A group penalization framework for detecting time-lagged microbiota-host associations

Area of Science:

• Microbiome research
• Host-microbe interactions
• Statistical modeling

Background:

• Longitudinal microbiome data is increasingly used to understand host-microbe dynamics.
• Existing methods often fail to identify specific time-lagged associations and their durations.
• Understanding these time-lagged effects is crucial for deciphering microbiome's impact on health.

Purpose of the Study:

• To develop a novel framework for identifying time-lagged associations between longitudinal microbial data and host health outcomes.
• To accurately determine the strength, sign, and time span of these microbial effects.
• To apply the framework to a real-world biological system.

Main Methods:

• Defined time-lagged effects by imposing a specific structure on longitudinal microbial measurements.
• Employed group penalization methods to identify associations, their strengths, signs, and time spans.
• Validated the approach through simulation studies and application to zebrafish data.

Main Results:

• The proposed framework accurately identifies time lags and estimates signal strengths in simulations.
• Specific gut microbial taxa exhibiting time-lagged effects on parasite worm burden in zebrafish were identified.
• The method successfully elucidates complex temporal relationships in microbiome-host interactions.

Conclusions:

• The developed framework provides a robust method for analyzing time-lagged microbiome effects.
• This approach enhances our understanding of how past microbial states influence current host health.
• The findings have implications for developing microbiome-based diagnostics and therapeutics.