Characterizing a Focused Landscape of Familial Acute Respiratory Distress Syndrome

Abstract

Background Acute respiratory distress syndrome (ARDS) affects approximately 190,600 patients per year in the United States, with mortality up to 45%. ARDS can occur as primary disease due to various factors (e.g. bacterial or viral pneumonia, gastric aspiration, lung contusion, toxic inhalation, and near drowning) or as secondary disease due to sepsis, pancreatitis, severe trauma, massive blood transfusion, and burn. We hypothesized that ARDS-affected individuals have patterns of variants in their physiological repertoire that can be tracked and utilized to complement clinical diagnosis and/or clinical monitoring. Methods The goals of this study were to: (1) characterize the landscape of variants within protein coding but we also studied UTR regions in ARDS using an Exome sequencing approach; (2), determine the variations in signaling pathways across ARDS; and (3) use computational approaches to explore the functional consequences of ARDS. Towards this we assessed an ARDS-affected individual in the context of unaffected individuals from the same family as well as unrelated ARDS cases, in order to elucidate underlying inheritance patterns of “private variants”. Private variants consist of variants shared by ARDS cases but not found in unaffected individuals. Results Whole exome sequencing yielded 3,516 variants represented by 2,354 genes. Of these, 128 variants were shared across all ARDS cases. Of these, there were 24 unique variants represented by 9 ARDS genes shared by the primary ARDS-case and unrelated individuals with ARDS. The overall genes identified and subsequent analysis, demonstrate that there are important biological pathways that distinguish ARDS cases from or from non-ARDS. These pathways include: cell-to-cell signaling interaction, cell growth and proliferation and cell morphology. The data also show a coordinated effort amongst biological processes such as liver hyperproliferation and cell death that underlie the pathogenesis of ARDS. Conclusions These in-silico discoveries demonstrate a role for private variants shared by ARDS cases to be leveraged as biomarkers for clinical diagnosis and/or monitoring of ARDS.