scholarly journals Genome-scale transcriptional regulatory network models of psychiatric and neurodegenerative disorders

2017 ◽  
Author(s):  
Jocelynn R. Pearl ◽  
Dani E. Bergey ◽  
Cory C. Funk ◽  
Bijoya Basu ◽  
Rediet Oshone ◽  
...  
Keyword(s):  
Human Brain ◽  
Binding Sites ◽  
Regulatory Network ◽  
Target Genes ◽  
Disease Risk ◽  
Transcriptional Regulatory Network ◽  
Specific Binding ◽  
Experimental Studies ◽  
Brain Diseases ◽  
Transcriptional Regulatory

AbstractGenetic and genomic studies suggest an important role for transcriptional regulatory changes in brain diseases, but roles for specific transcription factors (TFs) remain poorly understood. We integrated human brain-specific DNase I footprinting and TF-gene co-expression to reconstruct a transcriptional regulatory network (TRN) model for the human brain, predicting the brain-specific binding sites and target genes for 741 TFs. We used this model to predict core TFs involved in psychiatric and neurodegenerative diseases. Our results suggest that disease-related transcriptomic and genetic changes converge on small sets of disease-specific regulators, with distinct networks underlying neurodegenerative vs. psychiatric diseases. Core TFs were frequently implicated in a disease through multiple mechanisms, including differential expression of their target genes, disruption of their binding sites by disease-associated SNPs, and associations of the genetic loci encoding these TFs with disease risk. We validated our model’s predictions through systematic comparison to publicly available ChIP-seq and TF perturbation studies and through experimental studies in primary human neural stem cells. Combined genetic and transcriptional evidence supports roles for neuronal and microglia-enriched, MEF2C-regulated networks in Alzheimer’s disease; an oligodendrocyte-enriched, SREBF1-regulated network in schizophrenia; and a neural stem cell and astrocyte-enriched, POU3F2-regulated network in bipolar disorder. We provide our models of brain-specific TF binding sites and target genes as a resource for network analysis of brain diseases.

scholarly journals Screening Driving Transcription Factors in the Processing of Gastric Cancer

2016 ◽  
Vol 2016 ◽  
pp. 1-9 ◽  
Author(s):  
Guangzhong Xu ◽  
Kai Li ◽  
Nengwei Zhang ◽  
Bin Zhu ◽  
Guosheng Feng
Keyword(s):  
Gastric Cancer ◽  
Transcription Factors ◽  
Regulatory Network ◽  
Target Genes ◽  
Transcriptional Regulatory Network ◽  
Expression Profiles ◽  
Functional Enrichment ◽  
Regulatory Mechanisms ◽  
Integrated Analysis ◽  
Transcriptional Regulatory

Background. Construction of the transcriptional regulatory network can provide additional clues on the regulatory mechanisms and therapeutic applications in gastric cancer.Methods. Gene expression profiles of gastric cancer were downloaded from GEO database for integrated analysis. All of DEGs were analyzed by GO enrichment and KEGG pathway enrichment. Transcription factors were further identified and then a global transcriptional regulatory network was constructed.Results. By integrated analysis of the six eligible datasets (340 cases and 43 controls), a bunch of 2327 DEGs were identified, including 2100 upregulated and 227 downregulated DEGs. Functional enrichment analysis of DEGs showed that digestion was a significantly enriched GO term for biological process. Moreover, there were two important enriched KEGG pathways: cell cycle and homologous recombination. Furthermore, a total of 70 differentially expressed TFs were identified and the transcriptional regulatory network was constructed, which consisted of 566 TF-target interactions. The top ten TFs regulating most downstream target genes were BRCA1, ARID3A, EHF, SOX10, ZNF263, FOXL1, FEV, GATA3, FOXC1, and FOXD1. Most of them were involved in the carcinogenesis of gastric cancer.Conclusion. The transcriptional regulatory network can help researchers to further clarify the underlying regulatory mechanisms of gastric cancer tumorigenesis.

