Enhanced detection for antibiotic resistance genes in wastewater samples using a CRISPR-enriched metagenomic method

The spread of antibiotic resistance genes (ARGs) in the environment is a global public health concern. To date, over 5000 genes have been identified to express resistance to antibiotics. ARGs are usually low in abundance for wastewater samples, making them difficult to detect. Metagenomic sequencing and quantitative polymerase chain reaction (qPCR), two conventional ARG detection methods, have low sensitivity and low throughput limitations, respectively. We developed a CRISPR-Cas9-modified next-generation sequencing (NGS) method to enrich the targeted ARGs during library preparation. The false negative and false positive of this method were determined based on a mixture of bacterial isolates with known whole-genome sequences. Low values of both false negative (2/1208) and false positive (1/1208) proved the method's reliability. We compared the results obtained by this CRISPR-NGS and the conventional NGS method for six untreated wastewater samples. As compared to the ARGs detected in the same samples using the regular NGS method, the CRISPR-NGS method found up to 1189 more ARGs and up to 61 more ARG families in low abundances, including the clinically important KPC beta-lactamase genes in the six wastewater samples collected from different sources. Compared to the regular NGS method, the CRISPR-NGS method lowered the detection limit of ARGs from the magnitude of 10^-4 to 10^-5 as quantified by qPCR relative abundance. The CRISPR-NGS method is promising for ARG detection in wastewater. A similar workflow can also be applied to detect other targets that are in low abundance but of high diversity.