Exploring the known Antimicrobial resistance genes (ARGs)-microbial hosts associations

Until now, we have learned to visualise and analyse the ARG abundance table and how to find associations between ARGs and taxa’s using various univariate and multivariate statistical techniques.

Another manual way of finding these associations is to search for reference AMR databases. Some of these may also provide information regarding the potential microbial hosts that harbour or carry the ARGs of interest. One can be interested in finding the microbial hosts for a list of ARGs detected in differential abundance testing or identified through high-throughput qPCR.

Information about such relationships can be complex as one microbe can carry multiple ARGs, and single ARGs can be present across multiple microbes. However, it is straightforward to identify key players from a network perspective, for instance, by looking for ARGs found in multiple microbes or identifying those microbes that simultaneously contain multiple ARGs of interest. ResistoXplorer supports an advanced network-based visual analytics system to explore such complex ‘multiple-to-multiple’ relationships.

In ResistoXplorer, we have collected information for network-based microbial host associations exploration of ARGs from three AMR reference databases (i.e., ResFinder, CARD and ARDB). In addition, the same info has been gathered for antibacterial biocides/metals and antimicrobial peptide resistance (AMP) genes from the BacMet database and a recently published AMP dataset. These databases contain direct or indirect information regarding the microbial host for the reference ARGs. In the latter case, the information on microbial host associated with each of the reference ARGs has been collected from their corresponding GenBank accession number using a combination of text mining and manual curation, like in ResFinder and ARDB.

Moreover, we have also collected and organised the available functional annotation information of resistance genes into sets to facilitate functional enrichment analysis. This method, coupled with the network visualisation system, can better interpret antimicrobial resistance mechanisms and inform on possible dissemination routes of resistance genes.

The powerful and fully- featured network visualisation system in ResistoXplorer is based on HTML5 canvas and JavaScript (sigma.js).

We want to hear from you!

Can you think of a real application of associating a resistance gene with a specific microbe?

Share with us in the comments down below!