Ribosome•RelA structures reveal the mechanism of stringent response activation

  1. Anna B Loveland
  2. Eugene Bah
  3. Rohini Madireddy
  4. Ying Zhang
  5. Axel F Brilot
  6. Nikolaus Grigorieff  Is a corresponding author
  7. Andrei A Korostelev  Is a corresponding author
  1. University of Massachusetts Medical School, United States
  2. Mayo Medical School, United States
  3. Brandeis University, United States

Abstract

Stringent response is a conserved bacterial stress response underlying virulence and antibiotic resistance. RelA/SpoT-homolog proteins synthesize transcriptional modulators (p)ppGpp, allowing bacteria to adapt to stresses. RelA is activated during amino-acid starvation, when cognate deacyl-tRNA binds to the ribosomal A (aminoacyl-tRNA) site. We report four cryo-EM structures of E. coli RelA bound to the 70S ribosome, in the absence and presence of deacyl-tRNA accommodating in the 30S A site. The boomerang-shaped RelA with a wingspan of more than 100 Å wraps around the A/R (30S A-site/RelA-bound) tRNA. The CCA end of the A/R tRNA pins the central TGS domain against the 30S subunit, presenting the (p)ppGpp-synthetase domain near the 30S spur. The ribosome and A/R tRNA are captured in three conformations, revealing hitherto elusive states of tRNA engagement with the ribosomal decoding center. Decoding-center rearrangements are coupled with the step-wise 30S-subunit 'closure', providing insights into the dynamics of high-fidelity tRNA decoding.

Data availability

The following data sets were generated

Article and author information

Author details

  1. Anna B Loveland

    RNA Therapeutics Institute, University of Massachusetts Medical School, Worcester, United States
    Competing interests
    No competing interests declared.
  2. Eugene Bah

    Mayo Medical School, Rochester, United States
    Competing interests
    No competing interests declared.
  3. Rohini Madireddy

    RNA Therapeutics Institute, University of Massachusetts Medical School, Worcester, United States
    Competing interests
    No competing interests declared.
  4. Ying Zhang

    RNA Therapeutics Institute, University of Massachusetts Medical School, Worcester, United States
    Competing interests
    No competing interests declared.
  5. Axel F Brilot

    Department of Biochemistry, Brandeis University, Waltham, United States
    Competing interests
    No competing interests declared.
  6. Nikolaus Grigorieff

    Department of Biochemistry, Brandeis University, Waltham, United States
    For correspondence
    niko@grigorieff.org
    Competing interests
    Nikolaus Grigorieff, Reviewing editor, eLife.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-1506-909X
  7. Andrei A Korostelev

    RNA Therapeutics Institute, University of Massachusetts Medical School, Worcester, United States
    For correspondence
    andrei.korostelev@umassmed.edu
    Competing interests
    No competing interests declared.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-1588-717X

Funding

Howard Hughes Medical Institute

  • Nikolaus Grigorieff

National Institutes of Health (RO1 GM106105)

  • Andrei A Korostelev

National Institutes of Health (PO1 GM62580)

  • Nikolaus Grigorieff

Helen Hay Whitney Foundation

  • Anna B Loveland

The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.

Copyright

© 2016, Loveland et al.

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

  • 5,479
    views
  • 924
    downloads
  • 136
    citations

Views, downloads and citations are aggregated across all versions of this paper published by eLife.

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)

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

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

  1. Anna B Loveland
  2. Eugene Bah
  3. Rohini Madireddy
  4. Ying Zhang
  5. Axel F Brilot
  6. Nikolaus Grigorieff
  7. Andrei A Korostelev
(2016)
Ribosome•RelA structures reveal the mechanism of stringent response activation
eLife 5:e17029.
https://doi.org/10.7554/eLife.17029

Share this article

https://doi.org/10.7554/eLife.17029

Further reading

    1. Structural Biology and Molecular Biophysics
    Johannes Elferich, Lingli Kong ... Nikolaus Grigorieff
    Research Advance

    Images taken by transmission electron microscopes are usually affected by lens aberrations and image defocus, among other factors. These distortions can be modeled in reciprocal space using the contrast transfer function (CTF). Accurate estimation and correction of the CTF is essential for restoring the high-resolution signal in cryogenic electron microscopy (cryoEM). Previously, we described the implementation of algorithms for this task in the cisTEM software package (Grant et al., 2018). Here we show that taking sample characteristics, such as thickness and tilt, into account can improve CTF estimation. This is particularly important when imaging cellular samples, where measurement of sample thickness and geometry derived from accurate modeling of the Thon ring pattern helps judging the quality of the sample. This improved CTF estimation has been implemented in CTFFIND5, a new version of the cisTEM program CTFFIND. We evaluated the accuracy of these estimates using images of tilted aquaporin crystals and eukaryotic cells thinned by focused ion beam milling. We estimate that with micrographs of sufficient quality CTFFIND5 can measure sample tilt with an accuracy of 3° and sample thickness with an accuracy of 5 nm.

    1. Structural Biology and Molecular Biophysics
    Sneha Menon, Subinoy Adhikari, Jagannath Mondal
    Research Article

    The mis-folding and aggregation of intrinsically disordered proteins (IDPs) such as α-synuclein (αS) underlie the pathogenesis of various neurodegenerative disorders. However, targeting αS with small molecules faces challenges due to the lack of defined ligand-binding pockets in its disordered structure. Here, we implement a deep artificial neural network-based machine learning approach, which is able to statistically distinguish the fuzzy ensemble of conformational substates of αS in neat water from those in aqueous fasudil (small molecule of interest) solution. In particular, the presence of fasudil in the solvent either modulates pre-existing states of αS or gives rise to new conformational states of αS, akin to an ensemble-expansion mechanism. The ensembles display strong conformation-dependence in residue-wise interaction with the small molecule. A thermodynamic analysis indicates that small-molecule modulates the structural repertoire of αS by tuning protein backbone entropy, however entropy of the water remains unperturbed. Together, this study sheds light on the intricate interplay between small molecules and IDPs, offering insights into entropic modulation and ensemble expansion as key biophysical mechanisms driving potential therapeutics.