Life identification number (LIN) codes for the genomic taxonomy of Corynebacterium diphtheriae strains

Life identification number (LIN) codes for the genomic taxonomy of Corynebacterium diphtheriae strains | bioRxiv /* */ /* */ <!-- <!-- /*! * yepnope1.5.4 * (c) WTFPL, GPLv2 */ (function(a,b,c){function d(a){return"[object Function]"==o.call(a)}function e(a){return"string"==typeof a}function f(){}function g(a){return!a||"loaded"==a||"complete"==a||"uninitialized"==a}function h(){var a=p.shift();q=1,a?a.t?m(function(){("c"==a.t?B.injectCss:B.injectJs)(a.s,0,a.a,a.x,a.e,1)},0):(a(),h()):q=0}function i(a,c,d,e,f,i,j){function k(b){if(!o&&g(l.readyState)&&(u.r=o=1,!q&&h(),l.onload=l.onreadystatechange=null,b)){"img"!=a&&m(function(){t.removeChild(l)},50);for(var d in y[c])y[c].hasOwnProperty(d)&&y[c][d].onload()}}var j=j||B.errorTimeout,l=b.createElement(a),o=0,r=0,u={t:d,s:c,e:f,a:i,x:j};1===y[c]&&(r=1,y[c]=[]),"object"==a?l.data=c:(l.src=c,l.type=a),l.width=l.height="0",l.onerror=l.onload=l.onreadystatechange=function(){k.call(this,r)},p.splice(e,0,u),"img"!=a&&(r||2===y[c]?(t.insertBefore(l,s?null:n),m(k,j)):y[c].push(l))}function j(a,b,c,d,f){return q=0,b=b||"j",e(a)?i("c"==b?v:u,a,b,this.i++,c,d,f):(p.splice(this.i++,0,a),1==p.length&&h()),this}function k(){var a=B;return a.loader={load:j,i:0},a}var l=b.documentElement,m=a.setTimeout,n=b.getElementsByTagName("script")[0],o={}.toString,p=[],q=0,r="MozAppearance"in l.style,s=r&&!!b.createRange().compareNode,t=s?l:n.parentNode,l=a.opera&&"[object Opera]"==o.call(a.opera),l=!!b.attachEvent&&!l,u=r?"object":l?"script":"img",v=l?"script":u,w=Array.isArray||function(a){return"[object Array]"==o.call(a)},x=[],y={},z={timeout:function(a,b){return b.length&&(a.timeout=b[0]),a}},A,B;B=function(a){function b(a){var a=a.split("!"),b=x.length,c=a.pop(),d=a.length,c={url:c,origUrl:c,prefixes:a},e,f,g;for(f=0;f<d;f++)g=a[f].split("="),(e=z[g.shift()])&&(c=e(c,g));for(f=0;f<b;f++)c=x[f](c);return c}function g(a,e,f,g,h){var i=b(a),j=i.autoCallback;i.url.split(".").pop().split("?").shift(),i.bypass||(e&&(e=d(e)?e:e[a]||e[g]||e[a.split("/").pop().split("?")[0]]),i.instead?i.instead(a,e,f,g,h):(y[i.url]?i.noexec=!0:y[i.url]=1,f.load(i.url,i.forceCSS||!i.forceJS&&"css"==i.url.split(".").pop().split("?").shift()?"c":c,i.noexec,i.attrs,i.timeout),(d(e)||d(j))&&f.load(function(){k(),e&&e(i.origUrl,h,g),j&&j(i.origUrl,h,g),y[i.url]=2})))}function h(a,b){function c(a,c){if(a){if(e(a))c||(j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}),g(a,j,b,0,h);else if(Object(a)===a)for(n in m=function(){var b=0,c;for(c in a)a.hasOwnProperty(c)&&b++;return b}(),a)a.hasOwnProperty(n)&&(!c&&!--m&&(d(j)?j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}:j[n]=function(a){return function(){var b=[].slice.call(arguments);a&&a.apply(this,b),l()}}(k[n])),g(a[n],j,b,n,h))}else!c&&l()}var h=!!a.test,i=a.load||a.both,j=a.callback||f,k=j,l=a.complete||f,m,n;c(h?a.yep:a.nope,!!i),i&&c(i)}var i,j,l=this.yepnope.loader;if(e(a))g(a,0,l,0);else if(w(a))for(i=0;i (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0];var j=d.createElement(s);var dl=l!='dataLayer'?'&l='+l:'';j.src='//www.googletagmanager.com/gtm.js?id='+i+dl;j.type='text/javascript';j.async=true;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-M677548'); Skip to main content Home About Submit ALERTS / RSS Search for this keyword Advanced Search New Results Life identification number (LIN) codes for the genomic taxonomy of Corynebacterium diphtheriae strains View ORCID Profile Jose F. Delgado-Blas , View ORCID Profile Martin Rethoret-Pasty , View ORCID Profile Sylvain Brisse doi: https://doi.org/10.1101/2025.06.20.660692 Jose F. Delgado-Blas 1 Institut Pasteur, Université Paris Cité, Biodiversity and Epidemiology of Bacterial Pathogens , Paris, France Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Jose F. Delgado-Blas Martin Rethoret-Pasty 1 Institut Pasteur, Université Paris Cité, Biodiversity and Epidemiology of Bacterial Pathogens , Paris, France Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Martin Rethoret-Pasty Sylvain Brisse 1 Institut Pasteur, Université Paris Cité, Biodiversity and Epidemiology of Bacterial Pathogens , Paris, France 2 National Reference Center for the Corynebacteria of the diphtheriae Complex , Institut Pasteur, Paris, France 3 European Reference Laboratory for Diphtheria and Pertussis , Paris, France Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Sylvain Brisse For correspondence: sylvain.brisse{at}pasteur.fr Abstract Full Text Info/History Metrics Supplementary material Data/Code Preview PDF Abstract Background Corynebacterium diphtheriae , which causes diphtheria, remains a public health concern especially in regions with low vaccination coverage. While advances in genomic typing, such as core-genome Multi-Locus Sequence Typing (cgMLST, based on 1305 genes), have improved our ability for strain identification, a standardized and stable genomic taxonomy is still lacking. This study aimed to establish a consistent classification and nomenclature for C. diphtheriae strains using cgMLST-based Life Identification Number (LIN) codes. Methods Comparing 1,665 genomes from C. diphtheriae and its closely related species C. belfantii and C. rouxii , we observed population-level genetic discontinuities in cgMLST profiles dissimilarities, and established hierarchical taxonomic levels based on optimal allelic difference thresholds. Ten-level LIN codes were defined, encompassing broad population structure subdivisions and fine-scale epidemiological levels. The LIN code system was implemented into the BIGSdb-Pasteur platform, and nicknames derived from the 7-loci MLST sequence types were given to sublineages and clonal groups. Results cgMLST genetic thresholds were first defined at species (minimum of 1,240 allelic differences) and lineage levels (1,035 differences). Sublineages (SL), clonal groups (ClG), and genetic clusters (GC) were next defined with progressively finer allelic mismatch thresholds (500, 55, and 25 differences, respectively). A broad population diversity of C. diphtheriae was uncovered, with the distinction of >400 SLs and >1,000 GCs. For epidemiological purposes, five shallow-level thresholds (8, 4, 2 ,1, and 0 allelic mismatches were defined, completing the 10-level LIN code taxonomy. We illustrate LIN codes applicability to investigate the genetic diversity and transmission chains of relevant clusters, such as SL8 (the 1990s ex-USSR outbreak) or SL384 (involved in outbreaks in Yemen and Europe). Conclusions The cgMLST-based LIN code system provides a stable genomic taxonomy for strains of C. diphtheriae, C. rouxii and C. belfantii . By defining ten hierarchical levels of resolution, this system effectively captures its phylogenetic diversity, facilitating population biology research and epidemiological surveillance. The public availability of this system from the BIGSdb-Pasteur platform provides a standardized framework for diphtheria genomic epidemiology with potential to harmonize global surveillance of the resurgence of diphtheria. Background Corynebacterium diphtheriae is the classical agent of diphtheria, a severe respiratory infectious disease caused by strains that produce the diphtheria toxin (DT). DT is a potent exotoxin encoded by the tox gene, which is carried by a lysogenic prophage integrated into the bacterial chromosome 1 . C. diphtheriae strains can also cause cutaneous infections, and C. diphtheriae strains that are tox -negative can, in turn, cause invasive infections and are common in cutaneous infections. Recent work has shown that the presence of the tox gene in individual C. diphtheriae lineages can be stable during years or even decades 2 – 4 , highlighting the importance of precisely defining and identifying them for clinical and public health interest. Furthermore, understanding the mobility of the tox gene across C. diphtheriae sublineages, and the evolutionary relationships among tox -positive and tox -negative strains, requires a robust genome-based strain classification and naming system. A considerable genetic diversity of C. diphtheriae strains was revealed by whole-genome sequencing 2 , 3 , 5 , 6 . Using a core-genome MLST (cgMLST) approach, a first-generation classification and nomenclature of C. diphtheriae strains was proposed 3 . However, this classification has two limitations. First, it only considered two infra-specific phylogenetic levels, dubbed sublineages and genetic clusters. While useful to classify isolates into deep subdivisions and to recognize groups of very closely related isolates that may be linked by recent transmission events, this classification lacks flexibility in its definition of closely related isolates, and a single level of grouping into genetic clusters may not fit all epidemiological investigations. Second, the previous classification was based on single-linkage clustering, a mathematical algorithm that is subject to instability due to group fusion whenever intermediate genotypes are sampled. Therefore, the previously proposed classification lacks the multilevel flexibility and stability needed for a taxonomic system. A recently developed stable strain taxonomy approach is known as Life Identification Numbers (LIN), or LIN codes 7 . LIN codes are composed of multiple hierarchical levels, which comprise numerical identifiers (integers) that capture the genetic similarity of bacterial genomes at each level. LIN codes based on cgMLST provide a proxy of genetic relationships among strains 8 , 9 . Besides, groups of LIN codes can be assigned simple nicknames to facilitate human-to-human communication on bacterial strains, thus creating a strain nomenclature that bridges population biology research, public health microbiology and clinical diagnostic. The main goal of this work was to define a cgMLST-based LIN code genomic taxonomy for strains of C. diphtheriae and its closely related species C. belfantii and C. rouxii 10 , 11 , which are often misidentified as C. diphtheriae in diagnostic practice. We investigated the population structure and infra-specific phylogenetic discontinuities within this species group, defined 10 hierarchical classification levels (from species level down to cgMLST identity), created a nickname nomenclature by inheritance from the 7-gene MLST identifiers, and integrated the LIN codes taxonomy into the BIGSdb-Pasteur platform. A second aim was to illustrate the benefits of the LIN codes taxonomy to address population biology and epidemiological surveillance questions in C. diphtheriae . Methods Database and cgMLST definition C. diphtheriae is a member of the C. diphtheriae species complex (CdSC), which also includes, among others, Corynebacterium belfantii and Corynebacterium rouxii 10 – 12 . Together, these three species are referred to as C. diphtheriae sensu lato , as most currently used laboratory methods, including MALDI-TOF 13 , 14 or PCR diagnostics 15 , 16 , do not distinguish them from C. diphtheriae. Given that all C. belfantii and C. rouxii isolates were so far reported as tox -negative, there is a need for genomic sequence-based identification. The BIGSdb-Pasteur platform, which relies on the Bacterial Isolate Genome Sequence Database (BIGSdb) web application and database system 17 , contains an implementation dedicated to the CdSC ( https://bigsdb.pasteur.fr/diphtheria/ ). This resource is made of (i) genomic sequences and isolates provenance data (the isolates and genomes database), and (ii) gene sequence alleles and MLST profiles definitions (the alleles and profiles database, also known as the nomenclature database). The isolates and genomes database contains a large (> 4600 genomic sequences, January 2025) collection of high-quality CdSC genomic assemblies, their related metadata, and their genotyping information as defined in the alleles and profiles database. In turn, the alleles and profiles database hosts all the allele definitions (allele number and corresponding sequence) of the defined loci, and MLST profiles, i.e. , collections of loci, such as the cgMLST scheme previously defined for C. diphtheriae sensu lato , which comprises 1,305 loci 3 . To avoid defining cgST types with too many missing loci, each unique allelic profile is assigned to a core-genome sequence type (cgST), only when the number of missing loci does not exceed 130 loci (10%) of the total number of loci in the scheme. For the present work, a total of 1,665 public records of C. diphtheriae sensu lato were used for population structure analysis: 1,611 C. diphtheriae genomes from previous studies 6 , 18 , and 44 C. belfantii and 10 C. rouxii public genomes retrieved from BIGSdb-Pasteur by April 17 th , 2024. These records represented isolates from 50 countries located in all continents (except Antarctica), collected between 1894 and 2022, comprising a total of 1,434 distinct cgSTs. Population structure analyses To assess the population structure, MSTclust v0.21b ( https://gitlab.pasteur.fr/GIPhy/MSTclust ) was used to generate a Minimum Spanning Tree (MST)-based clustering of cgMLST allelic profiles and to evaluate all clustering thresholds, i.e ., from 1 to 1,305 allelic differences. All pairwise comparisons of allelic profiles between the 1,665 C. diphtheriae sensu lato genomes were computed using RStudio v4.3.2 19 and the R package tidyverse 20 , excluding missing loci from the allelic mismatch counts. The density distribution of pairwise allelic mismatches was used to identify genetic discontinuities at different phylogenetic depths. Clustering statistics (Silhouette coefficient for the cluster consistency and adjusted Wallace coefficient for the cluster stability) were obtained for all allelic cgMLST thresholds (0-1,305 allelic mismatches) with MSTclust, and graphically visualized with RStudio using tidyverse and ggplot2 21 packages. Allelic difference thresholds for population partitions were defined based on the distribution of Silhouette values, adjusted Wallace coefficients and allelic mismatch counts. Clustering thresholds defined by MSTclust were compared with those suggested from an analysis with pHierCC v1.24 22 . Creation of the LIN code taxonomy A ten-level cgMLST-based LIN code system was established. The chosen levels were based on optimal population structure thresholds (for the fifth first ones), and shallow-level thresholds were added for epidemiological purposes (see Results). Within the LIN codes, the taxonomic resolution increases from left to right in a hierarchical manner: as the allelic distance thresholds decrease, each level is nested within the previous (left) one 7 – 9 . The cgMLST-based LIN code taxonomy was implemented in the BIGSdb-Pasteur platform diphtheria database. All defined cgST genotypes ( i.e. , corresponding to cgMLST profiles with less than 130 missing loci) were assigned a LIN code, and LIN code assignation was performed at once for the 3,247 cgST profiles (June 4 th , 2024). The number of partitions created for each LIN code level can vary according to the profile input order, and was minimized by following an input order defined by Prim’s algorithm as implemented in the LIN codes functionality of BIGSdb 8 , 9 . The entire contents of the CdSC isolates database as of 26 August 2024 was incorporated into the LIN code taxonomy, representing 3,161 genomes. Circle packing charts to visualize the LIN code-based population structure were generated with RStudio v4.3.2 using the R packages tidyverse , ggraph and igraph 23 , 24 . Likewise, alluvial plots displaying the concordance of LIN code levels were generated with RStudio v4.3.2 using tidyverse , ggplot2 , ggalluvial and dplyr 25 , 26 . Practical application and assessment of the LIN code taxonomy The complete LIN codes were analyzed with the tool LINtree v0.1 ( https://gitlab.pasteur.fr/GIPhy/LINtree ) to generate prefix trees, which can be used as proxies to visualize the phylogenetic diversity of a dataset 9 . For comparison, actual phylogenetic trees of each dataset were constructed as follows: alignments of nucleotide sequences from all loci included in the cgMLST scheme were obtained with MAFFT v6.8 27 applying default parameters, and then concatenated; recombinogenic positions were filtered out from the alignments using Gubbins 3.2.0 28 with default parameters; and phylogenetic trees were generated with IQ-TREE 2 v2.3.2 29 from the recombination-free concatenated alignments, selecting the K3Pu+F+I model with ModelFinder Plus and applying 1000 bootstrap replicates, the ultrafast bootstrap approximation and the option to optimize bootstrap support 30 , 31 . In addition, BactDating v1.1.2 was used to estimate the evolutionary rate and generate dated phylogenies for some specific clusters, incorporating the isolation year information from BIGSdb metadata and applying a ‘ strictgamma ’ model with 1000 MCMC iterations 32 . LIN prefix tree-phylogenetic tree comparisons were graphically displayed as tanglegram plots performed with RStudio and using the R packages dplyr , dendextend , ape and phylogram 33 – 35 . Besides, one project was created for each dataset under study on the Microreact platform 36 ( https://microreact.org/ ) in order to integrate and visualize the LIN trees together with corresponding metadata ( e.g. , country, isolation year, or source) and genetic traits ( e.g. , biovar genetic determinants or tox allele) of the C. diphtheriae isolates. Results Population structure and taxonomic levels The Silhouette consistency coefficient obtained from the 1,665 cgMLST profiles exhibited low values (∼0.1) in the 1,000 - 1,305 allelic mismatches range, which corresponded to a significant peak of pairwise allelic differences values (1,100-1,200) (Additional file 1). This peak underlines the large genomic heterogeneity of the C. diphtheriae sensu lato population, as a large proportion of loci had distinct alleles in pairwise comparisons. In this range, the Silhouette curve displayed two local optima, with higher consistency (Silhouette coefficient) and stability (adjusted Wallace coefficient) values, at both sides of the allelic mismatch peak, supporting the definition of two deep phylogenetic thresholds. A first peak was defined at 1,240 allelic mismatches, corresponding to the taxonomic level for species, and a second threshold was defined at 1,035 mismatches (here defined as the “lineage” level). Contrarily, in the range between 100 and 900 allelic differences, the silhouette curve reached a plateau with high consistency and stability coefficients (>0.6 and >0.8, respectively) and few pairwise allelic mismatch peaks (Additional file 1). The threshold of 500 allelic mismatches, which was previously established for sublineage (SL) definition from single-linkage clustering 3 , was located in the middle of this silhouette plateau and presented a high stability coefficient. Therefore, this threshold was retained to define sublineages (SL), with the benefit that correspondence with former SL assignations would be maximal. Regarding the 0-100 allelic difference range, a threshold with high stability values was chosen at 55 allelic mismatches, defining a novel taxonomic level, which we defined as clonal group (ClG) (Additional file 1). The threshold at 25 allelic differences, corresponding to the previously established threshold for genetic cluster (GC) definition 3 , was supported by our current analysis (Additional file 1) and therefore retained. These