1887

Abstract

, a gram-negative bacterium, is a common pathogen causing nosocomial infection. The drug-resistance rate of is increasing year by year, posing a severe threat to public health worldwide. has been listed as one of the pathogens causing the global crisis of antimicrobial resistance in nosocomial infections. We need to explore the drug resistance of for clinical diagnosis. Single nucleotide polymorphisms (SNPs) are of high density and have rich genetic information in whole-genome sequencing (WGS), which can affect the structure or expression of proteins. SNPs can be used to explore mutation sites associated with bacterial resistance.

Machine learning methods can detect genetic features associated with the drug resistance of from whole-genome SNP data.

This work used Fast Feature Selection (FFS) and Codon Mutation Detection (CMD) machine learning methods to detect genetic features related to drug resistance of from whole-genome SNP data.

WGS data on resistance of strains to four antibiotics (tetracycline, gentamicin, imipenem, amikacin) were downloaded from the European Nucleotide Archive (ENA). Sequence alignments were performed with to complete SNP calling using HS11286 chromosome as the reference genome. The FFS algorithm was applied to feature selection of the SNP dataset. The training set was constructed based on mutation sites with mutation frequency >0.995. Based on the original SNP training set, 70% of SNPs were randomly selected from each dataset as the test set to verify the accuracy of the training results. Finally, the resistance genes were obtained by the CMD algorithm and .

The number of strains resistant to tetracycline, gentamicin, imipenem and amikacin was 931, 1048, 789 and 203, respectively. Machine learning algorithms were applied to the SNP training set and test set, and 28 and 23 resistance genes were predicted, respectively. The 28 resistance genes in the training set included 22 genes in the test set, which verified the accuracy of gene prediction. Among them, some genes (KPHS_35310, KPHS_18220, KPHS_35880, etc.) corresponded to known resistance genes (, , , etc). Logistic regression classifiers were established based on the identified SNPs in the training set. The area under the curves (AUCs) of the four antibiotics was 0.939, 0.950, 0.912 and 0.935, showing a strong ability to predict bacterial resistance.

Machine learning methods can effectively be used to predict resistance genes and associated SNPs. The FFS and CMD algorithms have wide applicability. They can be used for the drug-resistance analysis of any microorganism with genomic variation and phenotypic data. This work lays a foundation for resistance research in clinical applications.

Loading

Article metrics loading...

/content/journal/jmm/10.1099/jmm.0.001474
2021-11-23
2024-03-28
Loading full text...

Full text loading...

