Throughout our lives, we are exposed and infected by a diverse community of pathogens from viruses and bacteria to parasitic worms. In humans, what combination of pathogens you are infected by matters as these organisms can interact with each other in remarkable ways that can alter the outcome of an infection. For example, people co-infected by HIV (human immunodeficiency virus) and tuberculosis (tb – a disease caused by Mycobacterium bacteria) experience heightened symptoms of each pathogen and are a much higher risk of dying compared to people infected by just one of these pathogens. HIV interferes with the immune system that not only allows tb to grow faster but also increases the chances of that individual transmitting the bacteria. This is an example of a positive or ‘facilitative interaction’ between pathogens in ecological speak. In contrast, pathogens can compete as well (a negative interaction) and is some cases this can protect us from disease. For example, co-infection between certain parasitic worms can actually be protective of malaria (see Nacher, 2011 below). Further, we know it is possible that interactions between pathogens can be dependent on the order of infection (see Hoverman et al. for more on this). But how do we test for these specific interactions, particularly in wildlife? Humans and wildlife are exposed and infected by a diverse range of organisms; how could we work out which ones to test? It is unfeasible to test every combination in the lab and even then, how would we know what combination actually occurs in the wild?
In this paper, we harnessed recent advances in ecological statistics and network theory to quantify associations between pathogens in a wild population of lions in the Serengeti in Tanzania. We label them associations as we can’t be 100% sure that they actually represent real interactions between pathogens (you’d need to do lab experiments for that which are difficult to do for wildlife). Based on over 10 years of exposure and infection data from a wide variety of pathogens that infect lions, we were able to establish which pathogens were positively or negatively associated with others. As we have been monitoring these lions often since birth, we were able to deduce the likely order of infection or exposure and work out if a pathogen that a lion was exposed to early in life could impact which pathogen they were exposed to as adults. These statistical methods are also useful as they can start to untangle if these associations could be just due to environmental factors (i.e. the lion got co-infected by two pathogens because of an ecological preference of these pathogens) rather than a potential biological mechanism.
The associations we found using these methods were often surprising but reflected what has been established in human lab-based studies which is promising. For example, we found a strong negative association between Rift Valley Fever (RVF -a mosquito-borne virus that infects lion as well as cattle and sheep leading to sometimes devastating economic loss) and felid equivalent to HIV (FIV). FIV infects nearly 100% of lions as cubs, whereas RVF infection is more likely to occur later in life. Interestingly RVF has similar molecular machinery to a group of viruses that are known to inhibit the growth of HIV, so it is possible that the same mechanism exists for lions as well. Similarly, we found a strong negative association between feline coronavirus (in the virus family that causes severe acute respiratory syndrome or SARS in humans) and one type of FIV also. Coronaviruses are considered possible candidate vaccines for HIV, so again laboratory work from human medicine provided some support for our findings.
We didn’t just find negative associations either, we also detected strong positive interaction between the tick-borne Babesia protozoans and canine distemper virus (CDV). This co-infection pattern has been identified previously and is likely the underlying factor that caused this lion population to crash by over 33% in the 1990s. Lions are may be able to withstand a CDV epidemic in isolation but when combined with Babesia in a co-infection, this can lead to serious population declines for this species (see Munson et al for some more details). Our study shows that it didn’t matter which species of Babesia either, all of the species we included had these strong positive associations with CDV.
We can’t prove conclusively that these pathogens actually interact within a lion based on these statistical methods alone. However, we can provide a valuable ‘shortlist’ of possible interactions that occur in a wild population that can be tested using cell-level experiments in a lab – we obviously don’t want to actually test these hypotheses out on lions themselves. Given how common interactions between pathogens are and the potentially positive or negative outcomes of them for the host, our approach coupled with lab-work can provide important insights to understanding pathogen dynamics in wild populations.
Nacher (20111): https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3192711/
Hoverman et al (2013): https://www.ncbi.nlm.nih.gov/pubmed/23754306?dopt=Abstract
Munson et al (2008) : https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0002545
A link to the paper here: https://onlinelibrary.wiley.com/doi/full/10.1111/ele.13250