five clustering thresholds were supported by pHierCC population evaluation (Additional file 2). Last, four smaller thresholds ( i.e. , 8, 4, 2 and 1 allelic mismatches) were selected to create high-resolution taxonomic groups that may be useful for epidemiological purposes. In all, the LIN code system provides a 10-level genome-based classification system for C. diphtheriae sensu lato strains ( Table 1 ). View this table: View inline View popup Download powerpoint Table 1. Overview of the 10 taxonomic levels forming the cgMLST-based LIN codes for C. diphtheriae sensu lato . cgMLST-based LIN codes and nicknames for taxonomic levels Based on the ten above-defined allelic distance thresholds (including threshold 0, which defines LIN code identity), a LIN code taxonomy was created in the BIGSdb-Pasteur diphtheria database. The population structure of C. diphtheriae sensu lato based on the LIN codes is illustrated in Figure 1 , showing the large diversity of LIN codes and their nested relationships. Download figure Open in new tab Figure 1. The hierarchical structure of C. diphtheriae sensu lato population based on LIN codes. Each bacterial strain is defined by a unique 10-level LIN code, in which each LIN code prefix is partitioned starting from 0. In this circle packing chart, each LIN code prefix is represented by a circle, which is coloured according to the level (from 1 to 10, as shown in the left legend) and nested within their corresponding higher/parental LIN code prefixes. The circle sizes display the number of genomes encompassed by each LIN code prefix, and an extra all-encompassing circle (grey) represents the entire C. diphtheriae sensu lato population. The first LIN code prefix corresponds to the species, as indicated in the plot, and the labels for the LIN code prefixes from level 2 to level 5 for those comprising ≥30 genomes are also displayed in the plot. Considering the relationships between the adjacent levels of SL and ClG, whereas almost two thirds (63.5%) of the SLs were formed by a single ClG, some others comprised a larger diversity, with up to ≥10 ClGs (2.04%). When comparing LIN code levels with the STs from the classical 7-loci MLST scheme ( Figure 2 ), a high concordance with the SL and ClG taxonomic levels was evident, confirmed by high values of the adjusted Rand indexes ( R t =0.945 and R t =0.932, respectively). To facilitate communication and ensure continuity with the widely used nomenclature based on 7-gene MLST 37 , nicknames were assigned to SL (LIN code prefix of size 3) and ClG (prefixes of size 4) groups 8 following the inheritance approach based on the most represented ST among their isolates 3 ( Figure 2 ). Download figure Open in new tab Figure 2. Concordance and nicknaming of C. diphtheriae sensu lato LIN code levels. The alluvial plot shows the hierarchical relationship of LIN code prefixes between level 1 ( i.e. , species) and level 5 ( i.e. , genetic cluster), and with the 7-gene MLST classification ( i.e. , STs) (bottom labels). The inter-level links are coloured according to the different genetic clusters to facilitate the visual tracking. The nicknames for LIN code prefixes comprising ≥30 genomes are displayed next to the white bars representing each level partitions, which are ordered by decreasing total counts ( Y -axis). Sublineage (SL) and clonal group (ClG) nicknames are based on the most represented ST among their members, as shown in the plot. The lineage level is indicated by the 2-level prefixes of LIN codes, whereas genetic cluster (GC) nicknames were assigned by decreasing total counts in the BIGSdb-Pasteur database. Alluvial plots were generated with RStudio v4.3.2 using tidyverse , ggplot2 , ggalluvial and dplyr 25 , 26 . In order to also facilitate communication on genetic clusters (allele difference threshold of 25; 5-level LIN code prefixes), which are useful in a broad screening approach to recognize closely related isolates potentially involved in case clusters or outbreaks 3 , GC nicknames were assigned, starting from 1, with GCs being ordered by decreasing total counts (as assessed in the BIGSdb-Pasteur database, accessed July 23 rd , 2024). As the inheritance or total count rules can only be applied once, subsequently, novel SL, ClG and GC nicknames will be assigned by incrementing by 1 the nicknames already defined in the database. For SL and GC identifiers that were previously assigned based on single-linkage clustering 3 , the equivalence between the previously defined SLs and GCs identifiers, and the newly established LIN code-based nicknames, was made available on BIGSdb-Pasteur (see CdSC home page) and in Additional file 3. Given that the first LIN code level corresponds to the taxonomic species level, the Linnaean taxon name of the three species was used as nickname: C. diphtheriae (prefix 0_), C. rouxii (prefix 1_) and C. belfantii (prefix 2_; Figures 1 and 2). We found a complete concordance between this cgMLST-based classification and the species-level ANI-derived or phylogeny-based classification of genomes. The lineage level, represented by the 2-level prefixes of LIN codes, represented the broadest intra-species classification categories. C. rouxii genomes were classified into two main lineages (1_0 and 1_1) and all C. belfantii belonged to the same lineage (2_0; Figure 2 ). In turn, C. diphtheriae genomes were segregated into 27 different lineages (0_0 to 0_26), even though most C. diphtheriae (90.2%) were grouped together in lineage 0_0. Some of the C. diphtheriae lineages showed association with specific epidemiological traits, supporting the evolutionary significance of these lineages; for example, lineage 0_23 comprised 58 C. diphtheriae isolates from horses (93.6% of this lineage members), whereas no other lineage comprised isolates from this host species. In addition, lineage 0_23 was subdivided into 8 sublineages, each comprising isolates from horses, showing a concordance between their grouping at lineage level and their host source. No nickname system was created for the lineage level, as referencing them using the two-bin identifiers appears convenient. Main C. diphtheriae sublineages, clonal groups, genetic clusters and LIN codes The 3-level LIN code prefixes divided C. diphtheriae sensu lato into 388 sublineages, including the most numerous SL377 (0_0_21, 316 isolates), SL574 (0_0_85, 300 isolates) and SL384 (0_0_18, 186 isolates), which were associated with the 2022 European outbreak in