Predicting cryptic links in host-parasite networks

Evolutionary history as the main driver of cohesive groups' hierarchical organization in flea-mammal interaction networks

Cohesive species groups (components, sectors, modules, and subgraphs) represent parts of an ecological network with a substantially higher density of interactions than the surrounding parts. We investigated cohesive groups in 108 flea-mammal networks from all over the world and tested whether these groups are hierarchically organized, that is, whether groups at the higher level are composed of groups at the lower level, thus representing a network structure. We measured congruence between groups, using congruence coefficients, and asked whether the extent of hierarchical organization differs between biogeographic realms, different biomes, and different climatic zones. We also tested whether coefficients of congruence between cohesive groups are affected by environmental variables (amount of green vegetation, precipitation, and air temperature). We found that (i) cohesive groups of species in these networks are hierarchically organized and (ii) the strength of this organization differs significantly between networks from different biogeographic realms but is not generally affected by surrounding environmental conditions such as vegetation type and climate. In other words, the structure of flea-mammal networks, in terms of the hierarchical organization of cohesive groups, seems to be determined, first and foremost, by the evolutionary history of flea-mammal interactions, that is, by processes and events of the past. We conclude that the impact of evolutionary history on the network structure appeared to be stronger than that of the contemporary environment.

Evidence for the Vacated Niche Hypothesis in Parasites of Invasive Mammals

Species redistribution and invasion are becoming increasingly common due to climate change and anthropogenic impacts. Understanding the resultant shifts in host-parasite associations is important for anticipating disruptions to host communities, disease cycles, and conservation efforts. In this paper, we bring together the enemy release and vacated niche hypotheses to relate parasite acquisition and retention, two distinct yet intertwined processes that play out during host invasion. Using the Global Mammal Parasite Database, we test for net enemy release based on differences in parasite species richness, and we develop a novel taxonomic null modeling approach to demonstrate that parasites fill vacated niches. We find evidence of net enemy release, and our taxonomic null models indicate replacement of lost parasites by taxonomically similar acquired ones, over and above what might be expected by chance. Our work suggests that both enemy release and vacated niche hypotheses provide valuable frameworks through which to understand and predict changing host-parasite associations, which may include insights on how climate change and anthropogenic influences perturb and reorganize communities and ecosystems.

Predicting host range expansion in parasitic mites using a global mammalian-acarine dataset

Multi-host parasites pose greater health risks to wildlife, livestock, and humans than single-host parasites, yet our understanding of how ecological and biological factors influence a parasite's host range remains limited. Here, we assemble the largest and most complete dataset on permanently parasitic mammalian mites and build a predictive model assessing the probability of single-host parasites to become multi-hosts, while accounting for potentially unobserved host-parasite links and class imbalance. This model identifies statistically significant predictors related to parasites, hosts, climate, and habitat disturbance. The most important predictors include the parasite's contact level with the host immune system and two variables characterizing host phylogenetic similarity and spatial co-distribution. Our model reveals an overrepresentation of mites associated with Rodentia (rodents), Chiroptera (bats), and Carnivora in the multi-host risk group. This highlights both the potential vulnerability of these hosts to parasitic infestations and the risk of serving as reservoirs of parasites for new hosts. In addition, we find independent macroevolutionary evidence that supports our prediction of several single-host species of Notoedres, the bat skin parasites, to be in the multi-host risk group, demonstrating the forecasting potential of our model.

Dissimilarity in flea and host assemblages and their interaction networks along a spatial distance gradient: different patterns revealed by different network dissimilarity metrics

We investigated the distance-decay pattern (an increase in dissimilarity with increasing geographic distance) in regional assemblages of fleas and their small mammalian hosts, as well as their interaction networks, in four biogeographic realms. Dissimilarity of assemblages (βtotal) was partitioned into species richness differences (βrich) and species replacement (βrepl) components. Dissimilarity of networks was assessed using two metrics: (a) whole network dissimilarity (βWN) partitioned into species replacement (βST) and interaction rewiring (βOS) components and (b) D statistics, measuring dissimilarity in the pure structure of the networks, without using information on species identities and calculated for hosts-shared-by-fleas networks (Dh) and fleas-shared-by-hosts networks (Df). We asked whether the distance-decay pattern (a) occurs among interactor assemblages or their interaction networks; (b) depends on the network dissimilarity metric used; and (c) differs between realms. The βtotal and βrepl of flea and host assemblages increased with distance in all realms except for host assemblages in the Afrotropics. βrich for flea and host assemblages increased with distance in the Nearctic only. In networks, βWN and βST demonstrated a distance-decay pattern, whereas βOS was mainly spatially invariant except in the Neotropics. Correlations of Dh or Df and geographic distance were mostly non-significant. We conclude that investigations of dissimilarity in interaction networks should include both types of dissimilarity metrics (those that consider partner identities and those that consider the pure structure of networks). This will allow elucidating the predictability of some facets of network dissimilarity and the unpredictability of other facets.

