Biology subjects

Messer, P. W.

Publications and source records attributed to Messer, P. W..

At least 19 recordsLinked to original sources

bioRxiv2025

Predicting the invasiveness of threshold-dependent gene drives

Gene drives hold great promise for controlling disease vectors or invasive species due to their capacity to rapidly spread through a population from a small initial release. This same property also raises serious concerns about unintended spillover into non-target populations. Threshold-dependent gene drive systems, which can spread only when introduced above a critical population frequency, have been proposed as a more controllable alternative, yet their invasion dynamics in spatially structured populations remain poorly understood. Here, we analyze invasion criteria for threshold-dependent gene drives in continuous-space populations using deterministic reaction-diffusion models and individual-based simulations that better capture the stochasticity of real-world populations. We find substantial variability in invasion outcomes in the individual-based models. Low-threshold modification drives with small fitness costs frequently spread across a wide range of release sizes, including introductions far below those required to succeed in diffusion models. In contrast, threshold-dependent suppression drives exhibit qualitatively different behavior: stochastic effects at low density can often disrupt wavefronts or produce persistent chasing cycles, generally reducing invasion success relative to diffusion-model expectations. Overall, our results show that the spatial containment of threshold-dependent gene drives is more complex than predicted by non-spatial or purely deterministic models, highlighting the importance of spatially explicit analyses when evaluating their real-world performance.

evolutionary biology↗

bioRxiv2025

SimHumanity: Using SLiM 5.0 to run whole-genome simulations of human evolution

The reconstruction of human evolutionary history has undergone repeated advances, each made possible by methodological innovations. In recent decades, genetic and genomic data played a central role in the reconstruction of major evolutionary events such as the out-of-Africa migration, and genetic simulations of human evolutionary history have come to play a major role in testing more specific hypotheses including proposed patterns of migration and admixture with archaic hominins. Increasing computational power has allowed human evolutionary history to be modeled at ever-larger scales, but simulations that encompass the complete human genome, including sex chromosomes and mitochondrial DNA, have been difficult due to the lack of support for whole-genome models in commonly used evolutionary simulation frameworks. With the recent introduction of SLiM 5 such simulations are now straightforward to construct, allowing the easy simulation of humans at whole-genome scale under different demographic models and evolutionary dynamics. We here present three versions of a reusable, customizable, open-source SLiM 5 model for simulating the molecular evolution of the full human genome. We also show some simple analyses of results from the model, to illustrate its utility. We hope this model, which we have nicknamed "SimHumanity" in jest, will facilitate further progress in the field of human evolutionary simulations.

evolutionary biology↗

bioRxiv2025

SLiM 5: Eco-evolutionary simulations across multiple chromosomes and full genomes

Evolutionary simulations of multiple chromosomes, even up to the scale of full-genome simulations, are becoming increasingly important in population genetics and evolutionary ecology. Unfortunately, the popular simulation framework SLiM has always been intrinsically limited to simulations of a single diploid chromosome. Modeling multiple chromosomes of different types, such as sex chromosomes, has always been cumbersome even with scripting, presenting a substantial barrier to the development of full-genome simulations. Here we present SLiM 5, a major extension of SLiMs capabilities for simulating multiple chromosomes. Modeling up to 256 chromosomes is now possible, and each chromosome may belong to any of a wide variety of types - not just autosomes (diploid and haploid), but also sex chromosomes (X, Y, Z, and W), haploid mitochondrial and chloroplast DNA, and more. This new functionality is integrated across all of SLiM, including not only the mechanics of reproduction and inheritance, but also input and output of multi-chromosome data in formats like VCF, and tree-sequence recording across multiple chromosomes. New recipes in the SLiM manual demonstrate these new features, and SLiMs graphical modeling environment, SLiMgui, has been extended in many ways for the visualization of multi-chromosome models. These new features will open new horizons and enable a heightened level of realism for full-genome simulations.

evolutionary biology↗

bioRxiv2025

Gene drive dynamics in plants: the role of seedbanks

Gene drives offer revolutionary potential for the management of problematic plant populations, such as invasive weeds and herbicide-resistant species, by rapidly spreading desired genetic alterations. Two recent studies have provided the first experimental demonstrations of engineered CRISPR gene drive systems in plants (CAIN and ClvR). However, the successful application of such systems in the field will critically depend on an accurate understanding of plant-specific life-history traits, especially seed dormancy, a ubiquitous yet frequently overlooked eco-evolutionary force. In this study, we develop the first comprehensive modeling framework for gene drives in plant populations that incorporates a persistent soil seedbank. We show how the presence of a seedbank can significantly slow gene drive spread but also reduce the genetic load required to achieve population elimination. Furthermore, we show that seedbanks substantially increase the required introduction frequency of threshold-dependent gene drives, which could prevent establishment in some cases, yet also provide an intrinsic biosafety mechanism for confining a highly efficient drive to a target population. Our study highlights the need to incorporate seedbank dynamics into gene drive strategies to ensure realistic predictions and successful field applications.

