Biology subjects

Bonhoeffer, S.

Publications and source records attributed to Bonhoeffer, S..

At least 19 recordsLinked to original sources

bioRxiv2026

Chromosome, Plasmid, or Both: Short-Term Dynamics and Long-Term Outcomes of Plasmid Cost Compensation under Trade-Offs

Conjugative plasmids drive the dissemination of antimicrobial resistance genes and other conditionally beneficial accessory genes, but otherwise inflict fitness costs on their hosts that limit their spread. These costs can be ameliorated by compensatory mutations on the chromosome, the plasmid, or both combined (combined compensation). Mutants with plasmid-borne or chromosomal compensation differ in how they spread and compete, making the location of compensation an important determinant of plasmid and antimicrobial resistance (AMR) dynamics. We collate experimental data on plasmid-host co-evolution which, albeit limited, suggest compensatory benefits differ by location and are highest for combined compensation. We develop and analyse a mathematical model of compensatory evolution, finding that the long-term location of compensation is mainly determined by the highest cost reduction. Short term, however, succession dynamics arise from differences between locations: for instance, plasmid-borne compensation spreads horizontally, initially dominates, and can even facilitate the establishment of chromosomal or combined compensation. Strong trade-offs between compensation and either resistance or conjugation render compensation non-viable, but only conjugation trade-offs are location-dependent, disadvantaging plasmid-borne compensation and generating oscillatory dynamics. Our findings suggest plasmid-borne compensation may initially accelerate AMR-plasmid spread, whereas long-term chromosomal or combined compensation may enable hosts to accumulate multiple AMR-plasmids, promoting multidrug resistance.

evolutionary biology↗

bioRxiv2026

Antimicrobial Combination Effects at Subinhibitory Doses do not Reliably Predict Effects at Inhibitory Concentrations

Assessing whether drug combinations synergise or antagonise is difficult for several reasons: (i) measuring bacterial death rates at clinically relevant inhibitory drug concentrations is methodologically challenging, (ii) there is no unifying definition of what constitutes synergy or antagonism, and (iii) both synergy and antagonism may be concentration- and mixing-ratio-dependent. To assess how well sub-inhibitory measurements predict inhibitory behaviour, we quantified drug interactions for 15 pairwise drug combinations on a concentration checkerboard covering a wide range of inhibitory and sub-inhibitory concentrations. To this end, we tracked the population dynamics of 8640 bioluminescent E. coli cultures by recording their light-intensity trajectories. To handle time-varying treatment effects and allow fair comparisons between drugs with distinct killing dynamics, we used a time-weighted net growth rate {psi} to summarise each trajectory and assigned interaction labels (synergistic/independent/antagonistic) based on Bliss independence and Loewe additivity. We found that the interaction label depends on both the concentration and the mixing ratio, frequently changing between the sub-inhibitory and inhibitory regimes. Characterising drug combinations at a single sub-inhibitory concentration is therefore not sufficient. Instead, their combined effects should be assessed at the conditions of their intended use.

microbiology↗

bioRxiv2026

Transfer potential of F-like plasmids in Escherichia coli differs by animal environment

Plasmids play a key role in the spread of virulence and antimicrobial resistance genes to new genetic backgrounds. Genetic variation in the transfer operon, which contain the genes responsible for conjugation, can lead to substantial differences in transfer potential even between closely related plasmids. However, it is not clear how much genetic diversity there is in transfer operons of natural bacterial populations. Here, we analyse the prevalence and transfer potential of F-like plasmids, a clinically important family of plasmids in Enterobacteriaceae, by searching for the presence of essential conjugation genes in 1200 Escherichia coli genomes isolated from three livestock-associated environments. We find that the fraction of F-like transfer operons that are functionally complete was significantly higher in poultry than in bovine and swine associated bacteria. This difference was not captured in methods that use the presence of replication genes to estimate plasmid prevalence. Confounders such as the phylogenetic relatedness of E. coli or the presence of antibiotic resistance could not explain these significant differences in transfer potential. Instead, it seems the poultry environment selects for plasmids with high transfer potential, as it also contained more conjugative plasmid types per isolate. While we find environment specific differences in overall plasmid frequency, patterns of transfer gene presence/absence were similar across the three environments. Regulatory and exclusion genes are the exception to this pattern, suggesting environment specific modulation of transfer rates. This highlights the use of genomic data to uncover environment specific differences in plasmid prevalence and transfer potential, revealing the selection pressures shaping horizontal gene transfer in these environments.

