Biology subjects

Uecker, H.

Publications and source records attributed to Uecker, H..

At least 19 recordsLinked to original sources

bioRxiv2026

Microbially Assisted Rescue of Host Populations

Rapid environmental change can drive populations into decline but host genomes may adapt in time to rescue the population from extinction. Host-associated microbes may alter this race. We here develop a population-genetics model to characterize the dynamics and consequences of microbe-assisted host population rescue. In our model, we replace one locus of a classical two-locus system with microbial carriage. Under weak selection the microbial variables settle to a quasi-microbial equilibrium (QME) conditional on the host allele, reducing the coupled dynamics to host allele frequency and abundance. The reduction identifies two microbial contributions that shape rescue. Additive benefits of microbial carriage on host fitness raise host growth immediately without affecting evolution and lift the floor of the demographic rescue-U. By contrast, positive interactions between the rescue allele and the microbe accelerate evolution and narrow the rescue-U instead of buffering initial decline. A microbe can therefore buy time, speed adaptation, or be required for recovery when neither contribution suffices alone. Imperfect microbial inheritance, however, can impede microbe-assisted rescue. When selection acts through fecundity and carriers transmit the microbe to only some offspring, adaptation generates non-carrying descendants faster than transmission replaces them. When that loss is sufficiently strong, recovery can fail even though the allele, the microbe, and their interaction are each individually beneficial. We call this selection-amplified dilution. Together, our model integrates evolutionary rescue theory with multilocus population genetics and evolutionary epidemiology to define a framework of microbe-assisted host rescue, thereby identifying key factors driving or compromising the rescue process.

evolutionary biology↗

bioRxiv2025

How do mate-finding Allee effects affect evolutionary rescue?

Harsh environmental change can put populations at risk of extinction, requiring rapid adaptation for persistence. In sexually reproducing populations, the challenge of finding mates at low densities can impose a strong demographic Allee effect. When such a population falls below the Allee threshold, either through a reduction of the population size or an increase in the threshold, successful adaptation relies on overcoming the Allee effect, which gets increasingly difficult as the population declines. Despite mate-finding Allee effects being common, most models of evolutionary rescue assume that mating is assured even at low densities. Here, we set up a population genetic model for evolutionary rescue of a population below its Allee threshold. For the analysis, we combine stochastic computer simulations with mathematical arguments. As expected, mate limitation can severely impede rescue but the extent differs across sexual systems. We further show that mate limitation shifts the optimal sex ratio for dioecious populations, alters optimal evolutionary routes when there are trade-offs between increasing mate-finding efficiency and fecundity, and enhances the importance of standing genetic variation relative to de novo mutants. Overall, our results highlight the importance of accounting for positive density dependence in the assessment of a populations scope for evolutionary rescue.

evolutionary biology↗

bioRxiv2025

Leaving Academia: Insights from Evolutionary Biologists on Their Career Transitions and Job Satisfaction

Many who have obtained PhDs in evolutionary biology will ultimately pursue careers that fall outside a narrow definition of an academic career. At the same time, PhD students and supervisors of PhD students are often ill-informed about career options outside of academia. Here, we report on a survey of evolutionary biologists who have pursued non-academic careers, to understand what careers they pursue, how they transitioned into those careers, how well prepared they were, and how satisfied they are with their current work. Overall, the message from this survey is positive- evolutionary biologists are readily employable outside of academia, generally well-prepared for those jobs, and report high levels of satisfaction in their non-academic careers. We also highlight areas where preparation for non-academic careers could be improved, which might be addressed by individual mentors or PhD training programmes.

evolutionary biology↗

bioRxiv2025

Resistance variation and bacterial interactions shape the adaptation of a genetically diverse bacterial population to antimicrobial treatment

Bacterial infections are often polymicrobial and subject to the evolution of antimicrobial resistance (AMR). Existing knowledge on AMR in such polymicrobial infections usually relies on observational patient data, for which cause-effect relationships are difficult to infer, or on studying interactions between different bacterial species, ignoring the commonly encountered variation within species. Here, we therefore asked how mixed populations with strains from the same species evolve under antibiotic treatment. We used a genetically diverse population of the high-risk human pathogen Pseudomonas aeruginosa, and first identified strain variation in both AMR and pairwise bacteria-bacteria interactions, the latter ranging from beneficial, neutral, to competitive. Using experimental evolution, we subsequently demonstrate that the response to selection by different antibiotic treatments is significantly influenced by AMR strain variation, bacterial interactions, and also spatial population structure. Moreover, de novo AMR evolution was additionally impacted by variation in resistance rates towards the two considered antibiotics. A second evolution experiment emphasized the central role of strain variation and bacterial interactions in determining the evolutionary outcome. We conclude that ecological dynamics in genetically diverse pathogen populations are key for our general understanding of infection characteristics and AMR evolution, and, therefore, deserve particular attention during treatment of polymicrobial infections.