evolutionary biology↗

bioRxiv2024

Population dynamics in spatial suppression gene drive models and the effect of resistance, density dependence, and life history

Due to their super-Mendelian inheritance, gene drive systems have the potential to provide revolutionary solutions to critical public health and environmental problems. For suppression drives, however, spatial structure can cause "chasing" population dynamics that may postpone target population elimination or even cause the drive to fail. In chasing, wild-type individuals elude the drive and recolonize previously suppressed areas. The drive can re-enter these recolonized areas, but often is not able to catch up to wild-type and finally eliminate it. Previous methods for chasing detection are only suitable to limited parameter ranges. In this study with expanded parameter ranges, we found that the shift from chasing dynamics to static equilibrium outcomes is continuous as drive performance is reduced. To quantify this, we defined a Weighted Average Nearest Neighbor statistic to assess the clustering degree during chasing, while also characterizing chasing by the per-generation chance of population elimination and drive loss. To detect chasing dynamics in local areas and to detect the start of chasing, we implemented Density-Based Spatial Clustering of Applications with Noise. Using these techniques, we determined the effect of arena size, resistance allele formation rate in both the germline and in the early embryo from maternally deposited Cas9, life history and reproduction strategies, and density-dependent growth curve shape on chasing outcomes. We found that larger real-world areas will be much more vulnerable to chasing and that species with overlapping generations, fecundity-based density dependence, and concave density-dependent growth curves have smaller and more clustered local chasing with a greater chance of eventual population elimination. We also found that embryo resistance and germline resistance hinder drive performance in different ways. These considerations will be important for determining the necessary drive performance parameters needed for success in different species, and whether future drives could potentially be considered as release candidates.

ecology↗

bioRxiv2024

Signatures of selective sweeps in continuous-space populations

Selective sweeps describe the process by which an adaptive mutation arises and rapidly fixes in the population, thereby removing genetic variation in its genomic vicinity. The expected signatures of selective sweeps are relatively well understood in panmictic population models, yet natural populations often extend across larger geographic ranges where individuals are more likely to mate with those born nearby. To investigate how such spatial population structure can affect sweep dynamics and signatures, we simulated selective sweeps in populations inhabiting a two-dimensional continuous landscape. The maximum dispersal distance of offspring from their parents can be varied in our simulations from an essentially panmictic population to scenarios with increasingly limited dispersal. We find that in low-dispersal populations, adaptive mutations spread more slowly than in panmictic ones, while recombination becomes less effective at breaking up genetic linkage around the sweep locus. Together, these factors result in a trough of reduced genetic diversity around the sweep locus that looks very similar across dispersal rates. We also find that the site frequency spectrum around hard sweeps in low-dispersal populations becomes enriched for intermediate-frequency variants, making these sweeps appear softer than they are. Furthermore, haplotype heterozygosity at the sweep locus tends to be elevated in low-dispersal scenarios as compared to panmixia, contrary to what we observe in neutral scenarios without sweeps. The haplotype patterns generated by these hard sweeps in low-dispersal populations can resemble soft sweeps from standing genetic variation that arose from substantially older alleles. Our results highlight the need for better accounting for spatial population structure when making inferences about selective sweeps.

evolutionary biology↗

bioRxiv2024

Population genetics meets ecology: a guide to individual-based simulations in continuous landscapes

Individual-based simulation has become an increasingly crucial tool for many fields of population biology. However, continuous geography is important to many applications, and implementing realistic and stable simulations in continuous space presents a variety of difficulties, from modeling choices to computational efficiency. This paper aims to be a practical guide to spatial simulation, helping researchers to implement individual-based simulations and avoid common pitfalls. To do this, we delve into mechanisms of mating, reproduction, density-dependent feedback, and dispersal, all of which may vary across the landscape, discuss how these affect population dynamics, and describe how to parameterize simulations in convenient ways (for instance, to achieve a desired population density). We also demonstrate how to implement these models using the current version of the individual-based simulator, SLiM. We additionally discuss natural selection - in particular, how genetic variation can affect demographic processes. Finally, we provide four short vignettes: simulations of pikas that shift their range up a mountain as temperatures rise; mosquitoes that live in rivers as juveniles and experience seasonally changing habitat; cane toads that expand across Australia, reaching 120 million individuals; and monarch butterflies whose populations are regulated by an explicitly modeled resource (milkweed).

