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Abstract Background Transmissible cancers lie at the intersection of oncology and infectious disease, two traditionally divergent fields for which gene expression studies are particularly useful for identifying the molecular basis of phenotypic variation. In oncology, transcriptomics studies, which characterize the expression of thousands of genes, have identified processes leading to heterogeneity in cancer phenotypes and individual prognoses. More generally, transcriptomics studies of infectious diseases characterize interactions between host, pathogen, and environment to better predict population-level outcomes. Tasmanian devils have been impacted dramatically by a transmissible cancer (devil facial tumor disease; DFTD) that has led to widespread population declines. Despite initial predictions of extinction, populations have persisted at low levels, due in part to heterogeneity in host responses, particularly between sexes. However, the processes underlying this variation remain unknown. Results We sequenced transcriptomes from healthy and DFTD-infected devils, as well as DFTD tumors, to characterize host responses to DFTD infection, identify differing host-tumor molecular interactions between sexes, and investigate the extent to which tumor gene expression varies among host populations. We found minimal variation in gene expression of devil lip tissues, either with respect to DFTD infection status or sex. However, 4088 genes were differentially expressed in tumors among our sampling localities. Pathways that were up- or downregulated in DFTD tumors relative to normal tissues exhibited the same patterns of expression with greater intensity in tumors from localities that experienced DFTD for longer. No mRNA sequence variants were associated with expression variation. Conclusions Expression variation among localities may reflect morphological differences in tumors that alter ratios of normal-to-tumor cells within biopsies. Phenotypic variation in tumors may arise from environmental variation or differences in host immune response that were undetectable in lip biopsies, potentially reflecting variation in host-tumor coevolutionary relationships among sites that differ in the time since DFTD arrival. 

Infectious diseases are a major threat for biodiversity conservation and can exert strong influence on wildlife population dynamics. Understanding the mechanisms driving infection rates and epidemic outcomes requires empirical data on the evolutionary trajectory of pathogens and host selective processes. Phylodynamics is a robust framework to understand the interaction of pathogen evolutionary processes with epidemiological dynamics, providing a powerful tool to evaluate disease control strategies. Tasmanian devils have been threatened by a fatal transmissible cancer, devil facial tumour disease (DFTD), for more than two decades. Here we employ a phylodynamic approach using tumour mitochondrial genomes to assess the role of tumour genetic diversity in epidemiological and population dynamics in a devil population subject to 12 years of intensive monitoring, since the beginning of the epidemic outbreak. DFTD molecular clock estimates of disease introduction mirrored observed estimates in the field, and DFTD genetic diversity was positively correlated with estimates of devil population size. However, prevalence and force of infection were the lowest when devil population size and tumour genetic diversity was the highest. This could be due to either differential virulence or transmissibility in tumour lineages or the development of host defence strategies against infection. Our results support the view that evolutionary processes and epidemiological trade‐offs can drive host‐pathogen coexistence, even when disease‐induced mortality is extremely high. We highlight the importance of integrating pathogen and population evolutionary interactions to better understand long‐term epidemic dynamics and evaluating disease control strategies.

 

Understanding mechanisms that underlie species range limits is at the core of evolutionary ecology. Asymmetric gene flow between larger core populations and smaller edge populations can swamp local adaptation at the range edge and inhibit further range expansion. However, empirical tests of this theory are exceedingly rare. We tested the hypothesis that asymmetric gene flow can constrain local adaptation and thereby species’ range limits in an endemic US salamander (Ambystoma barbouri) by determining if gene flow is asymmetric between the core and peripheries of the species’ geographic distribution and testing whether local adaptation is swamped at range edges with a reciprocal transplant experiment. Using putatively neutral loci from populations across three core‐to‐edge transects that covered nearly the entire species’ geographic range, we found evidence for asymmetric, core‐to‐edge gene flow along western and northern transects, but not along a southern transect. Subsequently, the reciprocal transplant experiment suggested that northern and western edge populations are locally adapted despite experiencing asymmetric gene flow, yet have lower fitness in their respective home regions than those of centre population. Conversely, southern populations exhibit low deme quality, experiencing high mortality regardless of where they were reared, probably due to harsher edge habitat conditions. Consequently, we provide rare species‐wide evidence that local adaptation can occur despite asymmetric gene flow, though migration from the core may prohibit range expansion by reducing fitness in edge populations. Further, our multitransect study shows that multiple, nonmutually exclusive mechanisms can lead to range limits within a single species.

 

Understanding spatial patterns of genetic differentiation and local adaptation is critical in a period of rapid environmental change. Climate change and anthropogenic development have led to population declines and shifting geographic distributions in numerous species. The streamside salamander,Ambystoma barbouri, is an endemic amphibian with a small geographic range that predominantly inhabits small, ephemeral streams. AsA. barbouriis listed as near‐threatened by the IUCN, we describe range‐wide patterns of genetic differentiation and adaptation to assess the species’ potential to respond to environmental change. We use outlier scans and genetic‐environment association analyses to identify genomic variation putatively underlying local adaptation across the species’ geographic range. We find evidence for adaptation with a polygenic architecture and a set of candidate SNPs that identify genes putatively contributing to local adaptation. Our results build on earlier work that suggests that someA. barbouripopulations are locally adapted despite evidence for asymmetric gene flow between the range core and periphery. Taken together, the body of work describing the evolutionary genetics of range limits inA. barbourisuggests that the species may be unlikely to respond naturally to environmental challenges through a range shift orin situadaptation. We suggest that management efforts such as assisted migration may be necessary in future.

