bioRxivMolecular BiologyPreprint
Decoding Transcriptional Memory in Yeast Heat Shock and the Functional Implication of the RNA Binding Protein Mip6
Ana Tejada Colón, Joan Serrano‐QuíleziD, Carme Nuño‐CabanesiD, Susana Rodríguez‐NavarroiD
Abstract
Cells not only adapt to environmental changes, but they also "remember" specific signals, allowing them to respond more rapidly to future stressors. This phenomenon, known as transcriptional memory, is orchestrated by a complex interplay of epigenetics, transcription regulators and RNA metabolism factors. Although transcriptional memory has been well-studied in various contexts, its role in the heat shock (HS) response of yeast remains largely unexplored. In our study, we delve into the dynamics of HS memory in wild-type yeast and a mip6{Delta} mutant, which lacks the RNA-binding protein Mip6. Notably, Mip6 has been shown to influence the expression of key stress-related genes and maintain low Msn2/4-dependent transcript levels under standard conditions. Our transcriptomic analysis offers novel insights into how yeast cells remember HS exposure. We uncover striking differences in gene expression patterns depending on whether genes are induced or repressed during HS memory. Furthermore, we find that an initial 15-minute heat shock triggers a response that becomes attenuated with just 5 additional minutes of stress. While the response kinetics between memory and non-memory conditions are similar, we report a subtle but important role for Mip6 in fine-tuning transcriptional memory and adaptation to heat stress. Biochemical and genetic evidence also suggests that Mip6 cooperates with alternative survival pathways independent of MSN2/4, and functionally interacts with the Rpd3 histone deacetylase complex, a key player in transcriptional memory and the HS response. These findings open up new avenues for understanding the molecular mechanisms behind heat stress adaptation in eukaryotes.
- organism: yeast
- assay: bulk RNA-seq
- research focus: differential expression
- research focus: gene regulatory network inference
- approach: statistical model
- Citations
- 0
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