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Background: Tuberculosis remains a global health threat, and the World Health Organization reports a limited reduction in disease incidence rates, including both new and relapse cases. Therefore, studies targeting tuberculosis transmission chains and recurrent episodes are crucial for developing the most effective control measures. Methods: Epidemiologically linked tuberculosis patient clusters were identified during the source case investigation for pediatric tuberculosis patients. Only M. Relevant clinical and epidemiological data were obtained from patient medical records. Results: We investigated 18 clusters comprising active tuberculosis patients 29 of whom were children at the time of diagnosis; nine patients experienced recurrent episodes. Isolates of six clusters were drug-resistant. Within clusters, the maximum genetic distance between closely related isolates was only 5—11 single nucleotide variants SNVs. However, unidentified active tuberculosis cases within the cluster, the variable mycobacterial mutation rate in dormant and active states, and low M. Conclusion: The complex approach of integrating tuberculosis cluster WGS-data-based median-joining networks with relevant epidemiological and clinical data proved valuable in delineating epidemiologically linked patient transmission chains and deciphering causes of recurrent tuberculosis episodes within clusters. Tuberculosis TB is an infectious disease caused by the Mycobacterium tuberculosis Mtb complex belonging mycobacteria. It is still a devastating public health threat worldwide, and the second leading cause of death from a single infectious agent, after coronavirus disease COVID The reduction of TB incidence rate, including both new and recurrent cases, is one of the main WHO End TB Strategy objectives; however, the incidence rate reduction of only 8. Therefore, it is crucial to understand TB transmission patterns and dynamics in countries with different-level incidence rates, precisely track the transmission chains, decipher recurrent TB causes, and define host- and pathogen-related contributing factors for transmission and recurrence events to achieve adequate disease management. This knowledge is particularly important for drug-resistant TB case control since it appears that drug resistance is more likely to be transmitted than acquired 3. Thus, in-depth characterization of TB clusters and recurrent TB cases by using epidemiological and clinical data combined with Mtb isolate molecular analysis is highly important. Contact investigation is a systematic non-molecular epidemiological approach to studying TB transmission, which is an important component of TB control programs in low-incidence countries. It is centered on an index patient—the initial new or recurrent TB case—and is aimed at identifying yet undiagnosed active TB cases and individuals harboring latent Mtb infection. Notably, an index patient is not necessarily a source case, who transmitted TB infection to one or more individuals 4 — 6. An opposite process—reverse contact investigation, also known as source case investigation, —is often conducted mostly to identify a source case for a TB-infected child 6 , 7. However, established epidemiological links need to be complemented by molecular Mtb typing results demonstrating that Mtb isolates obtained from these patients share the same genotyping pattern. Conventional genotyping methods, such as mycobacterial interspersed repetitive unit-variable number of tandem repeat MIRU-VNTR , insertion sequence restriction fragment length polymorphism RFLP , and spoligotyping, have been successfully applied for this purpose for decades 9 — Unfortunately, these methods lack sufficient discriminative power to confirm recent person-to-person transmission or distinguish remotely related Mtb strains belonging to the same genotype, which can lead to false clustering These genotyping methods were also used to determine the causes of recurrent TB episodes 14 — TB recurrence is caused by either endogenous reactivation of previously acquired Mtb infection, or exogenous reinfection with a new Mtb strain As in TB cluster investigation, the application of any conventional genotyping method for recurrent TB case analysis also has limitations and is only helpful in distinguishing reinfection with Mtb strain of distinct genotype. Whole genome sequencing WGS of Mtb has demonstrated a higher resolution when investigating TB transmission chains 13 , 19 — 22 and recurrent TB episodes 23 — 25 than conventional genotyping methods. The application of WGS allows the detection of differing single nucleotide variants SNVs and therefore - the measurement of genetic relatedness between studied Mtb isolates. Moreover, the pattern of SNV accumulation is thought to indicate the direction of TB transmission within clusters 19 , Furthermore, results of recently conducted studies also demonstrated, that analyzing pairwise SNV distances to determine the cause of recurrent TB episodes is insufficient to recognize reinfection with closely related Mtb strain. This observation is particularly important in the case of recurrent TB patients who have been involved in local TB transmission chains with epidemiologically