• Federica Palma, 
• Melanie Hennart, 
• Keith A. Jolley, 
• Chiara Crestani, 
• Kelly L. Wyres, 
• Sebastien Bridel, 
• Corin A. Yeats, 
• Bryan Brancotte, 
• Brice Raffestin, 
• Sophia David

x

Abstract

Citation: Palma F, Hennart M, Jolley KA, Crestani C, Wyres KL, Bridel S, et al. (2026) Life Identification Numbers: A strain nomenclature approach to aid epidemiological surveillance of bacterial pathogens. PLoS Biol 24(6): e3003781. https://doi.org/10.1371/journal.pbio.3003781

Published: June 4, 2026

Copyright: © 2026 Palma et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Funding: This work was supported by the Gates Foundation (INV-025280 to DMA, INV-077266 to KEH), and by European Union’s Horizon 2020 research and innovation programme (Grant Agreement No. 773830 to Sy.B). This work was also supported financially by the French Government’s Investissement d’Avenir program Laboratoire d’Excellence “Integrative Biology of Emerging Infectious Diseases” (ANR-10-LABX-62-IBEID to Sy.B). BIGSdb development was funded by a Wellcome Trust Biomedical Resource grant (218205/Z/19/Z to MCJM). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Competing interests: I have read the journal’s policy and the authors of this manuscript have the following competing interests: KH is an Academic Editor at PLOS Biology.

Abbreviations: ANI, Average Nucleotide Identity; CG, clonal groups; cgMLST, core genome multilocus sequence typing; HierCC, Hierarchical clustering; KpSC, Klebsiella pneumoniae Species Complex; LIN, Life Identification Number; MLST, multilocus sequence typing; SL, sublineages; SNPs, single-nucleotide polymorphisms; ST, sequence type; WGS, whole-genome sequencing

Introduction

Taxonomies of bacterial strains responsible for infectious diseases are essential resources to ensure effective communication in population biology, epidemiological surveillance, and public health response to outbreaks. As illustrated by the SARS-CoV-2 variant nomenclature, simple nicknames (e.g., Alpha, Delta, Omicron) can greatly improve communication among multiple actors, including the public, in the face of public health threats [1,2]. Strain taxonomies are therefore needed to precisely recognize and define variants with properties of special medical interest, such as antimicrobial resistance, high virulence, or vaccine escape.

Taxonomic systems are based on three pillars: classification, nomenclature, and identification. Currently, there are neither classification nor nomenclature standards to define sublineages, variants, types, or clones (hereafter, collectively called ‘strains’) within bacterial species [3]. Linnean taxonomy encompasses classification levels from Phylum down to subspecies, but the latter is seldom used for bacterial species as it is not a practical solution to describe strains. Ad-hoc phenotypic (e.g., serotypes) and genotypic (e.g., sequence types) methods have long been used to differentiate strains from particular species but have shown limitations in terms of universal applicability, reproducibility of classification, or level of resolution. However, the advent of universally applicable whole-genome sequencing (WGS, see Box 1) has advanced the potential to refine and generalize strain taxonomy (Box 1) by providing the maximal discrimination power needed for epidemiological surveillance, while being broadly applicable as a harmonized approach across pathogen Phyla [4–6]. Yet, few attempts have been made to devise genomic taxonomies of strains and evaluate their general applicability. With WGS now being implemented worldwide in all sectors of microbiology (e.g., medical, veterinary, food, environmental), a precise and universal procedure for describing bacterial strains has become a key need to translate WGS data into relevant information that would support epidemiological surveillance, outbreak investigations, cross-niche or between-host transmission tracking, and public health actions that need international and cross-sectoral coordination. In this Essay, we introduce a new approach to describing bacterial strains and explain its benefits for stable definition and labeling of intra-species groups from the deepest phylogenetic lineages through epidemiologically important clonal groups down to outbreak strains.

