Dataset Information

Rapid Detection of Mobilized Colistin Resistance using a Nucleic Acid Based Lab-on-a-Chip Diagnostic System.

ABSTRACT: The increasing prevalence of antimicrobial resistance is a serious threat to global public health. One of the most concerning trends is the rapid spread of Carbapenemase-Producing Organisms (CPO), where colistin has become the last-resort antibiotic treatment. The emergence of colistin resistance, including the spread of mobilized colistin resistance (mcr) genes, raises the possibility of untreatable bacterial infections and motivates the development of improved diagnostics for the detection of colistin-resistant organisms. This work demonstrates a rapid response for detecting the most recently reported mcr gene, mcr-9, using a portable and affordable lab-on-a-chip (LoC) platform, offering a promising alternative to conventional laboratory-based instruments such as real-time PCR (qPCR). The platform combines semiconductor technology, for non-optical real-time DNA sensing, with a smartphone application for data acquisition, visualization and cloud connectivity. This technology is enabled by using loop-mediated isothermal amplification (LAMP) as the chemistry for targeted DNA detection, by virtue of its high sensitivity, specificity, yield, and manageable temperature requirements. Here, we have developed the first LAMP assay for mcr-9 - showing high sensitivity (down to 100 genomic copies/reaction) and high specificity (no cross-reactivity with other mcr variants). This assay is demonstrated through supporting a hospital investigation where we analyzed nucleic acids extracted from 128 carbapenemase-producing bacteria isolated from clinical and screening samples and found that 41 carried mcr-9 (validated using whole genome sequencing). Average positive detection times were 6.58 ± 0.42?min when performing the experiments on a conventional qPCR instrument (n?=?41). For validating the translation of the LAMP assay onto a LoC platform, a subset of the samples were tested (n?=?20), showing average detection times of 6.83 ± 0.92?min for positive isolates (n?=?14). All experiments detected mcr-9 in under 10?min, and both platforms showed no statistically significant difference (p-value?>?0.05). When sample preparation and throughput capabilities are integrated within this LoC platform, the adoption of this technology for the rapid detection and surveillance of antimicrobial resistance genes will decrease the turnaround time for DNA detection and resistotyping, improving diagnostic capabilities, patient outcomes, and the management of infectious diseases.

SUBMITTER: Rodriguez-Manzano J

PROVIDER: S-EPMC7242339 | biostudies-literature | 2020 May

REPOSITORIES: biostudies-literature

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Rapid Detection of Mobilized Colistin Resistance using a Nucleic Acid Based Lab-on-a-Chip Diagnostic System.

Rodriguez-Manzano Jesus J Moser Nicolas N Malpartida-Cardenas Kenny K Moniri Ahmad A Fisarova Lenka L Pennisi Ivana I Boonyasiri Adhiratha A Jauneikaite Elita E Abdolrasouli Alireza A Otter Jonathan A JA Bolt Frances F Davies Frances F Didelot Xavier X Holmes Alison A Georgiou Pantelis P

Scientific reports 20200521 1

The increasing prevalence of antimicrobial resistance is a serious threat to global public health. One of the most concerning trends is the rapid spread of Carbapenemase-Producing Organisms (CPO), where colistin has become the last-resort antibiotic treatment. The emergence of colistin resistance, including the spread of mobilized colistin resistance (mcr) genes, raises the possibility of untreatable bacterial infections and motivates the development of improved diagnostics for the detection of ...[more]

PMID: 32439986

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Project description:BackgroundThe high incidence of bacterial genes that confer resistance to last-resort antibiotics, such as colistin, caused by mobilized colistin resistance (mcr) genes, poses an unprecedented threat to human health. Understanding the spread, evolution, and distribution of such genes among human populations will help in the development of strategies to diminish their occurrence. To tackle this problem, we investigated the distribution and prevalence of potential mcr genes in the human gut microbiome using a set of bioinformatics tools to screen the Unified Human Gastrointestinal Genome (UHGG) collection for the presence, synteny and phylogeny of putative mcr genes, and co-located antibiotic resistance genes.ResultsA total of 2079 antibiotic resistance genes (ARGs) were classified as mcr genes in 2046 metagenome assembled genomes (MAGs), distributed across 1596 individuals from 41 countries, of which 215 were identified in plasmidial contigs. The genera that presented the largest number of mcr-like genes were Suterella and Parasuterella. Other potential pathogens carrying mcr genes belonged to the genus Vibrio, Escherichia and Campylobacter. Finally, we identified a total of 22,746 ARGs belonging to 21 different classes in the same 2046 MAGs, suggesting multi-resistance potential in the corresponding bacterial strains, increasing the concern of ARGs impact in the clinical settings.ConclusionThis study uncovers the diversity of mcr-like genes in the human gut microbiome. We demonstrated the cosmopolitan distribution of these genes in individuals worldwide and the co-presence of other antibiotic resistance genes, including Extended-spectrum Beta-Lactamases (ESBL). Also, we described mcr-like genes fused to a PAP2-like domain in S. wadsworthensis. These novel sequences increase our knowledge about the diversity and evolution of mcr-like genes. Future research should focus on activity, genetic mobility and a potential colistin resistance in the corresponding strains to experimentally validate those findings.

Project description:Colistin is one of the last-resort antibiotics for infections caused by multidrug-resistant Gram-negative bacteria. However, the wide spread of novel plasmid-carrying colistin resistance genes mcr-1 and its variants substantially compromise colistin's therapeutic effectiveness and pose a severe danger to public health. To detect colistin-resistant microorganisms induced by mcr genes, rapid and reliable antibiotic susceptibility testing (AST) is imminently needed. In this study, we identified an RNA-based AST (RBAST) to discriminate between colistin-susceptible and mcr-1-mediated colistin-resistant bacteria. After short-time colistin treatment, RBAST can detect differentially expressed RNA biomarkers in bacteria. Those candidate mRNA biomarkers were successfully verified within colistin exposure temporal shifts, concentration shifts, and other mcr-1 variants. Furthermore, a group of clinical strains were effectively distinguished by using the RBAST approach during the 3-h test duration with over 93% accuracy. Taken together, our findings imply that certain mRNA transcripts produced in response to colistin treatment might be useful indicators for the development of fast AST for mcr-positive bacteria. IMPORTANCE The emergence and prevalence of mcr-1 and its variants in humans, animals, and the environment pose a global public health threat. There is a pressing urgency to develop rapid and accurate methods to identify MCR-positive colistin-resistant bacteria in the clinical samples, providing a basis for subsequent effective antibiotic treatment. Using the specific mRNA signatures, we develop an RNA-based antibiotic susceptibility testing (RBAST) for effectively distinguishing colistin-susceptible and mcr-1-mediated colistin-resistant strains. Meanwhile, the detection efficiency of these RNA biomarkers was evidenced in other mcr variants-carrying strains. By comparing with the traditional AST method, the RBAST method was verified to successfully characterize a set of clinical isolates during 3 h assay time with over 93% accuracy. Our study provides a feasible method for the rapid detection of colistin-resistant strains in clinical practice.

Dataset Information

Rapid Detection of Mobilized Colistin Resistance using a Nucleic Acid Based Lab-on-a-Chip Diagnostic System.

Publications

Rapid Detection of Mobilized Colistin Resistance using a Nucleic Acid Based Lab-on-a-Chip Diagnostic System.

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