closely linked individuals 32 — Latvia, a Baltic state in Northern Europe, is a low-moderate TB incidence country, however, it is one of the WHO priority countries in the European region due to relatively high drug-resistant TB prevalence In , a total number of new and recurrent TB cases was reported 19 per , population and 39 The widespread MDR and pre-extensively drug-resistant pre-XDR Mtb strains have been a significant concern for Latvian healthcare authorities for decades now, and numerous Mtb genotypes mostly belonging to the Lineages 2 and 4 were identified among both drug-resistant and susceptible isolates acquired from TB patients in various studies, emphasizing the TB as a major health threat for Latvian population 37 — In the present study, we recreated median-joining networks of multiple TB clusters based on the differing SNVs between Mtb isolates and interpreted them using previously conducted epidemiological investigation results and relevant clinical data to 1 verify if the known index case was also the initial source case; 2 identify the source cases for pediatric TB patients involved in studied clusters and compare acquired results with epidemiological data; 3 determine the causes of recurrent TB cases within clusters; 4 identify contributing and interfering factors for transmission chain delineation in the studied sample set. In addition, we also analyzed the genetic diversity of Mtb strains within clusters and between all Mtb isolates belonging to the same genotype and described the SNV accumulation pattern by comparing SNV profiles of closely related Mtb strains. In this study, we retrospectively investigated previously defined epidemiologically linked and Mtb -isolate-genotype-matched TB patient clusters that were first discovered during the source case investigation conducted for pediatric TB patients, who were diagnosed in — and had culture-confirmed TB disease. Surveillance of these TB clusters, contact tracing, and conventional Mtb isolate genotyping of all active TB cases potentially involved in these transmission chains were conducted until the end of All clusters consisted of both pediatric and adult active TB patients. Active TB cases epidemiologically linked to pediatric TB patients were classified according to the degree of exposure as recommended previously 4 : 1 individuals who shared the same household with a potential source of infection were classified as close household contacts; 2 individuals, who had regular, prolonged contact with a potential source of infection but did not live in the same household, for instance, relatives, neighbors, close friends, and classmates, were classified as close nonhousehold contacts; 3 individuals, who spent less time with a potential source of infection, for instance, school staff, passengers regularly using the same public transport, and acquaintances, were classified as casual contacts. We can confirm that all methods were carried out following relevant guidelines and regulations in the Declaration of Helsinki. The geospatial distribution of each cluster was visualized in Tableau Desktop software v Mtb cultivation, pDST, mycobacterial DNA extraction, and Mtb isolate spoligotyping for all investigated TB cases were conducted at the clinical laboratory of the Centre of Tuberculosis and Lung Diseases, while sputum smear microscopy was performed at the hospital, where each patient was admitted. Mtb cultivation and pDST for each patient were performed at the time of diagnosis, while Mtb DNA extraction and spoligotyping were carried out during source case investigation for pediatric TB patients and continued during the following contact tracing for each active TB patient potentially involved in one of these clusters. Mycobacterial DNA samples were extracted from Mtb cultures grown on Lowenstein-Jensen LJ media according to the cetyltrimethylammonium bromide protocol Afterward, spoligotyping was conducted using commercially available reagent kits Isogen Life Science, Netherlands; later—Ocimum Biosolutions, India based on the previously published protocol Only epidemiologically linked TB patients with identical Mtb isolate spoligotype patterns were subjected to WGS and detailed transmission chain analysis. Analyzed TB cases in each cluster were arranged according to the specimen collection date in chronological order starting with the known index case. For those Mtb isolates, when pDST was conducted by both methods, results acquired from testing on both solid and liquid media were compared and combined in the phenotypic drug resistance profile. If results were mismatched, and the Mtb isolate demonstrated resistance to any medication in only one of the performed tests, the isolate was considered resistant to this medication. Reads of a maximum of base pairs were produced. The bioinformatics pipeline applied for WGS data of studied Mtb isolates was described previously We applied a threshold of 12 SNVs between Mtb isolates to confirm the involvement of each TB case in one of the studied clusters. Sequence alignment files BAM generated by the snippy tool v4. The significance of all detected variants was assessed using the catalog of Mtb variants and their association with drug resistance developed by WHO Variants of yet