Revealing 29 sets of independently modulated genes inStaphylococcus aureus, their regulators, and role in key physiological response

The ability ofStaphylococcus aureusto infect many different tissue sites is enabled, in part, by its transcriptional regulatory network (TRN) that coordinates its gene expression to respond to different environments. We elucidated the organization and activity of this TRN by applying independent component analysis to a compendium of 108 RNA-sequencing expression profiles from twoS. aureusclinical strains (TCH1516 and LAC). ICA decomposed theS. aureustranscriptome into 29 independently modulated sets of genes (i-modulons) that revealed: 1) High confidence associations between 21 i-modulons and known regulators; 2) an association between an i-modulon and σS, whose regulatory role was previously undefined; 3) the regulatory organization of 65 virulence factors in the form of three i-modulons associated with AgrR, SaeR, and Vim-3; 4) the roles of three key transcription factors (CodY, Fur, and CcpA) in coordinating the metabolic and regulatory networks; and 5) a low-dimensional representation, involving the function of few transcription factors of changes in gene expression between two laboratory media (RPMI, cation adjust Mueller Hinton broth) and two physiological media (blood and serum). This representation of the TRN covers 842 genes representing 76% of the variance in gene expression that provides a quantitative reconstruction of transcriptional modules inS. aureus, and a platform enabling its full elucidation.

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Revealing 29 sets of independently modulated genes in Staphylococcus aureus, their regulators and role in key physiological responses

10.1101/2020.03.18.997296 ◽

2020 ◽

Cited By ~ 4

Author(s):

Saugat Poudel ◽

Hannah Tsunemoto ◽

Yara Seif ◽

Anand Sastry ◽

Richard Szubin ◽

...

Keyword(s):

Gene Expression ◽

Staphylococcus Aureus ◽

Transcription Factors ◽

Regulatory Network ◽

Transcriptional Regulatory Network ◽

Physiological Responses ◽

Cellular Systems ◽

Transcriptional Regulatory ◽

Successful Infection ◽

Its Gene

AbstractThe ability of Staphylococcus aureus to infect many different tissue sites is enabled, in part, by its Transcriptional Regulatory Network (TRN) that coordinates its gene expression to respond to different environments. We elucidated the organization and activity of this TRN by applying Independent Component Analysis (ICA) to a compendium of 108 RNAseq expression profiles from two S. aureus clinical strains (TCH1516 and LAC). ICA decomposed the S. aureus transcriptome into 29 independently modulated sets of genes (i-modulons) that revealed (1) high confidence associations between 21 i-modulons and known regulators; (2) an association between an i-modulon and σS, whose regulatory role was previously undefined; (3) the regulatory organization of 65 virulence factors in the form of three i-modulons associated with AgrR, SaeR and Vim-3, (4) the roles of three key transcription factors (codY, Fur and ccpA) in coordinating the metabolic and regulatory networks; and (5) a low dimensional representation, involving the function of few transcription factors, of changes in gene expression between two laboratory media (RPMI, CAMHB) and two physiological media (blood and serum). This representation of the TRN covers 842 genes representing 76% of the variance in gene expression that provides a quantitative reconstruction of transcriptional modules in S. aureus, and a platform enabling its full elucidation.Significance StatementStaphylococcus aureus infections impose an immense burden on the healthcare system. To establish a successful infection in a hostile host environment, S. aureus must coordinate its gene expression to respond to a wide array of challenges. This balancing act is largely orchestrated by the Transcriptional Regulatory Network (TRN). Here, we present a model of 29 independently modulated sets of genes that form the basis for a segment of the TRN in clinical USA300 strains of S. aureus. Using this model, we demonstrate the concerted role of various cellular systems (e.g. metabolism, virulence and stress response) underlying key physiological responses, including response during blood infection.

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Screening Driving Transcription Factors in the Processing of Gastric Cancer

Gastroenterology Research and Practice ◽

10.1155/2016/8431480 ◽

2016 ◽

Vol 2016 ◽

pp. 1-9 ◽

Cited By ~ 5

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.

