Substrate-based kinase activity inference identifies MK2 as driver of colitis

Abstract

Inflammatory bowel disease (IBD) is a chronic and debilitating disorder that has few treatment options due to a lack of comprehensive understanding of its molecular pathogenesis. We used multiplexed mass spectrometry to collect high-content information on protein phosphorylation in two different mouse models of IBD. Because the biological function of the vast majority of phosphorylation sites remains unknown, we developed Substrate-based Kinase Activity Inference (SKAI), a methodology to infer kinase activity from phosphoproteomic data. This approach draws upon prior knowledge of kinase-substrate interactions to construct custom lists of kinases and their respective substrate sites, termed kinase-substrate sets that employ prior knowledge across organisms. This expansion as much as triples the amount of prior knowledge available. We then used these sets within the Gene Set Enrichment Analysis framework to infer kinase activity based on increased or decreased phosphorylation of its substrates in a dataset. When applied to the phosphoproteomic datasets from the two mouse models, SKAI predicted largely non-overlapping kinase activation profiles. These results suggest that chronic inflammation may arise through activation of largely divergent signaling networks. However, the one kinase inferred to be activated in both mouse models was mitogen-activated protein kinase-activated protein kinase 2 (MAPKAPK2 or MK2), a serine/threonine kinase that functions downstream of p38 stress-activated mitogen-activated protein kinase. Treatment of mice with active colitis with ATI450, an orally bioavailable small molecule inhibitor of the MK2 pathway, reduced inflammatory signaling in the colon and alleviated the clinical and histological features of inflammation. These studies establish MK2 as a therapeutic target in IBD and identify ATI450 as a potential therapy for the disease.

Keywords: gene set enrichment analysis; inflammation; kinases; mouse model; proteomics.