scholarly journals Sharing and specificity of co-expression networks across 35 human tissues

2014 ◽  
Author(s):  
Emma Pierson ◽  
GTEx Consortium ◽  
Daphne Koller ◽  
Alexis Battle ◽  
Sara Mostafavi
Keyword(s):  
Regulatory Networks ◽  
Specific Gene ◽  
Human Tissues ◽  
Expression Data ◽  
Rna Seq ◽  
Web Tool ◽  
Tissue Specific ◽  
Tissue Specific Gene ◽  
Share Data ◽  
Gene Regulatory

To understand the regulation of tissue-specific gene expression, the GTEx Consortium generated RNA-seq expression data for more than thirty distinct human tissues. This data provides an opportunity for deriving shared and tissue-specific gene regulatory networks on the basis of co-expression between genes. However, a small number of samples are available for a majority of the tissues, and therefore statistical inference of networks in this setting is highly underpowered. To address this problem, we infer tissue-specific gene co-expression networks for 35 tissues in the GTEx dataset using a novel algorithm, GNAT, that uses a hierarchy of tissues to share data between related tissues. We show that this transfer learning approach increases the accuracy with which networks are learned. Analysis of these networks reveals that tissue-specific transcription factors are hubs that preferentially connect to genes with tissue-specific functions. Additionally, we observe that genes with tissue-specific functions lie at the peripheries of our networks. We identify numerous modules enriched for Gene Ontology functions, and show that modules conserved across tissues are especially likely to have functions common to all tissues, while modules that are upregulated in a particular tissue are often instrumental to tissue-specific function. Finally, we provide a web tool, available at mostafavilab.stat.ubc.ca/GNAT, which allows exploration of gene function and regulation in a tissue-specific manner.

scholarly journals Gene Networks and Pathways for Plasma Lipid Traits via Multi-tissue Multi-omics Systems Analysis

2020 ◽  
pp. jlr.RA120000713
Author(s):  
Montgomery Blencowe ◽  
In Sook Ahn ◽  
Zara Saleem ◽  
Helen Luk ◽  
Ingrid Cely ◽  
...  
Keyword(s):  
Lipid Metabolism ◽  
Gene Regulatory Networks ◽  
Lipid Content ◽  
Regulatory Networks ◽  
Plasma Lipid ◽  
Specific Gene ◽  
Tissue Specific ◽  
Tissue Specific Gene ◽  
Gene Regulatory ◽  
Lipid Regulation

Genome-wide association studies (GWAS) have implicated ~380 genetic loci for plasma lipid regulation. However, these loci only explain 17-27% of the trait variance and a comprehensive understanding of the molecular mechanisms has not been achieved. In this study, we utilized an integrative genomics approach leveraging diverse genomic data from human populations to investigate whether genetic variants associated with various plasma lipid traits, namely total cholesterol (TC), high and low density lipoprotein cholesterol (HDL and LDL), and triglycerides (TG), from GWAS were concentrated on specific parts of tissue-specific gene regulatory networks. In addition to the expected lipid metabolism pathways, gene subnetworks involved in ‘interferon signaling’, ‘autoimmune/immune activation’, ‘visual transduction’, and ‘protein catabolism’ were significantly associated with all lipid traits. Additionally, we detected trait-specific subnetworks, including cadherin-associated subnetworks for LDL, glutathione metabolism for HDL, valine, leucine and isoleucine biosynthesis for TC, and insulin signaling and complement pathways for TG. Finally, utilizing gene-gene relations revealed by tissue-specific gene regulatory networks, we detected both known (e.g. APOH, APOA4, and ABCA1) and novel (e.g. F2 in adipose tissue) key regulator genes in these lipid-associated subnetworks. Knockdown of the F2 gene (Coagulation Factor II, Thrombin) in 3T3-L1 and C3H10T1/2 adipocytes reduced gene expression of Abcb11, Apoa5, Apof, Fabp1, Lipc, and Cd36, reduced intracellular adipocyte lipid content, and increased extracellular lipid content, supporting a link between adipose thrombin and lipid regulation. Our results shed light on the complex mechanisms underlying lipid metabolism and highlight potential novel targets for lipid regulation and lipid-associated diseases.

