Blair Matthew F, Garner Emily, Ji Pan, Pruden Amy
Via Department of Civil and Environmental Engineering, Virginia Tech, Blacksburg, Virginia 24061, United States.
Wadsworth Department of Civil and Environmental Engineering, West Virginia University, Morgantown, West Virginia 26506, United States.
Environ Sci Technol. 2024 Sep 11;58(38):16877-90. doi: 10.1021/acs.est.4c04679.
As water reuse applications expand, there is a need for more comprehensive means to assess water quality. Microbiome analysis could provide the ability to supplement fecal indicators and pathogen profiling toward defining a "healthy" drinking water microbiota while also providing insight into the impact of treatment and distribution. Here, we utilized 16S rRNA gene amplicon sequencing to identify signature features in the composition of microbiota across a wide spectrum of water types (potable conventional, potable reuse, and nonpotable reuse). A clear distinction was found in the composition of microbiota as a function of intended water use (e.g., potable vs nonpotable) across a very broad range of U.S. water systems at both the point of compliance (Betadisper > 0.01; ANOSIM < 0.01, -stat = 0.71) and point of use (Betadisper > 0.01; ANOSIM < 0.01, -stat = 0.41). Core and discriminatory analysis further served in identifying distinct differences between potable and nonpotable water microbiomes. Taxa were identified at both the phylum (Desulfobacterota, Patescibacteria, and Myxococcota) and genus ( and ) levels that effectively discriminated between potable and nonpotable waters, with the most discriminatory taxa being core/abundant in nonpotable waters (with few exceptions, such as being abundant in potable conventional waters). The approach and findings open the door to the possibility of microbial community signature profiling as a water quality monitoring approach for assessing efficacy of treatments and suitability of water for intended use/reuse application.
随着水再利用应用的扩大，需要更全面的方法来评估水质。微生物群落分析能够补充粪便指标和病原体分析，以定义“健康”的饮用水微生物群，同时还能深入了解处理和分配的影响。在此，我们利用16S rRNA基因扩增子测序来识别广泛水类型（饮用水常规、饮用水再利用和非饮用水再利用）中微生物群组成的特征。在美国非常广泛的水系统中，无论是在合规点（贝塔离散度>0.01；相似性分析<0.01，R统计量=0.71）还是使用点（贝塔离散度>0.01；相似性分析<0.01，R统计量=0.41），都发现微生物群组成因预期用水（如饮用水与非饮用水）而有明显差异。核心和判别分析进一步用于识别饮用水和非饮用水微生物群之间的明显差异。在门（脱硫杆菌门、帕氏菌门和粘球菌门）和属（未提及具体属名）水平上鉴定出了有效区分饮用水和非饮用水的分类群，其中最具判别性的分类群在非饮用水中是核心/丰富的（少数例外情况，如在饮用水常规水中丰富）。该方法和研究结果为微生物群落特征分析作为一种水质监测方法打开了大门，可用于评估处理效果和水用于预期用途/再利用应用的适用性。