Transcriptional regulatory network analysis of developing human erythroid progenitors reveals patterns of coregulation and potential transcriptional regulators

2006 ◽  
Vol 28 (1) ◽  
pp. 114-128 ◽  
Author(s):  
M. A. Keller ◽  
S. Addya ◽  
R. Vadigepalli ◽  
B. Banini ◽  
K. Delgrosso ◽  
...  
Keyword(s):  
Network Analysis ◽  
Blood Cell ◽  
Regulatory Network ◽  
Target Genes ◽  
Transcriptional Regulatory Network ◽  
Expression Patterns ◽  
Transcriptional Regulators ◽  
Erythroid Progenitors ◽  
Hematopoietic Stem ◽  
Transcriptional Regulatory

Deciphering the molecular basis for human erythropoiesis should yield information benefiting studies of the hemoglobinopathies and other erythroid disorders. We used an in vitro erythroid differentiation system to study the developing red blood cell transcriptome derived from adult CD34+ hematopoietic progenitor cells. mRNA expression profiling was used to characterize developing erythroid cells at six time points during differentiation ( days 1, 3, 5, 7, 9, and 11). Eleven thousand seven hundred sixty-three genes (20,963 Affymetrix probe sets) were expressed on day 1, and 1,504 genes, represented by 1,953 probe sets, were differentially expressed (DE) with 537 upregulated and 969 downregulated. A subset of the DE genes was validated using real-time RT-PCR. The DE probe sets were subjected to a cluster metric and could be divided into two, three, four, five, or six clusters of genes with different expression patterns in each cluster. Genes in these clusters were examined for shared transcription factor binding sites (TFBS) in their promoters by comparing enrichment of each TFBS relative to a reference set using transcriptional regulatory network analysis. The sets of TFBS enriched in genes up- and downregulated during erythropoiesis were distinct. This analysis identified transcriptional regulators critical to erythroid development, factors recently found to play a role, as well as a new list of potential candidates, including Evi-1, a potential silencer of genes upregulated during erythropoiesis. Thus this transcriptional regulatory network analysis has yielded a focused set of factors and their target genes whose role in differentiation of the hematopoietic stem cell into distinct blood cell lineages can be elucidated.

scholarly journals Human transcriptional gene regulatory network compiled from 14 data resources

2021 ◽  
Author(s):  
VIJAYKUMAR Yogesh MULEY ◽  
Rainer Koenig
Keyword(s):  
Regulatory Network ◽  
Transcriptional Repression ◽  
Target Genes ◽  
Transcriptional Regulatory Network ◽  
Confidence Score ◽  
Expression Of Genes ◽  
Transcriptional Regulatory ◽  
Significant Gene ◽  
Spatio Temporal ◽  
Weak Expression

Transcriptional regulatory network (TRN) orchestrates spatio-temporal expression of genes to generate cellular responses for survival. The transcription factors (TF) regulating expression of their target genes (TG) are the fundamental units of TRN. Several databases have been developed to catalogue human TRN based on low- and high-throughput experimental and computational studies considering their importance in understanding cellular physiology. However, literature lacks comparative assessment on the strength and weakness of each database. In this study, we compared over 2.2 million regulatory pairs between 1,379 TF and 22,518 TG assembled from 14 data resources. Our study reveals that the TF and TG were common across data resources but not their regulatory pairs. We observed that the TF and TG of the regulatory pairs showed weak expression correlation, significant gene ontology overlap, co-citations in PubMed and low numbers of TF-TG pairs representing transcriptional repression relationships. Furthermore, each TF-TG regulatory pair assigned a combined confidence score reflecting its reliability based on its presence in multiple databases and co-expression. The TRN containing 2,246,598 TF-TG pairs, of which, 44,284 with the information on TF′s activating or repressing effects on their TG is available upon request. This study brings the information about transcriptional regulation scattered over the literature and databases at one place in the form of one of the most comprehensive and complete human TRNs assembled to date, which will be a valuable resource for benchmarking TRN prediction tools, and to the scientific community working in functional genomics, gene expression and gene regulation analysis.

scholarly journals Spatial organization of the transcriptional regulatory network of Saccharomyces cerevisiae

