Forecasting the dynamics of a complex microbial community using integrated meta-omics.

Area of Science:

• Microbiology
• Environmental Science
• Biotechnology

Background:

• Predicting complex microbial community behavior is crucial for sustainable operation of biotechnological processes like biological wastewater treatment plants (BWWTPs).
• Longitudinal meta-omics data provides insights into microbial community dynamics.

Purpose of the Study:

• To develop a predictive model for microbial community behavior in BWWTPs.
• To link temporal signals in microbial communities to ecological events and environmental parameters.
• To forecast microbial community dynamics and gene expression over extended periods.

Main Methods:

• Analysis of 14 months of longitudinal meta-omics data from a BWWTP anaerobic tank.
• Identification and summarization of 17 temporal signals explaining 91.1% of temporal variance.
• Forecasting of temporal signals over five years using validated models.
• Prediction of gene abundance and expression based on forecasted signals and environmental variables.

Main Results:

• 17 temporal signals were identified, explaining over 91% of community variance.
• Forecasts for six signals were accurate, suggesting phenomena like predation cycles.
• Gene abundance and expression were predicted with a coefficient of determination ≥0.87 for three years.
• The study demonstrates the ability to forecast open microbial ecosystem dynamics.

Conclusions:

• Microbial community dynamics in BWWTPs can be predicted using temporal signals and environmental interactions.
• This predictive capability supports the sustainable operation of complex biotechnological processes.
• The findings offer a novel approach to understanding and managing open microbial ecosystems.