Tuberculosis super-spreading due to interterritorial mobility and prolonged diagnostic delay: A call for an integrated and enhanced analysis

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Saleeb, Silvia Vallejo-Godoy, Andrea Marcos-Abellán, and 18 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8893959/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Whole genome sequencing has revolutionised the precision with which we can delineate tuberculosis (TB) transmission. However, the majority of genomic epidemiology surveillance in TB is restricted to the analysis of geographically limited populations, which impairs the identification of cross-regional transmission. In this study, we delineate a complex transmission event in Spain involving 13 cases, characterised by the convergence of i) interterritorial transmission due to the mobility of migrant cases, ii) superspreading due to an undiagnosed advanced TB case resulting from a prolonged diagnostic delay, iii) extensive exposures attributable to substantial social gatherings, iv) involvement of 6 different nationalities and autochthonous cases, and iv) two independent populations where the majority of cases were exposed or diagnosed, respectively. The final understanding of this transmission event was only possible following the integration of sequencing data obtained from different populations, the refinement of interviews with patients to cover social networks at both the diagnostic and exposure populations, the design of tailored laboratory assays to fast-track new cases based on targeted sequencing of the strain marker single-nucleotide polymorphisms (SNPs) and the evolutionary analysis of the SNPs identified in the cluster. This study may serve as an illustration of the integrative efforts and simultaneous strategic, methodological and analytical improvements that are required to address the numerous novel challenges arising for a proper surveillance of TB transmission in our current, increasingly complex, epidemiological scenario. Recent transmission genomic clusters tuberculosis interterritorial diagnostic delay Figures Figure 1 Figure 1 Figure 2 Figure 3 Figure 3 Introduction Whole genome sequencing has enhanced the precision with which we can delineate tuberculosis (TB) transmission, facilitating the implementation of control measures by targeting the most active transmission hotspots in a population ( 1 ). The migratory phenomenon is frequently associated to vulnerable living conditions for migrants in the host countries, which has increased the complexity of the transmission dynamics of the disease, giving rise to several challenges in terms of the proper surveillance of transmission to improve its control. The initial challenge pertains to the necessity of expanding our analytical inquiry to consider the underlying factors contributing to TB in migrants. We need to consider i) the reactivation of infections due to exposures in the countries of origin, ii) recent transmission due to infections upon arrival in the host country ( 2 ), or iii) exposures along the migratory journey ( 3 , 4 ). The increased mobility of migrants has prompted us to expand our perspective beyond our own individual populations, encompassing cross-border transmission. The advent of transnational surveillance has facilitated the identification of the multinational dissemination of strains, whether MDR or susceptible ( 5 , 6 ). However, analogous endeavours to incorporate interterritorial transmission within a nation, due to internal migration, are considerably less prevalent. This paucity is attributable to the absence of integrative initiatives among the diverse populations within the country with genomic data at hand. A further challenge to be addressed when contemplating vulnerable populations is the increased propensity for diagnostic delay. The cornerstone of TB prevention is early diagnosis, which in turn prompts the more expeditious initiation of contact tracing efforts for effective TB control surveillance. We need to acknowledge the role of patients with prolonged undiagnosed infections as a reservoir for transmission, which may be a contributing factor to the escalating number of secondary cases. Furthermore, it is anticipated that diagnostic delay is more probable among migrants ( 7 ), who frequently encounter delays in establishing communication with the healthcare system and seeking medical assistance. This results in more extended exposures to infection and an elevated risk of onward transmission ( 8 – 10 ). The challenges associated with the increased complexity of the current TB epidemiological scenario necessitate a shift from the conventional epidemiological investigation, which is based on contact tracing. This approach is inadequate in providing the detailed information required to elucidate the intricate links, which are often non-obvious, among cases within a genomic cluster. A more sophisticated and comprehensive social network analysis is required to elucidate the underlying factors that facilitate the transmission of this phenomenon in its intricately complex nature ( 11 , 12 ). It is notable that several of these challenges coincided in the complex transmission event that is the focus of this study: i) interterritorial transmission involving multiple nationalities, associated to the mobility of migrant cases, ii) superspreading as a consequence of a prolonged diagnostic delay and iii) initially cryptic exposure contexts. In order to address the aforementioned challenges, a series of simultaneous enhancements were implemented. These enhancements included the integration of sequencing data obtained from diverse populations, exhaustive interviews of patients to ascertain their social networks, newly designed, tailored laboratory assays to fast-track new cases in the population, not well covered by sequencing efforts, and a refined analysis of the genomic data from cases in the cluster. Materials and Methods Sample collection The isolates from cases in Almeria involved in the cluster under study were analysed within a long-term TB transmission surveillance program which is running in Almeria (including all the province and the capital), Spain, since 2003. All prospective positive cultures from the total population of Almeria are dispatched on a weekly basis to our laboratory in Madrid, Spain, for the purpose of genomic analysis. Genomic analysis was performed based on high-throughput Illumina sequencing until December 2023 and since then by weekly nanopore sequencing of the cases diagnosed each preceding week. The sequences from Madrid used in the interterritorial analysis corresponded to the TB-STARS project, the aim of which is to characterise genomically (by Illumina sequencing) TB transmission in Madrid (in Madrid city and the rest of the province) involving minors (2020–2026). The sample from Madrid used to track retrospectively the presence of the clusters strain corresponded to the centralized collection stored at -80°C at Gregorio Marañón Hospital since 2018. This collection comprised all Mycobacterium tuberculosis (MTB) isolates (one isolate per patient) cultured in Madrid region hospitals. DNA extraction and purification Each positive MGIT (Mycobacteria Growth Indicator Tube) culture (4–5 mL) was centrifuged at 8,500 rpm at 4°C for 40 minutes. The resulting pellet was resuspended in ATL buffer (a cell lysis buffer), followed by heat inactivation at 95°C for 15 minutes. The process of DNA extraction and purification was conducted in accordance with the protocol outlined by the manufacturer of the QIAamp DNA Mini Kit (Qiagen, Hilden, Germany). For the retrospective tracking of the cluster strain in Madrid frozen cultures were thawed. For each isolate, 200 µL were subjected to heat inactivation at 95°C for 15 minutes, after which it was used directly for PCR without further purification. Genomic analysis Illumina library preparation, sequencing and bioinformatic analysis Libraries were prepared using the Nextera XT kit (Illumina, San Diego, USA) according to the manufacturer's instructions and pooled for sequencing on a MiSeq or Nextseq instrument (2x151bp). Sequence analysis was performed using an in-house pipeline deposited on Git-Hub: https://github.com/MG-IiSGM/autosnippy . The workflow of this pipeline follows the same steps as previously described ( 13 ), using a hypothetical MTB ancestral genome ( 14 ) as a reference. Finally, genomic distances between sequences were calculated using Jaccard similarity and Hamming distance metrics to generate distance matrices. Nanopore library preparation, sequencing and bioinformatic analysis Prospective members of the cluster were then subjected to sequencing on a MinION long-read platform (Oxford Nanopore Technologies) using the Rapid PCR Barcoding Kit (SQK-RPB114). The base-calling and annotation processes were executed utilising our proprietary pipeline (MG-IiSGM/prokaION), as previously described ( 15 ). The identification of species was conducted using the Kraken2 v2.1.3 and Mash v2.3 software. Reads were mapped against a hypothetical MTB ancestral genome (similar to H37Rv, with ancestral nucleotide positions inferred by maximum likelihood) using minimap2 v2.28, followed by single-nucleotide polymorphisms (SNPs) calling. Variant annotation was performed using SnpEff v5.1. Cluster analysis In order to assign new cases to the cluster, new sequences are to be introduced into the global phylogeny that has been obtained for the 1,173 cases sequenced in Almeria. In instances where a new sequence was found to be in close proximity to a preexisting sequence that had been assigned to be part of the cluster under study, either within the same clade or as a sister taxon originating from the same rooted branch, all related sequences were processed in order to generate a pairwise SNP distance matrix. A threshold of 12 SNP differences was applied: sequences within this limit were considered as potentially part of the same cluster, whereas sequences exceeding this threshold were classified as outgroup "orphans". Alignments and SNPs were visualized inspected using the IGV (Integrative Genomics Viewer) programme. Median-joining networks of genome-related isolates were constructed from the SNP matrix generated by PopART software ( 16 ). The cluster assignment was defined as a maximum of five differential SNPs, once visually confirmed, between sequences. Analysis of heterozygous calls The classification of SNPs was conducted in accordance with their allele frequency. Variants exhibiting a frequency above 70% were designated as fixed, whereas those ranging from 10% to 70% were categorised as heterozygous calls. In the interest of reducing the impact of sequencing noise and false positives, variants with a frequency below 10% were excluded from further consideration. Upon identification, heterozygous SNPs were subjected to thorough inspection at the identical positions across all cluster isolates. To confirm the presence and frequency of these variants, isolates containing heterozygous variants were subjected to further analysis, namely re-sequencing to assure its call based in both Illumina and nanopore sequencing. Targeted tracking of the cluster strain Identification of cluster marker SNPs Common SNPs across the cluster members were then compared against an in-house global reference database. This database comprised the SNPs from 109,648 high-quality sequences from publicly available datasets, representing the geographic and phylogenetic diversity of MTB lineages 1–9. The sequences included in the database were selected according to rigorous selection criteria. Only Illumina-generated datasets with complete metadata, exclusive taxonomic assignment to MTB, and reaching high-quality thresholds (> 70% of the genome covered at > 20× depth and 20×, allele frequency > 70%). Subsequent to the comparison, the cluster common SNPs found in the global database were filtered out to retain only the final specific cluster-marker-SNPs. In the event of there being multiple marker SNPs within the same gene, these are not given consideration, as it is deemed highly improbable that such an accumulation would occur in MTB. Multiplex PCR for targeted sequencing Primers design Primers were designed by a machine-learning model using TOAST (Tuberculosis Optimised Amplicon Sequencing Tool), an automated pipeline that generates amplicon sets based on user-defined mutation targets. The tool's functionality encompasses the evaluation of candidate primers with regard to their GC content, melting temperature, propensity for self- and hetero-dimer formation, and potential for off-target binding. The software is openly available at https://github.com/linfeng-wang/TOAST and described in detail in a recent preprint ( https://doi.org/10.1101/2025.01.13.632698 ). The final design resulted in the production of 350–488 bp nine amplicons, with the marker SNP situated approximately at their midpoint (Supplementary Table). Multiplex PCR The mixture for the multiplex PCR comprised a final concentration of 0.5 µM for each of the nine primer pairs, in a final volume of 25 µl, with a MgCl₂ concentration of 3 mM, using the QIAGEN Multiplex PCR Kit (Qiagen, Hilden, Germany). The PCR conditions that were utilised are outlined below: an initial heat activation step at 95°C for 15 minutes, denaturation at 94°C for 30 seconds, 35 cycles of annealing at 58–60°C, and extension at 72°C for 90 seconds, followed by a final extension step at 72°C for 10 minutes. A positive control corresponding to an isolate belonging to the cluster was included in the run. The PCR was applied to crude boiled extracts from the stored (frozen) isolates, obviating the necessity for subculturing or purification. Amplicon-targeted sequencing PCR products from the retrospective isolates were prepared for sequencing using the Rapid Barcoding Kit (SQK-RBK114.24, Oxford Nanopore Technologies). Libraries (up to 24) were loaded onto MinION flow cells (R10.4.1, FLOW-MIN114) with a maximum of 800 ng total DNA per run (50 ng per sample), in accordance with the manufacturer's recommendations. The run was terminated when > 80% of the amplicons, for each of the isolates, had been covered at a minimum of 30× depth of coverage. The amplicons were then subjected to further analysis and mapping using an in-house pipeline ( https://github.com/MG-IiSGM/prokaION ). The analytical workflow comprised the following steps: (i) conversion of raw pod5 files into fastq format through basecalling and barcoding with Dorado v0.9.6; (ii) quality control using Chopper v0.8.0 and NanoPlot v1.41.0 to ensure the retention of the most reliable data; and (iii) read mapping with ngmlr v0.2.7, followed by SNP calling with Freebayes v1.3.6, using a pseudo-consensus reference