scholarly journals Kidney cell type specific changes in the chromatin and transcriptome landscapes following epithelial Hdac1and Hdac2 knockdown

2021 ◽  
Author(s):  
Kelly A. Hyndman ◽  
David K Crossman
Keyword(s):  
Chromatin Structure ◽  
Kidney Cell ◽  
Histone Deacetylases ◽  
Cell Types ◽  
Chromatin Accessibility ◽  
Open Chromatin ◽  
Electrolyte Balance ◽  
Transcript Expression ◽  
Single Nucleus ◽  
Cell Type Specific

Recent studies have identified at least 20 different kidney cell types based upon chromatin structure and gene expression. Histone deacetylases (HDACs) are epigenetic transcriptional repressors via deacetylation of histone lysines resulting in inaccessible chromatin. We reported that kidney epithelial HDAC1 and HDAC2 activity is critical for maintaining a healthy kidney and preventing fluid-electrolyte abnormalities. However, to what extent does Hdac1/Hdac2 knockdown affect chromatin structure and subsequent transcript expression in the kidney? To answer this question, we used single nucleus Assay for Transposase-Accessible Chromatin-sequencing (snATAC-seq) and snRNA-seq to profile kidney nuclei from male and female, control and littermate kidney epithelial Hdac1/Hdac2 knockdown mice. Hdac1/Hdac2 knockdown resulted in significant changes in the chromatin structure predominantly within the promoter region of gene loci involved in fluid-electrolyte balance such as the aquaporins, with both increased and decreased accessibility captured. Moreover, Hdac1/Hdac2 knockdown resulted different gene loci being accessible with a corresponding increased transcript number in the kidney, but among all mice only 24-30% of chromatin accessibility agreed with transcript expression (e.g. open chromatin, increased transcript). To conclude, although chromatin structure does affect transcription, ~70% of the differentially expressed genes cannot be explained by changes in chromatin accessibility and HDAC1/HDAC2 had a minimal effect on these global patterns. Yet, the genes that are targets of HDAC1 and HDAC2 are critically important for maintaining kidney function.

scholarly journals Impact of regulatory variation across human iPSCs and differentiated cells

2016 ◽  
Author(s):  
Nicholas E. Banovich ◽  
Yang I. Li ◽  
Anil Raj ◽  
Michelle C. Ward ◽  
Peyton Greenside ◽  
...  
Keyword(s):  
Complex Disease ◽  
Cell Types ◽  
Chromatin Accessibility ◽  
Open Chromatin ◽  
Cell Type ◽  
Regulatory Variation ◽  
Differentiated Cells ◽  
Human Ipscs ◽  
Cell Type Specific ◽  
The Impact

AbstractInduced pluripotent stem cells (iPSCs) are an essential tool for studying cellular differentiation and cell types that are otherwise difficult to access. We investigated the use of iPSCs and iPSC-derived cells to study the impact of genetic variation across different cell types and as models for studies of complex disease. We established a panel of iPSCs from 58 well-studied Yoruba lymphoblastoid cell lines (LCLs); 14 of these lines were further differentiated into cardiomyocytes. We characterized regulatory variation across individuals and cell types by measuring gene expression, chromatin accessibility and DNA methylation. Regulatory variation between individuals is lower in iPSCs than in the differentiated cell types, consistent with the intuition that developmental processes are generally canalized. While most cell type-specific regulatory quantitative trait loci (QTLs) lie in chromatin that is open only in the affected cell types, we found that 20% of cell type-specific QTLs are in shared open chromatin. Finally, we developed a deep neural network to predict open chromatin regions from DNA sequence alone and were able to use the sequences of segregating haplotypes to predict the effects of common SNPs on cell type-specific chromatin accessibility.

scholarly journals Mapping genetic effects on cell type-specific chromatin accessibility and annotating complex trait variants using single nucleus ATAC-seq

