Machine-learning from Pseudomonas putida KT2440 transcriptomes reveals its transcriptional regulatory network

Lim, Hyun Gyu; Rychel, Kevin; Sastry, Anand V.; Bentley, Gayle J.; Mueller, Joshua; Schindel, Heidi S.; Larsen, Peter E.; Laible, Philip D.; Guss, Adam M.; Niu, Wei; Johnson, Christopher W.; Beckham, Gregg T.; Feist, Adam M.; Palsson, Bernhard O.

doi:10.1016/j.ymben.2022.04.004

Machine-learning from Pseudomonas putida KT2440 transcriptomes reveals its transcriptional regulatory network

Journal Article · Wed Apr 27 00:00:00 EDT 2022 · Metabolic Engineering

DOI:https://doi.org/10.1016/j.ymben.2022.04.004· OSTI ID:1868499

Lim, Hyun Gyu ^[1]; Rychel, Kevin ^[2]; Sastry, Anand V. ^[2]; Bentley, Gayle J. ^[3]; Mueller, Joshua ^[4]; Schindel, Heidi S. ^[5]; Larsen, Peter E. ^[6]; Laible, Philip D. ^[6]; Guss, Adam M. ^[7]; Niu, Wei ^[4]; Johnson, Christopher W. ^[3]; Beckham, Gregg T. ^[3]; Feist, Adam M. ^[8]; Palsson, Bernhard O. ^[8]

Univ. of California San Diego, La Jolla, CA (United States); Joint BioEnergy Institute (JBEI), Emeryville, CA (United States)
Univ. of California San Diego, La Jolla, CA (United States)
National Renewable Energy Lab. (NREL), Golden, CO (United States). Renewable Resources and Enabling Science Center; Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States). Agile BioFoundry
Univ. of Nebraska, Lincoln, NE (United States)
Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)
Argonne National Lab. (ANL), Lemont, IL (United States)
Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States). Agile BioFoundry; Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)
Univ. of California San Diego, La Jolla, CA (United States); Joint BioEnergy Institute (JBEI), Emeryville, CA (United States); Technical Univ. of Denmark, Lyngby (Denmark). Novo Nordisk Foundation Center for Biosustainability

Bacterial gene expression is orchestrated by numerous transcription factors (TFs). Elucidating how gene expression is regulated is fundamental to understanding bacterial physiology and engineering it for practical use. In this study, a machine-learning approach was applied to uncover the genome-scale transcriptional regulatory network (TRN) in Pseudomonas putida KT2440, an important organism for bioproduction. We performed independent component analysis of a compendium of 321 high-quality gene expression profiles, which were previously published or newly generated in this study. We identified 84 groups of independently modulated genes (iModulons) that explain 75.7% of the total variance in the compendium. With these iModulons, we (i) expand our understanding of the regulatory functions of 39 iModulon associated TFs (e.g., HexR, Zur) by systematic comparison with 1993 previously reported TF-gene interactions; (ii) outline transcriptional changes after the transition from the exponential growth to stationary phases; (iii) capture group of genes required for utilizing diverse carbon sources and increased stationary response with slower growth rates; (iv) unveil multiple evolutionary strategies of transcriptome reallocation to achieve fast growth rates; and (v) define an osmotic stimulon, which includes the Type VI secretion system, as coordination of multiple iModulon activity changes. Taken together, this study provides the first quantitative genome-scale TRN for P. putida KT2440 and a basis for a comprehensive understanding of its complex transcriptome changes in a variety of physiological states.

View Accepted Manuscript (DOE)

Research Organization:: National Renewable Energy Laboratory (NREL), Golden, CO (United States); Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)

Sponsoring Organization:: USDOE Office of Energy Efficiency and Renewable Energy (EERE), Bioenergy Technologies Office

Grant/Contract Number:: AC02-05CH11231; AC05-00OR22725; AC36-08GO28308

OSTI ID:: 1868499

Alternate ID(s):: OSTI ID: 1878678

Report Number(s):: NREL/JA-2A00-82394; MainId:83167; UUID:bbe39dbb-10b9-4fc6-9c07-7c0398aefcf1; MainAdminID:64522

Journal Information:: Metabolic Engineering, Journal Name: Metabolic Engineering Vol. 72; ISSN 1096-7176

Publisher:: ElsevierCopyright Statement

Country of Publication:: United States

Language:: English

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09 BIOMASS FUELS
59 BASIC BIOLOGICAL SCIENCES
Pseudomonas putida
independent component analysis
machine learning
systems biology
transcriptome

Machine-learning from Pseudomonas putida KT2440 transcriptomes reveals its transcriptional regulatory network

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