Journal: Molecular Biology and Evolution

Abbreviation

Mol Biol Evol

Publisher

Oxford University Press

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ISSN

0737-4038
1537-1719

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Publications 1 - 10 of 88
  • Bayesian Phylodynamic Inference of Multitype Population Trajectories Using Genomic Data
    Item type: Journal Article
    Vaughan, Timothy G.; Stadler, Tanja (2025)
    Molecular Biology and Evolution
    Phylodynamic methods provide a coherent framework for the inference of population parameters directly from genetic data. They are an important tool for understanding both the spread of epidemics as well as long-term macroevolutionary trends in speciation and extinction. In particular, phylodynamic methods based on multitype birth-death models have been used to infer the evolution of discrete traits, the movement of individuals or pathogens between geographic locations or host types, and the transition of infected individuals between disease stages. In these models, population heterogeneity is treated by assigning individuals to different discrete types. Typically, methods which allow inference of parameters under multitype birth-death models integrate over the possible birth-death trajectories (i.e. the type-specific population size functions) to reduce the computational demands of the inference. As a result, it has not been possible to use these methods to directly infer the dynamics of trait-specific population sizes, infected host counts or other such demographic quantities. In this article, we present a method which infers these multitype trajectories with minimal additional computational cost beyond that of existing methods. We demonstrate the practicality of our approach by applying it to a previously published set of Middle East respiratory syndrome coronavirus genomes, inferring the numbers of human and camel cases through time, together with the number and timing of spillovers from the camel reservoir. This application highlights the multitype population trajectory's ability to elucidate properties of the population which are not directly ancestral to its sampled members.
  • Origin and domestication of the fungal wheat pathogen Mycosphaerella graminicola via sympatric speciation
    Item type: Journal Article
    Stukenbrock, Eva H.; Banke, Soren; Javan-Nikkhah, Mohammad; et al. (2007)
    Molecular Biology and Evolution
  • How Good Are Statistical Models at Approximating Complex Fitness Landscapes?
    Item type: Journal Article
    Du Plessis, Louis; Leventhal, Gabriel E.; Bonhoeffer, Sebastian (2016)
    Molecular Biology and Evolution
    Fitness landscapes determine the course of adaptation by constraining and shaping evolutionary trajectories. Knowledge of the structure of a fitness landscape can thus predict evolutionary outcomes. Empirical fitness landscapes, however, have so far only offered limited insight into real-world questions, as the high dimensionality of sequence spaces makes it impossible to exhaustively measure the fitness of all variants of biologically meaningful sequences. We must therefore revert to statistical descriptions of fitness landscapes that are based on a sparse sample of fitness measurements. It remains unclear, however, how much data are required for such statistical descriptions to be useful. Here, we assess the ability of regression models accounting for single and pairwise mutations to correctly approximate a complex quasi-empirical fitness landscape. We compare approximations based on various sampling regimes of an RNA landscape and find that the sampling regime strongly influences the quality of the regression. On the one hand it is generally impossible to generate sufficient samples to achieve a good approximation of the complete fitness landscape, and on the other hand systematic sampling schemes can only provide a good description of the immediate neighborhood of a sequence of interest. Nevertheless, we obtain a remarkably good and unbiased fit to the local landscape when using sequences from a population that has evolved under strong selection. Thus, current statistical methods can provide a good approximation to the landscape of naturally evolving populations.
  • Purifying Selection Determines the Short-Term Time Dependency of Evolutionary Rates in SARS-CoV-2 and pH1N1 Influenza
    Item type: Journal Article
    Ghafari, Mahan; Du Plessis, Louis; Raghwani, Jayna; et al. (2022)
    Molecular Biology and Evolution
