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Title: The Escherichia coli transcriptome mostly consists of independently regulated modules

Abstract

Underlying cellular responses is a transcriptional regulatory network (TRN) that modulates gene expression. A useful description of the TRN would decompose the transcriptome into targeted effects of individual transcriptional regulators. Here, we apply unsupervised machine learning to a diverse compendium of over 250 high-quality Escherichia coli RNA-seq datasets to identify 92 statistically independent signals that modulate the expression of specific gene sets. We show that 61 of these transcriptomic signals represent the effects of currently characterized transcriptional regulators. Condition-specific activation of signals is validated by exposure of E. coli to new environmental conditions. The resulting decomposition of the transcriptome provides: a mechanistic, systems-level, network-based explanation of responses to environmental and genetic perturbations; a guide to gene and regulator function discovery; and a basis for characterizing transcriptomic differences in multiple strains. Taken together, our results show that signal summation describes the composition of a model prokaryotic transcriptome.

Authors:
ORCiD logo [1];  [1];  [1];  [1]; ORCiD logo [1];  [1]; ORCiD logo [1];  [1]; ORCiD logo [1]; ORCiD logo [2]
+ Show Author Affiliations
  1. Univ. of California San Diego, La Jolla, CA (United States)
  2. Univ. of California San Diego, La Jolla, CA (United States); Novo Nordisk Foundation Center for Biosustainability, Lyngby (Denmark)
Publication Date:
Research Org.:
Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States). National Energy Research Scientific Computing Center (NERSC)
Sponsoring Org.:
USDOE Office of Science (SC); Novo Nordisk Foundation Center for Biosustainability
OSTI Identifier:
1624220
Grant/Contract Number:  
AC02-05CH11231; NNF10CC1016517
Resource Type:
Accepted Manuscript
Journal Name:
Nature Communications
Additional Journal Information:
Journal Volume: 10; Journal Issue: 1; Journal ID: ISSN 2041-1723
Publisher:
Nature Publishing Group
Country of Publication:
United States
Language:
English
Subject:
59 BASIC BIOLOGICAL SCIENCES; science & technology - other topics; bacterial systems biology; data processing; gene regulatory networks; machine learning; regulatory networks

Citation Formats

Sastry, Anand V., Gao, Ye, Szubin, Richard, Hefner, Ying, Xu, Sibei, Kim, Donghyuk, Choudhary, Kumari Sonal, Yang, Laurence, King, Zachary A., and Palsson, Bernhard O. The Escherichia coli transcriptome mostly consists of independently regulated modules. United States: N. p., 2019. Web. doi:10.1038/s41467-019-13483-w.
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Sastry, Anand V., Gao, Ye, Szubin, Richard, Hefner, Ying, Xu, Sibei, Kim, Donghyuk, Choudhary, Kumari Sonal, Yang, Laurence, King, Zachary A., & Palsson, Bernhard O. The Escherichia coli transcriptome mostly consists of independently regulated modules. United States. https://doi.org/10.1038/s41467-019-13483-w
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Sastry, Anand V., Gao, Ye, Szubin, Richard, Hefner, Ying, Xu, Sibei, Kim, Donghyuk, Choudhary, Kumari Sonal, Yang, Laurence, King, Zachary A., and Palsson, Bernhard O. Wed . "The Escherichia coli transcriptome mostly consists of independently regulated modules". United States. https://doi.org/10.1038/s41467-019-13483-w. https://www.osti.gov/servlets/purl/1624220.
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@article{osti_1624220,
title = {The Escherichia coli transcriptome mostly consists of independently regulated modules},
author = {Sastry, Anand V. and Gao, Ye and Szubin, Richard and Hefner, Ying and Xu, Sibei and Kim, Donghyuk and Choudhary, Kumari Sonal and Yang, Laurence and King, Zachary A. and Palsson, Bernhard O.},
abstractNote = {Underlying cellular responses is a transcriptional regulatory network (TRN) that modulates gene expression. A useful description of the TRN would decompose the transcriptome into targeted effects of individual transcriptional regulators. Here, we apply unsupervised machine learning to a diverse compendium of over 250 high-quality Escherichia coli RNA-seq datasets to identify 92 statistically independent signals that modulate the expression of specific gene sets. We show that 61 of these transcriptomic signals represent the effects of currently characterized transcriptional regulators. Condition-specific activation of signals is validated by exposure of E. coli to new environmental conditions. The resulting decomposition of the transcriptome provides: a mechanistic, systems-level, network-based explanation of responses to environmental and genetic perturbations; a guide to gene and regulator function discovery; and a basis for characterizing transcriptomic differences in multiple strains. Taken together, our results show that signal summation describes the composition of a model prokaryotic transcriptome.},
doi = {10.1038/s41467-019-13483-w},
journal = {Nature Communications},
number = 1,
volume = 10,
place = {United States},
year = {Wed Dec 04 00:00:00 EST 2019},
month = {Wed Dec 04 00:00:00 EST 2019}
}
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Journal Article:
Free Publicly Available Full Text
Publisher's Version of Record
https://doi.org/10.1038/s41467-019-13483-w
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Figures / Tables:

Fig. 1 Fig. 1: ICA extracts regulatory signals from expression data. a Given three microphones recording three people speaking simultaneously, each microphone records each voice (i.e. signal) at different volumes (i.e. signal strengths) based on their relative distances. Using only these measured mixed signals, ICA recovers the original signals and their relativemore » signal strengths by maximizing the statistical independence of the recovered signals. The mixed signals (X) are a linear combination of the matrix of recovered source signals (S) and the mixing matrix (A) that represents the relative strength of each source signal in the mixed output signals. This relationship is mathematically described as X= SA. b An expression profile under a specific condition can be likened to a microphone in a cell, measuring the combined effects of all transcriptional regulators. c Schematic illustration of ICA applied to a gene expression compendium. See Supplementary Fig. 1a, b for additional details on data quality. The example TF is a dual regulator that primarily upregulates genes, and is activated by the green circular metabolite. Example experimental conditions shown are a TF knock-out, wild-type, and wild-type grown on medium supplemented with the activating metabolite. Each column of X contains an individual expression profile across 3923 genes in E. coli. d Each component (column of S) contains a coefficient for each gene. These coefficients are scaled by the component’s condition-specific activities (row in A) to form the component’s contribution to the transcriptomic compendium e. The sum of the contributions from the 92 components reconstructs most of the variance in the original compendium. f Independent components are converted into i-modulons by removing all genes with coefficients within a significance threshold (indicated in gray). Significant genes may have either positive (red) or negative (blue) coefficients. g Distribution of i-modulon categories. Categories of regulatory i-modulons are labeled in bold font. Genomic i-modulons account for single gene knock-outs, large deletions or duplications of genomic regions. Biological i-modulons contain genes enriched for a specific function, but are not linked to a specific transcriptional regulator. For more information, see Supplementary Fig. 1 and Supplementary Table 1.« less
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Large-Scale Mapping and Validation of Escherichia coli Transcriptional Regulation from a Compendium of Expression Profiles
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Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2
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  • DOI: 10.1016/j.jbi.2010.07.001

Genetic Control of the 2-Keto-3-Deoxy- d -Gluconate Metabolism in Escherichia coli K-12: kdg Regulon
journal, February 1974

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    • Lastiri-Pancardo, Gustavo; Mercado-Hernández, Jonathan S.; Kim, Juhyun
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    • Galán-Vásquez, Edgardo; Perez-Rueda, Ernesto
    • Frontiers in Molecular Biosciences, Vol. 6
    • DOI: 10.3389/fmolb.2019.00139

    Dynamic landscape of protein occupancy across the Escherichia coli chromosome
    journal, June 2021

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