Host and parasite identity interact in scale-dependent fashion to determine parasite community structure

Understanding the ecological assembly of parasite communities is critical to characterise how changing host and environmental landscapes will alter infection dynamics and outcomes. However, studies frequently assume that (a) closely related parasite species or those with identical life-history strategies are functionally equivalent, and (b) the same factors will drive infection dynamics for a single parasite across multiple host species, oversimplifying community assembly patterns. Here, we challenge these two assumptions using a naturally occurring host-parasite system, with the mussel Anodonta anatina infected by the digenean trematode Echinoparyphium recurvatum, and the snail Viviparus viviparus infected by both E. recurvatum and Echinostoma sp. By analysing the impact of temporal parasite dispersal, host species and size, and the impact of coinfection (moving from broader environmental factors to within-host dynamics), we show that neither assumption holds true, but at different ecological scales. The assumption that closely related parasites can be functionally grouped is challenged when considering dispersal to the host (i.e. larger scales), while the assumption that the same factors will drive infection dynamics for a single parasite across multiple host species is challenged when considering within-host interspecific competition (i.e. smaller scales). Our results demonstrate that host identity, parasite identity and ecological scale require simultaneous consideration in studies of parasite community composition and transmission.

Using phylogeographic link-prediction in primates to prioritize human parasite screening

Objectives

The ongoing risk of emerging infectious disease has renewed calls for understanding the origins of zoonoses and identifying future zoonotic disease threats. Given their close phylogenetic relatedness and geographic overlap with humans, non-human primates (NHPs) have been the source of many infectious diseases throughout human evolution. NHPs harbor diverse parasites, with some infecting only a single host species while others infect species from multiple families.

Materials and Methods

We applied a novel link-prediction method to predict undocumented instances of parasite sharing between humans and NHPs. Our model makes predictions based on phylogenetic distances and geographic overlap among NHPs and humans in six countries with high NHP diversity: Columbia, Brazil, Democratic Republic of Congo, Madagascar, China and Indonesia.

Results

Of the 899 human parasites documented in the Global Infectious Diseases and Epidemiology Network (GIDEON) database for these countries, 12% were shared with at least one other NHP species. The link prediction model identified an additional 54 parasites that are likely to infect humans but were not reported in GIDEON. These parasites were mostly host generalists, yet their phylogenetic host breadth varied substantially.

Discussion

As human activities and populations encroach on NHP habitats, opportunities for parasite sharing between human and non-human primates will continue to increase. Our study identifies specific infectious organisms to monitor in countries with high NHP diversity, while the comparative analysis of host generalism, parasite taxonomy, and transmission mode provides insights to types of parasites that represent high zoonotic risk.

Network embedding unveils the hidden interactions in the mammalian virome

Predicting host-virus interactions is fundamentally a network science problem. We develop a method for bipartite network prediction that combines a recommender system (linear filtering) with an imputation algorithm based on low-rank graph embedding. We test this method by applying it to a global database of mammal-virus interactions and thus show that it makes biologically plausible predictions that are robust to data biases. We find that the mammalian virome is under-characterized anywhere in the world. We suggest that future virus discovery efforts could prioritize the Amazon Basin (for its unique coevolutionary assemblages) and sub-Saharan Africa (for its poorly characterized zoonotic reservoirs). Graph embedding of the imputed network improves predictions of human infection from viral genome features, providing a shortlist of priorities for laboratory studies and surveillance. Overall, our study indicates that the global structure of the mammal-virus network contains a large amount of information that is recoverable, and this provides new insights into fundamental biology and disease emergence.