References

  1. Long SW, Olsen RJ, Eagar TN, Beres SB, Zhao P et al. Population genomic analysis of 1,777 extended-spectrum beta-lactamase-producing Klebsiella pneumoniae isolates, Houston, Texas: unexpected abundance of clonal group 307. mBio 2017; 8:e00489-17. [View Article] [PubMed]
    [Google Scholar]
  2. Snitkin ES, Zelazny AM, Thomas PJ, Stock F. NISC Comparative Sequencing Program et al. Tracking a hospital outbreak of carbapenem-resistant Klebsiella pneumoniae with whole-genome sequencing. Sci Transl Med 2012; 4:116r–148r [View Article]
    [Google Scholar]
  3. Hu F, Guo Y, Yang Y, Zheng Y, Wu S et al. Resistance reported from China antimicrobial surveillance network (CHINET) in 2018. Eur J Clin Microbiol Infect Dis 2019; 38:2275–2281 [View Article] [PubMed]
    [Google Scholar]
  4. Resistance reported from China antimicrobial surveillance network (CHINET) in 2019 Expert Committee on Rational Drug Use National Health Commission; 2020
    [Google Scholar]
  5. Baltekin Ö, Boucharin A, Tano E, Andersson DI, Elf J. Antibiotic susceptibility testing in less than 30 min using direct single-cell imaging. Proc Natl Acad Sci U S A 2017; 114:9170–9175 [View Article] [PubMed]
    [Google Scholar]
  6. Pancholi P, Carroll KC, Buchan BW, Chan RC, Dhiman N et al. Multicenter evaluation of the accelerate PhenoTest BC Kit for rapid identification and phenotypic antimicrobial susceptibility testing using morphokinetic cellular analysis. J Clin Microbiol 2018; 56:e01329-17. [View Article] [PubMed]
    [Google Scholar]
  7. Walker GT, Quan J, Higgins SG, Toraskar N, Chang W et al. Predicting antibiotic resistance in gram-negative bacilli from resistance genes. Antimicrob Agents Chemother 2019; 63:e02462-18. [View Article] [PubMed]
    [Google Scholar]
  8. Müller-Schulte E, Tuo MN, Akoua-Koffi C, Schaumburg F, Becker SL. High prevalence of ESBL-producing Klebsiella pneumoniae in clinical samples from central Côte d’Ivoire. Int J Infect Dis 2020; 91:207–209 [View Article] [PubMed]
    [Google Scholar]
  9. Lu S, Soeung V, Nguyen HAT, Long SW, Musser JM et al. Development and evaluation of a novel protein-based assay for specific detection of KPC β-Lactamases from Klebsiella pneumoniae clinical isolates. mSphere 2020; 5:e00918-19. [View Article] [PubMed]
    [Google Scholar]
  10. Davis JJ, Boisvert S, Brettin T, Kenyon RW, Mao C et al. Antimicrobial resistance prediction in PATRIC and RAST. Sci Rep 2016; 6:27930. [View Article] [PubMed]
    [Google Scholar]
  11. Freebern E, Santos DJA, Fang L, Jiang J, Parker Gaddis KL et al. GWAS and fine-mapping of livability and six disease traits in Holstein cattle. BMC Genomics 2020; 21:41 [View Article] [PubMed]
    [Google Scholar]
  12. Gao G, Gao D, Zhao X, Xu S, Zhang K et al. Genome-Wide association study-based identification of SNPs and haplotypes associated with goose reproductive performance and egg quality. Front Genet 2021; 12:360 [View Article]
    [Google Scholar]
  13. Lee JC, Espéli M, Anderson CA, Linterman MA, Pocock JM et al. Human SNP links differential outcomes in inflammatory and infectious disease to a FOXO3-regulated pathway. Cell 2013; 155:57–69 [View Article] [PubMed]
    [Google Scholar]
  14. Sengupta Chattopadhyay A, Hsiao C-L, Chang CC, Lian I-B, Fann CSJ. Summarizing techniques that combine three non-parametric scores to detect disease-associated 2-way SNP-SNP interactions. Gene 2014; 533:304–312 [View Article] [PubMed]
    [Google Scholar]
  15. Shang J, Zhang J, Lei X, Zhao W, Dong Y. EpiSIM: simulation of multiple epistasis, linkage disequilibrium patterns and haplotype blocks for genome-wide interaction analysis. Genes Genom 2013; 35:305–316 [View Article]
    [Google Scholar]
