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Natural transformation is the only mechanism of genetic exchange controlled by the recipient bacteria. We quantified its rates in 786 clinical strains of the human pathogens Legionella pneumophila (Lp) and 496 clinical and environmental strains of Acinetobacter baumannii (Ab). The analysis of transformation rates in the light of phylogeny revealed they evolve by a mixture of frequent small changes and a few large quick jumps across 6 orders of magnitude. In standard conditions close to half of the strains of Lp and a more than a third in Ab are below the detection limit and thus presumably non-transformable. Ab environmental strains tend to have higher transformation rates than the clinical ones. Transitions to non-transformability were frequent and usually recent, suggesting that they are deleterious and subsequently purged by natural selection. Accordingly, we find that transformation decreases genetic linkage in both species, which might accelerate adaptation. Intragenomic conflicts with chromosomal mobile genetic elements (MGEs) and plasmids could explain these transitions and a GWAS confirmed systematic negative associations between transformation and MGEs: plasmids and other conjugative elements in Lp, prophages in Ab, and transposable elements in both. In accordance with the hypothesis of modulation of transformation rates by genetic conflicts, transformable strains have fewer MGEs in both species and some MGEs inactivate genes implicated in the transformation with heterologous DNA (in Ab). Innate defense systems against MGEs are associated with lower transformation rates, especially restriction-modification systems. In contrast, CRISPR-Cas systems are associated with higher transformation rates suggesting that adaptive defense systems may facilitate cell protection from MGEs while preserving genetic exchanges by natural transformation. Ab and Lp have different lifestyles, gene repertoires, and population structure. Nevertheless, they exhibit similar trends in terms of variation of transformation rates and its determinants, suggesting that genetic conflicts could drive the evolution of natural transformation in many bacteria., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2024 Mazzamurro et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Acinetobacter baumannii is a globally distributed opportunistic pathogen in human health settings, including in intensive care units (ICUs). We investigated the contamination of a French small animal ICU with A. baumannii . We discovered repeated animal contamination by A. baumannii , and phylogenetic analysis traced contamination back to a potential foreign animal origin. Genomic analysis combined with antibiotic susceptibility testing revealed heteroresistance to penicillin and aminoglycoside mediated by insertion sequence dynamics and also suggest a potential cross-resistance to human-restricted piperacillin-tazobactam combination. The A. baumannii isolates of the animal ICU belong to the International Clone 2 commonly found in human health settings. Our results suggest a high adaptation of this lineage to healthcare settings and provide questions on the requirements for genetic determinants enabling adaptation to host and abiotic conditions.

Decisions in Environmental Risk Assessment (ERA) about impacts of chemical compounds on different species are based on critical effect indicators such as the 50% lethal concentration (LC 50 ). Regulatory documents recommend concentration-response (or concentration-effect) model fitting on standard toxicity test data to get LC 50 values. However, toxicokinetic-toxicodynamic (TKTD) models proved their efficiency to better exploit toxicity test data, at Tier-2 but also at Tier-1, delivering time-independent indicators. In particular, LC 50 values can be obtained from the reduced General Unified Threshold model of Survival (GUTS-RED) with both variants, Stochastic Death and Individual Tolerance, that include parameter h b , the background mortality. Estimating h b during the fitting process or not depends on studies and fitting habits, while it may strongly influence the other GUTS-RED parameters, and consequently the LC 50 estimate. We hypothesized that estimating h b from all data in all replicates over time should provide more precise LC 50 estimates. We then explored how estimating h b impacted: (i) GUTS-RED model parameters; (ii) goodness-of-fit criteria (fitting plot, posterior predictive check, parameter correlations); (iii) LC 50 accuracy and precision. We finally show that estimating h b does not impact the LC 50 precision while providing more accurate and precise GUTS parameter estimates. Hence, estimating h b would lead to a more protective ERA., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2023 The Author(s).)