Mao Yuqing, Shisler Joanna L, Nguyen Thanh H
Department of Civil and Environmental Engineering, The Grainger College of Engineering, University of Illinois Urbana-Champaign, IL, USA; Carl R. Woese Institute for Genomic Biology, University of Illinois Urbana-Champaign, IL, USA.
Carl R. Woese Institute for Genomic Biology, University of Illinois Urbana-Champaign, IL, USA; Department of Microbiology, University of Illinois Urbana-Champaign, IL, USA.
Water Res. 2025 Apr 15;274:123056. doi: 10.1016/j.watres.2024.123056. Epub 2024 Dec 26.
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 to 10 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.
抗生素抗性基因（ARGs）在环境中的传播是一个全球公共卫生问题。迄今为止，已鉴定出5000多种对抗生素具有抗性的基因。对于废水样本，ARGs的丰度通常较低，难以检测。宏基因组测序和定量聚合酶链反应（qPCR）这两种传统的ARG检测方法分别存在灵敏度低和通量低的局限性。我们开发了一种经CRISPR-Cas9修饰的下一代测序（NGS）方法，以在文库制备过程中富集目标ARGs。该方法的假阴性和假阳性是基于已知全基因组序列的细菌分离株混合物来确定的。低假阴性值（2/1208）和低假阳性值（1/1208）证明了该方法的可靠性。我们比较了这种CRISPR-NGS方法和传统NGS方法对六个未经处理的废水样本的检测结果。与使用常规NGS方法在相同样本中检测到的ARGs相比，CRISPR-NGS方法在低丰度下发现了多达1189个更多的ARGs和多达61个更多的ARG家族，包括从不同来源收集的六个废水样本中具有临床重要性的KPCβ-内酰胺酶基因。与常规NGS方法相比，CRISPR-NGS方法将ARGs的检测限从qPCR相对丰度量化的10数量级降低到了10数量级。CRISPR-NGS方法在废水ARG检测方面具有广阔前景。类似的工作流程也可应用于检测其他低丰度但多样性高的目标。