  16. Rishishwar L, Petit RA 3rd, Kraft CS, Jordan IK. Genome sequence-based discriminator for vancomycin-intermediate Staphylococcus aureus. J Bacteriol 2014; 196:940–948 [View Article] [PubMed]
    [Google Scholar]
  17. Pesesky MW, Hussain T, Wallace M, Patel S, Andleeb S et al. Evaluation of machine learning and rules-based approaches for predicting antimicrobial resistance profiles in gram-negative bacilli from whole genome sequence data. Front Microbiol 2016; 7:1887. [View Article] [PubMed]
    [Google Scholar]
  18. Gibson MK, Forsberg KJ, Dantas G. Improved annotation of antibiotic resistance determinants reveals microbial resistomes cluster by ecology. ISME J 2015; 9:207–216 [View Article] [PubMed]
    [Google Scholar]
  19. Drouin A, Giguère S, Déraspe M, Marchand M, Tyers M et al. Predictive computational phenotyping and biomarker discovery using reference-free genome comparisons. BMC Genomics 2016; 17:754 [View Article] [PubMed]
    [Google Scholar]
  20. Santerre JW, Davis JJ, Xia FF, Stevens R. Machine learning for antimicrobial resistance. arXiv preprint 2016
    [Google Scholar]
  21. Feretzakis G, Sakagianni A, Loupelis E, Kalles D, Martsoukou M et al. Using machine learning to predict antimicrobial resistance of Acinetobacter baumannii, Klebsiella pneumoniae and Pseudomonas aeruginosa strains. Stud Health Technol Inform 2021; 281:43–47 [View Article] [PubMed]
    [Google Scholar]
  22. Macesic N, Bear Don’t Walk OJ IV, Pe’er I, Tatonetti NP, Peleg AY et al. Predicting phenotypic polymyxin resistance in Klebsiella pneumoniae through machine learning analysis of genomic data. mSystems 2020; 5:e00656-19. [View Article] [PubMed]
    [Google Scholar]
  23. Xing Z, Ding W, Zhang S, Zhong L, Wang L et al. Machine learning-based differentiation of nontuberculous mycobacteria lung disease and pulmonary tuberculosis using CT images. Biomed Res Int 2020; 2020:6287545. [View Article] [PubMed]
    [Google Scholar]
  24. Nguyen M, Brettin T, Long SW, Musser JM, Olsen RJ et al. Developing an in silico minimum inhibitory concentration panel test for Klebsiella pneumoniae. Sci Rep 2018; 8:421. [View Article] [PubMed]
    [Google Scholar]
  25. Shi J, Yan Y, Links MG, Li L, Dillon J-AR et al. Antimicrobial resistance genetic factor identification from whole-genome sequence data using deep feature selection. BMC Bioinformatics 2019; 20:535 [View Article] [PubMed]
    [Google Scholar]
  26. Liu P, Li P, Jiang X, Bi D, Xie Y et al. Complete genome sequence of Klebsiella pneumoniae subsp. pneumoniae HS11286, a multidrug-resistant strain isolated from human sputum. J Bacteriol 2012; 194:1841–1842 [View Article] [PubMed]
    [Google Scholar]
  27. CLSI Performance Standards for Antimicrobial Susceptibility Testing, 27th Informational Supplement. CLSI supplement Wayne, PA: Clinical and Laboratory Standards Institute; 2017
    [Google Scholar]
  28. Kurtz S, Phillippy A, Delcher AL, Smoot M, Shumway M et al. Versatile and open software for comparing large genomes. Genome Biol 2004; 5:R12. [View Article] [PubMed]
    [Google Scholar]
  29. Purcell S, Neale B, Todd-Brown K, Thomas L, Ferreira MAR et al. PLINK: a tool set for whole-genome association and population-based linkage analyses. Am J Hum Genet 2007; 81:559–575 [View Article] [PubMed]
    [Google Scholar]
  30. Yan W. The study of identification of pathogenic SNP based on feature selection [D] Nanjing Agriculture University; 2017
    [Google Scholar]
  31. Petković M, Slavkov I, Kocev D, Džeroski S. Biomarker discovery by feature ranking: Evaluation on a case study of embryonal tumors. Comput Biol Med 2021; 128:104143 [View Article] [PubMed]
    [Google Scholar]
