1,153
Views
1
CrossRef citations to date
0
Altmetric
Research Article

In silico discovering relationship between bacteriophages and antimicrobial resistance

, , , &
Pages 14-23 | Received 15 Sep 2022, Accepted 21 Nov 2022, Published online: 04 Jan 2023

References

  • Calero-Cáceres W, Ye M, Balcázar JL. Bacteriophages as environmental reservoirs of antibiotic resistance. Trends Microbiol. 2019;27(7):570–577.
  • Colavecchio A, Cadieux B, Lo A, et al. Bacteriophages contribute to the spread of antibiotic resistance genes among foodborne pathogens of the enterobacteriaceae family–a review. Front Microbiol. 2017;8:1108.
  • Balcázar JL. Implications of bacteriophages on the acquisition and spread of antibiotic resistance in the environment. Int Microbiol. 2020;23(4):475–479.
  • Subirats J, Sànchez-Melsió A, Borrego CM, et al. Metagenomic analysis reveals that bacteriophages are reservoirs of antibiotic resistance genes. Int J Antimicrob Agents. 2016;48(2):163–167.
  • Enault F, Briet A, Bouteille L, et al. Phages rarely encode antibiotic resistance genes: a cautionary tale for virome analyses. ISME J. 2017;11(1):237–247.
  • Chevallereau A, Pons BJ, van Houte S, et al. Interactions between bacterial and phage communities in natural environments. Nat Rev Microbiol. 2022;20(1):49–62.
  • Auslander N, Gussow AB, Benler S, et al. Seeker: alignment-free identification of bacteriophage genomes by deep learning. Nucleic Acids Res. 2020;48(21):e121.
  • Strange JE, Leekitcharoenphon P, Møller FD, et al. Metagenomics analysis of bacteriophages and antimicrobial resistance from global urban sewage. Sci Rep. 2021;11(1):1–11.
  • Hendriksen RS, Munk P, Njage P, et al.; The Global Sewage Surveillance Project Consortium. Global monitoring of antimicrobial resistance based on metagenomics analyses of urban sewage. Nat Commun. 2019;10(1):1–12.
  • Danko D, Bezdan D, Afshinnekoo E, et al. Global genetic cartography of urban metagenomes and anti-microbial resistance. BioRxiv 724526; 2019. doi:10.1101/724526
  • Torres-Barceló C. The disparate effects of bacteriophages on antibiotic-resistant bacteria. Emerg Microbes Infect. 2018;7(1):1–12.
  • Do DT, Le TQT, Le NQK. Using deep neural networks and biological subwords to detect protein s-sulfenylation sites. Briefings Bioinf. 2021;22(3):bbaa128.
  • Le NQK. Fertility-gru: identifying fertility-related proteins by incorporating deep-gated recurrent units and original position-specific scoring matrix profiles. J Proteome Res. 2019;18(9):3503–3511.
  • Le NQK, Huynh T-T. Identifying snares by incorporating deep learning architecture and amino acid embedding representation. Front Physiol. 2019;10:1501.
  • http://camda2021.bioinf.jku.at/start; 2021.
  • Pawlowsky-Glahn V, Egozcue JJ, Tolosana-Delgado R. Modeling and analysis of compositional data. Chichester: John Wiley & Sons; 2015.
  • Chao A. Nonparametric estimation of the number of classes in a population. Scand J Stat. 1984;11(4):265–270.
  • Shannon CE, Weaver W. The mathematical theory of communication. Urbana (IL): University of Illinois Press; 1949.
  • Simpson EH. Measurement of diversity. Nature. 1949;163(4148):688.
  • Bray JR, Curtis JT. An ordination of the upland Forest communities of Southern Wisconsin. Ecol Monogr. 1957;27(4):325–349.
  • Tibshirani R. Regression shrinkage and selection via the lasso. J Roy Stat Soc: Ser B (Methodological). 1996;58(1):267–288.
  • Besag J, York J, Mollié A. Bayesian image restoration, with two applications in spatial statistics. Ann Inst Stat Math. 1991;43(1):1–20.
  • Menzel P, Ng KL, Krogh A. Fast and sensitive taxonomic classification for metagenomics with Kaiju. Nat Commun. 2016;7(1):11257–11259.
  • Mende DR, Letunic I, Huerta-Cepas J, et al. Progenomes: a resource for consistent functional and taxonomic annotations of prokaryotic genomes. Nucleic Acids Res. 2017;45(D1):D529–D534.