uncertain significance grading group 3 were interpreted as resistance-associated if they had been previously detected in drug-resistant strains. Multiple sequence core alignments which included all identified SNV sites were created using the snippy-core tool v4. The outgroup-rooted maximum likelihood phylogeny based on the core alignment of the whole dataset was estimated using IQ-TREE 2 v2. The phylogenetic tree was annotated on the iTOL public server v6. Median-joining networks based on the core alignments of all Mtb isolates belonging to the same sub-lineage were constructed in PopART software v1. The Mtb ancestral genome served as a reference point for the examination of genetic change direction in the networks. The genetic distance between Mtb isolates was calculated following the shortest path in the median-joining network. For this purpose, we also operated with network distances—the number of nodes traversed on the shortest path between two TB cases. Patients having extrapulmonary TB forms were considered non-contagious, while patients with positive sputum smear microscopy were considered more likely to transmit TB infection All calculations and statistical analyses were performed in RStudio software The average patient number per cluster and the average time between specimen collection from the first and last TB patients were reported for clusters belonging to the same sub-lineage. The distribution normality of patient age at the time of diagnosis, sequencing data quality score, and coverage depth were checked using three approaches: construction of both quantile diagram and boxplot and performing Shapiro—Wilk test; the median and interquartile range IQR were calculated for these data. The chi-square goodness of fit test with Bayes factor upper bound and effect size calculation was performed to assess the occurrence distribution of recently acquired SNV effects. The number of patients per cluster varied from three to 17, and 10 patients experienced recurrent active TB episodes. From one patient Mtb isolate could not be obtained, 13 Mtb isolates were excluded from the study based on the spoligotyping results, and two more Mtb isolates were not available at the time of WGS performance. Each isolate represented one active TB case. Eighty of sequenced Mtb isolates Sixty-four adults and 27 children experienced a single TB episode: there were 40 males In nine cases of TB recurrence, three male and four female patients were adults during both TB episodes, and two more male patients had their initial TB episode in adolescence. The median age at the time of diagnosis calculated per each TB episode was Pulmonary TB was the most common disease form in both adult and pediatric populations, including both episodes of all recurrent TB cases. Detailed information is included in Supplementary material S1. Therefore, close household and close nonhousehold contact types were the most prevalent among investigated clusters, and both contact types were determined between TB patients of 10 clusters D, F, G, H, I, L, N, P, Q, and R. Patients of three clusters C, K, and O had close household contact, and patients of two clusters B and M had close nonhousehold contact. In two clusters A and E , close household, close nonhousehold, and casual contact types were determined, and in one cluster J , both close household and casual contact types were identified. Figure 1. Geospatial distribution of studied tuberculosis clusters. The map displays geographical locations i. Each dot on the map corresponds to one of these locations. For all sequenced Mtb DNA samples, the median base quality score was All sequenced isolates were kept for further analysis. WGS-based genotyping and spoligotyping results were concordant. However, spoligotyping demonstrated greater discriminatory power in this dataset as in most cases multiple spoligotypes representing the same TB-Profiler-assigned sub-lineage were identified among studied isolates. Mtb isolates of investigated clusters belonged to the Lineages 2 and 4 Table 1. Sub-lineage 4. Other Lineage 4 sub-lineages were 4. Lineage 2 was represented by sub-lineage 2. WGS-predicted drug resistance profiles among isolates belonging to the same cluster were identical, except for isolates of cluster K, that demonstrated two different drug-resistance profiles. In drug-resistant Mtb isolates, resistance-associated variants for rifampicin, isoniazid, ethambutol, pyrazinamide, fluoroquinolones, streptomycin, amikacin, capreomycin, kanamycin, ethionamide, and para-aminosalicylic acid were detected, and there were seven disagreements between phenotypic and WGS-based DST results Figure 2. Figure 2. Comparison of phenotypic and WGS-based drug susceptibility testing results of drug-resistant M. Clusters E, D, and all but one isolates of cluster C case C1. Figure 3. Maximum-likelihood phylogeny of the studied dataset. Almost half of the studied Mtb isolates belonged to the sub-lineage 4. Seventeen Mtb isolates obtained from 14 pulmonary TB and two tuberculous pleurisy patients cases A6 and A8 had SIT42 corresponding pattern clusters A and B , and 30 Mtb isolates obtained from 24 pulmonary TB patients, one tuberculous meningitis patient case C2 , one simultaneous pulmonary TB and peripheral