2019 ◽  
Author(s):  
Dong-Qing Sun ◽  
Liu Tian ◽  
Bin-Guang Ma
Keyword(s):  
Saccharomyces Cerevisiae ◽  
Regulatory Network ◽  
Spatial Organization ◽  
Target Genes ◽  
Transcriptional Regulatory Network ◽  
3D Structure ◽  
Three Dimensional ◽  
3D Genome ◽  
Transcriptional Regulatory ◽  
Four Levels

AbstractTranscriptional regulatory network (TRN) is a directed complex network composed of all regulatory interactions between transcription factors and corresponding target genes. Recently, the three-dimensional (3D) genomics studies have shown that the 3D structure of the genome makes a difference to the regulation of gene transcription, which provides us with a novel perspective. In this study, we constructed the TRN of the budding yeast Saccharomyces cerevisiae and placed it in the context of 3D genome model. We analyzed the spatial organization of the yeast TRN on four levels: global feature, central nodes, hierarchical structure and network motifs. Our results suggested that the TRN of S. cerevisiae presents an optimized structure in space to adapt to functional requirement.

scholarly journals Genome-Wide Mapping of Binding Sites Reveals Multiple Biological Functions of the Transcription Factor Cst6p in Saccharomyces cerevisiae

mBio ◽  
2016 ◽  
Vol 7 (3) ◽  
Author(s):  
Guodong Liu ◽  
David Bergenholm ◽  
Jens Nielsen
Keyword(s):  
Saccharomyces Cerevisiae ◽  
Transcription Factor ◽  
Transcription Factors ◽  
Binding Sites ◽  
Regulatory Network ◽  
Deletion Mutant ◽  
Transcriptional Regulatory Network ◽  
Biological Processes ◽  
Genome Wide ◽  
Transcriptional Regulatory

ABSTRACT In the model eukaryote Saccharomyces cerevisiae , the transcription factor Cst6p has been reported to play important roles in several biological processes. However, the genome-wide targets of Cst6p and its physiological functions remain unknown. Here, we mapped the genome-wide binding sites of Cst6p at high resolution. Cst6p binds to the promoter regions of 59 genes with various biological functions when cells are grown on ethanol but hardly binds to the promoter at any gene when cells are grown on glucose. The retarded growth of the CST6 deletion mutant on ethanol is attributed to the markedly decreased expression of NCE103 , encoding a carbonic anhydrase, which is a direct target of Cst6p. The target genes of Cst6p have a large overlap with those of stress-responsive transcription factors, such as Sko1p and Skn7p. In addition, a CST6 deletion mutant growing on ethanol shows hypersensitivity to oxidative stress and ethanol stress, assigning Cst6p as a new member of the stress-responsive transcriptional regulatory network. These results show that mapping of genome-wide binding sites can provide new insights into the function of transcription factors and highlight the highly connected and condition-dependent nature of the transcriptional regulatory network in S. cerevisiae . IMPORTANCE Transcription factors regulate the activity of various biological processes through binding to specific DNA sequences. Therefore, the determination of binding positions is important for the understanding of the regulatory effects of transcription factors. In the model eukaryote Saccharomyces cerevisiae , the transcription factor Cst6p has been reported to regulate several biological processes, while its genome-wide targets remain unknown. Here, we mapped the genome-wide binding sites of Cst6p at high resolution. We show that the binding of Cst6p to its target promoters is condition dependent and explain the mechanism for the retarded growth of the CST6 deletion mutant on ethanol. Furthermore, we demonstrate that Cst6p is a new member of a stress-responsive transcriptional regulatory network. These results provide deeper understanding of the function of the dynamic transcriptional regulatory network in S. cerevisiae .

scholarly journals Gene co-regulation and co-expression in the aryl hydrocarbon receptor-mediated transcriptional regulatory network in the mouse liver

