摘要
      已经提出了一个基于宏基因组学的技术框架，用于评估FEAST作为一种基于ARG图谱的来源分配工具的潜力和效用。为此，分析了一大组宏基因组数据集，这些数据集与环境中ARGs的八种来源类型相关联。在604个源元基因组中总共发现了1089个不同的ARG，396个ARG指标被鉴定为源特异性指纹，以表征每种源类型。利用源指纹，使用“留一”交叉验证策略检查了FEAST的预测性能。此外，模拟了人工水槽群落，以评估ARGs来源分配的FEAST。FEAST的预测显示出高准确度值（0.933±0.046）和特异性值（0.959±0.041），证实了其适用于区分不同来源类型的样本。分配结果很好地反映了人工群落的预期产出，这些群落是用不同比例的源类型生成的，以模拟不同的污染水平。最后，将经过验证的FEAST应用于河流沉积物中ARGs的来源跟踪。结果表明，STP污水是ARGs的主要贡献者，平均贡献率为76%，其次是污泥（10%）和水产养殖污水（2.7%），这与该地区的实际环境基本一致。
Abstract
A metagenomics-based technological framework has been proposed for evaluating the potential and utility of FEAST as an ARG profile-based source apportionment tool. To this end, a large panel of metagenomic data sets was analyzed, associating with eight source types of ARGs in environments. Totally, 1089 different ARGs were found in the 604 source metagenomes, and 396 ARG indicators were identified as the source-specific fingerprints to characterize each of the source types. With the source fingerprints, predictive performance of FEAST was checked using "leave-one-out" cross-validation strategy. Furthermore, artificial sink communities were simulated to evaluate the FEAST for source apportionment of ARGs. The prediction of FEAST showed high accuracy values (0.933 ± 0.046) and specificity values (0.959 ± 0.041), confirming its suitability to discriminate samples from different source types. The apportionment results reflected well the expected output of artificial communities which were generated with different ratios of source types to simulate various contamination levels. Finally, the validated FEAST was applied to track the sources of ARGs in river sediments. Results showed STP effluents were the main contributor of ARGs, with an average contribution of 76 %, followed by sludge (10 %) and aquaculture effluent (2.7 %), which were basically consistent with the actual environment in the area.

https://www.sciencedirect.com/science/article/abs/pii/S0304389422019100