Abstract P484: Defining Disease Specific Cardiac Regulatory Networks

2021 ◽  
Vol 129 (Suppl_1) ◽  
Author(s):  
Sonja Lazarevic ◽  
Zhezhen Wang ◽  
Kaitlyn Shen ◽  
Margaret Gadek ◽  
Carlos Perez-Cervantes ◽  
...  
Keyword(s):  
Heart Failure ◽  
Regulatory Networks ◽  
Cardiac Fibroblasts ◽  
Regulatory Elements ◽  
Common Disease ◽  
Wild Type ◽  
Cell Type ◽  
Cell Type Specific ◽  
Gene Regulatory ◽  
Disease Specific

The transcriptional basis of homeostasis and disease is implied by the non-coding nature of genetic variation identified by GWAS. Functional genomic approaches to unveil the gene regulatory networks (GRNs) relevant to disease are therefore a high priority. Most models for transcriptional dysregulation presume that perturbation of a wild-type gene regulatory network causes disease risk. For example, the T-box transcription factor (TF) TBX5, is essential for atrial rhythm homeostasis and directly drives a physiologically relevant GRN composed of cardiac channel genes. Alternatively, the expression of many genes pertinent to cardiac pathology are upregulated after the removal of Tbx5, implicating a disease-specific GRN absent from the wild-type atrium. We applied TF-dependent noncoding RNA (ncRNA) profiling, using differential deep ncRNA sequencing from atria of wild-type and Tbx5 mutant mice, to identify TBX5-dependent enhancers and ncRNAs that were only activated following TBX5 removal. We hypothesized that these regulatory elements would reveal disease-response enhancers, essential for coping with atrial dysfunction. To identify the cell-specific regulatory elements, we generated cell-type specific Assay from Transposase-Accessible Chromatin (ATAC) datasets for left atrial tissue, cardiac fibroblasts, and cardiomyocytes. Overlap with the Tbx5 -repressed ncRNAs defined candidate cell-type specific regulatory elements. Candidate regulatory elements were identified upstream of Sox9, a known modulator of cardiac fibrosis, along with other cardiac stress-response pathways, including mediators of TGF-β signaling. Activation of the enhancer at Sox9 was confirmed in isolated cardiac fibroblasts treated with TGF-β. We hypothesized that the disease-acquired GRN in TBX5 mutant atria may be generalizable to other cardiac insults. We therefore examined the transcriptional and genomic changes in the left atria of the heart failure Transverse Aortic Constriction (TAC) mouse model. This analysis revealed remarkable correlation between differentially expressed genes and ncRNAs between TAC and TBX5 mutant disease models. The conservation of the coding and non-coding transcriptional response between arrhythmia and heart failure models supports a paradigm of a common disease-specific GRN that mediates the physiologic consequences of distinct cardiac diseases.

scholarly journals Integrative multi-omics analyses identify cell-type disease genes and regulatory networks across schizophrenia and Alzheimer’s disease

2020 ◽  
Author(s):  
Mufang Ying ◽  
Peter Rehani ◽  
Panagiotis Roussos ◽  
Daifeng Wang
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Gene Regulatory Networks ◽  
Regulatory Networks ◽  
Regulatory Elements ◽  
Disease Genes ◽  
Omics Data ◽  
Cell Type ◽  
Cellular Resolution ◽  
Gene Regulatory

AbstractStrong phenotype-genotype associations have been reported across brain diseases. However, understanding underlying gene regulatory mechanisms remains challenging, especially at the cellular level. To address this, we integrated the multi-omics data at the cellular resolution of the human brain: cell-type chromatin interactions, epigenomics and single cell transcriptomics, and predicted cell-type gene regulatory networks linking transcription factors, distal regulatory elements and target genes (e.g., excitatory and inhibitory neurons, microglia, oligodendrocyte). Using these cell-type networks and disease risk variants, we further identified the cell-type disease genes and regulatory networks for schizophrenia and Alzheimer’s disease. The celltype regulatory elements (e.g., enhancers) in the networks were also found to be potential pleiotropic regulatory loci for a variety of diseases. Further enrichment analyses including gene ontology and KEGG pathways revealed potential novel cross-disease and disease-specific molecular functions, advancing knowledge on the interplays among genetic, transcriptional and epigenetic risks at the cellular resolution between neurodegenerative and neuropsychiatric diseases. Finally, we summarized our computational analyses as a general-purpose pipeline for predicting gene regulatory networks via multi-omics data.

