scholarly journals Mechanisms of tissue-specific genetic regulation revealed by latent factors across eQTLs

2019 ◽  
Author(s):  
Yuan He ◽  
Surya B. Chhetri ◽  
Marios Arvanitis ◽  
Kaushik Srinivasan ◽  
François Aguet ◽  
...  
Keyword(s):  
Matrix Factorization ◽  
Tissue Specificity ◽  
Genetic Regulation ◽  
Regulation Of Gene Expression ◽  
Genetic Effect ◽  
Cell Types ◽  
Tissue Expression ◽  
Specific Gene ◽  
Disease Etiology ◽  
Tissue Specific

AbstractBackgroundGenetic regulation of gene expression, revealed by expression quantitative trait loci (eQTLs), varies across tissues in complex patterns ranging from highly tissue-specific effects to effects shared across many or all tissues. Improved characterization of these patterns may allow us to better understand the biological mechanisms that underlie tissue-specific gene regulation and disease etiology.ResultsWe develop a constrained matrix factorization model to learn patterns of tissue sharing and tissue specificity of eQTLs across 49 human tissues from the Genotype-Tissue Expression (GTEx) project. The learned factors include patterns reflecting tissues with known biological similarity or shared cell types, in addition to a dense factor representing a universal genetic effect across all tissues. To explore the regulatory mechanisms that generate tissue-specific patterns of expression, we evaluate chromatin state enrichment and identify specific transcription factors with binding sites enriched for eQTLs from each factor.ConclusionsOur results demonstrate that matrix factorization can be applied to learn the tissue specificity pattern of eQTLs and that it exhibits better biological interpretability than heuristic methods. We present a framework to characterize the tissue specificity of eQTLs, and we identify examples of tissue-specific eQTLs that may be driven by tissue-specific transcription factor (TF) binding, with relevance to interpretation of disease association.

scholarly journals sn-spMF: matrix factorization informs tissue-specific genetic regulation of gene expression

2020 ◽  
Vol 21 (1) ◽  
Author(s):  
Yuan He ◽  
◽  
Surya B. Chhetri ◽  
Marios Arvanitis ◽  
Kaushik Srinivasan ◽  
...  
Keyword(s):  
Gene Expression ◽  
Matrix Factorization ◽  
Genetic Regulation ◽  
Regulation Of Gene Expression ◽  
Tissue Expression ◽  
Disease Etiology ◽  
Tissue Specific ◽  
Specific Effects ◽  
Factorization Model ◽  
Biological Similarity

Abstract Genetic regulation of gene expression, revealed by expression quantitative trait loci (eQTLs), exhibits complex patterns of tissue-specific effects. Characterization of these patterns may allow us to better understand mechanisms of gene regulation and disease etiology. We develop a constrained matrix factorization model, sn-spMF, to learn patterns of tissue-sharing and apply it to 49 human tissues from the Genotype-Tissue Expression (GTEx) project. The learned factors reflect tissues with known biological similarity and identify transcription factors that may mediate tissue-specific effects. sn-spMF, available at https://github.com/heyuan7676/ts_eQTLs, can be applied to learn biologically interpretable patterns of eQTL tissue-specificity and generate testable mechanistic hypotheses.

scholarly journals Understanding Tissue-specific Gene Regulation

2017 ◽  
Author(s):  
Abhijeet R. Sonawane ◽  
John Platig ◽  
Maud Fagny ◽  
Cho-Yi Chen ◽  
Joseph N. Paulson ◽  
...  
Keyword(s):  
Transcription Factor ◽  
Transcriptional Control ◽  
Tissue Specificity ◽  
Enrichment Analysis ◽  
Tissue Expression ◽  
Gene Set Enrichment Analysis ◽  
Specific Gene ◽  
Tissue Specific ◽  
Network Nodes ◽  
Transcription Factor Expression

Although all human tissues carry out common processes, tissues are distinguished by gene expres-sion patterns, implying that distinct regulatory programs control tissue-specificity. In this study, we investigate gene expression and regulation across 38 tissues profiled in the Genotype-Tissue Expression project. We find that network edges (transcription factor to target gene connections) have higher tissue-specificity than network nodes (genes) and that regulating nodes (transcription factors) are less likely to be expressed in a tissue-specific manner as compared to their targets (genes). Gene set enrichment analysis of network targeting also indicates that regulation of tissue-specific function is largely independent of transcription factor expression. In addition, tissue-specific genes are not highly targeted in their corresponding tissue-network. However, they do assume bottleneck positions due to variability in transcription factor targeting and the influence of non-canonical regulatory interactions. These results suggest that tissue-specificity is driven by context-dependent regulatory paths, providing transcriptional control of tissue-specific processes.

