» Articles » PMID: 38861460

Leveraging Genome-scale Metabolic Models to Understand Aerobic Methanotrophs

Overview
Journal ISME J
Date 2024 Jun 11
PMID 38861460
Authors
Affiliations
Soon will be listed here.
Abstract

Genome-scale metabolic models (GEMs) are valuable tools serving systems biology and metabolic engineering. However, GEMs are still an underestimated tool in informing microbial ecology. Since their first application for aerobic gammaproteobacterial methane oxidizers less than a decade ago, GEMs have substantially increased our understanding of the metabolism of methanotrophs, a microbial guild of high relevance for the natural and biotechnological mitigation of methane efflux to the atmosphere. Particularly, GEMs helped to elucidate critical metabolic and regulatory pathways of several methanotrophic strains, predicted microbial responses to environmental perturbations, and were used to model metabolic interactions in cocultures. Here, we conducted a systematic review of GEMs exploring aerobic methanotrophy, summarizing recent advances, pointing out weaknesses, and drawing out probable future uses of GEMs to improve our understanding of the ecology of methane oxidizers. We also focus on their potential to unravel causes and consequences when studying interactions of methane-oxidizing bacteria with other methanotrophs or members of microbial communities in general. This review aims to bridge the gap between applied sciences and microbial ecology research on methane oxidizers as model organisms and to provide an outlook for future studies.

Citing Articles

A Study of the Community Relationships Between Methanotrophs and Their Satellites Using Constraint-Based Modeling Approach.

Esembaeva M, Kulyashov M, Kolpakov F, Akberdin I Int J Mol Sci. 2024; 25(22).

PMID: 39596533 PMC: 11594979. DOI: 10.3390/ijms252212469.

References
1.
Altieri A, Gedan K . Climate change and dead zones. Glob Chang Biol. 2014; 21(4):1395-406. DOI: 10.1111/gcb.12754. View

2.
Bosi E, Bacci G, Mengoni A, Fondi M . Perspectives and Challenges in Microbial Communities Metabolic Modeling. Front Genet. 2017; 8:88. PMC: 5478693. DOI: 10.3389/fgene.2017.00088. View

3.
Puri A, Schaefer A, Fu Y, Beck D, Greenberg E, Lidstrom M . Quorum Sensing in a Methane-Oxidizing Bacterium. J Bacteriol. 2016; 199(5). PMC: 5309911. DOI: 10.1128/JB.00773-16. View

4.
Feist A, Herrgard M, Thiele I, Reed J, Palsson B . Reconstruction of biochemical networks in microorganisms. Nat Rev Microbiol. 2009; 7(2):129-43. PMC: 3119670. DOI: 10.1038/nrmicro1949. View

5.
Geng J, Ji B, Li G, Lopez-Isunza F, Nielsen J . CODY enables quantitatively spatiotemporal predictions on in vivo gut microbial variability induced by diet intervention. Proc Natl Acad Sci U S A. 2021; 118(13). PMC: 8020746. DOI: 10.1073/pnas.2019336118. View