Mussel parasite richness and risk of extinction

Parasite conservation is important for the maintenance of ecosystem diversity and function. Conserving parasites relies first on understanding parasite biodiversity and second on estimating the extinction risk to that biodiversity. Although steps have been taken independently in both these areas, previous studies have overwhelmingly focused on helminths in vertebrate hosts over broad scales, providing low resolution and excluding a large proportion of possible host and parasite diversity. We estimated both total obligate parasite richness and parasite extinction risk in freshwater mussels (Unionidae and Margaritiferidae) from Europe and the United States to provide a case study for considering parasite conservation in a severely understudied system. We used currently reported host-parasite relationships to extrapolate parasite diversity to all possible mussel hosts and then used the threat levels of those hosts to estimate the extinction risk for both described and undescribed parasites. An estimated 67% of parasite richness in freshwater mussels is undescribed and over 80% of the most host-specific groups (digenean trematodes and ciliates) are undescribed. We estimated that 21% of this total parasite fauna is at immediate risk of extinction, corresponding to 60 unique species, many of which will likely go extinct before being described. Given the important roles parasites play in community structure and function and the strong ecosystem engineering capacities of freshwater mussels, such extinctions are likely to severely affect freshwater ecosystems. Our detailed study of mussel parasites provides compelling evidence for the hidden conservation threat to parasites through extinction cascades and shows parasites are deserving of immediate attention.

Collapse of terrestrial mammal food webs since the Late Pleistocene

Food webs influence ecosystem diversity and functioning. Contemporary defaunation has reduced food web complexity, but simplification caused by past defaunation is difficult to reconstruct given the sparse paleorecord of predator-prey interactions. We identified changes to terrestrial mammal food webs globally over the past ~130,000 years using extinct and extant mammal traits, geographic ranges, observed predator-prey interactions, and deep learning models. Food webs underwent steep regional declines in complexity through loss of food web links after the arrival and expansion of human populations. We estimate that defaunation has caused a 53% decline in food web links globally. Although extinctions explain much of this effect, range losses for extant species degraded food webs to a similar extent, highlighting the potential for food web restoration via extant species recovery.

Predicting the potential for zoonotic transmission and host associations for novel viruses

Host-virus associations have co-evolved under ecological and evolutionary selection pressures that shape cross-species transmission and spillover to humans. Observed virus-host associations provide relevant context for newly discovered wildlife viruses to assess knowledge gaps in host-range and estimate pathways for potential human infection. Using models to predict virus-host networks, we predicted the likelihood of humans as hosts for 513 newly discovered viruses detected by large-scale wildlife surveillance at high-risk animal-human interfaces in Africa, Asia, and Latin America. Predictions indicated that novel coronaviruses are likely to infect a greater number of host species than viruses from other families. Our models further characterize novel viruses through prioritization scores and directly inform surveillance targets to identify host ranges for newly discovered viruses.

Optimising predictive models to prioritise viral discovery in zoonotic reservoirs

Despite the global investment in One Health disease surveillance, it remains difficult and costly to identify and monitor the wildlife reservoirs of novel zoonotic viruses. Statistical models can guide sampling target prioritisation, but the predictions from any given model might be highly uncertain; moreover, systematic model validation is rare, and the drivers of model performance are consequently under-documented. Here, we use the bat hosts of betacoronaviruses as a case study for the data-driven process of comparing and validating predictive models of probable reservoir hosts. In early 2020, we generated an ensemble of eight statistical models that predicted host-virus associations and developed priority sampling recommendations for potential bat reservoirs of betacoronaviruses and bridge hosts for SARS-CoV-2. During a time frame of more than a year, we tracked the discovery of 47 new bat hosts of betacoronaviruses, validated the initial predictions, and dynamically updated our analytical pipeline. We found that ecological trait-based models performed well at predicting these novel hosts, whereas network methods consistently performed approximately as well or worse than expected at random. These findings illustrate the importance of ensemble modelling as a buffer against mixed-model quality and highlight the value of including host ecology in predictive models. Our revised models showed an improved performance compared with the initial ensemble, and predicted more than 400 bat species globally that could be undetected betacoronavirus hosts. We show, through systematic validation, that machine learning models can help to optimise wildlife sampling for undiscovered viruses and illustrates how such approaches are best implemented through a dynamic process of prediction, data collection, validation, and updating.

