The bacterial species concept remains controversial, yet it serves as a critical framework for all aspects of modern microbiology (Doolittle and Papke, 2006). The prevailing species definition describes a genomically coherent group of strains sharing high similarity in many independent phenotypic and ecological properties (Konstantinidis et al., 2006). The era of whole-genome sequencing (WGS) has seen average nucleotide identity (ANI) replace DNA-DNA hybridisation as the next-generation standard for microbial taxonomy (Wayne et al., 1987; Ciufo et al., 2018). Endorsed by the National Center for Biotechnology Information (NCBI) (Ciufo et al., 2018), ANI provides a precise, objective, and scalable method for delineation of species, defined as monophyletic groups of strains with genomes that exhibit at least 96% ANI (Jain et al., 2018; Richter and Rosselló-Móra, 2009).

Clostridioides (Clostridium) difficile is an important gastrointestinal pathogen that places a significant growing burden on health-care systems in many regions of the world (Guh et al., 2020). In both its 2013 (Centers for Disease Control and Prevention, 2013) and 2019 (Centers for Disease Control and Prevention, 2019) reports on antimicrobial resistance (AMR), the US Centers for Disease Control and Prevention rated C. difficile infection (CDI) as an urgent health threat, the highest level. Community-associated CDI has become more frequent (Guh et al., 2020) and is linked to sources of C. difficile in animals and the environment (Lim et al., 2020). Thus, over the last two decades, CDI has emerged as an important One Health issue (Lim et al., 2020).

Based on multi-locus sequence type (MLST), there are eight recognised monophyletic groups or ‘clades’ of C. difficile (Knight et al., 2015). Strains within these clades show many unique clinical, microbiological, and ecological features (Knight et al., 2015). Critical to the pathogenesis of CDI is the expression of the large clostridial toxins, TcdA and TcdB, and, in some strains, binary toxin (CDT), encoded by two separate chromosomal loci, the PaLoc and CdtLoc, respectively (Chandrasekaran and Lacy, 2017). Clade 1 (C1) contains over 200 toxigenic and non-toxigenic sequence types (STs) including many of the most prevalent strains causing CDI worldwide, for example, ST2, ST8, and ST17 (Knight et al., 2015). Several highly virulent CDT-producing strains, including ST1 (PCR ribotype [RT] 027), a lineage associated with major hospital outbreaks in North America, Europe, and Latin America (He et al., 2013), are found in clade 2 (C2). Comparatively little is known about clade 3 (C3), although it contains ST5 (RT 023), a toxigenic CDT-producing strain with characteristics that may make laboratory detection difficult (Shaw et al., 2020). C. difficile ST37 (RT 017) is found in clade 4 (C4) and, despite the absence of a toxin A gene, is responsible for much of the endemic CDI burden in Asia (Imwattana et al., 2019). Clade 5 (C5) contains several CDT-producing strains including ST11 (RTs 078, 126, and others), which are highly prevalent in production animals worldwide (Knight et al., 2019). The remaining so-called ‘cryptic’ clades (C-I, C-II, and C-III), first described in 2012 (Dingle et al., 2014; Didelot et al., 2012), contain over 50 STs from clinical and environmental sources (Dingle et al., 2014; Didelot et al., 2012; Janezic et al., 2016; Ramirez-Vargas and Rodriguez, 2020; Ramírez-Vargas et al., 2018). The evolution of the cryptic clades is poorly understood. Clade C-I strains can cause CDI; however, due to atypical toxin gene architecture, they may not be detected, thus their prevalence may have been underestimated (Ramírez-Vargas et al., 2018).

There are over 600 STs currently described, and some STs may have access to a gene pool of more than 10,000 genes (Knight et al., 2015; Knight et al., 2019; Knight et al., 2016). Considering such enormous diversity, and recent contentious taxonomic revisions (Lawson et al., 2016; Oren and Rupnik, 2018), we hypothesise that C. difficile comprises a complex of distinct species divided along the major evolutionary clades. In this study, whole-genome ANI, and pangenomic and Bayesian analyses are used to explore an international collection of over 12,000 C. difficile genomes, to provide new insights into ancestry, genetic diversity, and evolution of pathogenicity in this enigmatic pathogen.