tuberculous lymphadenitis patient case C5 , and one simultaneous pulmonary TB and tuberculous pleurisy patient case E7 belonged to SIT spoligotype clusters C-F. Mtb isolates of all but one clusters were drug-susceptible, while isolates of cluster B were pre-XDR. The average cluster size was 7. Figure 4. Median-joining networks of studied tuberculosis clusters. Networks illustrate genetic distances between Mtb isolates obtained from clustered patients with active TB and belonging to sub-lineages A 4. Using the Mtb ancestral genome as a reference point, networks demonstrate the direction of genetic changes between isolates. Each network node represents TB episodes of one or more patients infected with identical Mtb strains zero SNV distance. Symbol combinations i. Network nodes are scaled according to the number of identical Mtb isolates, while numbers in brackets along network branches indicate the number of differing SNVs between nodes. The network topology of cluster A revealed the possibility of unidentified TB cases belonging to this cluster, including the initial source of infection, as the cluster network split creating two branches with 7—10 SNV distance between Mtb isolate sub-groups. The star-like node pattern visualizing the first Mtb isolate sub-group suggested the presence of a super-spreader in the common node consisting of six identical Mtb isolates, however, no available epidemiological data could support it. Notably, the specimen of index case A1 was obtained Furthermore, a heterozygous base g. One patient in this cluster experienced a recurrent TB episode cases A2. Mtb isolates acquired from this patient were identical to isolates of four more patients, one of whom case A9 was diagnosed with active TB approximately 9 months earlier than case A2. Thus, reinfection is thought to be the cause of recurrence. However, the network did not indicate the direct transmission event between cases A3 and A7. The second sub-group of cluster A consisted of three family members cases A4, A5, and A8 , and a person who regularly used the same public transport case A6. According to the median-joining network, case A4 was the source of infection for pediatric TB case A8, and not case A5 as was suggested during the epidemiological investigation. Notably, case A4 unlike case A5 had negative sputum smear microscopy, however, it was also a potential source of infection for case A6 as demonstrated by the network. Similarly, the network topology of cluster B confirmed that the sputum-smear-negative case B3 was the source of infection for both pediatric TB cases B1 and B2. In cluster C, the network topology indicated that the source of infection causing at least three secondary cases C1. Notably, two heterozygous bases g. This finding indicated that case C1. It indicated that the source case of the second episode C1. Therefore, case C1. According to the cluster D median-joining network, the index case D1. The case D1. Considering very limited Mtb isolate genetic variability and close epidemiological link between all involved individuals, the source of infection for the pediatric TB case D4 could not be identified. In this cluster, the index case experienced TB recurrence cases D1. As the Mtb isolate of the second episode was identical with four other patient isolates, one of whom was diagnosed approximately 5 months earlier than the case D1. The network of cluster E split creating two branches and separating case E4 from others by 8—11 SNV-distance, suggesting that the initial source case was unidentified. Although this cluster involved individuals who came from different geographical areas Figure 1 and TB infection was transmitted in multiple households, educational institution, and among casual contacts, Mtb isolates acquired very limited genetic variability. Mtb isolates of nine TB cases were identical, including the index case E1, whose specimen was acquired According to epidemiological data, case E1 was the potential source of infection for E2, E3, E4, and E5. Unfortunately, a more detailed transmission chain could not be obtained based on WGS data due to tight specimen acquisition timeline between some patients and possible unidentified active TB cases belonging to this cluster, thus sources of infection for five pediatric TB patients cases E2, E3, E4, E7, and E9 could not be precisely identified. One patient in this cluster experienced a recurrent TB episode cases E5. Although recurrent TB isolates exhibited the difference of only 1 SNV, reinfection is thought to be the cause of recurrence, as the first episode had identical Mtb genomes with eight other cases, and, based on the specimen collection dates and epidemiological data, at least one case E10 could be the source of infection for the second episode. Notably, a small genetic distance range of 9—16 SNVs was identified between Mtb isolates of clusters D, and E, and all but one isolates of cluster C. The periods of TB infection transmission within these clusters overlapped Table 1 , and although one of the documented cluster E geographical locations Riga matched the residence place of patients involved in cluster C, location of the cluster D was approximately kilometers away. Nevertheless, considering the small genetic distances between Mtb