Biology subjects

Lehtinen, S.

Publications and source records attributed to Lehtinen, S..

9 recordsLinked to original sources

bioRxiv2026

Bacterial strain structure shapes the trajectory of antibiotic resistance genes from plasmid to chromosome

The evolutionary dynamics determining whether an antimicrobial resistance (AMR) gene resides on a plasmid or a chromosome are critical to understanding the spread of resistance. While theoretical models often predict that beneficial traits should ultimately integrate into the more stable chromosome, contemporary genomic data frequently shows a strong enrichment of resistance genes on mobile, yet costly and less stable plasmids. Here, we propose that the widespread plasmid-mediated resistance observed today may not represent a stable evolutionary equilibrium, but rather a snapshot of an ongoing process. Using a stochastic multi-strain model, we explore the role of strain diversity as a determinant of the timescale of this process. We suggest that the population structure of the bacterial host species, maintained by balancing selection, acts as a substantial barrier to the selective sweep of vertically transmitted chromosomal genes. Because horizontal transmission allows conjugative plasmids to readily cross these strain boundaries, our model indicates that plasmid-borne resistance can transiently dominate the population before chromosomal integration takes over, and that this transient dominance can last for decades. We illustrate these predictions by analysing antimicrobial resistance gene location in three common opportunistic gut pathogens. Furthermore, we show how extending the model to include co-selection between resistance genes on multi-drug resistance plasmids can lead to more complex dynamics that transiently reverse this plasmid-to-chromosome trajectory. Overall, our findings highlight how bacterial population structure can dictate the evolutionary trajectory of beneficial genes, suggesting that the current distribution of AMR genes between chromosome and plasmids is a prolonged transient state rather than a static endpoint.

evolutionary biology↗

bioRxiv2025

Emergent epistasis mediates the role of negative frequency-dependent selection in bacterial strain structure

Strain structure is a well-documented phenomenon in many pathogenic and commensal bacterial species, where distinct strains persist over time exhibiting stable associations between genetic or phenotypic traits. This structure is surprising, particularly in highly recombinogenic species like Streptococcus pneumoniae, because recombination typically breaks down linkage disequilibrium, the non-random association of alleles at different loci. Allelic diversity is a necessary condition of strain structure. Recent work suggests that multi-locus negative frequency-dependent selection (NFDS) acts to maintain diversity across bacterial genomes. Here, using modelling and genomic analysis, we show that multi-locus NFDS also shapes bacterial strain structure through emergent epistatic effects. We develop models of two NFDS mechanisms - metabolic niche differentiation and competition-colonisation trade-offs - and show that epistasis emerges readily in these models. Notably, both models generate frequency-dependent epistasis. Unlike classical epistasis, this acts to either reinforce or abolish existing strain structure, making observed allele associations contingent on the evolutionary history of the population. We then use a dataset of over 3000 S. pneumoniae genomes to test our model predictions, and make observations consistent with emerging epistatic effects on gene associations. Our results extend and generalise previous work on the role of antigen-specific acquired immunity (a diversity-maintaining mechanism) on strain structure. Overall, this works contributes to a better understanding of the evolutionary processes shaping the structure of bacterial populations, which is central to predictive modelling of multi-strain pathogens

evolutionary biology↗

bioRxiv2025

Cluster dispersal shapes microbial diversity during community assembly

Identifying the drivers of diversity remains a central challenge in microbial ecology. In microbiota, within-community diversity is often linked to host health, which makes it all the more important to understand. Since many communities assemble de novo, microbial dispersal plays a critical role in shaping community structure. While theoretical models typically assume microbes disperse individually, this overlooks cases where microbes disperse in clusters, such as, for example, during host feeding. Here, we investigate how cluster dispersal impacts microbial community assembly, species richness, between-community dissimilarity, and species abundance. We developed a model in which microbes disperse from a pool into communities as clusters and then replicate locally. Using both analytical and numerical approaches, we show that cluster dispersal promotes community homogenization by increasing within-community richness and reducing dissimilarity across communities, even at low dispersal rates. Moreover, it modulates the influence of local selection on microbial community assembly and, consequently, on species abundance. Our results demonstrate that cluster dispersal has distinct effects from simply increasing the dispersal rate. We discuss how these predictions can inform the interpretation of experimental data and demonstrate their utility by reanalyzing a gut microbiota dataset from Caenorhabditis elegans. This analysis reveals new evidence for the role of cluster dispersal in microbial community assembly.

ecology↗

bioRxiv2025

Quantifying the effects of antibiotic resistance and within-host competition on strain fitness in Streptococcus pneumoniae