scholarly journals Dynamic genetic regulation of gene expression during cellular differentiation

Science ◽  
2019 ◽  
Vol 364 (6447) ◽  
pp. 1287-1290 ◽  
Author(s):  
B. J. Strober ◽  
R. Elorbany ◽  
K. Rhodes ◽  
N. Krishnan ◽  
K. Tayeb ◽  
...  
Keyword(s):  
Gene Expression ◽  
Complex Traits ◽  
Genetic Regulation ◽  
Regulation Of Gene Expression ◽  
Cell Types ◽  
Specific Gene ◽  
Genetic Associations ◽  
Sequencing Data ◽  
Using Data ◽  
Induced Pluripotent

Genetic regulation of gene expression is dynamic, as transcription can change during cell differentiation and across cell types. We mapped expression quantitative trait loci (eQTLs) throughout differentiation to elucidate the dynamics of genetic effects on cell type–specific gene expression. We generated time-series RNA sequencing data, capturing 16 time points during the differentiation of induced pluripotent stem cells to cardiomyocytes, in 19 human cell lines. We identified hundreds of dynamic eQTLs that change over time, with enrichment in enhancers of relevant cell types. We also found nonlinear dynamic eQTLs, which affect only intermediate stages of differentiation and cannot be found by using data from mature tissues. These fleeting genetic associations with gene regulation may explain some of the components of complex traits and disease. We highlight one example of a nonlinear eQTL that is associated with body mass index.

scholarly journals Tissue specific regulation of transcription in endometrium and association with disease

2020 ◽  
Vol 35 (2) ◽  
pp. 377-393 ◽  
Author(s):  
Sally Mortlock ◽  
Raden I Kendarsari ◽  
Jenny N Fung ◽  
Greg Gibson ◽  
Fei Yang ◽  
...  
Keyword(s):  
Gene Expression ◽  
Association Study ◽  
Genetic Variants ◽  
Genetic Regulation ◽  
Regulation Of Gene Expression ◽  
Enrichment Analysis ◽  
Reproductive Traits ◽  
Genetic Effects ◽  
Genotype Data ◽  
Tissue Specific

