Functional profiling of spacecraft cleanroom microbiomes through genome-wide phenotype predictions

Current planetary protection approaches rely heavily on spore-based tests developed for Mars missions and may not adequately assess contamination risks for icy ocean worlds such as Europa. We developed a genome-based framework combining deep shotgun metagenomics and supervised machine learning to predict survival-relevant microbial traits in ESA JUICE launch-site cleanrooms. From 183 genome bins, 25 representative genomes were analyzed for traits including cryotolerance, desiccation tolerance, salt resilience, anaerobic metabolism, autotrophy, and sporulation. Several skin-associated microbes carried multiple relevant traits, and some appeared actively replicating. A broader meta-analysis of 1,868 genomes showed that trait profiles vary strongly within taxa, demonstrating that taxonomy alone is insufficient for risk assessment. This framework complements current planetary protection assays, helps to predict how microbes would survive in a new biotope, and supports functional, risk-informed contamination monitoring for future space missions.