Application value of metagenomic next-generation sequencing for antimicrobial resistance prediction in respiratory tract infections_West China Medical Journal

Authors：

WANG Jing ¹ , CHEN Bojiang ² , ZHOU Yongzhao ² ,  LI Weimin ^1,2

1. Precision Medicine Center, Precision Medicine Key Laboratory of Sichuan Province, West China Hospital, Sichuan University, Chengdu, Sichuan 610041, P. R. China;
2. Department of Respiratory and Critical Care Medicine, West China Hospital, Sichuan University, Chengdu, Sichuan 610041, P. R. China;

Corresponding author：

LI Weimin, Email: weimi003@scu.edu.cn

Keywords：

Metagenomic next-generation sequencing; antimicrobial susceptibility testing; pathogen; antimicrobial resistance

DOI：

10.7507/1002-0179.202206094

Video：

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Abstract Full text Figures/Tables Video References Cited by

Antimicrobial resistance is a rigorous health issue around the world. Because of the short turn-around-time and broad pathogen spectrum, culture-independent metagenomic next-generation sequencing (mNGS) is a powerful and highly efficient tool for clinical pathogen detection. The increasing question is whether mNGS is practical in the prediction of antimicrobial susceptibility. This review summarizes the current mNGS-based antimicrobial susceptibility testing technologies. The critical determinants of mNGS-based antibacterial resistance prediction have been comprehensively analyzed, including antimicrobial resistance databases, sequence alignment tools, detection tools for genomic antimicrobial resistance determinants, as well as resistance prediction models. The clinical challenges for mNGS-based antibacterial resistance prediction have also been reviewed and discussed.

Citation： WANG Jing, CHEN Bojiang, ZHOU Yongzhao, LI Weimin. Application value of metagenomic next-generation sequencing for antimicrobial resistance prediction in respiratory tract infections. West China Medical Journal, 2022, 37(8): 1121-1127. doi: 10.7507/1002-0179.202206094 Copy