scholarly journals Gene Networks and Pathways for Plasma Lipid Traits via Multi-tissue Multi-omics Systems Analysis

2020 ◽  
Author(s):  
Montgomery Blencowe ◽  
In Sook Ahn ◽  
Zara Saleem ◽  
Helen Luk ◽  
Ingrid Cely ◽  
...  
Keyword(s):  
Lipid Metabolism ◽  
Gene Regulatory Networks ◽  
Lipid Content ◽  
Regulatory Networks ◽  
Plasma Lipid ◽  
Specific Gene ◽  
Tissue Specific ◽  
Tissue Specific Gene ◽  
Gene Regulatory ◽  
Lipid Regulation

AbstractGenome-wide association studies (GWAS) have implicated ∼380 genetic loci for plasma lipid regulation. However, these loci only explain 17-27% of the trait variance and a comprehensive understanding of the molecular mechanisms has not been achieved. In this study, we utilized an integrative genomics approach leveraging diverse genomic data from human populations to investigate whether genetic variants associated with various plasma lipid traits, namely total cholesterol (TC), high and low density lipoprotein cholesterol (HDL and LDL), and triglycerides (TG), from GWAS were concentrated on specific parts of tissue-specific gene regulatory networks. In addition to the expected lipid metabolism pathways, gene subnetworks involved in ‘interferon signaling’, ‘autoimmune/immune activation’, ‘visual transduction’, and ‘protein catabolism’ were significantly associated with all lipid traits. Additionally, we detected trait-specific subnetworks, including cadherin-associated subnetworks for LDL, glutathione metabolism for HDL, valine, leucine and isoleucine biosynthesis for TC, and insulin signaling and complement pathways for TG. Finally, utilizing gene-gene relations revealed by tissue-specific gene regulatory networks, we detected both known (e.g. APOH, APOA4, and ABCA1) and novel (e.g. F2 in adipose tissue) key regulator genes in these lipid-associated subnetworks. Knockdown of the F2 gene (Coagulation Factor II, Thrombin) in 3T3-L1 and C3H10T1/2 adipocytes reduced gene expression of Abcb11, Apoa5, Apof, Fabp1, Lipc, and Cd36, reduced intracellular adipocyte lipid content, and increased extracellular lipid content, supporting a link between adipose thrombin and lipid regulation. Our results shed light on the complex mechanisms underlying lipid metabolism and highlight potential novel targets for lipid regulation and lipid-associated diseases.

scholarly journals DeepDRIM: a deep neural network to reconstruct cell-type-specific gene regulatory network using single-cell RNA-Seq Data

2021 ◽  
Author(s):  
Jiaxing Chen ◽  
Chinwang Cheong ◽  
Liang Lan ◽  
Xin Zhou ◽  
Jiming Liu ◽  
...  
Keyword(s):  
Neural Network ◽  
Single Cell ◽  
Regulatory Networks ◽  
Deep Neural Network ◽  
Neighborhood Context ◽  
Cellular Heterogeneity ◽  
Specific Gene ◽  
Rna Seq ◽  
Cell Type Specific ◽  
Gene Regulatory

AbstractSingle-cell RNA sequencing is used to capture cell-specific gene expression, thus allowing reconstruction of gene regulatory networks. The existing algorithms struggle to deal with dropouts and cellular heterogeneity, and commonly require pseudotime-ordered cells. Here, we describe DeepDRIM a supervised deep neural network that represents gene pair joint expression as images and considers the neighborhood context to eliminate the transitive interactions. Deep-DRIM yields significantly better performance than the other nine algorithms used on the eight cell lines tested, and can be used to successfully discriminate key functional modules between patients with mild and severe symptoms of coronavirus disease 2019 (COVID-19).

scholarly journals Tissue-specific 5-hydroxymethylcytosine landscape of the human genome