2020 ◽  
Author(s):  
Paola Benaglio ◽  
Jacklyn Newsome ◽  
Jee Yun Han ◽  
Joshua Chiou ◽  
Anthony Aylward ◽  
...  
Keyword(s):  
Complex Traits ◽  
Immune Cell ◽  
Cell Types ◽  
Chromatin Accessibility ◽  
Genetic Effects ◽  
Specific Cell ◽  
Gene Promoters ◽  
Cell Type ◽  
Single Nucleus ◽  
Cell Type Specific

AbstractGene regulation is highly cell type-specific and understanding the function of non-coding genetic variants associated with complex traits requires molecular phenotyping at cell type resolution. In this study we performed single nucleus ATAC-seq (snATAC-seq) and genotyping in peripheral blood mononuclear cells from 10 individuals. Clustering chromatin accessibility profiles of 66,843 total nuclei identified 14 immune cell types and sub-types. We mapped chromatin accessibility QTLs (caQTLs) in each immune cell type and sub-type which identified 6,248 total caQTLs, including those obscured from assays of bulk tissue such as with divergent effects on different cell types. For 3,379 caQTLs we further annotated putative target genes of variant activity using single cell co-accessibility, and caQTL variants were significantly correlated with the accessibility level of linked gene promoters. We fine-mapped loci associated with 16 complex immune traits and identified immune cell caQTLs at 517 candidate causal variants, including those with cell type-specific effects. At the 6q15 locus associated with type 1 diabetes, in line with previous reports, variant rs72928038 was a naïve CD4+ T cell caQTL linked to BACH2 and we validated the allelic effects of this variant on regulatory activity in Jurkat T cells. These results highlight the utility of snATAC-seq for mapping genetic effects on accessible chromatin in specific cell types and provide a resource for annotating complex immune trait loci.

scholarly journals Human and rat skeletal muscle single-nuclei multi-omic integrative analyses nominate causal cell types, regulatory elements, and SNPs for complex traits

2021 ◽  
Author(s):  
Peter Orchard ◽  
Nandini Manickam ◽  
Christa Ventresca ◽  
Swarooparani Vadlamudi ◽  
Arushi Varshney ◽  
...  
Keyword(s):  
Skeletal Muscle ◽  
Muscle Cell ◽  
Muscle Fiber ◽  
Cell Types ◽  
Chromatin Accessibility ◽  
Skeletal Muscle Cell ◽  
Rna Seq ◽  
Rat Skeletal Muscle ◽  
Integrative Analyses ◽  
Single Nucleus

Skeletal muscle accounts for the largest proportion of human body mass, on average, and is a key tissue in complex diseases and mobility. It is composed of several different cell and muscle fiber types. Here, we optimize single-nucleus ATAC-seq (snATAC-seq) to map skeletal muscle cell–specific chromatin accessibility landscapes in frozen human and rat samples, and single-nucleus RNA-seq (snRNA-seq) to map cell-specific transcriptomes in human. We additionally perform multi-omics profiling (gene expression and chromatin accessibility) on human and rat muscle samples. We capture type I and type II muscle fiber signatures, which are generally missed by existing single-cell RNA-seq methods. We perform cross-modality and cross-species integrative analyses on 33,862 nuclei and identify seven cell types ranging in abundance from 59.6% to 1.0% of all nuclei. We introduce a regression-based approach to infer cell types by comparing transcription start site–distal ATAC-seq peaks to reference enhancer maps and show consistency with RNA-based marker gene cell type assignments. We find heterogeneity in enrichment of genetic variants linked to complex phenotypes from the UK Biobank and diabetes genome-wide association studies in cell-specific ATAC-seq peaks, with the most striking enrichment patterns in muscle mesenchymal stem cells (∼3.5% of nuclei). Finally, we overlay these chromatin accessibility maps on GWAS data to nominate causal cell types, SNPs, transcription factor motifs, and target genes for type 2 diabetes signals. These chromatin accessibility profiles for human and rat skeletal muscle cell types are a useful resource for nominating causal GWAS SNPs and cell types.

scholarly journals Analysis of putative cis-regulatory elements regulating blood pressure variation

