4
views
0
recommends
+1 Recommend
0 collections
    0
    shares
      • Record: found
      • Abstract: found
      • Article: found
      Is Open Access

      Global Analysis of the Specificities and Targets of Endoribonucleases from Escherichia coli Toxin-Antitoxin Systems

      research-article
      a , b , a , b , c ,
      mBio
      American Society for Microbiology
      Escherichia coli, RNA-seq, endoribonucleases, toxin-antitoxin systems

      Read this article at

      Bookmark
          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          ABSTRACT

          Toxin-antitoxin systems are widely distributed genetic modules typically featuring toxins that can inhibit bacterial growth and antitoxins that can reverse inhibition. Although Escherichia coli encodes 11 toxins with known or putative endoribonuclease activity, the targets of most of these toxins remain poorly characterized. Using a new RNA sequencing (RNA-seq) pipeline that enables the mapping and quantification of RNA cleavage with single-nucleotide resolution, we characterized the targets and specificities of 9 endoribonuclease toxins from E. coli. We found that these toxins use low-information cleavage motifs to cut a significant proportion of mRNAs in E. coli, but not tRNAs or the rRNAs from mature ribosomes. However, all the toxins, including those that are ribosome dependent and cleave only translated RNA, inhibit ribosome biogenesis. This inhibition likely results from the cleavage of ribosomal protein transcripts, which disrupts the stoichiometry and biogenesis of new ribosomes and causes the accumulation of aberrant ribosome precursors. Collectively, our results provide a comprehensive, global analysis of endoribonuclease-based toxin-antitoxin systems in E. coli and support the conclusion that, despite their diversity, each disrupts translation and ribosome biogenesis.

          Related collections

          Most cited references48

          • Record: found
          • Abstract: found
          • Article: not found

          Fast gapped-read alignment with Bowtie 2.

          As the rate of sequencing increases, greater throughput is demanded from read aligners. The full-text minute index is often used to make alignment very fast and memory-efficient, but the approach is ill-suited to finding longer, gapped alignments. Bowtie 2 combines the strengths of the full-text minute index with the flexibility and speed of hardware-accelerated dynamic programming algorithms to achieve a combination of high speed, sensitivity and accuracy.
            Bookmark
            • Record: found
            • Abstract: not found
            • Article: not found

            Cutadapt removes adapter sequences from high-throughput sequencing reads

              Bookmark
              • Record: found
              • Abstract: found
              • Article: not found

              MUSCLE: multiple sequence alignment with high accuracy and high throughput.

              We describe MUSCLE, a new computer program for creating multiple alignments of protein sequences. Elements of the algorithm include fast distance estimation using kmer counting, progressive alignment using a new profile function we call the log-expectation score, and refinement using tree-dependent restricted partitioning. The speed and accuracy of MUSCLE are compared with T-Coffee, MAFFT and CLUSTALW on four test sets of reference alignments: BAliBASE, SABmark, SMART and a new benchmark, PREFAB. MUSCLE achieves the highest, or joint highest, rank in accuracy on each of these sets. Without refinement, MUSCLE achieves average accuracy statistically indistinguishable from T-Coffee and MAFFT, and is the fastest of the tested methods for large numbers of sequences, aligning 5000 sequences of average length 350 in 7 min on a current desktop computer. The MUSCLE program, source code and PREFAB test data are freely available at http://www.drive5. com/muscle.
                Bookmark

                Author and article information

                Contributors
                Role: Editor
                Journal
                mBio
                mBio
                mbio
                mBio
                American Society for Microbiology (1752 N St., N.W., Washington, DC )
                2150-7511
                21 September 2021
                Sep-Oct 2021
                21 September 2021
                : 12
                : 5
                : e02012-21
                Affiliations
                [a ] Department of Immunology and Infectious Diseases, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, USA
                [b ] Department of Biology, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
                [c ] Howard Hughes Medical Institute, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
                The Ohio State University
                Author information
                https://orcid.org/0000-0001-7565-9975
                https://orcid.org/0000-0002-8288-7607
                Article
                mBio02012-21 mbio.02012-21
                10.1128/mBio.02012-21
                8546651
                34544284
                931af1e5-0939-4d79-a095-764cdf03f5a8
                Copyright © 2021 Culviner et al.

                This is an open-access article distributed under the terms of the Creative Commons Attribution 4.0 International license.

                History
                : 7 July 2021
                : 18 August 2021
                Page count
                supplementary-material: 5, Figures: 6, Tables: 1, Equations: 0, References: 48, Pages: 19, Words: 12720
                Funding
                Funded by: HHS | National Institutes of Health (NIH), FundRef https://doi.org/10.13039/100000002;
                Award ID: P01AI143575
                Award Recipient :
                Funded by: HHS | National Institutes of Health (NIH), FundRef https://doi.org/10.13039/100000002;
                Award ID: R01GM082899
                Award Recipient :
                Funded by: Howard Hughes Medical Institute (HHMI), FundRef https://doi.org/10.13039/100000011;
                Award ID: Investigator
                Award Recipient :
                Categories
                Research Article
                bacteriology, Bacteriology
                Custom metadata
                September/October 2021

                Life sciences
                escherichia coli,rna-seq,endoribonucleases,toxin-antitoxin systems
                Life sciences
                escherichia coli, rna-seq, endoribonucleases, toxin-antitoxin systems

                Comments

                Comment on this article