skip to main content

DOE PAGESDOE PAGES

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

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.more » Our computational workflow comprehensively characterizes the predictive capabilities and systems-level functions of an organism’s TRN from disparate data types.« less
Authors:
 [1] ;  [1] ;  [1] ;  [2] ;  [1] ; ORCiD logo [1] ;  [1] ;  [1] ;  [1] ;  [3]
  1. Univ. of California, San Diego, CA (United States)
  2. Kyung Hee Univ., Yongin (Korea, Republic of)
  3. Univ. of California, San Diego, CA (United States); Technical Univ. of Denmark, Horsholm (Denmark)
Publication Date:
Grant/Contract Number:
SC0008701
Type:
Published Article
Journal Name:
Proceedings of the National Academy of Sciences of the United States of America
Additional Journal Information:
Journal Volume: 114; Journal Issue: 38; Journal ID: ISSN 0027-8424
Publisher:
National Academy of Sciences, Washington, DC (United States)
Research Org:
Univ. of California, San Diego, CA (United States)
Sponsoring Org:
USDOE
Country of Publication:
United States
Language:
English
Subject:
59 BASIC BIOLOGICAL SCIENCES
OSTI Identifier:
1378781
Alternate Identifier(s):
OSTI ID: 1465783

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., 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.. 2017. "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}
}