migrant populations 18 , or SL8 (0_0_0, 111 isolates), which was prevalent during the ex-USSR outbreak 3 and is still observed sporadically nowadays (last isolate observed: year 2022). In turn, there were a total of 834 clonal groups ( Table 1 ). A few clonal groups belonging to the aforementioned sublineages were overrepresented in the database, accounting for more than a quarter of the total records: ClG377 (0_0_21_0), ClG574 (0_0_85_1), ClG384 (0_0_18_0), and ClG8 (0_0_0_0) ( Figures 1 and 2). The LIN code taxonomy further distinguished a total of 1,049 genetic clusters, which correspond to the maximum level of genetic variation previously observed among documented C. diphtheriae outbreaks 3 . As expected, some frequent genetic clusters were observed within the most frequent sublineages and clonal groups, and the three main ones belonged to ClG574 (GC1, 0_0_85_1_0), ClG377 (GC2, 0_0_21_0_4) and ClG384 (GC3, 0_0_18_0_0; Figures 1 and 2) 18 . Contrarily, the diverse ClG8 was split up in multiple GCs, reflecting the longer evolutionary history of this clonal group. The epidemiological levels defined for surveillance purposes further divided the genetic clusters into finer groups, from the 6-level prefixes (1,415 groups) to the complete 10-level LIN codes (2,003 groups) ( Figures 1 and 2; Table 1 ). We illustrate below the use of LIN codes to investigate genotype-phenotype links and potential transmission within specific genetic clusters and epidemiological contexts. Biovar and tox gene associations with C. diphtheriae taxonomic levels Based on phenotypic properties (nitrogen reduction and glycogen or starch utilization), the classification of C. diphtheriae strains into biovars has been performed since the 1930s. Recently, these distinct biochemical profiles were linked to specific genetic markers: gene spuA and the narIJHGK gene cluster 6 . We evaluated the distribution of these genetic markers in the C. diphtheriae population using the LIN code classification of a dataset of 1,250 C. diphtheriae isolates previously characterized for their biovar and the two genetic markers 6 . The spuA gene was strongly associated with all LIN codes levels, starting from the C. diphtheriae lineage level. All lineages but one, 0_0, were uniform for this marker status (96.0%). At the sublineage level, the proportion of spuA -uniform groups was 97.9%; only five out of 202 sublineages from lineage 0_0 ( i.e. , SL5, SL122, SL228, SL295, and SL466) possessed both spuA profiles. The majority of 0_0 sublineages therefore only comprised either spuA -positive (33.15%) or spuA -negative (66.85%) isolates. In turn, the spuA profile was almost fully concordant with the clonal group level, except for one clonal group (ClG466) out of the total 464 (99.7%), which was formed by a main spuA -positive genetic cluster and two one-isolate spuA -negative genetic clusters. Therefore, LIN code classification at clonal group level is concordant with spuA presence with only one exception, and thus appears useful to predict biovar (Gravis, spuA -positive; versus Mitis or Belfanti, molecularly defined as spuA -negative). Last, the distribution of spuA was fully concordant with genetic clusters (583/583, 100%), thus implying a complete biovar homogeneity at the evolutionary scale of outbreaks or epidemiological clusters. Similarly, when looking at the tox gene presence and allele number as a function of LIN codes, we found a strong homogeneity at the level of sublineages ( tox presence: 89.4% of homogeneous groups; allele: 84.6%), clonal groups ( tox : 97.6%; allele: 95.9%) and genetic clusters ( tox : 98.6%; allele: 97.9%). This demonstrates the utility of these taxonomic levels as predictors of the tox status and even of the tox allele ( Figure 3 , Additional file 4). In other words, most tox alleles were strongly associated with specific sublineages or clonal groups. Nevertheless, some Mitis sublineages exhibited a broad diversity of tox alleles, particularly SL141 ( tox5 , tox13 and tox22 ), SL381 ( tox2 , tox3 , tox18 and tox20 ) and SL40 ( tox2 , tox3 , tox27 and tox37 ) (Additional file 4). Four tox alleles were identified in more than five distinct sublineages ( i.e. , tox2 , tox3 , tox8 and tox16 ), including the most prevalent one, tox2 , present in 112 isolates spanning 26 different SLs ( Figure 3 ), suggesting a successful dissemination through horizontal gene transfer. Download figure Open in new tab Figure 3. Distribution of taxonomic clusters and tox alleles in the C. diphtheriae population. The LIN tree in the center was generated using cgMLST-based LIN codes from a collection of 1,250 genomes previously published 6 . Concentric rings show taxonomic clusters and tox allele presence according to the legend on the right. Different lineages and sublineages are represented by alternate colours, and relevant lineages, sublineages (SL), clonal groups (ClG) and genetic clusters (GC) are indicated by labels next to their corresponding tree branches. Several recent studies have identified a higher tox gene prevalence in biovar Mitis isolates than in biovar Gravis ones ( or spuA -positive isolates) 6 . Here, the proportion of tox -positive isolates was similar for both spuA -positive (28.5%) and spuA -negative (31.7%) isolates, but the spuA -negative isolates displayed a much broader diversity of tox alleles (32 allelic variants) compared to the spuA -positive ones (6 allelic variants). Some narrow-range alleles were only found in all members of specific Gravis lineages ( e.g. , tox12 in 0_6 or tox21 in 0_11), but unlike at shallower LIN code levels, the tox status and allele predictions according to the lineage were limited (76.0% and 64.0%, respectively) ( Figure 3 ). Application of LIN codes to epidemiological studies of C. diphtheriae We first explored the LIN codes diversity of SL8, based on 91 isolates, which were mainly collected in Europe ( Figure 4A ). SL8 includes ex-USSR outbreak isolates 3 . Here, SL8 comprised a unique clonal group (ClG8) and all isolates were of 7-gene MLST type ST8, but there were 15 genetic clusters (ten of which comprised less than 10 isolates), highlighting the genetic diversity of SL8. The LIN codes prefix tree displayed a high concordance with the SNP-based phylogenetic tree (Additional file 5), thus providing a proxy to visualize isolates relationships. The tox gene status within SL8 was fully concordant with the genetic cluster segregation, with tox1 being the only tox allele in tox -positive GCs ( Figure 4A ). Of the 15 GCs within SL8, some were highly clonal, such as GC32, in which all isolates shared the same 7-level LIN code (0_0_0_0_5_0_0) and were collected between 2016 and 2017 from several German regions, suggesting clonal dissemination ( Figure 4A and 4B). On the other hand, other genetic clusters were more diverse, such as GC37 (0_0_0_0_2), whose members were isolated in Belarus over a period of 11 eleven years, or GC29 (0_0_0_0_0), which included isolates obtained in five different European countries, revealing international spread ( Figure 4A and 4B). Within some countries, SL8 isolates were genetically diverse, implying several transmission chains: 43 isolates from Germany belonged to five GCs, and from Belarus, eight distinct GCs were identified ( Figure 4B ). This genetic diversity likely reflects the persistence of SL8 and its diversification since the 1990’s ex-USSR outbreak; for example in Belarus, SL8 was isolated at least from 1996 to 2010 38 . Download figure Open in new tab Figure 4. LIN codes in the highly diverse C. diphtheriae SL8. A. LIN tree of the 91 included genomes. Tree tips are coloured according to the distinct genetic clusters (GC) (left legend), and leaf labels show the complete 10-level LIN codes. The country of isolation is indicated by the different ring colours (left legend), and the tox gene presence is indicated by a red star. B. Alluvial plot displaying the partition relationships based in LIN code prefixes from level 3 ( i.e. , sublineage) to level 10 ( i.e. , complete LIN code) (bottom labels), and the country of isolation (right labels). The inter-level links are coloured according to the different complete LIN codes and countries. The nicknames for LIN code prefixes comprising ≥5 genomes are displayed next to the white bars representing each level partitions, which are ordered by decreasing total counts ( Y -axis). We next revisited the endemicity and local transmission of C. diphtheriae in New Caledonia between 2015 and 2019, based on genomic data from a previous genomic epidemiological study 39 . The 58 C. diphtheriae isolates belonged to four distinct lineages, revealing the deep population diversity existing in the New Caledonia archipelago. As in the total dataset, lineage 0_0 was the most prevalent in New Caledonia, with 43 members, and was highly diverse, with 13 sublineages and 22 genetic clusters (Additional file 6A). The LIN codes prefix tree showed a high concordance with the phylogenetic structure of the population (Additional file 6A); however, there were exceptions where distinct LIN code lineages were located in the same deep branch of the cgSNP tree, such as 0_11 and 0_0. The SLs with the highest isolates counts were SL228 (0_0_30, n=21) and SL416 (0_0_47, n=16), which encompassed 10 and 1 GCs, respectively, illustrating how LIN codes are concordant with their different epidemiological patterns described in the original work: SL228 presented a wider geographical distribution compared to SL416. We next investigated LIN codes in SL384, an epidemiologically important C. diphtheriae sublineage, which was predominant in the Yemen outbreak and also frequently isolated during the 2022 outbreak in asylum seekers in Europe 18 . A total of 183 SL384 genomes were included, all collected in European countries (2022-2024) and in Yemen in 2018 40 . All SL384 members carried tox2 . SL384 was subdivided into three clonal groups and their corresponding three genetic clusters. However, two of these were rare, comprising only one (GC530) or two (GC208) isolates. In contrast, GC3 comprised all other SL384 isolates ( Figure 5A and 5B), suggesting a recent clonal expansion. Furthermore, GC3 included two main complete LIN codes that differed by only two allelic differences (0_0_18_0_0_7_0_0_0_0 and 0_0_18_0_0_7_0_0_6_0), which comprised most (93.4%) of the isolates belonging to the European outbreak, consistent with a very recent common ancestor, estimated in 2019 by Hoefer et al. 18 In contrast, the isolates from Yemen were distributed in 15 different 7-level LIN code prefixes (4 allelic differences), showing the broader GC3 diversity in Yemen ( Figure 5A and 5B). The contrasted diversity of LIN codes between Yemen and European outbreaks suggests two distinct epidemiological scenarios, namely an endemism of GC3 in Yemen versus a rapid spread in Europe. This case illustrates how LIN codes can be used to infer distinct epidemiological dynamics. Download figure Open in new tab Figure 5. LIN codes in the highly conserved C. diphtheriae SL384. A. LIN tree from a collection of 183 genomes from CdSC BIGSdb-Pasteur. Tree tips are coloured according to the distinct 9-level LIN code prefixes, and tree labels show the complete 10-level complete LIN codes. The country of isolation is indicated by the different ring colours (left legend). B. Alluvial plot displaying the cluster distributions based in LIN code prefixes from level 3 ( i.e. , sublineage) to level 10 ( i.e. , complete LIN code) (bottom labels), and the country of isolation (right labels). The inter-level links are coloured according to the different complete LIN codes and countries. The nicknames for LIN code prefixes comprising ≥5 genomes are displayed next to the white bars representing each level partitions, which are ordered by decreasing total counts ( Y -axis). As a fourth example, we looked into the LIN codes diversity of GC1, which was also one of the main clusters responsible for the 2022 European outbreak 18 . GC1 (0_0_85_1_0) belongs to SL574 (0_0_85), which was so conserved that GC1 members represented almost all isolates of SL574 (94.6%), distantly followed by GC26 (4.8%) and GC537 (0.3%). From the 140 GC1 genomes available, 10 distinct complete LIN codes were identified, six of which only comprised a single isolate (Additional file 7A). Notably, Switzerland and France were the reporting countries of four and three of these single-isolate LIN codes, respectively, showing higher GC1 diversity than other countries (Additional file 7B). Still, only two LIN codes comprised the vast majority of GC1 members: the main one was 0_0_85_1_0_0_0_0_0_0, which encompassed 92 isolates (65.7% of GC1) and was present in all participating European countries, except Italy; whereas 0_0_85_1_0_0_0_2_0_0 comprised 37 isolates (26.4%) and was distributed in all countries except for Norway (Additional file 7B). The observation of identical LIN codes in multiple countries illustrates