constructed from all 9 amplicons, with a quality filter set at 20. Following the identification of SNPs, they were subjected to rigorous inspection and visualisation on IGV to ensure accuracy. An in-house script was also applied during sequencing to provide real-time identification of strain-specific marker SNPs, to provide a faster preliminary preassignment of the strains to the cluster, requiring a minimum depth of 10× and an allele frequency of at least 0.7. The candidate SNPs identified in this step were subsequently confirmed on the final sequences after the completion of the run. Epidemiological data collection The Almeria TB Prevention and Control Program (TBPCP) obtained and analysed epidemiological data from cases in Almeria. The TBPCP constitutes a multidisciplinary team involving epidemiologists, microbiologists, professionals in preventive medicine units, nurses, social workers, professionals in primary care centres and three hospital tuberculosis clinical units. The TBPCP is integrated into the Epidemiological Surveillance System of Andalusia (SVEA), an extensive surveillance system integrated within the Spanish Epidemiological Surveillance Network (RENAVE). The incorporation of community health workers (CHWs) was initiated in 2003 through the establishment of collaboration agreements with local non-governmental organisations (NGOs) operating within the designated territory. These NGOs included the Red Cross and the Consortium of Entities for Comprehensive Action with Migrants. The primary function of the CHWs was to provide support to the public health system and to serve as cultural mediators and translators. Their contributions were found to be of paramount importance in the follow-up of TB cases and the conduct of contact tracing. A data collection form, which was designed based on previous molecular and genomic research projects carried out by the research group in Almeria, was used to record the main cluster characteristics, transmission environments and risk factors identified. Furthermore, individual-level data were obtained from the SVEA registries, which are responsible for the storage of case-based epidemiological information. SVEA registries are comprised of two distinct components: hospital data from preventive medicine units and community data from primary care epidemiology units. This information has been validated and contrasted by experienced epidemiologists. To supplement the findings, a comprehensive review of the patients' clinical records was conducted for the cases in clusters that required a more profound investigation. In Almería, patients were interviewed at the initial diagnosis and reinterviewed (in-person visits or telephone calls) during their follow-up, guided by the key findings provided by the genomic analysis. It allowed us to reorient the global investigation to obtain additional epidemiological data. In Madrid, the absence of a programme grounded in genomically oriented epidemiologically interviews of the cases, in addition to the lack of a genomic surveillance systematic population-based programme, hindered our ability to extract refined epidemiological data from the cases. Results Cluster initial identification and preliminary epidemiological investigation Case 1 corresponded to a 32-year-old male from Guinea-Bissau. The subject was diagnosed with pan-susceptible TB in Almeria on 20 th of February 2023 (Figure 1 and Table). The signs and symptoms had begun a minimum of one year prior to the diagnosis. The high bacillary load (4+) in all three sputa, together with the clinical findings at the time of diagnosis (severe caloric-protein malnutrition, extensive bilateral lung destruction) were indicative of a long-term progressive disease, consequent to a prolonged diagnostic delay. Indeed, TB was the underlying cause of death two months after diagnosis. Within a period of one month following the diagnosis of Case 1, three further cases were identified (Cases 2, 3 and 4, sub-Saharan migrants). Of the three subjects under consideration, two of them (Cases 2 and 3) were relatives of Case 1: his brother and brother-in-law, respectively (Figure 1). The four subjects were found to be clustered, as evidenced by genomic analysis (Lineage 4.1.2.1 strain; 0-1 SNPs between the cases). Over the subsequent 12-month period, two new cases were diagnosed (Cases 5 and 6, Figure 1, Table and Figure 2), which also corresponded to Case 1 relatives (his sister-in-law and nephew, respectively). The final case in the cluster (Case 7; Case 1 non-relative, Figure 1, Table) was diagnosed seven months later (October, 2024). A preliminary analysis of the cluster was that a patient with extensively advanced TB disease, attributable to an extended diagnostic delay, exhibited superspreader-like characteristics, contributing to a substantial and rapid transmission (0-1 SNP among the cases), predominantly involving their family members. However, two of the genomically-clustered cases (Cases 4 and 7) were not part of Case 1 family, thus necessitating a more comprehensive epidemiological investigation to ascertain the links involving them. Refined epidemiological investigation Cases 2, 4, 6 and 7 was re-interviewed in order to compile additional information to the general one obtained in the initial interview. From those interviews we identified that Case 1 and his nephew Case 6 stayed temporarily in Almeria visiting relatives, but since the end of 2019 they had established themselves in the Madrid region (in Town A), where they were neighbours and had a very close and frequent relationship. In the case of Case 1, their return to Almeria was necessary in December 2022, due to the severity of the disease, shortly before the diagnosis and subsequent death. The finding of Cases 1 and 6 residing in one of the towns in Madrid region, during a period in which Case 1 was likely to be highly contagious, prompted the introduction of this element, “stays in Madrid region”, in a round of interviews with the remaining cases of the cluster. This allowed us to identify that one of the two only cases without family links with Case 1 (Case 4) had also resided at a refugee centre in Madrid (in Town B, neighbouring to Town A, both in Madrid region) between September and December 2021 prior to its arrival in Almeria. During his time in Madrid region, he engaged in prolonged interactions with other sub-Saharan migrants in the Town A areas they typically frequented. A stay in Madrid region was also identified for Cases 2 and 5, who attended a significant social event on 22 nd of February 2022 in the same Town A, where Cases 1 and 6 had resided. This social event was attended by approximately 50 sub-Saharan migrants coming from various Spanish and other European cities. Following these series of exhaustive interviews guided by genomic data, only Case 7 did not exhibit either family links with the remaining cases or a history of residence in Madrid. A new interview looking for previously unconsidered activities, venues and events revealed that Case 7 and Case 1 had coincided in the context of a a three-day social gathering that took place in Almeria in May 2022, with guests from various Spanish and other European cities. Furthermore, it is noteworthy that Case 1 and Case 7 played central roles in that event, which justified a high interaction between them along the three days. No additional links or connections were identified between Case 1 and 7 and no other members of the cluster attended that three-day social gathering. Expanding the identification of other related cases in Madrid region Tracking of new cases in Madrid region by an integrative analysis of Madrid and Almeria sequences The identification of the stages of several of the clustered cases in Madrid, including the extended period during which Case 1 was likely to have been highly contagious, and the involvement of some of the cases in a significant social event in Madrid, provided substantiated evidence to suspect potential additional exposures and therefore clustered cases among those diagnosed in Madrid region. Unfortunately, unlike Almeria, no systematic universal genomic analysis is performed in Madrid, which impaired the systematic screening of new cases. However, different convenience subsamples from cases diagnosed in Madrid region had been sequenced within different research projects. The analysis of the sequences obtained in one of these running projects (TB-Stars) focused on tracking genomically the transmission involving paediatric/minor TB cases and their index cases diagnosed in Madrid region since 2020, led to the identification of two further cases sharing the same strain. They corresponded to the only two Spaniard cases in the cluster (Cases 8 and 9), father and 17-year-old daughter who frequently visited and stayed in her father's house, located in the same town A as Cases 1 and 6 lived, and it was previously considered that these constituted a self-limited family micro-epidemic. The cases under consideration were diagnosed in the same period as the four initial cases in Almeria (February/March 2023; Figure 1). Tracking of new cases in Madrid region by targeted sequencing analysis of isolates in Madrid The identification of another two clustered cases in towns in Madrid region justified further efforts to identify other cases that could not have been previously identified. Due to the scarce genomic information available for the TB cases in Madrid region, an alternative strategy was implemented in order to expedite the process for fast-track the presence of additional cases. The initial step in this process involved the identification of the 23 marker SNPs for the specific strain that was the subject of the cluster in study. The marker SNPs were obtained through the following procedure: i) identification of SNPs shared across all isolates within the cluster, ii) filtering of these SNPs against an in-house global database to remove those identified in other strains, iii) selection of unique marker SNPs that were exclusive to the cluster. A multiplex PCR was designed to amplify a subset of nine regions, including the selected marker SNPs. The presence/absence of the marker SNPs was determined by nanopore sequencing of the amplicons. This strategy was implemented on the isolates obtained since 2018 up to 2025 from: i) all TB cases diagnosed and residing in Town A and a neighbouring Town B and ii) all cases from all the remaining towns in Madrid region with West-African nationalities. A total of 164 isolates were analysed, with >80% of the 9 amplicons reaching a minimum coverage of 30× in less than 30 minutes of the sequencing run (Supplementary figure), which enabled the proper identification of the reference/marker alleles. This approach facilitated the identification of four new candidate cases (Cases 10-13, from 3 different nationalities (Table), all living in Town A, to be part of the cluster. Subsequent WGS from these cases confirmed that they were indeed part of the cluster (Figure 2). Additional epidemiological information to justify their relationships with the cluster could not be obtained as the dynamic of re-interviews guided by genomic data applied in Almeria is not implemented in Madrid region. Refined genomic evolutionary analysis of the cluster Despite the robust clustering of most of the cases (0-1 SNP, well below the most restrictive thresholds (only Case 13 showed 4 SNPs), to consider the role for recent transmission among them), the presence of differential SNPs acquired between some of the cases only offered the opportunity to perform a more refined evolutionary analysis of these SNPs (Figure 3). A meticulous examination of one SNP (SNP-A; the one differentiating Cases 1-7 and 10 from Cases 3, 8 and 9, Figure 3) revealed that, in addition to its fixation in Cases 3, 8 and 11, we identified its emergence in Case 1 (remaining in heterozygosis at 51%). Subsequent analysis failed to detect any traces of this SNP in the remaining cases. The identification of this emerging SNP-A in Case 1 offered the opportunity to perform a more refined evolutionary analysis of the cluster. In consideration of the fact that Case 7, who was exposed to Case 1 in May 2022, lacks SNP-A (Figure 2), and that this SNP was emerging in Case 1 at the time of their diagnostic sample (February 2023), it can be hypothesized that there were two most likely exposure stages for the cluster members, contingent on the presence or absence of this SNP in the infected cases. In all cases without the SNP, exposures were most likely to have occurred during the initial period of infectivity of Case 1 (at least before May 2022). However, the three cases with the SNP (Cases 3, 8 and 9) should have been exposed in the latest periods, between the moment when the SNP started to emerge in Case 1 (probably not before May 2022) and death. The proposed exposure opportunity for Case 3 is consistent, given that the subject arrived in Almeria in the summer of 2022, a mere nine months prior to diagnosis. Discussion The analysis of the transmission cluster presented here illustrates some of the major challenges being faced in the TB genomic epidemiology era. In the present study, a number of these challenges coincided in the same transmission event. The impact of interterritorial mobility of cases, the consequences of an undiagnosed case due to a prolonged diagnostic delay, the need to modify standard epidemiological research dynamics to reveal the true complexity of TB transmission were identified. The transition from genotyping methods, such as Mycobacterial Interspersed Repetitive Unit-Variable Number Tandem Repeat (MIRU-VNTR), to genomic analysis has resulted in a significant enhancement in the precision with which transmission clusters can be identified ( 17 ). Nevertheless, this enhancement is only of limited value if parallel progress is not made in the programmatic strategies that determine the application of genomics, and if the acquisition of epidemiological information continues to rely on the limited data provided by standard contact tracing. Starting with the analytical framework which encompasses the genomic analyses conducted for epidemiological purposes in the majority of populations, these analyses are constrained to the geographic boundaries of a city or province. This framework might have been applicable prior to the advent of migratory patterns or in the context of highly stable populations. However, most European cities are currently experiencing high levels of migration, which has had a significant impact on the dynamics of TB transmission. The issue of migrant mobility has been the subject of consideration within the context of genomic epidemiology studies at a transnational level. The integration of datasets from