  32. Li H. A statistical framework for SNP calling, mutation discovery, association mapping and population genetical parameter estimation from sequencing data. Bioinformatics 2011; 27:2987–2993 [View Article] [PubMed]
    [Google Scholar]
  33. Nielsen R, Paul JS, Albrechtsen A, Song YS. Genotype and SNP calling from next-generation sequencing data. Nat Rev Genet 2011; 12:443–451 [View Article] [PubMed]
    [Google Scholar]
  34. Thammasiri D, Meesad P. Adaboost ensemble data classification based on diversity of classifiers. AMR 2011; 403–408:3682–3687 [View Article]
    [Google Scholar]
  35. Tibshirani R. Regression shrinkage and selection via the lasso: a retrospective. J R Stat Soc 2011; 73:273–282 [View Article]
    [Google Scholar]
  36. Aimeenice Mutation types of SNP; 2019 https://www.cnblogs.com/rmliu/p/10946066.html
  37. Beheshti M, Ardebili A, Beheshti F, Lari AR, Siyadatpanah A et al. Tetracycline resistance mediated by tet efflux pumps in clinical isolates of Acinetobacter baumannii. Rev Inst Med Trop Sao Paulo 2020; 62:e88 [View Article] [PubMed]
    [Google Scholar]
  38. Jahantigh M, Samadi K, Dizaji RE, Salari S. Antimicrobial resistance and prevalence of tetracycline resistance genes in Escherichia coli isolated from lesions of colibacillosis in broiler chickens in Sistan, Iran. BMC Vet Res 2020; 16:267 [View Article] [PubMed]
    [Google Scholar]
  39. Gul H, Ali SS, Saleem S, Khan S, Khan J et al. Subtractive proteomics and immunoinformatics approaches to explore Bartonella bacilliformis proteome (virulence factors) to design B and T cell multi-epitope subunit vaccine. Infect Genet Evol 2020; 85:104551 [View Article] [PubMed]
    [Google Scholar]
  40. Zhou S, Zhuang Y, Zhu X, Yao F, Li H et al. YhjX regulates the growth of Escherichia coli in the presence of a subinhibitory concentration of gentamicin and mediates the adaptive resistance to gentamicin. Front Microbiol 2019; 10:1180. [View Article] [PubMed]
    [Google Scholar]
  41. Kim H-SL, Rodriguez RD, Morris SK, Zhao S, Donato JJ. Identification of a novel plasmid-borne gentamicin resistance gene in nontyphoidal salmonella isolated from retail Turkey. Antimicrob Agents Chemother 2020; 64:e00867-20. [View Article] [PubMed]
    [Google Scholar]
  42. Bárria C, Domingues S, Arraiano CM. Pneumococcal RNase R globally impacts protein synthesis by regulating the amount of actively translating ribosomes. RNA Biol 2019; 16:211–219 [View Article] [PubMed]
    [Google Scholar]
  43. Holden VI, Wright MS, Houle S, Collingwood A, Dozois CM et al. Iron acquisition and siderophore release by carbapenem-resistant sequence type 258 Klebsiella pneumoniae. mSphere 2018; 3:e00125-18. [View Article] [PubMed]
    [Google Scholar]
  44. Garneau-Tsodikova S, Labby KJ. Mechanisms of resistance to aminoglycoside antibiotics: overview and perspectives. MedChemComm 2016; 7:11–27 [View Article] [PubMed]
    [Google Scholar]
  45. Lin J, Nishino K, Roberts MC, Tolmasky M, Aminov RI et al. Mechanisms of antibiotic resistance. Front Microbiol 2015; 6:34. [View Article] [PubMed]
    [Google Scholar]
  46. Chang L, Hou Y, Zhu L, Wang Z, Chen G et al. Veliparib overcomes multidrug resistance in liver cancer cells. Biochem Biophys Res Commun 2020; 521:596–602 [View Article] [PubMed]
    [Google Scholar]
  47. Wu Z-X, Teng Q-X, Cai C-Y, Wang J-Q, Lei Z-N et al. Tepotinib reverses ABCB1-mediated multidrug resistance in cancer cells. Biochem Pharmacol 2019; 166:120–127 [View Article] [PubMed]
    [Google Scholar]
  48. Lob SH, Hackel MA, Kazmierczak KM, Young K, Motyl MR et al. In vitro activity of imipenem-relebactam against gram-negative ESKAPE Pathogens Isolated by Clinical Laboratories in the United States in 2015 (Results from the SMART Global Surveillance Program). Antimicrob Agents Chemother 2017; 61:e02209–e02216 [View Article] [PubMed]