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Genetic and pharmacological inactivation of adenosine A2A receptor reveals an Egr-2-mediated transcriptional regulatory network in the mouse striatum

Physiological Genomics ◽

10.1152/physiolgenomics.00068.2005 ◽

2005 ◽

Vol 23 (1) ◽

pp. 89-102 ◽

Cited By ~ 11

Author(s):

Liqun Yu ◽

Peter M. Haverty ◽

Juliana Mariani ◽

Yumei Wang ◽

Hai-Ying Shen ◽

...

Keyword(s):

Gene Expression ◽

Regulatory Network ◽

Molecular Mechanisms ◽

Transcriptional Regulatory Network ◽

Expression Profiles ◽

Mrna Level ◽

Gene Expression Profiles ◽

Bioinformatics Analyses ◽

Mouse Striatum ◽

Transcriptional Regulatory

The adenosine A2A receptor (A2AR) is highly expressed in the striatum, where it modulates motor and emotional behaviors. We used both microarray and bioinformatics analyses to compare gene expression profiles by genetic and pharmacological inactivation of A2AR and inferred an A2AR-controlled transcription network in the mouse striatum. A comparison between vehicle (VEH)-treated A2AR knockout (KO) mice (A2AR KO-VEH) and wild-type (WT) mice (WT-VEH) revealed 36 upregulated genes that were partially mimicked by treatment with SCH-58261 (SCH; an A2AR antagonist) and 54 downregulated genes that were not mimicked by SCH treatment. We validated the A2AR as a specific drug target for SCH by comparing A2AR KO-SCH and A2AR KO-VEH groups. The unique downregulation effect of A2AR KO was confirmed by comparing A2AR KO-SCH with WT-SCH gene groups. The distinct striatal gene expression profiles induced by A2AR KO and SCH should provide clues to the molecular mechanisms underlying the different phenotypes observed after genetic and pharmacological inactivation of A2AR. Furthermore, bioinformatics analysis discovered that Egr-2 binding sites were statistically overrepresented in the proximal promoters of A2AR KO-affected genes relative to the unaffected genes. This finding was further substantiated by the demonstration that the Egr-2 mRNA level increased in the striatum of both A2AR KO and SCH-treated mice and that striatal Egr-2 binding activity in the promoters of two A2AR KO-affected genes was enhanced in A2AR KO mice as assayed by chromatin immunoprecipitation. Taken together, these results strongly support the existence of an Egr-2-directed transcriptional regulatory network controlled by striatal A2ARs.

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Leveraging chromatin accessibility for transcriptional regulatory network inference in T Helper 17 Cells

10.1101/292987 ◽

2018 ◽

Cited By ~ 3

Author(s):

Emily R. Miraldi ◽

Maria Pokrovskii ◽

Aaron Watters ◽

Dayanne M. Castro ◽

Nicholas De Veaux ◽

...

Keyword(s):

Gene Expression ◽

Regulatory Networks ◽

Network Inference ◽

Transcriptional Regulatory Network ◽

Indirect Evidence ◽

T Helper ◽

Motif Analysis ◽

T Helper 17 ◽

Transcriptional Regulatory

AbstractTranscriptional regulatory networks (TRNs) provide insight into cellular behavior by describing interactions between transcription factors (TFs) and their gene targets. The Assay for Transposase Accessible Chromatin (ATAC)-seq, coupled with transcription-factor motif analysis, provides indirect evidence of chromatin binding for hundreds of TFs genome-wide. Here, we propose methods for TRN inference in a mammalian setting, using ATAC-seq data to influence gene expression modeling. We rigorously test our methods in the context of T Helper Cell Type 17 (Th17) differentiation, generating new ATAC-seq data to complement existing Th17 genomic resources (plentiful gene expression data, TF knock-outs and ChIP-seq experiments). In this resource-rich mammalian setting, our extensive benchmarking provides quantitative, genome-scale evaluation of TRN inference combining ATAC-seq and RNA-seq data. We refine and extend our previous Th17 TRN, using our new TRN inference methods to integrate all Th17 data (gene expression, ATAC-seq, TF KO, ChIP-seq). We highlight newly discovered roles for individual TFs and groups of TFs (“TF-TF modules”) in Th17 gene regulation. Given the popularity of ATAC-seq, which provides high-resolution with low sample input requirements, we anticipate that application of our methods will improve TRN inference in new mammalian systems, especially in vivo, for cells directly from humans and animal models.