isolates and hypothetical unidentified initial source cases, we suggest that closely related Mtb strains were transmitted within a wide geographical area in Latvia. The network topology of cluster F revealed that multiple TB cases belonging to this cluster remained unidentified, as only two cases F2 and F3 were directly connected, and a path between all other TB cases included one negative network step overturning the possibility of direct transmission events. This cluster involved two pediatric TB patients cases F1 and F3. Cases F2 and F3 had identical Mtb genomes, however, two heterozygous bases were called in F2 isolate: g. This finding indicated the TB transmission from case F2 to F3. It supported the epidemiological data suggesting that F2 was the potential source of infection for the pediatric TB case F3. In contrast, the source of infection for another pediatric TB case F1 could not be identified. Among 21 Mtb isolates belonging to the sub-lineage 4. Mtb isolates of cluster G were Hr, cluster H was fluoroquinolone- and streptomycin-resistant, and cluster I was drug-susceptible. The average cluster size was 6 patients range 4—8 , and the average time between specimen collection from the first and last cluster TB patients was Therefore, a latent infection period in at least one of these individuals, and possible unidentified active TB cases that could be potential sources of infection for all individuals involved is suspected. According to the specimen acquisition timeline, cases G2 and G3. Considering the positive sputum smear microscopy of case G3. One patient in this cluster experienced recurrent TB episode cases G3. Mtb isolates obtained from this patient were identical, and no potential source of infection for the second episode could be identified based on the network topology, epidemiological data, and specimen acquisition timeline, therefore reactivation as a recurrent TB cause is plausible. The network topology of cluster H corresponded to the specimen acquisition timeline, demonstrating the initial TB infection transmission within a household, and further spreading within an educational institution. This cluster involved four pediatric TB patients cases H2. A heterozygous base g. Due to the very low AF of this variant in the H2. Moreover, the case H1 specimen was acquired Although cases H2. Thus, for both H2 and H3 cases, reinfection is thought to be a cause of TB recurrence. The initial node of the cluster I median-joining network included cases I1, I2, and I4. Since only the index case I1 had positive sputum smear microscopy, we hypothesize that this TB patient was also the initial source case. The I1 case specimen was acquired There were two pediatric TB patients in this cluster cases I3 and I6. Based on the small difference of 0. A heterozygous base was detected in the Mtb isolate obtained from case I3: g. It indicated TB transmission from the adolescent case I3 to the adult individual case I5 , which was also supported by positive sputum smear microscopy of case I3. Although pediatric TB case I6 was strongly epidemiologically linked only with case I3, the median-joining network revealed, that I3 could not be the source of infection as I6 was only directly connected with I3 family members I1, I2, and I4. Furthermore, a long latent TB infection period of at least Among 14 Mtb isolates belonging to the sub-lineage 4. Three pulmonary TB patients were involved in each cluster, and the average time between specimen collection from the first and last cluster TB patients was Median-joining network topology of the clusters J and K revealed that initial source cases of both clusters remained unidentified, as both networks split creating two branches. However, the cluster J network supported the epidemiological data, which put forward the index case J1 as the source of infection for pediatric TB case J2. Although case J3 was defined as a contact of J1, the path between these cases included one negative and one positive step overturning the possibility of direct transmission events. Moreover, since the case J1 specimen was acquired Based on the differing SNV analysis between cluster K isolates, we suspect that there were two unidentified initial source cases, which were infected with remotely related Mtb strains both belonging to the SIT53 spoligotype but having different drug resistance patterns Hr and MDR. Unfortunately, due to apparent unidentified active TB cases in this cluster, identification of the source of infection for the pediatric TB case K4 or transmission analysis of both Mtb strains could not be performed. According to the cluster L median-joining network, the index case L1 was also the initial source of infection, whose specimen was obtained As network nodes of cases L2. One patient in this cluster had a recurrent TB episode cases L2. A 5 SNV distance including a heterozygous base was identified between L2. However, the L2. Thus, reactivation is a plausible recurrent TB cause in this case. Clusters N and O involved eight and three TB patients, respectively, and the time between specimen collection from the first and last cluster TB patients was The first specimen of cluster O was acquired As the residence places of the patients involved in these clusters lay