skip to main content
DOE PAGES title logo U.S. Department of Energy
Office of Scientific and Technical Information

Title: Global transcriptional regulatory network for Escherichia coli robustly connects gene expression to transcription factor activities

Abstract

Transcriptional regulatory networks (TRNs) have been studied intensely for >25 y. Yet, even for the Escherichia coli TRN—probably the best characterized TRN—several questions remain. Here, we address three questions: ( i ) How complete is our knowledge of the E. coli TRN; ( 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 unmodeledmore » 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.« less

Authors:
; ; ; ; ; ORCiD logo; ; ; ;
Publication Date:
Research Org.:
Univ. of California, San Diego, CA (United States)
Sponsoring Org.:
USDOE
OSTI Identifier:
1378781
Alternate Identifier(s):
OSTI ID: 1465783
Grant/Contract Number:  
SC0008701
Resource Type:
Published Article
Journal Name:
Proceedings of the National Academy of Sciences of the United States of America
Additional Journal Information:
Journal Name: Proceedings of the National Academy of Sciences of the United States of America Journal Volume: 114 Journal Issue: 38; Journal ID: ISSN 0027-8424
Publisher:
Proceedings of the National Academy of Sciences
Country of Publication:
United States
Language:
English
Subject:
59 BASIC BIOLOGICAL SCIENCES

Citation Formats

Fang, Xin, Sastry, Anand, Mih, Nathan, Kim, Donghyuk, Tan, Justin, Yurkovich, James T., Lloyd, Colton J., Gao, Ye, Yang, Laurence, and Palsson, Bernhard O. Global transcriptional regulatory network for Escherichia coli robustly connects gene expression to transcription factor activities. United States: N. p., 2017. Web. doi:10.1073/pnas.1702581114.
Fang, Xin, Sastry, Anand, Mih, Nathan, Kim, Donghyuk, Tan, Justin, Yurkovich, James T., Lloyd, Colton J., Gao, Ye, Yang, Laurence, & Palsson, Bernhard O. Global transcriptional regulatory network for Escherichia coli robustly connects gene expression to transcription factor activities. United States. doi:10.1073/pnas.1702581114.
Fang, Xin, Sastry, Anand, Mih, Nathan, Kim, Donghyuk, Tan, Justin, Yurkovich, James T., Lloyd, Colton J., Gao, Ye, Yang, Laurence, and Palsson, Bernhard O. Tue . "Global transcriptional regulatory network for Escherichia coli robustly connects gene expression to transcription factor activities". United States. doi:10.1073/pnas.1702581114.
@article{osti_1378781,
title = {Global transcriptional regulatory network for Escherichia coli robustly connects gene expression to transcription factor activities},
author = {Fang, Xin and Sastry, Anand and Mih, Nathan and Kim, Donghyuk and Tan, Justin and Yurkovich, James T. and Lloyd, Colton J. and Gao, Ye and Yang, Laurence and Palsson, Bernhard O.},
abstractNote = {Transcriptional regulatory networks (TRNs) have been studied intensely for >25 y. Yet, even for the Escherichia coli TRN—probably the best characterized TRN—several questions remain. Here, we address three questions: ( i ) How complete is our knowledge of the E. coli TRN; ( 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.},
doi = {10.1073/pnas.1702581114},
journal = {Proceedings of the National Academy of Sciences of the United States of America},
number = 38,
volume = 114,
place = {United States},
year = {2017},
month = {9}
}

Journal Article:
Free Publicly Available Full Text
Publisher's Version of Record
DOI: 10.1073/pnas.1702581114

Citation Metrics:
Cited by: 8 works
Citation information provided by
Web of Science

Save / Share:

Works referenced in this record:

Few regulatory metabolites coordinate expression of central metabolic genes in Escherichia coli
journal, January 2017

  • Kochanowski, Karl; Gerosa, Luca; Brunner, Simon F.
  • Molecular Systems Biology, Vol. 13, Issue 1
  • DOI: 10.15252/msb.20167402

Determining the Control Circuitry of Redox Metabolism at the Genome-Scale
journal, April 2014


Decoding genome-wide GadEWX-transcriptional regulatory networks reveals multifaceted cellular responses to acid stress in Escherichia coli
journal, August 2015

  • Seo, Sang Woo; Kim, Donghyuk; O’Brien, Edward J.
  • Nature Communications, Vol. 6, Issue 1
  • DOI: 10.1038/ncomms8970

