In the past twenty years, there has been a surge of interest in the role of disease on individual health and its effects on host populations. These studies and, indeed, the majority of disease control programmes of humans and domestic animals tend to consider individual infections in isolation. However, hosts are typically infected by many parasite species at any one time. For example, humans, particularly in the developing world, can be simultaneously co-infected with a variety of parasites: around 40.3 million people are currently infected with HIV/AIDS, over one third of the population worldwide has TB, and over one fourth has soil transmitted helminths. Importantly, these co-infecting parasites are unlikely to occur in isolation within each host, indeed there may be a vast network of interactions between them. These interactions may arise through direct competition between parasites within each host. However, they may also be indirect, possibly through competition for shared resources (bottom-up interactions) or via the host's immune system (top-down interactions). In this case, immune responses raised against one parasite may also affect other co-infecting parasite species. Alternatively, if the host is combating one parasite type it may not be able to mount an effective response against another. Therefore there may be a complex network of subtle, and difficult to detect interactions between parasite species that result in a diverse, interactive community within each individual host. Clearly, understanding how these communities are shaped is vital for the design of truly effective and sustainable disease control programs. If control approaches only consider one parasite species there may be unpredictable consequences for disease caused by other, co-infecting parasites. However, current approaches to measure parasite interactions are purely observational and, so far, have produced unclear information about their strength or existence. We propose to adopt a new, direct way of measuring interactions using classical community ecology perturbation experiments, by removing certain parasites from wild wood mice and measuring what happens to the remaining parasite species - if they increase after the target parasites have been removed then this suggests that the target species was previously suppressing their abundance. By repeating this process for all main parasite groups in the wood mice, we can build a more complete picture of how these parasite communities are shaped by the interactions between species. Putting all these interactions into a mathematical model will allow us to predict how such parasite communities will respond to more complex treatments, such as the removal of two species at the same time. If our model predictions prove accurate for more complex co-treatment strategies, then these within host network approaches may provide a vital tool for developing long-term disease control strategies in other host species, such as humans, domestic animals or wildlife threatened to extinction by infectious diseases. It is gradually being realised that parasite co-infections play an important role in the occurrence and management of many diseases of human concern. Given the increasing concerns about emerging infectious diseases around the globe, it has never been more pressing to develop a genuine understanding of the factors affecting parasite invasion, transmission, persistence, and control. This project will be a major step in that direction.
Most studies of parasite dynamics, and strategies for disease control in humans and domestic animals, consider each parasite species in isolation. However, hosts are typically infected by many parasite species at any one time. As shown by numerous lab studies, these parasites may interact in a variety of ways, potentially resulting in a complex within-host parasite community. Such interactions could be vital in determining the stability of parasite communities, the epidemiology of individual parasite species and their long-term responses to disease control strategies. However, evidence of the existence of interspecific parasite interactions, or their significance in shaping parasite communities in wild populations is rare, possibly due to the paucity of experimental studies aimed at detecting them. Here we conducted the first series of large-scale parasite removal experiments dedicated to quantifying interspecific parasite interactions within a wild mammal population. We have made several key findings:
1) Using large-scale parasite removal experiments we were able to to quantify within-host parasite interspecific interactions and validate the ability of classical approaches of parasite community ecology to detect these interactions. We will removed parasite functional groups (e.g. nematodes, coccidia or blood-borne parasites) from naturally occurring wood mice populations and measured the response for >20 other parasites species. We conducted both sustained and transient parasite removals, using various drug therapies, which allowed us to quantify the magnitude of direct versus indirect parasite interactions, the stability of the community to these perturbations and the reinvasion rate of the target parasite following cessation of treatment.
- One major finding of the grant was that when anthelmintic drugs were used to reduce nematode infections, their effects were short-lived, lasting less than 4 weeks, and resulted in a significant increase in other non-target parasites, specifically coccidia protozoans (Knowles et al. in review). In addition, we found that parasite communities were remarkable stable to these drug-based perturbations, with the parasite community returning to its pre-treatment levels within 4 weeks of drug treatment.
- Another major finding of the grant was that by comparing the results of these experimental perturbations to those obtained from classical, observation-based studies we demonstrated, for the first time, the lack of reliability of standard approaches in detecting interspecific parasite interactions. In fact, all standard methods for observing within host parasite interactions were unable to detect this strong negative interaction between nematodes and protozoans demonstrated in our experiments (Fenton et al. in prep).
2) Our next aim was to develop a model of within-host parasite community interactions, to predict the consequences for within-host community stability.
- To do this we built a general model of the within-host parasite communities, and specifically tested for positive or negative interactions between parasites, as well as how the outcome of these interactions varied dependent on whether the interactions were direct, top-down (via the immune response) or bottom up (via shared resources) This model was then used to test how different disease control strategies were affected, sometimes unexpectedly by these interactions (Griffiths et al. in prep).
- Next, we scaled up this modelling framework to include a diverse community of coinfecting parasites, and used community ecology theory and network tools to identify patterns of coinfections from humans, specifically we found that most parasites likely interact through bottom up, or shared resource, mechancisms, and that there is strong evidence of modularity in the coinfection network, suggesting that there are groups of parasites, immune components and resources that associate closely with each other in the network (Griffiths et al. in review).
- Following this, we collated a large data set of coinfection in humans and found that most coinfections result in increased parasite abundance of the coinfecting parasites, and reduced health in the human hosts (Griffiths et al. 2011).
- Lastly, we translated these findings to human mortality data from the UK, to see if coinfected invididuals had higher mortality rates, and if so, what types of coinfections were likely to contribute to higher death rates (Griffiths et al. in prep).
3) Next we aimed to assess whether models of within-host parasite interactions can be scaled up to test the efficacy of multiple treatment strategies. Based on our findings form Obj1 and predictions from the models developed in Obj. 2, we scaled up single-species removal experiments and tested the efficacy and outcome of co-treatment experiments in which multiple parasite taxa are targeted. We combined several anthelmintics with different modes of action, as well as combination therapies of anthelmintics and anti-protozoal drugs and found that, in some but not all cases, added therapies improved host health through reductions in several parasite species. However, we found in some cases, that these drugs blocked efficacies, and had no additional benefits to the host (Knowles et al. in prep).
4) Lastly we aimed to examine how host parasite community structure affects individual host health. While there is considerable information on how individual parasite species may affect host health, to date no one had explored how parasite communities as a whole impact host health.
- Following our various treatment experiments, we measured host survival and reproductive rates, so that we were able to assess how different parasite removal regimes impact individual host fitness. We found that, on average, nematodes were harmful to their hosts fitness. However, and importantly, this was not always the case. We found strong effects of parasite burden (intensity of infection) and these effects were not always linearly related to host survival. In addition, male and female mice differed significantly in the effect of nematodes on their survival. We found that treating a subset of mice, specifically males with low worm burdens, actually reduced their survival by >40% (Pedersen et al. in prep).
- In addition, the effect of worm infection and intensity had other larger scael effects, determining the outcome of the shape between worm burden and survival, the benefits or costs of treatment, and the level of within host parasite interactions (Knowles et al. in prep).
Overall, by combining parasite removal experiments with community ecology theory, we expanded the traditional 1-host-1-parasite framework to evaluate the causes of parasite community stability and the consequences for parasite dynamics and host health.