Danielle Wrenn from the Institute of Artic Biology at the University of Alaska Fairbanks presented at the Nanopore Community Meeting 2022 on “DART: a toolbox for the rapid detection of antibiotic resistance.” Wrenn acknowledged the 2022 Genomics Hackathon in January 2022. They used Nanopore and adaptive sampling. Wrenn explained that DART’s purpose is “the rapid detection of antibiotic resistance genes in environmental samples.” The group was interested in soils, as Wrenn explained soils can serve as reservoirs and places for horizontal gene transfer. DART stands for “Detection of Antibiotic Resistance Toolbox.” In developing DART, their question was: can adaptive sampling enrich for antibiotic resistance. Wrenn noted that antibiotic resistance genes can be very small: the smallest gene in the panel they used was 300 bp and the largest in the panel ~3000 bp. Their experimental design began with soil samples that were cultured, isolated, and DNA was extracted. They then created a mock community with six members with genomes known and antibiotic resistance genes (ARGs) were identified. They used the gene panel they created to identify 25 unique ARGs present in the mock community. The team then used the MinION Mk1B to perform 10 sequencing runs: 2 treatments, using adaptive sample, and with a control. Wrenn noted that they used the Flongle flow cells to stay within budget. This is of interest to me for the use of this approach in teaching settings. Wrenn showed findings that included ARG composition comparison between a control run and the use of adaptive sample, with adaptive sampling performing very well in enriching for ARGs. Wrenn did note that they used flow cells for two runs, and the second run consistently had fewer active pores and less output. The team concluded that the tool reduces time from sample collection to actionable data and “goes beyond simple detection to provide genomic context.” Importantly, the approach allows analysis in resource limited situations. Wrenn indicated that future work aims to increase the output and ARG yield and expand the ARG panel. Wrenn shared that they have expanded the panel to 200 genes and used battery packs and solar panels in the field. While they are still using mock communities, I think we could adapt and learn from this approach. I ended up going to Devin Drown’s lab Several approaches now use adaptive sampling to quickly identify AMRs, however, automated library prep methods like the VolTRAX and Miro are still not routine parts of the pipeline. I’m excited about trying some variations. I also learned that Wrenn was an undergraduate researcher at the time of the presentation!
