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Abstract Plasmids promote adaptation of bacteria by facilitating horizontal transfer of diverse genes, notably those conferring antibiotic resistance. Some plasmids, like those of the incompatibility group IncP-1, are known to replicate and persist in a broad range of bacteria. We investigated a poorly understood exception, the IncP-1β plasmid pBP136 from a clinical Bordetella pertussis isolate, which quickly became extinct in laboratory Escherichia coli populations. Through experimental evolution, we found that the inactivation of a previously uncharacterized plasmid gene, upf31, drastically improved plasmid persistence in E. coli. The gene inactivation caused alterations in the plasmid regulatory system, including decreased transcription of the global plasmid regulators (korA, korB, and korC) and numerous genes in their regulons. This is consistent with our findings that Upf31 represses its own transcription. It also caused secondary transcriptional changes in many chromosomal genes. In silico analyses predicted that Upf31 interacts with the plasmid regulator KorB at its C-terminal dimerization domain (CTD). We showed experimentally that adding the CTD of upf31/pBP136 to the naturally truncated upf31 allele of the stable IncP-1β archetype R751 results in plasmid destabilization in E. coli. Moreover, mutagenesis showed that upf31 alleles encoded on nearly half of the sequenced IncP-1β plasmids also possess this destabilization phenotype. While Upf31 might be beneficial in many hosts, we show that in E. coli some alleles have harmful effects that can be rapidly alleviated with a single mutation. Thus, broad-host-range plasmid adaptation to new hosts can involve fine-tuning their transcriptional circuitry through evolutionary changes in a single gene. 

Abstract Genes that undergo horizontal gene transfer (HGT) evolve in different genomic backgrounds. Despite the ubiquity of cross-species HGT, the effects of switching hosts on gene evolution remains understudied. Here, we present a framework to examine the evolutionary consequences of host-switching and apply this framework to an antibiotic resistance gene commonly found on conjugative plasmids. Specifically, we determined the adaptive landscape of this gene for a small set of mutationally connected genotypes in 3 enteric species. We uncovered that the landscape topographies were largely aligned with minimal host-dependent mutational effects. By simulating gene evolution over the experimentally gauged landscapes, we found that the adaptive evolution of the mobile gene in one species translated to adaptation in another. By simulating gene evolution over artificial landscapes, we found that sufficient alignment between landscapes ensures such “adaptive equivalency” across species. Thus, given adequate landscape alignment within a bacterial community, vehicles of HGT such as plasmids may enable a distributed form of genetic evolution across community members, where species can “crowdsource” adaptation. 

To increase our basic understanding of the ecology and evolution of conjugative plasmids, we need reliable estimates of their rate of transfer between bacterial cells. Current assays to measure transfer rate are based on deterministic modeling frameworks. However, some cell numbers in these assays can be very small, making estimates that rely on these numbers prone to noise. Here, we take a different approach to estimate plasmid transfer rate, which explicitly embraces this noise. Inspired by the classic fluctuation analysis of Luria and Delbrück, our method is grounded in a stochastic modeling framework. In addition to capturing the random nature of plasmid conjugation, our new methodology, the Luria–Delbrück method (“LDM”), can be used on a diverse set of bacterial systems, including cases for which current approaches are inaccurate. A notable example involves plasmid transfer between different strains or species where the rate that one type of cell donates the plasmid is not equal to the rate at which the other cell type donates. Asymmetry in these rates has the potential to bias or constrain current transfer estimates, thereby limiting our capabilities for estimating transfer in microbial communities. In contrast, the LDM overcomes obstacles of traditional methods by avoiding restrictive assumptions about growth and transfer rates for each population within the assay. Using stochastic simulations and experiments, we show that the LDM has high accuracy and precision for estimation of transfer rates compared to the most widely used methods, which can produce estimates that differ from the LDM estimate by orders of magnitude.