Cited by 10Bacteriophages and microbial interactionsGenomics and Phylogenetic StudiesEvolution and Genetic Dynamics

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A pangenome analysis of ESKAPE bacteriophages: the underrepresentation may impact machine learning models

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Lee, Jeesu, Hunter, Branden, Shim, Hyunjin·2024·Frontiers in Molecular Biosciences

Published version of a preprint

Abstract

Bacteriophages are the most prevalent biological entities in the biosphere. However, limitations in both medical relevance and sequencing technologies have led to a systematic underestimation of the genetic diversity within phages. This underrepresentation not only creates a significant gap in our understanding of phage roles across diverse biosystems but also introduces biases in computational models reliant on these data for training and testing. In this study, we focused on publicly available genomes of bacteriophages infecting high-priority ESKAPE pathogens to show the extent and impact of this underrepresentation. First, we demonstrate a stark underrepresentation of ESKAPE phage genomes within the public genome and protein databases. Next, a pangenome analysis of these ESKAPE phages reveals extensive sharing of core genes among phages infecting the same host. Furthermore, genome analyses and clustering highlight close nucleotide-level relationships among the ESKAPE phages, raising concerns about the limited diversity within current public databases. Lastly, we uncover a scarcity of unique lytic phages and phage proteins with antimicrobial activities against ESKAPE pathogens. This comprehensive analysis of the ESKAPE phages underscores the severity of underrepresentation and its potential implications. This lack of diversity in phage genomes may restrict the resurgence of phage therapy and cause biased outcomes in data-driven computational models due to incomplete and unbalanced biological datasets.

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Research works printed but not linked: 54

