Paving the Way for Precise Diagnostics of Antimicrobial Resistant Bacteria

Overview

Journal Front Mol Biosci

Specialty Biology

Date 2022 Aug 29

PMID 36032670

Authors

Hao Wang

Chenhao Jia

Hongzhao Li

Rui Yin

Jiang Chen

Yan Li

Min Yue

Affiliations

Soon will be listed here.

Abstract

The antimicrobial resistance (AMR) crisis from bacterial pathogens is frequently emerging and rapidly disseminated during the sustained antimicrobial exposure in human-dominated communities, posing a compelling threat as one of the biggest challenges in humans. The frequent incidences of some common but untreatable infections unfold the public health catastrophe that antimicrobial-resistant pathogens have outpaced the available countermeasures, now explicitly amplified during the COVID-19 pandemic. Nowadays, biotechnology and machine learning advancements help create more fundamental knowledge of distinct spatiotemporal dynamics in AMR bacterial adaptation and evolutionary processes. Integrated with reliable diagnostic tools and powerful analytic approaches, a collaborative and systematic surveillance platform with high accuracy and predictability should be established and implemented, which is not just for an effective controlling strategy on AMR but also for protecting the longevity of valuable antimicrobials currently and in the future.

Citing Articles

Antimicrobial Susceptibility of Isolates Causing Community-Acquired Urinary Tract Infections: Comparison of Methods.

Cardoso A, Flores V, do Rosario G, Succar J, Berbert L, Oliveira M Microorganisms. 2025; 13(2).

PMID: 40005598 PMC: 11857815. DOI: 10.3390/microorganisms13020231.

Artificial intelligence in predicting pathogenic microorganisms' antimicrobial resistance: challenges, progress, and prospects.

Li Y, Cui X, Yang X, Liu G, Zhang J Front Cell Infect Microbiol. 2024; 14:1482186.

PMID: 39554812 PMC: 11564165. DOI: 10.3389/fcimb.2024.1482186.

A global genome dataset for Salmonella Gallinarum recovered between 1920 and 2024.

Jia C, Huang L, Zhou H, Cao Q, Wang Z, He F Sci Data. 2024; 11(1):1094.

PMID: 39375387 PMC: 11458892. DOI: 10.1038/s41597-024-03908-7.

Development and evaluation of rapid and accurate one-tube RPA-CRISPR-Cas12b-based detection of mcr-1 and tet(X4).

Wang Y, Chen H, Pan Q, Wang J, Jiao X, Zhang Y Appl Microbiol Biotechnol. 2024; 108(1):345.

PMID: 38801527 PMC: 11129972. DOI: 10.1007/s00253-024-13191-6.

Tackling the Antimicrobial Resistance "Pandemic" with Machine Learning Tools: A Summary of Available Evidence.

Rusic D, Kumric M, Seselja Perisin A, Leskur D, Bukic J, Modun D Microorganisms. 2024; 12(5).

PMID: 38792673 PMC: 11123121. DOI: 10.3390/microorganisms12050842.

References

Steinberger-Levy I, Shifman O, Zvi A, Ariel N, Beth-Din A, Israeli O . A Rapid Molecular Test for Determining Yersinia pestis Susceptibility to Ciprofloxacin by the Quantification of Differentially Expressed Marker Genes. Front Microbiol. 2016; 7:763. PMC: 4871873. DOI: 10.3389/fmicb.2016.00763. View

Shariati A, Dadashi M, Moghadam M, van Belkum A, Yaslianifard S, Darban-Sarokhalil D . Global prevalence and distribution of vancomycin resistant, vancomycin intermediate and heterogeneously vancomycin intermediate Staphylococcus aureus clinical isolates: a systematic review and meta-analysis. Sci Rep. 2020; 10(1):12689. PMC: 7391782. DOI: 10.1038/s41598-020-69058-z. View

Knight G, Glover R, McQuaid C, Olaru I, Gallandat K, Leclerc Q . Antimicrobial resistance and COVID-19: Intersections and implications. Elife. 2021; 10. PMC: 7886324. DOI: 10.7554/eLife.64139. View

Hicks A, Wheeler N, Sanchez-Buso L, Rakeman J, Harris S, Grad Y . Evaluation of parameters affecting performance and reliability of machine learning-based antibiotic susceptibility testing from whole genome sequencing data. PLoS Comput Biol. 2019; 15(9):e1007349. PMC: 6743791. DOI: 10.1371/journal.pcbi.1007349. View

Smieszek T, Pouwels K, Dolk F, Smith D, Hopkins S, Sharland M . Potential for reducing inappropriate antibiotic prescribing in English primary care. J Antimicrob Chemother. 2018; 73(suppl_2):ii36-ii43. PMC: 5890667. DOI: 10.1093/jac/dkx500. View

