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This content will become publicly available on July 13, 2024

Title: mRNA decapping activators Pat1 and Dhh1 regulate transcript abundance and translation to tune cellular responses to nutrient availability
Abstract

We have examined the roles of yeast mRNA decapping-activators Pat1 and Dhh1 in repressing the translation and abundance of specific mRNAs in nutrient-replete cells using ribosome profiling, RNA-Seq, CAGE analysis of capped mRNAs, RNA Polymerase II ChIP-Seq, and TMT-mass spectrometry of mutants lacking one or both factors. Although the Environmental Stress Response (ESR) is activated in dhh1Δ and pat1Δ mutants, hundreds of non-ESR transcripts are elevated in a manner indicating cumulative repression by Pat1 and Dhh1 in wild-type cells. These mRNAs show both reduced decapping and diminished transcription in the mutants, indicating that impaired mRNA turnover drives transcript derepression in cells lacking Dhh1 or Pat1. mRNA degradation stimulated by Dhh1/Pat1 is not dictated by poor translation nor enrichment for suboptimal codons. Pat1 and Dhh1 also collaborate to reduce translation and protein production from many mRNAs. Transcripts showing concerted translational repression by Pat1/Dhh1 include mRNAs involved in cell adhesion or utilization of the poor nitrogen source allantoin. Pat1/Dhh1 also repress numerous transcripts involved in respiration, catabolism of non-preferred carbon or nitrogen sources, or autophagy; and we obtained evidence for elevated respiration and autophagy in the mutants. Thus, Pat1 and Dhh1 function as post-transcriptional repressors of multiple pathways normally activated only during nutrient limitation.

 
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Award ID(s):
1951332
NSF-PAR ID:
10490899
Author(s) / Creator(s):
; ; ; ; ; ; ; ;
Publisher / Repository:
Oxford University Press
Date Published:
Journal Name:
Nucleic Acids Research
Volume:
51
Issue:
17
ISSN:
0305-1048
Page Range / eLocation ID:
9314 to 9336
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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    APA-Scan utilizes either predicted or experimentally validated actionable polyadenylation signals as a reference for polyadenylation sites and calculates the quantity of long and short 3′-UTR transcripts in the RNA-seq data. APA-Scan works in three major steps: (i) calculate the read coverage of the 3′-UTR regions of genes; (ii) identify the potential APA sites and evaluate the significance of the events among two biological conditions; (iii) graphical representation of user specific event with 3′-UTR annotation and read coverage on the 3′-UTR regions. APA-Scan is implemented in Python3. Source code and a comprehensive user’s manual are freely available athttps://github.com/compbiolabucf/APA-Scan.

    Result

    APA-Scan was applied to both simulated and real RNA-seq datasets and compared with two widely used baselines DaPars and APAtrap. In simulation APA-Scan significantly improved the accuracy of 3′-UTR APA identification compared to the other baselines. The performance of APA-Scan was also validated by 3′-end-seq data and qPCR on mouse embryonic fibroblast cells. The experiments confirm that APA-Scan can detect unannotated 3′-UTR APA events and improve genome annotation.

    Conclusion

    APA-Scan is a comprehensive computational pipeline to detect transcriptome-wide 3′-UTR APA events. The pipeline integrates both RNA-seq and 3′-end-seq data information and can efficiently identify the significant events with a high-resolution short reads coverage plots.

     
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