Co-evolutionary dynamics of defense systems and anti-defense genes in chromosomes and mobile genetic elements of Enterobacteriaceae

Technical Summary: Co-evolutionary Dynamics of Defense Systems and Anti-Defense Genes in Enterobacteriaceae

Problem Statement
Bacterial defense systems (DEFs) and mobile genetic elements (MGEs) engage in a complex evolutionary arms race. While it is established that DEFs are often encoded within MGEs (such as plasmids, prophages, and integrative conjugative elements), the specific contribution of the genomic compartment (chromosome vs. MGE) to the evolutionary turnover of these systems remains unclear. Furthermore, the quantitative evolutionary dynamics of anti-defense genes (anti-DEFs)—which suppress host immunity—relative to their target DEFs, and whether their trajectories are coupled, have not been systematically characterized across large-scale phylogenomic datasets. This study addresses the lack of quantitative comparisons regarding gene gain, loss, expansion, and reduction rates for DEFs and anti-DEFs across different genomic backgrounds within the Enterobacteriaceae family.

Methodology
The authors analyzed 6,907 high-quality genomes from 213 species of Enterobacteriaceae. The workflow involved:

1. Genome Processing and Annotation: Genomes were filtered for completeness (>99%) and contamination (<1%). Genes were predicted and annotated using pyrodigal-gv and geNomad. MGEs (prophages, plasmids, ICEs/IMEs, and satellites) were identified using specific tools (geNomad, Platon, SatelliteFinder, ICEfinder-opt), with strict cross-validation criteria to distinguish chromosomal from MGE-associated genes.
2. Defense System Identification: DEFs and anti-DEFs were identified using DefenseFinder. Orthologous gene families (OGs) were constructed using OrthoFinder.
3. Evolutionary Rate Modeling: A phylogenetic birth–death model implemented in Count was used to quantify four evolutionary processes: gene gain, gene loss, expansion (copy-number increase), and reduction (copy-number decrease). This was applied to OGs classified by genomic location (chromosome, prophage, plasmid/ICE/IME).
4. Statistical Analysis:
   • Turnover Comparisons: Generalized Linear Mixed-Effects Models (GLMMs) with Gamma distributions were fitted to compare rates between DEFs and non-defense (ND) genes, and between different genomic compartments.
   • Coupling Analysis: Partial Spearman correlations, controlling for branch length and assessed via phylogenetic Brownian motion simulations, were used to determine if gains and losses of DEFs and anti-DEFs are evolutionarily coupled across lineages.
   • Stratification: Analyses were performed both at the whole-genome level and stratified by specific genomic compartments and defense system subtypes (e.g., CRISPR-Cas, RM, CBASS).

Key Results

1. Genomic Background Drives Turnover Bias: DEF turnover differs significantly from the background dynamics of the genomic elements carrying them.

   • Chromosomes: Chromosomal DEFs (cDEFs) exhibited the highest turnover rates (gain, loss, expansion, and reduction) compared to chromosomal non-defense genes.
   • MGEs: In contrast, DEFs located on prophages showed the lowest turnover rates, displaying greater stability and fewer copy-number fluctuations than the surrounding prophage gene pool. Plasmid and ICE/IME-associated DEFs showed intermediate turnover rates.
   • Balance: Despite differences in absolute rates, the relative balance between gain/loss and expansion/reduction remained similar across chromosomes and MGEs.

2. Anti-Defense Dynamics: Anti-DEFs generally exhibit higher loss, expansion, and reduction rates than their target DEFs, particularly when located on MGEs.

   • On prophages and plasmids/ICEs/IMEs, anti-DEFs showed significantly higher loss and copy-number plasticity than DEFs.
   • On chromosomes, anti-DEFs showed lower gain rates than DEFs but similar rates of loss and copy-number change.
   • Stratified analysis confirmed these trends for specific systems, such as anti-CBASS and anti-RM, which showed higher loss and copy-number change rates than their respective targets.

3. Evolutionary Coupling: Gains and losses of DEFs and anti-DEFs are positively correlated across bacterial lineages.

   • This coupling was observed at the whole-genome level and within specific MGE compartments (prophages and plasmids).
   • Positive associations were found for both "matching" (antagonistic) pairs (e.g., RM Type I–ArdA/ArdU, CRISPR-Cas I-E–AcrIE9) and "non-matching" pairs.
   • The turnover of MGE-associated DEFs and anti-DEFs was also correlated with the turnover of broader functional gene categories (COGs), suggesting that their dynamics are often part of larger-scale changes in MGE content.

Significance and Claims
The paper claims that the genomic background (chromosome vs. MGE) is a primary determinant of the flux of defense systems. The authors propose that the high turnover of chromosomal DEFs may reflect a "defense outsourcing" strategy, where bacteria preferentially lose chromosomal defenses in favor of MGE-borne ones, or that chromosomal defense islands are inherently more plastic due to lateral transduction and proximity to mobile elements.

The study provides empirical support for the hypothesis that the evolutionary trajectories of DEFs and anti-DEFs are coupled. This coupling is driven not only by direct arms-race dynamics (ecological coupling between matching pairs) but also by the broader background turnover of MGEs that carry non-matching defense and counter-defense systems. The findings suggest that the retention of prophage-associated DEFs may be a result of selective maintenance or domestication, whereas the rapid loss of chromosomal DEFs highlights the cost-benefit trade-offs in maintaining immunity in a stable genomic environment.

The authors remain modest regarding their conclusions, noting that statistical power is limited for species with few genomes and that the analysis is restricted to Enterobacteriaceae. They acknowledge that the functional status of some MGE-associated DEFs (e.g., whether they are complete systems or reduced derivatives) requires further direct analysis.