Bacterial strain taxonomies

Several bacterial strain taxonomies have emerged in recent years. One of them, applied to bacterial pathogens with low amounts of genetic diversity (i.e., evolutionary recent pathogens), relies on recognizing notable branches in phylogenetic trees, defined by specific diagnostic single-nucleotide polymorphisms (SNPs) [7,8]. A similar approach, the Pango nomenclature, was successfully applied to SARS-CoV-2 variants [2]. Unfortunately, these phylogenetic approaches face challenges raised by the need to update phylogenetic trees and define novel lineages. Another classification approach, PopPunk, relies on pairwise comparisons of unaligned genomes (based on k-mers, see Box 1) to create groups within bacterial populations, and is scalable to large and diverse datasets [9]. However, among the broad range of methods developed for bacterial strain typing and group naming [10,11], MLST (see Box 1), based on the analysis of a few (typically seven) conserved loci, was established over the past two decades as the method of choice for most bacterial species [12–14]. Indeed, major strengths of MLST are its standardization, as it relies on well-defined fixed sets of genetic markers, and its ease of interpretation and portability. Classic MLST schemes form the basis of widely adopted ‘sequence type’ (ST) taxonomies [15] in most bacterial species, which are maintained, expanded, and made available to the international community through the platforms BIGSdb [16] and EnteroBase [17].

The logical extension of MLST at the genome scale, known as core genome MLST (cgMLST, see Box 1), uses thousands of conserved gene loci, leading to the definition of core genome sequence types, or cgST [4,18]. However, because of their much higher resolution, any cgST will match with only a tiny fraction of bacterial isolates from a given bacterial species, making the cgST a less useful nomenclatural element than the classic ST for tracing genetic relationships through broad space and time scales. To define phylogenetic associations among similar cgSTs, which together might represent meaningful groups of particular medical or epidemiological interest (a common way of conceptualizing the informal notion of a ‘strain’), cgMLST allele profiles can be grouped at chosen levels of dissimilarity, resulting in multilevel classifications. A single-linkage clustering was initially used to create these higher-level groups from cgMLST data [19], but by design, this approach suffers from a lack of stability, as preexisting groups can merge when intermediate genotypes are sampled. To address this issue, profiles can be assigned to the most closely related preexisting group. This approach, called Hierarchical clustering (HierCC), represents the first multilevel bacterial strain taxonomy system based on cgMLST [20]. HierCC is implemented in the platform EnteroBase, where taxonomies for strains of Salmonella, Escherichia coli, and other important bacterial pathogens are maintained [17,21].

Amongst the various efforts to align the taxonomy of all life forms with genomic data, a novel multilevel classification system was proposed by Vinatzer and colleagues, using multi-position numerical codes attributed to each individual genome [22,23]. These codes, called Life Identification Numbers (LIN), were designed to encompass all domains of life in a single, unified taxonomy, based on the Average Nucleotide Identity (ANI) metric (Box 1) [24]. A database of these ANI-based LIN codes, GenomeRxiv (initially called LINbase), was set up to enable global development and use of this taxonomic approach [25].

Given that the pairwise ANI estimates between genomes can be imprecise for nearly identical strains, particularly when draft genomes are significantly fragmented, some of us proposed combining LIN codes with the cgMLST approach in order to design novel taxonomies of bacterial strains within species [26]. We found that the use of pairwise dissimilarities between cgMLST profiles, rather than ANI estimates between genomes, provides greater reproducibility in appraising small-scale genome relationships. Hence, cgMLST-based LIN codes (hereafter, LIN codes for short) combine the strengths of both approaches.

Below, we present this LIN code approach and its recent improvements, including its implementation in the widely used genotyping platforms BIGSdb and Pathogenwatch [16,27], and a LIN code nicknaming procedure to facilitate the designation of familiar intra-species groups of key importance in either biological research or epidemiological surveillance. We also illustrate how the LIN codes can be used to address questions in population biology and genomic epidemiology, using the case of the Klebsiella pneumoniae Species Complex (KpSC), a phenotypically and genetically diverse ubiquitous pathogenic group [28].

cgMLST-based LIN coding and missing data handling

LIN codes are series of numeric codes that reflect genomic similarity between organisms. cgMLST-based LIN code systems consist of multiple (e.g., 10) predefined positions (or bins), each corresponding to a range of pairwise cgMLST profile similarity values, together representing a partition of the complete range (0%–100%). From left to right, the positions of the code correspond to decreasing allele mismatch dissimilarity (i.e., increasing similarity). The leftmost bins are thus used to classify deep phylogenetic divisions, whereas the rightmost bins distinguish recently evolved variants. Analogous to the classification levels of Linnean taxonomy (e.g., Phylum–Class–Order–Family–Genus–species), the LIN codes thus capture from left to right, the membership of a genome to taxonomic groups of increasing relatedness.