Through phylogenomic analysis of the largest and most diverse collection of C. difficile genomes to date, we identified major incoherence in C. difficile taxonomy, provide the first WGS-based phylogeny for the Peptostreptococcaceae, and provide new insight into intra-species diversity and evolution of pathogenicity in this major One Health pathogen.

Our analysis found high nucleotide identity (ANI >97%) between C. difficile clades C1–4, indicating that strains from these four clades (comprising 560 known STs) belong to the same species. On the other hand, ANI between C5 and C1–4 is on the borderline of the accepted species threshold (95.9–96.2%). This degree of speciation likely reflects the unique ecology of C5 – a lineage comprising 33 known STs, which is well established in non-human animal reservoirs worldwide and associated with CDI in the community setting (Knight and Riley, 2019). Conversely, we identified major taxonomic incoherence among the three cryptic clades and C1–5, evident by ANI values (compared to ST3, C1) far below the species threshold (~91%, C-I; ~94%, C-II; and ~89%, C-III). Similar ANI value differences were seen between the cryptic clades themselves, indicating that they are as divergent from each other as they are individually from C1–5. This extraordinary level of discontinuity is substantiated by our core genome and Bayesian analyses. Our study estimated the most recent common ancestor of C. difficile clades C1–4 and C1–5 existed between 0.46 to 2.77 mya and between 1.11 to 6.71 mya, respectively, whereas the common ancestors of clades C-I, C-II, and C-III were estimated to have existed at least 1.5–75 million years before the common ancestor of C1–5. For context, divergence dates for other notable pathogens range from 10 million years (Ma) (Campylobacter coli and C. jejuni) (Sheppard and Maiden, 2015), 47 Ma (Burkholderia pseudomallei and B. thailandensis) (Yu et al., 2006), and 120 Ma (Escherichia coli and Salmonella enterica) (Ochman et al., 1999). Corresponding whole-genome ANI values for these species are 86, 94, and 82%, respectively (Supplementary file 1j).

Although BEAST provided wider confidence intervals (and therefore less certainty compared to BactDating), it estimates the time of divergence for all clades within the same order of magnitude and, importantly, provides robust support for the same branching order of clades with clade C-III the most ancestral of lineages, followed by the emergence of C-I, C-II, and C5. After this point, there appears to have been rapid population expansion into the four closely related clades described today, which include many of the most prevalent strains causing healthcare-associated CDI worldwide (Knight et al., 2015). We acknowledge that the dating of ancient taxa is often imprecise and that using a strict clock model for such a diverse set of taxa leads to considerable uncertainty in divergence estimates. However, we tried to mitigate this as much as possible by using two independent tools and evaluated multiple molecular clock estimates (covering almost an order of magnitude), ultimately using the same fixed clock model as Kumar et al., 2019 (2.5 × 10⁻⁹–10.5 × 10⁻⁸). The branching order of the clades is robust, supported by comprehensive and independent comparative genomic and phylogenomic analyses. Notwithstanding this finding, if variations in the molecular clock happen over time and across lineages, which is likely the case for such a genetically diverse spore-forming pathogen, then the true age ranges for C. difficile clade emergence are likely far greater (and therefore less certain) than we report here.