microbiology↗

bioRxiv2025

Modeling assembly dynamics and stability of microbial communities

Predicting the species composition of microbial communities is a problem that has proven surprisingly difficult, including in fully controlled microbial systems in a lab setting. Few organisms are able to establish themselves in an community with no others, even if nutrients are provided. At the same time some species are observed to be unable to stably coexist with certain other species. We here present a model attempting to reduce the dynamics of community assembly to its most basic components, while preserving its salient features. The model deals not with abundances or densities of bacterial populations or resources, but only with their presence or absence. Similarly, only three types of discrete relationships between species are considered: species excluding each other, and mutualistic dependence on and production of nutrients in the form of exometabolites. Despite these simplifications, the model system still exhibits rich dynamics, including extinction cascades and emerging stability against invasion. We derive conditions for these to occur and compare our results with existing knowledge of microbial communities. Our results provide a novel approach for the theoretical study of microbial communities and their stability. SignificanceUnderstanding the processes governing the community composition of microbial ecosystems is an as-yet unsolved problem. In this article, we propose a novel rule-based model for the assembly and stability of communities of microbial species. Despite its simplicity the model exhibits rich dynamics, including critical points and extinction cascades. It presupposes no knowledge of specific population parameters or exact resource or species abundances, but focuses on pairwise interactions between species and their metabolites. The framework may be useful for understanding how real microbial communities arise and what causes them to break down.

ecology↗

bioRxiv2025

Cross-scale effect of microbiota dynamics on resistance

Antimicrobial resistance (AMR) is often carried asymptomatically in the human gut microbiota, yet the role of this carriage in AMR persistence and transmission remains poorly understood. To address this gap, we developed a cross-scale model that links within-host microbiota dynamics of extended-spectrum beta-lactamase (ESBL)-producing and non-ESBL-producing bacteria with transmission across hospital and community settings. The model incorporates plasmid exchange, antibiotic effects, and host heterogeneity. It integrates diverse data sources, including microbiota dynamics, community antibiotic consumption, and detailed surveillance data from a large university hospital, complemented by literature estimates. This model identifies mechanisms that enable coexistence of resistant and sensitive strains at both individual and population levels, revealing host heterogeneity as a critical driver of resistance persistence. Unlike classic epidemiological models, our analysis finds a continuum of colonization states within individuals rather than discrete resistant or sensitive categories, and highlights that realistic detection thresholds may substantially underestimate resistance prevalence. Finally, we use this framework to evaluate interventions such as hospital screening and isolation, showing that their impact strongly depends on local transmission dynamics and resistance prevalence. By bridging within-host and population processes, our model provides a novel tool for investigating AMR spread and optimizing prevention strategies in healthcare and community environments.

ecology↗

bioRxiv2025

High-Throughput Quantification of Population Dynamics using Luminescence

The dynamics of bacterial population decline at antibiotic concentrations above the minimum inhibitory concentration (MIC) remain poorly characterized. This is because measuring colony-forming units (CFU), the standard assay to quantify inhibition, is slow, labour-intensive, costly, and can be unreliable at high drug concentrations. Luminescence assays are widely used to quantify population dynamics at subinhibitory concentrations, yet their limitations and reliability at super-MIC concentrations remain underexplored. To fill this gap, we compared luminescence- and CFU-based rates across 20 antimicrobials. In our experiments luminescence- and CFU-based rates did not differ significantly for half of them. For the other half, CFU-based estimates of rates of decline were consistently higher. The estimates differed for two main reasons: First, because light intensity tracks biomass more closely than population size, luminescence declined more slowly than the population when bacteria filamented. Second, CFU-based estimates indicated a steeper decline when antimicrobial treatment reduced the number of colonies formed per plated bacterium. This effect can result from changes in clustering behaviour, physiological changes that impair culturability, or antimicrobial carry-over. Thus, the suitability of luminescence to quantify bacterial decline depends on the physiological effects of the antimicrobial used (e.g. filamentation) and whether the quantity of interest is cell number or biomass. Within these limitations, luminescence can serve as an efficient, high-throughput alternative for quantifying bacterial dynamics at super-MIC concentrations.

microbiology↗

bioRxiv2025

misosoup: A metabolic modeling tool for identifying minimal microbial communities reveals pervasive cross-feeding-driven niche expansion

