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Metabolic flux sensing of sugars in Saccharomyces cerevisiae

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2026-01-09

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Li, Ang. 2026. Metabolic flux sensing of sugars in Saccharomyces cerevisiae. Doctoral Dissertation, Harvard University Graduate School of Arts and Sciences.

Abstract

A canonical view of nutrient sensing is that cells sense the concentration of nutrients. However, it is sometimes beneficial to regulate cellular processes based on the metabolic flux through a pathway, instead of the concentration of a nutrient. The mechanisms to achieve metabolic flux sensing remain largely unclear. The observation of flux-dependent regulation is also limited.

This dissertation work explores the phenomena and mechanisms of metabolic flux sensing of sugars in budding yeast Saccharomyces cerevisiae. Galactose and glucose are two types of sugar used by cells as carbon sources for growth. Cells sense the sugars for proper regulation of their metabolism. In the case of galactose, cells sense galactose for deciding the induction of the galactose-utilization (GAL) pathway. In the case of glucose, cells sense glucose for repressing genes involved in utilizing other carbon sources.

To study the metabolic sensing processes, first, a series of tools are developed to enable the necessary genetic perturbation and control of metabolism in living cells. A high-throughput imaging method is developed to monitor the perturbations and measure signaling readout of the pathway. Then, we study the signaling role of galactokinase Gal1 in the GAL pathway, and report that the enzyme can couple its catalytic activity to signaling, resulting in a signaling output proportional to the metabolic flux. Next, in the glucose repression pathway, I find that glucose repression depends on metabolic flux of glucose. However, I rule out the signaling role of hexokinase Hxk2, and find that Mig1 controls glucose repression via a non-canonical mechanism. Some candidate metabolites potentially for determining glucose repression are identified through differential perturbations of glycolytic metabolites and model predictions. Last, I describe a growth curve measurement device I developed, which enables easy and accurate measurement of growth curves.

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Biology, Microbiology, Genetics

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