Comparative ANI analysis of the cryptic clades with >5000 reference genomes across 21 phyla failed to provide a better match than C. difficile (89–94% ANI). Similarly, our revised ANI-based taxonomy of the Peptostreptococcaceae placed clades C-I, C-II, and C-III between C. difficile and C. mangenotii. Our analyses of the Clostridioides spp. highlights the major discordance between WGS data and 16S rRNA data, which has historically been used to classify bacterial species. In 2016, Lawson et al., 2016 used 16S rRNA data to categorise C. difficile and C. mangenotii as the sole members of the Clostridioides. These species have 94.7% similarity in 16S rRNA sequence identity, yet our findings indicate that C. mangenotii and C. difficile share 77% ANI and should not be considered within the same genus. The rate of 16S rRNA divergence in bacteria is estimated to be 1–2% per 50 Ma (Ochman et al., 1999). Contradicting our ANI and core genome data, 16S rRNA sequences were highly conserved across all eight clades. This indicates that in C. difficile 16S rRNA gene similarity correlates poorly with measures of genomic, phenotypic, and ecological diversity, as reported in other taxa such as Streptomyces, Bacillus, and Enterobacteriaceae (Janda and Abbott, 2007; Chevrette et al., 2019). Another interesting observation is that C5 and the three cryptic clades had a high proportion (>90%) of MLST alleles that were absent in other clades (Supplementary file 1e), suggesting minimal exchange of essential housekeeping genes between these clades. Whether this reflects divergence or convergence of two species, as seen in Campylobacter (Sheppard et al., 2008), is unknown. Taken together, these data strongly support the reclassification of C. difficile clades C-I, C-II, and C-III as novel independent Clostridioides genomospecies. There have been similar genome-based reclassifications in Bacillus (Liu et al., 2018), Fusobacterium (Kook et al., 2017), and Burkholderia (Loveridge et al., 2017). Also, a recent Consensus Statement (Murray et al., 2020) argues that the genomics and big data era necessitate easing of nomenclature rules to accommodate genome-based assignment of species status to nonculturable bacteria and those without ‘type material’, as is the case with these Clostridioides genomospecies.

We also found that the significant taxonomic incoherence observed in C. difficile was also evident in other medically important clostridia, supporting calls for taxonomic revisions (Lawson et al., 2016; Oren and Rupnik, 2018). The entire published collections of C. perfringens, C. sporogenes, and C. botulinum all contained sequenced strains with pairwise ANI below the 96% demarcation threshold, with 8% of C. sporogenes and 31% of C. botulinum sequenced strains below 90% ANI. These findings highlight a significant problem with the current classification of the clostridia and further demonstrate that high-resolution approaches such as whole-genome ANI can be a powerful tool for the re-classification of these bacteria (Lawson et al., 2016; Oren and Rupnik, 2018; Murray et al., 2020).

The NCBI SRA was dominated by C1 and C2 strains, both in number and diversity. This apparent bias reflects the research community’s efforts to sequence the most prominent strains causing CDI in regions with the highest burden, for example, ST1 from humans in Europe and North America. As such, there is a paucity of sequenced strains from diverse environmental sources, animal reservoirs, or regions associated with atypical phenotypes. Cultivation bias – a historical tendency to culture, preserve, and ultimately sequence isolates that are concordant with expected phenotypic criteria – comes at the expense of ‘outliers’ or intermediate phenotypes. Members of the cryptic clades fit this criterion. They were first identified in 2012 but have been overlooked due to atypical toxin architecture, which may compromise diagnostic assays (discussed below). Our updated MLST phylogeny shows as many as 55 STs across the three cryptic clades (C-I, n = 25; C-II, n = 9; C-III, n = 21) (Figure 2). There remains a further dozen ‘outliers’ that could either fit within these new taxa or be the first typed representative of additional genomospecies. The growing popularity of metagenomic sequencing of animal and environmental microbiomes will certainly identify further diversity within these taxa, including nonculturable strains (Stewart et al., 2018; Lu et al., 2015).