within approximately kilometer distance, we hypothesize that all TB cases could be caused by closely related Mtb strains, that were transmitted within this geographical area. The median-joining network topology revealed the possibility of unidentified TB cases belonging to both clusters: the cluster N network split creating two branches and separating the index case N1 from others by 2—7 SNV distance, while all paths connecting cluster O cases included one negative network step overturning the possibility of direct transmission events between patients. Notably, the evolutionary direction of SNV accumulation among cluster N Mtb isolates did not follow the specimen acquisition timeline. The case N3 specimen was obtained Similarly, the case N2 specimen was obtained Finally, specimens of the cases N5 and N6 were obtained Therefore, we suspect long latent TB infection periods in cases N5, N6, N7, and N8 as well as more unidentified active TB cases belonging to this cluster, that could be potential sources of infection for all individuals involved. There were two pediatric TB patients in this cluster cases N2 and N8 , and unfortunately, the potential sources of infection could not be determined due to previously discussed obstacles. Heterozygous bases were called in Mtb isolates obtained from the cases N4 and N5: g. This finding indicated the transmission of TB infection from the case N5 to N6. Furthermore, in N4 isolate g. Two heterozygous bases in the N4 Mtb isolate highlighted the possibility of two source cases. However, since the case N4 specimen was acquired Sixteen Mtb isolates obtained from 15 pulmonary TB patients belonged to the sub-lineage 2. Mtb isolates of the clusters Q and R were Hr and cluster P was drug-susceptible. The average cluster size was 5 patients range 3—6 , while the average time between specimen collection from the first and last cluster TB patients was All Mtb isolates of cluster R were identical, which interfered with the establishment of the transmission chain and infection source identification for pediatric TB case R3. However, since the index case R1 specimen was obtained The initial node of the cluster P median-joining network included the index case P1, pediatric TB case P3, and cases P4. Among these TB cases, only P1 had positive sputum smear microscopy, therefore we suspect that the index case P1 was also the initial source of infection for the family members, including pediatric TB case P3, according to the specimen acquisition timeline. One patient in this cluster experienced TB recurrence cases P4. Although recurrent TB isolates exhibited a small difference of 2 SNVs, the network nodes of two episodes were connected via an intermediate node representing case P6. Since cases P4 and P6 shared the same household and case P6 had positive sputum smear microscopy, reinfection is thought to be a cause of TB recurrence. The network topology of cluster Q revealed the possibility of unidentified TB cases belonging to this cluster, including the initial source of infection, as the cluster network split creating two branches. The index case Q1 specimen was acquired Thus, we hypothesize that cases Q2 and Q4 had long latent TB infection periods. According to the epidemiological data, case Q2 was defined as a potential source of infection for pediatric TB case Q3, while the median-joining network revealed that case Q4 transmitted TB infection to Q3. Notably, in the Mtb isolate of case Q6 two heterozygous bases were called: g. No SNV accumulation pattern could be identified in this Mtb sample collection as most variants occurred randomly in different positions across the whole Mtb genome. Only in three cases did two variants accumulate in the same genomic loci: p. SerAsn and p. LeuVal and p. No recently acquired variants were associated with drug resistance development. Among recently acquired SNVs, missense variants were the most prevalent The observations provided greater support for the uneven distribution of accumulated SNV effects H 1 than for the assumption that effects occur with equal frequency H 0. This study comprised TB transmission chain delineation of 18 clusters that involved active TB patients and recurrent TB cause determination for nine of these patients. The wide time frame of epidemiological surveillance allowed the thorough investigation of disease transmission in mixed adult and pediatric populations, determination of long latent TB infection periods, and description of genetic distances between numerous Mtb isolates belonging to different genotypes using the WGS approach. Nonetheless, only specific clusters involving culture-confirmed pediatric TB cases were presented in this study, and the analyzed dataset could not be considered representative of the total Mtb bacilli population circulating in Latvia. The sex ratio imbalance in the studied adult group corresponded to the global and European TB incidence 1 , 55 as there were more male than female patients. Furthermore, children are at higher risk of getting infected with Mtb after household exposure rather than community exposure 59 , 60 , which is also reflected in our study. Among 29 pediatric TB cases, family members of 24 children living in either the same or different households were involved