Genomic Characterization of XDR Mycobacterium tuberculosis Isolates in Argentina (2006-2015)

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Castello, Ezequiel J. Sosa, Josefina Campos, Johana Monteserin, and 13 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6456461/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 17 Nov, 2025 Read the published version in BMC Infectious Diseases → Version 1 posted 14 You are reading this latest preprint version Abstract Background: Tuberculosis (TB), caused by the intracellular bacterium Mycobacterium tuberculosis (Mtb), remains a significant global health challenge, with Mtb being the second leading infectious killer worldwide, following COVID-19. Despite over a century of research, the disease continues to pose a major threat, with an estimated one-fourth of the global population latently infected. According to the World Health Organization (WHO), approximately 1.25 million deaths were attributed to TB in 2023 alone. The emergence of multidrug-resistant (MDR) strains, resistant to isoniazid and rifampin, and extensively drug-resistant (XDR) strains, resistant to isoniazid, rifampin, a fluoroquinolone, and a second-line injectable aminoglycoside, further complicates the situation, posing significant challenges for healthcare systems. In Argentina, TB burden is moderate compared to other countries, with approximately 10,500 new cases and 1,000 deaths reported annually. While standard therapy is generally effective, XDR Mtb infections require prolonged and costly treatment and are often associated with a guarded prognosis. Methods: In this work, we applied whole-genome sequencing analysis to investigate XDR strains circulating in Argentina between 2006 and 2015. Genotypic variants of each isolate were compared against resistance-associated variant databases and subjected to local and global phylogenetic analyses. Results: The analysis revealed no common origins for the most frequently observed resistance mutations. Notable variants associated with resistance to first-line drugs included katG Ser315Thr and fabG1 -15C < T for isoniazid, rpoB Ser450Leu and Asp435Val for rifampin, embB Gly406Ala, and Met306Ile for ethambutol, as well as multiple variants in the pncA gene linked to pyrazinamide resistance. Conclusions: This study provides valuable insights into the molecular mechanisms of antibiotic resistance in M. tuberculosis , specifically focusing on XDR strains circulating in Argentina. The findings highlight the genetic diversity and complexity of resistance-associated variants, emphasizing the need for continued research and surveillance efforts to address this pressing global health threat. multiresistance genomics tuberculosis drugs XDR Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Background Tuberculosis (TB) remains a significant global health challenge, placing a substantial burden on healthcare systems and communities worldwide ( 1 ). Caused by Mycobacterium tuberculosis ( Mtb ), TB affects millions of individuals each year, leading to considerable morbidity and mortality ( 2 ). Despite concerted efforts to control the disease, TB persists, exacerbated by factors such as drug resistance, co-infections with HIV/AIDS, and socioeconomic disparities ( 3 ). Resistance to TB treatment is categorized based on first-line and second-line drugs. First-line drugs, including isoniazid, rifampicin, ethambutol, and pyrazinamide, form the backbone of standard TB treatment regimens due to their high efficacy and relatively low toxicity ( 4 , 5 ). When resistance to these key drugs develops, second-line drugs—such as fluoroquinolones (e.g. levofloxacin, moxifloxacin) and injectable agents (e.g. amikacin, capreomycin, kanamycin, and streptomycin)—have historically been used, despite being associated with higher toxicity and reduced efficacy ( 6 , 7 ). It is important to note that, according to the latest WHO guidelines, kanamycin and capreomycin are no longer recommended, and streptomycin is only used in cases of hepatotoxicity ( 8 ). However, during the period covered by this study, these drugs were still part of the recommended treatment regimens for multidrug-resistant TB. Streptomycin was historically part of the first-line TB treatment regimen as the first antibiotic discovered to be effective against Mtb ( 9 – 11 ). However, due to increasing resistance and more effective oral alternatives, the World Health Organization (WHO) has reclassified streptomycin as a second-line drug. It is now primarily used in cases of drug-resistant TB when other injectable agents are unavailable or contraindicated ( 12 ). Multidrug-resistant tuberculosis (MDR-TB) is defined as TB caused by Mycobacterium tuberculosis strains resistant to at least isoniazid and rifampicin, the two most potent first-line drugs ( 11 , 13 ). Extensively drug-resistant tuberculosis (XDR-TB) was historically defined as tuberculosis with additional resistance to fluoroquinolones and at least one second-line injectable drug (amikacin, capreomycin, or kanamycin). However, this definition has been updated in recent WHO guidelines to reflect changes in second-line treatment recommendations. The current definition classifies XDR-TB as a form of MDR-TB that is resistant to any fluoroquinolone and at least one of the Group A drugs, which currently include bedaquiline (BDQ), and linezolid (LZD). The definition used in this study reflects the classification that applied to the period covered by the analysis. The emergence and spread of MDR-TB and XDR-TB pose significant challenges to TB control programs worldwide, highlighting the need for continuous surveillance and the development of effective treatment strategies ( 2 , 14 – 16 ). In the 1990s, Argentina was identified by the WHO as a hotspot for multidrug-resistant tuberculosis (TB MDR). During this period, hospital outbreaks of MDR-TB associated with acquired immunodeficiency syndrome (AIDS) were documented in the country. Initially emerging in the capital city, the outbreak spread to nearby areas, reaching significant proportions. With over 800 diagnosed cases between 1992 and 2004, this outbreak met the criteria for an epidemic. In the early 2000s, an increase in MDR-TB cases was observed among patients who were neither HIV-positive nor had a history of prior tuberculosis treatment ( 17 ). Among the MDR strains identified in Argentina, strain M (which originated in Buenos Aires and surrounding districts) and strain Ra (Rosario, Argentina) have been the most predominant ( 11 , 18 ), contributing significantly to the persistence and transmission of MDR-TB in the country. In 2002, strain M was isolated from the country's first two patients diagnosed with XDR-TB. The evolution of Mtb is primarily influenced by TB control strategies, alongside socio-economic, environmental, and human migration patterns, all of which add complexity to efforts to combat the disease effectively. Notably, the pathogen's biology significantly impacts the global dissemination of the disease. Molecular studies have revealed a remarkable intra-species genetic diversity within Mtb , enabling its classification into nine major lineages, each displaying distinct affinities for specific geographic regions and human ethnic groups. These lineages are categorized as Indo-Oceanic (Lineage 1), East Asian (Lineage 2, including the Beijing sublineage), East African-Indian (Lineage 3), Euro-American (Lineage 4), West African (Lineage 5, M. africanum I), and West African (Lineage 6, M. africanum II). Recent phylogenomic analyses have identified additional lineages with more restricted distributions, including Lineage 7, found in the Horn of Africa; Lineage 8, recently described in Central Africa; and Lineage 9, a newly discovered lineage primarily located in East Africa ( 19 , 20 ). Lineages 1, 5, and 6 are considered "ancient," while lineages 2, 3, and 4 are classified as "modern" based on the presence or absence of the TbD1 genomic region, which is absent in modern lineages ( 21 – 23 ). Currently, the predominant Mtb strains circulating in the Americas were introduced by Europeans during colonization, with the Euro-American lineage (Lineage 4) being the most prevalent. Since different Mtb lineages dominate various regions worldwide, drug resistance acquisition may be influenced by the strains' pre-existing genetic background. The heterogeneity observed worldwide can be explained by the variability of these mutations ( 7 ). In other words, the pre-existing genetic profiles of certain Mtb strains may be preferentially associated with specific resistance-causing mutations, and the effect of these associations could modulate the biological fitness of the strains ( 24 ). The advent of advanced sequencing technologies, coupled with bioinformatics tools, has revolutionized our understanding of TB pathogenesis, drug resistance, and transmission patterns. High-throughput sequencing enables comprehensive genomic analysis of Mtb isolates, providing unprecedented insights into the molecular basis of drug resistance and virulence. By leveraging these technologies, researchers can gain insights into the intricate interplay between genetic determinants, host immunity, and environmental factors that shape TB epidemiology ( 25 – 27 ). Understanding the molecular basis of antibiotic resistance in Mtb is crucial for developing effective TB control strategies. Therefore, this work aimed to analyze the genetic basis of antibiotic resistance mechanisms in XDR clinical isolates obtained in Argentina between 2006 and 2015. By examining resistance mutations associated with different antibiotics—such as streptomycin, isoniazid, rifampicin, ethambutol, kanamycin, amikacin, capreomycin, pyrazinamide, ethionamide, and fluoroquinolones—we aimed to elucidate the molecular mechanisms driving drug resistance in M. tuberculosis strains circulating in Argentina ( 4 , 16 , 28 , 29 ). The findings from this study contribute to a better understanding of the genetic diversity and resistance patterns in Mtb , ultimately supporting efforts to improve diagnostic and therapeutic strategies for TB management. Methods Clinical isolates and strain selection Case selection was conducted with healthcare professionals from the “Servicio de Micobacterias at the Instituto Nacional de Enfermedades Infecciosas - Dr. Carlos G. Malbrán”. Cultures were initiated for 120 isolates, corresponding to all reported cases of patients with XDR Mtb between 2006 and 2015. Sufficient DNA was successfully extracted for sequencing from 49 isolates while 14 were excluded, due to their low average depth (< 15X) and/or low horizontal coverage. Identification and DST for first-line anti-TB drugs were performed at Muñiz Hospital, Cetrangolo, and other regional centers, while second-line DST and confirmation of XDR status were conducted at the TB National Reference Laboratory (NRL) at the