My lab uses genomic and computational approaches to identify the molecular basis of autoimmunity and fibrosis with an emphasis on systemic sclerosis (SSc). We identified the first molecular subsets in an autoimmune disease, similar to those that have now been well studied in cancer. We perform RNA-sequencing, analyses of tissue resident microbiome in patients, gene-gene network analyses and use machine learning approaches to interrogate the genetic and phenotypic changes in patients.
We have used a systems biology approach, building on tissue specific networks derived from public data, to analyze multiple cohorts of patients with SSc and related fibrotic conditions. SSc results in vascular dysfunction, fibrosis (hardening) of the skin, and inflammation. The involvement of internal organs (lungs and pulmonary system, esophagus, GI system and kidneys) is a major complication and the main cause of death. We have shown that a common mechanism is driving disease in multiple organ systems of patients with SSc, but also in patients with related conditions such as pulmonary fibrosis and pulmonary arterial hypertension. This means that drugs developed to treat one condition, may be applicable other conditions. These studies used novel network and machine learning methods to analyze publicly available data to identify these common mechanisms. The critical problem is now, what are the fundamental causes or triggers for developing SSc in the context of genetic risk factors?