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.
摘要：
已经提出了基于宏基因组学的技术框架，用于评估FEAST作为基于ARG配置文件的源分配工具的潜力和实用性。为此,分析了一大群宏基因组数据集，与环境中八种来源类型的ARG相关联。完全正确,在604个源宏基因组中发现了1089种不同的ARG，和396ARG指标被确定为来源特定的指纹，以表征每个来源类型。有了源指纹，使用“留一法”交叉验证策略检查FEAST的预测性能。此外，模拟了人工汇群落，以评估FEAST对ARGs源的分配。FEAST的预测具有较高的准确性值（0.933±0.046）和特异性值（0.959±0.041），确认其适合区分不同来源类型的样品。分配结果很好地反映了人工群落的预期产出，这些人工群落是用不同比例的源类型生成的，以模拟各种污染水平。最后,验证的FEAST用于跟踪河流沉积物中的ARGs来源。结果表明，STP流出物是ARGs的主要贡献者，平均贡献率为76%，其次是污泥(10%)和水产养殖废水(2.7%)，这与该地区的实际环境基本一致。
