The long-term objective of this proposal is to understand the molecular-level mechanism of metabolic flux regulation in human cells. In this proposal, we will investigate the subcellular localization-function relationship of human multienyzme metabolic complex that regulate glucose metabolism in human cells. Despite considerable advances in our knowledge of glycolytic enzymes and their complexes, it is still challenging to explain how the direction of glucose flux is spatially and/or temporally regulated at metabolic nodes between energy metabolism and anabolic biosynthetic pathways. Now, we provide compelling evidence that all the cytosolic, rate-determining enzymes in glucose metabolism are spatially organized into a multienzyme complex, namely the ?glucosome,? in various sizes in the cytoplasm of human cells. We hypothesize that the spatial assembly of glucosomes regulates the direction of glucose flux in a size-dependent manner at subcellular levels.
In Aim1, we will determine a precise metabolic function of each size of glucosome clusters at subcellular levels, thus providing a quantitative principle for their collective metabolic outcomes at ensemble levels. Quantitative secondary ion mass spectrometric imaging and 13C-metabolic flux assays will be performed to determine size-specific partition coefficients of glucosome clusters as their metabolic functions.
In Aim 2, we will provide mechanistic insights of how multiple metabolic pathways are reciprocally regulated as a network to govern metabolic shunts at subcellular levels. Intracellular fluorescence resonance energy transfer microscopy and in vitro immunoprecipitation will be used to map the network of protein-protein interactions and its alterations in differently sized clusters.
In Aim 3, we will address how glucosome clusters are spatially altered to functionally contribute to the cell cycle progression. Flow cytometry, time-lapse fluorescence live-cell imaging, cell synchronization and mass spectrometry will be employed to functionally correlate the sizes of glucosome clusters with the cell cycle. We envision that metabolic activities of the rate-determining enzymes in glucose metabolism are spatially regulated inside glucosome clusters to govern the direction of glucose flux in cells. The proposed research will significantly advance our understanding of glucose flux regulation at subcellular levels and thus its dysregulation in human metabolic diseases, like cancer and diabetes. Collectively, this new level of understanding will divulge the importance of a heretofore unrecognized metabolic compartment as a novel target for therapeutic intervention.

Public Health Relevance

Metabolic alterations in glucose metabolism are primarily responsible for various human metabolic diseases, like cancer and diabetes. However, our current knowledge of metabolism is not sufficient yet to explan how metabolic enyzmes in glucose metabolism communicate with each other in space and time to control glucose flux into certain directions at subcellular levels. The proposed research will reveal a fundamental mechanism of glucose flux regulation in human cells, thereby advancing our understanding of altered metabolism in human diseases.

Agency
National Institute of Health (NIH)
Institute
National Institute of General Medical Sciences (NIGMS)
Type
Research Project (R01)
Project #
5R01GM125981-02
Application #
9607600
Study Section
Macromolecular Structure and Function A Study Section (MSFA)
Program Officer
Barski, Oleg
Project Start
2017-12-05
Project End
2022-11-30
Budget Start
2018-12-01
Budget End
2019-11-30
Support Year
2
Fiscal Year
2019
Total Cost
Indirect Cost
Name
University of Maryland Balt CO Campus
Department
Chemistry
Type
Schools of Arts and Sciences
DUNS #
061364808
City
Baltimore
State
MD
Country
United States
Zip Code
21250
Schmitt, Danielle L; Sundaram, Anand; Jeon, Miji et al. (2018) Spatial alterations of De Novo purine biosynthetic enzymes by Akt-independent PDK1 signaling pathways. PLoS One 13:e0195989
Jeon, Miji; Kang, Hye-Won; An, Songon (2018) A Mathematical Model for Enzyme Clustering in Glucose Metabolism. Sci Rep 8:2696