Neisseria gonorrhoeae LIN codes provide a robust, multi-resolution lineage nomenclature

Taxonomic classification is necessary to unravel evolutionary relationships, while also enabling the establishment of nomenclatures that facilitate effective communication of where an organism falls on a phylogeny (Maiden et al., 2013). This is particularly important for lineages within bacterial species, which are often distinct in clinically relevant phenotypes including antimicrobial resistance (AMR) and virulence (Maiden et al., 2013). The ability to identify these lineages consistently via a stable nomenclature system is essential to epidemiological surveillance (Maiden et al., 2013).

In Neisseria gonorrhoeae, the identification of AMR-associated lineages is particularly relevant. The gonococcus is a multi-drug resistant pathogen included in the WHO priority list for AMR (WHO, 2018; Unemo and Nicholas, 2012). Globally, gonorrhoea causes a high burden of disease with an estimated 86.9 million new cases annually, which, if not successfully treated, can cause sequelae such as infertility and pelvic inflammatory disease (Unemo and Nicholas, 2012; Rowley et al., 2019). Facing the dual challenges of AMR surveillance and gonococcal vaccine development (Lyu et al., 2024), accurate characterisation of gonococcal population structure and consistent identification of key lineages is of utmost importance (Gottlieb et al., 2019; Harrison et al., 2020).

A variety of approaches has been applied to N. gonorrhoeae lineage taxonomy, which can cause confusion and miscommunication. Underlying these disparate nomenclatures is the complex population structure of the gonococcus; it is a highly recombinogenic organism, carrying out extensive horizontal gene transfer (HGT) between gonococci, and more rarely with other species (O’Rourke and Stevens, 1993; Unitt et al., 2024; Manoharan-Basil et al., 2022; Bennett et al., 2014). Polymorphisms are continually reassorted through this process, leading to low levels of linkage disequilibrium and disrupting clonal population structure (O’Rourke and Stevens, 1993; Hanage, 2016). Consequently, N. gonorrhoeae has been described as a ‘sexual clone’, reflecting the strong impact of HGT and the therefore weak clonal signal present in gonococcal genomes (Harrison et al., 2020; Harrison and Maiden, 2021). This low signal means that many molecular typing methods that are effective at capturing lineages in relatives such as N. meningitidis are not as reliable in N. gonorrhoeae (Harrison et al., 2020). The result has been a proliferation of differing approaches and nomenclatures seeking to capture N. gonorrhoeae taxonomy, particularly since 2010 (Harrison et al., 2016; Kwong et al., 2016; Golparian et al., 2021).

Most of these methods are Multi-Locus Sequence Typing (MLST) based. MLST is a widely used molecular approach for characterising bacterial isolates through the combination of alleles across a set of genes (an ‘allelic profile’; Maiden et al., 1998). Each unique combination of alleles across the profile represents a sequence type (ST), which can be used, where they correspond with clonal inheritance, to characterise lineages (Maiden et al., 2013). Isolates can be further grouped based on these STs, using methods such as goeBURST (Francisco et al., 2009) or single linkage clustering (Everitt et al., 2011).

MLST is a powerful approach that has had widespread applications in the analysis of bacterial lineages (Maiden et al., 2013); however, conventional MLST approaches such as 7-locus MLST use only a small number of loci, which can make this system more vulnerable to disruptive HGT. In bacteria such as N. gonorrhoeae, HGT is extensive enough that it affects the housekeeping genes used in 7-locus MLST (Harrison et al., 2020). This makes 7-locus MLST a sub-optimal tool when applied in N. gonorrhoeae typing, as two isolates with the same ST may not actually be closely related, having come by the same ST by HGT rather than clonal inheritance (Harrison et al., 2020). This process affects several gonococcal typing systems, including NG-MAST (Harrison et al., 2020; Kwong et al., 2016) and NG-STAR (Harrison et al., 2020; Golparian et al., 2021).

Typing systems using larger numbers of loci can address this problem. For example, core gene MLST (cgMLST) is an extension of the MLST approach that uses a profile of hundreds of core genes to gain higher resolution, while also diluting the taxonomy-jumbling effects of HGT of some of those loci (Maiden et al., 2013; Harrison et al., 2020; Maiden and Harrison, 2016; Palma et al., 2024). In N. gonorrhoeae cgMLST v1, genes were defined as core if they were present in >95% of genomes, resulting in a core gene list (scheme) of >1600 core genes (Harrison et al., 2020). If two isolates were identical across this scheme, allowing for up to 50 loci to be unannotated, they would belong to the same core genome sequence type (cgST; Harrison et al., 2020). Single-linkage clustering was then applied to define core genome ‘groups’ based on various threshold levels of allelic differences within and between groups (Harrison et al., 2020). This clustering was necessary as the large number of loci used in cgMLST results in large numbers of unique cgSTs.

