Prediction of antimicrobial resistance in clinical Campylobacter jejuni isolates from whole-genome sequencing data

Abstract Campylobacter jejuni is recognised as the leading cause of bacterial gastroenteritis in industrialised countries. Although the majority of Campylobacter infections are self-limiting, antimicrobial treatment is necessary in severe cases. Therefore, the development of antimicrobial resistance (AMR) in Campylobacter is a growing public health challenge and surveillance of AMR is important for bacterial disease control. The aim of this study was to predict antimicrobial resistance in C. jejuni from whole-genome sequencing data. A total of 516 clinical C. jejuni isolates collected between 2014 and 2017 were subjected to WGS. Resistance phenotypes were determined by standard broth dilution, categorising isolates as either susceptible or resistant based on epidemiological cutoffs for six antimicrobials: ciprofloxacin, nalidixic acid, erythromycin, gentamicin, streptomycin, and tetracycline. Resistance genotypes were identified using an in-house database containing reference genes with known point mutations and the presence of resistance genes was determined using the ResFinder database and four bioinformatical methods (modified KMA, ABRicate, ARIBA, and ResFinder Batch Upload). We identified seven resistance genes including tet(O), tet(O/32/O), ant(6)-Ia, aph(2″)-If, blaOXA, aph(3′)-III, and cat as well as mutations in three genes: gyrA, 23S rRNA, and rpsL. There was a high correlation between phenotypic resistance and the presence of known resistance genes and/or point mutations. A correlation above 98% was seen for all antimicrobials except streptomycin with a correlation of 92%. In conclusion, we found that WGS can predict antimicrobial resistance with a high degree of accuracy and have the potential to be a powerful tool for AMR surveillance.

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Integrating Whole-Genome Sequencing Data Into Quantitative Risk Assessment of Foodborne Antimicrobial Resistance: A Review of Opportunities and Challenges

Frontiers in Microbiology ◽

10.3389/fmicb.2019.01107 ◽

2019 ◽

Vol 10 ◽

Cited By ~ 20

Author(s):

Lucie Collineau ◽

Patrick Boerlin ◽

Carolee A. Carson ◽

Brennan Chapman ◽

Aamir Fazil ◽

...

Keyword(s):

Risk Assessment ◽

Antimicrobial Resistance ◽

Whole Genome Sequencing ◽

Genome Sequencing ◽

Quantitative Risk Assessment ◽

Whole Genome Sequencing Data ◽

Whole Genome ◽

Sequencing Data

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Recommendations To Address the Difficulties Encountered When Determining Linezolid Resistance from Whole-Genome Sequencing Data

Antimicrobial Agents and Chemotherapy ◽

10.1128/aac.00613-18 ◽

2018 ◽

Vol 62 (8) ◽

Cited By ~ 2

Author(s):

Alicia G. Beukers ◽

Henrik Hasman ◽

Kristin Hegstad ◽

Sebastiaan J. van Hal

Keyword(s):

Whole Genome Sequencing ◽

Genome Sequencing ◽

23S Rrna ◽

Whole Genome Sequencing Data ◽

Rrna Gene ◽

Whole Genome ◽

Sequencing Data ◽

Content Type ◽

Numbering System ◽

Linezolid Resistance

ABSTRACT Mutations associated with linezolid resistance within the V domain of 23S rRNA are annotated using an Escherichia coli numbering system. The 23S rRNA gene varies in length, nucleotide sequence, and copy number among bacterial species. Consequently, this numbering system is not intuitive and can lead to confusion when mutation sites are being located using whole-genome sequencing data. Using the mutation G2576T as an example, we demonstrate the difficulties associated with using the E. coli numbering system.

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Discordant bioinformatic predictions of antimicrobial resistance from whole-genome sequencing data of bacterial isolates: An inter-laboratory study

10.1101/793885 ◽

2019 ◽

Cited By ~ 1

Author(s):

Ronan M. Doyle ◽

Denise M. O’Sullivan ◽

Sean D. Aller ◽

Sebastian Bruchmann ◽

Taane Clark ◽

...

Keyword(s):

Antimicrobial Resistance ◽

Whole Genome Sequencing ◽

Genome Sequencing ◽

Laboratory Study ◽

Clinical Microbiology ◽

Sequence Data ◽

Clinical Samples ◽

Whole Genome Sequencing Data ◽

Whole Genome ◽

Sequencing Data

AbstractBackgroundAntimicrobial resistance (AMR) poses a threat to public health. Clinical microbiology laboratories typically rely on culturing bacteria for antimicrobial susceptibility testing (AST). As the implementation costs and technical barriers fall, whole-genome sequencing (WGS) has emerged as a ‘one-stop’ test for epidemiological and predictive AST results. Few published comparisons exist for the myriad analytical pipelines used for predicting AMR. To address this, we performed an inter-laboratory study providing sets of participating researchers with identical short-read WGS data sequenced from clinical isolates, allowing us to assess the reproducibility of the bioinformatic prediction of AMR between participants and identify problem cases and factors that lead to discordant results.MethodsWe produced ten WGS datasets of varying quality from cultured carbapenem-resistant organisms obtained from clinical samples sequenced on either an Illumina NextSeq or HiSeq instrument. Nine participating teams (‘participants’) were provided these sequence data without any other contextual information. Each participant used their own pipeline to determine the species, the presence of resistance-associated genes, and to predict susceptibility or resistance to amikacin, gentamicin, ciprofloxacin and cefotaxime.ResultsIndividual participants predicted different numbers of AMR-associated genes and different gene variants from the same clinical samples. The quality of the sequence data, choice of bioinformatic pipeline and interpretation of the results all contributed to discordance between participants. Although much of the inaccurate gene variant annotation did not affect genotypic resistance predictions, we observed low specificity when compared to phenotypic AST results but this improved in samples with higher read depths. Had the results been used to predict AST and guide treatment a different antibiotic would have been recommended for each isolate by at least one participant.ConclusionsWe found that participants produced discordant predictions from identical WGS data. These challenges, at the final analytical stage of using WGS to predict AMR, suggest the need for refinements when using this technology in clinical settings. Comprehensive public resistance sequence databases and standardisation in the comparisons between genotype and resistance phenotypes will be fundamental before AST prediction using WGS can be successfully implemented in standard clinical microbiology laboratories.

