Department of Medicine, University of Colorado Anschutz Medical Campus, Aurora, CO, 80045, USA.
Skaggs School of Pharmacy and Pharmaceutical Sciences, University of Colorado Anschutz Medical Campus, 80045CO, Aurora, USA.
BMC Bioinformatics. 2019 Nov 28;20(1):614. doi: 10.1186/s12859-019-3176-8.
Untargeted metabolomics of host-associated samples has yielded insights into mechanisms by which microbes modulate health. However, data interpretation is challenged by the complexity of origins of the small molecules measured, which can come from the host, microbes that live within the host, or from other exposures such as diet or the environment.
We address this challenge through development of AMON: Annotation of Metabolite Origins via Networks. AMON is an open-source bioinformatics application that can be used to annotate which compounds in the metabolome could have been produced by bacteria present or the host, to evaluate pathway enrichment of host verses microbial metabolites, and to visualize which compounds may have been produced by host versus microbial enzymes in KEGG pathway maps.
AMON empowers researchers to predict origins of metabolites via genomic information and to visualize potential host:microbe interplay. Additionally, the evaluation of enrichment of pathway metabolites of host versus microbial origin gives insight into the metabolic functionality that a microbial community adds to a host:microbe system. Through integrated analysis of microbiome and metabolome data, mechanistic relationships between microbial communities and host phenotypes can be better understood.
对宿主相关样本进行非靶向代谢组学研究，可以深入了解微生物调节健康的机制。然而，由于所测量小分子的来源的复杂性，数据解释受到了挑战，这些小分子可能来自宿主、宿主内的微生物，也可能来自饮食或环境等其他暴露源。
我们通过开发 AMON（通过网络注释代谢物的起源）来解决这一挑战。AMON 是一个开源的生物信息学应用程序，可用于注释代谢组中的哪些化合物可能是由存在的细菌或宿主产生的，评估宿主与微生物代谢物的途径富集，以及在 KEGG 途径图中可视化哪些化合物可能是由宿主与微生物酶产生的。
AMON 使研究人员能够通过基因组信息预测代谢物的起源，并可视化潜在的宿主与微生物相互作用。此外，对宿主与微生物来源的途径代谢物的富集评估，深入了解微生物群落为宿主-微生物系统增加的代谢功能。通过对微生物组和代谢组数据的综合分析，可以更好地理解微生物群落与宿主表型之间的机制关系。