different countries has enabled the identification of cross-border transmissions ( 3 , 6 , 18 , 19 ). Indeed, the ECDC initiative entitled EpiPulse is dedicated to the study of cross-border transmission. This initiative has recently described a multi-country cluster involving cases from the Horn of Africa ( 5 ). However, there has been a paucity of research into the issue of national-level mobility, particularly in relation to interterritorial migration within the same country, due to the fact that migrants' home/family towns frequently differ from their work towns. The dearth of data concerning the integration of cross-regional transmission in genomic epidemiology studies pertaining to tuberculosis appears to be undergoing a shift, as evidenced by the emergence of several recent articles, all originating from China, that have begun to address this issue ( 20 – 22 ). The findings of the two studies centred on Shenzhen ranged from 16.8% of 142 clusters involving cross-regional transmission, when only a selection of districts was sampled ( 20 ), to up to 52% of 119 clusters when all 11 districts in Shenzhen were included ( 22 ). The findings of both studies indicated that internal migrants were more likely to be involved in a cross-regional cluster. Consequently, a significant proportion of cross-regional clusters may be overlooked if the analytical framework does not exceed the conventional geographic boundaries, thus being constrained to specific populations. The present study provides a clear illustration of this phenomenon; the precise definition of the extension of the cluster necessitated the integration of genomic data from two independent populations, Madrid and Almeria (541 kms apart). It is anticipated that the findings of this study are not expected to be exclusive to interactions between these two populations. Indeed, a recent study conducted in Spain also identified interterritorial transmission between two other regions, Catalonia and the Community of Valencia ( 23 ). It is necessary to acknowledge that the complete magnitude and extent of the studied cluster is likely to be larger. The involvement of two social gatherings of great magnitude, frequented by individuals from different Spanish cities and even European countries, raises concerns about a potential unidentified larger impact beyond the two populations integrated in our analysis. Of particular concern is the social gathering in Almeria, which occurred during a period when Case 1 should have been highly infectious, as evidenced by the infection of Case 7, who coincided exclusively with Case 1 at that event. These elements collectively underscore the necessity for a paradigm shift in the conventional framework within which the field of TB genomic epidemiology is predominantly confined. Secondly, it is imperative also to acknowledge that the expansion beyond the confines of single populations not only entails the integration of genomic data from independent populations, but it should be complemented by the elimination of geographic limitations in the epidemiological investigation of patients. It is widely acknowledged that the data obtained through standard contact tracing methods is inadequate for comprehending the intricate nature of TB transmission, particularly in the context of genomically supported epidemiological surveillance ( 24 ). A number of improvements have been made to genomic analysis to enhance the inferences that can be made from it, including the integration of spatial analysis ( 25 ) and the incorporation of an additional layer of phylodynamic analysis ( 26 ). These modifications provide the capability to identify index cases and establish a probable chronology for the transmission. Furthermore, advances have been made in the epidemiological field, with the development of more sophisticated epidemiological analyses underpinned by social networks, complemented by interviews and structured questionnaires, in conjunction with genomic analysis. Despite their still limited application, these methodologies have been demonstrated to be essential ( 11 , 27 ) in revealing the true nature of the clusters, identifying hitherto unreported social interactions, and delineating the locations frequently visited. The integration of a similar more refined approach to our epidemiological investigation, guided by genomic findings, was instrumental in elucidating the intricate connections between the cases and the exposure contexts, which were initially opaque, thereby underscoring the complexity of reconstructing complete epidemiological chains when patients move between territories. Furthermore, they played a pivotal role in elucidating the intricacies inherent in a cluster, wherein the diagnosis and exposure scenarios, which are typically coincidental, were, in our case, mostly split. The majority of cases were diagnosed in Almeria, while a significant proportion of exposures took place in Madrid. It means that the epidemiological investigation not only has to pursue enhanced access to a more extensive array of social networks among the cases in the diagnostic setting but also ensure the incorporation of patients' exposure context information, when these two contexts differ. Another coincidental challenging factor in our study was the likely prolonged period of infectiousness of Case 1 . The patient's severe clinical presentation, high bacillary load, and rapid death after diagnosis reflected a long-term diagnostic delay and highlighted the risk posed by undiagnosed, infectious TB patients, especially when they are highly mobile. The aforementioned factors were found to contribute to the superspreading phenomenon, thereby justifying the magnitude of the cluster. However, it is important to note that undiagnosed cases continue to be a prevalent issue, particularly among specific population groups. It has been reported that a high prevalence of undiagnosed pulmonary TB has been identified in specific prisons, thus indicating that these institutions may act as reservoirs for ongoing transmission ( 28 ). A comparable phenomenon has been documented in vulnerable groups. A study conducted in Kenya estimated that up to 54% of undiagnosed TB cases were concentrated in informal settlements ( 29 ). It is estimated that each undiagnosed case may result in 10–15 secondary cases ( 29 ), and various reports have documented the association between diagnostic delay and genomic clustering ( 30 ). Patients who experience delays have been shown to have 2.57 times the risk of being part of genomic clusters ( 31 ). Non-diagnosed cases with diagnostic delay have been identified as significant triggers and major contributors to the spread of TB in outbreaks ( 11 , 27 , 32 , 33 ). A range of 37–247 days has been documented for delays in the diagnosis of index cases, with longer delays being associated with a higher number of secondary cases ( 34 ). A genomic consequence of the prolonged diagnostic delay in Case 1 is the identification of a SNP that emerged during the period of diagnostic delay, likely due to microevolution along the bacterial viability period preceding the diagnosis. This SNP was detected as fixed in three cases within the cluster and absent in the remaining cases, which led us to propose the distribution of cases across two exposure time periods. The tracking of the emergence of heterozygous SNP has enabled the establishment of a more precise chronology and refinement of the most likely case-case relationships in other long-term extensional clusters ( 35 ). Evolutionary analysis of clusters, with specific attention paid to the distribution of differential SNPs among clustered cases, adds value to the standard exploitation of genomic data, beyond just exploiting them to ruling in or out clustered cases depending on the number of differential SNPs between the cases ( 1 ). In a preceding study, we employed within-cluster differential SNPs to categorise a proportion of cases as candidates for probable reactivations or diagnostic delays/subclinical TB ( 36 ). With regard to the usefulness of focusing on specific SNPs we further utilised them, now the cluster common SNPs, to enhance the identification of new cases in Madrid within the cluster, among those patients who had not been reached by our sequencing efforts. The strategy of tracking relevant strains by targeting marker SNPs has been employed by our team in numerous previous studies ( 3 , 6 , 18 , 37 , 38 ). A proposal was also made for a more efficient tracking of the most prevalent MDR strains across the EU. This would be achieved by identifying their marker SNPs ( 35 ). In our preceding efforts, the marker SNPs were targeted by allele-specific PCRs, which limited the number of marker SNPs that could be interrogated to the number of fluorophore channels available in a qPCR device. In the present study, we have refined our strategy by integrating nanopore sequencing to analyse the presence of the marker SNPs in the amplicons. This approach overcomes the limitation in the number of SNPs that can be targeted, thereby enhancing specificity. The efficiency of this strategy in quickly identifying new cases associated with a cluster, directly on crude extracts from stored frozen isolates, offers a scalable alternative to WGS for public health surveillance, thereby enhancing the tracking of relevant strains beyond territorial boundaries. In the pursuit of enhanced TB control in the present epidemiological context, characterised by within-country migrant mobility, a transition to a novel operational framework is imperative, one that exceeds the confines of regional boundaries. We must circumvent geographic fragmentation, firstly, at the genomic level, which entails the integration of genomic data procured across all pertinent territories; and secondly, at the epidemiological level, entailing the expansion of the epidemiological research paradigm beyond the confines of the diagnostic setting. In order to achieve this novel, expanded vision, there is a necessity to incorporate additional layers of enhancement. Firstly, methodological, with the aim of simplifying the tracking of strains involved in cross-regional transmission. Secondly, epidemiological, with the objective of introducing structured interviews with patients to enrich the data acquired, in conjunction with genomic analysis and guided by their findings. Finally, analytical, with the purpose of conducting a detailed examination of the SNP content of the clusters to extract the most valuable information according to chronologies and case-case relationships. Declarations Mycobacteria Infections Madrid Study Group The following members are listed: Jaime Esteban, María Cabrera, María Simón Sacristán, Diego Domingo, Carlos Toro Rueda, Cristina Loras, Felipe Pérez-García, Sara Hernández Egido, Laura Barrado, María Jesús Ruíz Serrano, Marta Tato Diez, Paula López Roa, Jesús García Martínez, Irene Díaz de la Torre, José Valverde, Laura Viñuela, Laura Pérez-Lago and Darío García de Viedma. Acknowledgements COST-Action-AdvanceTB (CA21164), and Computing facilities at CETA-CIEMAT with ERDF funds. Funding ISCIII (PI21/01823; PI25/00911, AC25/00023; PI25/01033, PI23/01700), a PFIS contract to SBS (FI21/00145), Junta de Andalucía (AP-0062-2021-C2-F2 and PI-0284-2024). SEPAR2023: 1401/2023, CIBER - Consorcio Centro de Investigación Biomédica en Red (CB06/06/0058, CB21/13/00044). Ethical Approval This study in Almeria was approved by Junta de Andalucía Ethical Committee (References 60/2017 and 98/2023). The study in Madrid was approved by the Ethical Committee for Research at Gregorio Marañón Hospital (Ref PI23/01700). All sequences were encrypted to anonymize any associated personal information. Data availability statement The sequences generated were deposited in the ENA (project number PRJEB105429). Conflicts of interest The author(s) declare that there are no conflicts of interest. References Walker TM, Ip CLC, Harrell RH, Evans JT, Kapatai G, Dedicoat MJ, et al. Whole-genome sequencing to delineate Mycobacterium tuberculosis outbreaks: A retrospective observational study. Lancet Infect Dis. 2013;13(2). Abascal E, Pérez-Lago L, Martínez-Lirola M, Chiner-Oms Á, Herranz M, Chaoui I, et al. 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Redefinition of transmission clusters by accessing to additional diversity in Mycobacterium tuberculosis through long-read sequencing. Pathog Glob Health [Internet]. 2025;00(00):1–11. Available from: https://doi.org/10.1080/20477724.2025.2555926 Leigh JW, Bryant D. POPART: Full-feature software for haplotype network construction. Methods Ecol Evol. 2015;6(9). Jajou R, de Neeling A, van Hunen R, de Vries G, Schimmel H, Mulder A, et al. Correction: Epidemiological links between tuberculosis cases identified twice as efficiently by whole genome sequencing than conventional molecular typing: A population-based study(PLoS ONE (2018)13:4 (e0195413) DOI: 10.1371/journal.pone.0195413). Vol. 13, PLoS ONE. 2018. Abascal E, Herranz M, Acosta F, Agapito J, Cabibbe AM, Monteserin J, et al. Screening of inmates transferred to Spain reveals a Peruvian prison as a reservoir of persistent Mycobacterium tuberculosis MDR strains and mixed infections. Sci Rep. 2020;10(1). Tagliani E, Anthony R, Kohl TA, De Neeling A, Nikolayevskyy V, Ködmön C, et al. Use of a whole genome sequencingbased approach for Mycobacterium tuberculosis surveillance in Europe in 2017-2019: An ECDC pilot study. Eur Respir J. 2021;57(1). Li M, Quan Z, Xu P, Takiff H, Gao Q. Internal migrants as drivers of long-distance cross-regional transmission of tuberculosis in China. Clin Microbiol Infect. 2025 Jan 1;31(1):71–7. Yu LJ, Ji PS, Ren X, Wang YH, Lv CL, Geng MJ, et al. Inter-city movement pattern of notifiable infectious diseases in China: a social network analysis. Lancet Reg Heal - West Pacific. 2025 Jan 1;54. Hong C, Ge J, Gui J, Che X, Li Y, Zhuo Z, et al. Cross-District Transmission of Tuberculosis in a High-Mobility City in China: Implications for Regional Collaboration in Infectious Disease Control. Infect Drug Resist. 2025;18:1551–60. Saludes V, Cano P, Antuori A, Mendioroz J. Cruzando fronteras: evidencia genómica de la transmisión intercomunitaria de la tuberculosis en España. 