Microbial survival and function often depend on metabolic interactions within communities. Therefore, a central question in disentangling microbial organization is determining which minimal groups of species are able to thrive in a given medium - referred to as "minimal communities". Answering this question is essential for understanding microbial distribution, enhancing laboratory cultivation, and designing synthetic communities (SynComs). Here, we introduce misosoup, a Python package for identifying minimal communities (MInimal Supplying community Search). Through genome-scale constraint-based metabolic modeling, misosoup enables the systematic identification of communities that support microbial growth in environments where individual species fail to survive alone. We validate misosoup against experimentally verified minimal communities, demonstrating its ability to predict known cooperative interactions, cocultures, and consortia with biotechnological potential. We further illustrate the use of misosoup to investigate broad microbial ecology questions by applying it to a set of 60 marine microbes, finding pervasive cross-feeding-driven niche expansion, and showing how the detailed outputs provided by misosoup facilitate research on hot topics such as the identification of functional groups. In summary, misosoup provides a powerful tool for microbial ecology and community design, with potential applications in both research and biotechnological innovation. ImportanceMicrobes often rely on each other to survive, especially in environments where they cant live alone. Understanding which small groups of microbes can thrive together--called minimal communities--is key to improving lab research, designing synthetic ecosystems, and exploring how microbes spread in nature. To support this, we developed misosoup, a Python tool that identifies these communities using advanced metabolic modeling. misosoup helps scientists discover how microbes cooperate by sharing nutrients, a process known as metabolic cross-feeding. When tested on sets of species from different origins, the tool showed that species could thrive in more environments when part of a group. This highlights the importance of teamwork in microbial life. misosoup not only predicts these interactions but also provides detailed insights that can guide ecological studies and biotechnological innovation. By revealing how microbes support each other, misosoup contributes to a deeper understanding of lifes interconnectedness and offers tools for solving real-world challenges.

bioinformatics↗

bioRxiv2025

Parallel evolution of full-length genomes in a long-term evolution experiment with phage ΦX174

The study of evolution in organisms with high mutation rates requires detailed data on the genomic composition of the population. Here we report on an innovative use of high-throughput sequencing technology combined with rapid experimental evolution that allowed us to follow the genetic diversification of four independent bacteriophage populations that we evolved without actively imposing any selection pressures (in addition to those inherent in the bacterial cell environment) for 412 generations in unprecedented detail. Tracing over 80000 bacteriophage genomes, representing 884 distinct genotypes, we find that the patterns of viral diversification result in largely non-congruent genotype distributions, but also document multiple instances of parallel evolution. Unlike previous observations of parallelism in viral evolution, we do not just see the parallel evolution of mutations at single sites, but on the level of full-length genomes. Computer simulations that recapitulate our experiments in great detail show that this degree of parallelism is inconsistent with neutral evolution. We further show that the observed extent of parallel evolution biases phylodynamic analysis of migration rates, and wrongly estimates significant migration between the independent evolution lines. Our approach is applicable to many other viruses and paves the way to advanced population genetic investigations that aim to shed light on phenomena that relate to genetic linkage across the genome.

evolutionary biology↗

bioRxiv2025

Viral Simulation Reveals Overestimation Bias in Within-Host Phylodynamic Migration Rate Estimates Under Selection

Phylodynamic methods are widely used to infer the population dynamics of viruses between and within hosts. For HIV-1, these methods have been used to estimate migration rates between different anatomical compartments within a host. These methods typically assume that the genomic regions used for reconstruction are evolving without selective pressure, even though other parts of the viral genome are known to experience strong selection. In this study, we investigate how selection affects phylodynamic migration rate estimates. To this end, we developed a novel agent-based simulation tool, virolution, to simulate the evolution of virus within two anatomical compartments of a host. Using this tool, we generated viral sequences and genealogies assuming both, neutral evolution and purifying selection that is concordant in both compartments. We found that, under the selection regime, migration rates are significantly over-estimated with a stochastic mixture model and a structured coalescent model in the Bayesian inference framework BEAST2. Our results reveal that commonly used phylogeographic methods, which assume neutral evolution, can significantly bias migration rate estimates in selective regimes. This study underscores the need for assessing the robustness of phylodynamic analysis with respect to more realistic selection regimes.

evolutionary biology↗

bioRxiv2024

Assessing the role of bacterial innate and adaptive immunity as barriers to conjugative plasmids

Plasmids are ubiquitous mobile genetic elements, that can be either costly or beneficial for their bacterial host. In response to constant viral threat, bacteria have evolved various immune systems, such as the prevalent restriction modification (RM) (innate immunity) and CRISPR-Cas systems (adaptive immunity). At the molecular level, both systems also target plasmids, but the consequences of these interactions for plasmid spread are unclear. Using a modeling approach, we show that RM and CRISPR-Cas are effective as barriers against the spread of costly plasmids, but not against beneficial ones. Consequently, bacteria can profit from the adaptive advantages that beneficial plasmids confer even in the presence of bacterial immunity. While plasmids that are costly for bacteria may persist for a certain period in the bacterial population, RM and CRISPR-Cas pose a substantial burden for such plasmids, which can eventually drive them to extinction. Finally, we demonstrate that the selection pressure imposed by bacterial immunity on costly plasmids can be circumvented through a diversity of escape mechanisms and highlight how plasmid carriage might be common despite bacterial immunity. In summary, the population-level outcome of interactions between plasmids and defense systems in a bacterial population is closely tied to plasmid cost: Beneficial plasmids can persist at high prevalence in bacterial populations despite defense systems, while costly plasmids may face substantial reduction in prevalence or even extinction.

