Abstract
Glucose, the primary cellular energy source, is metabolized through glycolysis initiated by the rate-limiting enzyme hexokinase (HK). In energy-demanding tissues like the brain, HK1 is the dominant isoform, primarily localized on mitochondria, and is crucial for efficient glycolysis–oxidative phosphorylation coupling and optimal energy generation. This study unveils a unique mechanism regulating HK1 activity, glycolysis and the dynamics of mitochondrial coupling, mediated by the metabolic sensor enzyme O-GlcNAc transferase (OGT). OGT catalyses reversible O-GlcNAcylation, a post-translational modification influenced by glucose flux. Elevated OGT activity induces dynamic O-GlcNAcylation of the regulatory domain of HK1, subsequently promoting the assembly of the glycolytic metabolon on the outer mitochondrial membrane. This modification enhances the mitochondrial association with HK1, orchestrating glycolytic and mitochondrial ATP production. Mutation in HK1’s O-GlcNAcylation site reduces ATP generation in multiple cell types, specifically affecting metabolic efficiency in neurons. This study reveals a previously unappreciated pathway that links neuronal metabolism and mitochondrial function through OGT and the formation of the glycolytic metabolon, providing potential strategies for tackling metabolic and neurological disorders.
| Original language | English |
|---|---|
| Pages (from-to) | 1712-1735 |
| Number of pages | 24 |
| Journal | Nature Metabolism |
| Volume | 6 |
| Issue number | 9 |
| DOIs | |
| State | Published - Sep 2024 |
Funding
We gratefully acknowledge the invaluable contributions of the Pekkurnaz laboratory members, as well as the generous sharing of key instrument resources by E. Hui. We also extend our appreciation to the technical team members of the University of California San Diego Biomolecular/Proteomics Mass Spectrometry Facility and Nikon Imaging Center for their expert assistance, and Arizona State University Research Computing for providing High Performance Computing resources. This project was made possible by the support of a grant from the National Institutes of Health (NIH) to G.P. (R35GM128823), NIH (2T32GM007240) to S.B.Y., NIH (5T32GM133351) to A.A.A., NIH to M.H. (R01NS094219), University of California San Diego TRELS fellowship to A.Z., NIH (5T32NS007220) to V.L., NIH (5T32NS007220-40) to Z.W., NIH (5T32EB009380-15) to N.M.C, the San Diego IRACDA Scholars Program (K12GM068524) to R.S., URS Ledell Family Research Scholarship for Science and Engineering to A.Z., NSF Graduate Research Fellowship (2020298734) to J.W.V and NSF (MCB-1942763) to A.S. We also acknowledge the Gordon and Betty Moore Foundation (7555.04) and the Chan Zuckerberg Initiative DAF, an advised fund of Silicon Valley Community Foundation (2020-222005) for their contributions to this project.