Major genetic discontinuity and novel toxigenic species in Clostridioides difficile taxonomy

  1. Daniel R Knight  Is a corresponding author
  2. Korakrit Imwattana
  3. Brian Kullin
  4. Enzo Guerrero-Araya
  5. Daniel Paredes-Sabja
  6. Xavier Didelot
  7. Kate E Dingle
  8. David W Eyre
  9. César Rodríguez
  10. Thomas V Riley  Is a corresponding author
  1. Murdoch University, Australia
  2. University of Western Australia, Australia
  3. University of Cape Town, South Africa
  4. Universidad Andrés Bello, Chile
  5. Universidad Andrés Bello, United Kingdom
  6. University of Warwick, United Kingdom
  7. University of Oxford, United Kingdom
  8. Universidad de Costa Rica, Costa Rica

Abstract

Clostridioides difficile infection (CDI) remains an urgent global One Health threat. The genetic heterogeneity seen across C. difficile underscores its wide ecological versatility and has driven the significant changes in CDI epidemiology seen in the last 20 years. We analysed an international collection of over 12,000 C. difficile genomes spanning the eight currently defined phylogenetic clades. Through whole-genome average nucleotide identity, and pangenomic and Bayesian analyses, we identified major taxonomic incoherence with clear species boundaries for each of the recently described cryptic clades CI-III. The emergence of these three novel genomospecies predates clades C1-5 by millions of years, rewriting the global population structure of C. difficile specifically and taxonomy of the Peptostreptococcaceae in general. These genomospecies all show unique and highly divergent toxin gene architecture, advancing our understanding of the evolution of C. difficile and close relatives. Beyond the taxonomic ramifications, this work may impact the diagnosis of CDI.

Data availability

All data generated or analysed during this study are included in the manuscript and Supplementary Data which is hosted at Figshare http://doi.org/10.6084/m9.figshare.12471461.Data files on figshare include:[1] Full MLST data for all 12000+ C. difficile genomes (Fig 1).[2] Whole-genome ANI analyses (Table 1, Fig 3, Fig 5).[3] Tree files for phylogenetic analyses (Fig 2, Fig 4).[4] Pangenome data (Fig 6).[5] Pan-GWAS data (Table 2).[6] Comparative genomic analysis of virulence gene architecture (Fig 7).Note: Regarding the question below - Did your work use any previously published datasets (e.g., DNA sequence data, clinical trial data, field data)?We retrieved the entire collection of C. difficile genomes (taxid ID 1496) held at the NCBI Sequence Read Archive [https://www.ncbi.nlm.nih.gov/sra/]. The raw dataset (as of 1st January 2020) comprised 12,621 genomes. These genomes comprise hundreds, maybe thousands of publications. The individual accession numbers for all genomes analysed in this study are provided in the Supplementary Data at http://doi.org/10.6084/m9.figshare.12471461.

Article and author information

Author details

  1. Daniel R Knight

    Murdoch University, Murdoch, Australia
    For correspondence
    daniel.knight@murdoch.edu.au
    Competing interests
    No competing interests declared.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-9480-4733
  2. Korakrit Imwattana

    School of Biomedical Sciences, University of Western Australia, Nedlands, Australia
    Competing interests
    No competing interests declared.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-2538-9775
  3. Brian Kullin

    Department of Pathology, University of Cape Town, Cape Town, South Africa
    Competing interests
    No competing interests declared.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-5460-1977
  4. Enzo Guerrero-Araya

    Microbiota-Host Interactions and Clostridia Research Group, Universidad Andrés Bello, Santiago, Chile
    Competing interests
    No competing interests declared.
  5. Daniel Paredes-Sabja

    Microbiota-Host Interactions and Clostridia Research Group, Universidad Andrés Bello, Santiago, United Kingdom
    Competing interests
    No competing interests declared.
  6. Xavier Didelot

    University of Warwick, Coventry, United Kingdom
    Competing interests
    No competing interests declared.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-1885-500X
  7. Kate E Dingle

    Nuffield Department of Clinical Medicine, University of Oxford, Oxford, United Kingdom
    Competing interests
    No competing interests declared.
  8. David W Eyre

    Big Data Institute, University of Oxford, Oxford, United Kingdom
    Competing interests
    David W Eyre, DWE declares lecture fees from Gilead, outside the submitted work..
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-5095-6367
  9. César Rodríguez

    Facultad de Microbiología & Centro de Investigación en Enfermedades Tropicales (CIET), Universidad de Costa Rica, San José, Costa Rica
    Competing interests
    No competing interests declared.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-5599-0652
  10. Thomas V Riley

