I hypothesize that recovery from infection is an active process that differs from the route we take from health to pathology;I want to understand what properties make patients resilient to infections. In other words, how do we get sick AND recover? My goal is to repair the dissonance between microbial pathogenesis as we study it in the lab and the way we treat patients with infections. As scientists studying infections we mostly define the mechanisms controlling how sick a host gets. The philosophy is that we can help people if we can prevent them from getting extremely ill. Unfortunately, what this means is that we are great at defining just how sick a patient will be when they show up in the ER;we leave it to the physician to clear the infection and return the patient to a healthy state. The route back to health may involve the same physiological mechanisms that define the route to sickness but it may not;we dont know because we dont study recovery. I am proposing a new paradigm for studying host-microbe interactions where we use whole animals to define the properties governing recovery (the route back from sickness) and resilience (the ability of the host to be perturbed and then return to its original state). I will identify fundamental principles governing recovery and to establish a conceptual framework in which this neglected but important phase of disease can be pursued. I will use the following three aims to dissect this problem.
Aim 1 : Test if mechanisms known to dictate decline are involved in recovery. Known resistance and tolerance mechanisms will be tested for their role in recovering from bacterial infections. This will help describe the attributes that control the shape of a recovery curve.
Aim 2 : Identify novel mechanisms involved in recovery. We will perform a forward genetic screen for mutants with altered ability to recover from infections. This will lead us towards new biology by asking an open question about what is required for recovery rather than asking is process X required for recovery? Aim 3: Develop a predictive systems-level model of resilience. We will use mathematical models to define resilient systems that are either healthy or stuck in persistent infections or pro-inflammatory states and then test these models using the tools developed in the first two aims.
| Louie, Alexander; Song, Kyung Han; Hotson, Alejandra et al. (2016) How Many Parameters Does It Take to Describe Disease Tolerance? PLoS Biol 14:e1002435 |
| Torres, Brenda Y; Oliveira, Jose Henrique M; Thomas Tate, Ann et al. (2016) Tracking Resilience to Infections by Mapping Disease Space. PLoS Biol 14:e1002436 |
| Hotson, Alejandra Guzmán; Schneider, David S (2015) Drosophila melanogaster Natural Variation Affects Growth Dynamics of Infecting Listeria monocytogenes. G3 (Bethesda) 5:2593-600 |
| Dillman, Adler R; Schneider, David S (2015) Defining Resistance and Tolerance to Cancer. Cell Rep 13:884-7 |
| Chambers, Moria C; Song, Kyung Han; Schneider, David S (2012) Listeria monocytogenes infection causes metabolic shifts in Drosophila melanogaster. PLoS One 7:e50679 |
