scholarly journals Gene Expression Networks in the Drosophila Genetic Reference Panel

2019 ◽  
Author(s):  
Logan J. Everett ◽  
Wen Huang ◽  
Shanshan Zhou ◽  
Mary Anna Carbone ◽  
Richard F. Lyman ◽  
...  
Keyword(s):  
Gene Expression ◽  
Transposable Elements ◽  
Dna Sequences ◽  
Quantitative Trait ◽  
Regulatory Networks ◽  
Target Genes ◽  
Reference Panel ◽  
Modern Biology ◽  
Specific Expression ◽  
Drosophila Genetic Reference Panel

SummaryA major challenge in modern biology is to understand how naturally occurring variation in DNA sequences affects complex organismal traits through networks of intermediate molecular phenotypes. Here, we performed deep RNA sequencing of 200 Drosophila Genetic Reference Panel inbred lines with complete genome sequences, and mapped expression quantitative trait loci for annotated genes, novel transcribed regions (most of which are long noncoding RNAs), transposable elements and microbial species. We identified host variants that affect expression of transposable elements, independent of their copy number, as well as microbiome composition. We constructed sex-specific expression quantitative trait locus regulatory networks. These networks are enriched for novel transcribed regions and target genes in heterochromatin and euchromatic regions of reduced recombination, and genes regulating transposable element expression. This study provides new insights regarding the role of natural genetic variation in regulating gene expression and generates testable hypotheses for future functional analyses.

Mammalian genome evolution as a result of epigenetic regulation of transposable elements

2014 ◽  
Vol 5 (3) ◽  
pp. 183-194 ◽  
Author(s):  
Reuben M. Buckley ◽  
David L. Adelson
Keyword(s):  
Gene Expression ◽  
Transposable Elements ◽  
Epigenetic Regulation ◽  
Dna Sequences ◽  
Defense Mechanisms ◽  
Regulatory Networks ◽  
Regulation Of Gene Expression ◽  
Epigenetic Marks ◽  
Rna Molecules ◽  
Mammalian Genomes

AbstractTransposable elements (TEs) make up a large proportion of mammalian genomes and are a strong evolutionary force capable of rewiring regulatory networks and causing genome rearrangements. Additionally, there are many eukaryotic epigenetic defense mechanisms able to transcriptionally silence TEs. Furthermore, small RNA molecules that target TE DNA sequences often mediate these epigenetic defense mechanisms. As a result, epigenetic marks associated with TE silencing can be reestablished after epigenetic reprogramming – an event during the mammalian life cycle that results in widespread loss of parental epigenetic marks. Furthermore, targeted epigenetic marks associated with TE silencing may have an impact on nearby gene expression. Therefore, TEs may have driven species evolution via their ability to heritably alter the epigenetic regulation of gene expression in mammals.

Targeting gene expression to the head: the Drosophila orthodenticle gene is a direct target of the Bicoid morphogen

Development ◽  
1998 ◽  
Vol 125 (21) ◽  
pp. 4185-4193 ◽  
Author(s):  
Q. Gao ◽  
R. Finkelstein
Keyword(s):  
Gene Expression ◽  
Target Genes ◽  
Regulatory Element ◽  
Regulatory Region ◽  
Repeated Sequence ◽  
Drosophila Embryo ◽  
Sequence Motif ◽  
Specific Expression ◽  
Gap Gene

The Bicoid (Bcd) morphogen establishes the head and thorax of the Drosophila embryo. Bcd activates the transcription of identified target genes in the thoracic segments, but its mechanism of action in the head remains poorly understood. It has been proposed that Bcd directly activates the cephalic gap genes, which are the first zygotic genes to be expressed in the head primordium. It has also been suggested that the affinity of Bcd-binding sites in the promoters of Bcd target genes determines the posterior extent of their expression (the Gene X model). However, both these hypotheses remain untested. Here, we show that a small regulatory region upstream of the cephalic gap gene orthodenticle (otd) is sufficient to recapitulate early otd expression in the head primordium. This region contains two control elements, each capable of driving otd-like expression. The first element has consensus Bcd target sites that bind Bcd in vitro and are necessary for head-specific expression. As predicted by the Gene X model, this element has a relatively low affinity for Bcd. Surprisingly, the second regulatory element has no Bcd sites. Instead, it contains a repeated sequence motif similar to a regulatory element found in the promoters of otd-related genes in vertebrates. Our study is the first demonstration that a cephalic gap gene is directly regulated by Bcd. However, it also shows that zygotic gene expression can be targeted to the head primordium without direct Bcd regulation.