By analysing 260 STs across eight clades, we provide the most comprehensive pangenome analysis of C. difficile to date. Importantly, we also show that the choice of algorithm significantly affects pangenome estimation. The C. difficile pangenome was determined to be open (i.e. an unlimited gene repertoire) and vast in scale (over 17,000 genes), much larger than previous estimates (~10,000 genes), which mainly considered individual clonal lineages (Knight et al., 2019; Knight et al., 2016). Conversely, comprising just 12.8% of its genetic repertoire (2232 genes), the core genome of C. difficile is remarkably small, consistent with earlier WGS and microarray-based studies describing ultralow genome conservation in C. difficile (Knight et al., 2015; Scaria et al., 2010). Considering only C1–5, the pangenome reduced in size by 12% (2082 genes); another 519 genes were lost when considering only C1–4. These findings are consistent with our taxonomic data, suggesting that the cryptic clades, and to a lesser extent C5, contribute a significant proportion of evolutionarily divergent and unique loci to the gene pool. A large open pangenome and small core genome are synonymous with a sympatric lifestyle, characterised by cohabitation with, and extensive gene transfer between, diverse communities of prokarya and archaea (Medini et al., 2005). Indeed, C. difficile shows a highly mosaic genome comprising many phages, plasmids, and integrative and conjugative elements (Knight et al., 2015), and has adapted to survival in multiple niches including the mammalian gastrointestinal tract, water, soil and compost, and invertebrates (Knight and Riley, 2019).

Through a robust Pan-GWAS approach, we identified loci that are enriched or unique in the genomospecies. C-I strains were associated with the presence of transporter AbgB and absence of a mannose-type phosphotransferase (PTS) system. In E. coli, AbgAB proteins allow it to survive on exogenous sources of folate (Carter et al., 2007). In many enteric species, the mannose-type PTS system is essential for catabolism of fructosamines such as glucoselysine and fructoselysine, abundant components of rotting fruit and vegetable matter (Miller et al., 2015). C-II strains contained Zn transporter loci znuA and yeiR, in addition to Zn transporter ZupT, which is highly conserved across all eight C. difficile clades. S. enterica and E. coli harbour both znuA/yeiR and ZupT loci, enabling survival in Zn-depleted environments (Sabri et al., 2009). C-III strains were associated with major gene clusters encoding systems for ethanolamine catabolism, heavy metal transport, and spermidine uptake. The C-III eut gene cluster encoded six additional kinases, transporters, and transcription regulators absent from the highly conserved eut operon found in other clades. Ethanolamine is a valuable source of carbon and/or nitrogen for many bacteria, and eut gene mutations (in C1/C2) impact toxin production in vivo (Nawrocki et al., 2018). The C-III metal transport gene cluster encoded a chelator of heavy metal ions and a multi-component transport system with specificity for iron, nickel, and glutathione. The conserved spermidine operon found in all C. difficile clades is thought to play an important role in various stress responses including during iron limitation (Berges et al., 2018). The additional, divergent spermidine transporters found in C-III were similar to regions in closely related genera Romboutsia and Paeniclostridium (data not shown). Together, these data provide preliminary insights into the biology and ecology of the genomospecies. Most differential loci identified were responsible for extra or alternate metabolic processes, some not previously reported in C. difficile. It is therefore tempting to speculate that the evolution of alternate biosynthesis pathways in these species reflects distinct ancestries and metabolic responses to evolving within markedly different ecological niches.

This work demonstrates the presence of toxin genes on PaLoc and CdtLoc structures in all three genomospecies, confirming their clinical relevance. Monotoxin PaLocs were characterised by the presence of tcdR, tcdB, and tcdE, the absence of tcdA and tcdC, and flanking by transposases and recombinases which mediate LGT (Ramirez-Vargas and Rodriguez, 2020; Ramírez-Vargas et al., 2018; Monot et al., 2015). These findings support the notion that the classical bi-toxin PaLoc common to clades C1–5 was derived by multiple independent acquisitions and stable fusion of monotoxin PaLocs from ancestral clostridia (Monot et al., 2015). Moreover, the presence of syntenic PaLoc and CdtLoc (in ST369, C-I), the latter featuring two copies of cdtA and cdtR, and a recombinase (xerC), further supports this PaLoc fusion hypothesis (Monot et al., 2015).