National Institute of Infectious Diseases Dr. Carlos G. Malbrán (ANLIS). In all cases, patient interaction was exclusively managed by the physician, and inclusion in the project was based on the physician's recommendation and clinical history analysis. All samples were anonymized. Microbiological and molecular studies All isolates were grown on Löwenstein-Jensen slants and identified as M. tuberculosis through biochemical and molecular tests. DST was performed using the reference standard proportion method in the Löwenstein-Jensen medium and/or BACTEC MGIT 960 system (Becton Dickinson, MD) under international standards ( 30 ). A multiplex allele-specific PCR (MAS-PCR) was conducted on all isolates to detect mutations associated with INH and RIF resistance (codons katG315, inhA-15, rpoB450, 445, and 425) according to a modified protocol described elsewhere ( 31 ). Genotyping was performed by spoligotyping and MIRU-VNTR according to standard procedures ( 32 , 33 ), followed by a comparison with SITVITWEB ( 34 ) and MIRU-VNTRplus database ( 35 ). Genome sequencing For whole-genome sequencing (WGS), isolates were re-cultured on Löwenstein-Jensen slants. DNA was extracted following a standard protocol for mycobacteria ( 36 ). Genomic libraries were prepared using the Nextera® XT DNA Sample Preparation Kit (Illumina) according to the manufacturer’s instructions, with individual libraries indexed using the Nextera® XT Index Kit. Paired-end reads were generated for all isolates using the Illumina MiSeq platform at Unidad Operativa Centro Nacional de Genómica y Bioinformática, ANLIS. All sequencing reads were deposited in the NCBI Sequence Read Archive (SRA) under accession number PRJNA646920. Resistance Variants Calling Pipeline The quality of the sequencing reads for each experiment was assessed using FastQC version 0.11.5 ( 37 ), and bases affected by biases or low quality were trimmed with Trimmomatic (reads shorter than 36 bp and mean Q < 20 were filtered out) ( 37 , 38 ). Subsequently, the reads from each strain were aligned to the Mtb H37Rv reference genome (NCBI NC_000962.3) using BWA( 39 ), and the alignment was processed in BAM format with Samtools. Next, variant calling was performed using GATK( 40 ), and finally, the variants were annotated using SnpEff ( 41 ). To determine the genotypic resistances of each strain, the variants obtained in the previous step were crossed with a custom database that integrates data from variants present in TBProfiler( 42 ), 2023 WHO “Catalogue of mutations in Mycobacterium tuberculosis complex and their association with drug resistance - second edition”, KvarQ v0.12.2( 43 ), TBDream( 44 ), and CARD RGI 5.1.1( 45 ) with bibliographic data up to December 2023 ( https://github.com/florenciacastello/tb_resistance/resistanceTB_2024.csv ) Phylogenetic analysis Sequence assembly was performed by applying the variants identified in each sample to the reference genome using Samtools. Subsequently, low coverage areas and repetitive regions such as IS6110, PGRSs, CRISPRs, and VNTR were masked, as these regions often contain many sequencing/mapping errors. Next, multiple sequence alignment (MSA) of all samples was carried out using MAFFT with default parameters. Before conducting the phylogenetic analysis, the optimal evolutionary model, substitution rates, nucleotide frequencies, and other relevant parameters were determined using ModelTest. The phylogenetic analysis was performed by comparing the samples with representative samples of the main M. tuberculosis lineages and sublineages ( 46 ) downloaded from NCBI. Finally, a Maximum Likelihood (ML) tree was constructed using RaXML( 47 ), with parameters determined by jModelTest. Processing Pipelines The pipelines described above were implemented using the Python language,. The processing of isolates was divided into three scripts. All external programs are called through Docker, a platform for distributing and using programs transparently, which minimizes installation requirements: having Python and Docker installed. The code and usage instructions are available online in a GitHub repository ( https://github.com/florenciacastello/tb_resistance ). The processing of the sample group, which takes raw VCFs and generates a phylogeny, was not developed in an automated manner, as it is an exploratory process. This means several variant selection strategies were tested (with and without filtering repeated regions, using only SNPs or also short insertions and deletions, using a program to predict phylogeny parameters, or testing those used in the literature for Mtb in similar works). Results Whole-genome sequencing data from 49 clinical isolates were initially mapped against the Mtb H37Rv reference genome. This collection represents approximately 30% of all XDR available isolates in Argentina from 2006 to 2015. After applying coverage and sequencing depth filters, 16 samples were discarded, leaving us with a final total of 33 samples. Following quality filtering and the exclusion of single-nucleotide polymorphisms (SNPs) in problematic genomic regions, 292 high-confidence SNPs were identified. These variants distinguished the 33 isolates, with an average pairwise SNP distance of 10.9 (Supplementary Table 1). For a more detailed characterization of the samples, spacer oligonucleotide typing (spoligotyping) was performed. Our analysis indicated that 6 out of 33 samples (18.1%) lacked a specific spoligotype. We classified the samples into four main groups based on the spoligotyping profile. The Haarlem (H) group (21.2%, H2 n = 7), LAM group (27.2%, including LAM5 and LAM3, n = 9), and T group (27.2%, including T, T1 and Tuscany, T2, and T3, n = 9), collectively representing 75.6% of the samples. Among these groups, H2 was the most frequently identified spoligotype (n = 7) (Fig. 1 ). To contextualize our findings on a global scale, we constructed a phylogenetic tree using representative strains from each phylogenetic variant identified in our dataset ( 48 , 49 ). For this analysis, we included a diverse selection of global Mtb strains previously characterized by Sekizuka et al. ( 50 ), allowing for a comparison between our isolates and major phylogenetic lineages worldwide. The resulting tree confirmed the presence of distinct sublineages among our samples and suggested that the strains in our collection may not originate from a single phylogenetic lineage, potentially indicating multiple independent introductions or diversification events. These findings align with global trends, where M. tuberculosis lineages exhibit substantial geographic structuring and convergent evolution ( 51 , 52 ), underscoring the importance of detailed phylogenetic analysis in tracking transmission dynamics. Additionally, the phylogenetic tree further supports the assignment of isolates to specific sublineages, such as Haarlem, LAM, and T, consistent with prior spoligotyping results, reinforcing the robustness of our characterization (Fig. 2 ). Furthermore, spoligotyping analysis of the studied samples confirmed their assignment to Lineage 4 Euro-American, in agreement with previous genotyping studies conducted in Argentina ( 53 ). Notably, approximately 80% of Mtb isolates in Buenos Aires belong to this lineage, predominantly comprising the T, LAM, and Haarlem families. This distribution reflects the historical influence of Hispanic colonization and recent immigration waves from the Mediterranean and neighboring countries. Isolates from the TGS-TB project are labeled with the corresponding phylogenetic lineage followed by the number of representatives (in parenthesis) if multiple isolates belong to the same lineage. This labeling convention also applies to M and Ra strain isolates. Three annotation columns accompany the project isolates: " In Silico Lin" (lineage determined computationally), " In Vitro Spol" (experimentally determined spoligotype), and " In Silico Spol" (spoligotype predicted In Silico ). Missing values in the experimental spoligotype column indicate no laboratory experiment was performed, whereas missing values in the In Silico columns denote inconclusive results. Beyond lineage classification, understanding the genetic determinants of drug resistance is crucial for characterizing XDR Mtb isolates. Therefore, we analyzed resistance-associated variants, summarizing the most frequent mutations in Fig. 3 . Overall, there was a strong correlation between phenotypic drug resistance and predictions based on the presence or absence of known resistance mutations for the four first-line drugs (isoniazid, rifampicin, ethambutol, and streptomycin), three second-line injectables (amikacin, kanamycin, and capreomycin), and fluoroquinolones. Since all isolates were phenotypically characterized as XDR, resistance to isoniazid and rifampicin was expected. The identified mutations further support this classification, reinforcing the reliability of our genetic resistance profiling. For the four first-line drugs, the predominantly identified mutations were (Fig. 3 ): Isoniazid: katG Ser315Thr (75.7% of samples), followed by fabG1 -15 C > T (24.2%). Rifampicin: rpoB Ser450Leu (72.7%), followed by rpoB Asp435Val (21.2%). Ethambutol: Two predominant SNPs were detected in the embB gene: Gly406Ala (27.2%) and Met306Ile (42.4%). Pyrazinamide: pncA Gly10Pro (33,33%), followed by pncA Arg154Gly (25%). Regarding streptomycin, three predominant mutations were identified: a frameshift mutation at position 110 (24.2%) and a Leu16Arg substitution (39.4%) in the gid gene, and the 1401 A > G SNP in rrs (72.7%). Notably, since streptomycin shares its target with the injectable aminoglycosides studied (kanamycin, amikacin, and capreomycin), the rrs 1401 A > G mutation was also predominantly found in these drugs. For fluoroquinolones, four predominant SNPs were identified in the gyrA gene: Asp94Gly (21.2%), Ala90Val (15.2%) and Asp94Ala (12.1%), Asp94His (12.1%). It is important to note that, for most drugs, the total percentage exceeded 100%, as multiple variations were found in most samples. Figures 4 and 5 compare the groups defined by spoligotyping with the identified resistance profiles to explore the relationship between phylogenetic classification and drug resistance patterns. This analysis aimed to assess whether the clustering observed through spoligotyping correlates with distinct resistance signatures. The phylogenetic trees of the strains analyzed in this work (Figs. 4 and 5 ) confirm that all isolates belong to Lineage 4 (Euro-American), except for one Beijing isolate (Lineage 2). Five major groups were confirmed based on the spoligotyping profile: H, LAM3, LAM5, T, and T-Tuscany. Group H includes all isolates molecularly characterized as H2 or H3. Within the group, the same profile of resistance mutations is observed. All samples harbor the katG Ser315Thr mutation, which confers resistance to isoniazid (INH). For rifampicin, ethambutol, pyrazinamide, and streptomycin resistance, the majority of samples share specific mutations: rpoB Ser450Leu (rifampicin), embB Gly406Ala (ethambutol), pncA Gln10Pro (pyrazinamide) and rrs 1401A > G along with gid Val100fs (streptomycin). This pattern suggests a potential common phylogenetic origin for these resistance mutations. Notably, sample 20394 carries a double mutation at codon 435 of the rpoB gene, resulting in Asp435Gly, previously associated with rifampicin resistance by Napier G. et al. ( 5 ). All isolates in this group harbor the rrs 1401A > G mutation, associated with resistance to second-line injectable drugs ( 54 , 55 ). In contrast, fluoroquinolone resistance exhibits greater variability, with distinct mutations identified across different isolates, including gyrA Asp94Gly, gyrA Asp94His, gyrA Asp94Ala, gyrA Ala90Val, gyrB Ala504Val, and gyrB Arg446Cys ( 56 – 60 ). The LAM5 group includes all isolates identified as belonging to this lineage through experimental and In Silico spoligotyping. However, sample 11880 was classified as LAM5 based on the experimental spoligotyping, whereas In Silico spoligotyping assigned it to LAM3. Furthermore, its clustering with LAM3 isolates supports this classification; therefore, sample 11880 was excluded from the LAM5 group. All LAM5 group isolates share the same INH, RIF, PZA, and EMB resistance genotypes. Regarding second-line aminoglycosides, isolate 13429 is the only one lacking the rrs 1401A > G variant (STR). Additionally, isolates 13429 and 13431 are the only ones without a variant at codon 94 of gyrA gene for fluoroquinolones within the group. The phylogenetic tree is consistent with the hypothesis that mutation at gene gyrA codon 94 (Asp) may have arisen in a common ancestor of the LAM5 isolates in this group, while the rrs 1401A > G mutation appears to have been lost in isolate 13429, potentially as a result of a later evolutionary event. The T-Tuscany group consists of two isolates, 22372 and 20246, which were experimentally characterized as belonging to the T lineage. Regarding resistance variants, these isolates do not harbor katG mutations associated with INH resistance but instead carry resistance mechanisms through fabG1 promoter variants. Additionally, they share the rrs 1401A > G mutation for aminoglycoside resistance, embB Met306Ile for ethambutol resistance ( 61 ), and gyrA Asp94Gly for fluoroquinolone resistance. Notably, neither isolate carries pncA mutations associated with PZA resistance. In the global tree, Group LAM3 clusters with samples of sublineage 4.3.2, with isolate 25203 positioned near the outbreak of the Ra strain, which has been characterized as sublineage 4.3 (LAM3 108) and exhibits an MDR resistance profile( 16 ). All LAM3 isolates share the same resistance variants for INH and RIF. Isolates 11401 and 11880 also share resistance mechanisms for STR ( rpsL Lys88Arg) and fabG1 -inhA − 15C > T promoter variants, responsible for INH and ETH resistance. These two isolates share a KAN resistance variant in the eis − 12G > A promoter, differing from the rrs 1401A > G variant associated with KAN in most studied isolates. While no FLQ resistance variants were found, these isolates are known to be phenotypically resistant. Notably, despite being experimentally classified as LAM5, isolate 11880 exhibits an In Silico spoligotype and phylogenetic placement consistent with LAM3. The T1 group includes nine isolates, all characterized as T spoligotypes both In Silico and experimentally. The group can be further subdivided into two subgroups, both defined by the rpoB Ser450Leu variant associated with RIF resistance. The first subgroup comprises isolates 22468 and 10010, in which INH resistance is mediated by the katG Ser315Thr mutation alongside genotypic variants associated with EMB resistance. The second subgroup includes isolates 17817 and 20483, where INH resistance is mediated by fabG1 -15C > T while no EMB resistance mutations were detected. Adjacent to the T1 group in the phylogenetic tree, isolate 16561 is classified as T5. It is one of the two isolates lacking an INH resistance mutation while exhibiting phenotypic resistance. Additionally, it harbors the rpoB Gln432Pro variant, which is associated with rifampicin resistance. Other isolates exhibit ambiguous classifications. Further analysis is needed to determine the underlying causes. The phylogenetic analysis, together with resistance profiles, highlights the prevalence of mutations rpoB Ser450Leu and rpoB Gln432Leu, which are strongly associated with RIF resistance, as well as katG Ser315Thr, the most common mutation conferring INH resistance, followed by mutations in the fabG1 promoter. While katG Ser315Thr is strongly associated with high resistance levels, fabG1 promoter mutations are also found in Argentinean lineages but confer lower levels of resistance ( 62 ). Variants such as rrs 1401 A > G are also frequent, albeit to a lesser extent. Resistance mutations for EMB and PZA appear in specific phylogenetic groups, suggesting a shared evolutionary origin. In contrast, resistance mechanisms for FLQ and ETH seem to have emerged more recently and do not exhibit exclusive associations with particular phylogenetic branches. Multiple mutations associated with resistance to the same drug were identified, as detailed in Figs. 4 and 5 . However, some phenotypically resistant strains lacked known high-confidence resistance mutations reported in the literature (one for isoniazid, three for ethambutol, six for pyrazinamide, and two for streptomycin). Despite this, we identified previously undescribed mutations that might contribute to resistance to the antibiotics under study. For sample 16561, a novel conservative in-frame insertion was found at position 1440 of the katG gene (protein position 404), an insertion not yet reported as associated with an AMR in the latest TBprofiler and WHO AMR 2023 databases ( 63 ) adds an extra alanine codon between katG 's two peroxidase domains (positions 404–405) according to InterPro analysis (Pfam ID: PF00141, Prosite ID: IPR002016), suggesting further research potential. Consistent with this observation, insertions in the katG gene (Rv1908c) are frequently observed and contribute to isoniazid resistance [19–22]. In the pyrazinamide false-negative analysis, a thorough manual analysis was performed using IGV program on the remaining samples (15213, 10900, 11880, and 22468), focusing on the genes pncA , panD , rpsA , Rv1258c , Rv3236c , and their respective promoters to identify poorly characterized mutations or other artifacts that could explain their phenotypic resistance. In sample 22468, the complete absence of the pncA gene was observed. The complete absence of the pncA gene in sample 22468 is a significant finding, as this gene is essential for the pyrazinamide conversion into its active form, pyrazinoic acid. The total loss or deletion of pncA has been identified as a mechanism of pyrazinamide resistance in Mtb ( 64 , 65 ). For sample 15213, two SNPs of interest were identified: Rv3236c Ala370Thr and a synonymous variant in the rpsA gene (636A > C; Arg340Arg). The first mutation has consistently been reported alongside a secondary mutation ( 66 ), the latter is mentioned in the “Catalogue of Mutations in Mycobacterium tuberculosis Complex and their Association with Drug Resistance – Second Edition” ( 63 ) as a rare mutation for PZA with low PPV. For samples 10900 and 11880, no mutations or regions with low coverage were found that could account for their resistance. Further investigation is required. Identifying resistance-associated mutations in EMB-resistant organisms is one of the greatest challenges when diagnosing antibiotic resistance through variant analysis. It is believed that the resistance mechanism to this antibiotic in tuberculosis is not solely attributed to the embABC cassette but may result from a combination of different variants across multiple genes, making accurate detection challenging in some cases ( 63 , 67 – 71 ). In our samples, the three false negatives (11401, 11880, and 17817) shared the synonymous SNP embC Arg927Arg which has been widely reported for all isolates (resistant and sensitives) in the PolyTB database ( http://pathogenseq.lshtm.ac.uk/polytb ). Brossier et al. suggest that this SNP could have originated from a sequencing artifact in the Mtb reference strain H37Rv, as recorded in GenBank. (accession number AL123456.3) ( 72 ). On the other hand, sample 17817 presented multiple previously unreported variants, including four missenses ( embR Phe283Leu, embR Cys294Gly, embB Phe642Ser, embB Asn675Thr ), one disruptive inframe insertion ( embA 3347 C < CCG) and two synonymous variants ( embR Cys288Cys and the one described above). For samples 11401 and 11880, no variants were found in any reported gene most commonly associated with ethambutol resistance (seven genes were manually reviewed in IGV to verify the information: embABC , iniABC , and embR ). In addition, both samples show optimal vertical coverage (< 30X), and no insertion sequences (IS) were detected that could explain the phenotypic resistance. Further investigation will be essential to unravel these findings. Additionally, resistance-associated variants were detected for Delamanid, Linezolid, and other second-line drugs, even though these drugs were not administered at the time of the study. For Delamanid, we identified one high-confidence mutation (fbiC Ala855fs) and seven low-confidence mutations (three in fbiC -Ile406Val, V al410Gly, Val415Gly- and four in fbiA -Ala30Thr, Gln120Arg, Ile208Val, and one synonymous variant). Regarding Linezolid, we detected two low-confidence synonymous mutations in rplC and one high-confidence mutation ( rplC Cys154Arg). No resistance-associated mutations were found for Bedaquiline or Fosfomycin. In conclusion, these findings reveal a diverse landscape of resistance mutations among XDR M. tuberculosis isolates in Argentina, with notable lineage-specific and convergent mutations. Our findings offer a comprehensive view of both established and lesser-known mutations, enriching the understanding of resistance patterns and evolutionary pathways in these isolates. Discussion and conclusions The study of extensively drug-resistant (XDR) tuberculosis (TB) in Argentina provides critical insights into the evolution, genetic diversity, and complexity of antibiotic resistance in Mycobacterium tuberculosis ( Mtb ). Despite substantial progress in TB research and the development of drug resistance profiling techniques, XDR-TB remains a significant public health threat worldwide. In Argentina, where TB burden is moderate but concerning, especially with rising drug-resistant strains, our findings contribute to a better understanding of the genetic determinants of XDR-TB in the region and highlight the importance of genomic