Jason E. Shoemaker, PhD

Assistant Professor

932 Benedum Hall | 3700 O’Hara Street | Pittsburgh, PA 15261 P: 412-624-3318

jason.shoemaker@pitt.edu www.shoemakerlab.pitt.edu

Systems Immunology

We all fear disease but the unfortunate truth is that our bodies are often responsible for the damage wrought during disease progression. Our bodies have evolved several internalized sentinel programs that compromise our immune response. When functioning effectively, our immune response rapidly detects threatening pathogens and activates processes that assess and respond appropriately. But, occasionally, such as during certain viral and bacterial infections, our body responds too aggressively to the pathogen. These overly aggressive responses by our immune system results in major tissue inflammation and can greatly complicate patient recovery. We seek to develop immuno-modulatory therapies that work with the immune response to improve patient health.

Big Data-Driven Modeling of Lung Inflammation

Inflammation is a complex, multicellular process that is generally a protective response to a pathogen, but inflammation often becomes dysregulated, resulting in greater and occasionally fatal damage. New experimental techniques allow us to quantify changes across the genome in response to a pathogen, but the cellularly diverse environment makes it difficult to determine which genetic events are associated with inflammation and what specific immune cell populations may play a role in the response. We are developing approaches that combine individual gene expression profiles, dynamic clustering, and time-course gene expression to unravel the key genetic events associated with severe inflammation. Our early tools can be found at

www.influenza-x.org/~jshoemaker/cten/

How Does Herpes Avoid Immune Detection?

Herpes simplex virus (HSV) and HIV are DNA viruses and both are very competent at avoiding our immune response. We are now working with our collaborators at UPMC to better understand how our cells detect DNA viruses and by what mechanisms might the virus be able to either avoid detection (Fig.1). We are developing mathematical models of the molecular interactions responsible for DNA virus detection and immune activation. Uniquely, these models can exploit immune response dynamics to identify hidden, disease-associated signaling mechanisms. Then combined with machine learning and protein similarity data, these models can provide deep insight into viral escape.

Fig. 1