2024;2014–5. Asghar RJ, Patlan DE, Miner MC, Rhodes HD, Solages A, Katz DJ, et al. Limited utility of name-based tuberculosis contact investigations among persons using illicit drugs: Results of an outbreak investigation. J Urban Heal. 2009;86(5). Lan Y, Rancu I, Chitwood MH, Sobkowiak B, Nyhan K, Lin HH, et al. Integrating genomic and spatial analyses to describe tuberculosis transmission: a scoping review. Vol. 6, The Lancet Microbe. Elsevier Ltd; 2025. Xu Y, Cancino-Munoz I, Torres-Puente M, Villamayor LM, Borrás R, Borrás-Máñez M, et al. High-resolution mapping of tuberculosis transmission: Whole genome sequencing and phylogenetic modelling of a cohort from Valencia Region, Spain. PLoS Med. 2019;16(10). Gardy JL, Johnston JC, Sui SJH, Cook VJ, Shah L, Brodkin E, et al. Whole-Genome Sequencing and Social-Network Analysis of a Tuberculosis Outbreak. N Engl J Med. 2011;364(8). Teketel T, Agide FD, Yirga Y, Hamdalla T, Beykaso G. Undiagnosed Pulmonary Tuberculosis Among Incarcerated Individuals and Its Overlooked Transmission Risk for the Community in Central Ethiopia. Can J Infect Dis Med Microbiol. 2025;2025(1). Kunjok DM, Mwangi JG, Kairu-Wanyoike S, Kinyua J, Mambo S. Spatial epidemiology of tuberculosis diagnostic delays, healthcare access disparities, and socioeconomic inequities in Nairobi County, Kenya. PLoS One. 2025 Aug 1;20(8 August). Liu X-J, F-x T, Y-f Y, J-h L, F-h Y, C-l B, et al. REVIEWED BY Whole-genome sequencing to characterize the genetic structure and transmission risk of Mycobacterium tuberculosis in Yichang city of China [Internet]. Available from: https://ngdc.cncb.ac.cn/ Wang M, Zhang Y, Huang C, Li J, Shen X, Zhao G, et al. A Whole-Genome Sequencing-Based Study to Delineate the Risk and Characteristics of Tuberculosis Transmission in an Insular Population Over 10 Years in Shanghai. Front Microbiol. 2022 Feb 16;12. Bao H, Liu K, Wu Z, Wang X, Chai C, He T, et al. Tuberculosis outbreaks among students in mainland China: a systematic review and meta-analysis. BMC Infect Dis. 2019;19(1). takiff-et-al-2012-epidemiological-evidence-of-the-spread-of-a-mycobacterium-tuberculosis-strain-of-the-beijing-genotype. Xu Z, Liu H, Liu Y, Tang Y, Tan Y, Hu P, et al. Whole-Genome Sequencing and Epidemiological Investigation of Tuberculosis Outbreaks in High Schools in Hunan, China. Infect Drug Resist. 2022;15:5149–60. de Neeling AJ, Tagliani E, Ködmön C, van der Werf MJ, van Soolingen D, Cirillo DM, et al. Characteristic SNPs defining the major multidrug-resistant Mycobacterium tuberculosis clusters identified by EuSeqMyTB to support routine surveillance, EU/EEA, 2017 to 2019. Eurosurveillance. 2024 Mar 21;29(12). Rodríguez-Grande C, Vallejo-Godoy S, Martínez-Lirola M, Saleeb SM, Buenestado-Serrano S, Barroso-García P, et al. A long-term refined genomic analysis of tuberculosis clusters to discriminate between ongoing transmission, reactivations or diagnostic delays [Internet]. 2025. Available from: https://www.researchsquare.com/article/rs-6057121/v1 Paul R, Lorenzo F, López B, Alegre MG, Couvin D, Rastogi N, et al. Outbreak Caused by Multidrug-Resistant Mycobacterium Tuberculosis with Unusual Combination of Resistance Mutations, Northern Argentina, 2006–2022. Emerg Infect Dis. 2025 Mar 1;31(3):601–6. Acosta F, Norman A, Sambrano D, Batista V, Mokrousov I, Shitikov E, et al. Probable long-term prevalence for a predominant Mycobacterium tuberculosis clone of a Beijing genotype in Colon, Panama. Transbound Emerg Dis. 2021 Jul 1;68(4):2229–38. Table Table: Socio-demographic data for isolates in cluster. Patient ID Case Age Gender Country of origin Place of diagnosis Year of diagnosis 3115 1 32 Male Guinea Bissau Almeria, Spain 2023 3113 2 38 Male Guinea Bissau Almeria, Spain 2023 3121 3 23 Male Gambia Almeria, Spain 2023 3139 4 22 Male Burkina Faso Almeria, Spain 2023 3217 5 29 Female Guinea Bissau Almeria, Spain 2023 3306 6 24 Male Spain-Senegal* Almeria, Spain 2024 3388 7 24 Female Spain- Guinea Bissau* Almeria, Spain 2024 40202819 8 53 Male Spain Madrid, Spain 2023 40164234 9 17 Female Spain Madrid, Spain 2023 44279605 10 36 Male Senegal Madrid, Spain 2024 40133926 11 33 Male Senegal Madrid, Spain 2022 42335996 12 37 Female Equatorial Guinea Madrid, Spain 2021 44280854 13 27 Male Guinea Madrid, Spain 2024 *Second-generation immigrants born in Spain. Additional Declarations The authors declare no competing interests. Supplementary Files Supplementary.docx Supplementary Table :Primers (and their corresponding coordinates) included in the multiplex PCR targeting strain-marker SNPs SupplFigure.pptx Supplementary Figure: Rapid detection of cluster-specific marker positions in a selection of eight isolates during the first hour of sequencing. (A) Average coverage of the nine marker SNPs along the sequencing run (minutes). (B) Percentage of marker positions with coverage ≥10× over the course of the run. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8893959","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":592568165,"identity":"2f0e1d19-d8cb-479c-8ebc-c794963ca3f5","order_by":0,"name":"Sheri M. 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2","display":"","copyAsset":false,"role":"figure","size":47547,"visible":true,"origin":"","legend":"\u003cp\u003eGenomic network for the cluster members. Each dot corresponds to a SNP. Cases within the same box were infected by identical strains (0 SNPs between them).\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-8893959/v1/a15589e3ca135c70bc1bcaee.png"},{"id":103504316,"identity":"08ecb25e-7b79-49ae-ad93-147a3f955be2","added_by":"auto","created_at":"2026-02-26 13:19:10","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":268945,"visible":true,"origin":"","legend":"\u003cp\u003eGraphical description of the two likely exposure stages to Case 1, supported on the emergence of SNP-A and the dates of proved exposure between Cases 1 and 7.\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-8893959/v1/484f1b68bfa2112a224c72c8.png"},{"id":103351923,"identity":"7c7cbbcf-6cbf-49cf-841d-f4e5aac59c54","added_by":"auto","created_at":"2026-02-24 17:22:04","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":268945,"visible":true,"origin":"","legend":"\u003cp\u003eGraphical description of the two likely exposure stages to Case 1, supported on the emergence of SNP-A and the dates of proved exposure between Cases 1 and 7.\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-8893959/v1/11523315110c824cbed95e66.png"},{"id":106993875,"identity":"73a3d1a2-5306-45b7-947d-f9cc96c39bcc","added_by":"auto","created_at":"2026-04-15 14:59:34","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1714583,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8893959/v1/dcbb0f95-976e-48c9-b49f-c5928ba73b25.pdf"},{"id":103166737,"identity":"713908aa-d100-4d31-ab55-bee97120250e","added_by":"auto","created_at":"2026-02-22 12:42:00","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":22513,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSupplementary Table :\u003c/strong\u003ePrimers (and their corresponding coordinates) included in the multiplex PCR targeting strain-marker SNPs\u003c/p\u003e","description":"","filename":"Supplementary.docx","url":"https://assets-eu.researchsquare.com/files/rs-8893959/v1/69982abaa149d63bbfb5fa46.docx"},{"id":103166740,"identity":"a09e302f-e85e-46b1-8517-83da11fab2ca","added_by":"auto","created_at":"2026-02-22 12:42:00","extension":"pptx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":709958,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSupplementary Figure:\u003c/strong\u003e Rapid detection of cluster-specific marker positions in a selection of eight isolates during the first hour of sequencing. \u003cstrong\u003e(A)\u003c/strong\u003e Average coverage of the nine marker SNPs along the sequencing run (minutes).\u003cbr\u003e\n \u003cstrong\u003e(B)\u003c/strong\u003e Percentage of marker positions with coverage ≥10× over the course of the run.\u003c/p\u003e","description":"","filename":"SupplFigure.pptx","url":"https://assets-eu.researchsquare.com/files/rs-8893959/v1/d7e5c53f80b8f4466b2e626c.pptx"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003e\u003cstrong\u003eTuberculosis super-spreading due to interterritorial mobility and prolonged diagnostic delay: A call for an integrated and enhanced analysis\u003c/strong\u003e\u003c/p\u003e","fulltext":[{"header":"Introduction","content":"\u003cp\u003eWhole genome sequencing has enhanced the precision with which we can delineate tuberculosis (TB) transmission, facilitating the implementation of control measures by targeting the most active transmission hotspots in a population (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e). The migratory phenomenon is frequently associated to vulnerable living conditions for migrants in the host countries, which has increased the complexity of the transmission dynamics of the disease, giving rise to several challenges in terms of the proper surveillance of transmission to improve its control.\u003c/p\u003e \u003cp\u003eThe initial challenge pertains to the necessity of expanding our analytical inquiry to consider the underlying factors contributing to TB in migrants. We need to consider i) the reactivation of infections due to exposures in the countries of origin, ii) recent transmission due to infections upon arrival in the host country (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e), or iii) exposures along the migratory journey (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe increased mobility of migrants has prompted us to expand our perspective beyond our own individual populations, encompassing cross-border transmission. The advent of transnational surveillance has facilitated the identification of the multinational dissemination of strains, whether MDR or susceptible (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e). However, analogous endeavours to incorporate interterritorial transmission within a nation, due to internal migration, are considerably less prevalent. This paucity is attributable to the absence of integrative initiatives among the diverse populations within the country with genomic data at hand.\u003c/p\u003e \u003cp\u003eA further challenge to be addressed when contemplating vulnerable populations is the increased propensity for diagnostic delay. The cornerstone of TB prevention is early diagnosis, which in turn prompts the more expeditious initiation of contact tracing efforts for effective TB control surveillance. We need to acknowledge the role of patients with prolonged undiagnosed infections as a reservoir for transmission, which may be a contributing factor to the escalating number of secondary cases. Furthermore, it is anticipated that diagnostic delay is more probable among migrants (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e), who frequently encounter delays in establishing communication with the healthcare system and seeking medical assistance. This results in more extended exposures to infection and an elevated risk of onward transmission (\u003cspan additionalcitationids=\"CR9\" citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe challenges associated with the increased complexity of the current TB epidemiological scenario necessitate a shift from the conventional epidemiological investigation, which is based on contact tracing. This approach is inadequate in providing the detailed information required to elucidate the intricate links, which are often non-obvious, among cases within a genomic cluster. A more sophisticated and comprehensive social network analysis is required to elucidate the underlying factors that facilitate the transmission of this phenomenon in its intricately complex nature (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIt is notable that several of these challenges coincided in the complex transmission event that is the focus of this study: i) interterritorial transmission involving multiple nationalities, associated to the mobility of migrant cases, ii) superspreading as a consequence of a prolonged diagnostic delay and iii) initially cryptic exposure contexts. In order to address the aforementioned challenges, a series of simultaneous enhancements were implemented. These enhancements included the integration of sequencing data obtained from diverse populations, exhaustive interviews of patients to ascertain their social networks, newly designed, tailored laboratory assays to fast-track new cases in the population, not well covered by sequencing efforts, and a refined analysis of the genomic data from cases in the cluster.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eSample collection\u003c/h2\u003e \u003cp\u003eThe isolates from cases in Almeria involved in the cluster under study were analysed within a long-term TB transmission surveillance program which is running in Almeria (including all the province and the capital), Spain, since 2003. All prospective positive cultures from the total population of Almeria are dispatched on a weekly basis to our laboratory in Madrid, Spain, for the purpose of genomic analysis. Genomic analysis was performed based on high-throughput Illumina sequencing until December 2023 and since then by weekly nanopore sequencing of the cases diagnosed each preceding week.\u003c/p\u003e \u003cp\u003eThe sequences from Madrid used in the interterritorial analysis corresponded to the TB-STARS project, the aim of which is to characterise genomically (by Illumina sequencing) TB transmission in Madrid (in Madrid city and the rest of the province) involving minors (2020\u0026ndash;2026).\u003c/p\u003e \u003cp\u003eThe sample from Madrid used to track retrospectively the presence of the clusters strain corresponded to the centralized collection stored at -80\u0026deg;C at Gregorio Mara\u0026ntilde;\u0026oacute;n Hospital since 2018. This collection comprised all \u003cem\u003eMycobacterium tuberculosis\u003c/em\u003e (MTB) isolates (one isolate per patient) cultured in Madrid region hospitals.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eDNA extraction and purification\u003c/h3\u003e\n\u003cp\u003eEach positive MGIT (Mycobacteria Growth Indicator Tube) culture (4\u0026ndash;5 mL) was centrifuged at 8,500 rpm at 4\u0026deg;C for 40 minutes. The resulting pellet was resuspended in ATL buffer (a cell lysis buffer), followed by heat inactivation at 95\u0026deg;C for 15 minutes. The process of DNA extraction and purification was conducted in accordance with the protocol outlined by the manufacturer of the QIAamp DNA Mini Kit (Qiagen, Hilden, Germany).\u003c/p\u003e \u003cp\u003eFor the retrospective tracking of the cluster strain in Madrid frozen cultures were thawed. For each isolate, 200 \u0026micro;L were subjected to heat inactivation at 95\u0026deg;C for 15 minutes, after which it was used directly for PCR without further purification.\u003c/p\u003e\n\u003ch3\u003eGenomic analysis\u003c/h3\u003e\n\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eIllumina library preparation, sequencing and bioinformatic analysis\u003c/h2\u003e \u003cp\u003eLibraries were prepared using the Nextera XT kit (Illumina, San Diego, USA) according to the manufacturer's instructions and pooled for sequencing on a MiSeq or Nextseq instrument (2x151bp).