Argo overview
The global proliferation of antibiotic resistance genes (ARGs) poses a significant threat to the efficacy of antibiotic-based treatments for diseases. Effective monitoring of ARGs across both spatial and temporal dimensions is essential to understanding their transmission and implementing preventive measures.
A research team led by Professor Tong Zhang from the Department of Civil Engineering of Faculty of Engineering at the University of Hong Kong (HKU) has developed a computational tool, Argo, designed to accurately track ARGs in environmental samples, providing insights into their dissemination and associated risks.
"Short-read sequencing method is currently used as a high-throughput DNA sequencing technique that generates large volumes of short DNA fragments, typically 150 base pairs. However, it often fails to provide information on the hosts of ARGs," explained Professor Zhang. "Without detailed host information, it becomes challenging to accurately assessing the risks of ARGs and tracing their transmission, hindering our understanding of their impact on human health and the environment."
Argo utilises long-read sequencing, a method that can generate DNA fragments significantly longer than 150 base pairs, to rapidly identify and quantify ARGs in environmental metagenomes. By assigning taxonomic labels to read clusters (collections of reads that overlap to each other), Argo significantly enhances ARGs detection resolution. The key difference between Argo and existing tools lies in its method of grouping and analysing DNA fragments based on their overlaps, assigning labels to these groups rather than individual reads. Argo has a distinct advantage in host identification accuracy, providing a more comprehensive ARG profile.
Professor Zhang elaborated, "It is like solving a puzzle. Initially, we group DNA fragment pieces based on shared features like colour, making it easier to identify and label the locations of overlapping or similar pieces in groups. Our research showcased that Argo's read-overlapping approach achieved the lowest misclassification rate in comparison to other tools through simulations. For a 10 Gbp (10^10 base pairs) metagenomic sample, Argo typically completes analysis within 20 minutes using 32 CPU threads."
While long-read sequencing remains costly for achieving high throughput, the team considers the new method vital in addressing the growing threat posed by ARGs. Professor Zhang concluded, "Argo has the potential to standardise ARGs surveillance and enhance our ability to trace the origins and dissemination pathways of ARGs, contributing to efforts to tackle the global health threat of antimicrobial resistance (AMR)."
The research paper, "Species-resolved profiling of antibiotic resistance genes in complex metagenomes through long-read overlapping with Argo," was published in Nature Communications: https://www.nature.com/articles/s41467-025-57088-y.
About Professor Tong Zhang
Professor Tong Zhang leads the Environmental Microbiome Engineering and Biotechnology Laboratory at the Department of Civil Engineering of Faculty of Engineering at HKU. His research focuses on the "environmental microbiome." He has conducted pioneering work on the emerging topic of the "Environmental Dimension of Antibiotic Resistance" in the microbiome field.