The process of LIN code assignment from cgMLST data, first proposed in Hennart and colleagues [26], is summarized in Fig 1 (and formalized in Section A in S1 Appendix). A LIN code is created for each distinct cgST. The system is initialized by creating, for a selected initial cgST, a LIN code with the integer value 0 at every bin. The initial cgST can be chosen randomly or using a reference strain of the species or group under consideration (e.g., the first strain that was sequenced, or the taxonomic type strain). The next steps are the same for each subsequent individual cgST. Each incoming cgST is matched against all already LIN-encoded cgSTs in order to identify its most similar one (hereafter, the reference cgST), based on the fraction of allele mismatches across cgMLST loci. For creating the novel LIN code, the pivot bin is defined as the bin in which the observed allele similarity falls, and the novel LIN code is then created in three steps (Fig 1): copying the LIN code prefix of the reference cgST (i.e., from the leftmost bin up to the pivot bin (excluded)); incrementing by 1 the maximum integer value observed in the pivot bin among the cgST(s) sharing the same prefix used in the previous step; and attributing the integer value 0 at the bins downstream of the pivot, corresponding to initialization of the novel subdivision created at the pivot bin level.

The cgSTs differ from classic STs in an important way: due to the often-fragmented nature of genome sequence assemblies and because many core genes are dispensable in bacteria, the cgST must accommodate missing data. If two cgSTs (new and reference) have a 100% similarity (i.e., no allele mismatch among the loci called in both profiles), the LIN code of the reference is simply assigned to the new cgST. This can happen when the new cgST differs from the reference only by its missing data pattern, such pairs (or groups) being called coincident cgSTs (see Section B in S1 Appendix). Consequently, a single LIN code can correspond to multiple coincident cgSTs.

An important implication of the encoding process is that LIN codes are created definitively, as are the assignments for LIN codes to individual cgSTs. Hence, LIN codes are stable by design, and the incorporation of novel genomes will never affect preexisting LIN codes and their assignments. This provides trust in LIN code referencing and stability in comparisons across time.

How to design and use LIN codes

An initial requirement for any cgMLST-based LIN code taxonomy implementation is a cgMLST scheme that has been designed and thoroughly validated to ensure the stability of the taxonomy from its inception. Subsequent removal of loci (e.g., because they are too infrequently called) or changing of their template (e.g., choosing an upstream start codon) would result in potential inconsistencies with previously defined LIN codes. Therefore, it is advisable to define LIN code taxonomies following careful evaluation of the cgMLST schemes from which they are derived, particularly if these are intended for broad usage.

The scope of applicability of the cgMLST scheme is also an important consideration. MLST or cgMLST schemes are typically used for a single species, and less frequently for an entire genus (e.g., more than 90% ANI). In rare cases, an intermediate category called a species complex is covered: these correspond to groups of closely related species that are sometimes misidentified in routine microbiology diagnostic processes. The applicability of the cgMLST scheme (and related LIN code taxonomy) should ideally be broadened, but increasing the phylogenetic breadth will be at the expense of the core genome size, reducing the discriminatory power of the scheme.

While any number of bins (up to the number of loci in the cgMLST scheme) can be chosen to create a LIN code system, it is recommended to guide their definition by analyzing the population structure of the species in order to propose phylogenetically informative bin thresholds. Several methods have been designed to find optimal ranges of dissimilarities that optimize the reliability of the subsequent classifications [20,26,29].

Deep levels might also correspond to previously recognized subdivisions and might therefore be optimized to match these previous classifications. For example, in the KpSC scheme, the distinction of its phylogroups (taxonomic species or subspecies) was used as a guide to define the two deepest thresholds [26], as detailed in Section G in S1 Appendix.

For epidemiological levels, bin thresholds can be selected to reflect epidemiological surveillance practice. In a hypothetical example, four different alleles might be typically used to define clusters and trigger outbreak investigations; in this case, using a LIN code bin associated with a threshold of four allele differences would be congruent with this practice. Broader epidemiological thresholds might yield more false positives (sporadic isolates unrelated to a given common source outbreak) in detecting genetic clusters, but will on the other hand capture outbreaks during which more diversity has accumulated. It is therefore advised to use a set of epidemiological thresholds which will be useful in different situations, from the most stringent (i.e., one single allelic difference) to more relaxed ones.

