Biology subjects

Han, B. A.

Publications and source records attributed to Han, B. A..

13 recordsLinked to original sources

bioRxiv2026

From receptor binding to biogeography: Multi-scale prediction of filovirus hosts in bats

Forecasting zoonotic risk requires identifying which host species are biologically susceptible to infection, yet susceptibility is rarely predicted using frameworks that integrate molecular mechanisms with macroecology. Filoviruses, a diverse group of bat-associated viruses that include Ebola and Marburg viruses, illustrate this challenge: viral entry depends on interactions between viral glycoproteins and the host receptor NPC1, and host ecology and distribution determine opportunity of viral entry. Additionally, receptor sequence data used for informing viral entry are available for only a small fraction of bat species. Here, we extend virus-specific susceptibility prediction across the global diversity of bats by integrating experimentally measured and physicochemically inferred virus-receptor binding strengths with phylogenetic, ecological, and environmental data. Using boosted regression models trained on binding assay labels, we generate predictions of NPC1-mediated binding strength for more than 1,300 bat species. Predicted susceptibility is strongly structured by evolutionary relationships, with high binding concentrated in particular bat lineages, but is further differentiated within clades by morphology, life-history strategy, and environmental context. Strikingly, macroevolutionary structure alone recovers interaction patterns originally derived from amino acid-level physicochemistry, indicating that information about receptor-mediated compatibility is recoverable from host evolutionary history and ecological traits. Predicted high binding strength extends well beyond historically recognized outbreak regions, suggesting that the fundamental host range of filoviruses may be substantially broader than their currently realized distribution. By scaling receptor biology to global host diversity, this multi-scale framework expands mechanistic susceptibility forecasting beyond species with available molecular data and provides a generalizable approach for integrating molecular and ecological information in zoonotic prediction.

ecology↗

bioRxiv2025

Museum collections and machine learning guide discovery of novel coronaviruses and paramyxoviruses

Natural history museum collections are valuable but underutilized resources for viral discovery, offering opportunities to test hypotheses about viral occurrence across space, time, and taxonomic groups. We developed machine learning models of bat host suitability to guide coronavirus and paramyxovirus screening of 1330 and 491 tissues, respectively, in a museum collection. For the first time, we recovered coronavirus (n = 16) and paramyxovirus (n = 3) sequences from archived museum tissues, confirming three novel coronavirus host species and three novel paramyxovirus host species (3% and 33% prediction success rate, respectively). These sequences included a SARS-like coronavirus and an orthoparamyxovirus from Angolan Rhinolophus fumigatus specimens collected in June 2019, suggesting that viruses with epidemic potential may be more widespread in sub-Saharan Africa than previously believed. Our study demonstrates the value of combining predictive modeling and collections-based viral discovery, particularly for filling outstanding sampling gaps and investigating changes in host-virus associations over time.

ecology↗

bioRxiv2024

Integrating predictors of host condition into spatiotemporal multi-scale models of virus shedding

Understanding where and when pathogens occur in the environment has implications for reservoir population health and infection risk. In reservoir hosts, infection status and pathogen shedding are affected by processes interacting across different scales: from landscape features affecting host location and transmission to within-host processes affecting host immunity and infectiousness. While uncommonly done, simultaneously incorporating processes across multiple scales may improve pathogen shedding predictions. In Australia, the black flying fox (Pteropus alecto) is a natural host for the zoonotic Hendra virus, which is hypothesized to cause latent infections in bats. Re-activation and virus shedding may be triggered by poor host condition, leading to virus excretion through urine. Here, we developed a statistical modeling approach that combined data at multiple spatial and temporal scales to capture ecological and biological processes potentially affecting virus shedding. We parameterized these models using existing datasets and compared model performance to under-roost virus shedding data from 2011-2014 in 23 roosts across a 1200-km transect. Our approach enabled comparisons among multiple model structures to determine which variables at which scales are most influential for accurate predictions of virus shedding in space and time. We identified environmental predictors and temporal lags of these features that were important for determining where reservoirs are located and multiple independent proxies for reservoir condition. The best-performing multi-scale model delineated periods of low and high virus prevalence, reflecting observed shedding patterns from pooled under-roost samples. Incorporating regional indicators of food scarcity enhanced model accuracy while incorporating other stress indicators at local scales confounded this signal. This multiscale modeling approach enabled the combination of processes from different ecological scales and identified environmental variables influencing Hendra virus shedding, highlighting how integrating data across scales may improve risk forecasts for other pathogen systems.