\u003c/p\u003e \u003cp\u003eSequence analysis was performed using an in-house pipeline deposited on Git-Hub: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://github.com/MG-IiSGM/autosnippy\u003c/span\u003e\u003cspan address=\"https://github.com/MG-IiSGM/autosnippy\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. The workflow of this pipeline follows the same steps as previously described (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e), using a hypothetical MTB ancestral genome (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e) as a reference. Finally, genomic distances between sequences were calculated using Jaccard similarity and Hamming distance metrics to generate distance matrices.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eNanopore library preparation, sequencing and bioinformatic analysis\u003c/h3\u003e\n\u003cp\u003eProspective members of the cluster were then subjected to sequencing on a MinION long-read platform (Oxford Nanopore Technologies) using the Rapid PCR Barcoding Kit (SQK-RPB114). The base-calling and annotation processes were executed utilising our proprietary pipeline (MG-IiSGM/prokaION), as previously described (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e). The identification of species was conducted using the Kraken2 v2.1.3 and Mash v2.3 software. Reads were mapped against a hypothetical MTB ancestral genome (similar to H37Rv, with ancestral nucleotide positions inferred by maximum likelihood) using minimap2 v2.28, followed by single-nucleotide polymorphisms (SNPs) calling. Variant annotation was performed using SnpEff v5.1.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eCluster analysis\u003c/h2\u003e \u003cp\u003eIn order to assign new cases to the cluster, new sequences are to be introduced into the global phylogeny that has been obtained for the 1,173 cases sequenced in Almeria. In instances where a new sequence was found to be in close proximity to a preexisting sequence that had been assigned to be part of the cluster under study, either within the same clade or as a sister taxon originating from the same rooted branch, all related sequences were processed in order to generate a pairwise SNP distance matrix. A threshold of 12 SNP differences was applied: sequences within this limit were considered as potentially part of the same cluster, whereas sequences exceeding this threshold were classified as outgroup \"orphans\".\u003c/p\u003e \u003cp\u003eAlignments and SNPs were visualized inspected using the IGV (Integrative Genomics Viewer) programme. Median-joining networks of genome-related isolates were constructed from the SNP matrix generated by PopART software (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e). The cluster assignment was defined as a maximum of five differential SNPs, once visually confirmed, between sequences.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eAnalysis of heterozygous calls\u003c/h3\u003e\n\u003cp\u003eThe classification of SNPs was conducted in accordance with their allele frequency. Variants exhibiting a frequency above 70% were designated as fixed, whereas those ranging from 10% to 70% were categorised as heterozygous calls. In the interest of reducing the impact of sequencing noise and false positives, variants with a frequency below 10% were excluded from further consideration.\u003c/p\u003e \u003cp\u003eUpon identification, heterozygous SNPs were subjected to thorough inspection at the identical positions across all cluster isolates. To confirm the presence and frequency of these variants, isolates containing heterozygous variants were subjected to further analysis, namely re-sequencing to assure its call based in both Illumina and nanopore sequencing.\u003c/p\u003e\n\u003ch3\u003eTargeted tracking of the cluster strain\u003c/h3\u003e\n\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eIdentification of cluster marker SNPs\u003c/h2\u003e \u003cp\u003eCommon SNPs across the cluster members were then compared against an in-house global reference database. This database comprised the SNPs from 109,648 high-quality sequences from publicly available datasets, representing the geographic and phylogenetic diversity of MTB lineages 1\u0026ndash;9. The sequences included in the database were selected according to rigorous selection criteria. Only Illumina-generated datasets with complete metadata, exclusive taxonomic assignment to MTB, and reaching high-quality thresholds (\u0026gt;\u0026thinsp;70% of the genome covered at \u0026gt;\u0026thinsp;20\u0026times; depth and \u0026lt;\u0026thinsp;25% of the genome uncovered) were included. The global database contains a total of 2,042,025 polymorphic positions (allele depth\u0026thinsp;\u0026gt;\u0026thinsp;20\u0026times;, allele frequency\u0026thinsp;\u0026gt;\u0026thinsp;70%). Subsequent to the comparison, the cluster common SNPs found in the global database were filtered out to retain only the final specific cluster-marker-SNPs. In the event of there being multiple marker SNPs within the same gene, these are not given consideration, as it is deemed highly improbable that such an accumulation would occur in MTB.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eMultiplex PCR for targeted sequencing\u003c/h2\u003e \u003cdiv id=\"Sec13\" class=\"Section3\"\u003e \u003ch2\u003ePrimers design\u003c/h2\u003e \u003cp\u003ePrimers were designed by a machine-learning model using TOAST (Tuberculosis Optimised Amplicon Sequencing Tool), an automated pipeline that generates amplicon sets based on user-defined mutation targets. The tool's functionality encompasses the evaluation of candidate primers with regard to their GC content, melting temperature, propensity for self- and hetero-dimer formation, and potential for off-target binding. The software is openly available at \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://github.com/linfeng-wang/TOAST\u003c/span\u003e\u003cspan address=\"https://github.com/linfeng-wang/TOAST\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e and described in detail in a recent preprint (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1101/2025.01.13.632698\u003c/span\u003e\u003cspan address=\"10.1101/2025.01.13.632698\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). The final design resulted in the production of 350\u0026ndash;488 bp nine amplicons, with the marker SNP situated approximately at their midpoint (Supplementary Table).\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eMultiplex PCR\u003c/h2\u003e \u003cp\u003eThe mixture for the multiplex PCR comprised a final concentration of 0.5 \u0026micro;M for each of the nine primer pairs, in a final volume of 25 \u0026micro;l, with a MgCl₂ concentration of 3 mM, using the QIAGEN Multiplex PCR Kit (Qiagen, Hilden, Germany). The PCR conditions that were utilised are outlined below: an initial heat activation step at 95\u0026deg;C for 15 minutes, denaturation at 94\u0026deg;C for 30 seconds, 35 cycles of annealing at 58\u0026ndash;60\u0026deg;C, and extension at 72\u0026deg;C for 90 seconds, followed by a final extension step at 72\u0026deg;C for 10 minutes. A positive control corresponding to an isolate belonging to the cluster was included in the run.\u003c/p\u003e \u003cp\u003eThe PCR was applied to crude boiled extracts from the stored (frozen) isolates, obviating the necessity for subculturing or purification.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eAmplicon-targeted sequencing\u003c/h2\u003e \u003cp\u003ePCR products from the retrospective isolates were prepared for sequencing using the Rapid Barcoding Kit (SQK-RBK114.24, Oxford Nanopore Technologies). Libraries (up to 24) were loaded onto MinION flow cells (R10.4.1, FLOW-MIN114) with a maximum of 800 ng total DNA per run (50 ng per sample), in accordance with the manufacturer's recommendations. The run was terminated when \u0026gt;\u0026thinsp;80% of the amplicons, for each of the isolates, had been covered at a minimum of 30\u0026times; depth of coverage.\u003c/p\u003e \u003cp\u003eThe amplicons were then subjected to further analysis and mapping using an in-house pipeline (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://github.com/MG-IiSGM/prokaION\u003c/span\u003e\u003cspan address=\"https://github.com/MG-IiSGM/prokaION\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). The analytical workflow comprised the following steps: (i) conversion of raw pod5 files into fastq format through basecalling and barcoding with Dorado v0.9.6; (ii) quality control using Chopper v0.8.0 and NanoPlot v1.41.0 to ensure the retention of the most reliable data; and (iii) read mapping with ngmlr v0.2.7, followed by SNP calling with Freebayes v1.3.6, using a pseudo-consensus reference constructed from all 9 amplicons, with a quality filter set at 20. Following the identification of SNPs, they were subjected to rigorous inspection and visualisation on IGV to ensure accuracy.\u003c/p\u003e \u003cp\u003eAn in-house script was also applied during sequencing to provide real-time identification of strain-specific marker SNPs, to provide a faster preliminary preassignment of the strains to the cluster, requiring a minimum depth of 10\u0026times; and an allele frequency of at least 0.7. The candidate SNPs identified in this step were subsequently confirmed on the final sequences after the completion of the run.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eEpidemiological data collection\u003c/h2\u003e \u003cp\u003eThe Almeria TB Prevention and Control Program (TBPCP) obtained and analysed epidemiological data from cases in Almeria. The TBPCP constitutes a multidisciplinary team involving epidemiologists, microbiologists, professionals in preventive medicine units, nurses, social workers, professionals in primary care centres and three hospital tuberculosis clinical units. The TBPCP is integrated into the Epidemiological Surveillance System of Andalusia (SVEA), an extensive surveillance system integrated within the Spanish Epidemiological Surveillance Network (RENAVE). The incorporation of community health workers (CHWs) was initiated in 2003 through the establishment of collaboration agreements with local non-governmental organisations (NGOs) operating within the designated territory. These NGOs included the Red Cross and the Consortium of Entities for Comprehensive Action with Migrants. The primary function of the CHWs was to provide support to the public health system and to serve as cultural mediators and translators. Their contributions were found to be of paramount importance in the follow-up of TB cases and the conduct of contact tracing.\u003c/p\u003e \u003cp\u003eA data collection form, which was designed based on previous molecular and genomic research projects carried out by the research group in Almeria, was used to record the main cluster characteristics, transmission environments and risk factors identified. Furthermore, individual-level data were obtained from the SVEA registries, which are responsible for the storage of case-based epidemiological information. SVEA registries are comprised of two distinct components: hospital data from preventive medicine units and community data from primary care epidemiology units. This information has been validated and contrasted by experienced epidemiologists. To supplement the findings, a comprehensive review of the patients' clinical records was conducted for the cases in clusters that required a more profound investigation. In Almer\u0026iacute;a, patients were interviewed at the initial diagnosis and reinterviewed (in-person visits or telephone calls) during their follow-up, guided by the key findings provided by the genomic analysis. It allowed us to reorient the global investigation to obtain additional epidemiological data.\u003c/p\u003e \u003cp\u003eIn Madrid, the absence of a programme grounded in genomically oriented epidemiologically interviews of the cases, in addition to the lack of a genomic surveillance systematic population-based programme, hindered our ability to extract refined epidemiological data from the cases.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003ch2\u003e\u003cstrong\u003eCluster initial identification and preliminary epidemiological investigation\u003c/strong\u003e\u003c/h2\u003e\n\u003cp\u003eCase 1 corresponded to a 32-year-old male from Guinea-Bissau. The subject was diagnosed with pan-susceptible TB in Almeria on 20\u003csup\u003eth\u0026nbsp;\u003c/sup\u003eof February 2023 (Figure 1 and Table). The signs and symptoms had begun a minimum of one year prior to the diagnosis. The high bacillary load (4+) in all three sputa, together with the clinical findings at the time of diagnosis (severe caloric-protein malnutrition, extensive bilateral lung destruction) were indicative of a long-term progressive disease, consequent to a prolonged diagnostic delay. Indeed, TB was the underlying cause of death two months after diagnosis.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eWithin a period of one month following the diagnosis of Case 1, three further cases were identified (Cases 2, 3 and 4, sub-Saharan migrants). Of the three subjects under consideration, two of them (Cases 2 and 3) were relatives of Case 1: his brother and brother-in-law, respectively (Figure 1). The four subjects were found to be clustered, as evidenced by genomic analysis (Lineage 4.1.2.1 strain; 0-1 SNPs between the cases).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eOver the subsequent 12-month period, two new cases were diagnosed (Cases 5 and 6, Figure 1, Table and Figure 2), which also corresponded to Case 1 relatives (his sister-in-law and nephew, respectively). The final case in the cluster (Case 7; Case 1 non-relative, Figure 1, Table) was diagnosed seven months later (October, 2024).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eA preliminary analysis of the cluster was that a patient with extensively advanced TB disease, attributable to an extended diagnostic delay, exhibited superspreader-like characteristics, contributing to a substantial and rapid transmission (0-1 SNP among the cases), predominantly involving their family members. However, two of the genomically-clustered cases (Cases 4 and 7) were not part of Case 1 family, thus necessitating a more comprehensive epidemiological investigation to ascertain the links involving them.\u003c/p\u003e\n\u003ch2\u003e\u003cstrong\u003eRefined epidemiological investigation\u0026nbsp;\u003c/strong\u003e\u003c/h2\u003e\n\u003cp\u003eCases 2, 4, 6 and 7 was re-interviewed in order to compile additional information to the general one obtained in the initial interview. From those interviews we identified that Case 1 and his nephew Case 6 stayed temporarily in Almeria visiting relatives, but since the end of 2019 they had established themselves in the Madrid region (in Town A), where they were neighbours and had a very close and frequent relationship. In the case of Case 1, their return to Almeria was necessary in December 2022, due to the severity of the disease, shortly before the diagnosis and subsequent death.