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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.

 

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. 

A plethora of intrinsic and environmental factors have been shown to influence the length of telomeres, the protector of chromosome ends. Despite the growing interest in infection–telomere interactions, there is very limited knowledge on how transmissible cancers influence telomere maintenance. An emblematic example of transmissible cancer occurs in the Tasmanian devil (Sarcophilus harrisii), whose populations have been dramatically reduced by infectious cancer cells. To investigate associations between telomere dynamics and the transmissible cancer, we used longitudinal data from a Tasmanian devil population that has been exposed to the disease for over 15 years. We detected substantial temporal variation in individual telomere length (TL), and a positive significant association between TL and age, as well as a marginally significant trend for devils with devil facial tumour disease (DFTD) having longer telomeres. A proportional hazard analysis yielded no significant effect of TL on the development of DFTD. Like previous studies, we show the complexity that TL dynamics may exhibit across the lifetime of organisms. Our work highlights the importance of long‐term longitudinal sampling for understanding the effects of wildlife diseases on TL.

 

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.