A genomic catalog of Earth’s bacterial and archaeal symbionts
Abstract
1 SUMMARY Microbial symbiosis drives the functional and phylogenomic diversification of life on Earth, yet remains underexplored due to culturing challenges. This study employed machine learning (ML) to predict symbiotic lifestyles in hundreds of thousands of microbial genomes from diverse environmental metagenome samples and reference genomes. Predictions were performed using symclatron , a novel ML framework developed here to identify genomic signatures of symbionts. Predictions were deposited in a novel Symbiont Genomes catalog (SymGs). The results indicated that 15-23% of uncultivated microbes likely engage in symbiotic relationships with other organisms and are present in half of all known bacterial and archaeal phyla. We also identified genomic signatures of symbiotic lifestyles, including the loss of certain metabolic functions and the differential presence of others that may enable host-dependent living. The symclatron software and the SymGs catalog represent valuable resources for studying symbioses, facilitating future mechanistic investigations of host-microbe associations.
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Authors: Juan C. Villada, Yumary M. Vasquez, Gitta Szabó, E. W. Ainley Walker, Miguel F. Romero, Sarina Qin, Neha Varghese, Emiley A. Eloe‐Fadrosh, Nikos C. Kyrpides, Ramūnas Stepanauskas, Christopher Francis, Rick Cavicchioli, Katherine Trina McMahon, Steven Hallam, Wen‐Tso Liu, T. Mock, James Tiedje, Jiarong Guo, Axel Visel, Tanja Woyke, Frederik Schulz
Institutions: University of British Columbia, The Ohio State University, UNSW Sydney, Michigan State University, Stanford University, University of Illinois Urbana-Champaign, University of Wisconsin–Madison, University of California, Santa Cruz, University of East Anglia, Lawrence Berkeley National Laboratory, Bigelow Laboratory for Ocean Sciences, Joint Genome Institute, University of California, Merced