BACKGROUND Microorganisms in activated sludge (AS) play key roles in the wastewater treatment processes. However, their ecological behaviors and differences from microorganisms in other environments have mainly been studied using the 16S rRNA gene that may not truly represent in situ functions. RESULTS Here, we present 2045 archaeal and bacterial metagenome-assembled genomes (MAGs) recovered from 1.35 Tb of metagenomic data generated from 114 AS samples of 23 full-scale wastewater treatment plants (WWTPs). We found that the AS MAGs have obvious plant-specific features and that few proteins are shared by different WWTPs, especially for WWTPs located in geographically distant areas. Further, we developed a novel machine learning approach that can distinguish between AS MAGs and MAGs from other environments based on the clusters of orthologous groups of proteins with an accuracy of 96%. With the aid of machine learning, we also identified some functional features (e.g., functions related to aerobic metabolism, nutrient sensing/acquisition, and biofilm formation) that are likely vital for AS bacteria to adapt themselves in wastewater treatment bioreactors. CONCLUSIONS Our work reveals that, although the bacterial species in different municipal WWTPs could be different, they may have similar deterministic functional features that allow them to adapt to the AS systems. Also, we provide valuable genome resources and a novel approach for future investigation and better understanding of the microbiome of AS and other ecosystems. Video Abtract.

中文翻译：

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机器学习辅助的数千个基因组草图分析揭示了活性污泥过程的特定特征。

背景技术活性污泥（AS）中的微生物在废水处理过程中起关键作用。但是，主要使用可能无法真正代表原位功能的16S rRNA基因研究了它们的生态行为和与其他环境中微生物的差异。结果在这里，我们介绍了从23个大型废水处理厂（WWTP）的114个AS样品中产生的1.35 Tb宏基因组学数据中回收的2045个古细菌和细菌基因组组装的基因组（MAGs）。我们发现AS MAG具有明显的植物特有特征，几乎没有蛋白质被不同的WWTP共享，特别是对于位于地理遥远地区的WWTP。进一步，我们开发了一种新颖的机器学习方法，可以基于直系同源蛋白质组的簇区分AS MAG和其他环境中的MAG，准确度为96％。借助机器学习，我们还确定了一些功能特性（例如，与有氧代谢，营养素感测/获取和生物膜形成有关的功能），对于AS细菌适应废水处理生物反应器可能至关重要。结论我们的工作表明，尽管不同市政污水处理厂中的细菌种类可能不同，但它们可能具有相似的确定性功能特征，从而使其能够适应AS系统。此外，我们还提供了宝贵的基因组资源和新颖的方法，可用于未来的调查以及对AS和其他生态系统的微生物组的更好理解。