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PreprintCited by 4Genomics and Phylogenetic StudiesBacterial Genetics and BiotechnologyEffects of Radiation Exposure

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TolRad: A model for predicting radiation tolerance using Pfam annotations identifies novel radiosensitive bacterial species from reference genomes and MAGs

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Sweet, Philip, Burroughs, Matthew, Jang, Sungyeon, Contreras, Lydia·2023·Microbiology Spectrum
DOI 10.1101/2023.11.02.562514PMC PMC11466087

Preprint — published as 10.1128/spectrum.03838-23

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Abstract

The trait of ionizing radiation (IR) tolerance is variable between bacterial species, with radiosensitive bacteria succumbing to acute doses around 100Gy and extremophiles able to survive doses exceeding 10,000Gy. While survival screens have identified multiple highly radioresistant bacteria, such systemic searches have not been conducted for radiosensitive bacteria. The taxonomy-level diversity of IR of intolerance across bacteria is poorly understood, as are genetic elements that influence IR sensitivity. Using the protein domain frequencies from 61 bacterial species with experimentally determined IR D10 values (the dose at which only 10% of the population survives) we trained TolRad, a random forest binary classifier, to distinguish between radiosensitive bacteria (D10 < 200Gy) and radiation tolerant bacteria (D10 > 200Gy). On the hidden species, TolRad had an accuracy of 0.900. We applied TolRad to 152 UniProt-hosted bacterial proteomes, including 37 strains from the ATCC Human Microbiome Collection, and classified 34 species as radiosensitive. Whereas IR intolerance (D10 < 200Gy) in the training dataset had been confined to the phylum Proteobacterium , this initial TolRad screen identified radiosensitive bacteria in 2 additional phyla. We experimentally validated the predicted radiosensitivity of a key species of the human microbiome from the Bacteroidota phyla. To demonstrate that TolRad can be applied to Metagenome-Assembled Genome (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 phylum 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.

