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 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 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.

scholarly journals Transcriptional regulatory network controlling the ontogeny of hematopoietic stem cells

2020 ◽  
Vol 34 (13-14) ◽  
pp. 950-964 ◽  
Author(s):  
Peng Gao ◽  
Changya Chen ◽  
Elizabeth D. Howell ◽  
Yan Li ◽  
Joanna Tober ◽  
...  
Keyword(s):  
Stem Cells ◽  
Hematopoietic Stem Cells ◽  
Regulatory Network ◽  
Transcriptional Regulatory Network ◽  
Hematopoietic Stem ◽  
Transcriptional Regulatory

scholarly journals Genome-Wide Phylogenetic Analysis, Expression Pattern, and Transcriptional Regulatory Network of the Pig C/EBP Gene Family

2021 ◽  
Vol 17 ◽  
pp. 117693432110413
Author(s):  
Chaoxin Zhang ◽  
Tao Wang ◽  
Tongyan Cui ◽  
Shengwei Liu ◽  
Bing Zhang ◽  
...  
Keyword(s):  
Gene Expression ◽  
Phylogenetic Analysis ◽  
Regulatory Network ◽  
Target Genes ◽  
Transcriptional Regulatory Network ◽  
Expression Patterns ◽  
Enrichment Analysis ◽  
Functional Enrichment ◽  
Genome Wide ◽  
A Genome

The CCAAT/enhancer binding protein (C/EBP) transcription factors (TFs) regulate many important biological processes, such as energy metabolism, inflammation, cell proliferation etc. A genome-wide gene identification revealed the presence of a total of 99 C/EBP genes in pig and 19 eukaryote genomes. Phylogenetic analysis showed that all C/EBP TFs were classified into 6 subgroups named C/EBPα, C/EBPβ, C/EBPδ, C/EBPε, C/EBPγ, and C/EBPζ. Gene expression analysis showed that the C/EBPα, C/EBPβ, C/EBPδ, C/EBPγ, and C/EBPζ genes were expressed ubiquitously with inconsistent expression patterns in various pig tissues. Moreover, a pig C/EBP regulatory network was constructed, including C/EBP genes, TFs and miRNAs. A total of 27 feed-forward loop (FFL) motifs were detected in the pig C/EBP regulatory network. Based on the RNA-seq data, gene expression patterns related to FFL sub-network were analyzed in 27 adult pig tissues. Certain FFL motifs may be tissue specific. Functional enrichment analysis indicated that C/EBP and its target genes are involved in many important biological pathways. These results provide valuable information that clarifies the evolutionary relationships of the C/EBP family and contributes to the understanding of the biological function of C/EBP genes.

scholarly journals Multiple transcription factors co-regulate the Mycobacterium tuberculosis adaptation response to vitamin C

BMC Genomics ◽  
2019 ◽  
Vol 20 (1) ◽  
Author(s):  
Malobi Nandi ◽  
Kriti Sikri ◽  
Neha Chaudhary ◽  
Shekhar Chintamani Mande ◽  
Ravi Datta Sharma ◽  
...  
Keyword(s):  
Gene Expression ◽  
Transcription Factor ◽  
Mycobacterium Tuberculosis ◽  
Vitamin C ◽  
Large Scale ◽  
Target Genes ◽  
Transcriptional Regulatory Network ◽  
Transcriptional Regulators ◽  
Transcriptional Regulatory ◽  
Modulation Of Gene Expression

Abstract Background Latent tuberculosis infection is attributed in part to the existence of Mycobacterium tuberculosis in a persistent non-replicating dormant state that is associated with tolerance to host defence mechanisms and antibiotics. We have recently reported that vitamin C treatment of M. tuberculosis triggers the rapid development of bacterial dormancy. Temporal genome-wide transcriptome analysis has revealed that vitamin C-induced dormancy is associated with a large-scale modulation of gene expression in M. tuberculosis. Results An updated transcriptional regulatory network of M.tuberculosis (Mtb-TRN) consisting of 178 regulators and 3432 target genes was constructed. The temporal transcriptome data generated in response to vitamin C was overlaid on the Mtb-TRN (vitamin C Mtb-TRN) to derive insights into the transcriptional regulatory features in vitamin C-adapted bacteria. Statistical analysis using Fisher’s exact test predicted that 56 regulators play a central role in modulating genes which are involved in growth, respiration, metabolism and repair functions. Rv0348, DevR, MprA and RegX3 participate in a core temporal regulatory response during 0.25 h to 8 h of vitamin C treatment. Temporal network analysis further revealed Rv0348 to be the most prominent hub regulator with maximum interactions in the vitamin C Mtb-TRN. Experimental analysis revealed that Rv0348 and DevR proteins interact with each other, and this interaction results in an enhanced binding of DevR to its target promoter. These findings, together with the enhanced expression of devR and Rv0348 transcriptional regulators, indicate a second-level regulation of target genes through transcription factor- transcription factor interactions. Conclusions Temporal regulatory analysis of the vitamin C Mtb-TRN revealed that there is involvement of multiple regulators during bacterial adaptation to dormancy. Our findings suggest that Rv0348 is a prominent hub regulator in the vitamin C model and large-scale modulation of gene expression is achieved through interactions of Rv0348 with other transcriptional regulators.

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 phylogenetic analysis, expression pattern, and transcriptional regulatory network of the pig C/EBP gene family

2020 ◽  
Author(s):  
Chaoxin Zhang ◽  
Tao Wang ◽  
Shengwei Liu ◽  
Bing Zhang ◽  
Xue Li ◽  
...  
Keyword(s):  
Phylogenetic Analysis ◽  
Regulatory Network ◽  
Target Genes ◽  
Transcriptional Regulatory Network ◽  
Expression Patterns ◽  
Enrichment Analysis ◽  
Functional Enrichment Analysis ◽  
Functional Enrichment ◽  
Rna Seq ◽  
Group I

Abstract Background: The vertebrate C/EBP transcription factors regulate many important biological processes, such as cell proliferation, differentiation, signal transduction, inflammation, and energy metabolism. The first C/EBP protein was identified in rat liver nuclei. Development of sequencing technology resulted in identification of the C/EBP genes in various species. In this study, a bioinformatics approach was used to determine the distribution of the members of the C/EBP family in vertebrates. A phylogenetic tree was constructed to analyze the C/EBP genes in vertebrates. Based on RNA-seq data, the expression patterns of pig C/EBP members in various tissues were analyzed. In addition, a gene transcription regulatory network was constructed with pig C/EBP members as the core.Results: We identified a total of 92 C/EBP genes in 17 vertebrate genomes. Phylogenetic analysis showed that all C/EBP TFs were classified into two groups; group I contained C/EBPβ TFs, and group II contained the remaining C/EBP TFs. The C/EBPα, C/EBPβ, C/EBPδ, C/EBPγ, and C/EBPζ genes were expressed ubiquitously with inconsistent expression patterns in various tissues. Moreover, a pig C/EBP regulatory network was constructed, including C/EBP genes, TFs, and miRNAs. A total of 39 FFL motifs were detected in the pig C/EBP regulatory network. Based on the RNA-seq data, gene expression patterns related to this FFL sub-network were analyzed in 27 adult Duroc tissues. Certain FFL motifs may be tissue specific. Functional enrichment analysis indicated that C/EBP and its target genes are involved in many important biological pathways. Conclusions: These results provide valuable information that clarifies the evolutionary relationships of the C/EBP family and contributes to the understanding of the biological function of C/EBP genes.

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 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.

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