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1. Antimicrobial Resistance Collaborators. Global burden of bacterial antimicrobial resistance in 2019: a systematic analysis. Lancet, 2022, 399(10325): 629-655.
2. 宏基因组分析和诊断技术在急危重症感染应用专家共识组. 宏基因组分析和诊断技术在急危重症感染应用的专家共识. 中华急诊医学杂志, 2019, 28(2): 151-155.
3. 《中华传染病杂志》编辑委员会. 中国宏基因组学第二代测序技术检测感染病原体的临床应用专家共识. 中华传染病杂志, 2020, 38(11): 681-689.
4. 中华医学会检验医学分会. 高通量宏基因组测序技术检测病原微生物的临床应用规范化专家共识. 中华检验医学杂志, 2020, 43(12): 1181-1195.
5. 中华医学会检验医学分会临床微生物学组, 中华医学会微生物学与免疫学分会临床微生物学组, 中国医疗保健国际交流促进会临床微生物与感染分会. 宏基因组高通量测序技术应用于感染性疾病病原检测中国专家共识. 中华检验医学杂志, 2021, 44(2): 107-120.
6. 中华医学会检验医学分会. 宏基因组测序病原微生物检测生物信息学分析规范化管理专家共识. 中华检验医学杂志, 2021, 44(9): 799-807.
7. 韩东升, 马筱玲, 吴文娟. 病原体宏基因组高通量测序医院实验室本地化之路: 现状和挑战. 中华检验医学杂志, 2022, 45(2): 100-104.
8. Ransom EM, Potter RF, Dantas G, et al. Genomic prediction of antimicrobial resistance: ready or not, here it comes!. Clin Chem, 2020, 66(10): 1278-1289.
9. Ruppé E, d’Humières C, Armand-Lefèvre L. Inferring antibiotic susceptibility from metagenomic data: dream or reality?. Clin Microbiol Infect, 2022: S1198-743X(22)00229-4.
10. Tyson GH, McDermott PF, Li C, et al. WGS accurately predicts antimicrobial resistance in Escherichia coli. J Antimicrob Chemother, 2015, 70(10): 2763-2769.
11. Moradigaravand D, Palm M, Farewell A, et al. Prediction of antibiotic resistance in Escherichia coli from large-scale pan-genome data. PLoS Comput Biol, 2018, 14(12): e1006258.
12. Quan TP, Bawa Z, Foster D, et al. Evaluation of whole-genome sequencing for mycobacterial species identification and drug susceptibility testing in a clinical setting: a large-scale prospective assessment of performance against line probe assays and phenotyping. J Clin Microbiol, 2018, 56(2): e01417-e01480.
13. Yang Y, Niehaus KE, Walker TM, et al. Machine learning for classifying tuberculosis drug-resistance from DNA sequencing data. Bioinformatics, 2018, 34(10): 1666-1671.
14. Chen ML, Doddi A, Royer J, et al. Beyond multidrug resistance: leveraging rare variants with machine and statistical learning models in Mycobacterium tuberculosis resistance prediction. EBioMedicine, 2019, 43: 356-369.
15. Kuang X, Wang F, Hernandez KM, et al. Accurate and rapid prediction of tuberculosis drug resistance from genome sequence data using traditional machine learning algorithms and CNN. Sci Rep, 2022, 12(1): 2427.
16. Mason A, Foster D, Bradley P, et al. Accuracy of different bioinformatics methods in detecting antibiotic resistance and virulence factors from Staphylococcus aureus whole-genome sequences. J Clin Microbiol, 2018, 56(9): e01815-e01817.
17. Alam MT, Petit RA 3rd, Crispell EK, et al. Dissecting vancomycin-intermediate resistance in Staphylococcus aureus using genome-wide association. Genome Biol Evol, 2014, 6(5): 1174-1185.
18. Deng X, Memari N, Teatero S, et al. Whole-genome sequencing for surveillance of invasive pneumococcal diseases in Ontario, Canada: rapid prediction of genotype, antibiotic resistance and characterization of emerging serotype 22F. Front Microbiol, 2016, 7: 2099.
19. Zankari E, Hasman H, Kaas RS, et al. Genotyping using whole-genome sequencing is a realistic alternative to surveillance based on phenotypic antimicrobial susceptibility testing. J Antimicrob Chemother, 2013, 68(4): 771-777.
20. Kos VN, Deraspe M, McLaughlin RE, et al. The resistome of Pseudomonas aeruginosa in relationship to phenotypic susceptibility. Antimicrob Agents Chemother, 2015, 59(1): 427-436.
21. Nguyen M, Brettin T, Long SW, et al. Developing an in silico minimum inhibitory concentration panel test for Klebsiella pneumoniae. Sci Rep, 2018, 8(1): 421.
22. Eyre DW, De Silva D, Cole K, et al. WGS to predict antibiotic MICs for Neisseria gonorrhoeae. J Antimicrob Chemother, 2017, 72(7): 1937-1947.
23. Su M, Satola SW, Read TD. Genome-based prediction of bacterial antibiotic resistance. J Clin Microbiol, 2019, 57(3): e01405-18.
24. Kozarewa I, Armisen J, Gardner AF, et al. Overview of target enrichment strategies. Curr Protoc Mol Biol, 2015, 112(21): 7.21.1-7.21.23.
25. Allicock OM, Guo C, Uhlemann AC, et al. BacCapSeq: a platform for diagnosis and characterization of bacterial infections. MBio, 2018, 9(5): e02007-e02018.
26. Ferreira I, Lepuschitz S, Beisken S, et al. Culture-free detection of antibiotic resistance markers from native patient samples by hybridization capture sequencing. Microorganisms, 2021, 9(8): 1672.
27. Chen H, Bai X, Gao Y, et al. Profile of bacteria with ARGs among real-world samples from ICU admission patients with pulmonary infection revealed by metagenomic NGS. Infect Drug Resist, 2021, 14: 4993-5004.
28. Liu H, Zhang Y, Yang J, et al. Application of mNGS in the etiological analysis of lower respiratory tract infections and the prediction of drug resistance. Microbiol Spectr, 2022, 10(1): e0250221.