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Fig 1 Construction of TolRad. (A) Histogram distribution of the train/test set. Each count is the mean D10 of a unique species. A total of 61 species were used to train the model, representing 121 experimentally determined D10 values. (B) Workflow used for training TolRad. (C) Performance of TolRad on the train set. Numbers are a unique species. 1, Acinetobacter calcoaceticus; 2, Aeromonas hydrophila; 3, Aeromonas salmonicida; 4, Aquifex pyrophilus; 5, Bacillus pumilus; 6, Bacillus sphaericus; 7, Campylobacter coli; 8, Campylobacter jejuni; 9, Campylobacter lari; 10, Coxiella burnetiid; 11, Deinococcus geothermalis; 12, Deinococcus radiodurans; 13, Enterobacter sp. BIGb0383; 14, Enterococcus faecium; 15, Enterococcus faecalis; 16, Escherichia coli; 17, Kineococcus radiotolerans; 18, Klebsiella variicola; 19, Kocuria rhizophila; 20, Lactococcus lactis; 21, Listeria monocytogenes; 22, Methylobacterium radiotoleran; 23, Micrococcus luteus; 24, Morganella morganii; 25, Mycobacterium smegmatis; 26, Mycobacterium tuberculosis; 27, Proteus vulgaris; 28, Pseudomonas putida; 29, Rhodococcus erythropolis; 30, Rubrobacter radiotolerans; 31, Rubrobacter xylanophilus; 32, Salmonella Senftenberg; 33, Salmonella enterica subsp. enterica serovar Heidelberg; 34, Salmonella paratyphi; 35, Serratia marcescens; 36, Shewanella putrefaciens; 37, Staphylococcus aureus; 38, Staphylococcus epidermidis; 39, Stenotrophomonas maltophilia; 40, Vibrio parahaemolyticus. Graph IDs can also be found in Table S3. Color denotes the classification assigned by TolRad. Red denotes a species classified as radiosensitive. Blue denotes a species classified as tolerant. The dashed line at 200 Gy is the cutoff used for defining radiosensitive vs tolerant species. (D) Same as (C), but for the train set classifications. 1, Acinetobacter radioresistens; 2, Bacillus cereus; 3, Bifidobacterium breve; 4, Deinococcus ficus; 5, Lactobacillus acidophilus; 6, Neisseria gonorrhoeae; 7, Paenibacillus amylolyticus; 8, Pediococcus pentosaceus; 9, Priestia megaterium; 10, Salmonella enterica; 11, Salmonella muenster; 12, Salmonella typhimurium; 13, Shewanella oneidensis; 14, Shigella boydii; 15, Shigella flexneri; 16, Shigella sonnei; 17, Spirosoma radiotolerans; 18, Streptococcus thermophilus; 19, Thermus thermophilus; 20, Vibrio cholera O1; 21, Yersinia enterocolitica.
Fig 1 Construction of TolRad. (A) Histogram distribution of the train/test set. Each count is the mean D10 of a unique species. A total of 61 species were used to train the model, representing 121 experimentally determined D10 values. (B) Workflow used for training TolRad. (C) Performance of TolRad on the train set. Numbers are a unique species. 1, Acinetobacter calcoaceticus; 2, Aeromonas hydrophila; 3, Aeromonas salmonicida; 4, Aquifex pyrophilus; 5, Bacillus pumilus; 6, Bacillus sphaericus; 7, Campylobacter coli; 8, Campylobacter jejuni; 9, Campylobacter lari; 10, Coxiella burnetiid; 11, Deinococcus geothermalis; 12, Deinococcus radiodurans; 13, Enterobacter sp. BIGb0383; 14, Enterococcus faecium; 15, Enterococcus faecalis; 16, Escherichia coli; 17, Kineococcus radiotolerans; 18, Klebsiella variicola; 19, Kocuria rhizophila; 20, Lactococcus lactis; 21, Listeria monocytogenes; 22, Methylobacterium radiotoleran; 23, Micrococcus luteus; 24, Morganella morganii; 25, Mycobacterium smegmatis; 26, Mycobacterium tuberculosis; 27, Proteus vulgaris; 28, Pseudomonas putida; 29, Rhodococcus erythropolis; 30, Rubrobacter radiotolerans; 31, Rubrobacter xylanophilus; 32, Salmonella Senftenberg; 33, Salmonella enterica subsp. enterica serovar Heidelberg; 34, Salmonella paratyphi; 35, Serratia marcescens; 36, Shewanella putrefaciens; 37, Staphylococcus aureus; 38, Staphylococcus epidermidis; 39, Stenotrophomonas maltophilia; 40, Vibrio parahaemolyticus. Graph IDs can also be found in Table S3. Color denotes the classification assigned by TolRad. Red denotes a species classified as radiosensitive. Blue denotes a species classified as tolerant. The dashed line at 200 Gy is the cutoff used for defining radiosensitive vs tolerant species. (D) Same as (C), but for the train set classifications. 1, Acinetobacter radioresistens; 2, Bacillus cereus; 3, Bifidobacterium breve; 4, Deinococcus ficus; 5, Lactobacillus acidophilus; 6, Neisseria gonorrhoeae; 7, Paenibacillus amylolyticus; 8, Pediococcus pentosaceus; 9, Priestia megaterium; 10, Salmonella enterica; 11, Salmonella muenster; 12, Salmonella typhimurium; 13, Shewanella oneidensis; 14, Shigella boydii; 15, Shigella flexneri; 16, Shigella sonnei; 17, Spirosoma radiotolerans; 18, Streptococcus thermophilus; 19, Thermus thermophilus; 20, Vibrio cholera O1; 21, Yersinia enterocolitica.CC BY · PubMed Central
Fig 2 Application of TolRad to bacteria isolated from the human microbiome. (A) Counts of bacteria species by phyla. Phylum diversity and tolerance predictions (by color) within the train/test set, the ATCC-hosted NIH Human Microbiome Project, and additional species isolated from the human skin, oral, and gut microbiome. (B) Survival, determined via CFU, of Bacteroides thetaiotaomicron at 10, 40, and 70 Gy. From three technical replicates, the D10 was determined to be 110 Gy. Survival at 200 Gy is predicted to be 1.8%. (C) Survival, determined via CFU, of Acinetobacter baumannii at 10, 70, and 170 Gy. The D10 was determined to be 400 Gy. (D) Survival, determined via CFU, of Pseudomonas aeruginosa at 10, 70, and 200 Gy. The D10 was determined to be 130 Gy.
Fig 2 Application of TolRad to bacteria isolated from the human microbiome. (A) Counts of bacteria species by phyla. Phylum diversity and tolerance predictions (by color) within the train/test set, the ATCC-hosted NIH Human Microbiome Project, and additional species isolated from the human skin, oral, and gut microbiome. (B) Survival, determined via CFU, of Bacteroides thetaiotaomicron at 10, 40, and 70 Gy. From three technical replicates, the D10 was determined to be 110 Gy. Survival at 200 Gy is predicted to be 1.8%. (C) Survival, determined via CFU, of Acinetobacter baumannii at 10, 70, and 170 Gy. The D10 was determined to be 400 Gy. (D) Survival, determined via CFU, of Pseudomonas aeruginosa at 10, 70, and 200 Gy. The D10 was determined to be 130 Gy.CC BY · PubMed Central
Fig 3 (A) Workflow for generating TolRad predictions on de novo genome annotations. First, the genome sequence is downloaded. EggNOG-Mapper is used to assign Pfams. Pfam abundance is calculated. TolRad classifications are made. Then, Pfam domains were randomly removed, and the classification was determined again. (B) De novo genome annotations were generated for the 61 species of the train/test set. Species numbers (graph ID) are in Table S3. These “mock MAGS” were classified using TolRad. Color denotes the classification assigned by TolRad. Red denotes a species classified as radiosensitive. Blue denotes a species classified as tolerant. The dashed line at 200 Gy is the cutoff used for defining radiosensitive vs tolerant species. (C) The mock MAGs were randomly degraded to 90%, 80%, 70%, 60%, and 50%. The Pfam abundances were recalculated along with the TolRad classification. The percent of correct classifications at each level is reported. (D) The number of missing predictors at each level of degradation within the mock MAGs from (C).
Fig 3 (A) Workflow for generating TolRad predictions on de novo genome annotations. First, the genome sequence is downloaded. EggNOG-Mapper is used to assign Pfams. Pfam abundance is calculated. TolRad classifications are made. Then, Pfam domains were randomly removed, and the classification was determined again. (B) De novo genome annotations were generated for the 61 species of the train/test set. Species numbers (graph ID) are in Table S3. These “mock MAGS” were classified using TolRad. Color denotes the classification assigned by TolRad. Red denotes a species classified as radiosensitive. Blue denotes a species classified as tolerant. The dashed line at 200 Gy is the cutoff used for defining radiosensitive vs tolerant species. (C) The mock MAGs were randomly degraded to 90%, 80%, 70%, 60%, and 50%. The Pfam abundances were recalculated along with the TolRad classification. The percent of correct classifications at each level is reported. (D) The number of missing predictors at each level of degradation within the mock MAGs from (C).CC BY · PubMed Central
Fig 4 Application of TolRad to three MAG collections. (A) As described in Fig. 3, de novo annotations were assigned to previously published environmentally collected MAGs. The fraction of previously published MAGs classified as radiosensitive (red) or tolerant (blue) across MAGs collected from the HSM, a glacial stream in the CHA, the HGM, and the water column of the deep ocean (DO). (B) Counts of MAGs, by phylum, from the deep ocean collection that were classified as radiosensitive or tolerant.
Fig 4 Application of TolRad to three MAG collections. (A) As described in Fig. 3, de novo annotations were assigned to previously published environmentally collected MAGs. The fraction of previously published MAGs classified as radiosensitive (red) or tolerant (blue) across MAGs collected from the HSM, a glacial stream in the CHA, the HGM, and the water column of the deep ocean (DO). (B) Counts of MAGs, by phylum, from the deep ocean collection that were classified as radiosensitive or tolerant.CC BY · PubMed Central

