Implementing landscape genetics in molecular epidemiology to determine drivers of vector-borne disease: A malaria case study

This study employs landscape genetics to investigate the environmental drivers of a deadly vector-borne disease, malaria caused by Plasmodium falciparum, in a more spatially comprehensive manner than any previous work. With 1804 samples from 44 sites collected in western Kenya in 2012 and 2013, we performed resistance surface analysis to show that Lake Victoria acts as a barrier to transmission between areas north and south of the Winam Gulf. In addition, Mantel correlograms clearly showed significant correlations between genetic and geographic distance over short distances (less than 70 km). In both cases, we used an identity-by-state measure of relatedness tailored to find highly related individual parasites in order to focus on recent gene flow that is more relevant to disease transmission. To supplement these results, we performed conventional population genetics analyses, including Bayesian clustering methods and spatial ordination techniques. These analyses revealed some differentiation on the basis of geography and elevation and a cluster of genetic similarity in the lowlands north of the Winam Gulf of Lake Victoria. Taken as a whole, these results indicate low overall genetic differentiation in the Lake Victoria region, but with some separation of parasite populations north and south of the Winam Gulf that is explained by the presence of the lake as a geographic barrier to gene flow. We recommend similar landscape genetics analyses in future molecular epidemiology studies of vector-borne diseases to extend and contextualize the results of traditional population genetics.

Coupling spatial statistics with social network analysis to estimate distinct risk areas of disease circulation to improve risk-based surveillance

Most animal disease surveillance systems concentrate efforts in blocking transmission pathways and tracing back infected contacts while not considering the risk of transporting animals into areas with elevated disease risk. Here, we use a suite of spatial statistics and social network analysis to characterize animal movement among areas with an estimated distinct risk of disease circulation to ultimately enhance surveillance activities. Our model utilized equine infectious anemia virus (EIAV) outbreaks, between-farm horse movements, and spatial landscape data from 2015 through 2017. We related EIAV occurrence and the movement of horses between farms with climate variables that foster conditions for local disease propagation. We then constructed a spatially explicit model that allows the effect of the climate variables on EIAV occurrence to vary through space (i.e., non-stationary). Our results identified important areas in which in-going movements were more likely to result in EIAV infections and disease propagation. Municipalities were then classified as having high 56 (11.3%), medium 48 (9.66%), and low 393 (79.1%) spatial risk. The majority of the movements were between low-risk areas, altogether representing 68.68% of all animal movements. Meanwhile, 9.48% were within high-risk areas, and 6.20% were within medium-risk areas. Only 5.37% of the animals entering low-risk areas came from high-risk areas. On the other hand, 4.91% of the animals in the high-risk areas came from low- and medium-risk areas. Our results demonstrate that animal movements and spatial risk mapping could be used to make informed decisions before issuing animal movement permits, thus potentially reducing the chances of reintroducing infection into areas of low risk.

Socio-ecological drivers of multiple zoonotic hazards in highly urbanized cities

The ongoing COVID-19 pandemic is a stark reminder of the devastating consequences of pathogen spillover from wildlife to human hosts, particularly in densely populated urban centers. Prevention of future zoonotic disease is contingent on informed surveillance for known and novel threats across diverse human-wildlife interfaces. Cities are a key venue for potential spillover events because of the presence of zoonotic pathogens transmitted by hosts and vectors living in close proximity to dense human settlements. Effectively identifying and managing zoonotic hazards requires understanding the socio-ecological processes driving hazard distribution and pathogen prevalence in dynamic and heterogeneous urban landscapes. Despite increasing awareness of the human health impacts of zoonotic hazards, the integration of an eco-epidemiological perspective into public health management plans remains limited. Here we discuss how landscape patterns, abiotic conditions, and biotic interactions influence zoonotic hazards across highly urbanized cities (HUCs) in temperate climates to promote their efficient and effective management by a multi-sectoral coalition of public health stakeholders. We describe how to interpret both direct and indirect ecological processes, incorporate spatial scale, and evaluate networks of connectivity specific to different zoonotic hazards to promote biologically-informed and targeted decision-making. Using New York City, USA as a case study, we identify major zoonotic threats, apply knowledge of relevant ecological factors, and highlight opportunities and challenges for research and intervention. We aim to broaden the toolbox of urban public health stakeholders by providing ecologically-informed, practical guidance for the evaluation and management of zoonotic hazards.