evolutionary biology↗

bioRxiv2024

e3SIM: epidemiological-ecological-evolutionary simulation framework for genomic epidemiology

Infectious disease dynamics are driven by the complex interplay of epidemiological, ecological, and evolutionary processes. Accurately modeling these interactions is crucial for understanding pathogen spread and informing public health strategies. However, existing simulators often fail to capture the dynamic interplay between these processes, resulting in oversimplified models that do not fully reflect real-world complexities in which the pathogens genetic evolution dynamically influences disease transmission. We introduce the epidemiological-ecological-evolutionary simulator (e3SIM), an open-source framework that concurrently models the transmission dynamics and molecular evolution of pathogens within a host population while integrating environmental factors. Using an agent-based, discrete-generation, forward-in-time approach, e3SIM incorporates compartmental models, host-population contact networks, and quantitative-trait models for pathogens. This integration allows for realistic simulations of disease spread and pathogen evolution. Key features include a modular and scalable design, flexibility in modeling various epidemiological and population-genetic complexities, incorporation of time-varying environmental factors, and a user-friendly graphical interface. We demonstrate e3SIMs capabilities through simulations of realistic outbreak scenarios with SARS-CoV-2 and Mycobacterium tuberculosis, illustrating its flexibility for studying the genomic epidemiology of diverse pathogen types.

bioinformatics↗

bioRxiv2024

Resource-explicit interactions in spatial population models

Continuous-space population models can yield significantly different results from their panmictic counterparts when assessing evolutionary, ecological, or population-genetic processes. However, the computational burden of spatial models is typically much greater than that of panmictic models due to the overhead of determining which individuals interact with one another and how strongly they interact. Though these calculations are necessary to model local competition that regulates the population density, they can lead to prohibitively long runtimes. Here, we present a novel modeling method in which the resources available to a population are abstractly represented as an additional layer of the simulation. Instead of interacting directly with one another, individuals interact indirectly via this resource layer. We find that this method closely matches other spatial models, yet can dramatically increase the speed of the model, allowing the simulation of much larger populations. Additionally, models structured in this manner exhibit other desirable characteristics, including more realistic spatial dynamics near the edge of the simulated area, and an efficient route for modeling more complex heterogeneous landscapes.

ecology↗

bioRxiv2023

New germline Cas9 promoters show improved performance for homing gene drive

Gene drive systems could be a viable strategy to prevent pathogen transmission or suppress vector populations by propagating drive alleles with super-Mendelian inheritance. CRISPR-based homing gene drives, perhaps the most powerful gene drive strategy, convert wild type alleles into drive alleles in heterozygotes with the help of Cas9 and gRNA. However, achieving successful outcomes with these drives often requires high performance. Specifically, it is desirable to identify Cas9 promoters that yield high drive conversion rates, minimize the formation rate of resistance alleles in both the germline and the early embryo, and limit somatic Cas9 expression. Thus far, high-performance promoters have only been discovered in Anopheles species. In Drosophila, the nanos promoter avoids leaky somatic expression, but at the cost of high embryo resistance from maternally deposited Cas9. To improve drive efficiency, we tested eleven Drosophila melanogaster germline promoters in several configurations. Some of the new promoters achieved higher drive conversion efficiency with minimal embryo resistance, but none could completely avoid somatic expression like nanos. However, such somatic expression often did not carry detectable fitness costs when the promoter-Cas9 elements supported a rescue homing drive targeting a haplolethal gene, suggesting somatic drive conversion. Based on our findings, we selected two Cas9 promoter lines for cage experiments with a 4-gRNA suppression drive. While one promoter exhibited substantial somatic effects, leading to a low drive equilibrium frequency, the other outperformed nanos, resulting in the successful suppression of the cage population. Overall, these novel Cas9 promoters hold potential advantages for homing drives in Drosophila species and may also possess valuable homologs in other organisms.

genetics↗

bioRxiv2023

Catching a wave: on the suitability of traveling-wave solutions in epidemiological modeling

Ordinary differential equation models such as the classical SIR model are widely used in epidemiology to study and predict infectious disease dynamics. However, these models typically assume that populations are homogeneously mixed, ignoring possible variations in disease prevalence due to spatial heterogeneity. To address this issue, reaction-diffusion models have been proposed as an alternative approach to modeling spatially continuous populations in which individuals move in a diffusive manner. In this study, we explore the conditions under which such spatial structure must be explicitly considered to accurately predict disease spread, and when the assumption of homogeneous mixing remains adequate. In particular, we derive a critical threshold for the diffusion coefficient below which disease transmission dynamics exhibit spatial heterogeneity. We validate our analytical results with individual-based simulations of disease transmission across a two-dimensional continuous landscape. Using this framework, we further explore how key epidemiological parameters such as the probability of disease establishment, its maximum incidence, and its final epidemic size are affected by incorporating spatial structure into SI, SIS, and SIR models. We discuss the implications of our findings for epidemiological modeling and identify design considerations and limitations for spatial simulation models of disease dynamics.