    High-throughput sequencing enables rapid genome sequencing during infectious disease outbreaks and provides an opportunity to quantify the evolutionary dynamics of pathogens in near real-time. One difficulty of undertaking evolutionary analyses over short timescales is the dependency of the inferred evolutionary parameters on the timespan of observation. Crucially, there are an increasing number of molecular clock analyses using external evolutionary rate priors to infer evolutionary parameters. However, it is not clear which rate prior is appropriate for a given time window of observation due to the time-dependent nature of evolutionary rate estimates. Here, we characterize the molecular evolutionary dynamics of SARS-CoV-2 and 2009 pandemic H1N1 (pH1N1) influenza during the first 12 months of their respective pandemics. We use Bayesian phylogenetic methods to estimate the dates of emergence, evolutionary rates, and growth rates of SARS-CoV-2 and pH1N1 over time and investigate how varying sampling window and data set sizes affect the accuracy of parameter estimation. We further use a generalized McDonald–Kreitman test to estimate the number of segregating nonneutral sites over time. We find that the inferred evolutionary parameters for both pandemics are time dependent, and that the inferred rates of SARS-CoV-2 and pH1N1 decline by ∼50% and ∼100%, respectively, over the course of 1 year. After at least 4 months since the start of sequence sampling, inferred growth rates and emergence dates remain relatively stable and can be inferred reliably using a logistic growth coalescent model. We show that the time dependency of the mean substitution rate is due to elevated substitution rates at terminal branches which are 2–4 times higher than those of internal branches for both viruses. The elevated rate at terminal branches is strongly correlated with an increasing number of segregating nonneutral sites, demonstrating the role of purifying selection in generating the time dependency of evolutionary parameters during pandemics.
  • Jointly Inferring the Dynamics of Population Size and Sampling Intensity from Molecular Sequences
    Item type: Journal Article
    Parag, Kris V.; Du Plessis, Louis; Pybus, Oliver G. (2020)
    Molecular Biology and Evolution
    Estimating past population dynamics from molecular sequences that have been sampled longitudinally through time is an important problem in infectious disease epidemiology, molecular ecology, and macroevolution. Popular solutions, such as the skyline and skygrid methods, infer past effective population sizes from the coalescent event times of phylogenies reconstructed from sampled sequences but assume that sequence sampling times are uninformative about population size changes. Recent work has started to question this assumption by exploring how sampling time information can aid coalescent inference. Here, we develop, investigate, and implement a new skyline method, termed the epoch sampling skyline plot (ESP), to jointly estimate the dynamics of population size and sampling rate through time. The ESP is inspired by real-world data collection practices and comprises a flexible model in which the sequence sampling rate is proportional to the population size within an epoch but can change discontinuously between epochs. We show that the ESP is accurate under several realistic sampling protocols and we prove analytically that it can at least double the best precision achievable by standard approaches. We generalize the ESP to incorporate phylogenetic uncertainty in a new Bayesian package (BESP) in BEAST2. We re-examine two well-studied empirical data sets from virus epidemiology and molecular evolution and find that the BESP improves upon previous coalescent estimators and generates new, biologically useful insights into the sampling protocols underpinning these data sets. Sequence sampling times provide a rich source of information for coalescent inference that will become increasingly important as sequence collection intensifies and becomes more formalized.
  • Mutational and Selective Processes Involved in Evolution during Bacterial Range Expansions
    Item type: Journal Article
    Bosshard, Lars; Peischl, Stephan; Ackermann, Martin; et al. (2019)
    Molecular Biology and Evolution
    Bacterial populations have been shown to accumulate deleterious mutations during spatial expansions that overall decrease their fitness and ability to grow. However, it is unclear if and how they can respond to selection in face of this mutation load. We examine here if artificial selection can counteract the negative effects of range expansions. We examined the molecular evolution of 20 mutator lines selected for fast expansions (SEL) and compared them to 20 other mutator lines freely expanding without artificial selection (CONTROL). We find that the colony size of all 20 SEL lines have increased relative to the ancestral lines, unlike CONTROL lines, showing that enough beneficial mutations are produced during spatial expansions to counteract the negative effect of expansion load. Importantly, SEL