This method provided a high-resolution classification of N. gonorrhoeae lineage taxonomy, while being less affected by HGT than 7-locus MLST, NG-STAR or NG-MAST (Harrison et al., 2020). However, single-linkage clustering is susceptible to group fusion when intermediate isolates are found that bridge the identity thresholds between linkage groups (Palma et al., 2024; Hennart et al., 2022). HGT can exacerbate this issue, homogenising sequences across lineages by spreading polymorphisms throughout the population. As a result, the Ng cgMLST v1 core genome group nomenclature was not stable and changed over time as more N. gonorrhoeae isolates were sampled, decreasing the resolution provided and potentially leading to confusion as the nomenclature shifts. Therefore, there is still a need for a N. gonorrhoeae lineage nomenclature that provides similarly effective discrimination between gonococcal lineages, while also remaining consistent over time.

Here, a definitive nomenclature for N. gonorrhoeae is proposed, using LIN codes. This isolate barcoding system has previously been applied to Klebsiella pneumoniae (Hennart et al., 2022) and Streptococcus pneumoniae (van Rensburg et al., 2024), where LIN codes were shown to provide a high resolution nomenclature, enabling accurate insight into the population structure of these organisms. LIN code combines the HGT-diluting effects of a cgMLST scheme with the stability of a numeric barcode (Palma et al., 2024; Hennart et al., 2022). This stability is derived from the fixed nature of these barcodes for each isolate, along with the flexibility of a multi-position numeric code which can increase infinitely as new variants are sequenced (Palma et al., 2024; Hennart et al., 2022; Vinatzer et al., 2017). The N. gonorrhoeae LIN code developed here was implemented in PubMLST, a freely accessible bacterial genomics database (RRID:SCR_012955) (Jolley et al., 2018). LIN codes are automatically assigned to all WGS uploaded to PubMLST, making this taxonomy easily applicable to old and new datasets, and encouraging the widespread use of this reliable nomenclature.

Accurate identification of bacterial lineages is necessary to examine links between bacterial population structure and characteristics such as AMR, and to enable surveillance of emergent or outbreak-associated variants (Maiden et al., 2013). However, in N. gonorrhoeae, widespread HGT reassorts DNA among isolates, disrupting clonal inheritance and consequently distorting phylogenetic trees (Harrison et al., 2020; Turner and Feil, 2007). This makes it difficult to accurately characterise gonococcal lineages, particularly when using conventional typing systems based on small numbers of genes, such as 7-locus MLST (Harrison et al., 2020; Hanage, 2016). Previously, the most reliable approach applied core genome MLST (cgMLST) to define core genome groups, using over 1000 genes to dilute the effects of HGT and facilitate high-resolution analyses (Maiden et al., 2013; Harrison et al., 2020; de Korne-Elenbaas et al., 2022). Unfortunately, this method suffers from cluster instability (Hennart et al., 2022; Turner and Feil, 2007). LIN code provides an alternative means to leverage the benefits of cgMLST within a stable classification system, which can accommodate new genotypes without the risk of disrupting established clustering (Palma et al., 2024; Hennart et al., 2022). Here, we developed a N. gonorrhoeae LIN code: an effective nomenclature for categorising, exploring, and discussing gonococcal population structure.

Evidence for the existence of distinct, persistent gonococcal lineages pre-dates whole genome sequencing, in the characterisation of auxotypes: related isolates that exhibit similar patterns of growth in media containing different combinations of key nutrients (Catlin, 1978). This may be due to metabolic competition amongst gonococcal strains, causing the population structure to segregate into distinct metabolic types (Gupta, 2024). Previous research has posited that selection can preserve such lineages even in the face of extensive HGT, as has been the case in immune selection for differing antigenic types in other bacterial species (Gupta, 2024; Watkins et al., 2016; Buckee et al., 2011). Sequence-based approaches including 7-locus MLST, NG-STAR, and cgMLST have confirmed that the N. gonorrhoeae population contains identifiable groups of related isolates, some of which are associated with clinically relevant phenotypes such as AMR (Harrison et al., 2020; Golparian et al., 2021; Golparian et al., 2024; Sánchez-Busó et al., 2022), with outbreak events (Smolarchuk et al., 2018), or with specific at-risk groups such as men who have sex with men (MSM; Sánchez-Busó et al., 2019; Williamson et al., 2019). However, some of these typing systems are prone to providing misleading classifications, where isolates that are not closely related appear to be, due to the effects of HGT within the small numbers of loci used to classify them. Consequently, while equivalent systems have proved highly effective at categorising the closely related N. meningitidis (Maiden, 2008), the extensive HGT observed across the gonococcal genome necessitates a whole genome approach to reliably identify related groups of N. gonorrhoeae isolates (Harrison et al., 2020). LIN code achieves this by applying cgMLST, using hundreds of loci belonging to a wide range of functional categories, including genes involved in metabolic pathways, genetic processing, and AMR. This allows the LIN code to capture gonococcal population structure with increased consistency and higher resolution than many previous approaches, providing fresh insight into the biology that underlies this species’ genetic composition.