evolutionary biology↗

bioRxiv2024

Effectiveness of CRISPR-Cas in Sensitizing Bacterial Populations with Plasmid-Encoded Antimicrobial Resistance

The spread of bacteria resistant to antibiotics poses a serious threat to human health. Genes that encode antibiotic resistance are often harbored on plasmids, extra-chromosomal DNA molecules found in bacteria. The emergence of multiresistance plasmids is particularly problematic and demands the development of new antibiotics and alternative strategies. CRISPR-Cas derived tools with their sequence specificity offer a promising new approach to combating antibiotic resistance. By introducing CRISPR-Cas encoding plasmids that target antibiotic resistance genes on plasmids, the susceptibility of bacteria to conventional antibiotics can be restored. However, genetic variation within bacterial populations can hinder the effectiveness of such CRISPR-Cas tools by allowing some mutant plasmids to evade CRISPR-mediated cleaving or gene silencing. In this study, we develop a model to test the effectiveness of CRISPR-Cas in sensitizing bacterial populations carrying resistance on non-transmissible plasmids and assess the success probability of a subsequent treatment with conventional antibiotics. abstract hgt We evaluate this probability according to the target interference mechanism, the copy number of the resistance-encoding plasmid, and its compatibility with the CRISPR-Cas encoding plasmid. Our results identify promising approaches to revert antibiotic resistance with CRISPR-Cas encoding plasmids: A DNA-cleaving CRISPR-Cas system on a plasmid incompatible with the targeted plasmid is most effective for low copy numbers, while for resistance plasmids with higher copy numbers gene silencing by CRISPR-Cas systems encoded on compatible plasmids is the superior solution.

evolutionary biology↗

bioRxiv2024

Evolutionary rescue of bacterial populations by heterozygosity on multicopy plasmids

Bacterial plasmids and other extrachromosomal DNA elements frequently carry genes with important fitness effects for their hosts. Multicopy plasmids can additionally carry distinct alleles of host-fitness-relevant genes on different plasmid copies, allowing for heterozygosity not possible for loci on haploid chromosomes. Plasmid-mediated heterozygosity may increase the fitness of bacterial cells in circumstances where there is an advantage to having multiple distinct alleles (heterozyogote advantage); however, plasmid-mediated heterozygosity is also subject to constant loss due to random segregation of plasmid copies on cell division. We analyze a multitype branching process model to study the evolution and maintenance of plasmid-mediated heterozygosity under a heterozygote advantage. We focus on an evolutionary rescue scenario in which a novel mutant allele on a plasmid must be maintained together with the wild-type allele to allow population persistance (although our results apply more generally to the maintenance of heterozygosity due to heterozygote advantage). We determine the probability of rescue and derive an analytical expression for the threshold on the fitness of heterozygotes required to overcome segregation and make rescue possible; this threshold decreases with increasing plasmids copy number. We further show that the formation of cointegrates from the fusion of plasmid copies increases the probability of rescue. Overall, our results provide a rigorous quantitative assessment of the conditions under which bacterial populations can adapt to multiple stressors through plasmid-mediated heterozygosity. Many of the results are furthermore applicable to the related problem of the maintenance of incompatible plasmids in the same cell under selection for both. MSC Classification92D15, 60J85

evolutionary biology↗

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

Is the distribution of plasmid lengths bimodal?