  1. Alexandre, 1988, Putative genes of a variant-specific antigen gene transcription unit in Trypanosoma brucei, Mol. Cell. Biol., 8, 2367, 10.1128/mcb.8.6.2367 · DOI
  2. Al-Shayeb, 2020, Clades of huge phages from across Earth’s ecosystems, Nature, 578, 425, 10.1038/s41586-020-2007-4 · DOI
  3. 2022, Global burden of bacterial antimicrobial resistance in 2019: a systematic analysis, Lancet, 399, 629, 10.1016/S0140-6736(21)02724-0 · DOI · Paper page
  4. Batstone, 2022, Phenotypic and genomic signatures of interspecies cooperation and conflict in naturally occurring isolates of a model plant symbiont, Proc. Biol. Sci., 289, 20220477, 10.1098/rspb.2022.0477 · DOI
  5. Berman, 2000, The protein Data Bank, Nucleic Acids Res., 28, 235, 10.1093/nar/28.1.235 · DOI · Paper page
  6. Bondy-Denomy, 2012, Bacteriophage genes that inactivate the CRISPR/Cas bacterial immune system, Nature, 493, 429, 10.1038/nature11723 · DOI
  7. Chen, 2018, PGAweb: a web server for bacterial pan-genome analysis, Front. Microbiol., 9, 1910, 10.3389/fmicb.2018.01910 · DOI
  8. Clokie, 2011, Phages in nature, Bacteriophage, 1, 31, 10.4161/bact.1.1.14942 · DOI
  9. DeSalle, 2020, Phylogenomics: a primer, 10.1201/9780429397547 · DOI
  10. Deviyani, 2022, Assessing dataset bias in computer vision, 10.13140/RG.2.2.19950.89924 · DOI
  11. Ding, 2018, panX: pan-genome analysis and exploration, Nucleic Acids Res., 46, e5, 10.1093/nar/gkx977 · DOI
  12. Fleischmann, 1995, Whole-genome random sequencing and assembly of Haemophilus influenzae rd, Science, 269, 496, 10.1126/science.7542800 · DOI
  13. Fouts, 2012, PanOCT: automated clustering of orthologs using conserved gene neighborhood for pan-genomic analysis of bacterial strains and closely related species, Nucleic Acids Res., 40, e172, 10.1093/nar/gks757 · DOI
  14. Fremin, 2022, Thousands of small, novel genes predicted in global phage genomes, Cell. Rep., 39, 110984, 10.1016/j.celrep.2022.110984 · DOI
  15. Galperin, 2001, Conserved “hypothetical” proteins: new hints and new puzzles, Comp. Funct. Genomics, 2, 14, 10.1002/cfg.66 · DOI
  16. Galperin, 2021, COG database update: focus on microbial diversity, model organisms, and widespread pathogens, Nucleic Acids Res., 49, D274, 10.1093/nar/gkaa1018 · DOI
  17. Gautreau, 2020, PPanGGOLiN: depicting microbial diversity via a partitioned pangenome graph, PLoS Comput. Biol., 16, e1007732, 10.1371/journal.pcbi.1007732 · DOI
  18. Gordillo Altamirano, 2019, Phage therapy in the postantibiotic era, Clin. Microbiol. Rev., 32, e00066, 10.1128/CMR.00066-18 · DOI
  19. Ho, Random decision forests
  20. Ho, The random subspace method for constructing decision forests
  21. Horiuchi, 1974, Studies on lambda virulent mutants. III. Action of the anti- and vir-repressor (cro-product) of lambda phage on the related lambdoid phages, Mol. Gen. Genet., 133, 57, 10.1007/BF00268677 · DOI
  22. Jones, 2024, A causal perspective on dataset bias in machine learning for medical imaging, Nat. Mach. Intell., 1, 10.1038/s42256-024-00797-8 · DOI
  23. Kalie, 2007, An interferon alpha2 mutant optimized by phage display for IFNAR1 binding confers specifically enhanced antitumor activities, J. Biol. Chem., 282, 11602, 10.1074/jbc.M610115200 · DOI
  24. Knowles, 2016, Lytic to temperate switching of viral communities, Nature, 531, 466, 10.1038/nature17193 · DOI
  25. Konstantinidis, 2005, Genomic insights that advance the species definition for prokaryotes, Proc. Natl. Acad. Sci. U. S. A., 102, 2567, 10.1073/pnas.0409727102 · DOI
  26. Li, 2003, OrthoMCL: identification of ortholog groups for eukaryotic genomes, Genome Res., 13, 2178, 10.1101/gr.1224503 · DOI · Paper page
  27. Liu, 2022, IPGA: a handy integrated prokaryotes genome and pan-genome analysis web service, iMeta, 1, e55, 10.1002/imt2.55 · DOI
  28. Mathew, 2019, Mixed viral-bacterial infections and their effects on gut microbiota and clinical illnesses in children, Sci. Rep., 9, 865, 10.1038/s41598-018-37162-w · DOI
  29. McNair, 2012, PHACTS, a computational approach to classifying the lifestyle of phages, Bioinformatics, 28, 614, 10.1093/bioinformatics/bts014 · DOI
  30. Merrikh, 2023, Replication-transcription conflicts in bacteria, Nat. Rev. Microbiol., 10, 449, 10.1038/nrmicro2800 · DOI
  31. Meyn, 1977, A protease inhibitor blocks SOS functions in Escherichia coli: antipain prevents lambda repressor inactivation, ultraviolet mutagenesis, and filamentous growth, Proc. Natl. Acad. Sci. U. S. A., 74, 1152, 10.1073/pnas.74.3.1152 · DOI
