Predicting the fine-scale spatial distribution of zoonotic reservoirs using computer vision.

Zoonotic diseases threaten human health worldwide and are often associated with anthropogenic disturbance. Predicting how disturbance influences spillover risk is critical for effective disease intervention but difficult to achieve at fine spatial scales. Here, we develop a method that learns the spatial distribution of a reservoir species from aerial imagery. Our approach uses neural networks to extract features of known or hypothesized importance from images. The spatial distribution of these features is then summarized and linked to spatially explicit reservoir presence/absence data using boosted regression trees. We demonstrate the utility of our method by applying it to the reservoir of Lassa virus, Mastomys natalensis, within the West African nations of Sierra Leone and Guinea. We show that, when trained using reservoir trapping data and publicly available aerial imagery, our framework learns relationships between environmental features and reservoir occurrence and accurately ranks areas according to the likelihood of reservoir presence.

Suppressing evolution in genetically engineered systems through repeated supplementation.

Genetically engineered organisms are prone to evolve in response to the engineering. This evolution is often undesirable and can negatively affect the purpose of the engineering. Methods that maintain the stability of engineered genomes are therefore critical to the successful design and use of genetically engineered organisms. One potential method to limit unwanted evolution is by taking advantage of the ability of gene flow to counter local adaption, a process of supplementation. Here, we investigate the feasibility of supplementation as a mechanism to offset the evolutionary degradation of a transgene in three model systems: a bioreactor, a gene drive, and a transmissible vaccine. In each model, continual introduction from a stock is used to balance mutation and selection against the transgene. Each system has its unique features. The bioreactor system is especially tractable and has a simple answer: The level of supplementation required to maintain the transgene at a frequency p ^ is approximately p ^ s , where s is the selective disadvantage of the transgene. Supplementation is also feasible in the transmissible vaccine case but is probably not practical to prevent the evolution of resistance against a gene drive. We note, however, that the continual replacement of even a small fraction of a large population can be challenging, limiting the usefulness of supplementation as a means of controlling unwanted evolution.

Bridging the gap: Using reservoir ecology and human serosurveys to estimate Lassa virus spillover in West Africa.

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.

Designing transmissible viral vaccines for evolutionary robustness and maximum efficiency.

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.

Westward range expansion from middle latitudes explains the Mississippi River discontinuity in a forest herb of eastern North America.

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.

Selfing ability and drift load evolve with range expansion.

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.

Bottlenecks and inbreeding depression in autotetraploids.

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.

Costs of selfing prevent the spread of a self-compatibility mutation that causes reproductive assurance.

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.