Competition plays a key role in shaping the structure and diversity of bacterial populations. In many clinically important bacterial species, strains compete at multiple scales: at the between-host scale for new hosts to colonise, and at the within-host scale during co-colonisation. Characterising these multiple facets of competition plays an important role in understanding bacterial ecology. This is particularly relevant for antibiotic resistance, where competition between antibiotic-susceptible and resistant strains determines resistance dynamics. In this work, we perform survival analyses on a large longitudinal dataset of Streptococcus pneumoniae carriage to quantify how within-host competition affects the rates of clearance and establishment of pneumococcal strains. We find that the presence of a within-host competitor is associated with a 33% increase in clearance and a 54% reduction in establishment. Priority effects and serotype differences partially predict the outcomes of this within-host competition. Further, we quantify the effects of antibiotic resistance on betweenand within-host components of fitness. Antibiotic consumption is associated with increased clearance rate for both susceptible and resistant strains, albeit to a higher extent in susceptible strains. In the absence of antibiotics, we find some evidence that resistance is associated with increased susceptibility to within-host competition, suggesting a fitness cost of resistance. Overall, our work provides quantitative insights into pneumococcal competition across scales and the role of this competition in shaping pneumococcal epidemiology.

ecology↗

bioRxiv2023

Persistence-colonization trade-off and niche differentiation enable the coexistence of E. coli phylogroups

Despite extensive literature on the pathogenicity and virulence of the opportunistic pathogen Escherichia coli, much less is known about its ecological and evolutionary dynamics as a commensal in healthy hosts. Based on two detailed longitudinal datasets on the gut microbiota of healthy adult individuals followed over months to years in France and in the USA, we identified a robust trade-off between the ability to establish in a new host (colonization) and the ability to remain in the host (residence). Major E. coli lineages (phylogroups) exhibited similar fitness but a diversity of strategies, from strong colonizers residing for a few days in the gut, to poor colonizers residing for years. Strains with the largest number of extra-intestinal virulence associated genes and highest pathogenicity resided for longest in hosts. Moreover, the residence time of a strain was reduced more strongly when it competed with other strains of the same phylogroup than of different phylogroups, suggesting niche differentiation between them. To investigate the consequences of the trade-off and niche differentiation for coexistence between strains, we developed a discrete-state Markov model describing the dynamics of E. coli in a population of hosts. We found that the trade-off and niche differentiation acted together as equalizing and stabilizing mechanisms enabling the coexistence of phylogroups over extended periods of time. Our model predicted that a reduction in transmission (e.g. better hygiene) would not alter the balance between phylogroups, while disturbance of the microbiome (e.g. antibiotics) would hinder residents strains such as those of the extra-intestinal pathogenic phylogroup B2.3.

evolutionary biology↗

bioRxiv2021

Plasmid co-infection: linking biological mechanisms to ecological and evolutionary dynamics

As infectious agents of bacteria and vehicles of horizontal gene transfer, plasmids play a key role in bacterial ecology and evolution. Plasmid dynamics are shaped not only by plasmid-host interactions, but also by ecological interactions between plasmid variants. These interactions are complex: plasmids can co-infect the same host cell and the consequences for the co-resident plasmid can be either beneficial or detrimental. Many of the biological processes that govern plasmid co-infection-from systems to exclude infection by other plasmids to interactions in the regulation of plasmid copy number per cell-are well characterised at a mechanistic level. Modelling plays a central role in translating such mechanistic insights into predictions about plasmid dynamics, and in turn, the impact of these dynamics on bacterial evolution. Theoretical work in evolutionary epidemiology has shown that formulating models of co-infection is not trivial, as some modelling choices can introduce unintended ecological assumptions. Here, we review how the biological processes that govern co-infection can be represented in a mathematical model, discuss potential modelling pitfalls, and analyse this model to provide general insights into how co-infection impacts eco-evolutionary outcomes. In particular, we demonstrate how beneficial and detrimental effects of co-infection give rise to frequency-dependent selection.

microbiology↗

bioRxiv2020

Epidemiological dynamics of bacteriocin competition and antibiotic resistance

Bacteriocins, toxic peptides involved in the competition between bacterial strains, are extremely diverse. Previous work on bacteriocin dynamics has highlighted the role of non-transitive rock-paper-scissors competition in maintaining the coexistence of different bacteriocin profiles. The focus to date has primarily been on bacteriocin interactions at the within-host scale. Yet, in species such as Streptococcus pneumoniae with relatively short periods of colonisation and limited within-host diversity, ecological outcomes are also shaped by processes at the epidemiological (between-host) scale. Here, we first investigate bacteriocin dynamics and diversity in epidemiological models. We find that in these models, bacteriocin diversity is more readily maintained than in within-host models, and with more possible combinations of coexisting bacteriocin profiles. Indeed, maintenance of diversity in epidemiological models does not require rock-paper-scissors dynamics; it can also occur through a competition-colonisation trade-off. Second, we investigate the link between bacteriocin diversity and diversity at antibiotic resistance loci. Previous work has proposed that bacterial duration of colonisation modulates the fitness of antibiotic resistance. Due to their inhibitory effects, bacteriocins are a plausible candidate for playing a role in the duration of colonisation episodes. We extend the epidemiological model of bacteriocin dynamics to incorporate an antibiotic resistance locus and demonstrate that bacteriocin diversity can indeed maintain the coexistence of antibiotic sensitive and resistant strains.