Explosive networking: The role of adaptive host radiations and ecological opportunity in a species-rich host-parasite assembly

Many species-rich ecological communities emerge from adaptive radiation events. Yet the effects of adaptive radiation on community assembly remain poorly understood. Here, we explore the well-documented radiations of African cichlid fishes and their interactions with the flatworm gill parasites Cichlidogyrus spp., including 10,529 reported infections and 477 different host-parasite combinations collected through a survey of peer-reviewed literature. We assess how evolutionary, ecological, and morphological parameters determine host-parasite meta-communities affected by adaptive radiation events through network metrics, host repertoire measures, and network link prediction. The hosts' evolutionary history mostly determined host repertoires of the parasites. Ecological and evolutionary parameters predicted host-parasite interactions. Generally, ecological opportunity and fitting have shaped cichlid-Cichlidogyrus meta-communities suggesting an invasive potential for hosts used in aquaculture. Meta-communities affected by adaptive radiations are increasingly specialised with higher environmental stability. These trends should be verified across other systems to infer generalities in the evolution of species-rich host-parasite networks.

Machine Learning and Its Applications for Protozoal Pathogens and Protozoal Infectious Diseases

In recent years, massive attention has been attracted to the development and application of machine learning (ML) in the field of infectious diseases, not only serving as a catalyst for academic studies but also as a key means of detecting pathogenic microorganisms, implementing public health surveillance, exploring host-pathogen interactions, discovering drug and vaccine candidates, and so forth. These applications also include the management of infectious diseases caused by protozoal pathogens, such as Plasmodium, Trypanosoma, Toxoplasma, Cryptosporidium, and Giardia, a class of fatal or life-threatening causative agents capable of infecting humans and a wide range of animals. With the reduction of computational cost, availability of effective ML algorithms, popularization of ML tools, and accumulation of high-throughput data, it is possible to implement the integration of ML applications into increasing scientific research related to protozoal infection. Here, we will present a brief overview of important concepts in ML serving as background knowledge, with a focus on basic workflows, popular algorithms (e.g., support vector machine, random forest, and neural networks), feature extraction and selection, and model evaluation metrics. We will then review current ML applications and major advances concerning protozoal pathogens and protozoal infectious diseases through combination with correlative biology expertise and provide forward-looking insights for perspectives and opportunities in future advances in ML techniques in this field.

A Review of the Impact of Climate Change on the Epidemiology of Gastrointestinal Nematode Infections in Small Ruminants and Wildlife in Tropical Conditions

Climate change is causing detrimental changes in living organisms, including pathogens. This review aimed to determine how climate change has impacted livestock system management, and consequently, what factors influenced the gastrointestinal nematodes epidemiology in small ruminants under tropical conditions. The latter is orientated to find out the possible solutions responding to climate change adverse effects. Climate factors that affect the patterns of transmission of gastrointestinal parasites of domesticated ruminants are reviewed. Climate change has modified the behavior of several animal species, including parasites. For this reason, new control methods are required for controlling parasitic infections in livestock animals. After a pertinent literature analysis, conclusions and perspectives of control are given.

The science of the host-virus network

Better methods to predict and prevent the emergence of zoonotic viruses could support future efforts to reduce the risk of epidemics. We propose a network science framework for understanding and predicting human and animal susceptibility to viral infections. Related approaches have so far helped to identify basic biological rules that govern cross-species transmission and structure the global virome. We highlight ways to make modelling both accurate and actionable, and discuss the barriers that prevent researchers from translating viral ecology into public health policies that could prevent future pandemics.

Forecasting parasite sharing under climate change

Species are shifting their distributions in response to climate change. This geographic reshuffling may result in novel co-occurrences among species, which could lead to unseen biotic interactions, including the exchange of parasites between previously isolated hosts. Identifying potential new host-parasite interactions would improve forecasting of disease emergence and inform proactive disease surveillance. However, accurate predictions of future cross-species disease transmission have been hampered by the lack of a generalized approach and data availability. Here, we propose a framework to predict novel host-parasite interactions based on a combination of niche modelling of future host distributions and parasite sharing models. Using the North American ungulates as a proof of concept, we show this approach has high cross-validation accuracy in over 85% of modelled parasites and find that more than 34% of the host-parasite associations forecasted by our models have already been recorded in the literature. We discuss potential sources of uncertainty and bias that may affect our results and similar forecasting approaches, and propose pathways to generate increasingly accurate predictions. Our results indicate that forecasting parasite sharing in response to shifts in host geographic distributions allow for the identification of regions and taxa most susceptible to emergent pathogens under climate change. This article is part of the theme issue 'Infectious disease macroecology: parasite diversity and dynamics across the globe'.