    School of Biomedical Sciences, University of Western Australia, Nedlands, Australia
    For correspondence
    thomas.riley@uwa.edu.au
    Competing interests
    No competing interests declared.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-1351-3740

Funding

Raine Medical Research Foundation

  • Daniel R Knight

National Health and Medical Research Council

  • Daniel R Knight

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

Copyright

© 2021, Knight 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

  • 3,434
    views
  • 440
    downloads
  • 61
    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. Daniel R Knight
  2. Korakrit Imwattana
  3. Brian Kullin
  4. Enzo Guerrero-Araya
  5. Daniel Paredes-Sabja
  6. Xavier Didelot
  7. Kate E Dingle
  8. David W Eyre
  9. César Rodríguez
  10. Thomas V Riley
(2021)
Major genetic discontinuity and novel toxigenic species in Clostridioides difficile taxonomy
eLife 10:e64325.
https://doi.org/10.7554/eLife.64325

Share this article

https://doi.org/10.7554/eLife.64325

Further reading

    1. Genetics and Genomics
    2. Neuroscience
    Monique Marylin Alves de Almeida, Yves De Repentigny ... Rashmi Kothary
    Research Article

    Spinal muscular atrophy (SMA) is caused by mutations in the Survival Motor Neuron 1 (SMN1) gene. While traditionally viewed as a motor neuron disorder, there is involvement of various peripheral organs in SMA. Notably, fatty liver has been observed in SMA mouse models and SMA patients. Nevertheless, it remains unclear whether intrinsic depletion of SMN protein in the liver contributes to pathology in the peripheral or central nervous systems. To address this, we developed a mouse model with a liver-specific depletion of SMN by utilizing an Alb-Cre transgene together with one Smn2B allele and one Smn1 exon 7 allele flanked by loxP sites. Initially, we evaluated phenotypic changes in these mice at postnatal day 19 (P19), when the severe model of SMA, the Smn2B/- mice, exhibit many symptoms of the disease. The liver-specific SMN depletion does not induce motor neuron death, neuromuscular pathology or muscle atrophy, characteristics typically observed in the Smn2B/- mouse at P19. However, mild liver steatosis was observed, although no changes in liver function were detected. Notably, pancreatic alterations resembled that of Smn2B/-mice, with a decrease in insulin-producing β-cells and an increase in glucagon-producingα-cells, accompanied by a reduction in blood glucose and an increase in plasma glucagon and glucagon-like peptide (GLP-1). These changes were transient, as mice at P60 exhibited recovery of liver and pancreatic function. While the mosaic pattern of the Cre-mediated excision precludes definitive conclusions regarding the contribution of liver-specific SMN depletion to overall tissue pathology, our findings highlight an intricate connection between liver function and pancreatic abnormalities in SMA.

    1. Computational and Systems Biology
    2. Genetics and Genomics
    Jia-Ying Su, Yun-Lin Wang ... Chien-Ling Lin
    Research Article

    Untranslated regions (UTRs) contain crucial regulatory elements for RNA stability, translation and localization, so their integrity is indispensable for gene expression. Approximately 3.7% of genetic variants associated with diseases occur in UTRs, yet a comprehensive understanding of UTR variant functions remains limited due to inefficient experimental and computational assessment methods. To systematically evaluate the effects of UTR variants on RNA stability, we established a massively parallel reporter assay on 6555 UTR variants reported in human disease databases. We examined the RNA degradation patterns mediated by the UTR library in two cell lines, and then applied LASSO regression to model the influential regulators of RNA stability. We found that UA dinucleotides and UA-rich motifs are the most prominent destabilizing element. Gain of UA dinucleotide outlined mutant UTRs with reduced stability. Studies on endogenous transcripts indicate that high UA-dinucleotide ratios in UTRs promote RNA degradation. Conversely, elevated GC content and protein binding on UA dinucleotides protect high-UA RNA from degradation. Further analysis reveals polarized roles of UA-dinucleotide-binding proteins in RNA protection and degradation. Furthermore, the UA-dinucleotide ratio of both UTRs is a common characteristic of genes in innate immune response pathways, implying a coordinated stability regulation through UTRs at the transcriptomic level. We also demonstrate that stability-altering UTRs are associated with changes in biobank-based health indices, underscoring the importance of precise UTR regulation for wellness. Our study highlights the importance of RNA stability regulation through UTR primary sequences, paving the way for further exploration of their implications in gene networks and precision medicine.