evolutionary biology↗

bioRxiv2020

Evolutionary mechanisms that determine which bacterial genes are carried on plasmids

The evolutionary pressures that determine the location (chromosomal or plasmid-borne) of bacterial genes are not fully understood. We investigate these pressures through mathematical modelling in the context of antibiotic resistance, which is often found on plasmids. Our central finding is that gene location is under positive frequency-dependent selection: the higher the frequency of one form of resistance compared to the other, the higher its relative fitness. This can keep moderately beneficial genes on plasmids, despite occasional plasmid loss. For these genes, positive frequency-dependence leads to a priority effect: whichever form is acquired first - through either mutation or horizontal gene transfer - has time to increase in frequency and thus become difficult to displace. Higher rates of horizontal transfer of plasmid-borne than chromosomal genes therefore predict moderately beneficial genes will be found on plasmids. Gene flow between plasmid and chromosome allows chromosomal forms to arise, but positive frequencydependent selection prevents these from establishing. Further modelling shows this effect is particularly pronounced when genes are shared across a large number of species, suggesting antibiotic resistance genes are often found on plasmids because they are moderately beneficial across many species. We also revisit previous theoretical work - relating to the role of local adaptation in explaining gene location and to plasmid persistence - in light of our findings. Impact StatementBacterial genes can either reside on the chromosome or on plasmids, extra-chromosomal genetic structures which can be transferred from cell to cell. The distribution of genes between plasmid and chromosome is not random: certain types of genes are particularly likely to be plasmid-associated. This includes a number of clinically important traits, such as antibiotic resistance and virulence factors. The evolutionary mechanisms that give rise to this pattern are not well understood. Plasmids are occasionally lost during cell replication and thus less reliably inherited than the chromosome, and genes are free to transition between plasmid and chromosome: so what keeps genes on plasmids? We address this question using a mathematical model. The key prediction from our model is that the relative fitness of chromosomal and plasmid-borne genes depends on their relative frequencies ( positive frequency-dependent selection). In other words, the fitness of a plasmid-borne gene will be higher in a population in which the chromosomal gene is rare (and vice-versa). This positive-frequency dependence can keep moderately beneficial genes on plasmids, despite occasional plasmid loss. This leads to a priority effect: whichever form of the gene (i.e. plasmid-borne or chromosomal) is acquired first has time to increase in frequency and thus become difficult to displace. Therefore, the relative rate of acquiring the gene on the plasmid vs the chromosome predicts where the gene will be found. Further modelling shows this effect is particularly pronounced when genes are beneficial across a large number of species. All together, the hypothesis that emerges from our work is that plasmid-borne genes are moderately beneficial; functional across a large number of species; and rarely acquired through chromosomal mutation. We suggest traits like antibiotic resistance are often found on plasmids because these genes commonly fulfill these criteria.

evolutionary biology↗

bioRxiv2017

Mechanisms that maintain coexistence of antibiotic sensitivity and resistance also promote high frequencies of multidrug resistance

Resistance against different antibiotics appears on the same bacterial strains more often than expected by chance, leading to high frequencies of multidrug resistance. There are multiple explanations for this observation, but these tend to be specific to subsets of antibiotics and/or bacterial species, whereas the trend is pervasive. Here, we consider the question in terms of strain ecology: explaining why resistance to different antibiotics is often seen on the same strain requires an understanding of the competition between strains with different resistance profiles. This work builds on models originally proposed to explain another aspect of strain competition: the stable coexistence of antibiotic sensitivity and resistance observed in a number of bacterial species. We first demonstrate a partial structural similarity in these models of coexistence. We then generalise this unified underlying model to multidrug resistance and show that models with this structure predict high levels of association between resistance to different drugs and high multidrug resistance frequencies. We test predictions from this model in six bacterial datasets and find them to be qualitatively consistent with observed trends. The higher than expected frequencies of multidrug resistance are often interpreted as evidence that these strains are out-competing strains with lower resistance multiplicity. Our work provides an alternative explanation that is compatible with long-term stability in resistance frequencies.\n\nAuthor summaryAntibiotic resistance is a serious public health concern, yet the ecology and evolution of drug resistance are not fully understood. This impacts our ability to design effective interventions to combat resistance. From a public health point of view, multidrug resistance is particularly problematic because resistance to different antibiotics is often seen on the same bacterial strains, which leads to high frequencies of multidrug resistance and limits treatment options. This work seeks to explain this trend in terms of strain ecology and the competition between strains with different resistance profiles. Building on recent work exploring why resistant bacteria are not out-competing sensitive bacteria, we show that models originally proposed to explain this observation also predict high multidrug resistance frequencies. These models are therefore a unifying explanation for two pervasive trends in resistance dynamics. In terms of public health, the implication of our results is that new resistances are likeliest to be found on already multidrug resistant strains and that changing patterns of prescription may not be enough to combat multidrug resistance.

evolutionary biology↗