scholarly journals Predicting gene regulatory networks from cell atlases

2020 ◽  
Author(s):  
Andreas Fønss Møller ◽  
Kedar Nath Natarajan
Keyword(s):  
Single Cell ◽  
Gene Regulatory Networks ◽  
Regulatory Networks ◽  
Cell Types ◽  
Mouse Cell ◽  
Cell Type ◽  
Integrated Network ◽  
Mouse Tissues ◽  
Cell Type Specific ◽  
Gene Regulatory

AbstractRecent single-cell RNA-sequencing atlases have surveyed and identified major cell-types across different mouse tissues. Here, we computationally reconstruct gene regulatory networks from 3 major mouse cell atlases to capture functional regulators critical for cell identity, while accounting for a variety of technical differences including sampled tissues, sequencing depth and author assigned cell-type labels. Extracting the regulatory crosstalk from mouse atlases, we identify and distinguish global regulons active in multiple cell-types from specialised cell-type specific regulons. We demonstrate that regulon activities accurately distinguish individual cell types, despite differences between individual atlases. We generate an integrated network that further uncovers regulon modules with coordinated activities critical for cell-types, and validate modules using available experimental data. Inferring regulatory networks during myeloid differentiation from wildtype and Irf8 KO cells, we uncover functional contribution of Irf8 regulon activity and composition towards monocyte lineage. Our analysis provides an avenue to further extract and integrate the regulatory crosstalk from single-cell expression data.SummaryIntegrated single-cell gene regulatory network from three mouse cell atlases captures global and cell-type specific regulatory modules and crosstalk, important for cellular identity.

scholarly journals Chromatin and gene-regulatory dynamics of the developing human cerebral cortex at single-cell resolution

2020 ◽  
Author(s):  
Alexandro E. Trevino ◽  
Fabian Müller ◽  
Jimena Andersen ◽  
Laksshman Sundaram ◽  
Arwa Kathiria ◽  
...  
Keyword(s):  
Single Cell ◽  
Network Models ◽  
Regulatory Elements ◽  
Autism Spectrum ◽  
Cell Type ◽  
Neural Network Models ◽  
Neurodevelopmental Disease ◽  
Gene Regulatory Elements ◽  
Cell Type Specific ◽  
Gene Regulatory

ABSTRACTGenetic perturbations of cerebral cortical development can lead to neurodevelopmental disease, including autism spectrum disorder (ASD). To identify genomic regions crucial to corticogenesis, we mapped the activity of gene-regulatory elements generating a single-cell atlas of gene expression and chromatin accessibility both independently and jointly. This revealed waves of gene regulation by key transcription factors (TFs) across a nearly continuous differentiation trajectory into glutamatergic neurons, distinguished the expression programs of glial lineages, and identified lineage-determining TFs that exhibited strong correlation between linked gene-regulatory elements and expression levels. These highly connected genes adopted an active chromatin state in early differentiating cells, consistent with lineage commitment. Basepair-resolution neural network models identified strong cell-type specific enrichment of noncoding mutations predicted to be disruptive in a cohort of ASD subjects and identified frequently disrupted TF binding sites. This approach illustrates how cell-type specific mapping can provide insights into the programs governing human development and disease.

scholarly journals Single Cell Transcriptomics Reveals Cell Type Specific Diversification in Human Heart Failure

2021 ◽  
Author(s):  
Andrew L Koenig ◽  
Irina Shchukina ◽  
Prabhakar S Andhey ◽  
Konstantin Zaitsev ◽  
Lulu Lai ◽  
...  
Keyword(s):  
Heart Failure ◽  
Single Cell ◽  
Human Heart ◽  
Integrated Analysis ◽  
Common Disease ◽  
Cell Type ◽  
Sequencing Data ◽  
Human Heart Failure ◽  
Cell Composition ◽  
Cell Type Specific