ecology↗

bioRxiv2024

Fast-lived Vertebrate Hosts Exhibit Higher Potential for Mosquito-borne Parasite Transmission

The emergence of mosquito-borne zoonoses has continually increased over the past decade, posing a significant global public health challenge. Ecological theory can point to the characteristics of populations that make them more likely to form reservoirs of disease. The pace of life hypothesis posits that species with more rapid reproduction and shorter lifespans are more likely to be disease reservoirs than their slower-living cousins. Mathematical modeling suggests theoretical conditions under which this hypothesis is correct for directly- and environmentally-transmitted pathogens but its applicability to mosquito-borne disease systems has yet to be examined. We parameterized a mechanistic model with host trait data to investigate the link between the position of a species on the fast-slow life history continuum and the potential for it to spread mosquitoborne diseases. We evaluated the resulting relationships for four medically important mosquito species across their thermal niches and for four pathogens characterized by infection duration and the level of susceptibility of the focal host to infection. Finally, because the fast-slow life history continuum differs across taxonomic ranks, we considered the theory in the context of two orders of vertebrate hosts, Rodentia and Primates. After parameterizing our model, we found that, near universally across all the axes of variation considered, fast-lived hosts have higher transmission potential than slow-lived hosts. There was one exception: slower-lived hosts have higher transmission potential for parasites that cause long-enduring infections to which these hosts are highly susceptible. Generally, however, these connections hinge on the often unknown links between immunological traits, population density, and pace of life. Our analyses highlight immunological traits as a key knowledge gap with strong influence over the potential for transmission. Further investigation of the pace of life hypothesis will require a better understanding of how, for mosquito-borne parasitic infections, susceptibility and infection duration vary across the members of a taxonomic order.

ecology↗

bioRxiv2024

Understanding Ecological Systems Using Knowledge Graphs: An Application to Highly Pathogenic Avian Influenza

Ecological systems are complex. Representing heterogeneous knowledge about ecological systems is a pervasive challenge because data are generated from many subdisciplines, exist in disparate sources, and only capture a subset of important interactions underpinning system structure, resilience, and dynamics. Knowledge graphs have been successfully applied to organize heterogeneous data systematically and to predict new linkages representing unobserved relationships in complex systems. Though not previously applied broadly in ecology, knowledge graphs have much to offer in an era of global change when system dynamics are responding to rapid changes across multiple scales simultaneously. We developed a knowledge graph to demonstrate the methods utility for ecological problems focused on highly pathogenic avian influenza (HPAI), a highly transmissible virus with a broad animal host range, wide geographic distribution, and rapid evolution with pandemic potential. We describe the development of a graph to include a wide range of data related to HPAI including pathogen-host associations, animal species distributions, and human population demographics, using a semantic ontology that defines relationships within the data and between datasets. We use the graph to perform a set of proof-of-concept analyses validating the method and identifying new relationships and features of HPAI ecology, underscoring the generalizable value of knowledge graphs to ecology including their utility in revealing previously known relationships between entities and generating testable hypotheses in support of a deeper mechanistic understanding of ecological systems.

ecology↗

bioRxiv2022

Phylogenetic and biogeographical traits predict unrecognized hosts of zoonotic leishmaniasis

