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The tools provided by genetic engineering can fundamentally alter our technological approaches to medicine, agriculture, and ecology. Through their use, crops have been designed to increase yield while reducing loss from pests and disease (Pellegrino, Bedini, Nuti, & Ercoli, 2018). Critical medications, such as insulin and the mammalian growth hormone‐inhibiting hormone, are now produced using bio‐engineered organisms (Itakura et al., 1977). Genetically engineered organisms have been proposed to help facilitate adaptive responses to climate change, suppress undesirable or invasive populations, and reverse the fixation of deleterious mutations in at‐risk populations of endangered plants and animals (Thomas et al., 2013). In addition, genetic engineering has made vaccines safer, more effective, faster to produce, and could make them easier to disseminate (Basinski et al., 2018; Chahal et al., 2016). Although promising, these new technologies all confront a common challenge: unwanted evolution that may undermine the intent of engineering. Transgenes, stretches of foreign DNA inserted into a host genome, evolve in the same way as any other genetic material. Consequently, if a transgene compromises fitness, selection favors its potentially rapid removal from the population (Sleight, Bartley, Lieviant, & Sauro, 2010; Willemsen & Zwart, 2019). Even when the insert does not reduce fitness, mutations can accumulate, leading to loss of function. This “evolutionary half‐life” poses a significant challenge for the design of successful genetically modified organisms and motivates the identification of designs that slow or arrest evolution of genetic modifications (Bull & Barrick, 2017). One possible solution to suppress evolution in genetically modified organisms comes from population genetics theory demonstrating that gene flow can impede local adaptation (Akerman & Burger, 2014; Bolnick & Nosil, 2007; Crow & Kimura, 1970; Haldane, 1957; Lenormand, 2002; Levene, 1953; Slatkin, 1973; Wright, 1940). In the classic “mainland‐island” models, migration from a mainland to an island can suppress island adaptation. By suppressing local adaptation, immigration can maintain costly phenotypes that would otherwise be displaced by evolution. An interesting question is whether “migration” can be used to maintain essential functions of genetically modified populations in the face of countering selection. In this context, repeated re‐introduction of genetically modified individuals into an evolving population is analogous to a constant level of immigration from a source fixed for locally unfavorable mutations. To address this question, we develop and analyze mathematical models to evaluate the efficacy of repeated introduction (henceforth “supplementation”) as a tool for controlling unwanted evolution in three contexts of genetically engineered populations: bioreactors, gene drives, and transmissible vaccines. In the first, we identify the level of supplementation required to maintain a stable frequency of a desired but deleterious transgene in the microbial population of a bioreactor. In the second, we evaluate the effect of supplementation on the spread and stability of gene drives that experience resistance evolution. In the third, we identify the amount of supplemental direct vaccination required of a transmissible vaccine to prevent its evolutionary decay while maintaining a high enough vaccine coverage to also protect the population against a pathogen.

It is often expected that temperate plants have expanded their geographical ranges northward from primarily southern refugia. Evidence for this hypothesis is mixed in eastern North American species, and there is increasing support for colonization from middle latitudes. We studied genome-wide patterns of variation in RADseq loci to test hypotheses concerning range expansion in a North American forest herb (Campanula americana). First, spatial patterns of genetic differentiation were determined. Then phylogenetic relationships and divergence times were estimated. Spatial signatures of genetic drift were also studied to identify the directionality of recent range expansion and its geographical origins. Finally, spatially explicit scenarios for the spread of plants across the landscape were compared, using variation in the population mutation parameter and Tajima's D. We found strong longitudinal subdivision, with populations clustering into groups west and east of the Mississippi River. While the southeastern region was probably part of a diverse Pleistocene refugium, there is little evidence that range expansion involved founders from these southern locales. Instead, declines in genetic diversity and the loss of rare alleles support a westward colonization wave from a middle latitude refugium near the southern Appalachian Mountains, with subsequent expansion from a Pleistocene staging ground in the Mississippi River Valley (0.51-1.27 million years ago). These analyses implicate stepping stone colonization from middle latitudes as an important mechanism of species range expansion in eastern North America. This study further demonstrates the utility of population genetics as a tool to infer the routes travelled by organisms during geographical range expansion.

