1. Computational and Systems Biology
Download icon

RNA polymerase errors cause splicing defects and can be regulated by differential expression of RNA polymerase subunits

  1. Lucas B Carey  Is a corresponding author
  1. Universitat Pompeu Fabra, Spain
Short Report
  • Cited 24
  • Views 7,052
  • Annotations
Cite this article as: eLife 2015;4:e09945 doi: 10.7554/eLife.09945

Abstract

Errors during transcription may play an important role in determining cellular phenotypes: the RNA polymerase error rate is >4 orders of magnitude higher than that of DNA polymerase and errors are amplified >1000-fold due to translation. However, current methods to measure RNA polymerase fidelity are low-throughout, technically challenging, and organism specific. Here I show that changes in RNA polymerase fidelity can be measured using standard RNA sequencing protocols. I find that RNA polymerase is error-prone, and these errors can result in splicing defects. Furthermore, I find that differential expression of RNA polymerase subunits causes changes in RNA polymerase fidelity, and that coding sequences may have evolved to minimize the effect of these errors. These results suggest that errors caused by RNA polymerase may be a major source of stochastic variability at the level of single cells.

Article and author information

Author details

  1. Lucas B Carey

    Department of Experimental and Health Sciences, Universitat Pompeu Fabra, Barcelona, Spain
    For correspondence
    lucas.carey@upf.edu
    Competing interests
    The authors declare that no competing interests exist.

Reviewing Editor

  1. Patrick Cramer, Max Planck Institute for Biophysical Chemistry, Germany

Publication history

  1. Received: July 8, 2015
  2. Accepted: October 26, 2015
  3. Accepted Manuscript published: December 10, 2015 (version 1)
  4. Version of Record published: December 29, 2015 (version 2)

Copyright

© 2015, Carey

This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.

Metrics

  • 7,052
    Page views
  • 435
    Downloads
  • 24
    Citations

Article citation count generated by polling the highest count across the following sources: Crossref, PubMed Central, Scopus.

Download links

A two-part list of links to download the article, or parts of the article, in various formats.

Downloads (link to download the article as PDF)

Download citations (links to download the citations from this article in formats compatible with various reference manager tools)

Open citations (links to open the citations from this article in various online reference manager services)

Further reading

    1. Computational and Systems Biology
    Martin Jinye Zhang et al.
    Research Article

    Aging is associated with complex molecular and cellular processes that are poorly understood. Here we leveraged the Tabula Muris Senis single-cell RNA-seq dataset to systematically characterize gene expression changes during aging across diverse cell types in the mouse. We identified aging-dependent genes in 76 tissue-cell types from 23 tissues and characterized both shared and tissue-cell-specific aging behaviors. We found that the aging-related genes shared by multiple tissue-cell types also change their expression congruently in the same direction during aging in most tissue-cell types, suggesting a coordinated global aging behavior at the organismal level. Scoring cells based on these shared aging genes allowed us to contrast the aging status of different tissues and cell types from a transcriptomic perspective. In addition, we identified genes that exhibit age-related expression changes specific to each functional category of tissue-cell types. Altogether, our analyses provide one of the most comprehensive and systematic characterizations of the molecular signatures of aging across diverse tissue-cell types in a mammalian system.

    1. Computational and Systems Biology
    Michael S Lauer et al.
    Research Article

    A previous report found an association of topic choice with race-based funding disparities among R01 applications submitted to the National Institutes of Health ('NIH') between 2011-2015. Applications submitted by African American or Black ('AAB') Principal Investigators ('PIs') skewed toward a small number of topics that were less likely to be funded (or 'awarded'). It was suggested that lower award rates may be related to topic-related biases of peer reviewers. However, the report did not account for differential funding ecologies among NIH Institutes and Centers ('ICs'). In a re-analysis, we find that 10% of 148 topics account for 50% of applications submitted by AAB PIs. These applications on 'AAB Preferred' topics were funded at lower rates, but peer review outcomes were similar. The lower rate of funding for these topics was primarily due to their assignment to ICs with lower award rates, not to peer-reviewer preferences.