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Title: An integrated approach to reconstructing genome-scale transcriptional regulatory networks

Abstract

Transcriptional regulatory networks (TRNs) program cells to dynamically alter their gene expression in response to changing internal or environmental conditions. In this study, we develop a novel workflow for generating large-scale TRN models that integrates comparative genomics data, global gene expression analyses, and intrinsic properties of transcription factors (TFs). An assessment of this workflow using benchmark datasets for the well-studied γ-proteobacterium Escherichia coli showed that it outperforms expression-based inference approaches, having a significantly larger area under the precision-recall curve. Further analysis indicated that this integrated workflow captures different aspects of the E. coli TRN than expression-based approaches, potentially making them highly complementary. We leveraged this new workflow and observations to build a large-scale TRN model for the α-Proteobacterium Rhodobacter sphaeroides that comprises 120 gene clusters, 1211 genes (including 93 TFs), 1858 predicted protein-DNA interactions and 76 DNA binding motifs. We found that ~67% of the predicted gene clusters in this TRN are enriched for functions ranging from photosynthesis or central carbon metabolism to environmental stress responses. We also found that members of many of the predicted gene clusters were consistent with prior knowledge in R. sphaeroides and/or other bacteria. Experimental validation of predictions from this R. sphaeroides TRN model showedmore » that high precision and recall was also obtained for TFs involved in photosynthesis (PpsR), carbon metabolism (RSP_0489) and iron homeostasis (RSP_3341). In addition, this integrative approach enabled generation of TRNs with increased information content relative to R. sphaeroides TRN models built via other approaches. We also show how this approach can be used to simultaneously produce TRN models for each related organism used in the comparative genomics analysis. Our results highlight the advantages of integrating comparative genomics of closely related organisms with gene expression data to assemble large-scale TRN models with high-quality predictions.« less

Authors:
 [1];  [1];  [1];  [2]
  1. Univ. of Wisconsin, Madison, WI (United States)
  2. Memorial Sloan-Kettering Cancer Center, New York, NY (United States)
Publication Date:
Research Org.:
Univ. of Wisconsin, Madison, WI (United States); Great Lakes Bioenergy Research Center (GLBRC), Madison, WI (United States)
Sponsoring Org.:
USDOE Office of Science (SC), Basic Energy Sciences (BES); USDOE Office of Science (SC), Advanced Scientific Computing Research (ASCR). Scientific Discovery through Advanced Computing (SciDAC)
OSTI Identifier:
1204400
Grant/Contract Number:  
FC02-07ER64494; FG02-04ER25627
Resource Type:
Accepted Manuscript
Journal Name:
PLoS Computational Biology (Online)
Additional Journal Information:
Journal Name: PLoS Computational Biology (Online); Journal Volume: 11; Journal Issue: 2; Journal ID: ISSN 1553-7358
Publisher:
Public Library of Science
Country of Publication:
United States
Language:
English
Subject:
59 BASIC BIOLOGICAL SCIENCES

Citation Formats

Imam, Saheed, Noguera, Daniel R., Donohue, Timothy J., and Leslie, Christina. An integrated approach to reconstructing genome-scale transcriptional regulatory networks. United States: N. p., 2015. Web. doi:10.1371/journal.pcbi.1004103.
Imam, Saheed, Noguera, Daniel R., Donohue, Timothy J., & Leslie, Christina. An integrated approach to reconstructing genome-scale transcriptional regulatory networks. United States. https://doi.org/10.1371/journal.pcbi.1004103
Imam, Saheed, Noguera, Daniel R., Donohue, Timothy J., and Leslie, Christina. Fri . "An integrated approach to reconstructing genome-scale transcriptional regulatory networks". United States. https://doi.org/10.1371/journal.pcbi.1004103. https://www.osti.gov/servlets/purl/1204400.
@article{osti_1204400,
title = {An integrated approach to reconstructing genome-scale transcriptional regulatory networks},
author = {Imam, Saheed and Noguera, Daniel R. and Donohue, Timothy J. and Leslie, Christina},
abstractNote = {Transcriptional regulatory networks (TRNs) program cells to dynamically alter their gene expression in response to changing internal or environmental conditions. In this study, we develop a novel workflow for generating large-scale TRN models that integrates comparative genomics data, global gene expression analyses, and intrinsic properties of transcription factors (TFs). An assessment of this workflow using benchmark datasets for the well-studied γ-proteobacterium Escherichia coli showed that it outperforms expression-based inference approaches, having a significantly larger area under the precision-recall curve. Further analysis indicated that this integrated workflow captures different aspects of the E. coli TRN than expression-based approaches, potentially making them highly complementary. We leveraged this new workflow and observations to build a large-scale TRN model for the α-Proteobacterium Rhodobacter sphaeroides that comprises 120 gene clusters, 1211 genes (including 93 TFs), 1858 predicted protein-DNA interactions and 76 DNA binding motifs. We found that ~67% of the predicted gene clusters in this TRN are enriched for functions ranging from photosynthesis or central carbon metabolism to environmental stress responses. We also found that members of many of the predicted gene clusters were consistent with prior knowledge in R. sphaeroides and/or other bacteria. Experimental validation of predictions from this R. sphaeroides TRN model showed that high precision and recall was also obtained for TFs involved in photosynthesis (PpsR), carbon metabolism (RSP_0489) and iron homeostasis (RSP_3341). In addition, this integrative approach enabled generation of TRNs with increased information content relative to R. sphaeroides TRN models built via other approaches. We also show how this approach can be used to simultaneously produce TRN models for each related organism used in the comparative genomics analysis. Our results highlight the advantages of integrating comparative genomics of closely related organisms with gene expression data to assemble large-scale TRN models with high-quality predictions.},
doi = {10.1371/journal.pcbi.1004103},
journal = {PLoS Computational Biology (Online)},
number = 2,
volume = 11,
place = {United States},
year = {Fri Feb 27 00:00:00 EST 2015},
month = {Fri Feb 27 00:00:00 EST 2015}
}