and CONTROL lines have similar numbers of mutations indicating that they evolved for the same number of generations and that increased fitness is not due to a purging of deleterious mutations. We find that loss of function mutations better explain the increased colony size of SEL lines than nonsynonymous mutations or a combination of the two. Interestingly, most loss of function mutations are found in simple sequence repeats (SSRs) located in genes involved in gene regulation and gene expression. We postulate that such potentially reversible mutations could play a major role in the rapid adaptation of bacteria to changing environmental conditions by shutting down expensive genes and adjusting gene expression.
  • Stochastic Gene Expression Influences the Selection of Antibiotic Resistance Mutations
    Item type: Journal Article
    Sun, Lei; Ashcroft, Peter; Ackermann, Martin; et al. (2020)
    Molecular Biology and Evolution
    Bacteria can resist antibiotics by expressing enzymes that remove or deactivate drug molecules. Here, we study the effects of gene expression stochasticity on efflux and enzymatic resistance. We construct an agent-based model that stochastically simulates multiple biochemical processes in the cell and we observe the growth and survival dynamics of the cell population. Resistance-enhancing mutations are introduced by varying parameters that control the enzyme expression or efficacy. We find that stochastic gene expression can cause complex dynamics in terms of survival and extinction for these mutants. Regulatory mutations, which augment the frequency and duration of resistance gene transcription, can provide limited resistance by increasing mean expression. Structural mutations, which modify the enzyme or efflux efficacy, provide most resistance by improving the binding affinity of the resistance protein to the antibiotic; increasing the enzyme’s catalytic rate alone may contribute to resistance if drug binding is not rate limiting. Overall, we identify conditions where regulatory mutations are selected over structural mutations, and vice versa. Our findings show that stochastic gene expression is a key factor underlying efflux and enzymatic resistances and should be taken into consideration in future antibiotic research.
  • Assessing the Role of Bacterial Innate and Adaptive Immunity as Barriers to Conjugative Plasmids
    Item type: Journal Article
    Siedentop, Berit Ada; Mediavilla, Carlota Losa; Kouyos, Roger D.; et al. (2024)
    Molecular Biology and Evolution
    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 (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 restriction modification and CRISPR-Cas are effective as barriers against the spread of costly plasmids, but not against beneficial ones. Consequently, bacteria can profit from the selective advantages that beneficial plasmids confer even in the presence of bacterial immunity. While plasmids that are costly for bacteria may persist in the bacterial population for a certain period, restriction modification and CRISPR-Cas 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 extinction.
  • The effect of multifunctionality on the rate of evolution in yeast
    Item type: Journal Article
    Salathé, Marcel; Ackermann, Martin; Bonhoeffer, Sebastian (2006)
    Molecular Biology and Evolution
  • Estimating epidemic incidence and prevalence from genomic data
    Item type: Journal Article
    Vaughan, Timothy G.; Leventhal, Gabriel E.; Rasmussen, David A.; et al. (2019)
    Molecular Biology and Evolution
    Modern phylodynamic methods interpret an inferred phylogenetic tree as a partial transmission chain providing information about the dynamic process of transmission and removal (where removal may be due to recovery, death, or behavior change). Birth–death and coalescent processes have been introduced to model the stochastic dynamics of epidemic spread under common epidemiological models such as the SIS and SIR models and are successfully used to infer phylogenetic trees together with transmission (birth) and removal (death) rates. These methods either integrate analytically over past incidence and prevalence to infer rate parameters, and thus cannot explicitly infer past incidence or prevalence, or allow such inference only in the coalescent limit of large population size. Here, we introduce a particle filtering framework to explicitly infer prevalence and incidence trajectories along with phylogenies and epidemiological model parameters from genomic sequences and case count data in a manner consistent with the underlying birth–death model. After demonstrating the accuracy of this method on simulated data, we use it to assess the prevalence through time of the early 2014 Ebola outbreak in Sierra Leone.
Publications 1 - 10 of 88