Uncovering Predictive Factors and Interventions for Restoring Microecological Diversity after Antibiotic Disturbance

Area of Science:

• Microbiology
• Computational Biology
• Host-Microbiome Interactions

Background:

• Antibiotic therapy can significantly reduce gut microbial diversity and alter composition, impacting host health.
• Gut microbiome recovery post-antibiotics may be influenced by specific microbial species.

Purpose of the Study:

• To develop a predictive model for gut microbiome recovery after antibiotic treatment.
• To identify key bacterial species associated with microbiome recovery (p-RABs).
• To explore additional indicators of successful recovery and potential intervention targets.

Main Methods:

• Utilized a novel ensemble learning framework to identify predictive recovery-associated bacterial species (p-RABs).
• Conducted metabolic support analysis to identify key species for reconstruction.
• Validated findings using a C57BL/6J male mouse model.

Main Results:

• Identified a set of common and predictive recovery-associated bacterial species (p-RABs).
• Higher taxonomic and functional diversity correlated with increased likelihood of successful recovery.
• Akkermansia muciniphila and Bacteroides uniformis were identified as potential key species for interventions.
• p-RABs demonstrated effectiveness in facilitating intestinal microbial reconstitution in a mouse model.

Conclusions:

• The developed p-RABs reliably predict gut microbiome recovery status under antibiotic treatment.
• The study validates a practical intervention scheme for gut microbiota reconstruction.
• Findings suggest diversity metrics and specific species can guide microbiome restoration strategies.