Cronos: A Machine Learning Pipeline for Description and Predictive Modeling of Microbial Communities Over Time

Area of Science:

• Microbiology
• Computational Biology
• Ecology

Background:

• Traditional microbial time-series analysis focuses on individual taxa, assuming uniform community responses.
• This assumption is often violated in complex systems like the human gut microbiome, where distinct microbial profiles exist.
• Understanding these variations is crucial for accurate ecological process interpretation.

Purpose of the Study:

• To propose an alternative approach for microbial time-series analysis focusing on community transitions rather than individual taxa.
• To develop and implement an R pipeline named Cronos for analyzing microbial community dynamics.
• To enhance the understanding of ecological processes and enable prediction of microbial community states.

Main Methods:

• Cronos analyzes microbial composition data, phylogenetic relationships, and metadata.
• It identifies and describes distinct microbial community clusters at each time point.
• The pipeline models transitions between clusters to predict future community states.

Main Results:

• Cronos was applied to infant gut microbiome data, revealing distinct trajectories for breastfed and formula-fed infants.
• These trajectories converged towards profiles similar to mature individuals.
• The study demonstrated Cronos' capability to identify and model community dynamics.

Conclusions:

• Tracking microbial community transitions offers a more robust approach to time-series analysis than focusing on individual taxa.
• Cronos provides a powerful tool for dissecting complex microbial dynamics and predicting community fate.
• The findings highlight the importance of community structure in understanding gut microbiome development.