Population genomics and geographic dispersal in Chagas disease vectors: Landscape drivers and evidence of possible adaptation to the domestic setting

Accurate prediction of vectors dispersal, as well as identification of adaptations that allow blood-feeding vectors to thrive in built environments, are a basis for effective disease control. Here we adopted a landscape genomics approach to assay gene flow, possible local adaptation, and drivers of population structure in Rhodnius ecuadoriensis, an important vector of Chagas disease. We used a reduced-representation sequencing technique (2b-RADseq) to obtain 2,552 SNP markers across 272 R. ecuadoriensis samples from 25 collection sites in southern Ecuador. Evidence of high and directional gene flow between seven wild and domestic population pairs across our study site indicates insecticide-based control will be hindered by repeated re-infestation of houses from the forest. Preliminary genome scans across multiple population pairs revealed shared outlier loci potentially consistent with local adaptation to the domestic setting, which we mapped to genes involved with embryogenesis and saliva production. Landscape genomic models showed elevation is a key barrier to R. ecuadoriensis dispersal. Together our results shed early light on the genomic adaptation in triatomine vectors and facilitate vector control by predicting that spatially-targeted, proactive interventions would be more efficacious than current, reactive approaches.

Re-infestation of recently insecticide-treated houses by wild/secondary triatomine, their potential adaptation to this new environment and capabilities to geographically disperse across multiple human communities jeopardise sustainable Chagas disease control. This is the first study in Chagas disease vectors that identifies genomic regions possibly linked to adaptations to the built environment and describes landscape drivers for accurate prediction of geographic dispersal. We sampled multiple domestic and wild Rhodnius ecuadoriensis population pairs across a mountainous terrain in southern Ecuador. We evidenced that triatomine movement from forest to built enviroments does occur at a high rate. In these highly connected population pairs we detected loci possibly linked to local adaptation among the genomic makers we evaluated and in doing so we pave the way for future triatomine genomic research. We highlighted that current haphazardous vector control in the zone will be hindered by reinfestation of triatomines from the forest. Instead, we recommend frequent and spatially-targeted vector control and provided a landacape genomic model that identifies highly connected and isolated triatomine populations to facilitate efficient vector control.

Age structuring and spatial heterogeneity in prion protein gene (PRNP) polymorphism in white-tailed deer

Chronic-wasting disease (CWD) is a prion-derived fatal neurodegenerative disease that has affected wild cervid populations on a global scale. Susceptibility has been linked unambiguously to several amino acid variants within the prion protein gene (PRNP). Quantifying their distribution across landscapes can provide critical information for agencies attempting to adaptively manage CWD. Here we attempt to further define management implications of PRNP polymorphism by quantifying the contemporary geographic distribution (i.e., phylogeography) of PRNP variants in hunter-harvested white-tailed deer (WTD; Odocoileus virginianus, N = 1433) distributed across Arkansas (USA), including a focal spot for CWD since detection of the disease in February 2016. Of these, PRNP variants associated with the well-characterized 96S non-synonymous substitution showed a significant increase in relative frequency among older CWD-positive cohorts. We interpreted this pattern as reflective of a longer life expectancy for 96S genotypes in a CWD-endemic region, suggesting either decreased probabilities of infection or reduced disease progression. Other variants showing statistical signatures of potential increased susceptibility, however, seemingly reflect an artefact of population structure. We also showed marked heterogeneity across the landscape in the prevalence of ‘reduced susceptibility’ genotypes. This may indicate, in turn, that differences in disease susceptibility among WTD in Arkansas are an innate, population-level characteristic that is detectable through phylogeographic analysis.