The DNA-binding network of Mycobacterium tuberculosi s
journal, January 2015

  • Minch, Kyle J.; Rustad, Tige R.; Peterson, Eliza J. R.
  • Nature Communications, Vol. 6, Issue 1
  • DOI: 10.1038/ncomms6829

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

DAVID: Database for Annotation, Visualization, and Integrated Discovery
journal, January 2003

  • Dennis, Glynn; Sherman, Brad T.; Hosack, Douglas A.
  • Genome Biology, Vol. 4, Issue 9, Article No. R60
  • DOI: 10.1186/gb-2003-4-9-r60

The PurR regulon in Escherichia coli K-12 MG1655
journal, May 2011

  • Cho, Byung-Kwan; Federowicz, Stephen A.; Embree, Mallory
  • Nucleic Acids Research, Vol. 39, Issue 15
  • DOI: 10.1093/nar/gkr307

A system‐level model for the microbial regulatory genome
journal, July 2014

  • Brooks, Aaron N.; Reiss, David J.; Allard, Antoine
  • Molecular Systems Biology, Vol. 10, Issue 7
  • DOI: 10.15252/msb.20145160

Gene Expression Profiling and the Use of Genome-Scale In Silico Models of Escherichia coli for Analysis: Providing Context for Content
journal, April 2009

  • Lewis, N. E.; Cho, B. -K.; Knight, E. M.
  • Journal of Bacteriology, Vol. 191, Issue 11
  • DOI: 10.1128/JB.00034-09

Genome-scale Analysis of Escherichia coli FNR Reveals Complex Features of Transcription Factor Binding
journal, June 2013


Metagenes and molecular pattern discovery using matrix factorization
journal, March 2004

  • Brunet, J. -P.; Tamayo, P.; Golub, T. R.
  • Proceedings of the National Academy of Sciences, Vol. 101, Issue 12
  • DOI: 10.1073/pnas.0308531101

Finding community structure in very large networks
journal, December 2004


Integrating high-throughput and computational data elucidates bacterial networks
journal, May 2004

  • Covert, Markus W.; Knight, Eric M.; Reed, Jennifer L.
  • Nature, Vol. 429, Issue 6987
  • DOI: 10.1038/nature02456

Detecting and Removing Inconsistencies between Experimental Data and Signaling Network Topologies Using Integer Linear Programming on Interaction Graphs
journal, September 2013


The Mycobacterium tuberculosis regulatory network and hypoxia
journal, July 2013

  • Galagan, James E.; Minch, Kyle; Peterson, Matthew
  • Nature, Vol. 499, Issue 7457
  • DOI: 10.1038/nature12337

Systems biology definition of the core proteome of metabolism and expression is consistent with high-throughput data
journal, August 2015

  • Yang, Laurence; Tan, Justin; O’Brien, Edward J.
  • Proceedings of the National Academy of Sciences, Vol. 112, Issue 34
  • DOI: 10.1073/pnas.1501384112

Nonsmooth nonnegative matrix factorization (nsNMF)
journal, March 2006

  • Pascual-Montano, A.; Carazo, J. M.; Kochi, K.
  • IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 28, Issue 3
  • DOI: 10.1109/TPAMI.2006.60

limma powers differential expression analyses for RNA-sequencing and microarray studies
journal, January 2015

  • Ritchie, Matthew E.; Phipson, Belinda; Wu, Di
  • Nucleic Acids Research, Vol. 43, Issue 7
  • DOI: 10.1093/nar/gkv007

Functional organisation of Escherichia coli transcriptional regulatory network
journal, August 2008

  • Martínez-Antonio, Agustino; Janga, Sarath Chandra; Thieffry, Denis
  • Journal of Molecular Biology, Vol. 381, Issue 1
  • DOI: 10.1016/j.jmb.2008.05.054

Stability-driven nonnegative matrix factorization to interpret spatial gene expression and build local gene networks
journal, April 2016

  • Wu, Siqi; Joseph, Antony; Hammonds, Ann S.
  • Proceedings of the National Academy of Sciences, Vol. 113, Issue 16
  • DOI: 10.1073/pnas.1521171113

Wisdom of crowds for robust gene network inference
journal, July 2012

  • Marbach, Daniel; Costello, James C.; Küffner, Robert
  • Nature Methods, Vol. 9, Issue 8
  • DOI: 10.1038/nmeth.2016