Abstract STUDY QUESTION Are genetic effects on endometrial gene expression tissue specific and/or associated with reproductive traits and diseases? SUMMARY ANSWER Analyses of RNA-sequence data and individual genotype data from the endometrium identified novel and disease associated, genetic mechanisms regulating gene expression in the endometrium and showed evidence that these mechanisms are shared across biologically similar tissues. WHAT IS KNOWN ALREADY The endometrium is a complex tissue vital for female reproduction and is a hypothesized source of cells initiating endometriosis. Understanding genetic regulation specific to, and shared between, tissue types can aid the identification of genes involved in complex genetic diseases. STUDY DESIGN, SIZE, DURATION RNA-sequence and genotype data from 206 individuals was analysed and results were compared with large publicly available datasets. PARTICIPANTS/MATERIALS, SETTING, METHODS RNA-sequencing and genotype data from 206 endometrial samples was used to identify the influence of genetic variants on gene expression, via expression quantitative trait loci (eQTL) analysis and to compare these endometrial eQTLs with those in other tissues. To investigate the association between endometrial gene expression regulation and reproductive traits and diseases, we conducted a tissue enrichment analysis, transcriptome-wide association study (TWAS) and summary data-based Mendelian randomisation (SMR) analyses. Transcriptomic data was used to test differential gene expression between women with and without endometriosis. MAIN RESULTS AND THE ROLE OF CHANCE A tissue enrichment analysis with endometriosis genome-wide association study summary statistics showed that genes surrounding endometriosis risk loci were significantly enriched in reproductive tissues. A total of 444 sentinel cis-eQTLs (P < 2.57 × 10−9) and 30 trans-eQTLs (P < 4.65 × 10−13) were detected, including 327 novel cis-eQTLs in endometrium. A large proportion (85%) of endometrial eQTLs are present in other tissues. Genetic effects on endometrial gene expression were highly correlated with the genetic effects on reproductive (e.g. uterus, ovary) and digestive tissues (e.g. salivary gland, stomach), supporting a shared genetic regulation of gene expression in biologically similar tissues. The TWAS analysis indicated that gene expression at 39 loci is associated with endometriosis, including five known endometriosis risk loci. SMR analyses identified potential target genes pleiotropically or causally associated with reproductive traits and diseases including endometriosis. However, without taking account of genetic variants, a direct comparison between women with and without endometriosis showed no significant difference in endometrial gene expression. LARGE SCALE DATA The eQTL dataset generated in this study is available at http://reproductivegenomics.com.au/shiny/endo_eqtl_rna/. Additional datasets supporting the conclusions of this article are included within the article and the supplementary information files, or are available on reasonable request. LIMITATIONS, REASONS FOR CAUTION Data are derived from fresh tissue samples and expression levels are an average of expression from different cell types within the endometrium. Subtle cell-specifc expression changes may not be detected and differences in cell composition between samples and across the menstrual cycle will contribute to sample variability. Power to detect tissue specific eQTLs and differences between women with and without endometriosis was limited by the sample size in this study. The statistical approaches used in this study identify the likely gene targets for specific genetic risk factors, but not the functional mechanism by which changes in gene expression may influence disease risk. WIDER IMPLICATIONS OF THE FINDINGS Our results identify novel genetic variants that regulate gene expression in endometrium and the majority of these are shared across tissues. This allows analysis with large publicly available datasets to identify targets for female reproductive traits and diseases. Much larger studies will be required to identify genetic regulation of gene expression that will be specific to endometrium. STUDY FUNDING/COMPETING INTEREST(S) This work was supported by the National Health and Medical Research Council (NHMRC) under project grants GNT1026033, GNT1049472, GNT1046880, GNT1050208, GNT1105321, GNT1083405 and GNT1107258. G.W.M is supported by a NHMRC Fellowship (GNT1078399). J.Y is supported by an ARC Fellowship (FT180100186). There are no competing interests.

scholarly journals Tissue-Specific Transcription Footprinting Using RNA PoI DamID (RAPID) in Caenorhabditis elegans

Genetics ◽  
2020 ◽  
Vol 216 (4) ◽  
pp. 931-945 ◽  
Author(s):  
Georgina Gómez-Saldivar ◽  
Jaime Osuna-Luque ◽  
Jennifer I. Semple ◽  
Dominique A. Glauser ◽  
Sophie Jarriault ◽  
...  
Keyword(s):  
Caenorhabditis Elegans ◽  
Rna Polymerase ◽  
Cell Types ◽  
Neuroendocrine Cells ◽  
Cell Physiology ◽  
Specific Gene ◽  
Cell Content ◽  
Tissue Specific ◽  
C Elegans ◽  
Active Transcription

Differential gene expression across cell types underlies development and cell physiology in multicellular organisms. Caenorhabditis elegans is a powerful, extensively used model to address these biological questions. A remaining bottleneck relates to the difficulty to obtain comprehensive tissue-specific gene transcription data, since available methods are still challenging to execute and/or require large worm populations. Here, we introduce the RNA Polymerase DamID (RAPID) approach, in which the Dam methyltransferase is fused to a ubiquitous RNA polymerase subunit to create transcriptional footprints via methyl marks on the DNA of transcribed genes. To validate the method, we determined the polymerase footprints in whole animals, in sorted embryonic blastomeres and in different tissues from intact young adults by driving tissue-specific Dam fusion expression. We obtained meaningful transcriptional footprints in line with RNA-sequencing (RNA-seq) studies in whole animals or specific tissues. To challenge the sensitivity of RAPID and demonstrate its utility to determine novel tissue-specific transcriptional profiles, we determined the transcriptional footprints of the pair of XXX neuroendocrine cells, representing 0.2% of the somatic cell content of the animals. We identified 3901 candidate genes with putatively active transcription in XXX cells, including the few previously known markers for these cells. Using transcriptional reporters for a subset of new hits, we confirmed that the majority of them were expressed in XXX cells and identified novel XXX-specific markers. Taken together, our work establishes RAPID as a valid method for the determination of RNA polymerase footprints in specific tissues of C. elegans without the need for cell sorting or RNA tagging.

scholarly journals Inducible cell-specific mouse models for paired epigenetic and transcriptomic studies of microglia and astroglia

