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The widespread emergence of antibiotic resistance genes (ARGs) represents a critical challenge to the effectiveness of antibiotic therapies across various diseases. To address this pressing issue, it is crucial to monitor ARGs both spatially and temporally, which will enable a deeper understanding of their spread and help in the formulation of preventive strategies.
A team of researchers, led by Professor Tong Zhang from the Department of Civil Engineering at the University of Hong Kong (HKU), has introduced a sophisticated computational tool known as Argo. This tool is specifically designed to monitor ARGs found in environmental samples, offering valuable insights into their distribution and related risks.
“The conventional short-read sequencing method, although widely adopted for high-throughput DNA sequencing, produces relatively short DNA fragments—typically around 150 base pairs. This approach often falls short in providing crucial information about the hosts carrying these ARGs,” stated Professor Zhang. “Without a clear understanding of the hosts, accurately assessing the risks associated with ARGs and tracing their transmission becomes difficult, thus obscuring their implications for public health and environmental safety.”
Argo leverages long-read sequencing technology, which is capable of generating significantly longer DNA fragments than the 150 base pair limit of traditional methods. This innovation allows for rapid identification and quantification of ARGs in environmental metagenomic samples. Unlike existing tools, Argo enhances detection by grouping overlapping DNA fragments into clusters and assigning taxonomic labels to these groups rather than focusing on individual reads. This method markedly improves the accuracy of host identification, leading to a more detailed profile of ARGs.
Professor Zhang elaborated, “Think of it as piecing together a puzzle. We initially group DNA fragments based on similar characteristics, such as color, which helps in accurately determining the placement of overlapping pieces. Our research demonstrated that Argo, through its read-overlapping methodology, achieved the lowest misclassification rates compared to other tools in simulation studies. For a sample with 10 billion base pairs, Argo can typically complete its analysis in about 20 minutes utilizing 32 CPU threads.”
Despite the cost challenges associated with high-throughput long-read sequencing, the research team believes that this novel approach is crucial in combating the escalating threat of ARGs. Professor Zhang concluded, “Argo has the potential to standardize monitoring of ARGs and improve our capacity to trace their sources and pathways of spread, playing a significant role in global efforts to mitigate the health crisis posed by antimicrobial resistance (AMR).”
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