A machine-learning approach to map landscape connectivity in Aedes aegypti with genetic and environmental data

Aedes mosquitoes are projected to continue expanding their ranges, which could expose millions more humans to the diseases they carry. The implementation of vector control methods ranging from traditional (e.g., insecticides) to cutting edge (e.g., genetic modification) could be improved with landscape connectivity maps and increased understanding of the factors that affect mosquito dispersal. Here we present an iterative random forest method for integrating genetic and environmental data to map landscape connectivity. We achieve a correlation of 0.83 between the model’s predicted genetic distance and actual genetic distance. We produce a genetic connectivity map for the southern tier of the United States and discuss important factors to consider in mosquito control, e.g., the release of genetically modified mosquitoes.

Mapping landscape connectivity is important for controlling invasive species and disease vectors. Current landscape genetics methods are often constrained by the subjectivity of creating resistance surfaces and the difficulty of working with interacting and correlated environmental variables. To overcome these constraints, we combine the advantages of a machine-learning framework and an iterative optimization process to develop a method for integrating genetic and environmental (e.g., climate, land cover, human infrastructure) data. We validate and demonstrate this method for the Aedes aegypti mosquito, an invasive species and the primary vector of dengue, yellow fever, chikungunya, and Zika. We test two contrasting metrics to approximate genetic distance and find Cavalli-Sforza–Edwards distance (CSE) performs better than linearized F_ST. The correlation (R) between the model’s predicted genetic distance and actual distance is 0.83. We produce a map of genetic connectivity for Ae. aegypti’s range in North America and discuss which environmental and anthropogenic variables are most important for predicting gene flow, especially in the context of vector control.

The effect of landscape and human settlement on the genetic differentiation and presence of Paragonimus species in Mesoamerica

Foodborne diseases are a neglected research area, and despite the existence of many tools for diagnosis and genetic studies, very little is known about the effect of the landscape on the genetic diversity and presence of parasites. One of these foodborne disease is paragonimiasis, caused by trematodes of the genus Paragonimus, which is responsible for a high number of infections in humans and wild animals. The main Paragonimus sp reported in Mesoamerica is Paragonimus mexicanus, yet there are doubts about its correct identification as a unique species throughout the region. This, together with a lack of detailed knowledge about their ecology, evolution and differentiation, may complicate the implementation of control strategies across the Mesoamerican region. We had the goal of delimiting the species of P. mexicanus found throughout Mesoamerica and determining the effect of landscape and geology on the diversity and presence of the parasite. We found support for the delimitation of five genetic groups. The genetic differentiation among these groups was positively affected by elevation and the isolation of river basins, while the parasite's presence was affected negatively only by the presence of human settlements. These results suggest that areas with lower elevation, connected rivers basins, and an absence of human settlements have low genetic differentiation and high P. mexicanus presence, which may increase the risk of Paragonimus infection. These demonstrate the importance of accurate species delimitation and consideration of the effect of landscape on Paragonimus in the proposal of adequate control strategies. However, other landscape variables cannot be discarded, including temperature, rainfall regime, and spatial scale (local, landscape and regional). These additional variables were not explored here, and should be considered in future studies.

Impact of Landscape on Host-Parasite Genetic Diversity and Distribution Using the Puumala orthohantavirus-Bank Vole System

In nature, host specificity has a strong impact on the parasite's distribution, prevalence, and genetic diversity. The host's population dynamics is expected to shape the distribution of host-specific parasites. In turn, the parasite's genetic structure is predicted to mirror that of the host. Here, we study the tandem Puumala orthohantavirus (PUUV)-bank vole system. The genetic diversity of 310 bank voles and 33 PUUV isolates from 10 characterized localities of Northeast France was assessed. Our findings show that the genetic diversity of both PUUV and voles, was positively correlated with forest coverage and contiguity of habitats. While the genetic diversity of voles was weakly structured in space, that of PUUV was found to be strongly structured, suggesting that the dispersion of voles was not sufficient to ensure a broad PUUV dissemination. Genetic diversity of PUUV was mainly shaped by purifying selection. Genetic drift and extinction events were better reflected than local adaptation of PUUV. These contrasting patterns of microevolution have important consequences for the understanding of PUUV distribution and epidemiology.