surveillance in guiding TB control strategies. This study characterized the genetic basis of antibiotic resistance to first- and second-line drugs used in tuberculosis treatment, analyzing clinical XDR Mycobacterium tuberculosis isolates from Argentina collected between 2006 and 2015. The resistance-associated variants identified in this study remained consistent with those previously reported for drug-resistant isolates in Argentina. For isoniazid, katG Ser315Thr and fabG1 -15C > T were the most frequent mutations, aligning with previous studies ( 15 , 28 , 73 ). For rifampicin resistance, rpoB Ser450Leu, a variant previously reported locally, remained prevalent, while Asp435Val, one of the most frequently identified mutations in other studies, was also commonly detected ( 28 , 53 ). For pyrazinamide (PZA), pncA mutations exhibited expected variability but were mostly contained within specific monophyletic branches, such as the H and LAM5 groups, suggesting phylogenetic constraints on PZA resistance evolution. Ethambutol resistance was primarily associated with the embB Gly406Ala and Met306Ile variants, consistent with local reports( 16 , 74 – 76 ), with the former being exclusive to the H group. For second-line aminoglycosides, the rrs 1401A > G mutation remained predominant, although it was not conserved throughout the phylogeny. Fluoroquinolone resistance-associated mutations in gyrA exhibited a more varied distribution in the tree compared to other antibiotics. However, the most frequent variant locations, Ala90 and Asp94, known to cause high MIC and found locally in FQL-resistant isolates, were present. Ethionamide resistance variants were found predominantly in the fabG1 promoter (also associated with INH resistance), with only one strain having a mutation in its target gene, inhA . The fabG1 -15C > T mutation seemed to be fixed in several groups but without a clear common origin. The occurrence of more than one resistance mutation for a given drug was not as uncommon as expected. However, mutations specific to a single strain were also highly prevalent. Resistance-associated mutations were not limited to these known markers. Certain isolates displayed unique mutations or combinations of mutations, which could suggest complex evolutionary mechanisms, such as convergent evolution or homoplasy, where resistance mutations emerge independently in separate lineages. This observation is crucial for understanding the adaptive landscape of Mtb , as different strains may develop resistance through distinct genetic pathways, possibly influenced by local epidemiological and selective pressures. We also identified less characterized mutations, such as novel insertions in pncA and katG , which warrant further investigation as they could contribute to pyrazinamide and isoniazid resistance, respectively. Some isolates exhibited phenotypic resistance despite lacking known resistance mutations, suggesting the presence of alternative resistance mechanisms ( 77 ), epigenetic factors influencing drug susceptibility ( 78 ), or technical limitations such as low sequencing coverage ( 78 , 79 ). These undetected cases underscore the importance of comprehensive genetic analysis and the need for updated resistance databases that include less commonly reported mutations, which could enhance diagnostic ( 79 ) accuracy for XDR-TB. We observed evidence of both lineage-specific resistance mutations and convergent evolution of resistance across different strains. The finding that XDR isolates were not derived from a single transmission event but rather multiple independent resistance acquisitions highlights the complexity of TB control efforts in Argentina. This suggests that strengthening infection control measures and targeted interventions are essential to prevent further spread. Future studies integrating genomic data with epidemiological and clinical data will be critical for designing more effective containment strategies. Finally, our work provides a detailed genomic characterization of XDR-TB isolates in Argentina, identifying both well-established and lesser-known resistance mutations. Our results highlight the complexity of resistance evolution in M. tuberculosis and underscore the importance of integrating WGS into routine TB surveillance and diagnosis in developing countries such as Argentina. Continued efforts to expand resistance mutation databases and improve molecular diagnostic tools are essential to combat the growing challenge of XDR-TB worldwide. Abbreviations A Amikacin AIDS Acquired Immunodeficiency Syndrome AMK Amikacin ANLIS Administración Nacional de Laboratorios e Institutos de Salud BACTEC MGIT Mycobacteria Growth Indicator Tube (BD system for culture and DST) BD Becton Dickinson BDQ Bedaquiline C Capreomycin CAP Capreomycin CARD RGI Comprehensive Antibiotic Resistance Database – Resistance Gene Identifier CONICET Consejo Nacional de Investigaciones Científicas y Técnicas CRISPR Clustered Regularly Interspaced Short Palindromic Repeats DNA Deoxyribonucleic Acid DST Drug Susceptibility Testing E Ethambutol EMB Ethambutol ETH Ethionamide F Fluoroquinolones FLQ Fluoroquinolones H Haarlem (a spoligotype family) HIV Human Immunodeficiency Virus I Isoniazid IGV Integrative Genomics Viewer INH Isoniazid IQR Interquartile Range IS Insertion Sequence KAN Kanamycin L LAM (Latin American-Mediterranean) lineage LAM Latin American-Mediterranean (a spoligotype family) LJ Löwenstein-Jensen medium LZD Linezolid M M strain of Mycobacterium tuberculosis MAS-PCR Multiplex Allele-Specific Polymerase Chain Reaction MAFFT Multiple Alignment using Fast Fourier Transform MDR Multidrug-Resistant MDR-TB Multidrug-Resistant Tuberculosis MIC Minimum Inhibitory Concentration MIRU-VNTR Mycobacterial Interspersed Repetitive Unit - Variable Number Tandem Repeat ML Maximum Likelihood Mtb Mycobacterium tuberculosis NCBI National Center for Biotechnology Information NRL National Reference Laboratory P Pyrazinamide PCR Polymerase Chain Reaction Pfam Protein Family database PZA Pyrazinamide RAxML Randomized Axelerated Maximum Likelihood R Rifampicin RIF Rifampicin rrs Ribosomal RNA gene commonly associated with aminoglycoside resistance S Streptomycin SNP Single-Nucleotide Polymorphism SRA Sequence Read Archive STR Streptomycin SITVITWEB International database for M. tuberculosis spoligotypes TB Tuberculosis TBProfiler A bioinformatics tool for predicting TB resistance from WGS data TGS-TB Total Genotyping Solution for Tuberculosis T T lineage (a spoligotype family) VNTR Variable Number Tandem Repeat VCF Variant Call Format WGS Whole-Genome Sequencing WHO World Health Organization XDR Extensively Drug-Resistant XDR-TB Extensively Drug-Resistant Tuberculosis Declarations Ethics approval and consent to participate This research has been approved by the INEI ANLIS research review board. Clinical Trial Clinical trial number: not applicable. Consent for publication Not applicable Availability of data and material Data is provided within the manuscript. The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request. Sequence data will be available in the European Nucleotide Archive (ENA) at the time of manuscript acceptance. Competing interests The authors declare that they have no competing interests. Funding Agencia Nacional de Promoción Científica y Tecnológica [ANPCyT, PICT START UP: PICT-2018-04663 to D.F.D.P.]. CONICET membership of the research career [D.F.D.P., M.M., A.T.], CONICET doctoral fellowship and support staff [M.C.P., F.S., F.A.C. and E.J.S.]. The funders had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication. Authors' contributions D.F.D.P., M.M., A.T., B.L., J.C. and N.S. conceived and designed the study. F.A.C. and D.F.D.P. wrote the main manuscript text. F.A.C. and E.J.S. prepared the figures with input from the other authors. J.C., J.Mo., T.P., N.S., R.P., B.L., M.M.M., and M.M.P. collected the samples and performed the wet lab experimental work. D.F.D.P., E.J.S., J.Me., L.G.G., M.C.P., F.S. and F.A.C. carried out the bioinformatic analyses. All authors reviewed and approved the final manuscript. Acknowledgements Not applicable. References World Health Organization. Global tuberculosis report 2023. World Health Organization; 2023. p. 75. Harding E. WHO global progress report on tuberculosis elimination. Lancet Respir Med. 2020;8(1):19. Duc PT, Nhat BS, Luyen LT. Establishing population pharmacokinetic model for pyrazinamide in pulmonary tuberculosis patients. 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Additional Declarations No competing interests reported. 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Palumbo","email":"","orcid":"","institution":"Instituto de Cálculo, UBA-CONICET","correspondingAuthor":false,"prefix":"","firstName":"Miranda","middleName":"C .","lastName":"Palumbo","suffix":""},{"id":455607855,"identity":"a6aee6b4-5892-4ff5-804a-767c1a529729","order_by":6,"name":"Federico Serral","email":"","orcid":"","institution":"Instituto de Cálculo, UBA-CONICET","correspondingAuthor":false,"prefix":"","firstName":"Federico","middleName":"","lastName":"Serral","suffix":""},{"id":455607856,"identity":"e6c3fdfc-2fd4-4d4f-9082-994da86fdf5a","order_by":7,"name":"Joaquín Messano","email":"","orcid":"","institution":"Instituto de Cálculo, UBA-CONICET","correspondingAuthor":false,"prefix":"","firstName":"Joaquín","middleName":"","lastName":"Messano","suffix":""},{"id":455607857,"identity":"f19415a9-2896-44a8-b3ae-8102ff796b81","order_by":8,"name":"L. 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J. Muñiz","correspondingAuthor":false,"prefix":"","firstName":"Mario","middleName":"","lastName":"Matteo","suffix":""},{"id":455607863,"identity":"6759b732-cbb2-4f44-a3a1-e434b1479d02","order_by":14,"name":"María M. Palomino","email":"","orcid":"","institution":"IQUIBICEN, CONICET","correspondingAuthor":false,"prefix":"","firstName":"María","middleName":"M.","lastName":"Palomino","suffix":""},{"id":455607864,"identity":"fdd995b7-54d7-400b-8ec9-850c6e0fa1ae","order_by":15,"name":"Marcelo Martí","email":"","orcid":"","institution":"IQUIBICEN, CONICET","correspondingAuthor":false,"prefix":"","firstName":"Marcelo","middleName":"","lastName":"Martí","suffix":""},{"id":455607865,"identity":"d89d3ce2-f5ba-4e2c-bf36-88b636d658b6","order_by":16,"name":"Darío Fernández Do Porto","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAwklEQVRIiWNgGAWjYDCCAyBkwywHZj8gXksaszGYnUCsFgaglsQGEE2UFr7jZx8e+JBgnT4/7PBDoC12croNBLRInkk3ODgjIT134+00A6CWZGOzAwS0GBxIYzjM++Nw7sbZCSAtBxK3EdRy/hnDYZ6Ew+mGs9M/EKnlRhpYS4K8dA6RtkjeeMYA8ovhBumcggMJBkT4he98GvMHYIjJy89O3/zhQ4WdHEEtCBeCVRoQqxwE5BtIUT0KRsEoGAUjCgAAscBMAGb0x/AAAAAASUVORK5CYII=","orcid":"","institution":"Instituto de Cálculo, UBA-CONICET","correspondingAuthor":true,"prefix":"","firstName":"Darío","middleName":"Fernández Do","lastName":"Porto","suffix":""}],"badges":[],"createdAt":"2025-04-15 15:53:13","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6456461/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6456461/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s12879-025-11913-3","type":"published","date":"2025-11-17T15:57:39+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":82624504,"identity":"7eba9718-de4c-4921-893d-ba228f0ab6a7","added_by":"auto","created_at":"2025-05-13 12:50:26","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":36419,"visible":true,"origin":"","legend":"\u003cp\u003eFrequency distribution of spoligotypes among XDR \u003cem\u003eM. tuberculosis\u003c/em\u003eisolates from Argentina (2006–2015).