scholarly journals Cell-type Specific Expression Quantitative Trait Loci Associated with Alzheimer Disease in Blood and Brain Tissue

2020 ◽  
Author(s):  
Devanshi Patel ◽  
Xiaoling Zhang ◽  
John J. Farrell ◽  
Jaeyoon Chung ◽  
Thor D. Stein ◽  
...  
Keyword(s):  
Gene Expression ◽  
Quantitative Trait ◽  
Expression Patterns ◽  
Regulation Of Gene Expression ◽  
Cell Types ◽  
Eqtl Analysis ◽  
Cell Type ◽  
Specific Expression ◽  
Cell Type Specific Expression ◽  
Cell Type Specific

ABSTRACTBecause regulation of gene expression is heritable and context-dependent, we investigated AD-related gene expression patterns in cell-types in blood and brain. Cis-expression quantitative trait locus (eQTL) mapping was performed genome-wide in blood from 5,257 Framingham Heart Study (FHS) participants and in brain donated by 475 Religious Orders Study/Memory & Aging Project (ROSMAP) participants. The association of gene expression with genotypes for all cis SNPs within 1Mb of genes was evaluated using linear regression models for unrelated subjects and linear mixed models for related subjects. Cell type-specific eQTL (ct-eQTL) models included an interaction term for expression of “proxy” genes that discriminate particular cell type. Ct-eQTL analysis identified 11,649 and 2,533 additional significant gene-SNP eQTL pairs in brain and blood, respectively, that were not detected in generic eQTL analysis. Of note, 386 unique target eGenes of significant eQTLs shared between blood and brain were enriched in apoptosis and Wnt signaling pathways. Five of these shared genes are established AD loci. The potential importance and relevance to AD of significant results in myeloid cell-types is supported by the observation that a large portion of GWS ct-eQTLs map within 1Mb of established AD loci and 58% (23/40) of the most significant eGenes in these eQTLs have previously been implicated in AD. This study identified cell-type specific expression patterns for established and potentially novel AD genes, found additional evidence for the role of myeloid cells in AD risk, and discovered potential novel blood and brain AD biomarkers that highlight the importance of cell-type specific analysis.

scholarly journals A compendium of uniformly processed human gene expression and splicing quantitative trait loci

2021 ◽  
Vol 53 (9) ◽  
pp. 1290-1299
Author(s):  
Nurlan Kerimov ◽  
James D. Hayhurst ◽  
Kateryna Peikova ◽  
Jonathan R. Manning ◽  
Peter Walter ◽  
...  
Keyword(s):  
Gene Expression ◽  
Quantitative Trait ◽  
Target Genes ◽  
Genome Wide Association Study ◽  
Cell Types ◽  
Summary Statistics ◽  
Genome Wide ◽  
Cell Type Specific ◽  
Trait Locus ◽  
Complex Human Traits

AbstractMany gene expression quantitative trait locus (eQTL) studies have published their summary statistics, which can be used to gain insight into complex human traits by downstream analyses, such as fine mapping and co-localization. However, technical differences between these datasets are a barrier to their widespread use. Consequently, target genes for most genome-wide association study (GWAS) signals have still not been identified. In the present study, we present the eQTL Catalogue (https://www.ebi.ac.uk/eqtl), a resource of quality-controlled, uniformly re-computed gene expression and splicing QTLs from 21 studies. We find that, for matching cell types and tissues, the eQTL effect sizes are highly reproducible between studies. Although most QTLs were shared between most bulk tissues, we identified a greater diversity of cell-type-specific QTLs from purified cell types, a subset of which also manifested as new disease co-localizations. Our summary statistics are freely available to enable the systematic interpretation of human GWAS associations across many cell types and tissues.

scholarly journals Identifying genetic modulators of the connectivity between transcription factors and their transcriptional targets

2016 ◽  
Vol 113 (13) ◽  
pp. E1835-E1843 ◽  
Author(s):  
Mina Fazlollahi ◽  
Ivor Muroff ◽  
Eunjee Lee ◽  
Helen C. Causton ◽  
Harmen J. Bussemaker
Keyword(s):  
Gene Expression ◽  
Transcription Factors ◽  
Gene Regulatory Networks ◽  
Regulatory Networks ◽  
Target Genes ◽  
Regulation Of Gene Expression ◽  
Nonsynonymous Mutation ◽  
Gene Regulatory ◽  
Genetic Modulators

