Selected abstract: NGS for characterization of clinical class 1 integrons from hospital effluents

Selected abstract: NGS for characterization of clinical class 1 integrons from hospital effluents

Background

Class 1 integrons are bacterial genetic elements widely involved in the spread of antibiotic resistance in the clinical setting. Located on plasmids, they can recruit, express and disseminate antibiotic resistance genes (ARGs) embedded within gene cassettes (GCs). GCs are mobilizable units that constitute the variable part of class 1 integrons. Up to 10 GCs can be present within a GC array and more than 130 GCs, encoding resistance to nearly all antibiotic families, have been described so far.  Class 1 integrons are present in high quantities in hospital effluents. Here, the objective was to extensively characterize the pool of class 1 integrons GCs from hospital effluents using NGS.

 

Methods

We extracted total DNA from 10 international hospital effluents. GC arrays of class 1 integrons were first amplified by end-point PCRs. Libraries were then prepared from PCR products using the library builder system and sequenced using the Ion Proton®. Reads were mapped against 436 known GCs. Unmapped reads were assembled as contigs using MIRA. Contigs longer than 500bp were screened for the presence of GCs using the IntegronFinder (IF) software. IF-positive contigs with no match in the Genbank database were manually analysed to confirm presence of novel GCs.

 

Results

In all effluents, raw read mapping revealed a high proportion of GCs with aminoglycosides and β-lactams ARGs. All ARGs represented 50-69% of all reads within each effluent. Each of the 10 effluents displayed its own GC signature. With unmapped reads, we obtained 236 IF-positive/Genbank-negative contigs among which we identified 73 novel GCs. Nine GCs hosted putative novel ARGs in seven classes of antibiotics.

 

Discussion

NGS combined to our workflow analysis constitute a powerful tool to extensively describe the GC pool of effluents. It enables to identify novel GCs and allows a quantitative description of the “cassettome” of a given effluent.