Machine learning analysis of RB-TnSeq fitness data predicts functional gene modules in Pseudomonas putida KT2440

Journal Article · Tue Mar 19 00:00:00 EDT 2024 · mSystems

DOI:https://doi.org/10.1128/msystems.00942-23· OSTI ID:2290392

Borchert, Andrew J.  ^[1]; Bleem, Alissa C.  ^[2]; Lim, Hyun Gyu  ^[3]; Rychel, Kevin ^[4]; Dooley, Keven D. ^[5]; Kellermyer, Zoe A. ^[1]; Hodges, Tracy L. ^[5]; Palsson, Bernhard O.  ^[6]; Beckham, Gregg T.  ^[2]; Hallam, ed., Steven J.

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1. Renewable Resources and Enabling Sciences Center, National Renewable Energy Laboratory, Golden, Colorado, USA, Center for Bioenergy Innovation, Oak Ridge National Laboratory, Oak Ridge, Tennessee, USA
2. Renewable Resources and Enabling Sciences Center, National Renewable Energy Laboratory, Golden, Colorado, USA, Center for Bioenergy Innovation, Oak Ridge National Laboratory, Oak Ridge, Tennessee, USA, Agile BioFoundry, Emeryville, California, USA
3. Department of Bioengineering, University of California San Diego, La Jolla, California, USA, Joint BioEnergy Institute, Emeryville, California, USA, Department of Biological Engineering, Inha University, Incheon, Korea
4. Department of Bioengineering, University of California San Diego, La Jolla, California, USA
5. Renewable Resources and Enabling Sciences Center, National Renewable Energy Laboratory, Golden, Colorado, USA, Agile BioFoundry, Emeryville, California, USA
6. Department of Bioengineering, University of California San Diego, La Jolla, California, USA, Joint BioEnergy Institute, Emeryville, California, USA, Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, Lyngby, Denmark, Department of Pediatrics, University of California, San Diego, California, USA

ABSTRACT There is growing interest in engineering Pseudomonas putida KT2440 as a microbial chassis for the conversion of renewable and waste-based feedstocks, and metabolic engineering of P. putida relies on the understanding of the functional relationships between genes. In this work, independent component analysis (ICA) was applied to a compendium of existing fitness data from randomly barcoded transposon insertion sequencing (RB-TnSeq) of P. putida KT2440 grown in 179 unique experimental conditions. ICA identified 84 independent groups of genes, which we call fModules (“functional modules”), where gene members displayed shared functional influence in a specific cellular process. This machine learning-based approach both successfully recapitulated previously characterized functional relationships and established hitherto unknown associations between genes. Selected gene members from fModules for hydroxycinnamate metabolism and stress resistance, acetyl coenzyme A assimilation, and nitrogen metabolism were validated with engineered mutants of P. putida . Additionally, functional gene clusters from ICA of RB-TnSeq data sets were compared with regulatory gene clusters from prior ICA of RNAseq data sets to draw connections between gene regulation and function. Because ICA profiles the functional role of several distinct gene networks simultaneously, it can reduce the time required to annotate gene function relative to manual curation of RB-TnSeq data sets. IMPORTANCE This study demonstrates a rapid, automated approach for elucidating functional modules within complex genetic networks. While Pseudomonas putida randomly barcoded transposon insertion sequencing data were used as a proof of concept, this approach is applicable to any organism with existing functional genomics data sets and may serve as a useful tool for many valuable applications, such as guiding metabolic engineering efforts in other microbes or understanding functional relationships between virulence-associated genes in pathogenic microbes. Furthermore, this work demonstrates that comparison of data obtained from independent component analysis of transcriptomics and gene fitness datasets can elucidate regulatory-functional relationships between genes, which may have utility in a variety of applications, such as metabolic modeling, strain engineering, or identification of antimicrobial drug targets.

• Research Organization: National Renewable Energy Laboratory (NREL), Golden, CO (United States)
• Sponsoring Organization: USDOE Office of Science (SC), Biological and Environmental Research (BER); USDOE Office of Energy Efficiency and Renewable Energy (EERE), Office of Sustainable Transportation. Bioenergy Technologies Office (BETO)
• Grant/Contract Number: AC36-08GO28308; AC02-05CH11231
• OSTI ID: 2290392
• Alternate ID(s): OSTI ID: 2316146
• Report Number(s): NREL/JA-2A00-89002; e00942-23
• Journal Information: mSystems, Journal Name: mSystems Vol. 9 Journal Issue: 3; ISSN 2379-5077
• Publisher: American Society for Microbiology
• Country of Publication: United States
• Language: English

Related Subjects

09 BIOMASS FUELS
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CHEMISTRY
amino acid metabolism
aromatic catabolism
functional genomics
independent component analysis
machine learning
Pseudomonas putida
RB-TnSeq
transposon insertion sequencing