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Transcriptional responses of acute glucose deprivation reveal a role for Snf12 and Spt20 in metabolic adaptation during stress

Aug 2026 · bioRxiv · 0 citations · 43 references
Biology

TL;DR

This dataset provides a comprehensive transcriptomic resource for dissecting stress signalling and driving novel hypothesis generation and identified cargo and stress-specific expression related to both eisosome components and surface transporters that are critical for metabolic adaptation.

Abstract

The budding yeast Saccharomyces cerevisiae is a well-established model organism to study cellular stress response and underlying mechanistic regulation. Although glucose starvation fundamentally alters gene regulation and cell behaviour, inconsistent deprivation protocols often trigger gross morphological artefacts. These non-specific changes confound findings by activating pathways independently of true glucose-signalling mechanisms. Furthermore, a thorough transcriptomic profile of glucose starvation using non-confounding conditions remains lacking. Consequently, the precise transcriptional impact of losing key metabolic regulators that mediate adaptation to glucose starvation remains undefined. Here we have employed a refined glucose starvation protocol, utilising raffinose exchange, which shows induction of vast transcriptional stress response with minimal impact on cellular morphology confirmed by label-free imaging. Transcriptomic profiling revealed shifts in metabolic regulation, ATP turnover, and cell-to-cell communication as acute glucose deprivation driving cells towards oxidation-driven metabolism. Additionally, we characterise transcriptional alterations seen in deletion mutants of SNF12 and SPT20, known regulators of cellular metabolism, showing previously unappreciated transcriptional conservation, in part mimicking glucose starvation response. Finally, we identified cargo and stress-specific expression related to both eisosome components and surface transporters that are critical for metabolic adaptation. Overall, this dataset provides a comprehensive transcriptomic resource for dissecting stress signalling and driving novel hypothesis generation.

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