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Reconstruction of a Global Transcriptional Regulatory Network for Control of Lipid Metabolism in Yeast by Using Chromatin Immunoprecipitation with Lambda Exonuclease Digestion

mSystems ◽

10.1128/msystems.00215-17 ◽

2018 ◽

Vol 3 (4) ◽

Cited By ~ 14

Author(s):

David Bergenholm ◽

Guodong Liu ◽

Petter Holland ◽

Jens Nielsen

Keyword(s):

Transcription Factor ◽

Transcription Factors ◽

Lipid Metabolism ◽

Chromatin Immunoprecipitation ◽

Regulatory Network ◽

Regulatory Networks ◽

Transcriptional Regulatory Network ◽

Lambda Exonuclease ◽

Transcriptional Regulatory ◽

Exonuclease Digestion

ABSTRACT To build transcription regulatory networks, transcription factor binding must be analyzed in cells grown under different conditions because their responses and targets differ depending on environmental conditions. We performed whole-genome analysis of the DNA binding of five Saccharomyces cerevisiae transcription factors involved in lipid metabolism, Ino2, Ino4, Hap1, Oaf1, and Pip2, in response to four different environmental conditions in chemostat cultures, which allowed us to keep the specific growth rate constant. Chromatin immunoprecipitation with lambda exonuclease digestion (ChIP-exo) enabled the detection of binding events at a high resolution. We discovered a large number of unidentified targets and thus expanded functions for each transcription factor (e.g., glutamate biosynthesis as a target of Oaf1 and Pip2). Moreover, condition-dependent binding of transcription factors in response to cell metabolic state (e.g., differential binding of Ino2 between fermentative and respiratory metabolic conditions) was clearly suggested. Combining the new binding data with previously published data from transcription factor deletion studies revealed the high complexity of the transcriptional regulatory network for lipid metabolism in yeast, which involves the combinatorial and complementary regulation by multiple transcription factors. We anticipate that our work will provide insights into transcription factor binding dynamics that will prove useful for the understanding of transcription regulatory networks. IMPORTANCE Transcription factors play a crucial role in the regulation of gene expression and adaptation to different environments. To better understand the underlying roles of these adaptations, we performed experiments that give us high-resolution binding of transcription factors to their targets. We investigated five transcription factors involved in lipid metabolism in yeast, and we discovered multiple novel targets and condition-specific responses that allow us to draw a better regulatory map of the lipid metabolism.

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Structural Changes in Transcriptional Regulatory Networks for Cell-type-specific Gene Expression During Hematopoiesis

International Journal of Biology and Biomedical Engineering ◽

10.46300/91011.2020.14.20 ◽

2020 ◽

Vol 14 ◽

Keyword(s):

Gene Expression ◽

Transcription Factors ◽

Regulatory Networks ◽

Structural Changes ◽

Specific Gene ◽

Cell Type ◽

Hematopoietic Stem ◽

Specific Gene Expression ◽

Transcriptional Regulatory ◽

Cell Type Specific

Hematopoiesis is an extensively studied model system for cell differentiation. Cell-type-specific gene expression patterns are observed during hematopoiesis. Gene expression is governed by regulatory networks composed of cell-type-specific transcription factors. Resolving the transcriptional regulatory network for cell-type-specific gene expression provides a promising means of understanding the mechanisms underlying cell fate decisions. In this study, transcriptional regulatory networks in hematopoietic stem and progenitor cells were predicted based on gene expression profiles and distributions of transcription factor binding motifs in the promoter regions of cell-type-specific transcription factors. In particular, structural changes that occur when pluripotent stem cells progress to lineage-committed progenitors were evaluated. Marked changes in the regulatory circuit of transcription throughout the differentiation process could be elucidated by network analysis. Modular structures were a frequently described feature of biological networks observed in estimated networks. Within a module, most transcription factors were found to be regulated by a small number of regulators acting as downstream targets. Certain regulators within these modules coincide with known key regulators of hematopoietic cell differentiation. In addition to the modular structure, a twolayered structure was clearly observed in progenitor regulatory networks. Transcription factors could be distinctly divided into regulators within the regulatory layer and into targets in the output layer according to their degree of distribution. The restriction of mutual regulation between transcription factors was remarkable in that it allowed for alterations in network structures between hematopoietic stem cells and progenitors. Thus, using this approach, the relationships among transcription factors could be revealed by a reduction in mutual regulation to form a modular structure within the regulatory network

