Edaphic controls on genome size and GC content of bacteria in soil microbial communities. (March 2023) - Chuckran, Peter F.; Flagg, Cody; Propster, Jeffrey; Rutherford, William A.; Sieradzki, Ella T.; Blazewicz, Steven J.; Hungate, Bruce; Pett-Ridge, Jennifer; Schwartz, Egbert; Dijkstra, Paul
Abstract: Nutrient limitation has been shown to reduce bacterial genome size and influence nucleotide composition; however, much of this work has been conducted in marine systems and the factors which shape soil bacterial genomic traits remain largely unknown. Here we determined average genome size, GC content, codon usage, and amino acid content from 398 soil metagenomes across a broad geographic range and used machine-learning to determine the environmental parameters that most strongly explain the distribution of these traits. We found that genomic trait averages were most related to pH, which we suggest is primarily due to the correlation of pH with several environmental parameters, particularly soil carbon content. Low pH soils had higher carbon to nitrogen ratios (C:N) and tended to have communities with lower GC content and larger genomes, potentially a response to increased physiological stress and a requirement for metabolic diversity. Conversely, communities in high pH and low soil C:N had smaller genomes and higher GC content—indicating potential resource driven selection against AT base pairs, which have a higher C:N than GC base pairs. Similarly, we found that nutrient conservation also applied to amino acid stoichiometry, where bacteria in soils with low C:N ratios tended to code for amino acids with lower C:N. Together, these relationships point towards fundamental mechanisms that underpin genome size, and nucleotide and amino acid selection in soil bacteria. Highlights: Community averaged genomic traits of 398 soil metagenomes and environmental data. Average genome size and GC-% negatively correlated, consistent with C limitation. Correlation between soil C:N and predicted amino acid mean C:N. Machine learning approach indicated pH strongest driver of genomic traits.