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Discordant bioinformatic predictions of antimicrobial resistance from whole-genome sequencing data of bacterial isolates: an inter-laboratory study

Microbial Genomics ◽

10.1099/mgen.0.000335 ◽

2020 ◽

Vol 6 (2) ◽

Cited By ~ 9

Author(s):

Ronan M. Doyle ◽

Denise M. O'Sullivan ◽

Sean D. Aller ◽

Sebastian Bruchmann ◽

Taane Clark ◽

...

Keyword(s):

Antimicrobial Resistance ◽

Whole Genome Sequencing ◽

Genome Sequencing ◽

Laboratory Study ◽

Whole Genome Sequencing Data ◽

Whole Genome ◽

Bacterial Isolates ◽

Sequencing Data

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Whole-Genome Sequencing Analysis Accurately Predicts Antimicrobial Resistance Phenotypes in Campylobacter spp.

Applied and Environmental Microbiology ◽

10.1128/aem.02873-15 ◽

2015 ◽

Vol 82 (2) ◽

pp. 459-466 ◽

Cited By ~ 132

Author(s):

S. Zhao ◽

G. H. Tyson ◽

Y. Chen ◽

C. Li ◽

S. Mukherjee ◽

...

Keyword(s):

Antimicrobial Resistance ◽

Whole Genome Sequencing ◽

Genome Sequencing ◽

Resistance Genes ◽

Antimicrobial Agents ◽

The United States ◽

23S Rrna ◽

Whole Genome ◽

Sequencing Analysis ◽

Content Type

ABSTRACTThe objectives of this study were to identify antimicrobial resistance genotypes forCampylobacterand to evaluate the correlation between resistance phenotypes and genotypes usingin vitroantimicrobial susceptibility testing and whole-genome sequencing (WGS). A total of 114Campylobacterspecies isolates (82C. coliand 32C. jejuni) obtained from 2000 to 2013 from humans, retail meats, and cecal samples from food production animals in the United States as part of the National Antimicrobial Resistance Monitoring System were selected for study. Resistance phenotypes were determined using broth microdilution of nine antimicrobials. Genomic DNA was sequenced using the Illumina MiSeq platform, and resistance genotypes were identified using assembled WGS sequences through blastx analysis. Eighteen resistance genes, includingtet(O),blaOXA-61,catA,lnu(C),aph(2″)-Ib,aph(2″)-Ic,aph(2′)-If,aph(2″)-Ig,aph(2″)-Ih,aac(6′)-Ie-aph(2″)-Ia,aac(6′)-Ie-aph(2″)-If,aac(6′)-Im,aadE,sat4,ant(6′),aad9,aph(3′)-Ic, andaph(3′)-IIIa, and mutations in two housekeeping genes (gyrAand 23S rRNA) were identified. There was a high degree of correlation between phenotypic resistance to a given drug and the presence of one or more corresponding resistance genes. Phenotypic and genotypic correlation was 100% for tetracycline, ciprofloxacin/nalidixic acid, and erythromycin, and correlations ranged from 95.4% to 98.7% for gentamicin, azithromycin, clindamycin, and telithromycin. All isolates were susceptible to florfenicol, and no genes associated with florfenicol resistance were detected. There was a strong correlation (99.2%) between resistance genotypes and phenotypes, suggesting that WGS is a reliable indicator of resistance to the nine antimicrobial agents assayed in this study. WGS has the potential to be a powerful tool for antimicrobial resistance surveillance programs.

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Absolute copy number fitting from shallow whole genome sequencing data

10.1101/2021.07.19.452658 ◽

2021 ◽

Author(s):

Carolin M Sauer ◽

Matthew D Eldridge ◽

Maria Vias ◽

James A Hall ◽

Samantha E Boyle ◽

...

Keyword(s):

Whole Genome Sequencing ◽

Cancer Cell ◽

Genome Sequencing ◽

Copy Number ◽

Point Mutations ◽

Whole Genome Sequencing Data ◽

Whole Genome ◽

Sequencing Data ◽

Tissue Samples ◽

Absolute Copy Number

Low-coverage or shallow whole genome sequencing (sWGS) approaches can efficiently detect somatic copy number aberrations (SCNAs) at low cost. This is clinically important for many cancers, in particular cancers with severe chromosomal instability (CIN) that frequently lack actionable point mutations and are characterised by poor disease outcome. Absolute copy number (ACN), measured in DNA copies per cancer cell, is required for meaningful comparisons between copy number states, but is challenging to estimate and in practice often requires manual curation. Using a total of 60 cancer cell lines, 148 patient-derived xenograft (PDX) and 142 clinical tissue samples, we evaluate the performance of available tools for obtaining ACN from sWGS. We provide a validated and refined tool called Rascal (relative to absolute copy number scaling) that provides improved fitting algorithms and enables interactive visualisation of copy number profiles. These approaches are highly applicable to both pre-clinical and translational research studies on SCNA-driven cancers and provide more robust ACN fits from sWGS data than currently available tools.

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