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.