2019 ◽  
Vol 94 (1) ◽  
pp. 113-126 ◽  
Author(s):  
Navya Josyula ◽  
Melvin E. Andersen ◽  
Norbert E. Kaminski ◽  
Edward Dere ◽  
Timothy R. Zacharewski ◽  
...  
Keyword(s):  
Aryl Hydrocarbon Receptor ◽  
Regulatory Network ◽  
Target Genes ◽  
Transcriptional Regulatory Network ◽  
Expression Patterns ◽  
Cellular Stress Response ◽  
Proximal Promoter ◽  
Promoter Regions ◽  
Aryl Hydrocarbon ◽  
Transcriptional Regulatory

AbstractFour decades after its discovery, the aryl hydrocarbon receptor (AHR), a ligand-inducible transcription factor (TF) activated by the persistent environmental contaminant 2,3,7,8-Tetrachlorodibenzo-p-dioxin (TCDD), remains an enigmatic molecule with a controversial endogenous role. Here, we have assembled a global map of the AHR gene regulatory network in female C57BL/6 mice orally gavaged with 30 µg/kg of TCDD from a combination of previously published gene expression and genome-wide TF-binding data sets. Using Kohonen self-organizing maps and subspace clustering, we show that genes co-regulated by common upstream TFs in the AHR network exhibit a pattern of co-expression. Directly bound, indirectly bound, and non-genomic AHR target genes exhibit distinct expression patterns, with the directly bound targets associated with highest median expression. Interestingly, among the directly bound AHR target genes, the expression level increases with the number of AHR-binding sites in the proximal promoter regions. Finally, we show that co-regulated genes in the AHR network activate distinct groups of downstream biological processes. Although the specific findings described here are restricted to hepatic effects under short-term TCDD exposure, this work describes a generalizable approach to the reconstruction and analysis of transcriptional regulatory cascades underlying cellular stress response, revealing network hierarchy and the nature of information flow from the initial signaling events to phenotypic outcomes. Such reconstructed networks can form the basis of a new generation of quantitative adverse outcome pathways.

scholarly journals Gene Coregulation and Coexpression in the Aryl Hydrocarbon Receptor-mediated Transcriptional Regulatory Network in the Mouse Liver

2018 ◽  
Author(s):  
Navya Josyula ◽  
Melvin E. Andersen ◽  
Norbert Kaminski ◽  
Edward Dere ◽  
Timothy R. Zacharewski ◽  
...  
Keyword(s):  
Gene Expression ◽  
Aryl Hydrocarbon Receptor ◽  
Regulatory Network ◽  
Mouse Liver ◽  
Target Genes ◽  
Transcriptional Regulatory Network ◽  
Chemical Exposure ◽  
Proximal Promoter ◽  
Aryl Hydrocarbon ◽  
Transcriptional Regulatory

AbstractTissue-specific network models of chemical-induced gene perturbation can improve our mechanistic understanding of the intracellular events leading to adverse health effects resulting from chemical exposure. The aryl hydrocarbon receptor (AHR) is a ligand-inducible transcription factor (TF) that activates a battery of genes and produces a variety of species-specific adverse effects in response to the potent and persistent environmental contaminant 2,3,7,8-tetrachlorodibenzo-p-dioxin (TCDD). Here we assemble a global map of the AHR gene regulatory network in TCDD-treated mouse liver from a combination of previously published gene expression and genome-wide TF binding data sets. Using Kohonen selforganizing maps and subspace clustering, we show that genes co-regulated by common upstream TFs in the AHR network exhibit a pattern of co-expression. Specifically, directly-bound, indirectly-bound and non-genomic AHR target genes exhibit distinct patterns of gene expression, with the directly bound targets generally associated with highest median expression. Further, among the directly bound AHR target genes, the expression level increases with the number of AHR binding sites in the proximal promoter regions. Finally, we show that co-regulated genes in the AHR network activate distinct groups of downstream biological processes, with AHR-bound target genes enriched for metabolic processes and enrichment of immune responses among AHR-unbound target genes, likely reflecting infiltration of immune cells into the mouse liver upon TCDD treatment. This work describes an approach to the reconstruction and analysis of transcriptional regulatory cascades underlying cellular stress response using bioinformatic and statistical tools.

Sign in / Sign up

Export Citation Format

Share Document