Bidirectional Roles of the Ccr4-Not Complex in Regulating Autophagy Before and after Nitrogen Starvation

Zhangyuan Yin,Zhihai Zhang,Yuchen Lei,Daniel J. Klionsky

Autophagy（2022）SCI 1区SCI 2区

Cited 4|Views9

Abstract

Macroautophagy/autophagy is a highly conserved catabolic process by which cytoplasmic constituents are delivered to the vacuole/lysosome for degradation and recycling. To maintain cellular homeostasis and prevent pathologies, the induction and amplitude of autophagy activity are finely controlled through regulation of ATG gene expression. Here we report that the Ccr4-Not complex in Saccharomyces cerevisiae has bidirectional roles in regulating autophagy before and after nutrient deprivation. Under nutrient-rich conditions, Ccr4-Not directly targets the mRNAs of several ATG genes in the core autophagy machinery to promote their degradation through deadenylation, thus contributing to maintaining autophagy at the basal level. Upon starvation, Ccr4-Not releases its repression of these ATG genes and switches its role to promote the expression of a different subset of ATG genes, which is required for sufficient autophagy induction and activity. These results reveal that the Ccr4-Not complex is indispensable to maintain autophagy at the appropriate amplitude in both basal and stress conditions.Abbreviations: AID, auxin-inducible degron; Ape1, aminopeptidase I; Atg, autophagy related; Cvt, cytoplasm-to-vacuole targeting; DMSO, dimethyl sulfoxide; IAA, indole-3-acetic acid; PA, protein A; RIP, RNA immunoprecipitation.

Translated text

Key words

ATG mRNA,autophagy,Ccr4-Not complex,deadenylation,nitrogen starvation,Pop2,Caf1,post-transcriptional regulation,RNA decay

求助PDF

上传PDF

Bibtex

AI Read Science

AI Summary

AI Summary is the key point extracted automatically understanding the full text of the paper, including the background, methods, results, conclusions, icons and other key content, so that you can get the outline of the paper at a glance.

Example

Background

Key content

Introduction

Methods

Results

Related work

Fund

Key content

Pretraining has recently greatly promoted the development of natural language processing (NLP)
We show that M6 outperforms the baselines in multimodal downstream tasks, and the large M6 with 10 parameters can reach a better performance
We propose a method called M6 that is able to process information of multiple modalities and perform both single-modal and cross-modal understanding and generation
The model is scaled to large model with 10 billion parameters with sophisticated deployment, and the 10 -parameter M6-large is the largest pretrained model in Chinese
Experimental results show that our proposed M6 outperforms the baseline in a number of downstream tasks concerning both single modality and multiple modalities We will continue the pretraining of extremely large models by increasing data to explore the limit of its performance

Upload PDF to Generate Summary

Must-Reading Tree

Example

Generate MRT to find the research sequence of this paper