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

      A bacterial pan-genome makes gene essentiality strain-dependent and evolvable

      research-article

      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

          Many bacterial species are represented by a pan-genome, whose genetic repertoire far outstrips that of any single bacterial genome. Here we investigate how a bacterial pan-genome might influence gene essentiality and whether essential genes that are initially critical for the survival of an organism can evolve to become non-essential. By using Transposon insertion sequencing (Tn-seq), whole-genome sequencing and RNA-seq on a set of 36 clinical Streptococcus pneumoniae strains representative of >68% of the species’ pan-genome, we identify a species-wide ‘essentialome’ that can be subdivided into universal, core strain-specific and accessory essential genes. By employing ‘forced-evolution experiments’, we show that specific genetic changes allow bacteria to bypass essentiality. Moreover, by untangling several genetic mechanisms, we show that gene essentiality can be highly influenced by and/or be dependent on: (1) the composition of the accessory genome, (2) the accumulation of toxic intermediates, (3) functional redundancy, (4) efficient recycling of critical metabolites and (5) pathway rewiring. While this functional characterization underscores the evolvability potential of many essential genes, we also show that genes with differential essentiality remain important antimicrobial drug target candidates, as their inactivation almost always has a severe fitness cost in vivo.

          Abstract

          Pan-genome analyses of clinical pneumococcal strains identify categories of essential genes and show that gene essentiality depends on strain genetic background.

          Related collections

          Most cited references77

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

          Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing

            Bookmark
            • Record: found
            • Abstract: found
            • Article: found
            Is Open Access

            RSEM: accurate transcript quantification from RNA-Seq data with or without a reference genome

            Background RNA-Seq is revolutionizing the way transcript abundances are measured. A key challenge in transcript quantification from RNA-Seq data is the handling of reads that map to multiple genes or isoforms. This issue is particularly important for quantification with de novo transcriptome assemblies in the absence of sequenced genomes, as it is difficult to determine which transcripts are isoforms of the same gene. A second significant issue is the design of RNA-Seq experiments, in terms of the number of reads, read length, and whether reads come from one or both ends of cDNA fragments. Results We present RSEM, an user-friendly software package for quantifying gene and isoform abundances from single-end or paired-end RNA-Seq data. RSEM outputs abundance estimates, 95% credibility intervals, and visualization files and can also simulate RNA-Seq data. In contrast to other existing tools, the software does not require a reference genome. Thus, in combination with a de novo transcriptome assembler, RSEM enables accurate transcript quantification for species without sequenced genomes. On simulated and real data sets, RSEM has superior or comparable performance to quantification methods that rely on a reference genome. Taking advantage of RSEM's ability to effectively use ambiguously-mapping reads, we show that accurate gene-level abundance estimates are best obtained with large numbers of short single-end reads. On the other hand, estimates of the relative frequencies of isoforms within single genes may be improved through the use of paired-end reads, depending on the number of possible splice forms for each gene. Conclusions RSEM is an accurate and user-friendly software tool for quantifying transcript abundances from RNA-Seq data. As it does not rely on the existence of a reference genome, it is particularly useful for quantification with de novo transcriptome assemblies. In addition, RSEM has enabled valuable guidance for cost-efficient design of quantification experiments with RNA-Seq, which is currently relatively expensive.
              Bookmark
              • Record: found
              • Abstract: found
              • Article: found
              Is Open Access

              SWISS-MODEL: homology modelling of protein structures and complexes

              Abstract Homology modelling has matured into an important technique in structural biology, significantly contributing to narrowing the gap between known protein sequences and experimentally determined structures. Fully automated workflows and servers simplify and streamline the homology modelling process, also allowing users without a specific computational expertise to generate reliable protein models and have easy access to modelling results, their visualization and interpretation. Here, we present an update to the SWISS-MODEL server, which pioneered the field of automated modelling 25 years ago and been continuously further developed. Recently, its functionality has been extended to the modelling of homo- and heteromeric complexes. Starting from the amino acid sequences of the interacting proteins, both the stoichiometry and the overall structure of the complex are inferred by homology modelling. Other major improvements include the implementation of a new modelling engine, ProMod3 and the introduction a new local model quality estimation method, QMEANDisCo. SWISS-MODEL is freely available at https://swissmodel.expasy.org.
                Bookmark

                Author and article information

                Contributors
                tvanopij@broadinstitute.org
                Journal
                Nat Microbiol
                Nat Microbiol
                Nature Microbiology
                Nature Publishing Group UK (London )
                2058-5276
                12 September 2022
                12 September 2022
                2022
                : 7
                : 10
                : 1580-1592
                Affiliations
                [1 ]GRID grid.208226.c, ISNI 0000 0004 0444 7053, Biology Department, , Boston College, ; Chestnut Hill, MA USA
                [2 ]GRID grid.240871.8, ISNI 0000 0001 0224 711X, Department of Infectious Diseases, , St Jude Children’s Research Hospital, ; Memphis, TN USA
                [3 ]GRID grid.270240.3, ISNI 0000 0001 2180 1622, Vaccine and Infectious Disease Division, , Fred Hutchinson Cancer Research Center, ; Seattle, WA USA
                [4 ]GRID grid.66859.34, ISNI 0000 0004 0546 1623, Broad Institute of MIT and Harvard, ; Cambridge, MA USA
                Author information
                http://orcid.org/0000-0001-6760-9003
                http://orcid.org/0000-0002-8423-6840
                http://orcid.org/0000-0001-8648-4189
                http://orcid.org/0000-0002-1798-1760
                http://orcid.org/0000-0001-6895-6795
                Article
                1208
                10.1038/s41564-022-01208-7
                9519441
                36097170
                75546f0a-8e42-4a85-b9fb-700a394cf898
                © The Author(s) 2022

                Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

                History
                : 15 February 2022
                : 21 July 2022
                Funding
                Funded by: FundRef https://doi.org/10.13039/100000060, U.S. Department of Health & Human Services | NIH | National Institute of Allergy and Infectious Diseases (NIAID);
                Award ID: R01 AI110724
                Award ID: U01 AI124302
                Award ID: R21 AI117247
                Award ID: U01 AI124302
                Award Recipient :
                Funded by: FundRef https://doi.org/10.13039/100000875, Pew Charitable Trusts;
                Funded by: FundRef https://doi.org/10.13039/100008601, Charles A. King Trust;
                Funded by: FundRef https://doi.org/10.13039/100000072, U.S. Department of Health & Human Services | NIH | National Institute of Dental and Craniofacial Research (NIDCR);
                Award ID: R01 DE027850
                Award Recipient :
                Categories
                Article
                Custom metadata
                © The Author(s), under exclusive licence to Springer Nature Limited 2022

                bacterial genetics,epistasis,mutagenesis,next-generation sequencing,transcriptomics

                Comments

                Comment on this article