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Real-time detection of antibiotic-resistance genes using Oxford Nanopore Technologies’ MinION

Poster

Date: 28th November 2016

“ARMA”: an analysis workflow for identification of antibiotic-resistant microorganisms in real time, with the potential for point-of-care use

Fig.1 Penicillin resistance in S. pneumoniae and antibiotic use

Antibiotic resistance increases with antibiotic use

Antibiotics and other antimicrobial agents are becoming less and less effective as microorganisms develop resistance to them. This is largely the result of the over-use of these medications over the past few decades (Fig. 1). Antimicrobial resistance is seen as one of the greatest threats to patients’ safety across the world.

Some bacteria are naturally resistant to certain classes of antibiotics, but acquired resistance is the more significant problem, and results from both new mutations and gene transfer between organisms.

Fig.2 VanA-type vancomycin-resistant Staphylococcus aureus

Inter-species gene transfer spreads antibiotic resistance

Horizontal gene transfer is considered to be the major cause of antibiotic resistance in bacteria. Here, genes that are responsible for resistance to one or more antibiotics in one species of bacterium can be transferred to other species by a variety of mechanisms. When another bacterium receives the genes, it acquires resistance to those antibiotics (Fig. 2). With this in mind, we sought to write an analysis workflow that could be run in real time in the cloud, which could take sequence reads generated from a bacterial sample and which would identify any antibiotic-resistance genes present, in real time.

Fig.3 Antibiotic resistance analysis report

Analysis workflow for antibiotic resistance allows real-time detection of resistance genes

We obtained resistance gene sequences and antibiotic-resistance ontology (ARO) from the Comprehensive Antibiotic Resistance Database (CARD, http://arpcard.mcmaster.ca/). The ARO describes how the genes are related to antibiotic drugs. During nanopore sequencing, as soon as a library strand passes through the nanopore, the data is available for basecalling. This allows us to perform analyses on individual reads in real time. The antibiotic-resistance workflow runs Oxford Nanopore Technologies’ standard 1D basecalling, and then uses lastal to align the 1D basecalls against the full set of antibiotic-resistance genes in the CARD database. The report highlights which alignments indicate resistance to a given antibiotic. Because many sequences in the resistance database are either duplicates or are closely related, we can expect identical or very similar alignments of reads against members of a given cluster (Fig. 3).

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