Bacteriophage holin and endolysin enzymes coordinate host cell lysis, phage release, and toxin secretion (Fortier, 2018). Monotoxin PaLocs comprising phage-derived holin (tcdE) and endolysin (cwlH) genes were first described in C-I strains (Monot et al., 2015). We have expanded this previous knowledge by demonstrating that syntenic tcdE and cwlH are present within monotoxin PaLocs across all three genomospecies. Moreover, since some strains contained cwlH but lacked toxin genes, this gene seems to be implicated in toxin acquisition. These data, along with the detection of a complete and functional (Riedel et al., 2017) CdtLoc contained within ΦSemix9P1 in ST343 (C-III), further substantiate the role of phages in the evolution of toxin loci in C. difficile and related clostridia (Fortier, 2018).

The CdtR and TcdR sequences of the new genomospecies are unique, and further work is needed to determine if these regulators display different mechanisms or efficiencies of toxin expression (Chandrasekaran and Lacy, 2017). The presence of dual copies of CdtR in ST369 (C-I) is intriguing as analogous duplications in PaLoc regulators have not been documented. One of these CdtR had a mutation at a key phosphorylation site (Asp61→Asn61) and possibly shows either reduced wild-type activity or non-functionality, as seen in ST11 (Bilverstone et al., 2019). This might explain the presence of a second CdtR copy.

TcdB alone can induce host innate immune and inflammatory responses leading to intestinal and systemic organ damage (Carter et al., 2015). Our phylogenetic analysis shows that TcdB sequences from the three genomospecies are related to TcdB in C2 members, specifically ST1 and ST41, both virulent lineages associated with international CDI outbreaks (He et al., 2013; Eyre et al., 2015), and causing classical or variant (C. sordellii-like) cytopathic effects, respectively (Lanis et al., 2010). It would be relevant to explore whether the divergent PaLoc and CdtLoc regions confer differences in biological activity, as these may present challenges for the development of effective broad-spectrum diagnostic assays, and vaccines. We have previously demonstrated that common laboratory diagnostic assays may be challenged by changes in the PaLoc of C-I strains (Ramírez-Vargas et al., 2018). The same might be true for monoclonal antibody-based treatments for CDI such as bezlotoxumab, known to have distinct neutralising activities against different TcdB subtypes (Shen et al., 2020).

Our findings highlight major incongruence in C. difficile taxonomy, identify differential patterns of diversity among major clades, and advance understanding of the evolution of the PaLoc and CdtLoc. While our analysis is limited solely to the genomic differences between C. difficile clades, our data provide a robust genetic foundation for future studies to focus on the phenotypic, ecological, and epidemiological features of these interesting groups of strains, including defining the biological consequences of clade-specific genes and pathogenic differences in vitro and in vivo. Our findings reinforce that the epidemiology of this important One Health pathogen is not fully understood. Enhanced surveillance of CDI and WGS of new and emerging strains to better inform the design of diagnostic tests and vaccines are key steps in combating the ongoing threat posed by C. difficile. Last, besides C. difficile, we also demonstrate that a similar approach can be applied to other clostridia making a useful tool for the reclassification of these taxa.

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 (Figure 1). [2] Whole-genome ANI analyses (Table 1, Figure 3, Figure 5). [3] Tree files for phylogenetic analyses (Figure 2, Figure 4). [4] Pangenome data (Figure 6). [5] Pan-GWAS data (Table 2). [6] Comparative genomic analysis of virulence gene architecture (Figure 7). We retrieved the entire collection of C. difficile genomes (taxid ID 1496) held at the NCBI SRA [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.