2020 ◽  
Vol 29 (11) ◽  
pp. 1922-1932
Author(s):  
Priyanka Nandakumar ◽  
Dongwon Lee ◽  
Thomas J Hoffmann ◽  
Georg B Ehret ◽  
Dan Arking ◽  
...  
Keyword(s):  
Blood Pressure ◽  
Association Studies ◽  
Specific Effect ◽  
Cell Types ◽  
Regulatory Elements ◽  
Open Chromatin ◽  
Genome Wide Association Studies ◽  
Cell Type ◽  
Functional Scores ◽  
Cell Type Specific

Abstract Hundreds of loci have been associated with blood pressure (BP) traits from many genome-wide association studies. We identified an enrichment of these loci in aorta and tibial artery expression quantitative trait loci in our previous work in ~100 000 Genetic Epidemiology Research on Aging study participants. In the present study, we sought to fine-map known loci and identify novel genes by determining putative regulatory regions for these and other tissues relevant to BP. We constructed maps of putative cis-regulatory elements (CREs) using publicly available open chromatin data for the heart, aorta and tibial arteries, and multiple kidney cell types. Variants within these regions may be evaluated quantitatively for their tissue- or cell-type-specific regulatory impact using deltaSVM functional scores, as described in our previous work. We aggregate variants within these putative CREs within 50 Kb of the start or end of ‘expressed’ genes in these tissues or cell types using public expression data and use deltaSVM scores as weights in the group-wise sequence kernel association test to identify candidates. We test for association with both BP traits and expression within these tissues or cell types of interest and identify the candidates MTHFR, C10orf32, CSK, NOV, ULK4, SDCCAG8, SCAMP5, RPP25, HDGFRP3, VPS37B and PPCDC. Additionally, we examined two known QT interval genes, SCN5A and NOS1AP, in the Atherosclerosis Risk in Communities Study, as a positive control, and observed the expected heart-specific effect. Thus, our method identifies variants and genes for further functional testing using tissue- or cell-type-specific putative regulatory information.

scholarly journals Yeast alpha 2 repressor positions nucleosomes in TRP1/ARS1 chromatin.

1990 ◽  
Vol 10 (5) ◽  
pp. 2247-2260 ◽  
Author(s):  
S Y Roth ◽  
A Dean ◽  
R T Simpson
Keyword(s):  
Binding Site ◽  
Mating Type ◽  
Chromatin Structure ◽  
Nucleosome Position ◽  
Cell Types ◽  
Alpha Cells ◽  
Cell Type ◽  
Yeast Plasmid ◽  
A Cells ◽  
Cell Type Specific

The yeast alpha 2 repressor suppresses expression of a-mating-type-specific genes in haploid alpha and diploid a/alpha cell types. We inserted the alpha 2-binding site into the multicopy TRP1/ARS1 yeast plasmid and examined the effects of alpha 2 on the chromatin structure of the derivative plasmids in alpha cells, and a/alpha cells. Whereas no effect on nucleosome position was observed in a cells, nucleosomes were precisely and stably positioned over sequences flanking the alpha 2 operator in alpha and a/alpha cells. In addition, when the alpha 2 operator was located upstream of the TRP1 gene, an extended array of positioned nucleosomes was formed in alpha cells and a/alpha cells, with formation of a nucleosome not present in a cells, and TRP1 mRNA production was substantially reduced. These data indicate that alpha 2 causes a positioning of nucleosomes over sequences proximal to its operator in TRP1/ARS1 chromatin and suggest that changes in chromatin structure may be related to alpha 2 repression of cell-type-specific genes.

scholarly journals Analysis of putative cis-regulatory elements regulating blood pressure variation

2019 ◽  
Author(s):  
Priyanka Nandakumar ◽  
Dongwon Lee ◽  
Thomas J. Hoffmann ◽  
Georg B. Ehret ◽  
Dan Arking ◽  
...  
Keyword(s):  
Gene Expression ◽  
Blood Pressure ◽  
Cell Types ◽  
Regulatory Elements ◽  
Open Chromatin ◽  
Genome Wide Association Studies ◽  
Cell Type ◽  
Functional Scores ◽  
Cell Type Specific ◽  
Different Tissues