\u003c/p\u003e\n\u003cp\u003eThe finding of Cases 1 and 6 residing in one of the towns in Madrid region, during a period in which Case 1 was likely to be highly contagious, prompted the introduction of this element, \u0026ldquo;stays in Madrid region\u0026rdquo;, in a round of interviews with the remaining cases of the cluster. This allowed us to identify that one of the two only cases without family links with Case 1 (Case 4) had also resided at a refugee centre in Madrid (in Town B, neighbouring to Town A, both in Madrid region) between September and December 2021 prior to its arrival in Almeria. During his time in Madrid region, he engaged in prolonged interactions with other sub-Saharan migrants in the Town A areas they typically frequented.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eA stay in Madrid region was also identified for Cases 2 and 5, who attended a significant social event on 22\u003csup\u003end\u003c/sup\u003e of February 2022 in the same Town A, where Cases 1 and 6 had resided. This social event was attended by approximately 50 sub-Saharan migrants coming from various Spanish and other European cities.\u003c/p\u003e\n\u003cp\u003eFollowing these series of exhaustive interviews guided by genomic data, only Case 7 did not exhibit either family links with the remaining cases or a history of residence in Madrid. A new interview looking for previously unconsidered activities, venues and events revealed that Case 7 and Case 1 had coincided in the context of a a three-day social gathering that took place in Almeria in May 2022, with guests from various Spanish and other European cities. Furthermore, it is noteworthy that Case 1 and Case 7 played central roles in that event, which justified a high interaction between them along the three days. No additional links or connections were identified between Case 1 and 7 and no other members of the cluster attended that three-day social gathering.\u003c/p\u003e\n\u003ch2\u003e\u003cstrong\u003eExpanding the identification of other related cases in Madrid region\u003c/strong\u003e\u003c/h2\u003e\n\u003ch3\u003e\u003cstrong\u003e\u003cem\u003eTracking of new cases in Madrid region by an integrative analysis of Madrid and Almeria sequences\u003c/em\u003e\u003c/strong\u003e\u003c/h3\u003e\n\u003cp\u003eThe identification of the stages of several of the clustered cases in Madrid, including the extended period during which Case 1 was likely to have been highly contagious, and the involvement of some of the cases in a significant social event in Madrid, provided substantiated evidence to suspect potential additional exposures and therefore clustered cases among those diagnosed in Madrid region.\u003c/p\u003e\n\u003cp\u003eUnfortunately, unlike Almeria, no systematic universal genomic analysis is performed in Madrid, which impaired the systematic screening of new cases. However, different convenience subsamples from cases diagnosed in Madrid region had been sequenced within different research projects. The analysis of the sequences obtained in one of these running projects (TB-Stars) focused on tracking genomically the transmission involving paediatric/minor TB cases and their index cases diagnosed in Madrid region since 2020, led to the identification of two further cases sharing the same strain. They corresponded to the only two Spaniard cases in the cluster (Cases 8 and 9), father and 17-year-old daughter who frequently visited and stayed in her father\u0026apos;s house, located in the same town A as Cases 1 and 6 lived, and it was previously considered that these constituted a self-limited family micro-epidemic. The cases under consideration were diagnosed in the same period as the four initial cases in Almeria (February/March 2023; Figure 1).\u0026nbsp;\u003c/p\u003e\n\u003ch3\u003e\u003cstrong\u003e\u003cem\u003eTracking of new cases in Madrid region by targeted sequencing analysis of isolates in Madrid\u003c/em\u003e\u003c/strong\u003e\u003c/h3\u003e\n\u003cp\u003eThe identification of another two clustered cases in towns in Madrid region justified further efforts to identify other cases that could not have been previously identified. Due to the scarce genomic information available for the TB cases in Madrid region, an alternative strategy was implemented in order to expedite the process for fast-track the presence of additional cases. The initial step in this process involved the identification of the 23 marker SNPs for the specific strain that was the subject of the cluster in study. The marker SNPs were obtained through the following procedure: i) identification of SNPs shared across all isolates within the cluster, ii) filtering of these SNPs against an in-house global database to remove those identified in other strains, iii) selection of unique marker SNPs that were exclusive to the cluster.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eA multiplex PCR was designed to amplify a subset of nine regions, including the selected marker SNPs. The presence/absence of the marker SNPs was determined by nanopore sequencing of the amplicons. This strategy was implemented on the isolates obtained since 2018 up to 2025 from: i) all TB cases diagnosed and residing in Town A and a neighbouring Town B and ii) all cases from all the remaining towns in Madrid region with West-African nationalities.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;A total of 164 isolates were analysed, with \u0026gt;80% of the 9 amplicons reaching a minimum coverage of 30\u0026times; in less than 30 minutes of the sequencing run (Supplementary figure), which enabled the proper identification of the reference/marker alleles. This approach facilitated the identification of four new candidate cases (Cases 10-13, from 3 different nationalities (Table), all living in Town A, to be part of the cluster. Subsequent WGS from these cases confirmed that they were indeed part of the cluster (Figure 2). Additional epidemiological information to justify their relationships with the cluster could not be obtained as the dynamic of re-interviews guided by genomic data applied in Almeria is not implemented in Madrid region.\u003c/p\u003e\n\u003ch2\u003e\u003cstrong\u003eRefined genomic evolutionary analysis of the cluster\u003c/strong\u003e\u003c/h2\u003e\n\u003cp\u003eDespite the robust clustering of most of the cases (0-1 SNP, well below the most restrictive thresholds (only Case 13 showed 4 SNPs), to consider the role for recent transmission among them), the presence of differential SNPs acquired between some of the cases only offered the opportunity to perform a more refined evolutionary analysis of these SNPs (Figure 3).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eA meticulous examination of one SNP (SNP-A; the one differentiating Cases 1-7 and 10 from Cases 3, 8 and 9, Figure 3) revealed that, in addition to its fixation in Cases 3, 8 and 11, we identified its emergence in Case 1 (remaining in heterozygosis at 51%). Subsequent analysis failed to detect any traces of this SNP in the remaining cases.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe identification of this emerging SNP-A in Case 1 offered the opportunity to perform a more refined evolutionary analysis of the cluster. In consideration of the fact that Case 7, who was exposed to Case 1 in May 2022, lacks SNP-A (Figure 2), and that this SNP was emerging in Case 1 at the time of their diagnostic sample (February 2023), it can be hypothesized that there were two most likely exposure stages for the cluster members, contingent on the presence or absence of this SNP in the infected cases. In all cases without the SNP, exposures were most likely to have occurred during the initial period of infectivity of Case 1 (at least before May 2022). However, the three cases with the SNP (Cases 3, 8 and 9) should have been exposed in the latest periods, between the moment when the SNP started to emerge in Case 1 (probably not before May 2022) and death. The proposed exposure opportunity for Case 3 is consistent, given that the subject arrived in Almeria in the summer of 2022, a mere nine months prior to diagnosis.\u0026nbsp;\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe analysis of the transmission cluster presented here illustrates some of the major challenges being faced in the TB genomic epidemiology era. In the present study, a number of these challenges coincided in the same transmission event. The impact of interterritorial mobility of cases, the consequences of an undiagnosed case due to a prolonged diagnostic delay, the need to modify standard epidemiological research dynamics to reveal the true complexity of TB transmission were identified.\u003c/p\u003e\n\u003cp\u003eThe transition from genotyping methods, such as Mycobacterial Interspersed Repetitive Unit-Variable Number Tandem Repeat (MIRU-VNTR), to genomic analysis has resulted in a significant enhancement in the precision with which transmission clusters can be identified (\u003cspan class=\"CitationRef\"\u003e17\u003c/span\u003e). Nevertheless, this enhancement is only of limited value if parallel progress is not made in the programmatic strategies that determine the application of genomics, and if the acquisition of epidemiological information continues to rely on the limited data provided by standard contact tracing.\u003c/p\u003e\n\u003cp\u003eStarting with the analytical framework which encompasses the genomic analyses conducted for epidemiological purposes in the majority of populations, these analyses are constrained to the geographic boundaries of a city or province. This framework might have been applicable prior to the advent of migratory patterns or in the context of highly stable populations. However, most European cities are currently experiencing high levels of migration, which has had a significant impact on the dynamics of TB transmission. The issue of migrant mobility has been the subject of consideration within the context of genomic epidemiology studies at a transnational level. The integration of datasets from different countries has enabled the identification of cross-border transmissions (\u003cspan class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e19\u003c/span\u003e). Indeed, the ECDC initiative entitled EpiPulse is dedicated to the study of cross-border transmission. This initiative has recently described a multi-country cluster involving cases from the Horn of Africa (\u003cspan class=\"CitationRef\"\u003e5\u003c/span\u003e). However, there has been a paucity of research into the issue of national-level mobility, particularly in relation to interterritorial migration within the same country, due to the fact that migrants\u0026apos; home/family towns frequently differ from their work towns.\u003c/p\u003e\n\u003cp\u003eThe dearth of data concerning the integration of cross-regional transmission in genomic epidemiology studies pertaining to tuberculosis appears to be undergoing a shift, as evidenced by the emergence of several recent articles, all originating from China, that have begun to address this issue (\u003cspan class=\"CitationRef\"\u003e20\u003c/span\u003e\u0026ndash;\u003cspan class=\"CitationRef\"\u003e22\u003c/span\u003e). The findings of the two studies centred on Shenzhen ranged from 16.8% of 142 clusters involving cross-regional transmission, when only a selection of districts was sampled (\u003cspan class=\"CitationRef\"\u003e20\u003c/span\u003e), to up to 52% of 119 clusters when all 11 districts in Shenzhen were included (\u003cspan class=\"CitationRef\"\u003e22\u003c/span\u003e). The findings of both studies indicated that internal migrants were more likely to be involved in a cross-regional cluster. Consequently, a significant proportion of cross-regional clusters may be overlooked if the analytical framework does not exceed the conventional geographic boundaries, thus being constrained to specific populations.\u003c/p\u003e\n\u003cp\u003eThe present study provides a clear illustration of this phenomenon; the precise definition of the extension of the cluster necessitated the integration of genomic data from two independent populations, Madrid and Almeria (541 kms apart). It is anticipated that the findings of this study are not expected to be exclusive to interactions between these two populations. Indeed, a recent study conducted in Spain also identified interterritorial transmission between two other regions, Catalonia and the Community of Valencia (\u003cspan class=\"CitationRef\"\u003e23\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003eIt is necessary to acknowledge that the complete magnitude and extent of the studied cluster is likely to be larger. The involvement of two social gatherings of great magnitude, frequented by individuals from different Spanish cities and even European countries, raises concerns about a potential unidentified larger impact beyond the two populations integrated in our analysis. Of particular concern is the social gathering in Almeria, which occurred during a period when Case \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e should have been highly infectious, as evidenced by the infection of Case 7, who coincided exclusively with Case \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e at that event. These elements collectively underscore the necessity for a paradigm shift in the conventional framework within which the field of TB genomic epidemiology is predominantly confined.\u003c/p\u003e\n\u003cp\u003eSecondly, it is imperative also to acknowledge that the expansion beyond the confines of single populations not only entails the integration of genomic data from independent populations, but it should be complemented by the elimination of geographic limitations in the epidemiological investigation of patients. It is widely acknowledged that the data obtained through standard contact tracing methods is inadequate for comprehending the intricate nature of TB transmission, particularly in the context of genomically supported epidemiological surveillance (\u003cspan class=\"CitationRef\"\u003e24\u003c/span\u003e). A number of improvements have been made to genomic analysis to enhance the inferences that can be made from it, including the integration of spatial analysis (\u003cspan class=\"CitationRef\"\u003e25\u003c/span\u003e) and the incorporation of an additional layer of phylodynamic analysis (\u003cspan class=\"CitationRef\"\u003e26\u003c/span\u003e). These modifications provide the capability to identify index cases and establish a probable chronology for the transmission. Furthermore, advances have been made in the epidemiological field, with the development of more sophisticated epidemiological analyses underpinned by social networks, complemented by interviews and structured questionnaires, in conjunction with genomic analysis. Despite their still limited application, these methodologies have been demonstrated to be essential (\u003cspan class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e27\u003c/span\u003e) in revealing the true nature of the clusters, identifying hitherto unreported social interactions, and delineating the locations frequently visited.