bioinformatics↗

bioRxiv2023

A framework for identifying fertility gene targets for mammalian pest control

O_LIFertility-targeted gene drives have been proposed as an ethical genetic approach for managing wild populations of vertebrate pests for public health and conservation benefit. C_LIO_LIThis manuscript introduces a framework to identify and evaluate target gene suitability based on biological gene function, gene expression, and results from mouse knockout models. C_LIO_LIThis framework identified 16 genes essential for male fertility and 12 genes important for female fertility that may be feasible targets for mammalian gene drives and other non-drive genetic pest control technology. Further, a comparative genomics analysis demonstrates the conservation of the identified genes across several globally significant invasive mammals. C_LIO_LIIn addition to providing important considerations for identifying candidate genes, our framework and the genes identified in this study may have utility in developing additional pest control tools such as wildlife contraceptives. C_LI

genetics↗

bioRxiv2023

Adaptations to water stress and pastoralism in the Turkana of northwest Kenya

The Turkana people inhabit arid regions of east Africa--where temperatures are high and water is scarce--and they practice subsistence pastoralism, such that their diet is primarily composed of animal products. Working with Turkana communities, we sequenced 367 genomes and identified 8 regions putatively involved in adaptation to water stress and pastoralism. One of these regions includes a putative enhancer for STC1--a kidney-expressed gene involved in the response to dehydration and the metabolism of purine-rich foods such as red meat. We show that STC1 is induced by antidiuretic hormone in humans, is associated with urea levels in the Turkana themselves, and is under strong selection in this population (s[~]0.041). This work highlights that partnerships with subsistence-level groups can lead to new models of human physiology with biomedical relevance.

evolutionary biology↗

bioRxiv2022

Inference of selective sweep parameters through supervised learning

A selective sweep occurs when positive selection drives an initially rare allele to high population frequency. In nature, the precise parameters of a sweep are seldom known: How strong was positive selection? Did the sweep involve only a single adaptive allele (hard sweep) or were multiple adaptive alleles at the locus sweeping at the same time (soft sweep)? If the sweep was soft, did these alleles originate from recurrent new mutations (RNM) or from standing genetic variation (SGV)? Here, we present a method based on supervised machine learning to infer such parameters from the patterns of genetic variation observed around a given sweep locus. Our method is trained on sweep data simulated with SLiM, a fast and flexible framework that allows us to generate training data across a wide spectrum of evolutionary scenarios and can be tailored towards the specific population of interest. Inferences are based on summary statistics describing patterns of nucleotide diversity, haplotype structure, and linkage disequilibrium, which are estimated across systematically varying genomic window sizes to capture sweeps across a wide range of selection strengths. We show that our method can accurately infer selection coefficients in the range 0.01 < s < 100 and classify sweep types between hard sweeps, RNM soft sweeps, and SGV soft sweeps with accuracy 69 % to 95 % depending on sweep strength. We also show that the method infers the correct sweep types at three empirical loci known to be associated with the recent evolution of pesticide resistance in Drosophila melanogaster. Our study demonstrates the power of machine learning for inferring sweep parameters from present-day genotyping samples, opening the door to a better understanding of the modes of adaptive evolution in nature. Author summaryAdaptation often involves the rapid spread of a beneficial genetic variant through the population in a process called a selective sweep. Here, we develop a method based on machine learning that can infer the strength of selection driving such a sweep, and distinguish whether it involved only a single adaptive variant (a so-called hard sweep) or several adaptive variants of independent origin that were simultaneously rising in frequency at the same genomic position (a so-called soft selective sweep). Our machine learning method is trained on simulated data and only requires data sampled from a single population at a single point in time. To address the challenge of simulating realistic datasets for training, we explore the behavior of the method under a variety of testing scenarios, including scenarios where the history of the population of interest was misspecified. Finally, to illustrate the accuracy of our method, we apply it to three known sweep loci that have contributed to the evolution of pesticide resistance in Drosophila melanogaster.

evolutionary biology↗

bioRxiv2022

Finding the strongest gene drive: Simulations reveal unexpected performance differences between Anopheles homing suppression drive candidates