Heart failure represents a major cause of morbidity and mortality worldwide. Single cell transcriptomics have revolutionized our understanding of cell composition and associated gene expression across human tissues. Through integrated analysis of single cell and single nucleus RNA sequencing data generated from 45 individuals, we define the cell composition of the healthy and failing human heart. We identify cell specific transcriptional signatures of heart failure and reveal the emergence of disease associated cell states. Intriguingly, cardiomyocytes converge towards a common disease associated cell state, while fibroblasts and myeloid cells undergo dramatic diversification. Endothelial cells and pericytes display global transcriptional shifts without changes in cell complexity. Collectively, our findings provide a comprehensive analysis of the cellular and transcriptomic landscape of human heart failure, identify cell type specific transcriptional programs and states associated with disease, and establish a valuable resource for the investigation of human heart failure.

scholarly journals Leveraging cell-type-specific regulatory networks to interpret genetic variants in abdominal aortic aneurysm

2021 ◽  
Vol 119 (1) ◽  
pp. e2115601119
Author(s):  
Shining Ma ◽  
Xi Chen ◽  
Xiang Zhu ◽  
Philip S. Tsao ◽  
Wing Hung Wong
Keyword(s):  
Gene Regulation ◽  
Abdominal Aortic Aneurysm ◽  
Aortic Aneurysm ◽  
Regulatory Networks ◽  
Cell Types ◽  
Regulatory Elements ◽  
Specific Gene ◽  
Cell Type ◽  
Abdominal Aortic ◽  
Cell Type Specific

Abdominal aortic aneurysm (AAA) is a common degenerative cardiovascular disease whose pathobiology is not clearly understood. The cellular heterogeneity and cell-type-specific gene regulation of vascular cells in human AAA have not been well-characterized. Here, we performed analysis of whole-genome sequencing data in AAA patients versus controls with the aim of detecting disease-associated variants that may affect gene regulation in human aortic smooth muscle cells (AoSMC) and human aortic endothelial cells (HAEC), two cell types of high relevance to AAA disease. To support this analysis, we generated H3K27ac HiChIP data for these cell types and inferred cell-type-specific gene regulatory networks. We observed that AAA-associated variants were most enriched in regulatory regions in AoSMC, compared with HAEC and CD4+ cells. The cell-type-specific regulation defined by this HiChIP data supported the importance of ERG and the KLF family of transcription factors in AAA disease. The analysis of regulatory elements that contain noncoding variants and also are differentially open between AAA patients and controls revealed the significance of the interleukin-6-mediated signaling pathway. This finding was further validated by including information from the deleteriousness effect of nonsynonymous single-nucleotide variants in AAA patients and additional control data from the Medical Genome Reference Bank dataset. These results shed important insights into AAA pathogenesis and provide a model for cell-type-specific analysis of disease-associated variants.

scholarly journals ANANSE: An enhancer network-based computational approach for predicting key transcription factors in cell fate determination

2020 ◽  
Author(s):  
Quan Xu ◽  
Georgios Georgiou ◽  
Gert Jan C. Veenstra ◽  
Huiqing Zhou ◽  
Simon J. van Heeringen
Keyword(s):  
Transcription Factors ◽  
Cell Fate ◽  
Gene Regulatory Networks ◽  
Regulatory Networks ◽  
Cell Types ◽  
Cell Fate Determination ◽  
Cell Type ◽  
Fate Determination ◽  
Cell Type Specific ◽  
Gene Regulatory

AbstractProper cell fate determination is largely orchestrated by complex gene regulatory networks centered around transcription factors. However, experimental elucidation of key transcription factors that drive cellular identity is currently often intractable. Here, we present ANANSE (ANalysis Algorithm for Networks Specified by Enhancers), a network-based method that exploits enhancer-encoded regulatory information to identify the key transcription factors in cell fate determination. As cell type-specific transcription factors predominantly bind to enhancers, we use regulatory networks based on enhancer properties to prioritize transcription factors. First, we predict genome-wide binding profiles of transcription factors in various cell types using enhancer activity and transcription factor binding motifs. Subsequently, applying these inferred binding profiles, we construct cell type-specific gene regulatory networks, and then predict key transcription factors controlling cell fate conversions using differential gene networks between cell types. This method outperforms existing approaches in correctly predicting major transcription factors previously identified to be sufficient for trans-differentiation. Finally, we apply ANANSE to define an atlas of key transcription factors in 18 normal human tissues. In conclusion, we present a ready-to-implement computational tool for efficient prediction of transcription factors in cell fate determination and to study transcription factor-mediated regulatory mechanisms. ANANSE is freely available at https://github.com/vanheeringen-lab/ANANSE.