  32. Michniewski, 2021, A new family of “megaphages” abundant in the marine environment, ISME Commun., 1, 58, 10.1038/s43705-021-00064-6 · DOI
  33. Page, 2015, Roary: rapid large-scale prokaryote pan genome analysis, Bioinformatics, 31, 3691, 10.1093/bioinformatics/btv421 · DOI · Paper page
  34. Paget, 2015, Bacterial sigma factors and anti-sigma factors: structure, function and distribution, Biomolecules, 5, 1245, 10.3390/biom5031245 · DOI
  35. Park, In silico optimization of RNA-protein interactions for CRISPR-Cas13-based antimicrobials, Biol. Direct, 17, 27, 10.1186/s13062-022-00339-5 · DOI
  36. Park, Rethinking protein drug design with highly accurate structure prediction of anti-CRISPR proteins, Pharmaceuticals, 15, 310, 10.3390/ph15030310 · DOI
  37. Park, 2023, Sequencing, fast and slow: profiling microbiomes in human samples with nanopore sequencing, Appl. Biosci., 2, 437, 10.3390/applbiosci2030028 · DOI
  38. Pilotto, 2021, Structural basis of RNA polymerase inhibition by viral and host factors, Nat. Commun., 12, 5523, 10.1038/s41467-021-25666-5 · DOI
  39. 2019, Prioritization of pathogens to guide discovery, research and development of new antibiotics for drug-resistant bacterial infections, including tuberculosis
  40. Santajit, 2016, Mechanisms of antimicrobial resistance in ESKAPE pathogens, Biomed. Res. Int., 2016, 2475067, 10.1155/2016/2475067 · DOI
  41. Santos, 2013, PANNOTATOR: an automated tool for annotation of pan-genomes, Genet. Mol. Res., 12, 2982, 10.4238/2013.August.16.2 · DOI
  42. Shim, Futuristic methods in virus genome evolution using the third-generation DNA sequencing and artificial neural networks, 485
  43. Shim, Feature learning of virus genome evolution with the nucleotide skip-gram neural network, Evol. Bioinform Online, 15, 1176934318821072, 10.1177/1176934318821072 · DOI
  44. Shim, 2022, Investigating the genomic background of CRISPR-Cas genomes for CRISPR-based antimicrobials, arXiv [q-bio.GN], 18, 11769343221103887, 10.1177/11769343221103887 · DOI
  45. Shim, 2023, Three innovations of next-generation antibiotics: evolvability, specificity, and non-immunogenicity, Antibiot. (Basel), 12, 204, 10.3390/antibiotics12020204 · DOI
  46. Shim, 2021, Diverse ATPase proteins in mobilomes constitute a large potential sink for prokaryotic host ATP, Front. Microbiol., 12, 691847, 10.3389/fmicb.2021.691847 · DOI
  47. Silpe, 2023, Small protein modules dictate prophage fates during polylysogeny, Nature, 620, 625, 10.1038/s41586-023-06376-y · DOI · Paper page
  48. Spoerel, 1979, A novel bacteriophage defence mechanism: the anti-restriction protein, Nature, 278, 30, 10.1038/278030a0 · DOI
  49. Steinegger, 2017, MMseqs2 enables sensitive protein sequence searching for the analysis of massive data sets, Nat. Biotechnol., 35, 1026, 10.1038/nbt.3988 · DOI · Paper page
  50. Suttle, 2005, Viruses in the sea, Nature, 437, 356, 10.1038/nature04160 · DOI
  51. Tonkin-Hill, 2020, Producing polished prokaryotic pangenomes with the Panaroo pipeline, Genome Biol., 21, 180, 10.1186/s13059-020-02090-4 · DOI
  52. Young, 2015, Phage therapy redux—what is to be done?, Science, 350, 1163, 10.1126/science.aad6791 · DOI
  53. Zhang, 2023, A pan-cancer analysis of anti-proliferative protein family genes for therapeutic targets in cancer, Sci. Rep., 13, 21607, 10.1038/s41598-023-48961-1 · DOI
  54. Zhou, 2020, Accurate reconstruction of bacterial pan- and core genomes with PEPPAN, Genome Res., 30, 1667, 10.1101/gr.260828.120 · DOI

Metadata

Authors
Lee, J., Hunter, B., & Shim, H.
Affiliations
  • Ghent University Global Campus (KR)
  • California State University, Fresno (US)
Corresponding author
Shim, Hyunjin
Venue
Frontiers in Molecular Biosciences
Published
2024-06-21
Volume
11
Pages
1395450-1395450
ISSN
2296-889X (ISSN-L)
OpenAlex
W4399901391
Cited by
10
References
5
Citations by year
2026: 5 · 2025: 5
FWCI
2.74
Type
journal article
Language
en
Open access
Locations
  • Frontiers in Molecular Biosciences (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)
Funders
California State University, Fresno
Topics
  • Life Sciences › Immunology and Microbiology › Virology › Bacteriophages and microbial interactions
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Phylogenetic Studies
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Evolution and Genetic Dynamics
Keywords
bacteriophages, ESKAPE pathogens, pangenome analysis, core genes, phage clustering, lytic phages, phage therapy

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