Year: 2026
Type: dissertation
Abstract: Cell signaling networks rely on both genetic diversity and precise regulatory mechanisms to achieve specificity and robustness. In eukaryotic genomes, gene duplication has generated large numbers of paralogs, yet the extent to which individual paralogs contribute uniquely to signaling pathways remains incompletely understood. In this dissertation, we use Saccharomyces cerevisiae as a genetically tractable model to investigate how paralogous signaling components and essential G protein regulators shape pathway output. We first performed a systematic analysis of paralog pairs in yeast glucose sensing pathways, developing a single cell transcriptional reporter to quantify responses to glucose and alternative carbon sources. By integrating genetic perturbations with metabolomics, cytosolic pH measurements, and proximity-based proteomics, we demonstrate that paralogs in yeast glucose sensing pathways are largely non-redundant. They exert both shared and distinct effects on transcription, metabolism, and protein interaction networks, providing insight into the evolutionary retention of duplicated genes. In parallel, we developed an auxin-inducible degron (AID) platform to overcome limitations in studying essential signaling proteins. Conditional depletion of the yeast Gα protein Gpa1 enabled direct functional analysis of disease-associated Gα variants linked to uveal melanoma and GNAO1 encephalopathy. This approach revealed mutant-specific and recessive signaling phenotypes that are masked in the presence of endogenous Gpa1. In summary, these studies establish complementary experimental frameworks for dissecting signaling specificity arising from gene duplication and pathogenic mutation, and highlight the power of conditional perturbation strategies to study mutants of essential proteins involved in G protein signaling mechanisms.
DOI: 10.17615/69hw-ev70
Author: Si, Yibo
Institution: University of North Carolina at Chapel Hill
Language: English
Subfield: Molecular Biology
Domain: Life Sciences
Open Access status: green
OpenAlex ID: W7162782595
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