The future of zoonotic risk prediction

In the light of the urgency raised by the COVID-19 pandemic, global investment in wildlife virology is likely to increase, and new surveillance programmes will identify hundreds of novel viruses that might someday pose a threat to humans. To support the extensive task of laboratory characterization, scientists may increasingly rely on data-driven rubrics or machine learning models that learn from known zoonoses to identify which animal pathogens could someday pose a threat to global health. We synthesize the findings of an interdisciplinary workshop on zoonotic risk technologies to answer the following questions. What are the prerequisites, in terms of open data, equity and interdisciplinary collaboration, to the development and application of those tools? What effect could the technology have on global health? Who would control that technology, who would have access to it and who would benefit from it? Would it improve pandemic prevention? Could it create new challenges? This article is part of the theme issue 'Infectious disease macroecology: parasite diversity and dynamics across the globe'.

Data Proliferation, Reconciliation, and Synthesis in Viral Ecology

The fields of viral ecology and evolution are rapidly expanding, motivated in part by concerns around emerging zoonoses. One consequence is the proliferation of host–virus association data, which underpin viral macroecology and zoonotic risk prediction but remain fragmented across numerous data portals. In the present article, we propose that synthesis of host–virus data is a central challenge to characterize the global virome and develop foundational theory in viral ecology. To illustrate this, we build an open database of mammal host–virus associations that reconciles four published data sets. We show that this offers a substantially richer view of the known virome than any individual source data set but also that databases such as these risk becoming out of date as viral discovery accelerates. We argue for a shift in practice toward the development, incremental updating, and use of synthetic data sets in viral ecology, to improve replicability and facilitate work to predict the structure and dynamics of the global virome.

Next-generation cophylogeny: unravelling eco-evolutionary processes

A fundamental question in evolutionary biology is how microevolutionary processes translate into species diversification. Cophylogeny provides an appropriate framework to address this for symbiotic associations, but historically has been primarily limited to unveiling patterns. We argue that it is essential to integrate advances from ecology and evolutionary biology into cophylogeny, to gain greater mechanistic insights and transform cophylogeny into a platform to advance understanding of interspecific interactions and diversification more widely. We discuss key directions, such as incorporating trait reconstruction and considering multiple scales of network organization, and highlight recent developments for implementation. A new quantitative framework is proposed to allow integration of relevant information, such as quantitative traits and assessment of the contribution of individual mechanisms to cophylogenetic patterns.

Predicting the zoonotic capacity of mammals to transmit 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.

Divide-and-conquer: machine-learning integrates mammalian and viral traits with network features to predict virus-mammal associations

Our knowledge of viral host ranges remains limited. Completing this picture by identifying unknown hosts of known viruses is an important research aim that can help identify and mitigate zoonotic and animal-disease risks, such as spill-over from animal reservoirs into human populations. To address this knowledge-gap we apply a divide-and-conquer approach which separates viral, mammalian and network features into three unique perspectives, each predicting associations independently to enhance predictive power. Our approach predicts over 20,000 unknown associations between known viruses and susceptible mammalian species, suggesting that current knowledge underestimates the number of associations in wild and semi-domesticated mammals by a factor of 4.3, and the average potential mammalian host-range of viruses by a factor of 3.2. In particular, our results highlight a significant knowledge gap in the wild reservoirs of important zoonotic and domesticated mammals' viruses: specifically, lyssaviruses, bornaviruses and rotaviruses.

