Genome-based predictions of metabolic preferences and substrate phenotypes in psychrotrophic bacteria from permafrost environments

Abstract

ABSTRACT Genomes reveal vast functional potential, but harbor genomic noise that obscures prediction of metabolic and environmental preferences. Genomic databases are skewed towards clinically relevant and easily cultivated bacteria, limiting predictions for diverse and underrepresented environmental taxa. Psychrotrophic bacteria, which can survive and grow in cold, nutrient-limited, dry, and saline environments, are especially underrepresented despite their relevance for understanding microbial responses to changing cold environments and potential biotechnological value given growth at low temperatures. Assembling complete genomes of 48 isolates from Alaskan permafrost, seasonally frozen active layer soils, and terrestrial ice, we used Kyoto Encyclopedia of Genes and Genomes (KEGG) ortholog annotations to evaluate the predictability of metabolic resource-use traits observed using phenotypic tests. Genome-predicted values for glycolytic versus gluconeogenic catabolic preference index, or sugar-acid preference (SAP), explained over 50% of the variance in empirically observed SAP. SAP was inversely correlated to genomic GC content, which follows phylum-level trends, indicating that coarse metabolic preference covaries with phylogeny. Regularized elastic net models offered a more granular view, linking KEGG genes to specific substrate utilization and sensitivity phenotypes and yielding moderate but reproducible accuracy (AUC 0.70–0.79) for 11 substrates, demonstrating that specific substrate responses may be predictable from relatively small subsets of KO genes. These results extend recent advances, such as the SAP metric, and highlight associations among genomic GC content, phylum, and broad metabolic strategy. Linking genomic content to phenotype using isolates is a necessary step toward predictive models of microbial function in environmental communities, and this work can be used for hypothesis generation, with applications towards more expansive data sets. IMPORTANCE Cold region soils and ice host psychrotrophic bacteria with metabolic traits and adaptations that enable persistence in harsh, resource-limited environments. However, these taxa are underrepresented in genomic reference databases dominated by well-studied, mesophilic organisms. This gap limits inference of ecological strategies and our ability to predict how these microbes may influence the large, thaw-vulnerable carbon reservoirs in permafrost. Here, we show that genomic GC content is associated with the sugar-versus-acid catabolic preference (SAP) of isolates across major phyla, suggesting that broad genomic features may provide a coarse signal of metabolic strategy. We demonstrate that a modified SAP metric, using binary (positive/negative) substrate utilization rather than detailed growth rate measurements, is moderately predictive, thus extending its application to slow-growing or difficult-to-culture taxa. Together, these advances broaden the toolkit for linking genome content to resource-use traits (phenotype) in poorly characterized, cold-adapted bacteria and offer a tractable entry point to broad prediction and hypothesis generation. Cold region soils and ice host psychrotrophic bacteria with metabolic traits and adaptations that enable persistence in harsh, resource-limited environments. However, these taxa are underrepresented in genomic reference databases dominated by well-studied, mesophilic organisms. This gap limits inference of ecological strategies and our ability to predict how these microbes may influence the large, thaw-vulnerable carbon reservoirs in permafrost. Here, we show that genomic GC content is associated with the sugar-versus-acid catabolic preference (SAP) of isolates across major phyla, suggesting that broad genomic features may provide a coarse signal of metabolic strategy. We demonstrate that a modified SAP metric, using binary (positive/negative) substrate utilization rather than detailed growth rate measurements, is moderately predictive, thus extending its application to slow-growing or difficult-to-culture taxa. Together, these advances broaden the toolkit for linking genome content to resource-use traits (phenotype) in poorly characterized, cold-adapted bacteria and offer a tractable entry point to broad prediction and hypothesis generation.