Diverse applications rely on engineering microbes to carry and express foreign transgenes. This engineered baggage rarely benefits the microbe and is thus prone to rapid evolutionary loss when the microbe is propagated. For applications where a transgene must be maintained for extended periods of growth, slowing the rate of transgene evolution is critical and can be achieved by reducing either the rate of mutation or the strength of selection. Because the benefits realized by changing these quantities will not usually be equal, it is important to know which will yield the greatest improvement to the evolutionary half-life of the engineering. Here, we provide a method for jointly estimating the mutation rate of transgene loss and the strength of selection favoring these transgene-free, revertant individuals. The method requires data from serial transfer experiments in which the frequency of engineered genomes is monitored periodically. Simple mathematical models are developed that use these estimates to predict the half-life of the engineered transgene and provide quantitative predictions for how alterations to mutation and selection will influence longevity. The estimation method and predictive tools have been implemented as an interactive web application, MuSe.

Colonization at expanding range edges often involves few founders, reducing effective population size. This process can promote the evolution of self-fertilization, but implicating historical processes as drivers of trait evolution is often difficult and requires an explicit model of biogeographic history. In plants, contemporary limits to outcrossing are often invoked as evolutionary drivers of self-fertilization, but historical expansions may shape mating system diversity, with leading-edge populations evolving elevated selfing ability. In a widespread plant, Campanula americana, we identified a glacial refugium in the southern Appalachian Mountains from spatial patterns of genetic drift among 24 populations. Populations farther from this refugium have smaller effective sizes and fewer rare alleles. They also displayed elevated heterosis in among-population crosses, reflecting the accumulation of deleterious mutations during range expansion. Although populations with elevated heterosis had reduced segregating mutation load, the magnitude of inbreeding depression lacked geographic pattern. The ability to self-fertilize was strongly positively correlated with the distance from the refugium and mutation accumulation—a pattern that contrasts sharply with contemporary mate and pollinator limitation. In this and other species, diversity in sexual systems may reflect the legacy of evolution in small, colonizing populations, with little or no relation to the ecology of modern populations.

The danger posed by emerging infectious diseases necessitates the development of new tools that can mitigate the risk of animal pathogens spilling over into the human population. One promising approach is the development of recombinant viral vaccines that are transmissible, and thus capable of self-dissemination through hard to reach populations of wild animals. Indeed, mathematical models demonstrate that transmissible vaccines can greatly reduce the effort required to control the spread of zoonotic pathogens in their animal reservoirs, thereby limiting the chances of human infection. A key challenge facing these new vaccines, however, is the inevitability of evolutionary change resulting from their ability to self-replicate and generate extended chains of transmission. Further, carrying immunogenic transgenes is often costly, in terms of metabolic burden, increased competition with the pathogen, or due to unintended interactions with the viral host regulatory network. As a result, natural selection is expected to favor vaccine strains that down-regulate or delete these transgenes resulting in increased rates of transmission and reduced efficacy against the target pathogen. In addition, efficacy and evolutionary stability will often be at odds; as when longer, more efficacious antigens experience faster rates of evolutionary decay. Here, we ask how such trade-offs influence the overall performance of transmissible vaccines. We find that evolutionary instability can substantially reduce performance, even for vaccine candidates with the ideal combination of efficacy and transmission. However, we find that, at least in some cases, vaccine stability and overall performance can be improved by the inclusion of a second, redundant antigen. Overall, our results suggest that the successful application of recombinant transmissible vaccines will require consideration of evolutionary dynamics and epistatic effects, as well as basic measurements of epidemiological features.