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INFERRING THE REGULATORY INTERACTION MODELS OF TRANSCRIPTION FACTORS IN TRANSCRIPTIONAL REGULATORY NETWORKS

Journal of Bioinformatics and Computational Biology ◽

10.1142/s0219720012500126 ◽

2012 ◽

Vol 10 (05) ◽

pp. 1250012 ◽

Cited By ~ 4

Author(s):

SHERINE AWAD ◽

NICHOLAS PANCHY ◽

SEE-KIONG NG ◽

JIN CHEN

Keyword(s):

Gene Expression ◽

Transcription Factors ◽

Gene Expression Data ◽

Regulatory Networks ◽

Target Genes ◽

Expression Data ◽

Regulatory Interaction ◽

Transcriptional Regulatory Networks ◽

Interaction Models ◽

Transcriptional Regulatory

Living cells are realized by complex gene expression programs that are moderated by regulatory proteins called transcription factors (TFs). The TFs control the differential expression of target genes in the context of transcriptional regulatory networks (TRNs), either individually or in groups. Deciphering the mechanisms of how the TFs control the differential expression of a target gene in a TRN is challenging, especially when multiple TFs collaboratively participate in the transcriptional regulation. To unravel the roles of the TFs in the regulatory networks, we model the underlying regulatory interactions in terms of the TF–target interactions' directions (activation or repression) and their corresponding logical roles (necessary and/or sufficient). We design a set of constraints that relate gene expression patterns to regulatory interaction models, and develop TRIM (Transcriptional Regulatory Interaction Model Inference), a new hidden Markov model, to infer the models of TF–target interactions in large-scale TRNs of complex organisms. Besides, by training TRIM with wild-type time-series gene expression data, the activation timepoints of each regulatory module can be obtained. To demonstrate the advantages of TRIM, we applied it on yeast TRN to infer the TF–target interaction models for individual TFs as well as pairs of TFs in collaborative regulatory modules. By comparing with TF knockout and other gene expression data, we were able to show that the performance of TRIM is clearly higher than DREM (the best existing algorithm). In addition, on an individual Arabidopsis binding network, we showed that the target genes' expression correlations can be significantly improved by incorporating the TF–target regulatory interaction models inferred by TRIM into the expression data analysis, which may introduce new knowledge in transcriptional dynamics and bioactivation.

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Current and emerging approaches to define intestinal epithelium-specific transcriptional networks

AJP Gastrointestinal and Liver Physiology ◽

10.1152/ajpgi.00362.2011 ◽

2012 ◽

Vol 302 (3) ◽

pp. G277-G286 ◽

Cited By ~ 13

Author(s):

Anders Krüger Olsen ◽

Mette Boyd ◽

Erik Thomas Danielsen ◽

Jesper Thorvald Troelsen

Keyword(s):

Transcription Factors ◽

Regulatory Networks ◽

Transcriptional Regulatory Network ◽

Tissue Type ◽

Intestinal Cell ◽

Future Research ◽

Transcriptional Networks ◽

Differentiation Process ◽

Transcriptional Regulatory Networks ◽

Transcriptional Regulatory

Upon developmental or environmental cues, the composition of transcription factors in a transcriptional regulatory network is deeply implicated in controlling the signature of the gene expression and thereby specifies the cell or tissue type. Novel methods including ChIP-chip and ChIP-Seq have been applied to analyze known transcription factors and their interacting regulatory DNA elements in the intestine. The intestine is an example of a dynamic tissue where stem cells in the crypt proliferate and undergo a differentiation process toward the villus. During this differentiation process, specific regulatory networks of transcription factors are activated to target specific genes, which determine the intestinal cell fate. The expanding genomewide mapping of transcription factor binding sites and construction of transcriptional regulatory networks provide new insight into how intestinal differentiation occurs. This review summarizes the current overview of the transcriptional regulatory networks driving epithelial differentiation in adult intestine. The novel technologies that have been implied to study these networks are presented and their prospects for implications in future research are also addressed.