AbstractHundreds of loci have been associated with blood pressure traits from many genome-wide association studies. We identified an enrichment of these loci in aorta and tibial artery expression quantitative trait loci in our previous work in ∼100,000 Genetic Epidemiology Research on Aging (GERA) study participants. In the present study, we subsequently focused on determining putative regulatory regions for these and other tissues of relevance to blood pressure, to both fine-map these loci by pinpointing genes and variants of functional interest within them, and to identify any novel genes.We constructed maps of putative cis-regulatory elements using publicly available open chromatin data for the heart, aorta and tibial arteries, and multiple kidney cell types. Sequence variants within these regions may be evaluated quantitatively for their tissue- or cell-type-specific regulatory impact using deltaSVM functional scores, as described in our previous work. In order to identify genes of interest, we aggregate these variants in these putative cis-regulatory elements within 50Kb of the start or end of genes considered as “expressed” in these tissues or cell types using publicly available gene expression data, and use the deltaSVM scores as weights in the well-known group-wise sequence kernel association test (SKAT). We test for association with both blood pressure traits as well as expression within these tissues or cell types of interest, and identify several genes, including MTHFR, C10orf32, CSK, NOV, ULK4, SDCCAG8, SCAMP5, RPP25, HDGFRP3, VPS37B, and PPCDC. Although our study centers on blood pressure traits, we additionally examined two known genes, SCN5A and NOS1AP involved in the cardiac trait QT interval, in the Atherosclerosis Risk in Communities Study (ARIC), as a positive control, and observed an expected heart-specific effect. Thus, our method may be used to identify variants and genes for further functional testing using tissue- or cell-type-specific putative regulatory information.Author SummarySequence change in genes (“variants”) are linked to the presence and severity of different traits or diseases. However, as genes may be expressed in different tissues and at different times and degrees, using this information is expected to more accurately identify genes of interest. Variants within the genes are essential, but also in the sequences (“regulatory elements”) that control the genes’ expression in different tissues or cell types. In this study, we aim to use this information about expression and variants potentially involved in gene expression regulation to better pinpoint genes and variants in regulatory elements of interest for blood pressure regulation. We do so by taking advantage of such data that are publicly available, and use methods to combine information about variants in aggregate within a gene’s putative regulatory elements in tissues thought to be relevant for blood pressure, and identify several genes, meant to enable experimental follow-up.

scholarly journals Accurate imputation of histone modifications using transcription

2020 ◽  
Author(s):  
Zhong Wang ◽  
Alexandra G. Chivu ◽  
Lauren A. Choate ◽  
Edward J. Rice ◽  
Donald C. Miller ◽  
...  
Keyword(s):  
Transcription Initiation ◽  
Cell Types ◽  
Chromatin Accessibility ◽  
Cell Type ◽  
Active Chromatin ◽  
Histone Marks ◽  
Repressive Mark ◽  
Cell Type Specific ◽  
The Relationship ◽  
Machine Learning Tool

AbstractWe trained a sensitive machine learning tool to infer the distribution of histone marks using maps of nascent transcription. Transcription captured the variation in active histone marks and complex chromatin states, like bivalent promoters, down to single-nucleosome resolution and at an accuracy that rivaled the correspondence between independent ChIP-seq experiments. The relationship between active histone marks and transcription was conserved in all cell types examined, allowing individual labs to annotate active functional elements in mammals with similar richness as major consortia. Using imputation as an interpretative tool uncovered cell-type specific differences in how the PRC2-dependent repressive mark, H3K27me3, corresponds to transcription, and revealed that transcription initiation requires both chromatin accessibility and an active chromatin environment demonstrating that initiation is less promiscuous than previously thought.

scholarly journals CellWalker integrates single-cell and bulk data to resolve regulatory elements across cell types in complex tissues

2019 ◽  
Author(s):  
Pawel F. Przytycki ◽  
Katherine S. Pollard
Keyword(s):  
Single Cell ◽  
Cell Types ◽  
Regulatory Elements ◽  
Cell Labeling ◽  
Open Chromatin ◽  
Specific Cell ◽  
Rna Seq ◽  
Data Types ◽  
Bulk Data ◽  
Cell Type Specific