    [Google Scholar]
  49. Diene SM, Rolain JM. Carbapenemase genes and genetic platforms in Gram-negative bacilli: Enterobacteriaceae, Pseudomonas and Acinetobacter species. Clin Microbiol Infect 2014; 20:831–838 [View Article] [PubMed]
    [Google Scholar]
  50. El Khoury JY, Maure A, Gingras H, Leprohon P, Ouellette M. Chemogenomic screen for imipenem resistance in gram-negative bacteria. mSystems 2019; 4:e00465-19. [View Article] [PubMed]
    [Google Scholar]
  51. Balhana RJC, Singla A, Sikder MH, Withers M, Kendall SL. Global analyses of TetR family transcriptional regulators in mycobacteria indicates conservation across species and diversity in regulated functions. BMC Genomics 2015; 16:479 [View Article]
    [Google Scholar]
  52. Perrone F, De Siena B, Muscariello L, Kendall SL, Waddell SJ et al. A novel TetR-like transcriptional regulator is induced in acid-nitrosative stress and controls expression of an efflux pump in Mycobacteria. Front Microbiol 2017; 8: [View Article]
    [Google Scholar]
  53. Wang SC, Davejan P, Hendargo KJ, Javadi-Razaz I, Chou A et al. Expansion of the Major Facilitator Superfamily (MFS) to include novel transporters as well as transmembrane-acting enzymes. Biochim Biophys Acta Biomembr 2020; 1862:183277 [View Article] [PubMed]
    [Google Scholar]
  54. Aguiar-Pulido V, Huang W, Suarez-Ulloa V, Cickovski T, Mathee K et al. Metagenomics, metatranscriptomics, and metabolomics approaches for microbiome analysis. Evol Bioinform Online 2016; 12:5–16 [View Article] [PubMed]
    [Google Scholar]
  55. Krause KM, Serio AW, Kane TR, Connolly LE. Aminoglycosides: an overview. Cold Spring Harb Perspect Med 2016; 6:a027029 [View Article]
    [Google Scholar]
  56. Forsberg KJ, Patel S, Wencewicz TA, Dantas G. The tetracycline destructases: a novel family of tetracycline-inactivating enzymes. Chem Biol 2015; 22:888–897 [View Article] [PubMed]
    [Google Scholar]
  57. Gasparrini AJ, Markley JL, Kumar H, Wang B, Fang L et al. Tetracycline-inactivating enzymes from environmental, human commensal, and pathogenic bacteria cause broad-spectrum tetracycline resistance. Commun Biol 2020; 3:241 [View Article] [PubMed]
    [Google Scholar]
  58. Zhu Y, Wang C, Schwarz S, Liu W, Yang Q et al. Identification of a novel tetracycline resistance gene, tet(63), located on a multiresistance plasmid from Staphylococcus aureus. J Antimicrob Chemother 2021; 76:576–581 [View Article] [PubMed]
    [Google Scholar]
  59. Hu F-P, Guo Y, Zhu D-M, Wang F, Jiang X-F et al. Resistance trends among clinical isolates in China reported from CHINET surveillance of bacterial resistance, 2005–2014. Clinical Microbiology and Infection 2016; 22:S9–S14 [View Article]
    [Google Scholar]
  60. Hu F, Guo Y, Zhu D, Wang F, Jiang X F et al. CHINET 2016 surveillance of bacterial resistance in China. Chin J Infect Chemother 2017; 17:481–491
    [Google Scholar]
  61. Hu F, Guo Y, Zhu D, Wang F, Jiang X F et al. 2017 surveillance of bacterial resistance in China. Chin J Infect Chemother 2018; 18:241–251
    [Google Scholar]
  62. Tulara NK. Nitrofurantoin and fosfomycin for extended spectrum beta-lactamases producing Escherichia coli and Klebsiella pneumoniae. J Glob Infect Dis 2018; 10:19–21 [View Article] [PubMed]
    [Google Scholar]
  63. Yang X, Liu L, Zhao D, Zhang X. Detection of drug resistance gene of carbapenem-resistant Klebsiella pneumoniae and molecular epidemiological study. Chongqing Medicine 2018; 47:1977–1980 [View Article]
    [Google Scholar]
