Dynamics of immune memory and learning in bacterial communities

Area of Science:

• Microbiology
• Immunology
• Theoretical Biology

Background:

• Adaptive immunity, found in organisms from bacteria to humans, provides memory of past infections.
• Despite differences, adaptive immunity across species shares features like emergent diversity and host-pathogen coevolution.
• A unifying conceptual model for these shared features is currently lacking.

Purpose of the Study:

• To propose and solve a phenomenological model for CRISPR-based adaptive immunity in microbes.
• To investigate the dynamics of immune diversity, host-pathogen coevolution, and fitness pressures within microbial populations.

Main Methods:

• Development of a simple phenomenological model for microbial CRISPR adaptive immunity.
• Analysis of coexisting bacteria and phage populations using the model.
• Definition and application of 'average immunity' as a summary parameter.
• Synthetic time-shift analyses on experimental data to study coevolutionary modalities.

Main Results:

• Immune diversity in bacteria and phages is coupled and emerges spontaneously.
• Bacteria exhibit a context-dependent lag in tracking phage evolution.
• Paradoxically, high immune diversity correlates with low overall CRISPR immunity.
• Immune cross-reactivity leads to distinct evolutionary dynamics, including an influenza-like traveling wave regime.

Conclusions:

• CRISPR adaptive immunity in microbes serves as a tractable model for understanding general adaptive immunity principles.
• The model elucidates coupled immune diversity, coevolutionary lags, and the relationship between diversity and immunity levels.
• Findings suggest parallels between microbial and vertebrate adaptive immune system dynamics.