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Author details

1. Daniel R Knight

   1. Medical, Molecular and Forensic Sciences, Murdoch University, Murdoch, Australia
   2. School of Biomedical Sciences, the University of Western Australia, Nedlands, Australia

   Contribution

   Conceptualization, Data curation, Formal analysis, Supervision, Funding acquisition, Validation, Investigation, Visualization, Methodology, Writing - original draft, Project administration, Writing - review and editing, Designed the study, carried out all aspects of experiments and collected the data, and analyzed data, and wrote the manuscript

   For correspondence

   daniel.knight@murdoch.edu.au

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-9480-4733

2. Korakrit Imwattana

   1. School of Biomedical Sciences, the University of Western Australia, Nedlands, Australia
   2. Department of Microbiology, Faculty of Medicine Siriraj Hospital, Mahidol University, Bangkok, Thailand

   Contribution

   Conceptualization, Data curation, Formal analysis, Investigation, Visualization, Methodology, Writing - original draft, Writing - review and editing

   Competing interests

   No competing interests declared

   "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

   Contribution

   Data curation, Formal analysis, Investigation, Visualization, Methodology, Writing - original draft, Writing - review and editing

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0001-5460-1977

4. Enzo Guerrero-Araya

   1. Microbiota-Host Interactions and Clostridia Research Group, Facultad de Ciencias de la Vida, Universidad Andrés Bello, Santiago, Chile
   2. Millenium Nucleus in the Biology of Intestinal Microbiota, Santiago, Chile

   Contribution

   Data curation, Formal analysis, Investigation, Visualization, Methodology, Writing - original draft, Writing - review and editing

   Competing interests

   No competing interests declared

5. Daniel Paredes-Sabja

   1. Microbiota-Host Interactions and Clostridia Research Group, Facultad de Ciencias de la Vida, Universidad Andrés Bello, Santiago, Chile
   2. Millenium Nucleus in the Biology of Intestinal Microbiota, Santiago, Chile
   3. Department of Biology, Texas A&M University, College Station, United States

   Contribution

   Formal analysis, Supervision, Investigation, Methodology, Writing - original draft, Writing - review and editing

   Competing interests

   No competing interests declared

6. Xavier Didelot

   School of Life Sciences and Department of Statistics, University of Warwick, Coventry, United Kingdom

   Contribution

   Formal analysis, Supervision, Investigation, Visualization, Methodology, Writing - original draft, Writing - review and editing

   Competing interests

   No competing interests declared

   "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, National Institute for Health Research (NIHR) Oxford Biomedical Research Centre, John Radcliffe Hospital, Oxford, United Kingdom

   Contribution

   Formal analysis, Investigation, Methodology, Writing - original draft, Writing - review and editing

   Competing interests

   No competing interests declared

8. David W Eyre

   Big Data Institute, Nuffield Department of Population Health, University of Oxford, National Institute for Health Research (NIHR) Oxford Biomedical Research Centre, John Radcliffe Hospital, Oxford, United Kingdom

   Contribution

   Formal analysis, Investigation, Methodology, Writing - original draft, Writing - review and editing

   Competing interests

   DWE declares lecture fees from Gilead, outside the submitted work.

   "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

   Contribution

   Data curation, Formal analysis, Supervision, Investigation, Visualization, Methodology, Writing - original draft, Writing - review and editing

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0001-5599-0652

10. Thomas V Riley

    1. Medical, Molecular and Forensic Sciences, Murdoch University, Murdoch, Australia
    2. School of Biomedical Sciences, the University of Western Australia, Nedlands, Australia
    3. Department of Microbiology, PathWest Laboratory Medicine, Queen Elizabeth II Medical Centre, Nedlands, Australia
    4. School of Medical and Health Sciences, Edith Cowan University, Joondalup, Australia

    Contribution

    Conceptualization, Resources, Formal analysis, Supervision, Investigation, Methodology, Writing - original draft, Project administration, Writing - review and editing

    For correspondence

    thomas.riley@uwa.edu.au

    Competing interests

    No competing interests declared

    "This ORCID iD identifies the author of this article:" 0000-0002-1351-3740

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