\u003c/p\u003e\n\u003cp\u003eThe integration of a similar more refined approach to our epidemiological investigation, guided by genomic findings, was instrumental in elucidating the intricate connections between the cases and the exposure contexts, which were initially opaque, thereby underscoring the complexity of reconstructing complete epidemiological chains when patients move between territories. Furthermore, they played a pivotal role in elucidating the intricacies inherent in a cluster, wherein the diagnosis and exposure scenarios, which are typically coincidental, were, in our case, mostly split. The majority of cases were diagnosed in Almeria, while a significant proportion of exposures took place in Madrid. It means that the epidemiological investigation not only has to pursue enhanced access to a more extensive array of social networks among the cases in the diagnostic setting but also ensure the incorporation of patients\u0026apos; exposure context information, when these two contexts differ.\u003c/p\u003e\n\u003cp\u003eAnother coincidental challenging factor in our study was the likely prolonged period of infectiousness of Case \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e. The patient\u0026apos;s severe clinical presentation, high bacillary load, and rapid death after diagnosis reflected a long-term diagnostic delay and highlighted the risk posed by undiagnosed, infectious TB patients, especially when they are highly mobile. The aforementioned factors were found to contribute to the superspreading phenomenon, thereby justifying the magnitude of the cluster. However, it is important to note that undiagnosed cases continue to be a prevalent issue, particularly among specific population groups. It has been reported that a high prevalence of undiagnosed pulmonary TB has been identified in specific prisons, thus indicating that these institutions may act as reservoirs for ongoing transmission (\u003cspan class=\"CitationRef\"\u003e28\u003c/span\u003e). A comparable phenomenon has been documented in vulnerable groups. A study conducted in Kenya estimated that up to 54% of undiagnosed TB cases were concentrated in informal settlements (\u003cspan class=\"CitationRef\"\u003e29\u003c/span\u003e). It is estimated that each undiagnosed case may result in 10\u0026ndash;15 secondary cases (\u003cspan class=\"CitationRef\"\u003e29\u003c/span\u003e), and various reports have documented the association between diagnostic delay and genomic clustering (\u003cspan class=\"CitationRef\"\u003e30\u003c/span\u003e). Patients who experience delays have been shown to have 2.57 times the risk of being part of genomic clusters (\u003cspan class=\"CitationRef\"\u003e31\u003c/span\u003e). Non-diagnosed cases with diagnostic delay have been identified as significant triggers and major contributors to the spread of TB in outbreaks (\u003cspan class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e32\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e33\u003c/span\u003e). A range of 37\u0026ndash;247 days has been documented for delays in the diagnosis of index cases, with longer delays being associated with a higher number of secondary cases (\u003cspan class=\"CitationRef\"\u003e34\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003eA genomic consequence of the prolonged diagnostic delay in Case \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e is the identification of a SNP that emerged during the period of diagnostic delay, likely due to microevolution along the bacterial viability period preceding the diagnosis. This SNP was detected as fixed in three cases within the cluster and absent in the remaining cases, which led us to propose the distribution of cases across two exposure time periods. The tracking of the emergence of heterozygous SNP has enabled the establishment of a more precise chronology and refinement of the most likely case-case relationships in other long-term extensional clusters (\u003cspan class=\"CitationRef\"\u003e35\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003eEvolutionary analysis of clusters, with specific attention paid to the distribution of differential SNPs among clustered cases, adds value to the standard exploitation of genomic data, beyond just exploiting them to ruling in or out clustered cases depending on the number of differential SNPs between the cases (\u003cspan class=\"CitationRef\"\u003e1\u003c/span\u003e). In a preceding study, we employed within-cluster differential SNPs to categorise a proportion of cases as candidates for probable reactivations or diagnostic delays/subclinical TB (\u003cspan class=\"CitationRef\"\u003e36\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003eWith regard to the usefulness of focusing on specific SNPs we further utilised them, now the cluster common SNPs, to enhance the identification of new cases in Madrid within the cluster, among those patients who had not been reached by our sequencing efforts. The strategy of tracking relevant strains by targeting marker SNPs has been employed by our team in numerous previous studies (\u003cspan class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e37\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e38\u003c/span\u003e). A proposal was also made for a more efficient tracking of the most prevalent MDR strains across the EU. This would be achieved by identifying their marker SNPs (\u003cspan class=\"CitationRef\"\u003e35\u003c/span\u003e). In our preceding efforts, the marker SNPs were targeted by allele-specific PCRs, which limited the number of marker SNPs that could be interrogated to the number of fluorophore channels available in a qPCR device. In the present study, we have refined our strategy by integrating nanopore sequencing to analyse the presence of the marker SNPs in the amplicons. This approach overcomes the limitation in the number of SNPs that can be targeted, thereby enhancing specificity. The efficiency of this strategy in quickly identifying new cases associated with a cluster, directly on crude extracts from stored frozen isolates, offers a scalable alternative to WGS for public health surveillance, thereby enhancing the tracking of relevant strains beyond territorial boundaries.\u003c/p\u003e\n\u003cp\u003eIn the pursuit of enhanced TB control in the present epidemiological context, characterised by within-country migrant mobility, a transition to a novel operational framework is imperative, one that exceeds the confines of regional boundaries. We must circumvent geographic fragmentation, firstly, at the genomic level, which entails the integration of genomic data procured across all pertinent territories; and secondly, at the epidemiological level, entailing the expansion of the epidemiological research paradigm beyond the confines of the diagnostic setting. In order to achieve this novel, expanded vision, there is a necessity to incorporate additional layers of enhancement. Firstly, methodological, with the aim of simplifying the tracking of strains involved in cross-regional transmission. Secondly, epidemiological, with the objective of introducing structured interviews with patients to enrich the data acquired, in conjunction with genomic analysis and guided by their findings. Finally, analytical, with the purpose of conducting a detailed examination of the SNP content of the clusters to extract the most valuable information according to chronologies and case-case relationships.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003e\u003cstrong\u003eMycobacteria Infections Madrid Study Group\u003c/strong\u003e\u003c/h2\u003e\n\u003cp\u003eThe following members are listed:\u0026nbsp;Jaime Esteban,\u0026nbsp;Mar\u0026iacute;a Cabrera,\u0026nbsp;Mar\u0026iacute;a Sim\u0026oacute;n Sacrist\u0026aacute;n,\u0026nbsp;Diego Domingo,\u0026nbsp;Carlos Toro Rueda,\u0026nbsp;Cristina Loras,\u0026nbsp;Felipe P\u0026eacute;rez-Garc\u0026iacute;a,\u0026nbsp;Sara Hern\u0026aacute;ndez Egido,\u0026nbsp;Laura Barrado,\u0026nbsp;Mar\u0026iacute;a Jes\u0026uacute;s Ru\u0026iacute;z Serrano,\u0026nbsp;Marta Tato Diez,\u0026nbsp;Paula L\u0026oacute;pez Roa,\u0026nbsp;Jes\u0026uacute;s Garc\u0026iacute;a Mart\u0026iacute;nez,\u0026nbsp;Irene D\u0026iacute;az de la Torre,\u0026nbsp;Jos\u0026eacute; Valverde,\u0026nbsp;Laura Vi\u0026ntilde;uela,\u0026nbsp;Laura P\u0026eacute;rez-Lago and Dar\u0026iacute;o Garc\u0026iacute;a de Viedma.\u003c/p\u003e\n\u003ch2\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/h2\u003e\n\u003cp\u003eCOST-Action-AdvanceTB (CA21164), and Computing facilities at CETA-CIEMAT with ERDF funds.\u0026nbsp;\u003c/p\u003e\n\u003ch2\u003e\u003cstrong\u003eFunding\u0026nbsp;\u003c/strong\u003e\u003c/h2\u003e\n\u003cp\u003eISCIII (PI21/01823; PI25/00911, AC25/00023; PI25/01033, PI23/01700), a PFIS contract to SBS (FI21/00145), Junta de Andaluc\u0026iacute;a (AP-0062-2021-C2-F2 and PI-0284-2024). SEPAR2023: 1401/2023, CIBER - Consorcio Centro de Investigaci\u0026oacute;n Biom\u0026eacute;dica en Red (CB06/06/0058, CB21/13/00044).\u003c/p\u003e\n\u003ch2\u003e\u003cstrong\u003eEthical Approval\u003c/strong\u003e\u003c/h2\u003e\n\u003cp\u003eThis study in Almeria was approved by Junta de Andaluc\u0026iacute;a Ethical Committee (References 60/2017 and 98/2023). The study in Madrid was approved by the Ethical Committee for Research at Gregorio Mara\u0026ntilde;\u0026oacute;n Hospital (Ref PI23/01700). All sequences were encrypted to anonymize any associated personal information.\u003c/p\u003e\n\u003ch2\u003e\u003cstrong\u003eData availability statement\u003c/strong\u003e\u003c/h2\u003e\n\u003cp\u003eThe sequences generated were deposited in the ENA (project number\u0026nbsp;PRJEB105429).\u003c/p\u003e\n\u003ch2\u003e\u003cstrong\u003eConflicts of interest\u003c/strong\u003e\u003c/h2\u003e\n\u003cp\u003eThe author(s) declare that there are no conflicts of interest.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eWalker TM, Ip CLC, Harrell RH, Evans JT, Kapatai G, Dedicoat MJ, et al. Whole-genome sequencing to delineate Mycobacterium tuberculosis outbreaks: A retrospective observational study. Lancet Infect Dis. 2013;13(2). \u003c/li\u003e\n\u003cli\u003eAbascal E, P\u0026eacute;rez-Lago L, Mart\u0026iacute;nez-Lirola M, Chiner-Oms \u0026Aacute;, Herranz M, Chaoui I, et al. 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Elife. 2022;11. \u003c/li\u003e\n\u003cli\u003eBuenestado-Serrano S, Mart\u0026iacute;nez-Lirola M, Herranz-Mart\u0026iacute;n M, Esteban J, Broncano-Lavado A, Molero-Salinas A, et al. Microevolution, reinfection and highly complex genomic diversity in patients with sequential isolates of Mycobacterium abscessus. Nat Commun. 2024;15(1). \u003c/li\u003e\n\u003cli\u003eComas Ĩ, Chakravartti J, Small PM, Galagan J, Niemann S, Kremer K, et al. Human T cell epitopes of Mycobacterium tuberculosis are evolutionarily hyperconserved. Nat Genet. 2010;42(6). \u003c/li\u003e\n\u003cli\u003eBuenestado-Serrano S, Vallejo-Godoy S, Escabias Machuca F, Barroso P, Mart\u0026iacute;nez-Lirola M, Cabezas T, et al. Redefinition of transmission clusters by accessing to additional diversity in Mycobacterium tuberculosis through long-read sequencing. Pathog Glob Health [Internet]. 2025;00(00):1\u0026ndash;11. Available from: https://doi.org/10.1080/20477724.2025.2555926\u003c/li\u003e\n\u003cli\u003eLeigh JW, Bryant D. POPART: Full-feature software for haplotype network construction. Methods Ecol Evol. 2015;6(9). \u003c/li\u003e\n\u003cli\u003eJajou R, de Neeling A, van Hunen R, de Vries G, Schimmel H, Mulder A, et al. Correction: Epidemiological links between tuberculosis cases identified twice as efficiently by whole genome sequencing than conventional molecular typing: A population-based study(PLoS ONE (2018)13:4 (e0195413) DOI: 10.1371/journal.pone.0195413). Vol. 13, PLoS ONE. 2018. \u003c/li\u003e\n\u003cli\u003eAbascal E, Herranz M, Acosta F, Agapito J, Cabibbe AM, Monteserin J, et al. Screening of inmates transferred to Spain reveals a Peruvian prison as a reservoir of persistent Mycobacterium tuberculosis MDR strains and mixed infections. Sci Rep. 2020;10(1). \u003c/li\u003e\n\u003cli\u003eTagliani E, Anthony R, Kohl TA, De Neeling A, Nikolayevskyy V, K\u0026ouml;dm\u0026ouml;n C, et al. Use of a whole genome sequencingbased approach for Mycobacterium tuberculosis surveillance in Europe in 2017-2019: An ECDC pilot study. Eur Respir J. 2021;57(1). \u003c/li\u003e\n\u003cli\u003eLi M, Quan Z, Xu P, Takiff H, Gao Q. Internal migrants as drivers of long-distance cross-regional transmission of tuberculosis in China. Clin Microbiol Infect. 2025 Jan 1;31(1):71\u0026ndash;7. \u003c/li\u003e\n\u003cli\u003eYu LJ, Ji PS, Ren X, Wang YH, Lv CL, Geng MJ, et al. Inter-city movement pattern of notifiable infectious diseases in China: a social network analysis. Lancet Reg Heal - West Pacific. 2025 Jan 1;54. \u003c/li\u003e\n\u003cli\u003eHong C, Ge J, Gui J, Che X, Li Y, Zhuo Z, et al. Cross-District Transmission of Tuberculosis in a High-Mobility City in China: Implications for Regional Collaboration in Infectious Disease Control. Infect Drug Resist. 2025;18:1551\u0026ndash;60. \u003c/li\u003e\n\u003cli\u003eSaludes V, Cano P, Antuori A, Mendioroz J. Cruzando fronteras: evidencia gen\u0026oacute;mica de la transmisi\u0026oacute;n intercomunitaria de la tuberculosis en Espa\u0026ntilde;a. 2024;2014\u0026ndash;5. \u003c/li\u003e\n\u003cli\u003eAsghar RJ, Patlan DE, Miner MC, Rhodes HD, Solages A, Katz DJ, et al. Limited utility of name-based tuberculosis contact investigations among persons using illicit drugs: Results of an outbreak investigation. J Urban Heal. 2009;86(5). \u003c/li\u003e\n\u003cli\u003eLan Y, Rancu I, Chitwood MH, Sobkowiak B, Nyhan K, Lin HH, et al. Integrating genomic and spatial analyses to describe tuberculosis transmission: a scoping review. Vol. 6, The Lancet Microbe. Elsevier Ltd; 2025. \u003c/li\u003e\n\u003cli\u003eXu Y, Cancino-Munoz I, Torres-Puente M, Villamayor LM, Borr\u0026aacute;s R, Borr\u0026aacute;s-M\u0026aacute;\u0026ntilde;ez M, et al. High-resolution mapping of tuberculosis transmission: Whole genome sequencing and phylogenetic modelling of a cohort from Valencia Region, Spain. PLoS Med. 2019;16(10). \u003c/li\u003e\n\u003cli\u003eGardy JL, Johnston JC, Sui SJH, Cook VJ, Shah L, Brodkin E, et al. Whole-Genome Sequencing and Social-Network Analysis of a Tuberculosis Outbreak. N Engl J Med. 2011;364(8). \u003c/li\u003e\n\u003cli\u003eTeketel T, Agide FD, Yirga Y, Hamdalla T, Beykaso G. Undiagnosed Pulmonary Tuberculosis Among Incarcerated Individuals and Its Overlooked Transmission Risk for the Community in Central Ethiopia. Can J Infect Dis Med Microbiol. 