  64. Huang F, Xu Y. Detection of drug resistance genes and homology of carbapenem-resistant Klebsiella pneumoniae. Chin J Infect Control 2018; 17:21–25 [View Article]
    [Google Scholar]
  65. Dörfer M, Heine D, König S, Gore S, Werz O et al. Melleolides impact fungal translation via elongation factor 2. Org Biomol Chem 2019; 17:4906–4916 [View Article] [PubMed]
    [Google Scholar]
  66. Emptage RP, Tonthat NK, York JD, Schumacher MA, Zhou P. Structural basis of lipid binding for the membrane-embedded tetraacyldisaccharide-1-phosphate 4’-kinase LpxK. J Biol Chem 2014; 289:24059–24068 [View Article] [PubMed]
    [Google Scholar]
  67. Malinverni JC, Silhavy TJ. An ABC transport system that maintains lipid asymmetry in the gram-negative outer membrane. Proc Natl Acad Sci U S A 2009; 106:8009–8014 [View Article] [PubMed]
    [Google Scholar]
  68. Raetz CRH, Whitfield C. Lipopolysaccharide endotoxins. Annu Rev Biochem 2002; 71:635–700 [View Article] [PubMed]
    [Google Scholar]
  69. Bolla JR, Su C-C, Delmar JA, Radhakrishnan A, Kumar N et al. Crystal structure of the Alcanivorax borkumensis YdaH transporter reveals an unusual topology. Nat Commun 2015; 6:6874. [View Article] [PubMed]
    [Google Scholar]
  70. Su C-C, Bolla JR, Kumar N, Radhakrishnan A, Long F et al. Structure and function of Neisseria gonorrhoeae MtrF illuminates a class of antimetabolite efflux pumps. Cell Rep 2015; 11:61–70 [View Article] [PubMed]
    [Google Scholar]
  71. Grinter R, Lithgow T. The crystal structure of the TonB-dependent transporter YncD reveals a positively charged substrate-binding site. Acta Crystallogr D Struct Biol 2020; 76:484–495 [View Article] [PubMed]
    [Google Scholar]
  72. Hu Y, Cong Y, Li S, Rao X, Wang G et al. Identification of in vivo induced protein antigens of Salmonella enterica serovar Typhi during human infection. Sci China C Life Sci 2009; 52:942–948 [View Article] [PubMed]
    [Google Scholar]
  73. Zhou W, Tsai A, Dattmore DA, Stives DP, Chitrakar I et al. Crystal structure of E. coli PRPP synthetase. BMC Struct Biol 2019; 19:1. [View Article] [PubMed]
    [Google Scholar]
  74. Zhang Z, Ordonez AA, Wang H, Li Y, Gogarty KR et al. Positron Emission Tomography Imaging with 2-[18F]F- p-Aminobenzoic Acid Detects Staphylococcus aureus Infections and Monitors Drug Response. ACS Infect Dis 2018; 4:1635–1644 [View Article] [PubMed]
    [Google Scholar]
  75. Loh JT, Xu S, Huo JX, Kim S-Y, Wang Y et al. Dok3-protein phosphatase 1 interaction attenuates Card9 signaling and neutrophil-dependent antifungal immunity. J Clin Invest 2019; 129:2717–2729 [View Article] [PubMed]
    [Google Scholar]
  76. Edwards MS, McLaughlin RW, Li J, Wan X, Liu Y et al. Putative virulence factors of Plesiomonas shigelloides. Antonie van Leeuwenhoek 2019; 112:1815–1826 [View Article] [PubMed]
    [Google Scholar]
  77. Leblanc SKD, Oates CW, Raivio TL. Characterization of the induction and cellular role of the BaeSR two-component envelope stress response of Escherichia coli. J Bacteriol 2011; 193:3367–3375 [View Article] [PubMed]
    [Google Scholar]
  78. Liu B, Wei Y, Zhang Y, Yang Q. Deep neural networks for high dimension, low sample size data. In Twenty-Sixth International Joint Conference on Artificial Intelligence vol. 19 California:August 2017 2017 pp 2287–2293 [View Article]
    [Google Scholar]
http://instance.metastore.ingenta.com/content/journal/jmm/10.1099/jmm.0.001474
Loading
/content/journal/jmm/10.1099/jmm.0.001474
Loading

Data & Media loading...

Supplements

Supplementary material 1

PDF
This is a required field
Please enter a valid email address
Approval was a Success
Invalid data
An Error Occurred
Approval was partially successful, following selected items could not be processed due to error