Award: OCE-2126631

Bacteria and Archaea drive nearly all biogeochemical cycles by performing physiological processes other forms of life cannot do, or live in places where other forms of life cannot exist. Seafloor methane seeps are one such environment, where in the cold, dark, crushing, anoxic environment rich microbial communities thrive based on foundational members that perform the anaerobic oxidation of methane (AOM). Yet, little is known about the specifics how these microbes interact with their environment so successfully. Genomic surveys of these methane seep communities have revealed tremendous novelty and variability, but this high genetic diversity poses a major barrier to uncovering what feature(s) are most impactful. AOM is performed by physical aggregates comprised of two symbionts, anaerobic methanotrophic archaea (ANME) and sulfate reducing bacteria (SRB). We separated and sequenced genomes for individual ANME/SRB aggregates from the surrounding sediment from multiple methane seeps. As a result, we obtained genomic data representing of hundreds of individual aggregates across environments. We discovered that very closely related strains of ANME, whose genomes were more than 97% identical, separated into distinct subgroups indicative of ecological variation within a species. By looking at the identity and pattern of genes within these subgroups, we discovered that these distinct but closely-related strains encoded distinct metabolic pathways, notably the differential presence of nitrogenase. This result suggests ecological differentiation among these subpopulations. Single-aggregate genomes from SRB partners exhibited similar patterns within the same samples, suggesting strain-level co-diversification among partners. By querying publicly available metagenomes from methane seeps, we found similar patterns of strain-level diversity occurs at methane seeps around the globe. These findings advance our collective understanding of the ecological variation possible within microbial species. Further, studying these questions drove the development of bioinformatic analyses suited for disentangling closely related microbiota that will be beneficial to researchers in related fields. Broadly, these findings show unprecedented fine-tuning of microbial adaptation in this system, and opens questions for how many other systems have similarly hidden ecological variation.

Last Modified: 02/27/2025
Modified by: Daniel R Utter