Predicting mammalian hosts in which novel coronaviruses can be generated

Novel pathogenic coronaviruses – such as SARS-CoV and probably SARS-CoV-2 – arise by homologous recombination between co-infecting viruses in a single cell. Identifying possible sources of novel coronaviruses therefore requires identifying hosts of multiple coronaviruses; however, most coronavirus-host interactions remain unknown. Here, by deploying a meta-ensemble of similarity learners from three complementary perspectives (viral, mammalian and network), we predict which mammals are hosts of multiple coronaviruses. We predict that there are 11.5-fold more coronavirus-host associations, over 30-fold more potential SARS-CoV-2 recombination hosts, and over 40-fold more host species with four or more different subgenera of coronaviruses than have been observed to date at >0.5 mean probability cut-off (2.4-, 4.25- and 9-fold, respectively, at >0.9821). Our results demonstrate the large underappreciation of the potential scale of novel coronavirus generation in wild and domesticated animals. We identify high-risk species for coronavirus surveillance.

Homologous recombination between co-infecting coronaviruses can produce novel pathogens. Here, Wardeh et al. develop a machine learning approach to predict associations between mammals and multiple coronaviruses and hence estimate the potential for generation of novel coronaviruses by recombination.

Network Analysis: Ten Years Shining Light on Host-Parasite Interactions

Biological interactions are key drivers of ecological and evolutionary processes. The complexity of such interactions hinders our understanding of ecological systems and our ability to make effective predictions in changing environments. However, network analysis allows us to better tackle the complexity of ecosystems because it extracts the properties of an ecological system according to the number and distribution of links among interacting entities. The number of studies using network analysis to solve ecological and evolutionary questions in parasitology has increased over the past decade. Here, we synthesise the contribution of network analysis toward disentangling host-parasite processes. Furthermore, we identify current trends in mainstream ecology and novel applications of network analysis that present opportunities for research on host-parasite interactions.

What determines parasite species richness across host species?

IN FOCUS

Dáttilo, W., Barrozo-Chávez, N., Lira-Noriega, A., Guevara, R., Villalobos, F., Santiago-Alarcon, D., Neves, F. S., Izzo, T., & Ribeiro, S. P. (2020). Species-level drivers of mammalian ectoparasite faunas. Journal of Animal Ecology. https://doi.org/10.1111/1365-2656.13216. The question of what drives the number of parasite species able to infect a given host species is still a largely open question, despite decades of research. Dáttilo and colleagues examine the potential drivers of ectoparasite species across a large set of host species to explore the taxonomic and trait drivers of host-parasite interactions. Here, we contextualize their findings, explore what is known about parasite species richness, and identify some potential next steps towards answers.

ASAPP: Architectural Similarity-Based Automated Pathway Prediction System and Its Application in Host-Pathogen Interactions

The significance of metabolic pathway prediction is to envision the viable unknown transformations that can occur provided the appropriate enzymes are present. It can facilitate the prediction of the consequences of host-pathogen interactions. In this article, we have proposed a new algorithm Architectural Similarity-based Automated Pathway Prediction (ASAPP) to predict metabolic pathways based on the structural similarity among the metabolites. ASAPP takes two-dimensional structure and molecular weight of metabolites as input, and generates a list of probable transformations without the knowledge of any externally established reactions, with an accuracy of 85.09 percent. ASAPP has also been applied to predict the outcome of pathogen liberated toxins on the carbohydrate and lipid pathways of the hosts. We have analyzed the disruption of host pathways in the presence of toxins, and have found that some metabolites in Glycolysis and the TCA cycle have a high chance of being the breakpoints in the pathway. The tool is available at http://asapp.droppages.com/.

A Bipartite Network Module-Based Project to Predict Pathogen-Host Association

Pathogen-host interactions play an important role in understanding the mechanism by which a pathogen can infect its host. Some approaches for predicting pathogen-host association have been developed, but prediction accuracy is still low. In this paper, we propose a bipartite network module-based approach to improve prediction accuracy. First, a bipartite network with pathogens and hosts is constructed. Next, pathogens and hosts are divided into different modules respectively. Then, modular information on the pathogens and hosts is added into a bipartite network projection model and the association scores between pathogens and hosts are calculated. Finally, leave-one-out cross-validation is used to estimate the performance of the proposed method. Experimental results show that the proposed method performs better in predicting pathogen-host association than other methods, and some potential pathogen-host associations with higher prediction scores are also confirmed by the results of biological experiments in the publically available literature.