LIN code applications in epidemiological surveillance and outbreak investigations

Bacterial species can harbor huge amounts of genetic diversity and are often structured genetically into recognizable sublineages. For example, in K. pneumoniae, sublineages including SL258, SL147, SL307, SL17, and SL23 have been recognized as globally distributed drivers of multidrug resistance and/or hypervirulent infections. These sublineages have been the subject of detailed studies that have led to defining their geographical spread and phylogenetic subgroups [33–37]. However, so far, these sublineages have been defined using a mix of 7-locus MLST, cgMLST, and ANI; a clear and simple definition, and a harmonized nomenclature, have been lacking, making it difficult to recognize them in subsequent studies.

One prominent example of how LIN codes provide clear definitions of sublineages and disambiguate MLST definitions is the case of hypervirulent sublineage ST23. WGS analyses demonstrated the polyphyletic status of ST23 [35], which conflates isolates from two distant phylogenetic branches that are appropriately separated into two LIN code sublineages (SL23: 0_0_429 and SL218: 0_0_115; Section K in S1 Appendix). Beyond the case of ST23, multiple distinct sublineages are conflated into single STs, but can be appropriately recognized by their distinctive LIN codes (Table A and Section K in S1 Appendix).

LIN codes can also help track dissemination at fine genetic scales within sublineages; what follows is an example using SL258, a major K. pneumoniae carbapenemase (KPC)-producing sublineage. SL258 is defined by the LIN code prefix 0_0_105 and encompasses all isolates from 7-gene ST11, ST258, ST340, ST512, and some others (see Fig 2). Its phylogenetic structure is depicted in Fig 5 (see Section L in S1 Appendix for methodological details) and shows that SL258 is divided into several subclades. These include CG258 (0_0_105_6), which contains all ST258 and ST512 isolates. LIN code bin 5 can further be used to distinguish major subclades within SL258, including those corresponding to ST340 (0_0_105_0_11), ST437 (0_0_105_1_1, and other subclades within ST11, some of which seem to be associated with recombination events that include the capsule (K) locus (KL column in Fig 5).

LIN codes can help distinguish between different subclades that are associated with the same ST and capsule locus, a combination often used to describe specific subclones. For example, LIN codes clearly distinguish three phylogenetically distinct subclades that are all ST11-KL64 (gray shading on the tree branches, Fig 5). One of these is the major lineage circulating in China (0_0_105_2_0_0_2, predominantly 0_0_105_2_0_0_2_17) that carries KPC-2 and often the iuc aerobactin virulence locus, which we use here as a marker for the K. pneumoniae virulence plasmid, as discussed broadly [38,39]. A second, unrelated ST11-KL64 subclade (0_0_105_0_0) is circulating in South America encoding KPC-2, but rarely iuc [40], while a third smaller clade (0_0_105_0_2) is detected primarily in Taiwan [41] rather than in mainland China (lacking KPC and with only one of eight genomes carrying iuc). These distinct clades are all referred to in the literature as ST11-KL64, despite representing phylogenetically distinct and likely unrelated, independently evolved lineages. This example shows how LIN code classification beneath the sublineage level can help recognize and name subgroups of medical and epidemiological relevance, which should be subject to enhanced surveillance.

Identifying outbreak strains and tracking strain diversification during outbreaks are key objectives of genomic epidemiology, as they provide capacity to quickly respond to outbreaks and prevent further infections. LIN codes could be used to detect outbreaks: when genomic sequencing is applied prospectively in surveillance programs, finding identical or nearly identical LIN codes would lead to a strong suspicion of transmission, and such strain groups could be flagged for more in-depth epidemiological analyses. Further, the multi-level nature of LIN codes provides convenient flexibility for ‘outbreak strain’ definition and thus for epidemiological investigations that include risk factors analyses (e.g., to associate infection with the outbreak strain with source exposure). Complementary ad-hoc genomic analyses using, for example, whole-genome SNPs and pangenome analysis, can be performed to further characterize the outbreak isolates. Importantly, the availability of a public cgMLST-based LIN code system simplifies communication about outbreak strains between laboratories and public health actors, making it straightforward to identify whether an outbreak strain at one hospital is closely related to an outbreak identified at another hospital based on similarity of LIN codes.

LIN codes can even subdivide isolates from single long-term outbreaks. Clades that diversify during an outbreak can be captured and labeled unambiguously with LIN codes (Section M in S1 Appendix), as shown using an Italian outbreak of SL147, a prominent multidrug-resistant international sublineage of K. pneumoniae [42]. LIN codes were also recently used to label isolates from eight regional K. pneumoniae outbreaks that occurred within Poland (including two caused by SL258 and SL147), which had initially been loosely defined based on SNPs, O and K serotypes, and bla_VIM carbapenemase-carrying integrons [43].

