Horizontal gene transfer (HGT) promotes the spread of genes within bacterial communities. Among the HGT mechanisms, natural transformation stands out as being encoded by the bacterial core genome. Natural transformation is often viewed as a way to acquire new genes and to generate genetic mixing within bacterial populations. Another recently proposed function is the curing of bacterial genomes of their infectious parasitic mobile genetic elements (MGEs). Here, we propose that these seemingly opposing theoretical points of view can be unified. Although costly for bacterial cells, MGEs can carry functions that are at points in time beneficial to bacteria under stressful conditions (e.g., antibiotic resistance genes). Using computational modeling, we show that, in stochastic environments, an intermediate transformation rate maximizes bacterial fitness by allowing the reversible integration of MGEs carrying resistance genes, although these MGEs are costly for host cell replication. Based on this dual function (MGE acquisition and removal), transformation would be a key mechanism for stabilizing the bacterial genome in the long term, and this would explain its striking conservation. IMPORTANCE Natural transformation is the acquisition, controlled by bacteria, of extracellular DNA and is one of the most common mechanisms of horizontal gene transfer, promoting the spread of resistance genes. However, its evolutionary function remains elusive, and two main roles have been proposed: (i) the new gene acquisition and genetic mixing within bacterial populations and (ii) the removal of infectious parasitic mobile genetic elements (MGEs). While the first one promotes genetic diversification, the other one promotes the removal of foreign DNA and thus genome stability, making these two functions apparently antagonistic. Using a computational model, we show that intermediate transformation rates, commonly observed in bacteria, allow the acquisition then removal of MGEs. The transient acquisition of costly MGEs with resistance genes maximizes bacterial fitness in environments with stochastic stress exposure. Thus, transformation would ensure both a strong dynamic of the bacterial genome in the short term and its long-term stabilization.
France was one of the first countries to be reached by the COVID-19 pandemics. Here, we analyse 196 SARS-Cov-2 genomes collected between Jan 24 and Mar 24 2020, and perform a phylodynamics analysis. In particular, we analyse the doubling time, reproduction number (Rt) and infection duration associated with the epidemic wave that was detected in incidence data starting from Feb 27. We show that a slowing down of the epidemic spread can be detected in Mar, which is consistent with the implementation of the national lock-down on Mar 17. The inferred distributions for the infection duration and Rt are in line with those estimated from contact tracing data. Overall, this analysis shows the potential to use sequence genomic data to inform public health decisions in an epidemic crisis context.
We introduce TiPS, an R-based simulation software to generate time series and genealogies associated with a population dynamics model. The approach is flexible since it can capture any model defined with a set of ordinary differential equations (ODE), and allow parameter values to vary over time periods. Computational time is minimal thanks to the use of the Rcpp package to compile the ODEs into a program corresponding to an implementation of the Gillespie algorithm. This software is particularly suited for epidemiology and phylodynamics, where there is a need to generate numerous phylogenies for a variety of infections life cycles, and in population genetics as well.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.