Dataset Information

A genomic data resource for predicting antimicrobial resistance from laboratory-derived antimicrobial susceptibility phenotypes.

ABSTRACT: Antimicrobial resistance (AMR) is a major global health threat that affects millions of people each year. Funding agencies worldwide and the global research community have expended considerable capital and effort tracking the evolution and spread of AMR by isolating and sequencing bacterial strains and performing antimicrobial susceptibility testing (AST). For the last several years, we have been capturing these efforts by curating data from the literature and data resources and building a set of assembled bacterial genome sequences that are paired with laboratory-derived AST data. This collection currently contains AST data for over 67 000 genomes encompassing approximately 40 genera and over 100 species. In this paper, we describe the characteristics of this collection, highlighting areas where sampling is comparatively deep or shallow, and showing areas where attention is needed from the research community to improve sampling and tracking efforts. In addition to using the data to track the evolution and spread of AMR, it also serves as a useful starting point for building machine learning models for predicting AMR phenotypes. We demonstrate this by describing two machine learning models that are built from the entire dataset to show where the predictive power is comparatively high or low. This AMR metadata collection is freely available and maintained on the Bacterial and Viral Bioinformatics Center (BV-BRC) FTP site ftp://ftp.bvbrc.org/RELEASE_NOTES/PATRIC_genomes_AMR.txt.

SUBMITTER: VanOeffelen M

PROVIDER: S-EPMC8575023 | biostudies-literature | 2021 Nov

REPOSITORIES: biostudies-literature

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A genomic data resource for predicting antimicrobial resistance from laboratory-derived antimicrobial susceptibility phenotypes.

VanOeffelen Margo M Nguyen Marcus M Aytan-Aktug Derya D Brettin Thomas T Dietrich Emily M EM Kenyon Ronald W RW Machi Dustin D Mao Chunhong C Olson Robert R Pusch Gordon D GD Shukla Maulik M Stevens Rick R Vonstein Veronika V Warren Andrew S AS Wattam Alice R AR Yoo Hyunseung H Davis James J JJ

Briefings in bioinformatics 20211101 6

Antimicrobial resistance (AMR) is a major global health threat that affects millions of people each year. Funding agencies worldwide and the global research community have expended considerable capital and effort tracking the evolution and spread of AMR by isolating and sequencing bacterial strains and performing antimicrobial susceptibility testing (AST). For the last several years, we have been capturing these efforts by curating data from the literature and data resources and building a set o ...[more]

PMID: 34379107

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Project description:BackgroundAntimicrobial-resistant (AMR) Neisseria gonorrhoeae is an urgent threat to public health, as strains resistant to at least one of the two last-line antibiotics used in empiric therapy of gonorrhoea, ceftriaxone and azithromycin, have spread internationally. Whole genome sequencing (WGS) data can be used to identify new AMR clones and transmission networks and inform the development of point-of-care tests for antimicrobial susceptibility, novel antimicrobials and vaccines. Community-driven tools that provide an easy access to and analysis of genomic and epidemiological data is the way forward for public health surveillance.MethodsHere we present a public health-focussed scheme for genomic epidemiology of N. gonorrhoeae at Pathogenwatch ( https://pathogen.watch/ngonorrhoeae ). An international advisory group of experts in epidemiology, public health, genetics and genomics of N. gonorrhoeae was convened to inform on the utility of current and future analytics in the platform. We implement backwards compatibility with MLST, NG-MAST and NG-STAR typing schemes as well as an exhaustive library of genetic AMR determinants linked to a genotypic prediction of resistance to eight antibiotics. A collection of over 12,000 N. gonorrhoeae genome sequences from public archives has been quality-checked, assembled and made public together with available metadata for contextualization.ResultsAMR prediction from genome data revealed specificity values over 99% for azithromycin, ciprofloxacin and ceftriaxone and sensitivity values around 99% for benzylpenicillin and tetracycline. A case study using the Pathogenwatch collection of N. gonorrhoeae public genomes showed the global expansion of an azithromycin-resistant lineage carrying a mosaic mtr over at least the last 10 years, emphasising the power of Pathogenwatch to explore and evaluate genomic epidemiology questions of public health concern.ConclusionsThe N. gonorrhoeae scheme in Pathogenwatch provides customised bioinformatic pipelines guided by expert opinion that can be adapted to public health agencies and departments with little expertise in bioinformatics and lower-resourced settings with internet connection but limited computational infrastructure. The advisory group will assess and identify ongoing public health needs in the field of gonorrhoea, particularly regarding gonococcal AMR, in order to further enhance utility with modified or new analytic methods.

Project description:Background: Antimicrobial resistance (AMR) is now considered one of the greatest global health threats. This is further compounded by a lack of new antibiotics in development. Antimicrobial stewardship programmes can improve and optimize the use of antibiotics, thereby increasing the cure rates of antibiotic treatment and decreasing the problem of AMR. In addition, diagnostic and antimicrobial stewardships in the pathology laboratories are useful tools to guide clinicians on patient treatment and to stop the inappropriate use of antibiotics in empirical treatment or narrow antibiotics. Medical Laboratory Scientists are at the forefront of performing antibiotics susceptibility testing in pathology laboratories, thereby helping clinicians to select the appropriate antibiotics for patients suffering from bacterial infections. Methods: This cross-sectional study surveyed personal antimicrobial usage, the knowledge and awareness on AMR, and antimicrobial stewardship, as well as barriers to antimicrobial susceptibility testing among medical laboratory scientists in Nigeria using pre-tested and validated questionnaires administered online. The raw data were summarized and exported in Microsoft Excel and further analyzed using IBM SPSS version 26. Results: Most of the respondents were males (72%) and 25-35 years old (60%). In addition, the BMLS degree was the highest education qualification most of the respondents (70%) achieved. Of the 59.2% of the respondents involved in antibiotics susceptibility testing, the disc diffusion method was the most commonly used (67.2%), followed by PCR/Genome-based detection (5.2%). Only a small percentage of respondents used the E-test (3.4%). The high cost of testing, inadequate laboratory infrastructure, and a lack of skilled personnel are the major barriers to performing antibiotics susceptibility testing. A higher proportion of a good AMR knowledge level was observed in male respondents (75%) than females (42.9%). The knowledge level was associated with the respondent's gender (p = 0.048), while respondents with a master's degree were more likely to possess a good knowledge level of AMR (OR: 1.69; 95% CI: 0.33, 8.61). Conclusion: The findings of this study indicate that Nigerian medical laboratory scientists had moderate awareness of AMR and antibiotic stewardship. It is necessary to increase investments in laboratory infrastructure and manpower training, as well as set up an antimicrobial stewardship programme to ensure widespread antibiotics susceptibility testing in hospitals, thereby decreasing empirical treatment and the misuse of antibiotics.

Dataset Information

A genomic data resource for predicting antimicrobial resistance from laboratory-derived antimicrobial susceptibility phenotypes.

Publications

A genomic data resource for predicting antimicrobial resistance from laboratory-derived antimicrobial susceptibility phenotypes.

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