TolRad, a model for predicting radiation tolerance using Pfam annotations, identifies novel radiosensitive bacterial species from reference genomes and MAGs.

Abstract

The trait of ionizing radiation (IR) tolerance is variable between bacterium, with species succumbing to acute doses as low as 60 Gy and extremophiles able to survive doses exceeding 10,000 Gy. While survival screens have identified multiple highly radioresistant bacteria, such systemic searches have not been conducted for IR-sensitive bacteria. The taxonomy-level diversity of IR sensitivity is poorly understood, as are genetic elements that influence IR sensitivity. Using the protein domain (Pfam) frequencies from 61 bacterial species with experimentally determined D₁₀ values (the dose at which only 10% of the population survives), we trained TolRad, a random forest binary classifier, to distinguish between radiosensitive (D₁₀ < 200 Gy) and radiation-tolerant (D₁₀ > 200 Gy) bacteria. On untrained species, TolRad had an accuracy of 0.900. We applied TolRad to 152 UniProt-hosted bacterial proteomes associated with the human microbiome, including 37 strains from the ATCC Human Microbiome Collection, and classified 34 species as radiosensitive. Whereas IR-sensitive species (D₁₀ < 200 Gy) in the training data set had been confined to the phylum Proteobacterium, this initial TolRad screen identified radiosensitive bacteria in two additional phyla. We experimentally validated the predicted radiosensitivity of a Bacteroidota species from the human microbiome. To demonstrate that TolRad can be applied to metagenome-assembled genomes (MAGs), we tested the accuracy of TolRad on Egg-NOG assembled proteomes (0.965) and partial proteomes. Finally, three collections of MAGs were screened using TolRad, identifying further phyla with radiosensitive species and suggesting that environmental conditions influence the abundance of radiosensitive bacteria.

Importance: Bacterial species have vast genetic diversity, allowing for life in extreme environments and the conduction of complex chemistry. The ability to harness the full potential of bacterial diversity is hampered by the lack of high-throughput experimental or bioinformatic methods for characterizing bacterial traits. Here, we present a computational model that uses de novo-generated genome annotations to classify a bacterium as tolerant of ionizing radiation (IR) or as radiosensitive. This model allows for rapid screening of bacterial communities for low-tolerance species that are of interest for both mechanistic studies into bacterial sensitivity to IR and biomarkers of IR exposure.