\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-6456461/v1/cbc9571f81ed4205469bcb66.png"},{"id":82623533,"identity":"63b16c39-7731-4e4c-84ca-f0e1ca4dbb7e","added_by":"auto","created_at":"2025-05-13 12:42:27","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":244471,"visible":true,"origin":"","legend":"\u003cp\u003eGlobal phylogenetic tree of XDR \u003cem\u003eMycobacterium tuberculosis\u003c/em\u003e isolates, including a subset of M strain and Ra strain samples, alongside isolates from the TGS-TB project \u003ca href=\"https://paperpile.com/c/SF6YXX/hEMG\"\u003e(50)\u003c/a\u003e.\u003c/p\u003e\n\u003cp\u003eIsolates from the TGS-TB project are labeled with the corresponding phylogenetic lineage followed by the number of representatives (in parenthesis) if multiple isolates belong to the same lineage. This labeling convention also applies to M and Ra strain isolates.\u003c/p\u003e\n\u003cp\u003eThree annotation columns accompany the project isolates: \"\u003cem\u003eIn Silico\u003c/em\u003e Lin\" (lineage determined computationally), \"\u003cem\u003eIn Vitro\u003c/em\u003e Spol\" (experimentally determined spoligotype), and \"\u003cem\u003eIn Silico\u003c/em\u003e Spol\" (spoligotype predicted \u003cem\u003eIn Silico\u003c/em\u003e). Missing values in the experimental spoligotype column indicate no laboratory experiment was performed, whereas missing values in the \u003cem\u003eIn Silico\u003c/em\u003e columns denote inconclusive results.\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-6456461/v1/d89fc64368ebc81093e8add9.png"},{"id":82624505,"identity":"20ef59e0-bbed-46ec-8263-90ceafc83710","added_by":"auto","created_at":"2025-05-13 12:50:27","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":127367,"visible":true,"origin":"","legend":"\u003cp\u003eBar chart displaying the distribution of resistance-associated variants across nine drugs. The X-axis lists the drugs (isoniazid [I], rifampicin [R], ethambutol [E], pyrazinamide [P], streptomycin [S], kanamycin [K], amikacin [A], capreomycin [C], and fluoroquinolones [F]) while the Y-axis represents the number of identified variants. Each bar corresponds to a specific drug and is segmented by color to represent individual variants. Labels are shown only for variants present in at least 10% of resistant isolates for each drug, highlighting the most prevalent mutations associated with resistance.\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-6456461/v1/c53c9c51a92f110b0572e5c4.png"},{"id":82623536,"identity":"0cb9c146-6137-47c9-83a8-4aa3d569d905","added_by":"auto","created_at":"2025-05-13 12:42:27","extension":"jpeg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":1391942,"visible":true,"origin":"","legend":"\u003cp\u003ePhylogenetic tree of XDR \u003cem\u003eMycobacterium tuberculosis\u003c/em\u003e isolates aligned with drug resistance profiles. Each branch of the tree corresponds to a sample listed in the adjacent table, which contains six columns: \"Sample\" (indicating the isolate identifier), \"Spoligotype\" (specifying the spoligotype classification), and four drug resistance columns (representing the phenotypic resistance profile for isoniazid (I), rifampicin (R), ethambutol (E), and streptomycin (S).\u003c/p\u003e","description":"","filename":"floatimage4.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-6456461/v1/8d36968c9b812cae61443b5f.jpeg"},{"id":82623537,"identity":"f6d6f097-fa36-45dc-b0c4-382d232ea364","added_by":"auto","created_at":"2025-05-13 12:42:27","extension":"jpeg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":1095387,"visible":true,"origin":"","legend":"\u003cp\u003ePhylogenetic tree of XDR \u003cem\u003eMycobacterium tuberculosis\u003c/em\u003eisolates aligned with drug resistance profiles. Each branch of the tree corresponds to a sample listed in the adjacent table, which contains six columns: \"Sample\" (indicating the isolate identifier), \"Spoligotype\" (specifying the spoligotype classification), and four drug resistance columns (representing the phenotypic resistance profile for kanamycin (K), amikacin (A), capreomycin (C), and fluoroquinolones (F)).\u003c/p\u003e","description":"","filename":"floatimage5.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-6456461/v1/368cc69fb51c0ab6457fa48c.jpeg"},{"id":96650119,"identity":"77b6a022-dfa0-49f2-8603-e4dd44243641","added_by":"auto","created_at":"2025-11-24 16:08:14","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3576508,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6456461/v1/bc26f794-707d-47bd-a032-d5bd6724cd12.pdf"},{"id":82623531,"identity":"2524d386-b4f5-4fc4-945f-0724be7a2f70","added_by":"auto","created_at":"2025-05-13 12:42:26","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":12379,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementarymaterial.docx","url":"https://assets-eu.researchsquare.com/files/rs-6456461/v1/c371b1af9dc9f0c510d373ca.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Genomic Characterization of XDR Mycobacterium tuberculosis Isolates in Argentina (2006-2015)","fulltext":[{"header":"Background","content":"\u003cp\u003eTuberculosis (TB) remains a significant global health challenge, placing a substantial burden on healthcare systems and communities worldwide (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e). Caused by \u003cem\u003eMycobacterium tuberculosis\u003c/em\u003e (\u003cem\u003eMtb\u003c/em\u003e), TB affects millions of individuals each year, leading to considerable morbidity and mortality (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e). Despite concerted efforts to control the disease, TB persists, exacerbated by factors such as drug resistance, co-infections with HIV/AIDS, and socioeconomic disparities (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eResistance to TB treatment is categorized based on first-line and second-line drugs. First-line drugs, including isoniazid, rifampicin, ethambutol, and pyrazinamide, form the backbone of standard TB treatment regimens due to their high efficacy and relatively low toxicity (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e). When resistance to these key drugs develops, second-line drugs\u0026mdash;such as fluoroquinolones (e.g. levofloxacin, moxifloxacin) and injectable agents (e.g. amikacin, capreomycin, kanamycin, and streptomycin)\u0026mdash;have historically been used, despite being associated with higher toxicity and reduced efficacy (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e). It is important to note that, according to the latest WHO guidelines, kanamycin and capreomycin are no longer recommended, and streptomycin is only used in cases of hepatotoxicity (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e). However, during the period covered by this study, these drugs were still part of the recommended treatment regimens for multidrug-resistant TB.\u003c/p\u003e \u003cp\u003eStreptomycin was historically part of the first-line TB treatment regimen as the first antibiotic discovered to be effective against \u003cem\u003eMtb\u003c/em\u003e (\u003cspan additionalcitationids=\"CR10\" citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e). However, due to increasing resistance and more effective oral alternatives, the World Health Organization (WHO) has reclassified streptomycin as a second-line drug. It is now primarily used in cases of drug-resistant TB when other injectable agents are unavailable or contraindicated (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eMultidrug-resistant tuberculosis (MDR-TB) is defined as TB caused by \u003cem\u003eMycobacterium tuberculosis\u003c/em\u003e strains resistant to at least isoniazid and rifampicin, the two most potent first-line drugs (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e). Extensively drug-resistant tuberculosis (XDR-TB) was historically defined as tuberculosis with additional resistance to fluoroquinolones and at least one second-line injectable drug (amikacin, capreomycin, or kanamycin). However, this definition has been updated in recent WHO guidelines to reflect changes in second-line treatment recommendations. The current definition classifies XDR-TB as a form of MDR-TB that is resistant to any fluoroquinolone and at least one of the Group A drugs, which currently include bedaquiline (BDQ), and linezolid (LZD). The definition used in this study reflects the classification that applied to the period covered by the analysis. The emergence and spread of MDR-TB and XDR-TB pose significant challenges to TB control programs worldwide, highlighting the need for continuous surveillance and the development of effective treatment strategies (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan additionalcitationids=\"CR15\" citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn the 1990s, Argentina was identified by the WHO as a hotspot for multidrug-resistant tuberculosis (TB MDR). During this period, hospital outbreaks of MDR-TB associated with acquired immunodeficiency syndrome (AIDS) were documented in the country. Initially emerging in the capital city, the outbreak spread to nearby areas, reaching significant proportions. With over 800 diagnosed cases between 1992 and 2004, this outbreak met the criteria for an epidemic. In the early 2000s, an increase in MDR-TB cases was observed among patients who were neither HIV-positive nor had a history of prior tuberculosis treatment (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e). Among the MDR strains identified in Argentina, strain M (which originated in Buenos Aires and surrounding districts) and strain Ra (Rosario, Argentina) have been the most predominant (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e), contributing significantly to the persistence and transmission of MDR-TB in the country. In 2002, strain M was isolated from the country's first two patients diagnosed with XDR-TB.