A dedicated diribonucleotidase resolves a key bottleneck as the terminal step of RNA degradation
- University of Maryland, United States
- Cornell University, United States
Abstract
Degradation of RNA polymers, an ubiquitous process in all cells, is catalyzed by specific subsets of endo- and exoribonucleases that together recycle RNA fragments into nucleotide monophosphate. In γ-proteobacteria, 3-'5' exoribonucleases comprise up to eight distinct enzymes. Among them, Oligoribonuclease (Orn) is unique as its activity is required for clearing short RNA fragments, which is important for cellular fitness. However, the molecular basis of Orn's unique cellular function remained unclear. Here we show that Orn exhibits exquisite substrate preference for diribonucleotides. Crystal structures of substrate-bound Orn reveal an active site optimized for diribonucleotides. While other cellular RNases process oligoribonucleotides down to diribonucleotide entities, Orn is the one and only diribonucleotidase that completes the terminal step of RNA degradation. Together, our studies indicate RNA degradation as a step-wise process with a dedicated enzyme for the clearance of a specific intermediate pool, diribonucleotides, that affects cellular physiology and viability.
Data availability
The atomic coordinates and structure factors have been deposited in the Protein Data Bank, www.rcsb.org (PDB ID codes 6N6A, 6N6C, 6N6D, 6N6E, 6N6F, 6N6G, 6N6H, 6N6I, 6N6J, and 6N6K). Source data files have been provided for Figures.
-
Vibrio cholerae Oligoribonuclease bound to pGGProtein Data Bank, 6N6A.
-
Vibrio cholerae Oligoribonuclease bound to pAAProtein Data Bank, 6N6C.
-
Vibrio cholerae Oligoribonuclease bound to pAGProtein Data Bank, 6N6D.
-
Vibrio cholerae Oligoribonuclease bound to pGAProtein Data Bank, 6N6E.
-
Vibrio cholerae Oligoribonuclease bound to pGCProtein Data Bank, 6N6F.
-
Vibrio cholerae Oligoribonuclease bound to pCGProtein Data Bank, 6N6G.
-
Vibrio cholerae Oligoribonuclease bound to pCpUProtein Data Bank, 6N6H.
Article and author information
Author details
Funding
National Institute of Allergy and Infectious Diseases (R01AI110740)
- Vincent T Lee
National Institute of General Medical Sciences (R01GM123609)
- Holger Sondermann
National Science Foundation (MCB1051440)
- Wade C Winkler
Cystic Fibrosis Foundation (LEE16G0)
- Vincent T Lee
National Institute of Diabetes and Digestive and Kidney Diseases (R01AI110740)
- Vincent T Lee
National Institute of General Medical Sciences (T32-GM080201)
- Cordelia A Weiss
National Institute of Allergy and Infectious Diseases (R01AI142400)
- Wade C Winkler
- Holger Sondermann
- Vincent T Lee
The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.
Reviewing Editor
- Bryce E Nickels, Rutgers University, United States
Version history
- Received: February 22, 2019
- Accepted: June 14, 2019
- Accepted Manuscript published: June 21, 2019 (version 1)
- Version of Record published: July 8, 2019 (version 2)
- Version of Record updated: July 10, 2019 (version 3)
- Version of Record updated: July 16, 2019 (version 4)
- Version of Record updated: January 28, 2022 (version 5)
Copyright
© 2019, Kim 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
-
- 1,989
- Page views
-
- 259
- Downloads
-
- 18
- 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)
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)
A dedicated diribonucleotidase resolves a key bottleneck as the terminal step of RNA degradation
eLife 8:e46313.
https://doi.org/10.7554/eLife.46313
Further reading
-
- Microbiology and Infectious Disease
Despite much progress, image processing remains a significant bottleneck for high-throughput analysis of microscopy data. One popular platform for single-cell time-lapse imaging is the mother machine, which enables long-term tracking of microbial cells under precisely controlled growth conditions. While several mother machine image analysis pipelines have been developed in the past several years, adoption by a non-expert audience remains a challenge. To fill this gap, we implemented our own software, MM3, as a plugin for the multidimensional image viewer napari. napari-MM3 is a complete and modular image analysis pipeline for mother machine data, which takes advantage of the high-level interactivity of napari. Here, we give an overview of napari-MM3 and test it against several well-designed and widely used image analysis pipelines, including BACMMAN and DeLTA. Researchers often analyze mother machine data with custom scripts using varied image analysis methods, but a quantitative comparison of the output of different pipelines has been lacking. To this end, we show that key single-cell physiological parameter correlations and distributions are robust to the choice of analysis method. However, we also find that small changes in thresholding parameters can systematically alter parameters extracted from single-cell imaging experiments. Moreover, we explicitly show that in deep learning-based segmentation, ‘what you put is what you get’ (WYPIWYG) – that is, pixel-level variation in training data for cell segmentation can propagate to the model output and bias spatial and temporal measurements. Finally, while the primary purpose of this work is to introduce the image analysis software that we have developed over the last decade in our lab, we also provide information for those who want to implement mother machine-based high-throughput imaging and analysis methods in their research.