The spatio-temporal distribution of leishmaniasis, a parasitic vector-borne zoonotic disease, is significantly impacted by land-use change and climate warming in the Americas. However, predicting and containing outbreaks is challenging as the zoonotic Leishmania system is highly complex: leishmaniasis (visceral, cutaneous and muco-cutaneous) in humans is caused by up to 14 different Leishmania species, and the parasite is transmitted by dozens of sandfly species and is known to infect almost twice as many wildlife species. Despite the already broad known host range, new hosts are discovered almost annually and Leishmania transmission to humans occurs in absence of a known host. As such, the full range of Leishmania hosts is undetermined, inhibiting the use of ecological interventions to limit pathogen spread and the ability to accurately predicting the impact of global change on disease risk. Here, we employed a machine learning approach to generate trait profiles of known zoonotic Leishmania wildlife hosts (mammals that are naturally exposed and susceptible to infection) and used trait-profiles of known hosts to identify potentially unrecognized hosts. We found that biogeography, phylogenetic distance, and study effort best predicted Leishmania host status. Traits associated with global change, such as agricultural land-cover, urban land-cover, and climate, were among the top predictors of host status. Most notably, our analysis suggested that zoonotic Leishmania hosts are significantly undersampled, as our model predicted just as many unrecognized hosts as unknown hosts. Overall, our analysis facilitates targeted surveillance strategies and improved understanding of the impact of environmental change on local transmission cycles. Author summaryLeishmaniasis is a zoonotic, vector borne disease of poverty with a high burden throughout the Americas: within Latin America there are an estimated 58,500 new cases per year and 54,050 years of life lost due to disability. Although the World Health Organization has targeted leishmaniasis for elimination and control by 2030, the disease remains a persistent threat. Across the Americas, particularly in Central America, the southeastern United States, and perimeters of the Amazon Basin, risk of infection is increasing in geographic extent and elevation. While it is known that Leishmania parasites, the causative agent of leishmaniasis, are maintained in the environment via a mammalian host, the full suite of wildlife hosts has yet to be documented, which significantly hinders control efforts. Here, we use machine learning and ecological and evolutionary trait profiles of known hosts to identify unrecognized potential wildlife hosts of Leishmania. We identify 136 mammals in the Americas that are likely to be exposed to and infected by zoonotic Leishmania in the wild. The high number of unrecognized potential hosts emphasizes a need to better invest in studying the ecological epidemiology of leishmaniasis. The study provides information and tools to support targeted intervention and management of this important poverty-associated disease.

ecology↗

bioRxiv2022

Data-Driven Predictions of Potential Leishmania Vectors in Latin America

The incidence of vector-borne diseases is rising as deforestation, climate change, and globalization bring humans in contact with arthropods that can transmit pathogens. In particular, incidence of American Cutaneous Leishmaniasis (ACL), a parasitic disease transmitted by sandflies, is increasing as previously intact habitats are cleared for agriculture and urban areas, potentially bringing people into contact with vectors and reservoir hosts. Previous evidence has identified dozens of sandfly species that have been infected with and/or transmit Leishmania parasites. However, there is an incomplete understanding of which sandfly species transmit the parasite, complicating efforts to limit disease spread. Here, we apply machine learning models (boosted regression trees) to leverage biological and geographical traits of known sandfly vectors to predict potential vectors. Additionally, we generate trait profiles of confirmed vectors and identify important factors in transmission. Our model performed well with an average out of sample accuracy of 86%. The models predict that synanthropic sandflies living in areas with greater canopy height, less human modification, and within an optimal range of rainfall are more likely to be Leishmania vectors. We also observed that generalist sandflies that are able to inhabit many different ecoregions are more likely to transmit the parasite. Our results suggest that Psychodopygus amazonensis and Nyssomia antunesi are unidentified potential vectors, and should be the focus of sampling and research efforts. Overall, we found that our machine learning approach provides valuable information for Leishmania surveillance and management in an otherwise complex and data sparse system. Author SummaryAmerican Cutaneous Leishmaniasis (ACL) is a neglected parasitic disease transmitted by sandflies in Latin America. There is an incomplete understanding of which sandfly species transmit the parasite, complicating efforts to limit disease spread. In this study, the authors created a database of sandfly traits, then used predictive models to determine important factors in disease transmission and how different climate and environmental variables affect ACL transmission. The models suggest that transmission occurs at the interface between domestic habitats and well-preserved forests. The authors also generate predictions of which sandflies might be transmitting the parasite that are not known vectors at the time, specifically Psychodopygus amazonensis and Nyssomia antunesi. This new knowledge can lead to a better understanding of the system of transmission, and can point to possible hotspots of the disease. The analysis can also help direct researchers to areas of interest for sampling studies, as well as specific sandflies to focus their effort on.