\u003c/p\u003e \u003cp\u003eThe evolution of \u003cem\u003eMtb\u003c/em\u003e is primarily influenced by TB control strategies, alongside socio-economic, environmental, and human migration patterns, all of which add complexity to efforts to combat the disease effectively. Notably, the pathogen's biology significantly impacts the global dissemination of the disease. Molecular studies have revealed a remarkable intra-species genetic diversity within \u003cem\u003eMtb\u003c/em\u003e, enabling its classification into nine major lineages, each displaying distinct affinities for specific geographic regions and human ethnic groups. These lineages are categorized as Indo-Oceanic (Lineage 1), East Asian (Lineage 2, including the Beijing sublineage), East African-Indian (Lineage 3), Euro-American (Lineage 4), West African (Lineage 5, \u003cem\u003eM. africanum\u003c/em\u003e I), and West African (Lineage 6, \u003cem\u003eM. africanum\u003c/em\u003e II). Recent phylogenomic analyses have identified additional lineages with more restricted distributions, including Lineage 7, found in the Horn of Africa; Lineage 8, recently described in Central Africa; and Lineage 9, a newly discovered lineage primarily located in East Africa (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e). Lineages 1, 5, and 6 are considered \"ancient,\" while lineages 2, 3, and 4 are classified as \"modern\" based on the presence or absence of the TbD1 genomic region, which is absent in modern lineages (\u003cspan additionalcitationids=\"CR22\" citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e). Currently, the predominant \u003cem\u003eMtb\u003c/em\u003e strains circulating in the Americas were introduced by Europeans during colonization, with the Euro-American lineage (Lineage 4) being the most prevalent.\u003c/p\u003e \u003cp\u003eSince different \u003cem\u003eMtb\u003c/em\u003e lineages dominate various regions worldwide, drug resistance acquisition may be influenced by the strains' pre-existing genetic background. The heterogeneity observed worldwide can be explained by the variability of these mutations (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e). In other words, the pre-existing genetic profiles of certain \u003cem\u003eMtb\u003c/em\u003e strains may be preferentially associated with specific resistance-causing mutations, and the effect of these associations could modulate the biological fitness of the strains (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe advent of advanced sequencing technologies, coupled with bioinformatics tools, has revolutionized our understanding of TB pathogenesis, drug resistance, and transmission patterns. High-throughput sequencing enables comprehensive genomic analysis of \u003cem\u003eMtb\u003c/em\u003e isolates, providing unprecedented insights into the molecular basis of drug resistance and virulence. By leveraging these technologies, researchers can gain insights into the intricate interplay between genetic determinants, host immunity, and environmental factors that shape TB epidemiology (\u003cspan additionalcitationids=\"CR26\" citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eUnderstanding the molecular basis of antibiotic resistance in \u003cem\u003eMtb\u003c/em\u003e is crucial for developing effective TB control strategies. Therefore, this work aimed to analyze the genetic basis of antibiotic resistance mechanisms in XDR clinical isolates obtained in Argentina between 2006 and 2015. By examining resistance mutations associated with different antibiotics\u0026mdash;such as streptomycin, isoniazid, rifampicin, ethambutol, kanamycin, amikacin, capreomycin, pyrazinamide, ethionamide, and fluoroquinolones\u0026mdash;we aimed to elucidate the molecular mechanisms driving drug resistance in \u003cem\u003eM. tuberculosis\u003c/em\u003e strains circulating in Argentina (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e). The findings from this study contribute to a better understanding of the genetic diversity and resistance patterns in \u003cem\u003eMtb\u003c/em\u003e, ultimately supporting efforts to improve diagnostic and therapeutic strategies for TB management.\u003c/p\u003e "},{"header":"Methods","content":"\u003cp\u003eClinical isolates and strain selection\u003c/p\u003e \u003cp\u003eCase selection was conducted with healthcare professionals from the \u0026ldquo;Servicio de Micobacterias at the Instituto Nacional de Enfermedades Infecciosas - Dr. Carlos G. Malbr\u0026aacute;n\u0026rdquo;. Cultures were initiated for 120 isolates, corresponding to all reported cases of patients with XDR \u003cem\u003eMtb\u003c/em\u003e between 2006 and 2015. Sufficient DNA was successfully extracted for sequencing from 49 isolates while 14 were excluded, due to their low average depth (\u0026lt;\u0026thinsp;15X) and/or low horizontal coverage. Identification and DST for first-line anti-TB drugs were performed at Mu\u0026ntilde;iz Hospital, Cetrangolo, and other regional centers, while second-line DST and confirmation of XDR status were conducted at the TB National Reference Laboratory (NRL) at the National Institute of Infectious Diseases Dr. Carlos G. Malbr\u0026aacute;n (ANLIS). In all cases, patient interaction was exclusively managed by the physician, and inclusion in the project was based on the physician's recommendation and clinical history analysis. All samples were anonymized.\u003c/p\u003e \u003cp\u003eMicrobiological and molecular studies\u003c/p\u003e \u003cp\u003eAll isolates were grown on L\u0026ouml;wenstein-Jensen slants and identified as \u003cem\u003eM. tuberculosis\u003c/em\u003e through biochemical and molecular tests. DST was performed using the reference standard proportion method in the L\u0026ouml;wenstein-Jensen medium and/or BACTEC MGIT 960 system (Becton Dickinson, MD) under international standards (\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e). A multiplex allele-specific PCR (MAS-PCR) was conducted on all isolates to detect mutations associated with INH and RIF resistance (codons katG315, inhA-15, rpoB450, 445, and 425) according to a modified protocol described elsewhere (\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eGenotyping was performed by spoligotyping and MIRU-VNTR according to standard procedures (\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e), followed by a comparison with SITVITWEB (\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e) and MIRU-VNTRplus database (\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eGenome sequencing\u003c/p\u003e \u003cp\u003eFor whole-genome sequencing (WGS), isolates were re-cultured on L\u0026ouml;wenstein-Jensen slants. DNA was extracted following a standard protocol for mycobacteria (\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e). Genomic libraries were prepared using the Nextera\u0026reg; XT DNA Sample Preparation Kit (Illumina) according to the manufacturer\u0026rsquo;s instructions, with individual libraries indexed using the Nextera\u0026reg; XT Index Kit. Paired-end reads were generated for all isolates using the Illumina MiSeq platform at Unidad Operativa Centro Nacional de Gen\u0026oacute;mica y Bioinform\u0026aacute;tica, ANLIS. All sequencing reads were deposited in the NCBI Sequence Read Archive (SRA) under accession number PRJNA646920.\u003c/p\u003e \u003cp\u003eResistance Variants Calling Pipeline\u003c/p\u003e \u003cp\u003eThe quality of the sequencing reads for each experiment was assessed using FastQC version 0.11.5 (\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e), and bases affected by biases or low quality were trimmed with Trimmomatic (reads shorter than 36 bp and mean Q\u0026thinsp;\u0026lt;\u0026thinsp;20 were filtered out) (\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e). Subsequently, the reads from each strain were aligned to the \u003cem\u003eMtb\u003c/em\u003e H37Rv reference genome (NCBI NC_000962.3) using BWA(\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e), and the alignment was processed in BAM format with Samtools. Next, variant calling was performed using GATK(\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e), and finally, the variants were annotated using SnpEff (\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eTo determine the genotypic resistances of each strain, the variants obtained in the previous step were crossed with a custom database that integrates data from variants present in TBProfiler(\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e), 2023 WHO \u0026ldquo;Catalogue of mutations in \u003cem\u003eMycobacterium tuberculosis\u003c/em\u003e complex and their association with drug resistance - second edition\u0026rdquo;, KvarQ v0.12.2(\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e), TBDream(\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e), and CARD RGI 5.1.1(\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e) with bibliographic data up to December 2023 (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://github.com/florenciacastello/tb_resistance/resistanceTB_2024.csv\u003c/span\u003e\u003cspan address=\"https://github.com/florenciacastello/tb_resistance/resistanceTB_2024.csv\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e)\u003c/span\u003e\u003c/p\u003e \u003cp\u003ePhylogenetic analysis\u003c/p\u003e \u003cp\u003eSequence assembly was performed by applying the variants identified in each sample to the reference genome using Samtools. Subsequently, low coverage areas and repetitive regions such as IS6110, PGRSs, CRISPRs, and VNTR were masked, as these regions often contain many sequencing/mapping errors. Next, multiple sequence alignment (MSA) of all samples was carried out using MAFFT with default parameters. Before conducting the phylogenetic analysis, the optimal evolutionary model, substitution rates, nucleotide frequencies, and other relevant parameters were determined using ModelTest. The phylogenetic analysis was performed by comparing the samples with representative samples of the main \u003cem\u003eM. tuberculosis\u003c/em\u003e lineages and sublineages (\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e) downloaded from NCBI. Finally, a Maximum Likelihood (ML) tree was constructed using RaXML(\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e), with parameters determined by jModelTest.\u003c/p\u003e \u003cp\u003eProcessing Pipelines\u003c/p\u003e \u003cp\u003eThe pipelines described above were implemented using the Python language,. The processing of isolates was divided into three scripts. All external programs are called through Docker, a platform for distributing and using programs transparently, which minimizes installation requirements: having Python and Docker installed. The code and usage instructions are available online in a GitHub repository (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://github.com/florenciacastello/tb_resistance\u003c/span\u003e\u003cspan address=\"https://github.com/florenciacastello/tb_resistance\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe processing of the sample group, which takes raw VCFs and generates a phylogeny, was not developed in an automated manner, as it is an exploratory process. This means several variant selection strategies were tested (with and without filtering repeated regions, using only SNPs or also short insertions and deletions, using a program to predict phylogeny parameters, or testing those used in the literature for \u003cem\u003eMtb\u003c/em\u003e in similar works).