The length of a plasmid is a key property which is linked to many aspects of plas-mid biology. When distributions of plasmid lengths are shown in the literature, they are usually plotted with length on a logarithmic scale. However, a quantity and its logarithm have distinct distributions which may differ considerably in shape. Mistaking the distribution of log-lengths for the distribution of lengths can therefore lead to distorted conclusions about the distribution; in particular, the distribution of log-lengths may be bimodal when the distribution of lengths is only unimodal. This particular confusion has arisen in the literature where the length distribution is often claimed to be bimodal based on examination of what is in fact the log-length distribution. While the length distribution is indeed bimodal within many bacterial families, it is not across the ensemble of all plasmids. We suggest that authors should be careful to show the plasmid length distribution, or to distinguish the two distributions, to avoid misleading inferences. HighlightsO_LIThe distributions of lengths and log-lengths of plasmids are different, and have considerably different shapes. C_LIO_LIThe typical practice of using a logarithmic scale for plasmid lengths leads to confusion between the two distributions. C_LIO_LIIn particular, the distribution of log-lengths can be bimodal when the distribution of lengths is not. C_LIO_LIThe length distribution within bacterial families is often bimodal, but across all plasmids, it is unimodal. C_LIO_LIClearly distinguishing between the distributions will ensure that biological conclusions drawn from them are robust. C_LI

microbiology↗

bioRxiv2023

How antimicrobial heteroresistance promotes bacterial adaptation to antimicrobial therapy - or not

Withdrawal StatementThe authors have withdrawn their manuscript owing to inconsistencies of results during a re-assessment of the experimental work, which now question main conclusions drawn and which require additional experiments. Therefore, the authors do not wish this work to be cited as reference for the project. If you have any questions, please contact the corresponding author

evolutionary biology↗

bioRxiv2023

Segregational drift hinders the evolution of antibiotic resistance on polyploid replicons

The emergence of antibiotic resistance under treatment depends on the availability of resistance alleles and their establishment in the population. Novel resistance alleles are encoded either in chromosomal or extrachromosomal genetic elements; both types may be present in multiple copies within the cell. However, the effect of polyploidy on the emergence of antibiotic resistance remains understudied. Here we show that the establishment of resistance alleles in microbial populations depends on the ploidy level. Evolving bacterial populations under selection for antibiotic resistance, we demonstrate that resistance alleles in polyploid elements are lost frequently in comparison to alleles in monoploid elements due to segregational drift. Integrating the experiments with a mathematical model, we find an agreement between the theoretical and empirical allele dynamics, confirming our understanding of the allele segregation process. Using the mathematical model, we further show that the effect of polyploidy on the establishment probability of beneficial alleles is strongest for low replicon copy numbers and plateaus for high replicon copy numbers. Our results suggest that the distribution of fitness effects for mutations that get fixed in a population depends on the replicon ploidy level. Our study indicates that strategies for drug treatment of bacterial infections should take into consideration the pathogen ploidy level.

microbiology↗

bioRxiv2022

Limits to adaptation on conjugative plasmids

Plasmids may carry genes coding for beneficial traits and thus contribute to adaptation of bacterial populations to environmental stress. Conjugative plasmids can horizontally transfer between cells, which a priori facilitates the spread of adaptive alleles. However, if the potential recipient cell is already colonized by another incompatible plasmid, successful transfer may be prevented. Competition between plasmids can thus limit horizontal transfer. Previous modeling has indeed shown that evolutionary rescue by a conjugative plasmid is hampered by incompatible resident plasmids in the population. If the rescue plasmid is a mutant variant of the resident plasmid, both plasmids transfer at the same rates. A high conjugation rate then has two, potentially opposing, effects - a direct positive effect on spread of the rescue plasmid and an increase in the fraction of resident plasmid cells. This raises the question whether a high conjugation rate always benefits evolutionary rescue. In this article, we systematically analyse three models of increasing complexity to disentangle the benefits and limits of increasing horizontal gene transfer in the presence of plasmid competition and plasmid costs. We find that the net effect can be positive or negative and that the optimal transfer rate is thus not always the highest one. These results can contribute to our understanding of the many facets of plasmid-driven adaptation and the wide range of transfer rates observed in nature.

evolutionary biology↗

bioRxiv2022

Sequential antibiotic therapy in the lab and in the patient

Laboratory experiments suggest that rapid cycling of antibiotics during the course of treatment could successfully counter resistance evolution. Drugs involving collateral sensitivity could be particularly suitable for such therapies. However, the environmental conditions in-vivo differ from those in-vitro. One key difference is that drugs can be switched abruptly in the lab, while in the patient, pharmacokinetic processes lead to changing antibiotic concentrations including periods of dose overlaps from consecutive administrations. During such overlap phases, drug-drug interactions may affect the evolutionary dynamics. To address the gap between the lab and potential clinical applications, we set up two models for comparison - a lab model and a pharmacokinetic-pharmacodynamic patient model. The analysis shows that in the lab, the most rapid cycling suppresses the bacterial population always at least as well as other regimens. For patient treatment, however, a little slower cycling can sometimes be preferable if the pharmacodynamic curve is steep or if drugs interact antagonistically. When resistance is absent prior to treatment, collateral sensitivity brings no substantial benefit unless the cell division rate is low and drug cycling slow. By contrast, drug-drug interactions strongly influence the treatment efficiency of rapid regimens, demonstrating their importance for the optimal choice of drug pairs.