Regulation of gene expression by transcription factors (TFs) is highly dependent on genetic background and interactions with cofactors. Identifying specific context factors is a major challenge that requires new approaches. Here we show that exploiting natural variation is a potent strategy for probing functional interactions within gene regulatory networks. We developed an algorithm to identify genetic polymorphisms that modulate the regulatory connectivity between specific transcription factors and their target genes in vivo. As a proof of principle, we mapped connectivity quantitative trait loci (cQTLs) using parallel genotype and gene expression data for segregants from a cross between two strains of the yeast Saccharomyces cerevisiae. We identified a nonsynonymous mutation in the DIG2 gene as a cQTL for the transcription factor Ste12p and confirmed this prediction empirically. We also identified three polymorphisms in TAF13 as putative modulators of regulation by Gcn4p. Our method has potential for revealing how genetic differences among individuals influence gene regulatory networks in any organism for which gene expression and genotype data are available along with information on binding preferences for transcription factors.

scholarly journals Genomic dissection of the cytokine-controlled STAT5 signaling network in liver

2008 ◽  
Vol 34 (2) ◽  
pp. 135-143 ◽  
Author(s):  
Atsushi Hosui ◽  
Lothar Hennighausen
Keyword(s):  
Gene Expression ◽  
Gene Expression Profiling ◽  
Expression Profiling ◽  
Target Genes ◽  
Global Gene Expression ◽  
Specific Expression ◽  
Partial Explanation ◽  
Downstream Target ◽  
Expression Of Genes ◽  
Global Gene Expression Profiling

Growth hormone (GH) controls the physiology and pathophysiology of the liver, and its signals are conducted by two members of the family of signal transducers and activators of transcription, STAT5A and STAT5B. Mice in which the Stat5a/b locus has been inactivated specifically in hepatocytes display GH resistance, the sex-specific expression of genes associated with liver metabolism and the cytochrome P-450 system is lost, and they develop hepatosteatosis. Several groups have shown by global gene expression profiling that a cadre of STAT5A/B target genes identify genetic cascades induced by GH and other cytokines. Evidence is accumulating that in the absence of STAT5A/B GH aberrantly activates STAT1 and STAT3 and their downstream target genes and thereby offers a partial explanation of some of the physiological alterations observed in Stat5a/b-null mice and human patients. We hypothesize that phenotypic changes observed in the absence of STAT5A/B are due to two distinct molecular consequences: first, the failure of STAT5A/B target genes to be activated by GH and second, the rerouting of GH signaling to other members of the STAT family. Rerouting of GH signaling to STAT1 and STAT3 might partially compensate for the loss of STAT5A/B, but it certainly activates biological programs distinct from STAT5A/B. Here we discuss the extent to which studies on global gene expression profiling have fostered a better understanding of the biology behind cytokine-STAT5A/B networks in hepatocytes. We also explore whether this wealth of information on gene activity can be used to further understand the roles of cytokines in liver disease.

scholarly journals Expression quantitative trait methylation analysis reveals methylomic associations with gene expression in childhood asthma

2020 ◽  
Author(s):  
Soyeon Kim ◽  
Erick Forno ◽  
Rong Zhang ◽  
Qi Yan ◽  
Nadia Boutaoui ◽  
...  
Keyword(s):  
Gene Expression ◽  
Puerto Rican ◽  
Childhood Asthma ◽  
Quantitative Trait ◽  
Target Genes ◽  
Regulation Of Gene Expression ◽  
Atopic Asthma ◽  
Nasal Airway ◽  
Airway Epithelial ◽  
Methylation Analysis

AbstractNasal airway epithelial methylation profiles have been associated with asthma, but the effects of such profiles on expression of distant cis-genes are largely unknown. We identified 16,867 significant methylation-gene expression pairs in nasal epithelium from Puerto Rican children and adolescents (with and without asthma) in an expression quantitative trait methylation (eQTM) analysis of cis-genes located within 1 Mb of the methylation probes tested. Most eQTM methylation probes were distant from their target genes, and more likely located in enhancer regions of their target genes in lung tissue than control probes. The top 500 eQTM genes were enriched in pathways for immune processes and epithelial integrity, and also more likely to be differentially expressed in atopic asthma. Moreover, we identified 5,934 paths through which methylation probes could affect atopic asthma through gene expression. Our findings suggest that distant epigenetic regulation of gene expression in airway epithelium plays a role in atopic asthma.