\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eWhole-genome sequencing data from 49 clinical isolates were initially mapped against the \u003cem\u003eMtb\u003c/em\u003e H37Rv reference genome. This collection represents approximately 30% of all XDR available isolates in Argentina from 2006 to 2015. After applying coverage and sequencing depth filters, 16 samples were discarded, leaving us with a final total of 33 samples. Following quality filtering and the exclusion of single-nucleotide polymorphisms (SNPs) in problematic genomic regions, 292 high-confidence SNPs were identified. These variants distinguished the 33 isolates, with an average pairwise SNP distance of 10.9 (Supplementary Table\u0026nbsp;1).\u003c/p\u003e \u003cp\u003eFor a more detailed characterization of the samples, spacer oligonucleotide typing (spoligotyping) was performed. Our analysis indicated that 6 out of 33 samples (18.1%) lacked a specific spoligotype. We classified the samples into four main groups based on the spoligotyping profile. The Haarlem (H) group (21.2%, H2 n\u0026thinsp;=\u0026thinsp;7), LAM group (27.2%, including LAM5 and LAM3, n\u0026thinsp;=\u0026thinsp;9), and T group (27.2%, including T, T1 and Tuscany, T2, and T3, n\u0026thinsp;=\u0026thinsp;9), collectively representing 75.6% of the samples. Among these groups, H2 was the most frequently identified spoligotype (n\u0026thinsp;=\u0026thinsp;7) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eTo contextualize our findings on a global scale, we constructed a phylogenetic tree using representative strains from each phylogenetic variant identified in our dataset (\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e, \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e). For this analysis, we included a diverse selection of global \u003cem\u003eMtb\u003c/em\u003e strains previously characterized by Sekizuka et al. (\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e), allowing for a comparison between our isolates and major phylogenetic lineages worldwide. The resulting tree confirmed the presence of distinct sublineages among our samples and suggested that the strains in our collection may not originate from a single phylogenetic lineage, potentially indicating multiple independent introductions or diversification events. These findings align with global trends, where \u003cem\u003eM. tuberculosis\u003c/em\u003e lineages exhibit substantial geographic structuring and convergent evolution (\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e, \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e), underscoring the importance of detailed phylogenetic analysis in tracking transmission dynamics. Additionally, the phylogenetic tree further supports the assignment of isolates to specific sublineages, such as Haarlem, LAM, and T, consistent with prior spoligotyping results, reinforcing the robustness of our characterization (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eFurthermore, spoligotyping analysis of the studied samples confirmed their assignment to Lineage 4 Euro-American, in agreement with previous genotyping studies conducted in Argentina (\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e). Notably, approximately 80% of \u003cem\u003eMtb\u003c/em\u003e isolates in Buenos Aires belong to this lineage, predominantly comprising the T, LAM, and Haarlem families. This distribution reflects the historical influence of Hispanic colonization and recent immigration waves from the Mediterranean and neighboring countries.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eIsolates from the TGS-TB project are labeled with the corresponding phylogenetic lineage followed by the number of representatives (in parenthesis) if multiple isolates belong to the same lineage. This labeling convention also applies to M and Ra strain isolates.\u003c/p\u003e \u003cp\u003eThree annotation columns accompany the project isolates: \"\u003cem\u003eIn Silico\u003c/em\u003e Lin\" (lineage determined computationally), \"\u003cem\u003eIn Vitro\u003c/em\u003e Spol\" (experimentally determined spoligotype), and \"\u003cem\u003eIn Silico\u003c/em\u003e Spol\" (spoligotype predicted \u003cem\u003eIn Silico\u003c/em\u003e). Missing values in the experimental spoligotype column indicate no laboratory experiment was performed, whereas missing values in the \u003cem\u003eIn Silico\u003c/em\u003e columns denote inconclusive results.\u003c/p\u003e \u003cp\u003eBeyond lineage classification, understanding the genetic determinants of drug resistance is crucial for characterizing XDR \u003cem\u003eMtb\u003c/em\u003e isolates. Therefore, we analyzed resistance-associated variants, summarizing the most frequent mutations in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. Overall, there was a strong correlation between phenotypic drug resistance and predictions based on the presence or absence of known resistance mutations for the four first-line drugs (isoniazid, rifampicin, ethambutol, and streptomycin), three second-line injectables (amikacin, kanamycin, and capreomycin), and fluoroquinolones. Since all isolates were phenotypically characterized as XDR, resistance to isoniazid and rifampicin was expected. The identified mutations further support this classification, reinforcing the reliability of our genetic resistance profiling.\u003c/p\u003e \u003cp\u003eFor the four first-line drugs, the predominantly identified mutations were (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e):\u003c/p\u003e \u003cp\u003eIsoniazid: \u003cem\u003ekatG\u003c/em\u003e Ser315Thr (75.7% of samples), followed by \u003cem\u003efabG1\u003c/em\u003e -15 C\u0026thinsp;\u0026gt;\u0026thinsp;T (24.2%).\u003c/p\u003e \u003cp\u003eRifampicin: \u003cem\u003erpoB\u003c/em\u003e Ser450Leu (72.7%), followed by \u003cem\u003erpoB\u003c/em\u003e Asp435Val (21.2%).\u003c/p\u003e \u003cp\u003eEthambutol: Two predominant SNPs were detected in the \u003cem\u003eembB\u003c/em\u003e gene: Gly406Ala (27.2%) and Met306Ile (42.4%).\u003c/p\u003e \u003cp\u003ePyrazinamide: \u003cem\u003epncA\u003c/em\u003e Gly10Pro (33,33%), followed by \u003cem\u003epncA\u003c/em\u003e Arg154Gly (25%).\u003c/p\u003e \u003cp\u003eRegarding streptomycin, three predominant mutations were identified: a frameshift mutation at position 110 (24.2%) and a Leu16Arg substitution (39.4%) in the \u003cem\u003egid\u003c/em\u003e gene, and the 1401 A\u0026thinsp;\u0026gt;\u0026thinsp;G SNP in \u003cem\u003errs\u003c/em\u003e (72.7%). Notably, since streptomycin shares its target with the injectable aminoglycosides studied (kanamycin, amikacin, and capreomycin), the \u003cem\u003errs\u003c/em\u003e 1401 A\u0026thinsp;\u0026gt;\u0026thinsp;G mutation was also predominantly found in these drugs. For fluoroquinolones, four predominant SNPs were identified in the \u003cem\u003egyrA\u003c/em\u003e gene: Asp94Gly (21.2%), Ala90Val (15.2%) and Asp94Ala (12.1%), Asp94His (12.1%). It is important to note that, for most drugs, the total percentage exceeded 100%, as multiple variations were found in most samples.\u003c/p\u003e \u003cp\u003eFigures 4 and \u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e5\u003c/span\u003e compare the groups defined by spoligotyping with the identified resistance profiles to explore the relationship between phylogenetic classification and drug resistance patterns. This analysis aimed to assess whether the clustering observed through spoligotyping correlates with distinct resistance signatures. The phylogenetic trees of the strains analyzed in this work (Figs.\u0026nbsp;4 and \u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e5\u003c/span\u003e) confirm that all isolates belong to Lineage 4 (Euro-American), except for one Beijing isolate (Lineage 2). Five major groups were confirmed based on the spoligotyping profile: H, LAM3, LAM5, T, and T-Tuscany.\u003c/p\u003e \u003cp\u003eGroup H includes all isolates molecularly characterized as H2 or H3. Within the group, the same profile of resistance mutations is observed. All samples harbor the \u003cem\u003ekatG\u003c/em\u003e Ser315Thr mutation, which confers resistance to isoniazid (INH). For rifampicin, ethambutol, pyrazinamide, and streptomycin resistance, the majority of samples share specific mutations: \u003cem\u003erpoB\u003c/em\u003e Ser450Leu (rifampicin), \u003cem\u003eembB\u003c/em\u003e Gly406Ala (ethambutol), \u003cem\u003epncA\u003c/em\u003e Gln10Pro (pyrazinamide) and \u003cem\u003errs\u003c/em\u003e 1401A\u0026thinsp;\u0026gt;\u0026thinsp;G along with \u003cem\u003egid\u003c/em\u003e Val100fs (streptomycin). This pattern suggests a potential common phylogenetic origin for these resistance mutations. Notably, sample 20394 carries a double mutation at codon 435 of the \u003cem\u003erpoB\u003c/em\u003e gene, resulting in Asp435Gly, previously associated with rifampicin resistance by Napier G. et al. (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e). All isolates in this group harbor the \u003cem\u003errs\u003c/em\u003e 1401A\u0026thinsp;\u0026gt;\u0026thinsp;G mutation, associated with resistance to second-line injectable drugs (\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e, \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e). In contrast, fluoroquinolone resistance exhibits greater variability, with distinct mutations identified across different isolates, including \u003cem\u003egyrA\u003c/em\u003e Asp94Gly, \u003cem\u003egyrA\u003c/em\u003e Asp94His, \u003cem\u003egyrA\u003c/em\u003e Asp94Ala, \u003cem\u003egyrA\u003c/em\u003e Ala90Val, \u003cem\u003egyrB\u003c/em\u003e Ala504Val, and \u003cem\u003egyrB\u003c/em\u003e Arg446Cys (\u003cspan additionalcitationids=\"CR57 CR58 CR59\" citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe LAM5 group includes all isolates identified as belonging to this lineage through experimental and \u003cem\u003eIn Silico\u003c/em\u003e spoligotyping. However, sample 11880 was classified as LAM5 based on the experimental spoligotyping, whereas \u003cem\u003eIn Silico\u003c/em\u003e spoligotyping assigned it to LAM3. Furthermore, its clustering with LAM3 isolates supports this classification; therefore, sample 11880 was excluded from the LAM5 group.