2020 ◽  
Vol 3 (1) ◽  
Author(s):  
Ana J. Chucair-Elliott ◽  
Sarah R. Ocañas ◽  
David R. Stanford ◽  
Victor A. Ansere ◽  
Kyla B. Buettner ◽  
...  
Keyword(s):  
Gene Expression ◽  
Affinity Purification ◽  
Regulation Of Gene Expression ◽  
Cell Types ◽  
Tissue Level ◽  
Cell Phenotype ◽  
Specific Gene ◽  
Cell Type ◽  
A Cell ◽  
Cell Type Specific

AbstractEpigenetic regulation of gene expression occurs in a cell type-specific manner. Current cell-type specific neuroepigenetic studies rely on cell sorting methods that can alter cell phenotype and introduce potential confounds. Here we demonstrate and validate a Nuclear Tagging and Translating Ribosome Affinity Purification (NuTRAP) approach for temporally controlled labeling and isolation of ribosomes and nuclei, and thus RNA and DNA, from specific central nervous system cell types. Analysis of gene expression and DNA modifications in astrocytes or microglia from the same animal demonstrates differential usage of DNA methylation and hydroxymethylation in CpG and non-CpG contexts that corresponds to cell type-specific gene expression. Application of this approach in LPS treated mice uncovers microglia-specific transcriptome and epigenome changes in inflammatory pathways that cannot be detected with tissue-level analysis. The NuTRAP model and the validation approaches presented can be applied to any brain cell type for which a cell type-specific cre is available.

scholarly journals Analysis of Gal4 Expression Patterns in Adult Drosophila Females

2020 ◽  
Vol 10 (11) ◽  
pp. 4147-4158
Author(s):  
Lesley N. Weaver ◽  
Tianlu Ma ◽  
Daniela Drummond-Barbosa
Keyword(s):  
Genetic Manipulation ◽  
Expression Patterns ◽  
Cell Types ◽  
Tissue Expression ◽  
Whole Body ◽  
Specific Gene ◽  
Specific Cell ◽  
Gene Pathway ◽  
Gal4 Expression ◽  
Adult Drosophila

Precise genetic manipulation of specific cell types or tissues to pinpoint gene function requirement is a critical step in studies aimed at unraveling the intricacies of organismal physiology. Drosophila researchers heavily rely on the UAS/Gal4/Gal80 system for tissue-specific manipulations; however, it is often unclear whether the reported Gal4 expression patterns are indeed specific to the tissue of interest such that experimental results are not confounded by secondary sites of Gal4 expression. Here, we surveyed the expression patterns of commonly used Gal4 drivers in adult Drosophila female tissues under optimal conditions and found that multiple drivers have unreported secondary sites of expression beyond their published cell type/tissue expression pattern. These results underscore the importance of thoroughly characterizing Gal4 tools as part of a rigorous experimental design that avoids potential misinterpretation of results as we strive for understanding how the function of a specific gene/pathway in one tissue contributes to whole-body physiology.

scholarly journals Redefining tissue specificity of genetic regulation of gene expression in the presence of allelic heterogeneity

2021 ◽  
Author(s):  
Marios Arvanitis ◽  
Karl Tayeb ◽  
Benjamin J Strober ◽  
Alexis Battle
Keyword(s):  
Gene Expression ◽  
Genetic Variants ◽  
Tissue Specificity ◽  
Genetic Regulation ◽  
Regulation Of Gene Expression ◽  
Genetic Effects ◽  
Allelic Heterogeneity ◽  
Common Genetic Variants ◽  
Causal Variants ◽  
Disease Loci

Understanding the mechanisms that underlie genetic regulation of gene expression is crucial to explaining the diversity that governs complex traits. Large scale expression quantitative trait locus (eQTL) studies have been instrumental in identifying genetic variants that influence the expression of target genes. However, a large fraction of disease-associated genetic variants have not been clearly explained by current eQTL data, frustrating attempts to use these data to comprehensively characterize disease loci. One notable observation from recent studies is that cis-eQTL effects are often shared across different cell types and tissues. This would suggest that common genetic variants impacting steady-state, adult gene expression are largely tolerated, shared across tissues, and less relevant to disease. However, allelic heterogeneity and complex patterns of linkage disequilibrium (LD) within each locus may skew the quantification of sharing of genetic effects between tissues, impede our ability to identify causal variants, and hinder the identification of regulatory effects for disease-associated genetic variants. Indeed, recent research suggests that multiple causal variants are often present in many eQTL and complex trait associated loci. Here, we re-analyze tissue-specificity of genetic effects in the presence of LD and allelic heterogeneity, proposing a novel method, CAFEH, that improves the identification of causal regulatory variants across tissues and their relationship to disease loci.