Forecasting the risk of pathogen spillover from reservoir populations of wild or domestic animals is essential for the effective deployment of interventions such as wildlife vaccination or culling. Due to the sporadic nature of spillover events and limited availability of data, developing and validating robust, spatially explicit, predictions is challenging. Recent efforts have begun to make progress in this direction by capitalizing on machine learning methodologies. An important weakness of existing approaches, however, is that they generally rely on combining human and reservoir infection data during the training process and thus conflate risk attributable to the prevalence of the pathogen in the reservoir population with the risk attributed to the realized rate of spillover into the human population. Because effective planning of interventions requires that these components of risk be disentangled, we developed a multi-layer machine learning framework that separates these processes. Our approach begins by training models to predict the geographic range of the primary reservoir and the subset of this range in which the pathogen occurs. The spillover risk predicted by the product of these reservoir specific models is then fit to data on realized patterns of historical spillover into the human population. The result is a geographically specific spillover risk forecast that can be easily decomposed and used to guide effective intervention. Applying our method to Lassa virus, a zoonotic pathogen that regularly spills over into the human population across West Africa, results in a model that explains a modest but statistically significant portion of geographic variation in historical patterns of spillover. When combined with a mechanistic mathematical model of infection dynamics, our spillover risk model predicts that 897,700 humans are infected by Lassa virus each year across West Africa, with Nigeria accounting for more than half of these human infections., Author summary The 2019 emergence of SARS-CoV-2 is a grim reminder of the threat animal-borne pathogens pose to human health. Even prior to SARS-CoV-2, the spillover of pathogens from animal reservoirs was a persistent problem, with pathogens such as Ebola, Nipah, and Lassa regularly but unpredictably causing outbreaks. Machine-learning models that anticipate when and where pathogen transmission from animals to humans is likely to occur would help guide surveillance efforts and preemptive countermeasures like information campaigns or vaccination programs. We develop a novel machine learning framework that uses datasets describing the distribution of a virus within its host and the range of its animal host, along with data on spatial patterns of human immunity, to infer rates of animal-to-human transmission across a region. By training the model on data from the animal host alone, our framework allows rigorous validation of spillover predictions using human data. We apply our framework to Lassa fever, a viral disease of West Africa that is spread to humans by rodents, and use the predictions to update estimates of Lassa virus infections in humans. Our results suggest that Nigeria is most at risk for the emergence of Lassa virus, and should be prioritized for outbreak-surveillance.

Inbreeding depression is dependent on the ploidy of populations and can inhibit the evolution of selfing. While polyploids should generally harbor less inbreeding depression than diploids at equilibrium, it has been unclear whether this pattern holds in non-equilibrium conditions following bottlenecks. We use stochastic individual-based simulations to determine the effects of population bottlenecks on inbreeding depression in diploids and autotetraploids, in addition to cases where neo-autotetraploids form from the union of unreduced gametes. With a ploidy-independent dominance function based on enzyme kinetics, inbreeding depression is generally lower in autotetraploids for fully and partially recessive mutations. Due to the sampling of more chromosomes during reproduction, bottlenecks generally reduce inbreeding depression to a lesser extent in autotetraploids. All else being equal, population bottlenecks may have ploidy-dependent effects for another reason-in some cases matings between close relatives temporarily increase inbreeding depression in autotetraploids by increasing the frequency of the heterozygous genotype harboring the most harmful mutations. When neo-autotetraploids are formed by few individuals, inbreeding depression is dramatically reduced, given extensive masking of harmful mutations following whole genome duplication. This effect persists as nascent tetraploids reach mutation-selection-drift balance, providing a transient period of permissive conditions favoring the evolution of selfing.