  • Malone B, Searle R, Malone B, et al. Updating the Australian digital soil texture mapping (part 1*): re-calibration of field soil texture class centroids and description of a field soil texture conversion algorithm. Soil Res. 2021;59(5):419–434.
  • Aitchison JA. Compositional data analysis. London (UK): Chapman and Hall; 1986.
  • Hinton AL, Mucha PJ. Differential compositional variation feature selection: a machine learning framework with log ratios for compositional metagenomic data. bioRxiv 2021.12.08.471758; 2021. doi:10.1101/2021.12.08.471758
  • Fernandes AD, Macklaim JM, Linn TG, et al. Anova-like differential expression (aldex) analysis for mixed population RNA-seq. PLoS One. 2013;8(7):e67019.
  • Xia Y, Sun J, Chen D-G. Community diversity measures and calculations. In: Xia Y, Sun J, Chen D-G, editors. Statistical analysis of microbiome data with R, ICSA book series in statistics. Singapore: Springer, 2018. p. 167–190.
  • Oksanen J, Blanchet FG, Kindt R, et al. Vegan: community ecology package. R Package Version 2.4-1; 2016.
  • Kuhn M, Wing J, Weston S, et al. caret: classification and regression training. r package version 6.0-86; 2020. Available at: https://cran.r-project.org/web/packages/caret/caret.pdf(accessed March 20, 2020).
  • Friedman JH, Hastie T, Tibshirani R. Regularization paths for generalized linear models via coordinate descent. J Stat Softw. 2010;33(1):1–22.
  • Hoerl AE, Kennard RW. Ridge regression: biased estimation for nonorthogonal problems. Technometrics. 1970;12(1):55–67.
  • Emmert-Streib F, Dehmer M. High-dimensional lasso-based computational regression models: regularization, shrinkage, and selection. MAKE. 2019;1(1):359–383.
  • Zhelyazkova M, Yordanova R, Mihaylov I, et al. Origin sample prediction and spatial modeling of antimicrobial resistance in metagenomic sequencing data. Front Genet. 2021;12:642991.
  • Zhelyazkova M, Yordanova R, Mihaylov I, et al. Bayesian hierarchical modelling for antimicrobial resistance. In: Sotirov SS, Pencheva T, Kacprzyk J, Atanassov KT, Sotirova E, Staneva G, editors. Contemporary methods in bioinformatics and biomedicine and their applications, lecture notes in networks and systems. Cham: Springer International Publishing; 2022. p. 79–87.
  • Lawson AB. Bayesian disease mapping: hierarchical modeling in spatial epidemiology. New York: Chapman and Hall/CRC; 2018.
  • Yang Y, Chen N, Chen T. Inference of environmental factor-microbe and microbe-microbe associations from metagenomic data using a hierarchical bayesian statistical model. Cell Syst. 2017;4(1):129–137.e5.
  • Ryan FJ. Application of machine learning techniques for creating urban microbial fingerprints. Biol Direct. 2019;14(1):13.
  • Lee D. Carbayes: an R package for Bayesian spatial modeling with conditional autoregressive priors. J Stat Softw. 2013;55(13):1–24.
  • Carlin BP, Chib S. Bayesian model choice via Markov chain Monte Carlo methods. J Roy Stat Soc: Ser B (Methodological). 1995;57(3):473–484.
  • Gittleman JL, Kot M. Adaptation: statistics and a null model for estimating phylogenetic effects. Syst Zool. 1990;39(3):227–241.
  • Geweke J. Evaluating the accuracy of sampling-based approaches to the calculations of posterior moments. Bayesian Stat. 1992;4:641–649.
  • Fillol-Salom A, Alsaadi A, de Sousa JAM, et al. Bacteriophages benefit from generalized transduction. PLoS Pathog. 2019;15(7):e1007888.
  • Hassan AY, Lin JT, Ricker N, et al. The age of phage: friend or foe in the new dawn of therapeutic and biocontrol applications? Pharmaceuticals. 2021;14(3):199.
  • Rodríguez-Rubio L, Serna C, Ares-Arroyo M, et al. Extensive antimicrobial resistance mobilization via multicopy plasmid encapsidation mediated by temperate phages. J Antimicrob Chemother. 2020;75(11):3173–3180.