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Using Chemoproteomic and Metabolomic Platforms to Identify Nodal Metabolic Pathways Important to Inflammation

  • Author(s): Hunerdosse, Devon
  • Advisor(s): Nomura, Daniel
  • et al.
Abstract

There are an increasing number of human pathologies that have been associated with altered metabolism, including obesity, diabetes, cancer, atherosclerosis, and neurodegenerative diseases. Most attention on metabolism has been focused on well-understood metabolic pathways and has largely ignored most of the biochemical pathways that operate in physiological and pathophysiological settings. This is, in part, because of the vast landscape of uncharacterized and yet-undiscovered enzymes and metabolites that operate in metabolism. One technology that has arisen to address this challenge is activity-based protein profiling (ABPP). ABPP uses activity-based chemical probes to broadly assess the functional states of characterized and uncharacterized enzymes alike across entire enzyme classes. ABPP, when coupled with inhibitor discovery platforms and functional metabolomic technologies has led to discoveries that increase our definition of known biochemical pathways to expand our knowledge of metabolism in human health and disease.

Being able to identify key nodal metabolic pathways will undoubtedly lead to new therapeutic strategies for combating diseases associated with metabolism. We are particularly interested in studying inflammatory metabolism, because chronic, low-grade inflammation is increasingly associated with many human pathologies. Although there are several successfully marketed small molecule anti-inflammatory drugs such as cyclooxygenase inhibitors and glucocorticoids, many of these compounds are also associated with various adverse cardiovascular or immunosuppressive effects. Thus, identifying novel anti-inflammatory small molecules and their biological targets is critical for developing safer and more effective treatment strategies for inflammatory diseases. We conducted a chemical genetics screen to identify small molecules that suppress the release of the pro-inflammatory cytokine TNFα from stimulated macrophages. We have used an enzyme class-directed chemical library for our screening efforts to facilitate subsequent target identification using ABPP. Using this strategy, we have found that KIAA1363 is a novel target for lowering certain pro-inflammatory cytokines through affecting key ether lipid metabolism pathways. This study highlights the application of combining chemical genetics with chemoproteomic and metabolomic approaches toward identifying and characterizing anti-inflammatory small molecules and their targets.

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