BACON: decoding the dynamic social networks of complex microbial communities at single-cell resolution.

Area of Science:

• Microbiology
• Computational Biology
• Systems Biology

Background:

• Microbial communities exhibit complex social behaviors governed by intercellular communication.
• Mapping these intricate interaction networks at a meaningful resolution remains a significant challenge in microbiology.

Purpose of the Study:

• To develop BACON, a computational framework for inferring quorum sensing-mediated communication networks in bacteria.
• To enable single-cell resolution analysis of microbial social interactions.

Main Methods:

• Developed BACON, a computational framework integrating a curated signaling database and statistical models.
• Inferred communication strength via coordinated gene expression (signal synthesis and receptor genes).
• Applied BACON to model bacterial systems (Bacillus subtilis, Escherichia coli) and human gut microbiomes.

Main Results:

• BACON accurately reconstructed communication trajectories in Bacillus subtilis and network dynamics in Escherichia coli under antibiotic stress.
• Uncovered diurnal cross-species signaling patterns in human gut microbiomes, independent of enterotypes.
• Identified conserved metabolic specializations in signal-responsive gut bacteria and a Pseudomonas aeruginosa virulence circuit in an ICU patient.

Conclusions:

• BACON provides a unified framework for analyzing bacterial social interactions across diverse environments.
• This approach facilitates understanding microbial sociology, combating antimicrobial resistance, and engineering synthetic microbial communities.