genome

Clinical and genetic associations of deep learning-derived cardiac magnetic resonance-based left ventricular mass

Effect of genomic and cellular environments on gene expression noise

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