evolutionary biology↗

bioRxiv2022

Fixation dynamics of beneficial alleles in prokaryotic polyploid chromosomes and plasmids

Theoretical population genetics has been mostly developed for sexually reproducing diploid and for monoploid (haploid) organisms, focusing on eukaryotes. The evolution of bacteria and archaea is often studied by models for the allele dynamics in monoploid populations. However, many prokaryotic organisms harbor multicopy replicons - chromosomes and plasmids - and theory for the allele dynamics in populations of polyploid prokaryotes remains lacking. Here we present a population genetics model for replicons with multiple copies in the cell. Using this model, we characterize the fixation process of a dominant beneficial mutation at two levels: the phenotype and the genotype. Our results show that, depending on the mode of replication and segregation, the fixation time of mutant phenotypes may precede the genotypic fixation time by many generations; we term this time interval the heterozygosity window. We furthermore derive concise analytical expressions for the occurrence and length of the heterozygosity window, showing that it emerges if the copy number is high and selection strong. Replicon ploidy thus allows for the maintenance of genetic variation following phenotypic adaptation and consequently for reversibility in adaptation to fluctuating environmental conditions.

evolutionary biology↗

bioRxiv2019

Evolutionary rescue and drug resistance on multicopy plasmids

Bacteria often carry "extra DNA" in form of plasmids in addition to their chromosome. Many plasmids have a copy number greater than one such that the genes encoded on these plasmids are present in multiple copies per cell. This has evolutionary consequences by increasing the mutational target size, by prompting the (transitory) co-occurrence of mutant and wild-type alleles within the same cell, and by allowing for gene dosage effects. We develop and analyze a mathematical model for bacterial adaptation to harsh environmental change if adaptation is driven by beneficial alleles on multicopy plasmids. Successful adaptation depends on the availability of advantageous alleles and on their establishment probability. The establishment process involves the segregation of mutant and wild-type plasmids to the two daughter cells, allowing for the emergence of mutant-homozygous cells over the course of several generations. To model this process, we use the theory of multi-type branching processes, where a type is defined by the genetic composition of the cell. Both factors - the number of adaptive alleles and their establishment probability - depend on the plasmid copy number, and they often do so antagonistically. We find that in the interplay of various effects, a lower or higher copy number may maximize the probability of evolutionary rescue. The decisive factor is the dominance relationship between mutant and wild-type plasmids and potential gene dosage effects. Results from a simple model of antibiotic degradation indicate that the optimal plasmid copy number may depend on the specific environment encountered by the population.

evolutionary biology↗

bioRxiv2019

How does habitat choice affect evolutionary rescue in subdivided populations?

Evolutionary rescue is the process by which a population, in response to an environmental change, successfully avoids extinction through adaptation. In spatially structured environments, dispersal can affect the probability of rescue. Here, we model an environment consisting of patches that degrade one after another, and we investigate the probability of rescue by a mutant adapted to the degraded habitat. We focus on the effects of dispersal and of immigration biases. We find that the probability of evolutionary rescue can undergo up to three phases: (i) starting from low dispersal rates, it increases with dispersal; (ii) at intermediate dispersal rates, it decreases; (iii) finally, at large dispersal rates, the probability of rescue increases again with dispersal, except if mutants are too counter-selected in not-yet-degraded patches. The probability of rescue is generally highest when mutant and wild-type individuals preferentially immigrate into patches that have already undergone environmental change. Additionally, we find that mutants that will eventually rescue the population most likely first appear in non-degraded patches, and that the relative contribution of standing genetic variation vs. de-novo mutations declines with increasing emigration rates. Overall, our results show that habitat choice, when compared to the often studied unbiased immigration scheme, can substantially alter the dynamics of population survival and adaptation to new environments.