Strommenger B, Kettlitz C, Werner G, Witte W . Multiplex PCR assay for simultaneous detection of nine clinically relevant antibiotic resistance genes in Staphylococcus aureus. J Clin Microbiol. 2003; 41(9):4089-94. PMC: 193808. DOI: 10.1128/JCM.41.9.4089-4094.2003. View

Farfour E, Lecuru M, Dortet L, Le Guen M, Cerf C, Karnycheff F . Carbapenemase-producing Enterobacterales outbreak: Another dark side of COVID-19. Am J Infect Control. 2020; 48(12):1533-1536. PMC: 7529666. DOI: 10.1016/j.ajic.2020.09.015. View

Jiang Z, Paudyal N, Xu Y, Deng T, Li F, Pan H . Antibiotic Resistance Profiles of Recovered From Finishing Pigs and Slaughter Facilities in Henan, China. Front Microbiol. 2019; 10:1513. PMC: 6622357. DOI: 10.3389/fmicb.2019.01513. View

Descours G, Desmurs L, Hoang T, Ibranosyan M, Baume M, Ranc A . Evaluation of the Accelerate Pheno™ system for rapid identification and antimicrobial susceptibility testing of Gram-negative bacteria in bloodstream infections. Eur J Clin Microbiol Infect Dis. 2018; 37(8):1573-1583. DOI: 10.1007/s10096-018-3287-6. View

10.

Paudyal N, Yue M . Antimicrobial Resistance in the "Dark Matter". Clin Infect Dis. 2019; 69(2):379-380. DOI: 10.1093/cid/ciz007. View

11.

Ren Y, Chakraborty T, Doijad S, Falgenhauer L, Falgenhauer J, Goesmann A . Prediction of antimicrobial resistance based on whole-genome sequencing and machine learning. Bioinformatics. 2021; 38(2):325-334. PMC: 8722762. DOI: 10.1093/bioinformatics/btab681. View

12.

Elbediwi M, Beibei W, Pan H, Jiang Z, Biswas S, Li Y . Genomic Characterization of carrying Serovar 4,[5],12:i:- ST 34 Clone Isolated From Pigs in China. Front Bioeng Biotechnol. 2020; 8:663. PMC: 7344297. DOI: 10.3389/fbioe.2020.00663. View

13.

Shinoda H, Taguchi Y, Nakagawa R, Makino A, Okazaki S, Nakano M . Amplification-free RNA detection with CRISPR-Cas13. Commun Biol. 2021; 4(1):476. PMC: 8055673. DOI: 10.1038/s42003-021-02001-8. View

14.

Rohde A, Hammerl J, Al Dahouk S . Rapid screening for antibiotic resistance elements on the RNA transcript, protein and enzymatic activity level. Ann Clin Microbiol Antimicrob. 2016; 15(1):55. PMC: 5035493. DOI: 10.1186/s12941-016-0167-8. View

15.

Gupta S, Padmanabhan B, Diene S, Lopez-Rojas R, Kempf M, Landraud L . ARG-ANNOT, a new bioinformatic tool to discover antibiotic resistance genes in bacterial genomes. Antimicrob Agents Chemother. 2013; 58(1):212-20. PMC: 3910750. DOI: 10.1128/AAC.01310-13. View

16.

Martens K, Hallin J, Warringer J, Liti G, Parts L . Predicting quantitative traits from genome and phenome with near perfect accuracy. Nat Commun. 2016; 7:11512. PMC: 4866306. DOI: 10.1038/ncomms11512. View

17.

Kim H, Lee S, Seo H, Kang B, Moon J, Lee K . Clustered Regularly Interspaced Short Palindromic Repeats-Mediated Surface-Enhanced Raman Scattering Assay for Multidrug-Resistant Bacteria. ACS Nano. 2020; 14(12):17241-17253. DOI: 10.1021/acsnano.0c07264. View

18.

Tomczyk S, Taylor A, Brown A, de Kraker M, El-Saed A, Alshamrani M . Impact of the COVID-19 pandemic on the surveillance, prevention and control of antimicrobial resistance: a global survey. J Antimicrob Chemother. 2021; 76(11):3045-3058. PMC: 8499888. DOI: 10.1093/jac/dkab300. View

19.

Choi J, Jung Y, Kim J, Kim S, Jung Y, Na H . Rapid antibiotic susceptibility testing by tracking single cell growth in a microfluidic agarose channel system. Lab Chip. 2012; 13(2):280-7. DOI: 10.1039/c2lc41055a. View

20.

Chen J, Ma E, Harrington L, Da Costa M, Tian X, Palefsky J . CRISPR-Cas12a target binding unleashes indiscriminate single-stranded DNase activity. Science. 2018; 360(6387):436-439. PMC: 6628903. DOI: 10.1126/science.aar6245. View