Testing predictability of disease outbreaks with a simple model of pathogen biogeography

Predicting disease emergence and outbreak events is a critical task for public health professionals and epidemiologists. Advances in global disease surveillance are increasingly generating datasets that are worth more than their component parts for prediction-oriented work. Here, we use a trait-free approach which leverages information on the global community of human infectious diseases to predict the biogeography of pathogens through time. Our approach takes pairwise dissimilarities between countries' pathogen communities and pathogens' geographical distributions and uses these to predict country-pathogen associations. We compare the success rates of our model for predicting pathogen outbreak, emergence and re-emergence potential as a function of time (e.g. number of years between training and prediction), pathogen type (e.g. virus) and transmission mode (e.g. vector-borne). With only these simple predictors, our model successfully predicts basic network structure up to a decade into the future. We find that while outbreak and re-emergence potential are especially well captured by our simple model, prediction of emergence events remains more elusive, and sudden global emergences like an influenza pandemic are beyond the predictive capacity of the model. However, these stochastic pandemic events are unlikely to be predictable from such coarse data. Together, our model is able to use the information on the existing country-pathogen network to predict pathogen outbreaks fairly well, suggesting the importance in considering information on co-occurring pathogens in a more global view even to estimate outbreak events in a single location or for a single pathogen.

Food web structure selects for parasite host range

Complex life cycle parasites, including helminths, use intermediate hosts for development and definitive hosts for reproduction, with interactions between the two host types governed by food web structure. I study how a parasite's intermediate host range is controlled by the diet breadth of definitive host species and the cost of parasite generalism, a putative fitness cost that assumes host range trades off against fitness derived from a host species. In spite of such costs, a benefit to generalism may occur when the definitive host exhibits a large diet breadth, enhancing transmission of generalist parasites via consumption of a broad array of infected intermediate hosts. I develop a simple theoretical model to demonstrate how different host range infection strategies are differentially selected for across a gradient of definitive host diet breadth according to the cost of generalism. I then use a parasitic helminth-host database in conjunction with a food web database to show that diet breadth of definitive hosts promotes generalist infection strategies at the intermediate host level, indicating relatively low costs of parasite generalism among helminths.

Global estimates of mammalian viral diversity accounting for host sharing

Present estimates suggest there are over 1 million virus species found in mammals alone, with about half a million posing a possible threat to human health. Although previous estimates assume linear scaling between host and virus diversity, we show that ecological network theory predicts a non-linear relationship, produced by patterns of host sharing among virus species. To account for host sharing, we fit a power law scaling relationship for host-virus species interaction networks. We estimate that there are about 40,000 virus species in mammals (including ~10,000 viruses with zoonotic potential), a reduction of two orders of magnitude from present projections of viral diversity. We expect that the increasing availability of host-virus association data will improve the precision of these estimates and their use in the sampling and surveillance of pathogens with pandemic potential. We suggest host sharing should be more widely included in macroecological approaches to estimating biodiversity.

Host Specificity in Variable Environments

Host specificity encompasses the range and diversity of host species that a parasite is capable of infecting and is considered a crucial measure of a parasite's potential to shift hosts and trigger disease emergence. Yet empirical studies rarely consider that regional observations only reflect a parasite's 'realized' host range under particular conditions: the true 'fundamental' range of host specificity is typically not approached. We provide an overview of challenges and directions in modelling host specificity under variable environmental conditions. Combining tractable modelling frameworks with multiple data sources that account for the strong interplay between a parasite's evolutionary history, transmission mode, and environmental filters that shape host-parasite interactions will improve efforts to quantify emerging disease risk in times of global change.

Analysis of Predicted Host-Parasite Interactomes Reveals Commonalities and Specificities Related to Parasitic Lifestyle and Tissues Tropism

The study of molecular host–parasite interactions is essential to understand parasitic infection and adaptation within the host system. As well, prevention and treatment of infectious diseases require a clear understanding of the molecular crosstalk between parasites and their hosts. Yet, large-scale experimental identification of host–parasite molecular interactions remains challenging, and the use of computational predictions becomes then necessary. Here, we propose a computational integrative approach to predict host—parasite protein—protein interaction (PPI) networks resulting from the human infection by 15 different eukaryotic parasites. We used an orthology-based approach to transfer high-confidence intraspecies interactions obtained from the STRING database to the corresponding interspecies homolog protein pairs in the host–parasite system. Our approach uses either the parasites predicted secretome and membrane proteins, or only the secretome, depending on whether they are uni- or multi-cellular, respectively, to reduce the number of false predictions. Moreover, the host proteome is filtered for proteins expressed in selected cellular localizations and tissues supporting the parasite growth. We evaluated the inferred interactions by analyzing the enriched biological processes and pathways in the predicted networks and their association with known parasitic invasion and evasion mechanisms. The resulting PPI networks were compared across parasites to identify common mechanisms that may define a global pathogenic hallmark. We also provided a study case focusing on a closer examination of the human–S. mansoni predicted interactome, detecting central proteins that have relevant roles in the human–S. mansoni network, and identifying tissue-specific interactions with key roles in the life cycle of the parasite. The predicted PPI networks can be visualized and downloaded at http://orthohpi.jensenlab.org.