evolutionary biology↗

bioRxiv2018

Antibiotic treatment protocols revisited: The challenges of a conclusive assessment by mathematical modeling

Hospital-acquired bacterial infections lead to prolonged hospital stays and increased mortality. The problem is exacerbated by antibiotic resistant strains that delay or impede effective treatment. To ensure a successful therapy and to manage antibiotic resistance, treatment protocols that draw on several different antibiotics might be used. This includes the administration of drug cocktails to individual patients (\"combination therapy\") but also the random assignment of drugs to different patients (\"mixing\") and a regular switch in the default drug used in the hospital from drug A to drug B and back (\"cycling\"). For the past 20 years, mathematical models have been used to assess the prospects of antibiotic combination therapy, mixing, and cycling. But while tendencies in their ranking across studies have emerged, the picture remains surprisingly inconclusive and incomplete. In this article, we review existing modeling studies and demonstrate by means of examples how methodological factors complicate the emergence of a consistent picture. These factors include the choice of the criterion by which the effects of the protocols are compared, the model implementation, and its analysis. We thereafter discuss how progress can be made and suggest future modeling directions.

evolutionary biology↗

bioRxiv2018

Aggressive or moderate drug therapy for infectious diseases? Trade-offs between different treatment goals at the individual and population levels

Antimicrobial resistance is one of the major public health threats of the 21st century. There is a pressing need to adopt more efficient treatment strategies in order to prevent the emergence and spread of resistant strains. The common approach is to treat patients with high drug doses, both to clear the infection quickly and to reduce the risk of de novo resistance. Recently, several studies have argued that, at least in some cases, low-dose treatments could be more suitable to reduce the within-host emergence of antimicrobial resistance. However, the choice of a drug dose may have consequences at the population level, which has received little attention so far.\n\nHere, we study the influence of the drug dose on resistance and disease management at the host and population levels. We develop a nested two-strain model and unravel trade-offs in treatment benefits between an individual and the community. We use several measures to evaluate the benefits of any dose choice. Two measures focus on the emergence of resistance, at the host level and at the population level. The other two focus on the overall treatment success: the outbreak probability and the disease burden. We find that different measures can suggest different dosing strategies. In particular, we identify situations where low doses minimize the risk of emergence of resistance at the individual level, while high or intermediate doses prove most beneficial to improve the treatment efficiency or even to reduce the risk of resistance in the population.\n\nAuthor summaryThe obvious goals of antimicrobial drug therapy are rapid patient recovery and low disease prevalence in the population. However, achieving these goals is complicated by the rapid evolution and spread of antimicrobial resistance. A sustainable treatment strategy needs to account for the risk of resistance and keep it in check. One parameter of treatment is the drug dosage, which can vary within certain limits. It has been proposed that lower doses may, in some cases, be more suitable than higher doses to reduce the risk of resistance evolution in any one patient. However, if lower doses prolong the period of infectiousness, such a strategy has consequences for the pathogen dynamics of both strains at the population level. Here, we set up a nested model of within-host and between-host dynamics for an acute self-limiting infection. We explore the consequences of drug dosing on several measures of treatment success: the risk of resistance at the individual and population levels and the outbreak probability and the disease burden of an epidemic. Our analysis shows that trade-offs may exist between optimal treatments under these various criteria. The criterion given most weight in the decision process ultimately depends on the disease and population under consideration.

epidemiology↗

bioRxiv2016

Modeling antimicrobial cycling and mixing: Differences arising from an individual-based versus a population-based perspective

BackgroundIn order to manage bacterial infections in hospitals in the face of antibiotic resistance, the two treatment protocols \"mixing\" and \"cycling\" have received considerable attention both from modelers and clinicians. However, the terms are not used in exactly the same way by both groups.\n\nObjectivesWe aim to investigate a model that comes closer to clinical practice and compare the predictions to the standard model.\n\nMethodsWe set up two deterministic models, implemented as a set of differential equations, for the spread of bacterial infections in a hospital. Following the traditional approach, the first model takes a population-based perspective. The second model, in contrast, takes the drug use of individual patients into account.\n\nResultsThe alternative model can indeed lead to different predictions than the standard model. We provide examples for which in the new model, the opposite strategy maximizes the number of uninfected patients or minimizes the rate of spread of double resistance.\n\nConclusionsWhile the traditional models provide valuable insight, care is needed in the interpretation of results.

epidemiology↗