Spatial population genetics in heavily managed species: Separating patterns of historical translocation from contemporary gene flow in white-tailed deer

Approximately 100 years ago, unregulated harvest nearly eliminated white-tailed deer (Odocoileus virginianus) from eastern North America, which subsequently served to catalyze wildlife management as a national priority. An extensive stock-replenishment effort soon followed, with deer broadly translocated among states as a means of re-establishment. However, an unintended consequence was that natural patterns of gene flow became obscured and pretranslocation signatures of population structure were replaced. We applied cutting-edge molecular and biogeographic tools to disentangle genetic signatures of historical management from those reflecting spatially heterogeneous dispersal by evaluating 35,099 single nucleotide polymorphisms (SNPs) derived via reduced-representation genomic sequencing from 1143 deer sampled statewide in Arkansas. We then employed Simpson's diversity index to summarize ancestry assignments and visualize spatial genetic transitions. Using sub-sampled transects across these transitions, we tested clinal patterns across loci against theoretical expectations of their response under scenarios of re-colonization and restricted dispersal. Two salient results emerged: (A) Genetic signatures from historic translocations are demonstrably apparent; and (B) Geographic filters (major rivers; urban centers; highways) now act as inflection points for the distribution of this contemporary ancestry. These results yielded a statewide assessment of contemporary population structure in deer as driven by historic translocations as well as ongoing processes. In addition, the analytical framework employed herein to effectively decipher extant/historic drivers of deer distribution in Arkansas is also applicable for other biodiversity elements with similarly complex demographic histories.

Describing the current status of Plasmodium falciparum population structure and drug resistance within mainland Tanzania using molecular inversion probes

High-throughput Plasmodium genomic data is increasingly useful in assessing prevalence of clinically important mutations and malaria transmission patterns. Understanding parasite diversity is important for identification of specific human or parasite populations that can be targeted by control programmes, and to monitor the spread of mutations associated with drug resistance. An up-to-date understanding of regional parasite population dynamics is also critical to monitor the impact of control efforts. However, this data is largely absent from high-burden nations in Africa, and to date, no such analysis has been conducted for malaria parasites in Tanzania countrywide. To this end, over 1,000 P. falciparum clinical isolates were collected in 2017 from 13 sites in seven administrative regions across Tanzania, and parasites were genotyped at 1,800 variable positions genome-wide using molecular inversion probes. Population structure was detectable among Tanzanian P. falciparum parasites, approximately separating parasites from the northern and southern districts and identifying genetically admixed populations in the north. Isolates from nearby districts were more likely to be genetically related compared to parasites sampled from more distant districts. Known drug resistance mutations were seen at increased frequency in northern districts (including two infections carrying pfk13-R561H), and additional variants with undetermined significance for antimalarial resistance also varied by geography. Malaria Indicator Survey (2017) data corresponded with genetic findings, including average region-level complexity-of-infection and malaria prevalence estimates. The parasite populations identified here provide important information on extant spatial patterns of genetic diversity of Tanzanian parasites, to which future surveys of genetic relatedness can be compared.

Ecological drivers of genetic connectivity for African malaria vectors Anopheles gambiae and An. arabiensis

Anopheles gambiae and An. arabiensis are major malaria vectors in sub-Saharan Africa. Knowledge of how geographical factors drive the dispersal and gene flow of malaria vectors can help in combatting insecticide resistance spread and planning new vector control interventions. Here, we used a landscape genetics approach to investigate population relatedness and genetic connectivity of An. gambiae and An. arabiensis across Kenya and determined the changes in mosquito population genetic diversity after 20 years of intensive malaria control efforts. We found a significant reduction in genetic diversity in An. gambiae, but not in An. arabiensis as compared to prior to the 20-year period in western Kenya. Significant population structure among populations was found for both species. The most important ecological driver for dispersal and gene flow of An. gambiae and An. arabiensis was tree cover and cropland, respectively. These findings highlight that human induced environmental modifications may enhance genetic connectivity of malaria vectors.

Comparative landscape genetics reveals differential effects of environment on host and pathogen genetic structure in Tasmanian devils (Sarcophilus harrisii) and their transmissible tumour

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.