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Metadata

Authors
Sweet, P., Burroughs, M., Jang, S., & Contreras, L.
Affiliations
The University of Texas at Austin (US)
Corresponding author
Contreras, Lydia
ORCID
Venue
Microbiology Spectrum
Publisher
openRxiv
Published
2023-11-02
Volume
12
Issue
10
Pages
e0383823-e0383823
ISSN
2165-0497 (ISSN-L)
DOI
10.1101/2023.11.02.562514
PMC
PMC11466087
OpenAlex
W4402272237
Cited by
4
References
1
Citations by year
2026: 2 · 2024: 2
FWCI
0.54
Type
journal article
Language
en
Open access
Licence
cc-by
Locations
  • Microbiology Spectrum (publishedVersion, cc-by)
  • bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by-nc-nd)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Funders
  • U.S. Department of Defense
  • Office of the Director of National Intelligence (W911NF22S0002)
  • Defense Threat Reduction Agency (HDTRA1-17-1-0025, HDTRA1)
  • U.S. Air Force (FA9550)
  • Intelligence Advanced Research Projects Activity
  • Air Force Office of Scientific Research (FA9550-, FA9550-20-1, FA9550-20-1-0131, FA9550-20)
Topics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Phylogenetic Studies
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Bacterial Genetics and Biotechnology
  • Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Effects of Radiation Exposure
Keywords
metagenome-assembled genomes, human microbiome, Bacteroidetes, extremophiles, radiation biomarkers, random forest classification
MeSH terms
Bacteria, Bacterial Proteins, Humans, Radiation Tolerance, Radiation, Ionizing, Genome, Bacterial, Proteome, Metagenome, Molecular Sequence Annotation, Microbiota

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