Putting Microorganisms on the Map: Continental-Scale Context for Thousands of Newly Sampled Microbial Genomes from North American Wetlands
Emily Bechtold1, Jared Ellenbogen1, Mikayla Borton1, Bridget McGivern1, Djennyfer Ferreira, David Hoyt2, Elisha Wood-Charlson3, William Riley4, Jorge Villa5, Christopher Henry3, Gil Bohrer6, Mike Wilkins1, and Kelly Wrighton1
1Colorado State University; 2Pacific Northwest National Laboratory; 3Argonne National Laboratory; 4Lawrence Berkeley National Laboratory; 5University of Louisiana; and 6The Ohio State University
Despite their vital roles in transforming nutrients and controlling greenhouse gas (GHG) fluxes in wetlands, microbial knowledge is often limited to taxonomic identity alone and rarely includes cross-site comparisons. The team proposes to address this knowledge gap using coordinated, reproducible field measurements collected across a wetland-methane continuum from at least 25 wetlands in the continental United States. The overarching project goal is to decode the unifying microbial properties governing soil carbon decomposition and methane fluxes, both within and among freshwater wetlands. This project tests the overarching hypothesis that microbial genomic attributes are conserved across high methane-emitting samples or wetlands, such that some level of biological representation into models will enhance predictions of soil methane fluxes. First, the team will use a cross-wetland approach to define the microbial membership, physiology, and interactions directly contributing to wetland methane production. Next, the team will uncover the microbial decomposition network features that classify high methane emitting wetlands. Using this information, the team will test the genomic resolution needed to make robust predictions of methane fluxes across regional and global models. These integrated field, laboratory, and modeling approaches will identify the biotic attributes conserved across methane-emitting wetlands, such that some level of biological representation into models will enhance predictions of soil methane fluxes.
Today wetland contributions to the global methane budget are estimated from ecosystem scale models. These models exclude representation of soil microbial metabolism or are based on incomplete or outdated knowledge on the physiological controllers on soil methane metabolism. Instead, these models use abiotic (e.g., temperature) and indirect biotic (e.g., gross primary production) variables to approximate the environmental states enabling soil methane flux. However, years of observations from more than 40 freshwater wetlands showed that these variables only partially predicted annual methane fluxes. The deviation of the predictions from observations indicates predictions derived only from abiotic variables incompletely represented methane flux, especially for the highest emitting wetlands. Team members posit that knowledge of methane-cycling microorganisms and their physiological networks will enhance freshwater wetland model predictions. In this proposed research the team will identify the microbial processes impacting wetland methane fluxes and evaluate their biogeographic conservation. Researchers will distill this content into an ecosystem model with the goal of closing “the gap” between measured and predicted methane fluxes from wetlands.
In the first year of this project, the team leveraged publicly available 16S amplicon data to begin addressing the biogeographical conservation of microbial community composition and metabolic function across wetlands. Researchers cataloged the microbial community membership from 1,118 wetland samples collected from nine geographically dispersed wetlands. Samples screened with 16S rRNA were also contextualized with soil chemistry and flux data, yet many sites also included genome-resolved metagenomics, transcriptomics, and metabolomics. The analysis shows that marshes, fens, and bogs have distinct microbial communities such that wetland type—more than geography, climate, or soil features—drives microbial community composition. Despite sitewide differences at the community level, researchers did observe six genera of methanogens (e.g., Methanoregula, Methanothrix) and four genera of methanotrophs (e.g., Methylobacter) were core across wetland samples. Notably, the three highest methane emitting wetlands, all marshes, shared methanogen and methanotroph membership and distribution patterns. In fact, the dominance of Methanoregula is a strong predictor of methane flux across wetlands. Researchers also show that wetlands with annual methane emission that deviate the most from temperature-based predictions had the highest Methanoregula relative abundance. Together this preliminary cross-wetland data illuminates the conservation of microorganisms across high methane-emitting wetlands, narrowing the diverse soil carbon cycling community to a “most-wanted” list of fruitful targets for genomic and metabolic network efforts, yielding process-based knowledge needed for biologically aware models.
To begin to illuminate the metabolic features of carbon decomposition in high methane emitting wetlands, the team created the second version of the Genome Resolved Open Watershed (GROW2) database. This public, genomic resource contains the identity and distribution of 26,000 unique microbial genomes from wetlands, including over 500 methanogen and methanotroph genomes. This spatial sampling scheme coupled to a breadth of ecological dimensions (e.g., wetland type, methane emission rate, land use) will enable us to systematically identify the microorganisms and metabolic networks associated with high methane-emitting wetlands. As a preliminary approach, the team performed site-specific, co-occurrence analysis to uncover the network of microorganisms coordinated to methanogens across each site. Linking the 16S rRNA data to the project’s GROW2 genomes, team members developed metabolic profiles for these methanogen connected taxa. Researchers show that obligate fermenters (e.g., many syntrophs) have the highest connectivity to the most abundant methanogens. Additionally, the highest methane-emitting wetlands had the least amount of methanogen connections, suggesting streamlined metabolic circuits may contribute to enhanced methane production across wetland soils. Ultimately, GROW2 is a living road map, articulating the power of microbiome science to decode microbial organismal and metabolic patterns at scales necessary for ingestion into predictive modeling frameworks. GROW2 is publicly available on KBase, engendering collaborative enterprises with the goal to advance a new era of climate-driven research in wetlands.
This research was supported by the DOE Office of Science, Office of Biological and Environmental Research (BER), grant no. DE-SC0023084. This program is supported by the U. S. Department of Energy, Office of Science, through the Genomic Science Program, Office of Biological and Environmental Research, under FWP ERKP123.