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Detection of Antimicrobial Resistance Using Proteomics and the Comprehensive Antibiotic Resistance Database: A Case Study.

ABSTRACT: PURPOSE:Antimicrobial resistance (AMR), especially multidrug resistance, is one of the most serious global threats facing public health. The authors proof-of-concept study assessing the suitability of shotgun proteomics as an additional approach to whole-genome sequencing (WGS) for detecting AMR determinants. EXPERIMENTAL DESIGN:Previously published shotgun proteomics and WGS data on four isolates of Campylobacter jejuni are used to perform AMR detection by searching the Comprehensive Antibiotic Resistance Database, and their detection ability relative to genomics screening and traditional phenotypic testing measured by minimum inhibitory concentration is assessed. RESULTS:Both genomic and proteomic approaches identify the wild-type and variant molecular determinants responsible for resistance to tetracycline and ciprofloxacin, in agreement with phenotypic testing. In contrast, the genomic method identifies the presence of the ?-lactamase gene, blaOXA - 61 , in three isolates. However, its corresponding protein product is detected in only a single isolate, consistent with results obtained from phenotypic testing.

SUBMITTER: Chen CY

PROVIDER: S-EPMC7378939 | biostudies-literature | 2020 Jul

REPOSITORIES: biostudies-literature

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Detection of Antimicrobial Resistance Using Proteomics and the Comprehensive Antibiotic Resistance Database: A Case Study.

Chen Chih-Yu CY Clark Clifford G CG Langner Stacie S Boyd David A DA Bharat Amrita A McCorrister Stuart J SJ McArthur Andrew G AG Graham Morag R MR Westmacott Garrett R GR Van Domselaar Gary G

Proteomics. Clinical applications 20200228 4

<h4>Purpose</h4>Antimicrobial resistance (AMR), especially multidrug resistance, is one of the most serious global threats facing public health. The authors proof-of-concept study assessing the suitability of shotgun proteomics as an additional approach to whole-genome sequencing (WGS) for detecting AMR determinants.<h4>Experimental design</h4>Previously published shotgun proteomics and WGS data on four isolates of Campylobacter jejuni are used to perform AMR detection by searching the Comprehen ...[more]

PMID: 31872964

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Project description:BackgroundAntimicrobial resistance (AMR) poses a major threat to human health. Whole-genome sequencing holds great potential for AMR identification; however, there remain major gaps in accurately and comprehensively detecting AMR across the spectrum of AMR-conferring determinants and pathogens.MethodsUsing 16 wild-type Burkholderia pseudomallei and 25 with acquired AMR, we first assessed the performance of existing AMR software (ARIBA, CARD, ResFinder, and AMRFinderPlus) for detecting clinically relevant AMR in this pathogen. B. pseudomallei was chosen due to limited treatment options, high fatality rate, and AMR caused exclusively by chromosomal mutation (i.e. single-nucleotide polymorphisms [SNPs], insertions-deletions [indels], copy-number variations [CNVs], inversions, and functional gene loss). Due to poor performance with existing tools, we developed ARDaP (Antimicrobial Resistance Detection and Prediction) to identify the spectrum of AMR-conferring determinants in B. pseudomallei.FindingsCARD, ResFinder, and AMRFinderPlus failed to identify any clinically-relevant AMR in B. pseudomallei; ARIBA identified AMR encoded by SNPs and indels that were manually added to its database. However, none of these tools identified CNV, inversion, or gene loss determinants, and ARIBA could not differentiate AMR determinants from natural genetic variation. In contrast, ARDaP accurately detected all SNP, indel, CNV, inversion, and gene loss AMR determinants described in B. pseudomallei (n≈50). Additionally, ARDaP accurately predicted three previously undescribed determinants. In mixed strain data, ARDaP identified AMR to as low as ~5% allelic frequency.InterpretationExisting AMR software packages are inadequate for chromosomal AMR detection due to an inability to detect resistance conferred by CNVs, inversions, and functional gene loss. ARDaP overcomes these major shortcomings. Further, ARDaP enables AMR prediction from mixed sequence data down to 5% allelic frequency, and can differentiate natural genetic variation from AMR determinants. ARDaP databases can be constructed for any microbial species of interest for comprehensive AMR detection.FundingNational Health and Medical Research Council (BJC, EPP, DSS); Australian Government (DEM, ES); Advance Queensland (EPP, DSS).

Project description:Emerging known and unknown pathogens create profound threats to public health. Platforms for rapid detection and characterization of microbial agents are critically needed to prevent and respond to disease outbreaks. Available detection technologies cannot provide broad functional information about known and novel organisms. As a step toward developing such a system, we have produced and tested a series of high-density functional gene arrays to detect elements of virulence and antibiotic resistance mechanisms. Our first generation array targets genes from Escherichia coli strains K12 and CFT073, Enterococcus faecalis and Staphylococcus aureus. We determined optimal probe design parameters for gene family detection and discrimination. When tested with organisms at varying phylogenetic distances from the four target strains, the array detected orthologs for the majority of targeted gene families present in bacteria belonging to the same taxonomic family. In combination with whole-genome amplification, the array detects femtogram concentrations of purified DNA, either spiked in to an aerosol sample background, or in combinations from one or more of the four target organisms. This is the first report of a high density NimbleGen microarray system targeting microbial antibiotic resistance and virulence mechanisms. By targeting virulence gene families as well as genes unique to specific biothreat agents, these arrays will provide important data about the pathogenic potential and drug resistance profiles of unknown organisms in environmental samples. Keywords: detection, pathogen, virulence mechanism In this report, we describe the process used to design our first generation functional array for highly sensitive detection of virulence and antibiotic resistance gene families. We discuss the probe design algorithms, including virulence gene sequence selection, and our protocols for sample preparation, amplification, labeling, hybridization, and data analysis. We present the results from experiments designed to assess whether the array can detect virulence gene orthologs from organisms without perfect match probes on the array, using both targeted mismatch probes and hybridizations to DNA from other organisms. Also, we report the results from limit of detection studies, using known amounts of bacterial DNA spiked into aerosol samples to measure the minimal concentration required for detection of virulence elements against a complex background.

Dataset Information

Detection of Antimicrobial Resistance Using Proteomics and the Comprehensive Antibiotic Resistance Database: A Case Study.

Publications

Detection of Antimicrobial Resistance Using Proteomics and the Comprehensive Antibiotic Resistance Database: A Case Study.

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