microbiology↗

bioRxiv2024

A network optimisation condition uncovers the role offunctional groups in the feasibility and dynamical stability of microbial model ecosystems.

Investigating mechanisms favouring stable coexistence of complex microbial communities is central to understand their formation and maintenance in the natural environment and, eventually, controlling and designing synthetic communities. In this work, we studied microbial model ecosystems in which consumption and secretion of resources was explicit, determining ecological interactions (e.g. competition for resources, facilitation, mutualism). We found a sufficient condition for the dynamical stability of microbial model ecosystems. This condition allowed us to derive an objective function whose optimization selected specific configurations of consumption and secretion of resources. Optimized configurations stood out as a compromise between having a large parameter space in which species coexistence was feasible (dominated by systems in which microbes do not secrete any resources), and having a high rate of return to equilibrium after a perturbation (dominated by systems in which every microbe secrete all resources). We explained the behaviour observed for optimized configurations by noting that they host sets of species with differentiated niches -as defined by their consumption and secretion strategies-, termed functional groups. We speculated that, since increasing the number of functional groups increased the number of niches, competition between species should be reduced, favouring coexistence. Therefore, our results suggest that the formation of functional groups have an important role in microbial coexistence, which has ramifications for the design of complex microbial communities.

ecology↗

bioRxiv2023

The many dimensions of combination therapy: How to combine antibiotics to limit resistance evolution

In combination therapy, bacteria are challenged with two or more antibiotics simultaneously. Ideally, separate mutations are required to adapt to each of them, which is a priori expected to hinder the evolution of full resistance. Yet, the success of this strategy ultimately depends on how well the combination controls the growth of bacteria with and without resistance mutations. To design a combination treatment, we need to choose drugs and their doses and decide how many drugs get mixed. Which combinations are good? To answer this question, we set up a stochastic pharmacodynamic model and determine the probability to successfully eradicate a bacterial population. We consider bacteriostatic and two types of bactericidal drugs - those that kill independent of replication and those that kill during replication. To establish results for a null model, we consider non-interacting drugs and implement the two most common models for drug independence - Loewe additivity and Bliss independence. Our results show that combination therapy is almost always better in limiting the evolution of resistance than administering just one drug, even though we keep the total drug dose constant for a fair comparison. Yet, exceptions exist for drugs with steep dose-response curves. Combining a bacteriostatic and a bactericidal drug which can kill non-replicating cells is particularly beneficial. Our results suggest that a 50:50 drug ratio - even if not always optimal - is usually a good and safe choice. Applying three or four drugs is beneficial for treatment of strains with large mutation rates but adding more drugs otherwise only provides a marginal benefit or even a disadvantage. By systematically addressing key elements of treatment design, our study provides a basis for future models which take further factors into account. It also highlights conceptual challenges with translating the traditional concepts of drug independence to the single-cell level.

evolutionary biology↗

bioRxiv2023

My host's enemy is my enemy: plasmids carrying CRISPR-Cas as a defence against phages

Bacteria are infected by mobile genetic elements like plasmids and virulent phages, and those infections significantly impact bacterial ecology and evolution. Recent discoveries reveal that some plasmids carry anti-phage immune systems like CRISPR-Cas, suggesting that plasmids may participate in the coevolutionary arms-race between virulent phages and bacteria. Intuitively, this seems reasonable as virulent phages kill the plasmids obligate host. However, the efficiency of CRISPR-Cas systems carried by plasmids can be expected to be lower than those carried by the chromosome due to continuous segregation loss, creating susceptible cells for phage amplification. To evaluate the anti-phage protection efficiency of CRISPR-Cas on plasmids, we develop a stochastic model describing the dynamics of a virulent phage infection against which a conjugative plasmid defends using CRISPR-Cas. We show that CRISPR-Cas on plasmids provides robust protection, except in limited parameter-sets. In these cases, high segregation favours phage outbreaks by generating a population of defenceless cells on which the phage can evolve and escape CRISPR-Cas immunity. We show that the phages ability to exploit segregation loss depends strongly on the evolvability of both CRISPR-Cas and the phage itself.