29. Wang K, Li P, Lin Y, et al. Metagenomic diagnosis for a culture-negative sample from a patient with severe pneumonia by nanopore and next-generation sequencing. Front Cell Infect Microbiol, 2020, 10: 182.
30. Cohen LJ, Han S, Huang YH, et al. Identification of the colicin V bacteriocin gene cluster by functional screening of a human microbiome metagenomic library. ACS Infect Dis, 2018, 4(1): 27-32.
31. Wigand J, Tansirichaiya S, Winje E, et al. Functional screening of a human saliva metagenomic DNA reveal novel resistance genes against sodium hypochlorite and chlorhexidine. BMC Oral Health, 2021, 21(1): 632.
32. Liu B, Pop M. ARDB--Antibiotic Resistance Genes Database. Nucleic Acids Res, 2009, 37(Database issue): D443-D447.
33. Gupta SK, Padmanabhan BR, Diene SM, et al. ARG-ANNOT, a new bioinformatic tool to discover antibiotic resistance genes in bacterial genomes. Antimicrob Agents Chemother, 2014, 58(1): 212-220.
34. Jia B, Raphenya AR, Alcock B, et al. CARD 2017: expansion and model-centric curation of the comprehensive antibiotic resistance database. Nucleic Acids Res, 2017, 45(D1): D566-D573.
35. Zankari E, Hasman H, Cosentino S, et al. Identification of acquired antimicrobial resistance genes. J Antimicrob Chemother, 2012, 67(11): 2640-2644.
36. Gibson MK, Forsberg KJ, Dantas G. Improved annotation of antibiotic resistance determinants reveals microbial resistomes cluster by ecology. ISME J, 2015, 9(1): 207-216.
37. Wallace JC, Port JA, Smith MN, et al. FARME DB: a functional antibiotic resistance element database. Database (Oxford), 2017, 2017: baw165.
38. Yin X, Jiang XT, Chai B, et al. ARGs-OAP v2.0 with an expanded SARG database and Hidden Markov Models for enhancement characterization and quantification of antibiotic resistance genes in environmental metagenomes. Bioinformatics, 2018, 34(13): 2263-2270.
39. Ruppé E, Ghozlane A, Tap J, et al. Prediction of the intestinal resistome by a three-dimensional structure-based method. Nat Microbiol, 2019, 4(1): 112-123.
40. Thai QK, Pleiss J. SHV Lactamase Engineering Database: a reconciliation tool for SHV β-lactamases in public databases. BMC Genomics, 2010, 11: 563.
41. Thai QK, Bös F, Pleiss J. The Lactamase Engineering Database: a critical survey of TEM sequences in public databases. BMC Genomics, 2009, 10: 390.
42. Bush K, Jacoby GA. Updated functional classification of beta-lactamases. Antimicrob Agents Chemother, 2010, 54(3): 969-976.
43. Srivastava A, Singhal N, Goel M, et al. CBMAR: a comprehensive β-lactamase molecular annotation resource. Database (Oxford), 2014, 2014: bau111.
44. CRyPTIC Consortium and the 100, 000 Genomes Project, Allix-Béguec C, Arandjelovic I, et al. Prediction of susceptibility to first-line tuberculosis drugs by DNA sequencing. N Engl J Med, 2018, 379(15): 1403-1415.
45. Flandrois JP, Lina G, Dumitrescu O. MUBII-TB-DB: a database of mutations associated with antibiotic resistance in Mycobacterium tuberculosis. BMC Bioinformatics, 2014, 15: 107.
46. Sandgren A, Strong M, Muthukrishnan P, et al. Tuberculosis drug resistance mutation database. PLoS Med, 2009, 6(2): e2.
47. Saha SB, Uttam V, Verma V. u-CARE: user-friendly comprehensive antibiotic resistance repository of Escherichia coli. J Clin Pathol, 2015, 68(8): 648-651.
48. 杨兵, 梁晶, 刘林梦, 等. 耐药基因数据库概述. 生物工程学报, 2020, 36(12): 2582-2597.
49. Inouye M, Dashnow H, Raven LA, et al. SRST2: rapid genomic surveillance for public health and hospital microbiology labs. Genome Med, 2014, 6(11): 90.
50. Clausen PT, Zankari E, Aarestrup FM, et al. Benchmarking of methods for identification of antimicrobial resistance genes in bacterial whole genome data. J Antimicrob Chemother, 2016, 71(9): 2484-2488.
51. Hunt M, Mather AE, Sánchez-Busó L, et al. ARIBA: rapid antimicrobial resistance genotyping directly from sequencing reads. Microb Genom, 2017, 3(10): e000131.
52. Rowe WPM, Winn MD. Indexed variation graphs for efficient and accurate resistome profiling. Bioinformatics, 2018, 34(21): 3601-3608.
53. Zankari E, Allesøe R, Joensen KG, et al. PointFinder: a novel web tool for WGS-based detection of antimicrobial resistance associated with chromosomal point mutations in bacterial pathogens. J Antimicrob Chemother, 2017, 72(10): 2764-2768.
54. Bankevich A, Nurk S, Antipov D, et al. SPAdes: a new genome assembly algorithm and its applications to single-cell sequencing. J Comput Biol, 2012, 19(5): 455-477.
55. Zerbino DR, Birney E. Velvet: algorithms for de novo short read assembly using de Bruijn graphs. Genome Res, 2008, 18(5): 821-829.
56. Simpson JT, Wong K, Jackman SD, et al. ABySS: a parallel assembler for short read sequence data. Genome Res, 2009, 19(6): 1117-1123.
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