Recent experiments have produced several Anopheles gambiae homing gene drives that disrupt female fertility genes, thereby eventually inducing population collapse. Such drives may be highly effective tools to combat malaria. One such homing drive, based on the zpg promoter driving CRISPR/Cas9, was able to eliminate a cage population of mosquitoes. A second version, purportedly improved upon the first by incorporating an X-shredder element (which biases inheritance towards male offspring), was similarly successful. Here, we re-analyze the data of each of these gene drives and suggest an alternative interpretation of their performance. We assess each suppression drive within an individual-based simulation framework that models mosquito population dynamics in continuous space. We find that the combined homing/X-shredder drive is actually less effective at population suppression within the context of our mosquito population model. In particular, the combined drive often fails to completely suppress the population, instead resulting in an unstable equilibrium between drive and wild-type alleles. By contrast, otherwise similar drives based on the nos promoter may prove to be more promising candidates for future development due to potentially superior performance.

genetics↗

bioRxiv2022

A model of functionally buffered deleterious mutations can lead to signatures of positive selection distinguishable from an evolutionary conflict model

AbstractSelective pressures on DNA sequences often result in departures from neutral evolution that can be captured by the McDonald-Kreitman (MK) test. However, the nature of such selective forces often remains unknown to experimentalists. Amino acid fixations driven by natural selection in protein coding genes are commonly associated with a genetic arms race or changing biological purposes, leading to proteins with new functionality. Here, we evaluate the expectations of a buffering mechanism driving selective amino acids to fixation, which is motivated by an observed phenotypic rescue of otherwise deleterious nonsynonymous substitutions at bag of marbles (bam) and Sex lethal (Sxl). These two important genes in Drosophila melanogaster were shown to experience strong episodic bursts of natural selection potentially due to infections of the endosymbiotic bacteria Wolbachia observed among multiple Drosophila species. Using simulations to implement and evaluate the evolutionary dynamics of a Wolbachia buffering model, we demonstrate that selectively fixed amino acid replacements will occur, but that proportion of adaptive amino acid fixations and the statistical power of the MK test to detect the departure from an equilibrium neutral model are both significantly lower than seen for an arms race/change-in-function model that favors proteins with diversified amino acids.

genetics↗

bioRxiv2021

Experimental demonstration of tethered gene drive systems for confined population modification or suppression

Homing gene drives hold great promise for the genetic control of natural populations. However, current homing systems are capable of spreading uncontrollably between populations connected by even marginal levels of migration. This could represent a substantial sociopolitical barrier to the testing or deployment of such drives and may generally be undesirable when the objective is only local population control, such as suppression of an invasive species outside of its native range. Tethered drive systems, in which a locally confined gene drive provides the CRISPR nuclease needed for a homing drive, could provide a solution to this problem, offering the power of a homing drive and confinement of the supporting drive. Here, we demonstrate the engineering of a tethered drive system in Drosophila, using a regionally confined CRISPR Toxin-Antidote Recessive Embryo (TARE) drive to support modification and suppression homing drives. Each drive was able to bias inheritance in its favor, and the TARE drive was shown to spread only when released above a threshold frequency in experimental cage populations. After the TARE drive had established in the population, it facilitated the spread of a subsequently released split homing modification drive (to all individuals in the cage) and of a homing suppression drive (to its equilibrium frequency). Our results show that the tethered drive strategy is a viable and easily engineered option for providing confinement of homing drives to target populations.

genetics↗

bioRxiv2021

A homing suppression gene drive with multiplexed gRNAs maintains high drive conversion efficiency and avoids functional resistance alleles

Gene drives are engineered alleles that can bias inheritance in their favor, allowing them to spread throughout a population. They could potentially be used to modify or suppress pest populations, such as mosquitoes that spread diseases. CRISPR/Cas9 homing drives, which copy themselves by homology-directed repair in drive/wild-type heterozygotes, are a powerful form of gene drive, but they are vulnerable to resistance alleles that preserve the function of their target gene. Such resistance alleles can prevent successful population suppression. Here, we constructed a homing suppression drive in Drosophila melanogaster that utilized multiplexed gRNAs to inhibit the formation of functional resistance alleles in its female fertility target gene. The selected gRNA target sites were close together, preventing reduction in drive conversion efficiency. The construct reached a moderate equilibrium frequency in cage populations without apparent formation of resistance alleles. However, a moderate fitness cost prevented elimination of the cage population, showing the importance of using highly efficient drives in a suppression strategy, even if resistance can be addressed. Nevertheless, our results experimentally demonstrate the viability of the multiplexed gRNAs strategy in homing suppression gene drives.

genetics↗