Lassa virus is a significant burden on human health throughout its endemic region in West Africa, with most human infections the result of spillover from the primary rodent reservoir of the virus, the natal multimammate mouse,M. natalensis. Here we develop a Bayesian methodology for estimating epidemiological parameters of Lassa virus within its rodent reservoir and for generating probabilistic predictions for the efficacy of rodent vaccination programs. Our approach uses Approximate Bayesian Computation (ABC) to integrate mechanistic mathematical models, remotely-sensed precipitation data, and Lassa virus surveillance data from rodent populations. Using simulated data, we show that our method accurately estimates key model parameters, even when surveillance data are available from only a relatively small number of points in space and time. Applying our method to previously published data from two villages in Guinea estimates the time-averagedR0of Lassa virus to be 1.658 and 1.453 for rodent populations in the villages of Bantou and Tanganya, respectively. Using the posterior distribution for model parameters derived from these Guinean populations, we evaluate the likely efficacy of vaccination programs relying on distribution of vaccine-laced baits. Our results demonstrate that effective and durable reductions in the risk of Lassa virus spillover into the human population will require repeated distribution of large quantities of vaccine.Author SummaryLassa virus is a chronic source of illness throughout West Africa, and is considered to be a threat for widespread emergence. Because most human infections result from contact with infected rodents, interventions that reduce the number of rodents infected with Lassa virus represent promising opportunities for reducing the public health burden of this disease. Evaluating how well alternative interventions are likely to perform is complicated by our relatively poor understanding of viral epidemiology within the reservoir population. Here we develop a novel statistical approach that couples mathematical models and viral surveillance data from rodent populations to robustly estimate key epidemiological parameters. Applying our method to existing data from Guinea yields well-resolved parameter estimates and allows us to simulate a variety of rodent vaccination programs. Together, our results demonstrate that rodent vaccination alone is unlikely to be an effective tool for reducing that public health burden of Lassa fever within West Africa.

Lassa virus is a significant burden on human health throughout its endemic region in West Africa, with most human infections the result of spillover from the primary rodent reservoir of the virus, the natal multimammate mouse, M. natalensis. Here we develop a Bayesian methodology for estimating epidemiological parameters of Lassa virus within its rodent reservoir and for generating probabilistic predictions for the efficacy of rodent vaccination programs. Our approach uses Approximate Bayesian Computation (ABC) to integrate mechanistic mathematical models, remotely-sensed precipitation data, and Lassa virus surveillance data from rodent populations. Using simulated data, we show that our method accurately estimates key model parameters, even when surveillance data are available from only a relatively small number of points in space and time. Applying our method to previously published data from two villages in Guinea estimates the time-averaged R0 of Lassa virus to be 1.74 and 1.54 for rodent populations in the villages of Bantou and Tanganya, respectively. Using the posterior distribution for model parameters derived from these Guinean populations, we evaluate the likely efficacy of vaccination programs relying on distribution of vaccine-laced baits. Our results demonstrate that effective and durable reductions in the risk of Lassa virus spillover into the human population will require repeated distribution of large quantities of vaccine.

In flowering plants, shifts from outcrossing to partial or complete self-fertilization have occurred independently thousands of times, yet the underlying adaptive processes are difficult to discern. Selfing's ability to provide reproductive assurance when pollination is uncertain is an oft-cited ecological explanation for its evolution, but this benefit may be outweighed by costs diminishing its selective advantage over outcrossing. We directly studied the fitness effects of a self-compatibility mutation that was backcrossed into a self-incompatible (SI) population of Leavenworthia alabamica, illuminating the direction and magnitude of selection on the mating-system modifier. In array experiments conducted in two years, self-compatible (SC) plants produced 17-26% more seed, but this advantage was counteracted by extensive seed discounting-the replacement of high-quality outcrossed seeds by selfed seeds. Using a simple model and simulations, we demonstrate that SC mutations with these attributes rarely spread to high frequency in natural populations, unless inbreeding depression falls below a threshold value (0.57 ≤ δthreshold ≤ 0.70) in SI populations. A combination of heavy seed discounting and inbreeding depression likely explains why outcrossing adaptations such as self-incompatibility are maintained generally, despite persistent input of selfing mutations, and frequent limits on outcross seed production in nature.