bioRxivGeneticsPreprint
Genome-wide analysis reveals the importance of histone acetyltransferase Esa1 in transcriptional regulation during nitrogen starvation
Uzair Khan, Krystal Cvetkovski, Matthew Werick, Cole A Tracy, Sara Fatima, Dimitra DialynakiiD, Daniel J. KlionskyiD, Chhabi K. GovindiD
Abstract
Macroautophagy/autophagy is a process that degrades intracellular components and is strongly triggered by nitrogen starvation (-N). Some ATG (autophagy related) genes are activated at the transcriptional level in nitrogen starvation; however, a full understanding of transcriptional induction and the role of chromatin during this process remains unclear. To address this, we measured the occupancy of RNA polymerase II (Pol II), histone H3, and acetylated H4 (H4Ac) under nutrient-rich and -N conditions by ChIP-seq. We found that most genes are rapidly downregulated within 15-30 min, including ribosomal protein (RP) and biogenesis (RiBi) genes. Meanwhile, genes involved in amino acid (AA) biosynthesis are upregulated, along with many ATG genes. Unexpectedly, RP and RiBi genes were reinduced by 3 hours. Furthermore, many upregulated genes remained active during prolonged starvation. Histones are typically removed from promoters during transcription activation. Consistent with this, we found that most induced genes exhibited histone eviction and increased H4 acetylation at their promoters, suggesting a possible role for histone acetylation in their activation. In line with this, depleting Esa1, an essential H4 histone acetyltransferase, nearly abolished the induction of ribosomal biosynthetic genes and many AA biosynthetic genes. Sustained activation of many genes during prolonged starvation highlights the vital role of transcription in supporting autophagy and cell survival. This is the first comprehensive study to detail changes in chromatin, histone acetylation, and transcription during nitrogen starvation, highlighting the importance of Esa1 and H4Ac in this process.
- assay: ChIP-seq
- research focus: differential expression
- research focus: gene regulatory network inference
- approach: statistical model
- Citations
- 0
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