scholarly journals scGRNom: a computational pipeline of integrative multi-omics analyses for predicting cell-type disease genes and regulatory networks

2021 ◽  
Vol 13 (1) ◽  
Author(s):  
Ting Jin ◽  
Peter Rehani ◽  
Mufang Ying ◽  
Jiawei Huang ◽  
Shuang Liu ◽  
...  
Keyword(s):  
Regulatory Networks ◽  
Regulatory Elements ◽  
General Purpose ◽  
Inhibitory Neurons ◽  
Specific Gene ◽  
Disease Genes ◽  
Computational Pipeline ◽  
Cell Type ◽  
Cell Type Specific ◽  
Learning Analysis

AbstractUnderstanding cell-type-specific gene regulatory mechanisms from genetic variants to diseases remains challenging. To address this, we developed a computational pipeline, scGRNom (single-cell Gene Regulatory Network prediction from multi-omics), to predict cell-type disease genes and regulatory networks including transcription factors and regulatory elements. With applications to schizophrenia and Alzheimer’s disease, we predicted disease genes and regulatory networks for excitatory and inhibitory neurons, microglia, and oligodendrocytes. Further enrichment analyses revealed cross-disease and disease-specific functions and pathways at the cell-type level. Our machine learning analysis also found that cell-type disease genes improved clinical phenotype predictions. scGRNom is a general-purpose tool available at https://github.com/daifengwanglab/scGRNom.

scholarly journals Cell-Type-Specific Gene Regulatory Networks Underlying Murine Neonatal Heart Regeneration at Single-Cell Resolution

Cell Reports ◽  
2020 ◽  
Vol 33 (10) ◽  
pp. 108472
Author(s):  
Zhaoning Wang ◽  
Miao Cui ◽  
Akansha M. Shah ◽  
Wei Tan ◽  
Ning Liu ◽  
...  
Keyword(s):  
Single Cell ◽  
Gene Regulatory Networks ◽  
Regulatory Networks ◽  
Specific Gene ◽  
Heart Regeneration ◽  
Cell Type ◽  
Neonatal Heart ◽  
Cell Type Specific ◽  
Gene Regulatory

scholarly journals scMomentum: Inference of Cell-Type-Specific Regulatory Networks and Energy Landscapes

2020 ◽  
Author(s):  
Larisa M. Soto ◽  
Juan P. Bernal-Tamayo ◽  
Robert Lehmann ◽  
Subash Balsamy ◽  
Xabier Martinez-de-Morentin ◽  
...  
Keyword(s):  
Single Cell ◽  
Gene Regulatory Networks ◽  
Regulatory Networks ◽  
Data Driven ◽  
Energy Landscapes ◽  
Cell Type ◽  
Cell Clustering ◽  
Cell Type Specific ◽  
Gene Regulatory ◽  
Python Package

AbstractRecent progress in single-cell genomics has generated multiple tools for cell clustering, annotation, and trajectory inference; yet, inferring their associated regulatory mechanisms is unresolved. Here we present scMomentum, a model-based data-driven formulation to predict gene regulatory networks and energy landscapes from single-cell transcriptomic data without requiring temporal or perturbation experiments. scMomentum provides significant advantages over existing methods with respect to computational efficiency, scalability, network structure, and biological application.AvailabilityscMomentum is available as a Python package at https://github.com/larisa-msoto/scMomentum.git

scholarly journals Cell-Type-Specific Gene Regulatory Networks Underlying Murine Neonatal Heart Regeneration at Single-Cell Resolution

Cell Reports ◽  
2021 ◽  
Vol 35 (8) ◽  
pp. 109211
Author(s):  
Zhaoning Wang ◽  
Miao Cui ◽  
Akansha M. Shah ◽  
Wei Tan ◽  
Ning Liu ◽  
...  
Keyword(s):  
Single Cell ◽  
Gene Regulatory Networks ◽  
Regulatory Networks ◽  
Specific Gene ◽  
Heart Regeneration ◽  
Cell Type ◽  
Neonatal Heart ◽  
Cell Type Specific ◽  
Gene Regulatory
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