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Bo He ◽  
Chao Zhang ◽  
Xiaoxue Zhang ◽  
Yu Fan ◽  
Hu Zeng ◽  
...  
Keyword(s):  
Gene Expression ◽  
Human Genome ◽  
Association Studies ◽  
Specific Gene ◽  
Epigenetic Mark ◽  
Genome Wide Association Studies ◽  
Human Tissues ◽  
Tissue Specific ◽  
Tissue Specific Gene ◽  
Specific Gene Expression

Abstract5-Hydroxymethylcytosine (5hmC) is an important epigenetic mark that regulates gene expression. Charting the landscape of 5hmC in human tissues is fundamental to understanding its regulatory functions. Here, we systematically profiled the whole-genome 5hmC landscape at single-base resolution for 19 types of human tissues. We found that 5hmC preferentially decorates gene bodies and outperforms gene body 5mC in reflecting gene expression. Approximately one-third of 5hmC peaks are tissue-specific differentially-hydroxymethylated regions (tsDhMRs), which are deposited in regions that potentially regulate the expression of nearby tissue-specific functional genes. In addition, tsDhMRs are enriched with tissue-specific transcription factors and may rewire tissue-specific gene expression networks. Moreover, tsDhMRs are associated with single-nucleotide polymorphisms identified by genome-wide association studies and are linked to tissue-specific phenotypes and diseases. Collectively, our results show the tissue-specific 5hmC landscape of the human genome and demonstrate that 5hmC serves as a fundamental regulatory element affecting tissue-specific gene expression programs and functions.

scholarly journals Inferring and analyzing gene regulatory networks from multi-factorial expression data: a complete and interactive suite

BMC Genomics ◽  
2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Océane Cassan ◽  
Sophie Lèbre ◽  
Antoine Martin
Keyword(s):  
Differential Expression ◽  
Gene Regulatory Networks ◽  
Regulatory Networks ◽  
Expression Profiles ◽  
Gene Clustering ◽  
Published Data ◽  
Expression Data ◽  
Rna Seq ◽  
Data Set ◽  
Gene Regulatory

Abstract Background High-throughput transcriptomic datasets are often examined to discover new actors and regulators of a biological response. To this end, graphical interfaces have been developed and allow a broad range of users to conduct standard analyses from RNA-seq data, even with little programming experience. Although existing solutions usually provide adequate procedures for normalization, exploration or differential expression, more advanced features, such as gene clustering or regulatory network inference, often miss or do not reflect current state of the art methodologies. Results We developed here a user interface called DIANE (Dashboard for the Inference and Analysis of Networks from Expression data) designed to harness the potential of multi-factorial expression datasets from any organisms through a precise set of methods. DIANE interactive workflow provides normalization, dimensionality reduction, differential expression and ontology enrichment. Gene clustering can be performed and explored via configurable Mixture Models, and Random Forests are used to infer gene regulatory networks. DIANE also includes a novel procedure to assess the statistical significance of regulator-target influence measures based on permutations for Random Forest importance metrics. All along the pipeline, session reports and results can be downloaded to ensure clear and reproducible analyses. Conclusions We demonstrate the value and the benefits of DIANE using a recently published data set describing the transcriptional response of Arabidopsis thaliana under the combination of temperature, drought and salinity perturbations. We show that DIANE can intuitively carry out informative exploration and statistical procedures with RNA-Seq data, perform model based gene expression profiles clustering and go further into gene network reconstruction, providing relevant candidate genes or signalling pathways to explore. DIANE is available as a web service (https://diane.bpmp.inrae.fr), or can be installed and locally launched as a complete R package.

scholarly journals Model of Tryptophan Metabolism, Readily Scalable Using Tissue-specific Gene Expression Data

2013 ◽  
Vol 288 (48) ◽  
pp. 34555-34566 ◽  
Author(s):  
Anne-Kristin Stavrum ◽  
Ines Heiland ◽  
Stefan Schuster ◽  
Pål Puntervoll ◽  
Mathias Ziegler
Keyword(s):  
Gene Expression ◽  
Gene Expression Data ◽  
Tryptophan Metabolism ◽  
Specific Gene ◽  
Expression Data ◽  
Tissue Specific ◽  
Tissue Specific Gene ◽  
Specific Gene Expression
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