scholarly journals Identification of key tissue-specific, biological processes by integrating enhancer information in maize gene regulatory networks

2020 ◽  
Author(s):  
Maud Fagny ◽  
Marieke Lydia Kuijjer ◽  
Maike Stam ◽  
Johann Joets ◽  
Olivier Turc ◽  
...  
Keyword(s):  
Gene Expression ◽  
Transcription Factor ◽  
Binding Sites ◽  
Regulatory Network ◽  
Tissue Specificity ◽  
Target Genes ◽  
Specific Gene ◽  
Tissue Specific ◽  
Genome Wide ◽  
Gene Regulatory

AbstractEnhancers are important regulators of gene expression during numerous crucial processes including tissue differentiation across development. In plants, their recent molecular characterization revealed their capacity to activate the expression of several target genes through the binding of transcription factors. Nevertheless, identifying these target genes at a genome-wide level remains a challenge, in particular in species with large genomes, where enhancers and target genes can be hundreds of kilobases away. Therefore, the contribution of enhancers to regulatory network is still poorly understood in plants. In this study, we investigate the enhancer-driven regulatory network of two maize tissues at different stages: leaves at seedling stage and husks (bracts) at flowering. Using a systems biology approach, we integrate genomic, epigenomic and transcriptomic data to model the regulatory relationship between transcription factors and their potential target genes. We identify regulatory modules specific to husk and V2-IST, and show that they are involved in distinct functions related to the biology of each tissue. We evidence enhancers exhibiting binding sites for two distinct transcription factor families (DOF and AP2/ERF) that drive the tissue-specificity of gene expression in seedling immature leaf and husk. Analysis of the corresponding enhancer sequences reveals that two different transposable element families (TIR transposon Mutator and MITE Pif/Harbinger) have shaped the regulatory network in each tissue, and that MITEs have provided new transcription factor binding sites that are involved in husk tissue-specificity.SignificanceEnhancers play a major role in regulating tissue-specific gene expression in higher eukaryotes, including angiosperms. While molecular characterization of enhancers has improved over the past years, identifying their target genes at the genome-wide scale remains challenging. Here, we integrate genomic, epigenomic and transcriptomic data to decipher the tissue-specific gene regulatory network controlled by enhancers at two different stages of maize leaf development. Using a systems biology approach, we identify transcription factor families regulating gene tissue-specific expression in husk and seedling leaves, and characterize the enhancers likely to be involved. We show that a large part of maize enhancers is derived from transposable elements, which can provide novel transcription factor binding sites crucial to the regulation of tissue-specific biological functions.

scholarly journals Cell-type specific cis-eQTLs in eight brain cell-types identifies novel risk genes for human brain disorders

2021 ◽  
Author(s):  
Julien Bryois ◽  
Daniela Calini ◽  
Will Macnair ◽  
Lynette Foo ◽  
Eduard Urich ◽  
...  
Keyword(s):  
Gene Expression ◽  
Disease Risk ◽  
Regulation Of Gene Expression ◽  
Cell Types ◽  
Specific Cell ◽  
Eqtl Analysis ◽  
Cell Type ◽  
Disease Etiology ◽  
Risk Genes ◽  
Cell Type Specific

Most expression quantitative trait loci (eQTL) studies to date have been performed in heterogeneous brain tissues as opposed to specific cell types. To investigate the genetics of gene expression in adult human cell types from the central nervous system (CNS), we performed an eQTL analysis using single nuclei RNA-seq from 196 individuals in eight CNS cell types. We identified 6108 eGenes, a substantial fraction (43%, 2620 out of 6108) of which show cell-type specific effects, with strongest effects in microglia. Integration of CNS cell-type eQTLs with GWAS revealed novel relationships between expression and disease risk for neuropsychiatric and neurodegenerative diseases. For most GWAS loci, a single gene colocalized in a single cell type providing new clues into disease etiology. Our findings demonstrate substantial contrast in genetic regulation of gene expression among CNS cell types and reveal genetic mechanisms by which disease risk genes influence neurological disorders.

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