scholarly journals hTFtarget: a comprehensive database for regulations of human transcription factors and their targets

2019 ◽  
Author(s):  
Qiong Zhang
Keyword(s):  
Gene Expression ◽  
Transcription Factors ◽  
Dna Sequences ◽  
Chromatin Immunoprecipitation ◽  
Target Genes ◽  
Gene Expression Regulation ◽  
Epigenetic Modification ◽  
Wide Range ◽  
Chromatin Immunoprecipitation Sequencing ◽  
User Friendly

Transcription factors (TFs) as key regulators play crucial roles in biological processes. The identification of TF-target regulatory relationships is a key step for revealing functions of TFs and their regulations on gene expression. The accumulated data of Chromatin immunoprecipitation sequencing (ChIP-Seq) provides great opportunities to discover the TF-target regulations across different conditions. In this study, we constructed a database named hTFtarget, which integrated huge human TF target resources (7,190 ChIP-Seq samples of 659 TFs and high confident TF binding sites of 699 TFs) and epigenetic modification information to predict accurate TF-target regulations. hTFtarget offers the following functions for users to explore TF-target regulations: 1) Browse or search general targets of a query TF across datasets; 2) Browse TF-target regulations for a query TF in a specific dataset or tissue; 3) Search potential TFs for a given target gene or ncRNA; 4) Investigate co-association between TFs in cell lines; 5) Explore potential co-regulations for given target genes or TFs; 6) Predict candidate TFBSs on given DNA sequences; 7) View ChIP-Seq peaks for different TFs and conditions in genome browser. hTFtarget provides a comprehensive, reliable and user-friendly resource for exploring human TF-target regulations, which will be very useful for a wide range of users in the TF and gene expression regulation community. hTFtarget is available at http://bioinfo.life.hust.edu.cn/hTFtarget.

scholarly journals Bayesian Linear Mixed Models for Motif Activity Analysis

2019 ◽  
Author(s):  
Simone Lederer ◽  
Tom Heskes ◽  
Simon J. van Heeringen ◽  
Cornelis A. Albers
Keyword(s):  
Gene Expression ◽  
Dna Sequences ◽  
Ridge Regression ◽  
Mixed Model ◽  
Target Genes ◽  
Linear Mixed Model ◽  
Covariance Structure ◽  
Regulatory Elements ◽  
Mathematical Explanation ◽  
Experimental Conditions

AbstractMotivationCellular identity and behavior is controlled by complex gene regulatory networks. Transcription factors (TFs) bind to specific DNA sequences to regulate the transcription of their target genes. On the basis of these TF motifs in cis-regulatory elements we can model the influence of TFs on gene expression. In such models of TF motif activity the data is usually modeled assuming a linear relationship between the motif activity and the gene expression level. A commonly used method to model motif influence is based on Ridge Regression. One important assumption of linear regression is the independence between samples. However, if samples are generated from the same cell line, tissue, or other biological source, this assumption may be invalid. This same assumption of independence is also applied to different, yet similar, experimental conditions, which may also be inappropriate. In theory, the independence assumption between samples could lead to loss in signal detection. Here we investigate whether a Bayesian model that allows for correlations results in more accurate inference of motif activities.ResultsWe extend the Ridge Regression to a Bayesian Linear Mixed Model, which allows us to model dependence between different samples. In a simulation study, we in-vestigate the differences between the two model assumptions. We show that our Bayesian Linear Mixed Model implementation outperforms Ridge Regression in a simulation scenario where the noise, the signal that can not be explained by TF motifs, is uncorrelated. However, we demonstrate that there is no such gain in performance if the noise has a similar covariance structure over samples as the signal that can be explained by motifs. We give a mathematical explanation to why this is the case. Using two representative real data sets we show that at most ∼ 40% of the signal is explained by motifs using the linear model. With these data there is no advantage to using the Bayesian Linear Mixed Model, due to the similarity of the covariance structure.Availability & ImplementationThe project implementation is available at https://github.com/Sim19/SimGEXPwMotifs.

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