Investigating the outcomes of virus coinfection within and across host species.

Interactions between coinfecting pathogens have the potential to alter the course of infection and can act as a source of phenotypic variation in susceptibility between hosts. This phenotypic variation may influence the evolution of host-pathogen interactions within host species and interfere with patterns in the outcomes of infection across host species. Here, we examine experimental coinfections of two Cripaviruses-Cricket Paralysis Virus (CrPV), and Drosophila C Virus (DCV)-across a panel of 25 Drosophila melanogaster inbred lines and 47 Drosophilidae host species. We find that interactions between these viruses alter viral loads across D. melanogaster genotypes, with a ~3 fold increase in the viral load of DCV and a ~2.5 fold decrease in CrPV in coinfection compared to single infection, but we find little evidence of a host genetic basis for these effects. Across host species, we find no evidence of systematic changes in susceptibility during coinfection, with no interaction between DCV and CrPV detected in the majority of host species. These results suggest that phenotypic variation in coinfection interactions within host species can occur independently of natural host genetic variation in susceptibility, and that patterns of susceptibility across host species to single infections can be robust to the added complexity of coinfection.

Infection History and Current Coinfection With Schistosoma mansoni Decreases Plasmodium Species Intensities in Preschool Children in Uganda.

Malaria-schistosomiasis coinfections are common in sub-Saharan Africa but studies present equivocal results regarding the interspecific relationships between these parasites. Through mixed-model analyses of a dataset of Ugandan preschool children, we explore how current coinfection and prior infection with either Schistosoma mansoni or Plasmodium species alter subsequent Plasmodium intensity, Plasmodium risk, and S mansoni risk. Coinfection and prior infections with S mansoni were associated with reduced Plasmodium intensity, moderated by prior Plasmodium infections, wealth, and host age. Future work should assess whether these interactions impact host health and parasite control efficacy in this vulnerable age group.

A One Health Approach to Child Stunting: Evidence and Research Agenda.

Stunting (low height for age) affects approximately one-quarter of children aged < 5 years worldwide. Given the limited impact of current interventions for stunting, new multisectoral evidence-based approaches are needed to decrease the burden of stunting in low- and middle-income countries (LMICs). Recognizing that the health of people, animals, and the environment are connected, we present the rationale and research agenda for considering a One Health approach to child stunting. We contend that a One Health strategy may uncover new approaches to tackling child stunting by addressing several interdependent factors that prevent children from thriving in LMICs, and that coordinated interventions among human health, animal health, and environmental health sectors may have a synergistic effect in stunting reduction.

Predicting the effects of parasite co-infection across species boundaries.

It is normal for hosts to be co-infected by parasites. Interactions among co-infecting species can have profound consequences, including changing parasite transmission dynamics, altering disease severity and confounding attempts at parasite control. Despite the importance of co-infection, there is currently no way to predict how different parasite species may interact with one another, nor the consequences of those interactions. Here, we demonstrate a method that enables such prediction by identifying two nematode parasite groups based on taxonomy and characteristics of the parasitological niche. From an understanding of the interactions between the two defined groups in one host system (wild rabbits), we predict how two different nematode species, from the same defined groups, will interact in co-infections in a different host system (sheep), and then we test this experimentally. We show that, as predicted, in co-infections, the blood-feeding nematode Haemonchus contortus suppresses aspects of the sheep immune response, thereby facilitating the establishment and/or survival of the nematode Trichostrongylus colubriformis; and that the T. colubriformis-induced immune response negatively affects H. contortus This work is, to our knowledge, the first to use empirical data from one host system to successfully predict the specific outcome of a different co-infection in a second host species. The study therefore takes the first step in defining a practical framework for predicting interspecific parasite interactions in other animal systems.

Breaking beta: deconstructing the parasite transmission function.

Transmission is a fundamental step in the life cycle of every parasite but it is also one of the most challenging processes to model and quantify. In most host-parasite models, the transmission process is encapsulated by a single parameter ß Many different biological processes and interactions, acting on both hosts and infectious organisms, are subsumed in this single term. There are, however, at least two undesirable consequences of this high level of abstraction. First, nonlinearities and heterogeneities that can be critical to the dynamic behaviour of infections are poorly represented; second, estimating the transmission coefficient ß from field data is often very difficult. In this paper, we present a conceptual model, which breaks the transmission process into its component parts. This deconstruction enables us to identify circumstances that generate nonlinearities in transmission, with potential implications for emergent transmission behaviour at individual and population scales. Such behaviour cannot be explained by the traditional linear transmission frameworks. The deconstruction also provides a clearer link to the empirical estimation of key components of transmission and enables the construction of flexible models that produce a unified understanding of the spread of both micro- and macro-parasite infectious disease agents.This article is part of the themed issue 'Opening the black box: re-examining the ecology and evolution of parasite transmission'.

