B091-0005
Global Distribution of High-Affinity Trace Gas Metabolisms in Oligotrophic Environments: A Potential Metabolic Survival Strategy and Gas Sink

Tuesday, 15 December 2020
Poster
Mae Kennedy, Tullis C Onstott, Zachary K Garvin and Janelle Arnold, Princeton University, Department of Geosciences, Princeton, NJ, United States
Abstract:
It has been hypothesized that certain oligotrophic soil environments, such as deserts and volcanic regions, select for high-affinity trace gas metabolisms as a survival tactic when other energy and carbon sources are less available. In such cases, atmospheric gas-oxidizing metabolisms used by chemolithothrophic organisms could be a major source of primary productivity for the microbial communities. The importance of these metabolisms in global biogeochemical cycles is often overlooked, and they have the potential to be relevant sinks for major trace gas species. Focusing on CO and H2 as primary gas substrates, we developed Hidden Markov Models (HMMs) for carbon monoxide dehydrogenase (CODH) and NiFe-hydrogenase genes, widely used as biomarkers for gas metabolisms, in order to search microbial metagenomes for evidence of these metabolisms. We used public databases to obtain metagenomic data from a variety of oligotrophic environments, including polar and hot deserts, caves, and volcanic/fumarolic soils. Searches for both genes yielded successful results across our selection of data. We found that high-affinity variants of the genes, such as Group 1h NiFe-hydrogenases, typically account for over 50% of all hydrogenases in our search results. Comparing the relative abundance of the major subgroups of CODHs (i.e., Types I and II) revealed that the functionally uncharacterized Type II-classified genes commonly represent double that of Type I, despite Type I sequences being historically associated with soil CO uptake. By investigating enzyme presence and its association with reported trace gas uptake in oligotrophic environments, this study utilizes statistical analyses with climatic and geochemical parameters to gain a better geographic and quantitative understanding of chemolithotrophic metabolisms.