Abstract EANA2024-86

Introduction: We investigated the potential of microbial contamination from spacecraft assembly cleanrooms to the icy moons of Jupiter, where habitable extra-terrestrial environments may exist. For planetary protection purposes, it is crucial to safeguard these precious environments from forward contamination by terrestrial microbes. However, unlike Mars, some of these icy moons, such as Europa, might harbor an entire liquid ocean under their icy crust. Therefore, common methods to estimate the survivability of microbes during space missions need to be re-evaluated, modified, or supplemented with modern state-of-the-art tools.

Objectives: Our objective was to develop and apply a new probabilistic approach that relies on genome-centric metagenomics and supervised machine learning to predict relevant microbial phenotypes from the entire sequence of a genome. This approach allows us to assess the likelihood of microbial survival on an icy moon based on their genomic features. We present a use case tested during ESA’s JUICE mission, which is on its way to exploring the icy moons around Jupiter.

Methods: We collected large-scale floor samples (~80m2) from two spacecraft assembly cleanrooms harboring the JUICE spacecraft before its launch. We performed deep shotgun-metagenomic sequencing on the samples (~200 million reads per sample), and obtained 183 bins and 19 high-quality representative MAGs (metagenome-assembled genomes). We used phenotype predictions based on supervised machine learning to infer potential microbial survivability on an icy moon, including models for desiccation resistance, sporulation, an anaerobic, thermophilic, or autotrophic lifestyle, cryo-tolerance, and halo resilience. We also profiled databases covering prokaryotes, eukaryotes, and viruses to get a comprehensive picture of all genetic signatures around the JUICE spacecraft.

Results: We found that most genomes were classified as common human skin residents. However, phenotype predictions based on their genome content still revealed phenotypic traits relevant for survival on an icy moon. According to these predictions, MAGs classified as Romboutsia_A, Chroococcidiopsidaceae, and Staphylococcus warneri, were positive for multiple relevant traits. We also identified 127 species from public repositories that had the potential to cover at least one trait and 17 species that covered multiple traits.

Conclusion: We developed and applied a new probabilistic approach based on genome-centric metagenomics and supervised machine learning to predict relevant microbial phenotypes from the entire sequence of a genome. We demonstrate its utility and flexibility for planetary protection purposes by assessing the potential of microbial contamination from human skin to the icy moons of Jupiter or Saturn. Our approach can complement standard spore assays and other methods to estimate the survivability of microbes during space missions, and help to safeguard the habitable extra-terrestrial environments from forward contamination.

Importance: Our study provides a novel and comprehensive perspective on the microbial diversity and survivability around the JUICE spacecraft and contributes to the advancement of planetary protection research and practice. Our study addresses the knowledge gap on microbial contaminants' functional potential, and the challenge of assessing the risk of forward contamination from spacecraft assembly cleanrooms.