in the same TB cluster. Previously conducted spoligotyping emphasized the wide Mtb genotype variability among studied TB clusters. Moreover, Mtb isolates of 13 individuals initially clustered with other TB patients based on the established epidemiological links were withdrawn from the study as they belonged to different spoligotypes than the clustered Mtb isolates. Herein, we also applied WGS-based strain genotyping to complement spoligotyping results. According to the SITVIT2 database 61 and recently conducted epidemiological TB studies 38 — 40 , all five sub-lineages and eight spoligotypes in our study had been reported previously in Latvia, while drug-resistant strains belonged to the spoligotypes frequently associated with drug resistance i. Mtb isolates belonging to the LAM family had been acquired from extrapulmonary TB patients before, however, this Mtb genotype was not predominant in studied populations 64 , On the contrary, each studied TB cluster of the T genotype involved only three patients, while isolates of three clusters belonged to spoligotypes that are not as widespread in Latvia i. Further detailed studies on differences in virulence, TB disease severity, transmissibility, as well as microbial tissue tropism between Mtb genotypes are needed to complement existing knowledge and improve TB monitoring strategies. Mtb strain genetic variability was assessed not only within TB clusters but also between all Mtb isolates belonging to the same sub-lineage by creating median-joining networks based on WGS data. The genetic distances between Mtb isolates of sub-lineages 2. On the other hand, clusters belonging to the sub-lineages 4. This data suggests greater genetic relatedness of isolates within the sub-lineage 4. Notably, small genetic distances of a maximum of 16 and 19 SNVs were detected between Mtb isolates of three sub-lineage 4. As Mtb strains belonging to the sub-lineage 4. This finding highlights the transmission of closely related Mtb strains in a low-moderate-TB-incidence country within geographical areas of variable size, and the necessity of countrywide TB cluster identification to assess the transmission patterns and Mtb strain genetic diversity thoroughly. Construction of TB cluster median-joining networks and differing SNV analysis between Mtb isolates using acquired WGS data significantly complemented previously conducted epidemiological investigation and spoligotyping results. Except for two cases, when patients involved in the genotype-matched TB cluster got infected with remotely related Mtb strains as indicated by WGS results, the maximum genetic distance within clusters varied between 5 and 11 SNVs, which corresponds to the commonly applied 12 SNV-threshold for epidemiologically relevant transmission cluster identification, which was also used in this study. Furthermore, none of the directly connected TB cases in the network exhibited a distance of more than 4 SNVs, when 5 SNV-distance is a widely accepted threshold for inferring recent transmission events between TB patients 19 , 20 , 27 , 28 , Among detected differing SNVs between closely related Mtb isolates, no drug-resistance-associated variants were detected, which indicated that no resistance-triggering factors were present in this study. However, several drawbacks of applying the SNV distance threshold have been reported previously. Secondly, the enhanced intra-patient Mtb microevolution might affect the genetic distances between isolates exceeding the proposed thresholds 68 , while the long periods of latent TB infection might cause significant delays in the precise delineation of transmission chains Thirdly, although the application of SNV distance thresholds provided valuable insights into TB transmission dynamics in different settings and helped to rule out those cases when TB transmission was unlikely 19 , 70 , the direction and timing of specific person-to-person transmission events mostly remained unclear 71 , Several computational tools have been developed to infer individual transmission events by combining pathogen genomic data with underlying epidemiological models which have been applied to Mtb WGS data in multiple TB transmission studies 67 , 72 — Nonetheless, generated transmission trees still should be interpreted critically by considering available clinical diagnosis, specimen collection date, sputum smear microscopy results and epidemiological geospatial analysis and contact tracing data regarding studied clusters 74 , Therefore, at this point, the development of a comprehensive algorithm allowing to precisely delineate TB transmission chains is still essential. Low mycobacterial mutation rate previously defined as 0. Indeed, most of the studied Mtb isolates demonstrated low genetic variability with the presence of identical isolates in 13 clusters, which limited the precision of complete transmission chain delineating. Some of these cases were solved by including heterozygous bases in the differing SNV analysis. Heterozygous variants reflect an incomplete SNV accumulation, which could potentially indicate the transmission direction within a cluster In four cases clusters F, I, H, and N , it helped to identify the putative direct transmission events between patients, and in three cases