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Journal Article

Nutrition or nature: using elementary flux modes to disentangle the complex forces shaping prokaryote pan-genomes


van Dijk,  Bram
Department Microbial Population Biology, Max Planck Institute for Evolutionary Biology, Max Planck Society;

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Garza, D. R., von Meijenfeldt, F. A. B., van Dijk, B., Boleij, A., Huynen, M. A., & Dutilh, B. E. (2022). Nutrition or nature: using elementary flux modes to disentangle the complex forces shaping prokaryote pan-genomes. BMC Ecology and Evolution, 22: 101. doi:10.1186/s12862-022-02052-3.

Cite as: https://hdl.handle.net/21.11116/0000-000B-440D-B
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.