Coinfection: doing the math.

A transmission model clarifies the effects of influenza on pneumococcal pneumonia and bridges the gap between individual animal experiments and human epidemiological data (Shrestha et al., this issue).

The relative contribution of co-infection to focal infection risk in children.

Co-infection is ubiquitous in people in the developing world but little is known regarding the potential for one parasite to act as a risk factor for another. Using generalized linear mixed modelling approaches applied to data from school-aged children from Zanzibar, Tanzania, we determined the strength of association between four focal infections (i.e. Ascaris lumbricoides, Trichuris trichiura, hookworm and self-reported fever, the latter used as a proxy for viral, bacterial or protozoal infections) and the prevalence or intensity of each of the helminth infections. We compared these potential co-infections with additional risk factors, specifically, host sex and age, socioeconomic status and physical environment, and determined what the relative contribution of each risk factor was. We found that the risk of infection with all four focal infections was strongly associated with at least one other infection, and that this was frequently dependent on the intensity of that other infection. In comparison, no other incorporated risk factor was associated with all focal infections. Successful control of infectious diseases requires identification of infection risk factors. This study demonstrates that co-infection is likely to be one of these principal risk factors and should therefore be given greater consideration when designing disease-control strategies. Future work should also incorporate other potential risk factors, including host genetics which were not available in this study and, ideally, assess the risks via experimental manipulation.

Pathogen interactions, population cycles, and phase shifts.

Interspecific pathogen interactions can profoundly affect pathogen population dynamics and the efficacy of control strategies. However, many pathogens exhibit cyclic abundance patterns (e.g., seasonality), and temporal asynchrony between interacting pathogens could reduce the impact of those interactions. Here we use an extension of our previously published model to investigate the effects of cycles on pathogen interaction. We demonstrate that host immune memory can maintain the impact of an interaction, even when the effector pathogen abundance is low or the pathogen is absent. Paradoxically, immune memory can result in pathogens interacting more strongly when temporally out of phase. We find that interactions between species can result in changes to the temporal pattern of the affected species. We further demonstrate that this may be observed in a natural host-pathogen system. Given the continuing debate regarding the relevance of pathogen interactions in natural systems and increasing concern about treatment strategies for coinfections, both the discovery of a shift in cycle in empirical data and the mechanism by which we identified it are important. Finally, because the model structure used here is analogous to models of a simple predator-prey system, we also consider the consequences of these findings in the context of that system.

Competition and mutualism among the gut helminths of a mammalian host.

Most animal species are infected with multiple parasite species; however, the role of interspecific parasite interactions in influencing parasite dynamics and shaping parasite communities has been unclear. Although laboratory studies have found evidence of cross-immunity, immunosuppression and competition, analyses of hosts in the field have generally concluded that parasite communities are little more than random assemblages. Here we present evidence of consistent interspecific interactions in a natural mammalian system, revealed through the analysis of parasite intensity data collected from a free-ranging rabbit (Oryctolagus cuniculus) population, sampled monthly for a period of 23 yr. The wild rabbit plays host to a diverse gut helminth community that reflects the communities seen in other economically important domestic herbivores. These findings suggest that parasite interactions could have profound implications for the dynamics of parasite communities. The efficacy of parasite control programmes could be jeopardized if such interactions are not taken into account. In contrast, a clear understanding of such interactions may provide the basis for the development of more environmentally acceptable methods of parasite control.

Mixed models: getting the best use of parasitological data.

Statistical analysis of parasitological data provides a powerful method for understanding the biological processes underlying parasite infection. However, robust and reliable analysis of parasitological data from natural and experimental infections is often difficult where: (1) the distribution of parasites between hosts is aggregated; (2) multiple measurements are made on the same individual host in longitudinal studies; or (3) data are from 'noisy' natural systems. Mixed models, which allow multiple error terms, provide an excellent opportunity to overcome these problems, and their application to the analysis of various types of parasitological data are reviewed here.