Characterizing the phylogenetic specialism-generalism spectrum of mammal parasites

The distribution of parasites across mammalian hosts is complex and represents a differential ability or opportunity to infect different host species. Here, we take a macroecological approach to investigate factors influencing why some parasites show a tendency to infect species widely distributed in the host phylogeny (phylogenetic generalism) while others infect only closely related hosts. Using a database on over 1400 parasite species that have been documented to infect up to 69 terrestrial mammal host species, we characterize the phylogenetic generalism of parasites using standard effect sizes for three metrics: mean pairwise phylogenetic distance (PD), maximum PD and phylogenetic aggregation. We identify a trend towards phylogenetic specialism, though statistically host relatedness is most often equivalent to that expected from a random sample of host species. Bacteria and arthropod parasites are typically the most generalist, viruses and helminths exhibit intermediate generalism, and protozoa are on average the most specialist. While viruses and helminths have similar mean pairwise PD on average, the viruses exhibit higher variation as a group. Close-contact transmission is the transmission mode most associated with specialism. Most parasites exhibiting phylogenetic aggregation (associating with discrete groups of species dispersed across the host phylogeny) are helminths and viruses.

Helminth metacommunity structure of wild rodents in a preserved area of the Atlantic Forest, Southeast Brazil

Abstract The helminth fauna and metacommunity structure of eight sympatric sigmodontine rodents were investigated at the Serra dos Órgãos National Park, an Atlantic Forest reserve located in the State of Rio de Janeiro, southeast Brazil. Rodents of the species Abrawayaomys ruschii, Akodon montensis, Blarinomys breviceps , Delomys dorsalis, Oligoryzomys flavescens, Oligoryzomys nigripes, Oxymycterus quaestor and Thaptomys nigrita were found infected with helminths. Akodon montensis presented the highest total helminth species richness, with six different species of helminths. The nematode Stilestrongylus lanfrediae was the most abundant and prevalent helminth species observed. The host-parasite network analysis showed little interactions among host species. Akodon montensis seems to act as a keystone-species in the rodent community. This species shared the nematodes Stilestrongylus aculeata with A. ruschii and Protospirura numidica criceticola with T. nigrita, and the cestode Rodentolepis akodontis with D. dorsalis. The congeners host species O. flavescens and O. nigripes shared the nematodes Guerrerostrongylus zetta and S. lanfrediae. The rodents B. breviceps and O. quaestor did not share any helminths with other hosts. The helminth metacommunity showed a random pattern on both infracommunity and component community levels, indicating different responses by each helminth species to the environmental gradient.

Estimating parasite host range

Estimating the number of host species that a parasite can infect (i.e. host range) provides key insights into the evolution of host specialism and is a central concept in disease ecology. Host range is rarely estimated in real systems, however, because variation in species relative abundance and the detection of rare species makes it challenging to confidently estimate host range. We applied a non-parametric richness indicator to estimate host range in simulated and empirical data, allowing us to assess the influence of sampling heterogeneity and data completeness. After validating our method on simulated data, we estimated parasite host range for a sparsely sampled global parasite occurrence database (Global Mammal Parasite Database) and a repeatedly sampled set of parasites of small mammals from New Mexico (Sevilleta Long Term Ecological Research Program). Estimation accuracy varied strongly with parasite taxonomy, number of parasite occurrence records, and the shape of host species-abundance distribution (i.e. the dominance and rareness of species in the host community). Our findings suggest that between 20% and 40% of parasite host ranges are currently unknown, highlighting a major gap in our understanding of parasite specificity, host-parasite network structure, and parasite burdens.