MEtaGenome-informed Antimicrobial resistance Surveillance: Harnessing long-read sequencing for an analytical, indicator and risk assessment framework

抗菌素耐药性（AMR）对人类、动物和作物构成威胁，需要采取协调一致的“同一个健康”方法。抗微生物药物耐药性影响到世卫组织《2030年可持续发展议程》的许多目标。抗生素耐药性的水平通常通过培养大肠杆菌等指示生物来量化，并确定其耐药性。在所有人类、动物、食物和环境样本中只观察一种指示生物，就像用显微镜观察一滴海水来研究海洋生物一样。在液滴之间可以发现有趣的差异，但这真的是全部吗？宏基因组测序是一种无需培养即可在样品中鉴定和定量AMR基因和物种的技术。目前使用短读测序更为普遍，但在需要AMR定量时受到限制。人们只能量化基因，而不能量化它们在哪里。在染色体上？或者在可传播的质粒或转座子中，它们很容易传播给病原体？我们建议使用长读纳米孔测序来开发一种“一站式”监测方法。长reads既包含抗性基因，也包含侧翼序列，从而确定了原生物体或可移动遗传元件的位置，同时也确定了与高传播风险相关的基因。我们建议整合传统方法，将监测扩展到非模式生物。通过与政府、工业界和学术界的利益攸关方合作，该项目将支持将长读元基因组数据转化为可操作的监测信息，以减少对人类健康的风险。

Antimicrobial resistance (AMR) is a threat to humans, animals, and crops, and requires a coordinated One Health approach. AMR affects many of the goals of the WHO 2030 Agenda for Sustainable Development. Levels of AMR are generally quantified by culturing indicator organisms like E. coli, and determining its resistance. Looking at only one indicator organism in all human, animal, food and environmental samples is like studying ocean life by looking at a single droplet of seawater with a microscope. Interesting differences can be found between droplets but is it really the entire picture?Metagenomic sequencing is a technique that allows identification and quantification of AMR genes and species in a sample without culturing. Currently the use of short read sequencing is more common but limited when AMR quantification is required. One can only quantify genes, not where they are. On a chromosome? Or in a transmittable plasmid or transposon, which can easily spread to pathogens?We propose to use long-read Nanopore sequencing to develop a 'one stop shop' surveillance method. Long reads contain both resistance genes and flanking sequences, thereby identifying the original organism or mobile genetic element, the location, but also genes associated with high transmission risk. We propose integrating into traditional methods to expand surveillance into non-model-organisms.By working with stakeholders in government, industry and academia, this project will support translation of long-read metagenome data into actionable surveillance information for the reduction of risk to human health.