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

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

doi:10.1016/j.ymben.2022.04.004

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

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

Synthetic Biology

Research output: Contribution to journal › Article › peer-review

29 Scopus citations

Abstract

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.

Original language	English
Pages (from-to)	297-310
Number of pages	14
Journal	Metabolic Engineering
Volume	72
DOIs	https://doi.org/10.1016/j.ymben.2022.04.004
State	Published - Jul 2022

Funding

This work conducted by the Joint BioEnergy Institute was supported by the Office of Science, Office of Biological and Environmental Research, of the U.S. Department of Energy (DOE) under Contract No. DE-AC02-05CH11231. This work was authored in part by Alliance for Sustainable Energy, LLC, the manager and operator of the National Renewable Energy Laboratory for the U.S. DOE under Contract No. DE-AC36-08GO28308. This work was also partially authored by Oak Ridge National Laboratory, which is managed by UT-Battelle, LLC, for the U.S. DOE under contract DE-AC05-00OR22725. Funding was provided by the U.S. DOE Office of Energy Efficiency and Renewable Energy Bioenergy Technologies Office (BETO) for the Agile BioFoundry. This work was also supported by the Nebraska Center for Energy Science Research at the University of Nebraska - Lincoln (Cycle-11 grant to W.N.). We also thank members of the Agile BioFoundry, Richard Szubin, Ying Hutchison, and Marc K. Abrams for helpful discussions.

Funders	Funder number
Nebraska Center for Energy Science Research
U.S. DOE Office of Energy Efficiency and Renewable Energy Bioenergy Technologies Office
University of Nebraska
U.S. Department of Energy	DE-AC02-05CH11231
U.S. Department of Energy
Office of Science
Biological and Environmental Research
National Renewable Energy Laboratory	DE-AC05-00OR22725, DE-AC36-08GO28308
National Renewable Energy Laboratory
Bioenergy Technologies Office

Keywords

Independent component analysis
Machine learning
Pseudomonas putida
Systems biology
Transcriptome

Access to Document

10.1016/j.ymben.2022.04.004

Cite this

Lim, H. G., Rychel, K., Sastry, A. V., Bentley, G. J., Mueller, J., Schindel, H. S., Larsen, P. E., Laible, P. D., Guss, A. M., Niu, W., Johnson, C. W., Beckham, G. T., Feist, A. M., & Palsson, B. O. (2022). Machine-learning from Pseudomonas putida KT2440 transcriptomes reveals its transcriptional regulatory network. Metabolic Engineering, 72, 297-310. https://doi.org/10.1016/j.ymben.2022.04.004

@article{81a993a10ed84f57b453f43e26d1dbee,

title = "Machine-learning from Pseudomonas putida KT2440 transcriptomes reveals its transcriptional regulatory network",

abstract = "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.",

keywords = "Independent component analysis, Machine learning, Pseudomonas putida, Systems biology, Transcriptome",

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

note = "Publisher Copyright: {\textcopyright} 2022",

year = "2022",

month = jul,

doi = "10.1016/j.ymben.2022.04.004",

language = "English",

volume = "72",

pages = "297--310",

journal = "Metabolic Engineering",

issn = "1096-7176",

publisher = "Elsevier",

}

Lim, HG, Rychel, K, Sastry, AV, Bentley, GJ, Mueller, J, Schindel, HS, Larsen, PE, Laible, PD, Guss, AM, Niu, W, Johnson, CW, Beckham, GT, Feist, AM & Palsson, BO 2022, 'Machine-learning from Pseudomonas putida KT2440 transcriptomes reveals its transcriptional regulatory network', Metabolic Engineering, vol. 72, pp. 297-310. https://doi.org/10.1016/j.ymben.2022.04.004

TY - JOUR

T1 - Machine-learning from Pseudomonas putida KT2440 transcriptomes reveals its transcriptional regulatory network

AU - Lim, Hyun Gyu

AU - Rychel, Kevin

AU - Sastry, Anand V.

AU - Bentley, Gayle J.

AU - Mueller, Joshua

AU - Schindel, Heidi S.

AU - Larsen, Peter E.

AU - Laible, Philip D.

AU - Guss, Adam M.

AU - Niu, Wei

AU - Johnson, Christopher W.

AU - Beckham, Gregg T.

AU - Feist, Adam M.

AU - Palsson, Bernhard O.

PY - 2022/7

Y1 - 2022/7

N2 - 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.

AB - 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.

KW - Independent component analysis

KW - Machine learning

KW - Pseudomonas putida

KW - Systems biology

KW - Transcriptome

UR - http://www.scopus.com/inward/record.url?scp=85129451494&partnerID=8YFLogxK

U2 - 10.1016/j.ymben.2022.04.004

DO - 10.1016/j.ymben.2022.04.004

M3 - Article

C2 - 35489688

AN - SCOPUS:85129451494

SN - 1096-7176

VL - 72

SP - 297

EP - 310

JO - Metabolic Engineering

JF - Metabolic Engineering

ER -

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

Abstract

Funding

Keywords

Access to Document

Other files and links

Fingerprint

Cite this