The LIN nomenclature conveys lineage information in the form of hierarchical clustering at sequential thresholds of tolerated allelic mismatch within a cgMLST scheme (i.e. the number of alleles differing out of the total list of loci in the scheme; Hennart et al., 2022; van Rensburg et al., 2024). Here, 11 thresholds were used, resulting in an 11-bin barcode. The leftmost numbers of the barcode represent clustering at the highest thresholds of allelic mismatch, here corresponding with LIN superlineage (up to 550 mismatches), LIN lineage (300 mismatches), and LIN sublineage (125 mismatches). Progressing across the barcode to the right, each number indicates clustering at an increasing level of allelic similarity, ultimately resulting in the delineation of highly related isolates that differ in a very small number of core loci (Figure 4; Palma et al., 2024; Hennart et al., 2022; van Rensburg et al., 2024). Subsets of thresholds, for example a prefix of the first three or four, may be used to define clustering down to a particular level of taxonomic similarity (Hennart et al., 2022; van Rensburg et al., 2024). If two isolates are highly related and therefore identical across their core gene profile, they will share the same LIN code.

The use of multiple thresholds enables the LIN code to relate back to several familiar nomenclatures, such as core genome groups and 7-locus MLST, which are both broadly equivalent to LIN lineages and NG-STAR CCs, approximately equivalent to LIN sublineages. The difference lies in the accuracy and stability of the LIN code versus its predecessors. Visualisation via phylogenetic trees demonstrated that the LIN codes related strongly to phylogenetic structure, capturing distinct clades without overlap. This is in contrast to previous nomenclatures such as 7-locus MLST, which can form polyphyletic groups due to the misleading effects of HGT (Harrison et al., 2020).

Furthermore, the multi-resolution, hierarchical nature of LIN codes also allows this nomenclature to encode more information about the relationship between isolates than conventional typing systems (Palma et al., 2024). For example, if a pair of isolates belongs to MLST 1 and 2, it can only be deduced that they differ in between 1 and 7 housekeeping genes. Meanwhile, if the same pair of isolates belongs to LIN lineages 0_2_1 and 0_2_2, this communicates that these isolates differ by no more than 550/1430 core gene alleles, but by more than 300/1430, quickly defining the degree of their relatedness (Figure 4). This is useful when comparing isolates, for example those associated with AMR. Previous approaches used to track AMR strains include both 7-locus MLST and NG-STAR, which types isolates based on their alleles across seven AMR genes (penA, mtrR, porB, ponA, gyrA, parC, and 23 S rRNA; Demczuk et al., 2017). This typing system has been successfully used in many analyses of AMR associated gonococci, and NG-STAR CCs have been recommended as a tool for characterising gonococcal lineages (Golparian et al., 2021; Golparian et al., 2024). However, due to HGT, specific NG-STAR types or clonal complexes do not always represent closely related isolates. LIN sublineages were able to discern isolates with a similar level of resolution to NG-STAR CCs, but higher reliability. This will facilitate more accurate tracking of AMR associated isolates, such as those belonging to LIN sublineage 0_2_0_1, when compared to the equivalent NG-STAR CC 90, which also includes unrelated isolates belonging to LIN superlineages 0_0 and 1_1. While NG-STAR effectively characterises an isolate’s AMR genotype (Demczuk et al., 2017), it cannot consistently distinguish related isolates. LIN code fulfils this role, while also providing increased information about the degree of relatedness of the isolates in question. Accurately defining this lineage structure will be important to elucidate how AMR emerges in distinct gonococcal lineages.