ecology↗

bioRxiv2021

A systematic review and meta-analysis of the potential non-human animal reservoirs and arthropod vectors of the Mayaro virus

Improving our understanding of Mayaro virus (MAYV) ecology is critical to guide surveillance and risk assessment. We conducted a PRISMA-adherent systematic review of the published and grey literature to identify potential arthropod vectors and non-human animal reservoirs of MAYV. We searched PubMed, Embase, Web of Science, SciELO and grey-literature sources including PAHO databases and dissertation repositories. Studies were included if they assessed MAYV virological/immunological measured occurrence in field-caught, domestic, or sentinel animals or in field-caught arthropods. We conducted an animal seroprevalence meta-analysis using a random effects model. We compiled granular georeferenced maps of non-human MAYV occurrence and graded the quality of the studies using a customized framework. Overall, 57 studies were eligible out of 1523 screened, published between the years 1961 and 2020. Seventeen studies reported MAYV positivity in wild mammals, birds, or reptiles and five studies reported MAYV positivity in domestic animals. MAYV positivity was reported in 12 orders of wild-caught vertebrates, most frequently in the orders Charadriiformes and Primate. Sixteen studies detected MAYV in wild-caught mosquito genera including Haemagogus, Aedes, Culex, Psorophora, Coquillettidia, and Sabethes. Vertebrate animals or arthropods with MAYV were detected in Brazil, Panama, Peru, French Guiana, Colombia, Trinidad, Venezuela, Argentina, and Paraguay. Among non-human vertebrates, the Primate order had the highest pooled prevalence (PP) at 13.1% (95% CI: 4.3-25.1%). From the three most studied primate genera we found the highest prevalence was in Alouatta (PP: 32.2%, 95% CI: 0.0-79.2%), followed by Callithrix (PP: 17.8%, 95% CI: 8.6-28.5%), and Cebus/Sapajus (PP: 3.7%, 95% CI: 0.0-11.1%). We further found that MAYV occurs in a wide range of vectors beyond Haemagogus spp. The quality of evidence behind these findings was variable and prompts calls for standardization of reporting of arbovirus occurrence. These findings support further risk emergence prediction, guide field surveillance efforts, and prompt further in-vivo studies to better define the ecological drivers of MAYV maintenance and potential for emergence. Author SummaryMayaro virus (MAYV) is an emerging tropical public health threat in the Americas. We conducted a georeferenced, quality-graded systematic review to evaluate the current evidence regarding MAYV occurrence in non-human vertebrates and arthropods. Overall, 57 studies were eligible out of 1523 screened, published between the years 1961 and 2020. Seventeen studies reported MAYV positivity in wild mammals, birds, or reptiles and five studies reported MAYV positivity in domestic animals. MAYV positivity was reported in 12 orders of wild-caught vertebrates, most frequently in the orders Charadriiformes and Primate. Our systematic review identified 12 orders of wild-caught vertebrates and seven mosquito genera with evidence of MAYV occurrence. Primates had the highest pooled MAYV prevalence according to a seroprevalence meta-analysis. The graded quality of evidence behind these findings was variable and prompts calls for standardization of reporting of MAYV and perhaps other emerging arbovirus occurrence in animals and vectors. This study provides important information for public health authorities and disease ecologists concerned with the growing threat of MAYV in Latin America. Our analysis provides a foundation for future laboratory and field studies focused on the MAYV transmission cycle.