the efficiency of this system to track the rapid spatial spread of outbreak-causing clones. Last, to investigate how C. diphtheriae LIN codes change during recorded transmission chains, we revisited in light of LIN codes, a long-term local transmission of an NTTB strain in England 4 . Eight isolates were collected between 2014 and 2021, in a rehabilitation skin clinic and households of patient(s). The eight isolates belonged to ST336, but nevertheless showed considerable phylogenetic diversity, with 7-199 SNPs and 3-109 cgMLST mismatches 4 . LIN codes show they belong to five distinct genetic clusters, two of which were detected more than once: GC1071 (0_0_246_0_0), with three isolates collected from the same patient, and GC1070 (0_0_246_0_1), which comprises two isolates from two household contacts of the mentioned patient (Additional file 8A). Three genetic clusters belong to the same clonal group (ClG10037, 0_0_246_0), and have originated from a most recent common ancestor (MRCA) estimated in the late 1990’s (Additional file 8B), which in turn belong to SL10037 (0_0_246). This sublineage encompasses three clonal groups involved in this epidemiological investigation (Additional file 8A), and exhibits an evolutionary rate of 1.3x10 -6 substitutions/site/year (95%CI 5.4x10 -7 -2.4x10 -6 ). It was estimated to have originated in the mid 1960’s (MRCA: 1964, 95% CI 1942-1991) (Additional file 8B), consistent with the large genomic diversity observed among the isolates. Discussion Taxonomic classification and nomenclature are needed for communication on bacterial strains in research and epidemiological surveillance. C. diphtheriae comes back as a significant public health threat, particularly in low- and middle-income countries (LMICs) with suboptimal vaccination coverage, and antimicrobial-resistant strains are emerging 18 , 41 . Previous work has focused on identifying C. diphtheriae phenotypic and genotypic groups, including through biotyping and other pre-molecular approaches. The 7-loci MLST scheme provided initial genotypic classification, but lacks the resolution needed for fine discrimination 3 . The 1,305-loci cgMLST extension of this approach now enables high-resolution characterization of C. diphtheriae isolates. While standardized classification and naming at multiple levels is needed for effective use of cgMLST data, such a taxonomic system has been lacking. Here, we developed a 10-level LIN code taxonomy for C. diphtheriae, C. belfantii and C. rouxii strains, and explored how the LIN code approach based on cgMLST data could help addressing epidemiological and evolutionary questions, as shown previously for other relevant bacterial pathogens, including Klebsiella pneumoniae and Streptococcus pneumoniae 8 , 42 . We analyzed the population structure of C. diphtheriae sensu lato in order to define phylogenetically informative LIN code thresholds. Our analysis showed a star-like phylogenetic structure, as most allelic differences corresponded to the upper and lower peaks in the distribution, separated by a wide plateau with few allelic distance values. This previously reported structuration into multiple deeply branching sublineages 3 , 5 , some of which have undergone recent clonal expansions, is thus confirmed here, based on a higher number of genomes and sublineages (from 114 sublineages represented previously, to 388 SLs herein). The creation and integration of a 10-level cgMLST-based LIN code system in the BIGSdb-Pasteur platform, enabled us to address various evolutionary and epidemiological questions. Exploring deep evolutionary questions can be facilitated by using LIN codes, as illustrated here by the diversity of tox alleles among sublineages, which showed that only a few tox alleles were highly prevalent and showed broad distribution across sublineages. The tox gene distribution itself varied within some clonal groups, such as ClG8, but was fully compatible with the genetic cluster classification level, suggesting a good predictive power of this classification level for clinical implications. The LIN code system also proved effective for epidemiological purposes at local and global levels. The escalating discrimination from genetic clusters (25 allelic mismatches) to complete LIN codes (0 allelic mismatches) is useful to reveal the finer-grained genetic diversity within emerging subpopulations resulting from recent clonal expansions. For example, studying the NTTB SL10037 protracted local transmission 4 , LIN codes revealed a large diversity with three clonal groups having diversified from a common ancestor, consistent with long-term local transmission from a reservoir of colonization and infections. On a broader scale, LIN codes illustrated that the epidemiological situation in New Caledonia and Yemen was characterized by diverse C. diphtheriae populations suggesting persistent endemic contexts ( Figure 6 , A and B). When investigating previously reported outbreaks, the analysis of LIN codes revealed distinct epidemiological dynamics, with more diversity having accumulated within SL8 (since the USSR 1990s outbreak) than within the recent expansions (Europe, 2022-2023) of SL574 and ST384 ( Figures 6C , 6D and 6E). More diverse populations may indicate a longer diversification time, a more heterogeneous population influx, or a higher genetic mutation rate. In sum, the inspection of LIN codes within a given population of interest can uncover recent emergence or endemism, shifts in strain prevalence, and the spread of subtypes, which should be monitored in priority for their antibiotic resistance or virulence. Broader geographic application of genomic sequencing for diphtheria surveillance will help defining the relative roles of spatial spread and importation versus endemism of local strains, which currently appears as an important feature of C. diphtheriae populations. Download figure Open in new tab Figure 6. LIN code-based graphic representations of C. diphtheriae populations from different geographic and epidemiological contexts. LIN code prefixes are represented by circles coloured according to the level (from 1 to 10, as shown in the left legend of the panels), which are nested within their corresponding higher/parental LIN code prefixes. The circle sizes display the number of genomes encompassed by each LIN code prefix, and the labels for the main LIN code prefixes are displayed in the plots, including the nicknames for levels 1 and 3 to 5. The headings at the top