As an allele-based method, LIN codes enable analysis of a large number of isolates rapidly and reproducibly (Palma et al., 2024; Hennart et al., 2022; Vinatzer et al., 2017). This is an improvement on phylogenetic tools used to identify gonococcal lineages which rely on nucleotide sequence alignments, requiring significant time and expertise to compute (Tonkin-Hill et al., 2019). LIN codes can replicate similar clustering, identifying the same lineages while being fully automated (Palma et al., 2024). For example, hierarchical Bayesian analyses (BAPs) have previously been used to delineate gonococcal population structure into two lineages: lineage A, associated with high-risk sexual networks and AMR, and lineage B, associated with lower risk networks and susceptibility (Sánchez-Busó et al., 2019). In our LIN code analysis, we observed that lineage A (BAPS 8, 7, and 9) corresponds to LIN lineages 0_2_0, 0_2_1 and 0_2_17, reaffirming that cgMLST is able to provide the same resolution as SNP-based techniques (Harrison et al., 2020). While BAPs analysis can be complex and time intensive to run, LIN codes can be used to rapidly identify lineage A gonococci simply by uploading WGS data to PubMLST. Within 24 hr of upload, any sequence data in which 1405/1430 core loci can be annotated (including private isolate records) will be assigned a Ng cgMLST v2 cgST; if this is a previously identified cgST, a LIN code will be assigned immediately; if a new cgST, the new LIN code will be assigned by the following Monday. Furthermore, these isolates can then be analysed in the context of the wider PubMLST gonococcal genome collection, which currently includes >28,000 public records and integrates accessible plugins such as GrapeTree (Zhou et al., 2018) and Genome Comparator (Jolley et al., 2018). This facilitates further investigation, for example in identifying an isolates’ relationship to known AMR strains, or in the analysis of suspected transmission linked isolates from within the same community.

To further illustrate the efficacy of LIN code, we reproduced an analysis of 170 global ceftriaxone-resistant isolates (Fifer et al., 2015). The original article’s methodology involved a WGS alignment and generation of a maximum likelihood tree in order to characterise eight major phylogroups (Fifer et al., 2015). The gonococcal LIN code instantly reproduced these clusters, while providing additional detail about each clade in the form of superlineage/sublineage divisions and simultaneously contextualising the isolates amongst the wider PubMLST database. For example, lineage 0_14_0 was over-represented in this ceftriaxone-resistant dataset when compared against 1000 randomly selected N. gonorrhoeae isolates from PubMLST. Also, LIN code was able to classify several divergent isolates that were not assigned to any phylogroup in the original publication and detected 15 instances of matching full-length LIN codes, meaning these isolates were identical across their core genome and could therefore represent isolates associated with transmission events (Harrison et al., 2020). The PubMLST interface facilitates in-depth analysis of these instances of shared LIN codes, allowing users to explore related isolates at various thresholds of allelic dissimilarity by viewing a table of similar isolates (as defined by LIN code) on an isolate’s information page (Figure 8). This enables quick investigation of any related isolates, including their location, year, allele at a particular locus or classification by other typing schemes such as NG-STAR.

Figure 8

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The genetic mix-and-matching performed by N. gonorrhoeae can make characterisation of its population structure difficult (Harrison et al., 2020). However, the LIN code nomenclature proposed here provides clarity, consistency, and stability in its description of N. gonorrhoeae lineages. The multi-resolution clustering intrinsic to LIN code facilitates a common language around lineage nomenclature at different epidemiological levels, from high divisions such as superlineage down to unique clones (Palma et al., 2024). In conclusion, N. gonorrhoeae LIN codes represent a portable, publicly available taxonomic nomenclature that has the potential to enhance surveillance of N. gonorrhoeae in order to benefit public health.

All whole genome sequence data used in this publication is publicly available in the https://pubmlst.org/organisms/neisseria-spp database. The specific datasets used are described in the supplementary files. Computational scripts are documented at https://doi.org/10.17504/protocols.io.4r3l21beqg1y/v1 (Unitt, 2025).

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Author details

1. Anastasia Unitt

   Nuffield Department of Population Health, University of Oxford, Oxford, United Kingdom

   Contribution

   Conceptualization, Data curation, Formal analysis, Validation, 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:" 0000-0003-1392-9786

2. Made A Krisna

   Department of Biology, University of Oxford, Oxford, United Kingdom

   Contribution

   Software, Methodology

   Competing interests

   No competing interests declared

3. Kasia M Parfitt

   Department of Biology, University of Oxford, Oxford, United Kingdom

   Contribution

   Formal analysis

   Competing interests

   No competing interests declared

4. Keith A Jolley

   Department of Biology, University of Oxford, Oxford, United Kingdom

   Contribution

   Resources, Software, Validation, Methodology

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-0751-0287

5. Martin CJ Maiden

   Department of Biology, University of Oxford, Oxford, United Kingdom

   Contribution

   Conceptualization, Supervision, Project administration, Writing – review and editing

   Competing interests

   No competing interests declared

6. Odile B Harrison

   Nuffield Department of Population Health, University of Oxford, Oxford, United Kingdom

   Contribution

   Conceptualization, Resources, Supervision, Project administration, Writing – review and editing

   For correspondence

   odile.harrison@ndph.ox.ac.uk

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-1623-0295

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