Lu Lu
Profile Url: lu-lu
Researcher at Massachusetts Institute of Technology
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Public good exploitation has been studied extensively from an evolutionary lens, but little is known about the occurrence and impact of public good exploiters in natural communities. Here, we develop a reverse ecology approach to systematically identify bacteria that can exploit public goods produced during the degradation of polysaccharides. Focusing on chitin - the second most abundant biopolymer on the planet, we show that public good exploiters hinder the growth of degraders and invade marine microbial communities during early stages of colonization. Unlike cheaters in social evolution, exploiters and polysaccharide degraders (cooperators) come together by a process of community assembly, belong to distant lineages and can stably coexist. Thus, our approach opens novel avenues to interpret the wealth of genomic data through an ecological lens.
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Video Abstract (AI generated)
Paper
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Genomic data has revealed that genotypic variants of the same species, i.e., strains, coexist and are abundant in natural microbial communities. However, it is not clear if strains are ecologically equivalent, or if they exhibit distinct interactions and dynamics. Here, we address this problem by tracking 10 microbial communities from the pitcher plant Sarracenia purpurea in the laboratory for more than 300 generations. Using metagenomic sequencing, we reconstruct their dynamics over time and across scales, from distant phyla to closely related genotypes. We find that interactions between naturally occurring strains govern eco-evolutionary dynamics. Surprisingly, even fine-scale variants differing only by 100 base pairs can exhibit vastly different dynamics. We show that these differences may stem from ecological interactions in the communities, which are specific to strains, not species. Finally, by analyzing genomic differences between strains, we identify major functional hubs such as transporters, regulators, and carbohydrate-catabolizing enzymes, which might be the basis for strain-specific interactions. Our work shows that strains are the relevant level of diversity at which to study the long-term dynamics of microbiomes.