Microbial Keystone Taxa Identification in Global Wastewater Treatment Plants Based on Deep Learning

Microorganisms play a vital role in maintaining the stability of the activated sludge (AS) ecosystem. Recent studies indicate that certain keystone taxa impact both composition and function, but independent of their abundance. However, an effective framework for identifying these taxa from numerous high-throughput sequencing data is still lacking, particularly without the challenging task of reconstructing the detailed microbial correlation network. We developed a deep learning framework that quantifies microbial impact values across samples, bypassing network reconstruction. This algorithm effectively avoids the tricky issue of differing keystoneness across species in various samples, making it applicable to AS assemblage samples from various environmental and climatic conditions. In this work, we applied this framework to the high-throughput sequencing of the global wastewater treatment sample and identified 61 candidate taxa as the keystones for the wastewater treatment process. We found that the temperature and dissolved oxygen are the primary factors influencing the keystone taxa. Moreover, we found that the increased connectivity of keystone taxa with other members promotes tighter integration within the activated sludge microbial community. In summary, this study introduces an expeditious framework for efficient keystone taxa identification and reveals their role in enhancing microbial community integration in wastewater treatment systems.微生物在维持活性污泥（Activated Sludge, AS）生态系统的稳定性中发挥着至关重要的作用。近期研究表明，部分关键类群（keystone taxa）可同时调控群落组成与功能，且其作用效果独立于自身丰度。然而，当前仍缺乏可从海量高通量测序数据中高效识别此类类群的有效框架，尤其是在无需开展难度极大的微生物关联网络重构工作的前提下。本研究开发了一款深度学习框架，可在无需重构关联网络的情况下，量化不同样本中微生物的影响权重。该算法可有效规避不同样本间物种关键度存在差异的棘手问题，使其可适用于不同环境与气候条件下的活性污泥群落样本。本研究将该框架应用于全球污水处理样本的高通量测序数据分析，最终筛选出61个候选类群作为污水处理过程的关键类群。研究发现，温度与溶解氧是影响关键类群的核心环境因子。此外，关键类群与其他群落成员的连接度提升，可促进活性污泥微生物群落的整合程度进一步增强。综上，本研究提出了一种高效的关键类群识别框架，并揭示了其在提升污水处理系统微生物群落整合度方面的重要作用。