Journal of Biological ChemistryGenomicsPeer-reviewed journal article
Cytoplasmic mRNA decay factors modulate RNA polymerase II processivity in 5’ and 3’ gene regions in yeast
Jonathan D. FischeriD, Yun S. SongiD, Nir YosefiD, Julia di IulioiD, L. Stirling ChurchmaniD, Mordechai ChoderiD
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Abstract
mRNA levels are determined by the balance between mRNA synthesis and decay. Factors that mediate both processes, including the 5 to 3 exonuclease Xrn1, are responsible for the cross talk between the two processes in a manner that buffers steady-state mRNA levels. However, these proteins roles in transcription remain elusive and controversial. Applying NET-seq to yeast cells, we show that Xrn1 functions mainly as a transcriptional activator and that its disruption manifests via the reduction of RNA polymerase II (Pol II) occupancy downstream of transcription start sites. We combine our data and novel mathematical modeling of transcription to suggest that transcription initiation and elongation of targeted genes is modulated by Xrn1. Furthermore, Pol II occupancy markedly increases near cleavage and polyadenylation sites in xrn1{Delta} cells while its activity decreases, a characteristic feature of backtracked Pol II. We also provide indirect evidence that Xrn1 is involved in transcription termination downstream of polyadenylation sites. Two additional decay factors, Dhh1 and Lsm1, seem to function similarly to Xrn1 in transcription, perhaps as a complex, while the decay factors Ccr4 and Rpb4 also perturb transcription in other ways. Interestingly, DFs are capable of differentiating between SAGA- and TFIID-dominated promoters. These two classes of genes respond differently to XRN1 deletion in mRNA synthesis and differentially utilize mRNA decay pathways, raising the possibility that one distinction between the two types of genes lies in the mechanism(s) that balance these processes.
- organism: yeast
- assay: DNA methylation
- research focus: gene regulatory network inference
- research focus: differential expression
- research focus: variant effect prediction
- approach: statistical model
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