clusters A, C, and D , heterozygous bases were harbored by the cluster index cases, indicating that these patients also were initial source cases. Moreover, in three isolates the presence of multiple heterogenous bases indicated, that the patient got infected either with two remotely related Mtb strains case K3 or from two other TB patients involved in the same cluster cases N4 and Q7. As the main objective of epidemiological investigation and conventional genotyping was defining the source of TB infection for pediatric patients, we assessed if WGS data could support the initial assumptions. Unfortunately, sources of infection could be precisely determined only for 11 of 29 pediatric TB patients either supporting the epidemiological data cases B1, B2, C2, F3, H2. The latter-mentioned finding also interfered with transmission chain delineation in clusters G, N, and Q, as Mtb isolates which were acquired earlier in the study period cases G1, N2, N3, N4, N5, N6, and Q1 harbored additional SNVs in comparison with directly connected isolates obtained Currently, available data on mycobacterial mutation rate during the latency period are controversial: some studies report, that Mtb accumulates new variants at a similar rate while being dormant 82 , 83 , and other studies imply that mutagenesis during latent infection is significantly slower than in the active state 84 , Therefore, we hypothesize that our data could indicate either a slower mutation rate of dormant mycobacteria or a faster rate during Mtb strain transmission between individuals; however, the mycobacterial mutation rate in both dormant and active states should be studied further. Finally, we also determined the causes of recurrent TB episodes for nine patients, who were involved in studied clusters. The construction of median-joining networks including all known epidemiologically linked and genotype-matched TB cases along with analysis of available clinical, epidemiological, and geospatial data allowed more precise TB recurrence cause prediction in several recently conducted studies 32 — These approaches significantly complement the commonly applied distinguishing algorithm based on SNV-distance identification between Mtb isolates that represent each active episode of recurrent TB patient allowing the determination of reinfection with clonal or closely related Mtb strain. Herein, excluding the apparent reinfection case in cluster C with 97 SNV distance between TB episodes, Mtb isolates obtained from the first and second episodes of eight patients were separated by a maximum of 5 SNVs. Based on the genetic distance thresholds of 5—12 SNVs proposed in several studies 23 — 25 , 29 , 30 , these cases would be classified as endogenous reactivations. However, only in two cases clusters G and L no potential sources of infection for the second episode could be identified based on the available epidemiological and geographical data, specimen acquisition timeline of the cluster, as well as network distances and topology. In the remaining six cases at least one potential source of infection for recurrent TB patients could be identified within the cluster, therefore reinfection was a more reliable recurrence cause. Overall, this comprehensive approach allowed the evaluation of the known index case as the initial source case and enabled the identification of putative infection sources for patients of interest, herein—pediatric TB patients. Unidentified active TB cases belonging to the cluster, variable Mtb mutation rate in active and dormant states, tight specimen collection date timeline of genetically identical Mtb isolates, and low Mtb genetic variability interfered with transmission chain delineation in this study, while the inclusion of heterozygous SNVs in differing variant analysis between cluster Mtb isolates assisted in the identification of direct transmission events. In our opinion, the applied cluster investigation approach could be implemented as a part of a local TB surveillance program if WGS of all acquired Mtb isolates is routinely performed. However, it would require substantial resources to conduct such an investigation countrywide even in a low-burden setting. The datasets presented in this study can be found in online repositories. The studies were conducted in accordance with the local legislation and institutional requirements. AVa: Investigation, Writing — original draft. 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Keywords: tuberculosis, WGS, transmission, network, recurrence, reactivation, reinfection. Public Health. The use, distribution or reproduction in other forums is permitted, provided the original author s and the copyright owner s are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher. Top bar navigation. About us About us. Sections Sections. About journal About journal. Article types Author guidelines Editor guidelines Publishing fees Submission checklist Contact editorial office. Public Health , 20 May Infectious Diseases: Epidemiology and Prevention. Unraveling tuberculosis patient cluster transmission chains: integrating WGS-based network with clinical and epidemiological insights. Table 1. Characteristics of the studied TB clusters.

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