ecology↗

bioRxiv2021

Predictors of zoonotic potential in helminths

Helminths are parasites that cause disease at considerable cost to public health and present a risk for emergence as novel human infections. Although recent research has elucidated characteristics conferring a propensity to emergence in other parasite groups (e.g. viruses), the understanding of factors associated with zoonotic potential in helminths remains poor. We applied an investigator-directed learning algorithm to a global dataset of mammal helminth traits to identify factors contributing to spillover of helminths from wild animal hosts into humans. We characterized parasite traits that distinguish between zoonotic and non-zoonotic species with greater than 88% accuracy. Results suggest that helminth traits relating to transmission (e.g. definitive and intermediate hosts) and geography (e.g. distribution) are more important to predicting zoonotic species than morphological or epidemiological traits. Whether or not a helminth causes infection in companion animals (cats and dogs) is the most important predictor of propensity to cause human infection. Finally, we identified helminth species with high modeled propensity to cause zoonosis (over 70%) that have not previously been deemed to be of risk. This work highlights the importance of prioritizing studies on the transmission of helminths that infect pets and points to the risks incurred by close associations with these animals.

ecology↗

bioRxiv2021

Predicting the zoonotic capacity of mammal species for SARS-CoV-2

Back and forth transmission of SARS-CoV-2 between humans and animals may lead to wild reservoirs of virus that can endanger efforts toward long-term control of COVID-19 in people, and protecting vulnerable animal populations that are particularly susceptible to lethal disease. Predicting high risk host species is key to targeting field surveillance and lab experiments that validate host zoonotic potential. A major bottleneck to predicting animal hosts is the small number of species with available molecular information about the structure of ACE2, a key cellular receptor required for viral cell entry. We overcome this bottleneck by combining species ecological and biological traits with 3D modeling of virus and host cell protein interactions using machine learning methods. This approach enables predictions about the zoonotic capacity of SARS-CoV-2 for over 5,000 mammals -- an order of magnitude more species than previously possible. The high accuracy predictions achieved by this approach are strongly corroborated by in vivo empirical studies. We identify numerous common mammal species whose predicted zoonotic capacity and close proximity to humans may further enhance the risk of spillover and spillback transmission of SARS-CoV-2. Our results reveal high priority areas of geographic overlap between global COVID-19 hotspots and potential new mammal hosts of SARS-CoV-2. With molecular sequence data available for only a small fraction of potential host species, predictive modeling integrating data across multiple biological scales offers a conceptual advance that may expand our predictive capacity for zoonotic viruses with similarly unknown and potentially broad host ranges.

ecology↗

bioRxiv2020

The evolutionary history of ACE2 usage within the coronavirus subgenus Sarbecovirus

SARS-CoV-1 and SARS-CoV-2 are not phylogenetically closely related; however, both use the ACE2 receptor in humans for cell entry. This is not a universal sarbecovirus trait; for example, many known sarbecoviruses related to SARS-CoV-1 have two deletions in the receptor binding domain of the spike protein that render them incapable of using human ACE2. Here, we report three sequences of a novel sarbecovirus from Rwanda and Uganda which are phylogenetically intermediate to SARS-CoV-1 and SARS-CoV-2 and demonstrate via in vitro studies that they are also unable to utilize human ACE2. Furthermore, we show that the observed pattern of ACE2 usage among sarbecoviruses is best explained by recombination not of SARS-CoV-2, but of SARS-CoV-1 and its relatives. We show that the lineage that includes SARS-CoV-2 is most likely the ancestral ACE2-using lineage, and that recombination with at least one virus from this group conferred ACE2 usage to the lineage including SARS-CoV-1 at some time in the past. We argue that alternative scenarios such as convergent evolution are much less parsimonious; we show that biogeography and patterns of host tropism support the plausibility of a recombination scenario; and we propose a competitive release hypothesis to explain how this recombination event could have occurred and why it is evolutionarily advantageous. The findings provide important insights into the natural history of ACE2 usage for both SARS-CoV-1 and SARS-CoV-2, and a greater understanding of the evolutionary mechanisms that shape zoonotic potential of coronaviruses. This study also underscores the need for increased surveillance for sarbecoviruses in southwestern China, where most ACE2-using viruses have been found to date, as well as other regions such as Africa, where these viruses have only recently been discovered.

