Predicting Bacterial Radiation Resistance Through Identifying Novel Genomic Features

Radiation resistance in bacteria is a critical trait with implications for biotechnology, medicine and environmental science. Deinococcus species possess unique genomic features that enable their extraordinary survival in extreme environments. However, the diversity of radiation resistance mechanisms across bacteria remains poorly explored. Here, we analysed the genomic diversity and functional gene repertoire of Deinococcus and extended the analysis to a broader set of radiation-resistant bacteria. We found that highly resistant strains tend to have smaller genomes and harbour more genes positively associated with radiation resistance, highlighting their potential roles in sustaining cellular homeostasis under radiation-induced stress. By integrating machine learning with curated genomic datasets, we identified key resistance-associated gene signatures and developed a predictive tool to quantify bacterial radiation resistance. This study offers new insights into the genomic basis of radiation resistance and provides a practical framework for its assessment in environmental and microbial engineering applications.