The Bacterial Response Regulator ArcA Uses a Diverse Binding Site Architecture to Regulate Carbon Oxidation Globally
journal, October 2013


Flexible structure alignment by chaining aligned fragment pairs allowing twists
journal, September 2003


Estimating mutual information
journal, June 2004


Deciphering Fur transcriptional regulatory network highlights its complex role beyond iron metabolism in Escherichia coli
journal, September 2014

  • Seo, Sang Woo; Kim, Donghyuk; Latif, Haythem
  • Nature Communications, Vol. 5, Issue 1
  • DOI: 10.1038/ncomms5910

Genome-scale reconstruction of the sigma factor network in Escherichia coli: topology and functional states
journal, January 2014


Shared control of gene expression in bacteria by transcription factors and global physiology of the cell
journal, January 2013

  • Berthoumieux, Sara; de Jong, Hidde; Baptist, Guillaume
  • Molecular Systems Biology, Vol. 9, Issue 1
  • DOI: 10.1038/msb.2012.70

Capturing Heterogeneity in Gene Expression Studies by Surrogate Variable Analysis
journal, January 2007


Evidence classification of high-throughput protocols and confidence integration in RegulonDB
journal, January 2013


An integrative, multi‐scale, genome‐wide model reveals the phenotypic landscape of E scherichia coli
journal, July 2014

  • Carrera, Javier; Estrela, Raissa; Luo, Jing
  • Molecular Systems Biology, Vol. 10, Issue 7
  • DOI: 10.15252/msb.20145108

Probabilistic integrative modeling of genome-scale metabolic and regulatory networks in Escherichia coli and Mycobacterium tuberculosis
journal, September 2010

  • Chandrasekaran, Sriram; Price, Nathan D.
  • Proceedings of the National Academy of Sciences, Vol. 107, Issue 41
  • DOI: 10.1073/pnas.1005139107

The architecture of ArgR-DNA complexes at the genome-scale in Escherichia coli
journal, March 2015

  • Cho, Suhyung; Cho, Yoo-Bok; Kang, Taek Jin
  • Nucleic Acids Research, Vol. 43, Issue 6
  • DOI: 10.1093/nar/gkv150

Multi-omics integration accurately predicts cellular state in unexplored conditions for Escherichia coli
journal, October 2016

  • Kim, Minseung; Rai, Navneet; Zorraquino, Violeta
  • Nature Communications, Vol. 7, Issue 1
  • DOI: 10.1038/ncomms13090

Genome-scale reconstruction of the Lrp regulatory network in Escherichia coli
journal, December 2008

  • Cho, B. -K.; Barrett, C. L.; Knight, E. M.
  • Proceedings of the National Academy of Sciences, Vol. 105, Issue 49
  • DOI: 10.1073/pnas.0807227105

Prokaryotic genome regulation: A revolutionary paradigm
journal, January 2012

  • Ishihama, Akira
  • Proceedings of the Japan Academy, Series B, Vol. 88, Issue 9
  • DOI: 10.2183/pjab.88.485

Deciphering the transcriptional regulatory logic of amino acid metabolism
journal, November 2011

  • Cho, Byung-Kwan; Federowicz, Stephen; Park, Young-Seoub
  • Nature Chemical Biology, Vol. 8, Issue 1
  • DOI: 10.1038/nchembio.710

COLOMBOS v3.0: leveraging gene expression compendia for cross-species analyses: Table 1.
journal, November 2015

  • Moretto, Marco; Sonego, Paolo; Dierckxsens, Nicolas
  • Nucleic Acids Research, Vol. 44, Issue D1
  • DOI: 10.1093/nar/gkv1251

Local and global regulation of transcription initiation in bacteria
journal, August 2016

  • Browning, Douglas F.; Busby, Stephen J. W.
  • Nature Reviews Microbiology, Vol. 14, Issue 10
  • DOI: 10.1038/nrmicro.2016.103

Model-based redesign of global transcription regulation
journal, February 2009

  • Carrera, Javier; Rodrigo, Guillermo; Jaramillo, Alfonso
  • Nucleic Acids Research, Vol. 37, Issue 5
  • DOI: 10.1093/nar/gkp022

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


Functional determinants of transcription factors in Escherichia coli: protein families and binding sites
journal, February 2003