Summary
A medRxiv preprint reports a ligation-mediated PCR method that used long-read sequencing to resolve genomic contexts around four clinically relevant antibiotic-resistance genes in wastewater. Across 13 Washington state treatment plants sampled twice, the method found distinct context clusters, including 11 putatively novel qnrS-associated clusters.
Researchers have developed a targeted sequencing method for finding the genetic surroundings of antibiotic-resistance genes in wastewater, where resistance DNA can be present at low levels and mixed with genetic material from many organisms. In a medRxiv preprint posted on September 13, 2026, the team used ligation-mediated PCR (LM-PCR) enrichment followed by long-read sequencing to study four clinically relevant resistance genes across 13 wastewater treatment plants in Washington state.
Each plant was sampled at two timepoints. The method identified distinct genomic-context cluster families associated with the resistance genes, including 11 qnrS-associated clusters that the authors describe as putatively novel because they had no matches in public sequence databases.
Contents
How the targeted method works
Wastewater is useful for population-level antimicrobial-resistance surveillance because it combines biological material shed by many people. It can therefore contain a broad mixture of bacteria and resistance genes from a community.
The challenge is that untargeted approaches, such as metagenomic sequencing, can miss clinically important genes when they occur at low abundance. The reported LM-PCR strategy addresses this by enriching DNA molecules containing a selected resistance gene and capturing the genomic sequence next to it. Long-read sequencing then reads through longer stretches around the target than a short targeted signal alone would provide.
The study focused on four antibiotic-resistance genes: blaCTX-M, blaKPC, blaOXA-48-like and qnrS. The analysis was designed to identify recurring genomic arrangements around those genes rather than simply count whether a gene was present.
What the wastewater samples revealed
Across all sampling timepoints, the researchers identified:
- seven genomic-context cluster families associated with blaKPC;
- 11 associated with blaCTX-M;
- 24 associated with qnrS; and
- one associated with blaOXA-48-like.
These numbers describe distinct families of surrounding genetic sequences, not the number of resistant bacteria or infections. They show that the same resistance determinant can occur in multiple genomic arrangements within complex wastewater samples.
The patterns differed between genes. The genomic contexts linked to blaCTX-M and blaKPC were comparatively conserved across clusters. qnrS, by contrast, appeared alongside a more diverse set of sequences. Eleven of the 24 qnrS-containing clusters were classified as putatively novel because the surrounding sequences did not match those in existing public databases.
Why the surrounding DNA matters
An antibiotic-resistance gene is part of a larger genetic structure. Its neighboring DNA can help researchers distinguish recurring arrangements and investigate how resistance determinants are distributed through bacterial communities and mobile genetic elements.
That information adds a layer beyond a simple presence-or-absence result. A surveillance programme that can repeatedly resolve these contexts could track changes in the genetic forms associated with clinically relevant resistance genes across wastewater samples. The authors describe LM-PCR as a scalable framework for monitoring the dissemination of such antimicrobial-resistance determinants.
The evidence comes from a preprint describing a genomic surveillance study of wastewater, not from a clinical trial or a study of patient outcomes. Its measurements show resistance-gene contexts in pooled environmental samples; they are not individual diagnostic results or direct estimates of infections. The qnrS findings are also described as putatively novel because of their lack of database matches, making continued sequencing and comparative analysis important for interpreting their biological and epidemiological significance.