Measuring Immunity With Transparent Hosts
Host controls of within-host dynamics: insight from an invertebrate system (2021) Stewart Merrill et al., The American Naturalist. https://doi.org/10.1086/715355
This is a guest post by Dr. Tara Stewart Merrill
Image Credit: Per Harald Olsen, NTNU, CC BY 2.0, Image Cropped
When it comes to understanding how parasites and pathogens spread, immune defenses may be an especially important factor. The immune system is the gatekeeper for parasites and pathogens (I’ll just use the term “pathogen” from here on out). Whether you are exposed to influenza, a parasitic worm, or a tick-borne bacterium, your immune response will determine the outcome of infection — either you will become infected (which benefits the pathogen’s reproduction) or you will not (which is a barrier to the pathogen’s reproduction). So now, picture a whole population of individuals. A room full of individuals with poor immune responses should result in more infections (and more transmission) than a room full of individuals with strong and robust immune defenses. By shaping the fate of pathogens, host immune defenses can shape transmission.
But here’s where we get to a problem. How do we measure immune defenses? The thing about pathogens is that many live inside their hosts, buried deep within host tissues, completely invisible to the traditional ecologist. Alongside these of course are the immune responses that fight pathogens. With sophisticated molecular techniques, we can track immune cell populations and pathogen infra-populations, the timing of antibody production and deployment, and a suite of other immunological effectors. But for the many ecologists who study wild snails, birds, beetles, chipmunks, etc., we don’t always have the tools worked out to measure immunity. Sometimes we don’t even know which particular immune defenses our study species use.
What my co-authors and I have tried to do in this paper was develop a simple mathematical model to broadly measure host immune defenses in host-pathogen systems about which we know very little.
What We Did
We developed, applied, and tested our model using the zooplankton, Daphnia, and its fungal pathogen, Metschnikowia. In this interaction, Daphnia consume infective fungal spores while suspension feeding. Those spores then develop inside the Daphnia body cavity, and about ten days later, infected Daphnia are full of thousands of new spores which are released when the host dies of the fungal infection. We exposed Daphnia from multiple distinct genotypes (genotype being the specific genes that an organism has) to this pathogenic fungus, every other day taking a subset of Daphnia from each genotype and examining them to identify which stage of infection they possessed. The states in our model included: exposed, infected, uninfected, and dead.
At the end of a ten-day observational period, we had acquired: i) longitudinal state data for each day (the proportion of individuals occupying each infection state), and ii) the susceptibility of each genotype (the prevalence of infections at day 10).
We then developed a four-state Markov model (a model that analyzes how a given individual/population changes from one state to another) to estimate the probabilities of all state-to-state transitions. By applying the model to the longitudinal state data, we could estimate the probability of infection (moving from exposed to infected), the probability of mortality (any movement into the dead state), the probability of barrier resistance (moving from exposed to uninfected) and the probability of internal clearance (moving from infected to uninfected). The two latter probabilities capture immune functional processes, where barrier resistance represents physical and chemical barriers that prevent infection, and internal clearance represents internal immunological defenses that kill established or developing infections.
What We Found
We approached the model output with a few competing hypotheses: while it was possible that barrier resistance (their ability to resist becoming infected) could cause variation in susceptibility, internal clearance (their ability to contract and then beat the infection) might do the same. They also might work together to shape susceptibility, or perhaps neither has any effect. By connecting the four different probabilities to the susceptibility we measured for each genotype, we found that internal clearance best explained variation in susceptibility.
We had identified that some form of immune defense occurring inside the host body cavity was important for controlling the outcome of infection. But could we get a step closer to identifying how this worked? During infection, the fungus undergoes five stages of morphological development inside the Daphnia, and we tinkered with the modeling approach to see whether we could hone in on the stage of infection that was most vulnerable to internal clearance. Ultimately, we found that the two earliest stages of infection were the most vulnerable to internal clearance, and that once the host reaches the third stage of infection, its probability of recovering declines to almost zero.
Finally, we were able to test the model using an amazing attribute of Daphnia: their transparency. We measured probabilities of barrier resistance and internal clearance more practically, by observing individuals under the microscope, tracking their individual infections through time, and calculating the proportion of hosts that recovered using either defense. And it turns out, the model-estimated probabilities were very close to these observed probabilities.
One of the more complicating factors of susceptibility (and the efficacy of immune defenses) is that they can be sensitive to pathogen dose. Our study used only one dose of fungal spores, so we were unable to evaluate whether these two defenses shift in their importance depending on the dose administered. For instance, it may be that at very low doses, barrier resistance becomes the key trait shaping susceptibility. While this was beyond the scope of our study, this may be an exciting way to move forward with the mathematical model and — even more broadly — in how we think about measuring and defining immune defenses.
We hope this model can be an exciting tool to apply to understudied host-pathogen interactions. The model and approach can help us pinpoint critical steps in the infection process, where hosts have the greatest opportunity to destroy a pathogen. Once these steps are identified, we can do more mechanistic work to identify the particular immunological mechanisms at play.
Yet even without this knowledge, the model provides us with estimates of immune functional traits (barrier resistance and internal clearance). By measuring these traits in hosts of different genetic backgrounds, or under varying experimental or natural conditions, we can begin unpacking how much variation exists in resistance and clearance, how these two immune defenses are shaped by genes and the environment, and what their variation means for infection and transmission. We’ve already started using these models to estimate how immune responses could shape the emergence and size of epidemics.
Dr. Tara Stewart Merrill is an ecologist interested in the drivers and impacts of infectious disease, from individual host-parasite interactions to the importance of parasites in ecosystems. You can read more about her research here, or follow her on Twitter here.