» Articles » PMID: 35974327

Nutrition or Nature: Using Elementary Flux Modes to Disentangle the Complex Forces Shaping Prokaryote Pan-genomes

Overview
Journal BMC Ecol Evol
Publisher Biomed Central
Date 2022 Aug 16
PMID 35974327
Authors
Affiliations
Soon will be listed here.
Abstract

Background: Microbial pan-genomes are shaped by a complex combination of stochastic and deterministic forces. Even closely related genomes exhibit extensive variation in their gene content. Understanding what drives this variation requires exploring the interactions of gene products with each other and with the organism's external environment. However, to date, conceptual models of pan-genome dynamics often represent genes as independent units and provide limited information about their mechanistic interactions.

Results: We simulated the stochastic process of gene-loss using the pooled genome-scale metabolic reaction networks of 46 taxonomically diverse bacterial and archaeal families as proxies for their pan-genomes. The frequency by which reactions are retained in functional networks when stochastic gene loss is simulated in diverse environments allowed us to disentangle the metabolic reactions whose presence depends on the metabolite composition of the external environment (constrained by "nutrition") from those that are independent of the environment (constrained by "nature"). By comparing the frequency of reactions from the first group with their observed frequencies in bacterial and archaeal families, we predicted the metabolic niches that shaped the genomic composition of these lineages. Moreover, we found that the lineages that were shaped by a more diverse metabolic niche also occur in more diverse biomes as assessed by global environmental sequencing datasets.

Conclusion: We introduce a computational framework for analyzing and interpreting pan-reactomes that provides novel insights into the ecological and evolutionary drivers of pan-genome dynamics.

Citing Articles

pan-Draft: automated reconstruction of species-representative metabolic models from multiple genomes.

De Bernardini N, Zampieri G, Campanaro S, Zimmermann J, Waschina S, Treu L Genome Biol. 2024; 25(1):280.

PMID: 39456096 PMC: 11515315. DOI: 10.1186/s13059-024-03425-1.

References
1.
Sela I, Wolf Y, Koonin E . Assessment of assumptions underlying models of prokaryotic pangenome evolution. BMC Biol. 2021; 19(1):27. PMC: 7874442. DOI: 10.1186/s12915-021-00960-2. View

2.
Wolf Y, Makarova K, Yutin N, Koonin E . Updated clusters of orthologous genes for Archaea: a complex ancestor of the Archaea and the byways of horizontal gene transfer. Biol Direct. 2012; 7:46. PMC: 3534625. DOI: 10.1186/1745-6150-7-46. View

3.
Pang T, Maslov S . Universal distribution of component frequencies in biological and technological systems. Proc Natl Acad Sci U S A. 2013; 110(15):6235-9. PMC: 3625286. DOI: 10.1073/pnas.1217795110. View

4.
Hosseini S, Martin O, Wagner A . Phenotypic innovation through recombination in genome-scale metabolic networks. Proc Biol Sci. 2016; 283(1839). PMC: 5046906. DOI: 10.1098/rspb.2016.1536. View

5.
Bolotin E, Hershberg R . Bacterial intra-species gene loss occurs in a largely clocklike manner mostly within a pool of less conserved and constrained genes. Sci Rep. 2016; 6:35168. PMC: 5062063. DOI: 10.1038/srep35168. View