microbiology↗

bioRxiv2022

A Genome-Scale Metabolic Model of Marine Heterotroph Vibrio splendidus sp. 1A01

While the Vibrio splendidus species is best known as an opportunistic pathogen in oysters, the Vibrio splendidus sp. 1A01 strain was first identified as an early colonizer of synthetic chitin particles incubated in seawater. To gain a better understanding of its metabolism, a genome-scale metabolic model (GSMM) of V. splendidus sp. 1A01 was reconstructed. GSMMs enable us to simulate all metabolic reactions in a bacterial cell using Flux Balance Analysis. A draft model was built using an automated pipeline from BioCyc. Manual curation was then performed based on experimental data, in part by gap-filling metabolic pathways and tailoring the models biomass reaction to V. splendidus sp. 1A01. The challenges of building a metabolic model for a marine microorganism like V. splendidus sp. 1A01 are described.

microbiology↗

bioRxiv2021

The effect of sequencing and assembly on the inference of horizontal gene transfer on chromosomal and plasmid phylogenies

The spread of antibiotic resistance genes on plasmids is a threat to human and animal health. Phylogenies of bacteria and their plasmids contain clues regarding the frequency of plasmid transfer events, as well as the co-evolution of plasmids and their hosts. However, whole genome sequencing data from diverse ecological or clinical bacterial samples is rarely used to study plasmid phylogenies and resistance gene transfer. This is partially due to the difficulty to extract plasmids from short-read sequencing data. Here, we use both short- and long-read sequencing data of 24 clinical extended-spectrum {beta}-lactamase producing Escherichia coli to estimate chromosomal and plasmid phylogenies. We compare the impact of different sequencing and assembly methodologies on these phylogenies and on the inference of horizontal gene transfer. We find chromosomal phylogenies can be estimated robustly with all methods, whereas plasmid phylogenies have more variable topology and branch lengths across the methods used. Specifically, hybrid methods that use long reads to resolve short-read assemblies (HybridSPAdes and Unicycler) perform better than those that started from long-reads during assembly graph generation (Canu). In contrast, the inference of plasmid and antibiotic resistance gene transfer using a parsimony-based criterion is mostly robust to the choice of sequencing and assembly method.

bioinformatics↗

bioRxiv2021

Turnover in life strategies recapitulates microbial succession on synthetic marine particles

Particulate organic matter (POM) in the ocean sustains diverse communities of bacteria that mediate the remineralization of organic complex matter. However, the variability of these particles and of the environmental conditions surrounding them present a challenge to the study of the ecological processes shaping particle-associated communities and their function. In this work, we utilise data from experiments in which coastal water communities were grown on synthetic particles to ask which are the most important ecological drivers of their assembly and associated traits. Combining 16S rRNA amplicon sequencing with shotgun metagenomics, together with an analysis of the full genomes of a subset of isolated strains, we were able to identify two-to-three distinct community classes, corresponding to early vs. late colonizers. We show that these classes are shaped by environmental selection (early colonizers) and facilitation (late colonizers), and find distinctive traits associated with each class. While early colonizers have a larger proportion of genes related to uptake of nutrients, motility and environmental sensing with few pathways enriched for metabolism, late colonizers devote a higher proportion of genes for metabolism, comprising a wide array of different pathways including metabolism of carbohydrates, amino acids and xenobiotics We find evidence in selected metabolic pathways for the existence of a trophic-chain topology connecting both classes. The interpretation of these traits suggests a distinction between early and late colonizers analogous to other classifications found in the literature, and we discuss connections with the classical distinction between r- and K-strategists.

microbiology↗

bioRxiv2021

Constrained optimisation of divisional load in hierarchically-organised tissues during homeostasis

It has been hypothesised that the structure of tissues and the hierarchy of differentiation from stem cell to terminally-differentiated cell play a significant role in reducing the incidence of cancer in that tissue. One specific mechanism by which this risk can be reduced is by minimising the number of divisions - and hence the mutational risk - that cells accumulate as they divide to maintain tissue homeostasis. Here we investigate a mathematical model of cell division in a hierarchical tissue, calculating and minimising the divisional load while constraining parameters such that homeostasis is maintained. We show that the minimal divisional load is achieved by binary division tress with progenitor cells incapable of selfrenewal. Contrary to the protection hypothesis, we find that an increased stem cell turnover can lead to lower divisional load. Furthermore, we find that the optimal tissue structure depends on the time horizon of the duration of homeostasis, with faster stem cell division favoured in short-lived organisms and more progenitor compartments favoured in longer-lived organisms.

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↗