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Abstract

Understanding the adaptations of microorganisms to their environment is key to predicting the stability and dynamics of microbial communities. To uncover molecular mechanisms of environmental response, we extracted genomic features from 13,554 prokaryotic isolates, and trained machine learning models to identify which ones are most strongly associated with the microbial salinity, temperature, oxygen, and pH preferences. To extract these features in high throughput, including gene families, non-coding RNAs (ncRNAs), oligonucleotides, and amino acid usage, we built FxTractor, a scalable and adjustable pipeline available at: https://github.com/MGXlab/FxTractor. We validated the performance of our models with experimental data from a newly isolated deep-sea extremophile belonging to the genus Limnochorda that is not well-represented among the ML training sets, showing strong agreement between predictions and the conditions used to isolate this strain. Our analysis revealed specific gene and ncRNA families associated with each of the four environmental parameters, uncovering both established and potentially new molecular mechanisms. Examples include the bacterial large Signaling Recognition Particle in isolates that are able to grow at high temperatures ([&ge;]55{degrees}C), suggesting a role in translational pausing and structural stability under thermal stress. We also found the anti-hemB ncRNA to be associated with low-salinity (<0.7% NaCl), indicating a conserved antisense mechanism regulating the energetic costs of heme biosynthesis. Together, these findings provide new insights into microbe-environment interactions, and show how FxTractor enables high throughput discovery of genomic associations.

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