When most people think about treating infections, they are actually thinking about changing resistance, the host’s ability to control microbe load. Our lab is more interested in disease tolerance, a second defense response that limits the negative impact of microbes on the host’s health, without reducing the microbe load. Together, resistance and tolerance contribute to host resilience to infection, by which we mean the host’s ability to return to its original state of health.
To describe infection, we built a simple mathematical model that contains three general components: the health of an animal, the microbe load, and the immune response. Damage can be caused by the immune system in a classic “double edged sword” model, where the immune response both reduces microbe load and causes pathology to the host. Not all pathology is caused by the immune system, though, and thus the microbes can also directly induce damage. We are now trying to measure the rate constants and parameters we proposed in our model.
Tolerance is a summary statement that reports the dose response of health in response to microbe load for a population of hosts. There are many ways to measure this property, and the point of doing this is that it gives us insight into the structure of our model. For example, we measure tolerance in a Listeria infection of fruit flies by measuring microbe loads two days post infection and median time to death. We find that this tolerance curve has a sigmoid shape. Since we know that sigmoids can be described mathematically using 3 to 4 parameters this, means that, to measure changes in disease tolerance, we need to determine if and how each of these parameters change. This lets us move beyond saying that a gene is necessary for an immune response and we can now determine whether a mechanism controls the sensitivity of a response or the maximum effect size.
To map the path infected hosts take from health through sickness and back to health, we use a simple cartoon to track the trajectory the host takes through “disease space”. Disease space is a multidimensional universe of symptoms and sick individuals trace a trajectory through this manifold as they sicken and recover or get stuck in a chronic state or die. We are interested in the shapes of these disease trajectories. Our plan is to find trajectories that are easy to describe and are shared across infections. We are particularly excited about circular paths, because these let us uniquely identify the position of sick hosts in disease space using just two parameters.
While we understand how individuals get sick from an infection, we do not yet know fully how they get better. We do know, however, that recovering from an infection is not simply the opposite of getting sick. Our disease maps show this as recovering individuals don’t simply retrace their path from sickness back to health but take a different route. We think that recovery involves multiple coordinated physiological processes, including tissue damage repair and return to baseline metabolism. We study recovery in a variety of infection models, including fruit flies infected with Listeria monocytogenes, where we propose that tweaking these metabolic and repair pathways will alter the rate of recovery from infection.
Genetic differences in the host affect pathogen growth, host recovery, and, therefore, infection outcome. To identify the causes of variation in resilience, we are screening a collection of 1000 diversity outbred mice. We plan to identify alleles important in both sickness and recovery. To do this, we are building multidimensional maps of disease space for these mice and are identifying mice that deviate from common trajectories.
While many immune cell types have been identified as important for various infections, we don’t understand the dynamic behavior of these cells. We are creating a resource detailing immune cell dynamics from the beginning to end of a resilient infection. This allows us to identify cell types that drive pathology then manipulate an infection to minimize damage. We are also investigating systems-level indicators of infection stage though behavioral parameters like feeding and movement to develop non-invasive methods of monitoring disease.