Single-cell and bulk genomics assays have complementary strengths and weaknesses, and alone neither strategy can fully capture regulatory elements across the diversity of cells in complex tissues. We present CellWalker, a method that integrates single-cell open chromatin (scATAC-seq) data with gene expression (RNA-seq) and other data types using a network model that simultaneously improves cell labeling in noisy scATAC-seq and annotates cell-type specific regulatory elements in bulk data. We demonstrate CellWalker’s robustness to sparse annotations and noise using simulations and combined RNA-seq and ATAC-seq in individual cells. We then apply CellWalker to the developing brain. We identify cells transitioning between transcriptional states, resolve enhancers to specific cell types, and observe that autism and other neurological traits can be mapped to specific cell types through their enhancers.

scholarly journals DNA Methylation Atlas of the Mouse Brain at Single-Cell Resolution

2020 ◽  
Author(s):  
Hanqing Liu ◽  
Jingtian Zhou ◽  
Wei Tian ◽  
Chongyuan Luo ◽  
Anna Bartlett ◽  
...  
Keyword(s):  
Dna Methylation ◽  
Mouse Brain ◽  
Spatial Organization ◽  
Brain Area ◽  
Cell Types ◽  
Regulatory Elements ◽  
Mammalian Brain ◽  
Open Chromatin ◽  
Cell Type ◽  
Single Nucleus

SummaryMammalian brain cells are remarkably diverse in gene expression, anatomy, and function, yet the regulatory DNA landscape underlying this extensive heterogeneity is poorly understood. We carried out a comprehensive assessment of the epigenomes of mouse brain cell types by applying single nucleus DNA methylation sequencing to profile 110,294 nuclei from 45 regions of the mouse cortex, hippocampus, striatum, pallidum, and olfactory areas. We identified 161 cell clusters with distinct spatial locations and projection targets. We constructed taxonomies of these epigenetic types, annotated with signature genes, regulatory elements, and transcription factors. These features indicate the potential regulatory landscape supporting the assignment of putative cell types, and reveal repetitive usage of regulators in excitatory and inhibitory cells for determining subtypes. The DNA methylation landscape of excitatory neurons in the cortex and hippocampus varied continuously along spatial gradients. Using this deep dataset, an artificial neural network model was constructed that precisely predicts single neuron cell-type identity and brain area spatial location. Integration of high-resolution DNA methylomes with single-nucleus chromatin accessibility data allowed prediction of high-confidence enhancer-gene interactions for all identified cell types, which were subsequently validated by cell-type-specific chromatin conformation capture experiments. By combining multi-omic datasets (DNA methylation, chromatin contacts, and open chromatin) from single nuclei and annotating the regulatory genome of hundreds of cell types in the mouse brain, our DNA methylation atlas establishes the epigenetic basis for neuronal diversity and spatial organization throughout the mouse brain.

scholarly journals Systematic clustering algorithm for chromatin accessibility data and its application to hematopoietic cells

2020 ◽  
Vol 16 (11) ◽  
pp. e1008422
Author(s):  
Azusa Tanaka ◽  
Yasuhiro Ishitsuka ◽  
Hiroki Ohta ◽  
Akihiro Fujimoto ◽  
Jun-ichirou Yasunaga ◽  
...  
Keyword(s):  
Data Reduction ◽  
Clustering Algorithm ◽  
High Throughput Sequencing ◽  
Hematopoietic Cells ◽  
Cell Types ◽  
Chromatin Accessibility ◽  
Open Chromatin ◽  
Genome Wide ◽  
Data Reduction Method ◽  
Effective Analysis

The huge amount of data acquired by high-throughput sequencing requires data reduction for effective analysis. Here we give a clustering algorithm for genome-wide open chromatin data using a new data reduction method. This method regards the genome as a string of 1s and 0s based on a set of peaks and calculates the Hamming distances between the strings. This algorithm with the systematically optimized set of peaks enables us to quantitatively evaluate differences between samples of hematopoietic cells and classify cell types, potentially leading to a better understanding of leukemia pathogenesis.

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