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A Tight Link between Orthologs and Bidirectional Best Hits in Bacterial and Archaeal Genomes
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The Pfam protein families database
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A novel method for accurate operon predictions in all sequenced prokaryotes
journal, February 2005


MEME SUITE: tools for motif discovery and searching
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RegulonDB version 7.0: transcriptional regulation of Escherichia coli K-12 integrated within genetic sensory response units (Gensor Units)
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RegPredict: an integrated system for regulon inference in prokaryotes by comparative genomics approach
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Evolution of Transcription Factor Binding Sites in Mammalian Gene Regulatory Regions: Conservation and Turnover
journal, July 2002


Making connections between novel transcription factors and their DNA motifs
journal, January 2005


Protein-protein interactions between CbbR and RegA (PrrA), transcriptional regulators of the cbb operons of Rhodobacter sphaeroides
journal, February 2009


Co-regulation of lipoamide dehydrogenase and 2-oxoglutarate dehydrogenase synthesis in Escherichia coli : characterisation of an ArcA binding site in the lpd promoter
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Pathways Involved in Reductant Distribution during Photobiological H 2 Production by Rhodobacter sphaeroides
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Genetic evidence that PpsR from Rhodobacter sphaeroides 2.4.1 functions as a repressor of puc and bchF expression.
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The catabolite repressor/activator (Cra) protein of enteric bacteria.
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Cyclic AMP in prokaryotes.
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Postgenomic Adventures with Rhodobacter sphaeroides
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MochiView: versatile software for genome browsing and DNA motif analysis
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Global insights into energetic and metabolic networks in Rhodobacter sphaeroides
journal, January 2013

  • Imam, Saheed; Noguera, Daniel R.; Donohue, Timothy J.
  • BMC Systems Biology, Vol. 7, Issue 1
  • DOI: 10.1186/1752-0509-7-89

DISTILLER: a data integration framework to reveal condition dependency of complex regulons in Escherichia coli
journal, January 2009


Dialogue on Reverse-Engineering Assessment and Methods: The DREAM of High-Throughput Pathway Inference
journal, October 2007

  • Stolovitzky, G.; Monroe, D.; Califano, A.
  • Annals of the New York Academy of Sciences, Vol. 1115, Issue 1
  • DOI: 10.1196/annals.1407.021

Large-Scale Mapping and Validation of Escherichia coli Transcriptional Regulation from a Compendium of Expression Profiles
journal, January 2007


Reconstruction of the Core and Extended Regulons of Global Transcription Factors
journal, July 2010


Convergence of the Transcriptional Responses to Heat Shock and Singlet Oxygen Stresses
journal, September 2012


Global Analysis of Photosynthesis Transcriptional Regulatory Networks
journal, December 2014


Towards a Rigorous Assessment of Systems Biology Models: The DREAM3 Challenges
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Enhancement of Vaccinia Virus Based Oncolysis with Histone Deacetylase Inhibitors
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Transcriptional Regulatory Network Analysis of MYB Transcription Factor Family Genes in Rice
journal, December 2015

  • Smita, Shuchi; Katiyar, Amit; Chinnusamy, Viswanathan
  • Frontiers in Plant Science, Vol. 6
  • DOI: 10.3389/fpls.2015.01157

Modulation of extracytoplasmic function (ECF) sigma factor promoter selectivity by spacer region sequence
journal, October 2017

  • Gaballa, Ahmed; Guariglia-Oropeza, Veronica; Dürr, Franziska
  • Nucleic Acids Research, Vol. 46, Issue 1
  • DOI: 10.1093/nar/gkx953

Common principles and best practices for engineering microbiomes
journal, September 2019

  • Lawson, Christopher E.; Harcombe, William R.; Hatzenpichler, Roland
  • Nature Reviews Microbiology, Vol. 17, Issue 12
  • DOI: 10.1038/s41579-019-0255-9

Modulation of extracytoplasmic function (ECF) sigma factor promoter selectivity by spacer region sequence
journal, October 2017

  • Gaballa, Ahmed; Guariglia-Oropeza, Veronica; Dürr, Franziska
  • Nucleic Acids Research, Vol. 46, Issue 1
  • DOI: 10.1093/nar/gkx953

CceR and AkgR Regulate Central Carbon and Energy Metabolism in Alphaproteobacteria
journal, February 2015


An experimentally supported model of the Bacillus subtilis global transcriptional regulatory network
journal, November 2015

  • Arrieta‐Ortiz, Mario L.; Hafemeister, Christoph; Bate, Ashley Rose
  • Molecular Systems Biology, Vol. 11, Issue 11
  • DOI: 10.15252/msb.20156236

Transcriptional Regulatory Network Analysis of MYB Transcription Factor Family Genes in Rice
journal, December 2015

  • Smita, Shuchi; Katiyar, Amit; Chinnusamy, Viswanathan
  • Frontiers in Plant Science, Vol. 6
  • DOI: 10.3389/fpls.2015.01157