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Global transcriptional regulatory network forEscherichia colirobustly connects gene expression to transcription factor activities

Proceedings of the National Academy of Sciences ◽

10.1073/pnas.1702581114 ◽

2017 ◽

Vol 114 (38) ◽

pp. 10286-10291 ◽

Cited By ~ 43

Author(s):

Xin Fang ◽

Anand Sastry ◽

Nathan Mih ◽

Donghyuk Kim ◽

Justin Tan ◽

...

Keyword(s):

Gene Expression ◽

Regulatory Networks ◽

Transcriptional Regulatory Network ◽

Machine Learning Algorithms ◽

Supervised Machine Learning ◽

Data Types ◽

High Confidence ◽

Specific Expression ◽

Surrogate Variable Analysis ◽

Transcriptional Regulatory

Transcriptional regulatory networks (TRNs) have been studied intensely for >25 y. Yet, even for theEscherichia coliTRN—probably the best characterized TRN—several questions remain. Here, we address three questions: (i) How complete is our knowledge of theE. coliTRN; (ii) how well can we predict gene expression using this TRN; and (iii) how robust is our understanding of the TRN? First, we reconstructed a high-confidence TRN (hiTRN) consisting of 147 transcription factors (TFs) regulating 1,538 transcription units (TUs) encoding 1,764 genes. The 3,797 high-confidence regulatory interactions were collected from published, validated chromatin immunoprecipitation (ChIP) data and RegulonDB. For 21 different TF knockouts, up to 63% of the differentially expressed genes in the hiTRN were traced to the knocked-out TF through regulatory cascades. Second, we trained supervised machine learning algorithms to predict the expression of 1,364 TUs given TF activities using 441 samples. The algorithms accurately predicted condition-specific expression for 86% (1,174 of 1,364) of the TUs, while 193 TUs (14%) were predicted better than random TRNs. Third, we identified 10 regulatory modules whose definitions were robust against changes to the TRN or expression compendium. Using surrogate variable analysis, we also identified three unmodeled factors that systematically influenced gene expression. Our computational workflow comprehensively characterizes the predictive capabilities and systems-level functions of an organism’s TRN from disparate data types.

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Identification of candidate master transcription factors within enhancer-centric transcriptional regulatory networks

10.1101/345413 ◽

2018 ◽

Cited By ~ 1

Author(s):

Alexander J. Federation ◽

Donald R. Polaski ◽

Christopher J. Ott ◽

Angela Fan ◽

Charles Y. Lin ◽

...

Keyword(s):

Transcription Factors ◽

Mammalian Cells ◽

Regulatory Networks ◽

Transcriptional Regulatory Network ◽

Regulation Of Gene Expression ◽

Cell Types ◽

Regulatory Elements ◽

Lymphocytic Leukemia ◽

Comparable Efficacy ◽

Transcriptional Regulatory

AbstractRegulation of gene expression through binding of transcription factors (TFs) to cis-regulatory elements is highly complex in mammalian cells. Genome-wide measurement technologies provide new means to understand this regulation, and models of TF regulatory networks have been built with the goal of identifying critical factors. Here, we report a network model of transcriptional regulation between TFs constructed by integrating genomewide identification of active enhancers and regions of focal DNA accessibility. Network topology is confirmed by published TF ChIP-seq data. By considering multiple methods of TF prioritization following network construction, we identify master TFs in well-studied cell types, and these networks provide better prioritization than networks only considering promoter-proximal accessibility peaks. Comparisons between networks from similar cell types show stable connectivity of most TFs, while master regulator TFs show dramatic changes in connectivity and centrality. Applying this method to study chronic lymphocytic leukemia, we prioritized several network TFs amenable to pharmacological perturbation and show that compounds targeting these TFs show comparable efficacy in CLL cell lines to FDA-approved therapies. The construction of transcriptional regulatory network (TRN) models can predict the interactions between individual TFs and predict critical TFs for development or disease.

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