2025;2025(1). \u003c/li\u003e\n\u003cli\u003eKunjok DM, Mwangi JG, Kairu-Wanyoike S, Kinyua J, Mambo S. Spatial epidemiology of tuberculosis diagnostic delays, healthcare access disparities, and socioeconomic inequities in Nairobi County, Kenya. PLoS One. 2025 Aug 1;20(8 August). \u003c/li\u003e\n\u003cli\u003eLiu X-J, F-x T, Y-f Y, J-h L, F-h Y, C-l B, et al. REVIEWED BY Whole-genome sequencing to characterize the genetic structure and transmission risk of Mycobacterium tuberculosis in Yichang city of China [Internet]. Available from: https://ngdc.cncb.ac.cn/\u003c/li\u003e\n\u003cli\u003eWang M, Zhang Y, Huang C, Li J, Shen X, Zhao G, et al. A Whole-Genome Sequencing-Based Study to Delineate the Risk and Characteristics of Tuberculosis Transmission in an Insular Population Over 10 Years in Shanghai. Front Microbiol. 2022 Feb 16;12. \u003c/li\u003e\n\u003cli\u003eBao H, Liu K, Wu Z, Wang X, Chai C, He T, et al. Tuberculosis outbreaks among students in mainland China: a systematic review and meta-analysis. BMC Infect Dis. 2019;19(1). \u003c/li\u003e\n\u003cli\u003etakiff-et-al-2012-epidemiological-evidence-of-the-spread-of-a-mycobacterium-tuberculosis-strain-of-the-beijing-genotype. \u003c/li\u003e\n\u003cli\u003eXu Z, Liu H, Liu Y, Tang Y, Tan Y, Hu P, et al. Whole-Genome Sequencing and Epidemiological Investigation of Tuberculosis Outbreaks in High Schools in Hunan, China. Infect Drug Resist. 2022;15:5149\u0026ndash;60. \u003c/li\u003e\n\u003cli\u003ede Neeling AJ, Tagliani E, K\u0026ouml;dm\u0026ouml;n C, van der Werf MJ, van Soolingen D, Cirillo DM, et al. Characteristic SNPs defining the major multidrug-resistant Mycobacterium tuberculosis clusters identified by EuSeqMyTB to support routine surveillance, EU/EEA, 2017 to 2019. Eurosurveillance. 2024 Mar 21;29(12). \u003c/li\u003e\n\u003cli\u003eRodr\u0026iacute;guez-Grande C, Vallejo-Godoy S, Mart\u0026iacute;nez-Lirola M, Saleeb SM, Buenestado-Serrano S, Barroso-Garc\u0026iacute;a P, et al. A long-term refined genomic analysis of tuberculosis clusters to discriminate between ongoing transmission, reactivations or diagnostic delays [Internet]. 2025. Available from: https://www.researchsquare.com/article/rs-6057121/v1\u003c/li\u003e\n\u003cli\u003ePaul R, Lorenzo F, L\u0026oacute;pez B, Alegre MG, Couvin D, Rastogi N, et al. Outbreak Caused by Multidrug-Resistant Mycobacterium Tuberculosis with Unusual Combination of Resistance Mutations, Northern Argentina, 2006\u0026ndash;2022. Emerg Infect Dis. 2025 Mar 1;31(3):601\u0026ndash;6. \u003c/li\u003e\n\u003cli\u003eAcosta F, Norman A, Sambrano D, Batista V, Mokrousov I, Shitikov E, et al. Probable long-term prevalence for a predominant Mycobacterium tuberculosis clone of a Beijing genotype in Colon, Panama. Transbound Emerg Dis. 2021 Jul 1;68(4):2229\u0026ndash;38. \u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Table","content":"\u003cp\u003e\u003cstrong\u003eTable:\u0026nbsp;\u003c/strong\u003eSocio-demographic data for isolates in cluster.\u0026nbsp;\u003c/p\u003e\n\u003cdiv align=\"\"\u003e\n \u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"635\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 91px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePatient ID\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 41px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCase\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 38px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAge\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eGender\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCountry of origin\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 156px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePlace of diagnosis\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 109px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eYear of diagnosis\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 91px;\"\u003e\n \u003cp\u003e3115\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 41px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 38px;\"\u003e\n \u003cp\u003e32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003eGuinea Bissau\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 156px;\"\u003e\n \u003cp\u003eAlmeria, Spain\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 109px;\"\u003e\n \u003cp\u003e2023\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 91px;\"\u003e\n \u003cp\u003e3113\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 41px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 38px;\"\u003e\n \u003cp\u003e38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003eGuinea Bissau\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 156px;\"\u003e\n \u003cp\u003eAlmeria, Spain\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 109px;\"\u003e\n \u003cp\u003e2023\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 91px;\"\u003e\n \u003cp\u003e3121\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 41px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 38px;\"\u003e\n \u003cp\u003e23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003eGambia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 156px;\"\u003e\n \u003cp\u003eAlmeria, Spain\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 109px;\"\u003e\n \u003cp\u003e2023\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 91px;\"\u003e\n \u003cp\u003e3139\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 41px;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 38px;\"\u003e\n \u003cp\u003e22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003eBurkina Faso\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 156px;\"\u003e\n \u003cp\u003eAlmeria, Spain\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 109px;\"\u003e\n \u003cp\u003e2023\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 91px;\"\u003e\n \u003cp\u003e3217\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 41px;\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 38px;\"\u003e\n \u003cp\u003e29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003eGuinea Bissau\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 156px;\"\u003e\n \u003cp\u003eAlmeria, Spain\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 109px;\"\u003e\n \u003cp\u003e2023\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 91px;\"\u003e\n \u003cp\u003e3306\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 41px;\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 38px;\"\u003e\n \u003cp\u003e24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003eSpain-Senegal*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 156px;\"\u003e\n \u003cp\u003eAlmeria, Spain\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 109px;\"\u003e\n \u003cp\u003e2024\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 91px;\"\u003e\n \u003cp\u003e3388\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 41px;\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 38px;\"\u003e\n \u003cp\u003e24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003eSpain- Guinea Bissau*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 156px;\"\u003e\n \u003cp\u003eAlmeria, Spain\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 109px;\"\u003e\n \u003cp\u003e2024\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 91px;\"\u003e\n \u003cp\u003e40202819\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 41px;\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 38px;\"\u003e\n \u003cp\u003e53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003eSpain\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 156px;\"\u003e\n \u003cp\u003eMadrid, Spain\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 109px;\"\u003e\n \u003cp\u003e2023\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 91px;\"\u003e\n \u003cp\u003e40164234\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 41px;\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 38px;\"\u003e\n \u003cp\u003e17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003eSpain\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 156px;\"\u003e\n \u003cp\u003eMadrid, Spain\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 109px;\"\u003e\n \u003cp\u003e2023\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 91px;\"\u003e\n \u003cp\u003e44279605\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 41px;\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 38px;\"\u003e\n \u003cp\u003e36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003eSenegal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 156px;\"\u003e\n \u003cp\u003eMadrid, Spain\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 109px;\"\u003e\n \u003cp\u003e2024\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 91px;\"\u003e\n \u003cp\u003e40133926\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 41px;\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 38px;\"\u003e\n \u003cp\u003e33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003eSenegal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 156px;\"\u003e\n \u003cp\u003eMadrid, Spain\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 109px;\"\u003e\n \u003cp\u003e2022\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 91px;\"\u003e\n \u003cp\u003e42335996\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 41px;\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 38px;\"\u003e\n \u003cp\u003e37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003eEquatorial Guinea\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 156px;\"\u003e\n \u003cp\u003eMadrid, Spain\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 109px;\"\u003e\n \u003cp\u003e2021\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 91px;\"\u003e\n \u003cp\u003e44280854\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 41px;\"\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 38px;\"\u003e\n \u003cp\u003e27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003eGuinea\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 156px;\"\u003e\n \u003cp\u003eMadrid, Spain\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 109px;\"\u003e\n \u003cp\u003e2024\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e*Second-generation immigrants born in Spain.\u003cbr\u003e\u0026nbsp;\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[{"identity":"09b4b3e3-1032-4387-b374-929848b418d6","identifier":"10.13039/501100004587","name":"Instituto de Salud Carlos III","awardNumber":"PI21/01823","order_by":0},{"identity":"37294bf8-fdfe-4acd-9b32-040c9666c1f1","identifier":"10.13039/501100004587","name":"Instituto de Salud Carlos III","awardNumber":"PI25/00911","order_by":1},{"identity":"74957aa7-f5ad-4c77-9050-24bfeba4ac5c","identifier":"10.13039/501100004587","name":"Instituto de Salud Carlos III","awardNumber":"PI25/01033","order_by":2},{"identity":"002077bd-f414-43bc-a592-db1ae1899263","identifier":"10.13039/501100004587","name":"Instituto de Salud Carlos III","awardNumber":"AC25/00023","order_by":3},{"identity":"cba973f8-cea8-4aa6-9a20-9faefe77f8ad","identifier":"10.13039/501100011011","name":"Junta de Andalucía","awardNumber":"AP-0062-2021-C2-F2","order_by":4},{"identity":"3ba8295b-66d2-482d-8224-2a612d5d42ae","identifier":"10.13039/501100011011","name":"Junta de Andalucía","awardNumber":"PI-0284-2024","order_by":5}],"hasAdminPriorityOnWorkflow":true,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"Gregorio Marañón General University Hospital","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Recent transmission, genomic clusters, tuberculosis, interterritorial, diagnostic delay","lastPublishedDoi":"10.21203/rs.3.rs-8893959/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8893959/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eWhole genome sequencing has revolutionised the precision with which we can delineate tuberculosis (TB) transmission. However, the majority of genomic epidemiology surveillance in TB is restricted to the analysis of geographically limited populations, which impairs the identification of cross-regional transmission. In this study, we delineate a complex transmission event in Spain involving 13 cases, characterised by the convergence of i) interterritorial transmission due to the mobility of migrant cases, ii) superspreading due to an undiagnosed advanced TB case resulting from a prolonged diagnostic delay, iii) extensive exposures attributable to substantial social gatherings, iv) involvement of 6 different nationalities and autochthonous cases, and iv) two independent populations where the majority of cases were exposed or diagnosed, respectively. The final understanding of this transmission event was only possible following the integration of sequencing data obtained from different populations, the refinement of interviews with patients to cover social networks at both the diagnostic and exposure populations, the design of tailored laboratory assays to fast-track new cases based on targeted sequencing of the strain marker single-nucleotide polymorphisms (SNPs) and the evolutionary analysis of the SNPs identified in the cluster. This study may serve as an illustration of the integrative efforts and simultaneous strategic, methodological and analytical improvements that are required to address the numerous novel challenges arising for a proper surveillance of TB transmission in our current, increasingly complex, epidemiological scenario.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e","manuscriptTitle":"Tuberculosis super-spreading due to interterritorial mobility and prolonged diagnostic delay: A call for an integrated and enhanced analysis","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-02-22 12:41:56","doi":"10.21203/rs.3.rs-8893959/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"ad360727-3027-4c82-af49-3de141ac1ead","owner":[],"postedDate":"February 22nd, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-02-22T12:41:56+00:00","versionOfRecord":[],"versionCreatedAt":"2026-02-22 12:41:56","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8893959","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8893959","identity":"rs-8893959","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}