Future directions and conclusions

Facilitating communication on the intra-species diversity of microbial strains is a key objective of strain taxonomies, which entail classification and naming of groups within species. In the field of epidemiological surveillance of bacterial pathogens, it has long been recognized that strain typing methods used for long-term and global strain tracking should be reproducible enough to enable internationally standardized nomenclatures, or ‘library typing systems’ [44]. LIN codes based on cgMLST benefit from the high standardization and reproducibility of the cgMLST approach and provide a flexible and robust way to classify, name, and identify subpopulations within bacterial species. The recognition of subpopulations associated with distinct phenotypes is an important raison d’être of taxonomies, and multilevel LIN codes strain taxonomies will advance our understanding of the links between genotypes and clinical phenotypes, vaccine coverage, and antimicrobial resistance.

Given the reproducibility of cgMLST, an outbreak strain may be recognized by different investigators based solely on its LIN code prefix. As LIN code prefixes are sufficient to define strain identity across countries or sectors, LIN codes provide a simple yet accurate solution for cross-border or other collaborative genomic surveillance investigations, without the need to share genomic sequences themselves (Fig 6). This possibility brought by shared strain taxonomies can alleviate issues around data confidentiality and sharing agreements, which are often an important barrier in genomic surveillance and rapid response to outbreaks under investigation by multiple institutional actors. Likewise, for the surveillance of particularly concerning strains, early warnings could be triggered based on the detection of the specific LIN codes of the targeted strains. LIN codes may thus become an integral part of epidemiological surveillance practice.

However, LIN codes have several limitations. As a classification system, they rely on comparing genomes with a central database, implying the need for maintenance of this resource, regular updates to define the novel taxonomic elements, and accessibility of the nomenclatural source database. As for other databases used for genomic epidemiology of pathogens, sustainability, security, and governance of such resources are important issues that need to be addressed [45]. As a genotyping and outbreak detection tool, LIN codes are limited by the discriminatory power of the cgMLST scheme they are based upon. Hence, they do not provide ultimate resolution, which can be provided by complementary approaches such as whole-genome SNPs. Furthermore, cgMLST LIN codes are limited by the breadth of applicability of the cgMLST approach, which is typically limited to single species or groups of closely related ones. Fortunately, the rapid pace of cgMLST developments to multiple novel bacterial species expands the possibilities of LIN code implementation.

In general, LIN codes represent a widely applicable strain taxonomy system, as illustrated by the rapid pace of developments of LIN code implementations to bacterial pathogens. Following the initial use case for the KpSC, cgMLST LIN codes taxonomies have been introduced for S. pneumoniae [46], Staphylococcus aureus [47], Moraxella catarrhalis [48], Neisseria gonorrhoeae [49], and Corynebacterium diphtheriae [50]. In addition, developments are ongoing for Neisseria meningitidis, Campylobacter jejuni, and Streptococcus pyogenes. These LIN code taxonomies use different numbers of bins and thresholds adapted to the population structure of each species, illustrating the flexibility of the LIN code approach. The rapid development and adoption of LIN code taxonomies is facilitated by their integration into the BIGSdb platforms at Institut Pasteur and at Oxford University. In addition, an implementation of cgMLST LIN codes for E. coli and Salmonella enterica has just been released on the last EnteroBase update.

The applicability of the cgMLST LIN codes to most other bacterial species should be straightforward, provided they comprise sufficient genetic diversity. This requirement excludes the so-called monomorphic pathogens [51], such as Mycobacterium tuberculosis or Salmonella enterica serotype Typhi, where phylogeny-based taxonomies based on whole-genome SNPs are considered more useful given their higher resolution compared to cgMLST. The cgMLST LIN code strategy can also be extended with minor adaptations to other organisms with predominantly clonal reproduction, such as protozoan parasites and fungi, even if they are not haploid, given the existence of MLST taxonomies for, for example, Candida albicans and Trypanosoma cruzi [52,53].

The wide adoption of cgMLST LIN code strain taxonomies has the potential to result in a universal approach for standardized bacterial genotyping that could greatly enhance microbial biodiversity studies, international genomic epidemiology, and infectious disease surveillance.

Acknowledgments

We thank François Lebreton for providing the Newick file of the Italian ST147 outbreak described in [42]. We acknowledge the help of the HPC Core Facility of the Institut Pasteur for this work.

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