evolutionary biology↗

bioRxiv2020

Predicting wildlife hosts of betacoronaviruses for SARS-CoV-2 sampling prioritization

Despite global investment in One Health disease surveillance, it remains difficult--and often very costly--to identify and monitor the wildlife reservoirs of novel zoonotic viruses. Statistical models can be used to guide sampling prioritization, but predictions from any given model may be highly uncertain; moreover, systematic model validation is rare, and the drivers of model performance are consequently under-documented. Here, we use bat hosts of betacoronaviruses as a case study for the data-driven process of comparing and validating predictive models of likely reservoir hosts. In the first quarter of 2020, we generated an ensemble of eight statistical models that predict host-virus associations and developed priority sampling recommendations for potential bat reservoirs and potential bridge hosts for SARS-CoV-2. Over more than a year, we tracked the discovery of 40 new bat hosts of betacoronaviruses, validated initial predictions, and dynamically updated our analytic pipeline. We find that ecological trait-based models perform extremely well at predicting these novel hosts, whereas network methods consistently perform roughly as well or worse than expected at random. These findings illustrate the importance of ensembling as a buffer against variation in model quality and highlight the value of including host ecology in predictive models. Our revised models show improved performance and predict over 400 bat species globally that could be undetected hosts of betacoronaviruses. Although 20 species of horseshoe bats (Rhinolophus spp.) are known to be the primary reservoir of SARS-like viruses, we find at least three-fourths of plausible betacoronavirus reservoirs in this bat genus might still be undetected. Our study is the first to demonstrate through systematic validation that machine learning models can help optimize wildlife sampling for undiscovered viruses and illustrates how such approaches are best implemented through a dynamic process of prediction, data collection, validation, and updating.

ecology↗

bioRxiv2020

The macroecology and evolution of avian competence for Borrelia burgdorferi

AimPredicting novel reservoirs of zoonotic pathogens would be improved by identifying inter-specific drivers of host competence, the ability to transmit pathogens to new hosts or vectors. Tick-borne pathogens can provide a useful model system, as larvae become infected only when feeding on a competent host during their first bloodmeal. For tick-borne diseases, competence has been best studied for Borrelia burgdorferi sensu lato (Bbsl), which causes Lyme borreliosis. Major reservoirs include several small mammal species, but birds may play an underrecognized role in human risk given their ability to disperse infected ticks across large spatial scales. Here, we provide a global synthesis of the ecological and evolutionary factors that determine the ability of bird species to infect larval ticks with Bbsl. LocationGlobal Time period1983 to 2019 Major taxa studiedBirds MethodsWe compiled a dataset of Bbsl competence across 183 bird species and applied meta-analysis, phylogenetic factorization, and boosted regression trees to describe spatial and temporal patterns in competence, characterize its phylogenetic distribution across birds, reconstruct its evolution, and evaluate the trait profiles associated with competent avian species. ResultsHalf of sampled bird species show evidence of competence for Bbsl. Competence displays moderate phylogenetic signal, has evolved multiple times across bird species, and is pronounced in the genus Turdus. Trait-based analyses distinguished competent birds with 80% accuracy and show that such species have low baseline corticosterone, exist on both ends of the pace-of-life continuum, breed and winter at high latitudes, and have broad migratory movements into their breeding range. We use these trait profiles to predict various likely but unsampled competent species, including novel concentrations of avian reservoirs within the Neotropics. Main conclusionOur results can generate new hypotheses for how birds contribute to the dynamics of tick-borne pathogens and help prioritize surveillance of likely but unsampled competent birds. Our findings further emphasize that birds display underrecognized variation in their contributions to enzootic cycles of Bbsl and the broader need to better consider competence in ecological and predictive studies of multi-host pathogens.

ecology↗