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Translation initiation events on structured eukaryotic mRNAs generate gene expression noise
Author(s) -
Estelle Dacheux,
Naglis Malys,
Xiang Meng,
Vinoy K. Ramachandran,
Pedro Mendes,
John E.G. McCarthy
Publication year - 2017
Publication title -
nucleic acids research
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 9.008
H-Index - 537
eISSN - 1362-4954
pISSN - 0305-1048
DOI - 10.1093/nar/gkx430
Subject(s) - biology , five prime untranslated region , stem loop , untranslated region , genetics , open reading frame , computational biology , three prime untranslated region , gene , bursting , eukaryotic translation , eukaryotic chromosome fine structure , gene expression , messenger rna , upstream open reading frame , translation (biology) , microbiology and biotechnology , peptide sequence , genome , neuroscience
Gene expression stochasticity plays a major role in biology, creating non-genetic cellular individuality and influencing multiple processes, including differentiation and stress responses. We have addressed the lack of knowledge about posttranscriptional contributions to noise by determining cell-to-cell variations in the abundance of mRNA and reporter protein in yeast. Two types of structural element, a stem-loop and a poly(G) motif, not only inhibit translation initiation when inserted into an mRNA 5΄ untranslated region, but also generate noise. The noise-enhancing effect of the stem-loop structure also remains operational when combined with an upstream open reading frame. This has broad significance, since these elements are known to modulate the expression of a diversity of eukaryotic genes. Our findings suggest a mechanism for posttranscriptional noise generation that will contribute to understanding of the generally poor correlation between protein-level stochasticity and transcriptional bursting. We propose that posttranscriptional stochasticity can be linked to cycles of folding/unfolding of a stem-loop structure, or to interconversion between higher-order structural conformations of a G-rich motif, and have created a correspondingly configured computational model that generates fits to the experimental data. Stochastic events occurring during the ribosomal scanning process can therefore feature alongside transcriptional bursting as a source of noise.

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