and the left of the figure indicate the geographic and epidemiological contexts, and the title of each panel specifies the geographical region and/or the C. diphtheriae sublineage. A: Endemic C. diphtheriae sensu lato population in New Caledonia. B: Endemic C. diphtheriae population in Yemen (including GC3 population). C: Endemic (post-epidemic) SL8 population in Europe. D: Epidemic SL384 population in Europe (driven by epidemic GC3 population). E: Epidemic SL574 population in Europe (including epidemic GC1 population, which is covered by the fully overlapping 0_0_85_1_0_0_0 lower-level circle). Conclusions We suggest that the adoption of the cgMLST-based LIN code system could mark a transformative step for the genomic taxonomy of C. diphtheriae . This system establishes a consistent framework for classification, while at the same time effectively mitigating the issues of instability caused by the merging of distinct groups associated with previously-used single-linkage clustering 3 . The LIN code system provides stability of taxonomic assignments, even at shallow levels, and enables differentiation among closely related isolates, which is essential to unify epidemiological tracking and outbreak investigations from different studies. Besides, the capability to elucidate relationships at different phylogenetic levels enriches our understanding of the evolutionary dynamics of C. diphtheriae sensu lato . The transition to the LIN code system should streamline communication on C. diphtheriae strains in research and public health contexts, ultimately contributing to more effective strategies for monitoring and managing this reemerging bacterial pathogen and the come-back of diphtheria. List of abbreviations ANI average nucleotide identity BIGSdb Bacterial Isolate Genome Sequence Database CdSC Corynebacterium diphtheriae species complex cgMLST core-genome Multi-Locus Sequence Typing cgST core-genome sequence type ClG Clonal group DT diphtheria toxin GC Genetic cluster LIN code Life Identification Number code LIN tree Life Identification Number-based tree LMIC low- and middle-income country MALDI-TOF Matrix Assisted Laser Desorption Ionization - Time of Flight MCMC Markov Chain Monte Carlo MLST Multi-Locus Sequence Typing MRCA most recent common ancestor MST minimum spanning tree NTTB strain non-toxigenic tox -bearing strain PCR polymerase chain reaction SL Sublineage SNP single nucleotide polymorphism ST sequence type Declarations Availability of data and materials All genomes, allelic profiles and datasets analysed during the current study are available at the Corynebacterium diphtheriae species complex (CdSC) BIGSdb-Pasteur database ( https://bigsdb.pasteur.fr/diphtheria/ ), including: - Badell, E. et al. Ongoing diphtheria outbreak in Yemen: a cross-sectional and genomic epidemiology study. Lancet Microbe 2 , e386–e396 (2021): https://bigsdb.pasteur.fr/cgi-bin/bigsdb/bigsdb.pl?db=pubmlst_diphtheria_isolates&page=project&project_id=3 - Hennart, M. et al. A global Corynebacterium diphtheriae genomic framework sheds light on current diphtheria reemergence. Peer Community J. 3 , (2023): https://bigsdb.pasteur.fr/cgi-bin/bigsdb/bigsdb.pl?db=pubmlst_diphtheria_isolates&page=project&project_id=14 - Tessier, E. et al. Genomic Epidemiology of Corynebacterium diphtheriae in New Caledonia. Microbiol. Spectr. 11 , e0461622 (2023): https://bigsdb.pasteur.fr/cgi-bin/bigsdb/bigsdb.pl?db=pubmlst_diphtheria_isolates&page=project&project_id=10 - Hoefer, A. et al. Phenotypic and genomic analysis of a large-scale Corynebacterium diphtheriae outbreak among migrant populations in Europe. 2023.11.10.23297228 Preprint at https://doi.org/10.1101/2023.11.10.23297228 (2023): https://bigsdb.pasteur.fr/cgi-bin/bigsdb/bigsdb.pl?db=pubmlst_diphtheria_isolates&page=project&project_id=17 Competing interests The authors declare that they have no competing interests. Funding JFDB receives support from Trond Mohn foundation through the KlebGAP project (grant TMF2019TMT03). This work used the computational and storage services provided by the IT Department of Institut Pasteur. This work received financial support from Institut Pasteur and from the French Government’s Investissement d’Avenir program Laboratoire d’Excellence Integrative Biology of Emerging Infectious Diseases (grant number ANR-10-LABX-62-IBEID). Authors’ contributions JFDB conducted the analyses and created the figures, with input from SB. MRP contributed to the implementation and curation of the LIN codes in the BIGSdb-Pasteur database. SB acquired funding and supervised the work. JDB and SB wrote the manuscript. Acknowledgements We thank Virginie Passet for curating data in the BIGSdb-Pasteur diphtheria database, Federica Palma and Solène Cottis for BIGSdb-Pasteur project management assistance, and Brice Raffestin and Bryan Brancotte for maintenance and installing updates of BIGSdb-Pasteur. We thank Keith Jolley (Oxford University) for continuous developments of the BIGSdb platform, including LIN codes functionalities. Footnotes https://bigsdb.pasteur.fr/diphtheria/ References 1. ↵ Corynebacterium Diphtheriae and Related Toxigenic Species: Genomics, Pathogenicity and Applications . ( Springer Netherlands , Dordrecht , 2014 ). doi: 10.1007/978-94-007-7624-1 . OpenUrl CrossRef 2. ↵ Hennart , M. et al. Population genomics and antimicrobial resistance in Corynebacterium diphtheriae . Genome Med . 12 , 107 ( 2020 ). 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Development of the Pneumococcal Genome Library, a core genome multilocus sequence typing scheme, and a taxonomic life identification number barcoding system to investigate and define pneumococcal population structure. Microb . Genomics 10 , 001280 ( 2024 ). OpenUrl View the discussion thread. Back to top Previous Next Posted June 21, 2025. Download PDF Supplementary Material Data/Code Email Thank you for your interest in spreading the word about bioRxiv. NOTE: Your email address is requested solely to identify you as the sender of this article. Your Email * Your Name * Send To * Enter multiple addresses on separate lines or separate them with commas. You are going to email the following Life identification number (LIN) codes for the genomic taxonomy of Corynebacterium diphtheriae strains Message Subject (Your Name) has forwarded a page to you from bioRxiv Message Body (Your Name) thought you would like to see this page from the bioRxiv website. 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