 

Emerging infectious diseases pose one of the greatest threats to human health and biodiversity. Phylodynamics is often used to infer epidemiological parameters essential for guiding intervention strategies for human viruses such as severe acute respiratory syndrome coronavirus 2 (SARS-Cov-2). Here, we applied phylodynamics to elucidate the epidemiological dynamics of Tasmanian devil facial tumor disease (DFTD), a fatal, transmissible cancer with a genome thousands of times larger than that of any virus. Despite prior predictions of devil extinction, transmission rates have declined precipitously from ~3.5 secondary infections per infected individual to ~1 at present. Thus, DFTD appears to be transitioning from emergence to endemism, lending hope for the continued survival of the endangered Tasmanian devil. More generally, our study demonstrates a new phylodynamic analytical framework that can be applied to virtually any pathogen. 

The North American tiger salamander species complex, including its best-known species, the Mexican axolotl, has long been a source of biological fascination. The complex exhibits a wide range of variation in developmental life history strategies, including populations and individuals that undergo metamorphosis; those able to forego metamorphosis and retain a larval, aquatic lifestyle (i.e., paedomorphosis); and those that do both. The evolution of a paedomorphic life history state is thought to lead to increased population genetic differentiation and ultimately reproductive isolation and speciation, but the degree to which it has shaped population- and species-level divergence is poorly understood. Using a large multilocus dataset from hundreds of samples across North America, we identified genetic clusters across the geographic range of the tiger salamander complex. These clusters often contain a mixture of paedomorphic and metamorphic taxa, indicating that geographic isolation has played a larger role in lineage divergence than paedomorphosis in this system. This conclusion is bolstered by geography-informed analyses indicating no effect of life history strategy on population genetic differentiation and by model-based population genetic analyses demonstrating gene flow between adjacent metamorphic and paedomorphic populations. This fine-scale genetic perspective on life history variation establishes a framework for understanding how plasticity, local adaptation, and gene flow contribute to lineage divergence. Many members of the tiger salamander complex are endangered, and the Mexican axolotl is an important model system in regenerative and biomedical research. Our results chart a course for more informed use of these taxa in experimental, ecological, and conservation research.

 

Emerging infectious diseases increasingly threaten wildlife populations. Most studies focus on managing short‐term epidemic properties, such as controlling early outbreaks. Predicting long‐term endemic characteristics with limited retrospective data is more challenging. We used individual‐based modeling informed by individual variation in pathogen load and transmissibility to predict long‐term impacts of a lethal, transmissible cancer on Tasmanian devil (Sarcophilus harrisii) populations. For this, we employed approximate Bayesian computation to identify model scenarios that best matched known epidemiological and demographic system properties derived from 10 yr of data after disease emergence, enabling us to forecast future system dynamics. We show that the dramatic devil population declines observed thus far are likely attributable to transient dynamics (initial dynamics after disease emergence). Only 21% of matching scenarios led to devil extinction within 100 yr following devil facial tumor disease (DFTD) introduction, whereasDFTDfaded out in 57% of simulations. In the remaining 22% of simulations, disease and host coexisted for at least 100 yr, usually with long‐period oscillations. Our findings show that pathogen extirpation or host–pathogen coexistence are much more likely than theDFTD‐induced devil extinction, with crucial management ramifications. Accounting for individual‐level disease progression and the long‐term outcome of devil–DFTDinteractions at the population‐level, our findings suggest that immediate management interventions are unlikely to be necessary to ensure the persistence of Tasmanian devil populations. This is because strong population declines of devils after disease emergence do not necessarily translate into long‐term population declines at equilibria. Our modeling approach is widely applicable to other host–pathogen systems to predict disease impact beyond transient dynamics.

 

Infectious diseases are strong drivers of wildlife population dynamics, however, empirical analyses from the early stages of pathogen emergence are rare. Tasmanian devil facial tumour disease (DFTD), discovered in 1996, provides the opportunity to study an epizootic from its inception. We use a pattern‐oriented diffusion simulation to model the spatial spread of DFTD across the species' range and quantify population effects by jointly modelling multiple streams of data spanning 35 years. We estimate the wild devil population peaked at 53 000 in 1996, less than half of previous estimates. DFTD spread rapidly through high‐density areas, with spread velocity slowing in areas of low host densities. By 2020, DFTD occupied >90% of the species' range, causing 82% declines in local densities and reducing the total population to 16 900. Encouragingly, our model forecasts the population decline should level‐off within the next decade, supporting conservation management focused on facilitating evolution of resistance and tolerance.

 

Genetic structure in host species is often used to predict disease spread. However, host and pathogen genetic variation may be incongruent. Understanding landscape factors that have either concordant or divergent influence on host and pathogen genetic structure is crucial for wildlife disease management. Devil facial tumour disease (DFTD) was first observed in 1996 and has spread throughout almost the entire Tasmanian devil geographic range, causing dramatic population declines. Whereas DFTD is predominantly spread via biting among adults, devils typically disperse as juveniles, which experience low DFTD prevalence. Thus, we predicted little association between devil and tumour population structure and that environmental factors influencing gene flow differ between devils and tumours. We employed a comparative landscape genetics framework to test the influence of environmental factors on patterns of isolation by resistance (IBR) and isolation by environment (IBE) in devils and DFTD. Although we found evidence for broad‐scale costructuring between devils and tumours, we found no relationship between host and tumour individual genetic distances. Further, the factors driving the spatial distribution of genetic variation differed for each. Devils exhibited a strong IBR pattern driven by major roads, with no evidence of IBE. By contrast, tumours showed little evidence for IBR and a weak IBE pattern with respect to elevation in one of two tumour clusters we identify herein. Our results warrant caution when inferring pathogen spread using host population genetic structure and suggest that reliance on environmental barriers to host connectivity may be ineffective for managing the spread of wildlife diseases. Our findings demonstrate the utility of comparative landscape genetics for identifying differential factors driving host dispersal and pathogen transmission.