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

The spread of antibiotic resistance genes (ARGs) in the environment is a global public health concern. To date, over 5,000 genes have been identified to express resistance to antibiotics. ARGs are usually low in abundance in environmental samples, making them difficult to detect. Metagenomic sequencing and 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 five 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 686 more ARGs and up to 29 more ARG families in low abundances, including the clinically important KPC beta-lactamase genes in the five wastewater samples collected from different sources. The CRISPR-NGS method increased the average read depths by 10-fold compared with the regular NGS method. The CRISPR-NGS method is promising in ARG detection in future wastewater surveillance projects. A similar workflow can also be applied to detect other targets that are low in abundance but highly multiplexed.