\u003c/p\u003e \u003cp\u003eAll LAM5 group isolates share the same INH, RIF, PZA, and EMB resistance genotypes. Regarding second-line aminoglycosides, isolate 13429 is the only one lacking the \u003cem\u003errs\u003c/em\u003e 1401A\u0026thinsp;\u0026gt;\u0026thinsp;G variant (STR). Additionally, isolates 13429 and 13431 are the only ones without a variant at codon 94 of \u003cem\u003egyrA\u003c/em\u003e gene for fluoroquinolones within the group. The phylogenetic tree is consistent with the hypothesis that mutation at gene \u003cem\u003egyrA\u003c/em\u003e codon 94 (Asp) may have arisen in a common ancestor of the LAM5 isolates in this group, while the \u003cem\u003errs\u003c/em\u003e 1401A\u0026thinsp;\u0026gt;\u0026thinsp;G mutation appears to have been lost in isolate 13429, potentially as a result of a later evolutionary event.\u003c/p\u003e \u003cp\u003eThe T-Tuscany group consists of two isolates, 22372 and 20246, which were experimentally characterized as belonging to the T lineage. Regarding resistance variants, these isolates do not harbor \u003cem\u003ekatG\u003c/em\u003e mutations associated with INH resistance but instead carry resistance mechanisms through \u003cem\u003efabG1\u003c/em\u003e promoter variants. Additionally, they share the \u003cem\u003errs\u003c/em\u003e 1401A\u0026thinsp;\u0026gt;\u0026thinsp;G mutation for aminoglycoside resistance, \u003cem\u003eembB\u003c/em\u003e Met306Ile for ethambutol resistance (\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e), and \u003cem\u003egyrA\u003c/em\u003e Asp94Gly for fluoroquinolone resistance. Notably, neither isolate carries \u003cem\u003epncA\u003c/em\u003e mutations associated with PZA resistance.\u003c/p\u003e \u003cp\u003eIn the global tree, Group LAM3 clusters with samples of sublineage 4.3.2, with isolate 25203 positioned near the outbreak of the Ra strain, which has been characterized as sublineage 4.3 (LAM3 108) and exhibits an MDR resistance profile(\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e). All LAM3 isolates share the same resistance variants for INH and RIF. Isolates 11401 and 11880 also share resistance mechanisms for STR (\u003cem\u003erpsL\u003c/em\u003e Lys88Arg) and \u003cem\u003efabG1\u003c/em\u003e-inhA \u0026minus;\u0026thinsp;15C\u0026thinsp;\u0026gt;\u0026thinsp;T promoter variants, responsible for INH and ETH resistance. These two isolates share a KAN resistance variant in the \u003cem\u003eeis\u003c/em\u003e \u0026minus;\u0026thinsp;12G\u0026thinsp;\u0026gt;\u0026thinsp;A promoter, differing from the \u003cem\u003errs\u003c/em\u003e 1401A\u0026thinsp;\u0026gt;\u0026thinsp;G variant associated with KAN in most studied isolates. While no FLQ resistance variants were found, these isolates are known to be phenotypically resistant. Notably, despite being experimentally classified as LAM5, isolate 11880 exhibits an \u003cem\u003eIn Silico\u003c/em\u003e spoligotype and phylogenetic placement consistent with LAM3.\u003c/p\u003e \u003cp\u003eThe T1 group includes nine isolates, all characterized as T spoligotypes both \u003cem\u003eIn Silico\u003c/em\u003e and experimentally. The group can be further subdivided into two subgroups, both defined by the \u003cem\u003erpoB\u003c/em\u003e Ser450Leu variant associated with RIF resistance. The first subgroup comprises isolates 22468 and 10010, in which INH resistance is mediated by the \u003cem\u003ekatG\u003c/em\u003e Ser315Thr mutation alongside genotypic variants associated with EMB resistance. The second subgroup includes isolates 17817 and 20483, where INH resistance is mediated by \u003cem\u003efabG1\u003c/em\u003e -15C\u0026thinsp;\u0026gt;\u0026thinsp;T while no EMB resistance mutations were detected.\u003c/p\u003e \u003cp\u003eAdjacent to the T1 group in the phylogenetic tree, isolate 16561 is classified as T5. It is one of the two isolates lacking an INH resistance mutation while exhibiting phenotypic resistance. Additionally, it harbors the \u003cem\u003erpoB\u003c/em\u003e Gln432Pro variant, which is associated with rifampicin resistance. Other isolates exhibit ambiguous classifications. Further analysis is needed to determine the underlying causes.\u003c/p\u003e \u003cp\u003eThe phylogenetic analysis, together with resistance profiles, highlights the prevalence of mutations \u003cem\u003erpoB\u003c/em\u003e Ser450Leu and \u003cem\u003erpoB\u003c/em\u003e Gln432Leu, which are strongly associated with RIF resistance, as well as \u003cem\u003ekatG\u003c/em\u003e Ser315Thr, the most common mutation conferring INH resistance, followed by mutations in the \u003cem\u003efabG1\u003c/em\u003e promoter. While \u003cem\u003ekatG\u003c/em\u003e Ser315Thr is strongly associated with high resistance levels, \u003cem\u003efabG1\u003c/em\u003e promoter mutations are also found in Argentinean lineages but confer lower levels of resistance (\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e). Variants such as \u003cem\u003errs\u003c/em\u003e 1401 A\u0026thinsp;\u0026gt;\u0026thinsp;G are also frequent, albeit to a lesser extent. Resistance mutations for EMB and PZA appear in specific phylogenetic groups, suggesting a shared evolutionary origin. In contrast, resistance mechanisms for FLQ and ETH seem to have emerged more recently and do not exhibit exclusive associations with particular phylogenetic branches.\u003c/p\u003e \u003cp\u003eMultiple mutations associated with resistance to the same drug were identified, as detailed in Figs.\u0026nbsp;4 and \u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e5\u003c/span\u003e. However, some phenotypically resistant strains lacked known high-confidence resistance mutations reported in the literature (one for isoniazid, three for ethambutol, six for pyrazinamide, and two for streptomycin). Despite this, we identified previously undescribed mutations that might contribute to resistance to the antibiotics under study. For sample 16561, a novel conservative in-frame insertion was found at position 1440 of the \u003cem\u003ekatG\u003c/em\u003e gene (protein position 404), an insertion not yet reported as associated with an AMR in the latest TBprofiler and WHO AMR 2023 databases (\u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e) adds an extra alanine codon between \u003cem\u003ekatG\u003c/em\u003e's two peroxidase domains (positions 404\u0026ndash;405) according to InterPro analysis (Pfam ID: PF00141, Prosite ID: IPR002016), suggesting further research potential. Consistent with this observation, insertions in the \u003cem\u003ekatG\u003c/em\u003e gene (Rv1908c) are frequently observed and contribute to isoniazid resistance [19\u0026ndash;22].\u003c/p\u003e \u003cp\u003eIn the pyrazinamide false-negative analysis, a thorough manual analysis was performed using IGV program on the remaining samples (15213, 10900, 11880, and 22468), focusing on the genes \u003cem\u003epncA\u003c/em\u003e, \u003cem\u003epanD\u003c/em\u003e, \u003cem\u003erpsA\u003c/em\u003e, \u003cem\u003eRv1258c\u003c/em\u003e, \u003cem\u003eRv3236c\u003c/em\u003e, and their respective promoters to identify poorly characterized mutations or other artifacts that could explain their phenotypic resistance. In sample 22468, the complete absence of the \u003cem\u003epncA\u003c/em\u003e gene was observed. The complete absence of the \u003cem\u003epncA\u003c/em\u003e gene in sample 22468 is a significant finding, as this gene is essential for the pyrazinamide conversion into its active form, pyrazinoic acid. The total loss or deletion of \u003cem\u003epncA\u003c/em\u003e has been identified as a mechanism of pyrazinamide resistance in \u003cem\u003eMtb\u003c/em\u003e (\u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e, \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e). For sample 15213, two SNPs of interest were identified: \u003cem\u003eRv3236c\u003c/em\u003e Ala370Thr and a synonymous variant in the \u003cem\u003erpsA\u003c/em\u003e gene (636A\u0026thinsp;\u0026gt;\u0026thinsp;C; Arg340Arg). The first mutation has consistently been reported alongside a secondary mutation (\u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e66\u003c/span\u003e), the latter is mentioned in the \u0026ldquo;Catalogue of Mutations in \u003cem\u003eMycobacterium tuberculosis\u003c/em\u003e Complex and their Association with Drug Resistance \u0026ndash; Second Edition\u0026rdquo; (\u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e) as a rare mutation for PZA with low PPV. For samples 10900 and 11880, no mutations or regions with low coverage were found that could account for their resistance. Further investigation is required.\u003c/p\u003e \u003cp\u003eIdentifying resistance-associated mutations in EMB-resistant organisms is one of the greatest challenges when diagnosing antibiotic resistance through variant analysis. It is believed that the resistance mechanism to this antibiotic in tuberculosis is not solely attributed to the \u003cem\u003eembABC\u003c/em\u003e cassette but may result from a combination of different variants across multiple genes, making accurate detection challenging in some cases (\u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e, \u003cspan additionalcitationids=\"CR68 CR69 CR70\" citationid=\"CR67\" class=\"CitationRef\"\u003e67\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e71\u003c/span\u003e). In our samples, the three false negatives (11401, 11880, and 17817) shared the synonymous SNP \u003cem\u003eembC\u003c/em\u003e Arg927Arg which has been widely reported for all isolates (resistant and sensitives) in the PolyTB database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://pathogenseq.lshtm.ac.uk/polytb\u003c/span\u003e\u003cspan address=\"http://pathogenseq.lshtm.ac.uk/polytb\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e).\u003c/span\u003e Brossier et al. suggest that this SNP could have originated from a sequencing artifact in the \u003cem\u003eMtb\u003c/em\u003e reference strain H37Rv, as recorded in GenBank. (accession number AL123456.3) (\u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e72\u003c/span\u003e). On the other hand, sample 17817 presented multiple previously unreported variants, including four missenses (\u003cem\u003eembR\u003c/em\u003e Phe283Leu, \u003cem\u003eembR\u003c/em\u003e Cys294Gly, \u003cem\u003eembB\u003c/em\u003e Phe642Ser, \u003cem\u003eembB\u003c/em\u003e Asn675Thr ), one disruptive inframe insertion (\u003cem\u003eembA\u003c/em\u003e 3347 C\u0026thinsp;\u0026lt;\u0026thinsp;CCG) and two synonymous variants (\u003cem\u003eembR\u003c/em\u003e Cys288Cys and the one described above). For samples 11401 and 11880, no variants were found in any reported gene most commonly associated with ethambutol resistance (seven genes were manually reviewed in IGV to verify the information: \u003cem\u003eembABC\u003c/em\u003e, \u003cem\u003einiABC\u003c/em\u003e, and \u003cem\u003eembR\u003c/em\u003e). In addition, both samples show optimal vertical coverage (\u0026lt;\u0026thinsp;30X), and no insertion sequences (IS) were detected that could explain the phenotypic resistance. Further investigation will be essential to unravel these findings.\u003c/p\u003e \u003cp\u003eAdditionally, resistance-associated variants were detected for Delamanid, Linezolid, and other second-line drugs, even though these drugs were not administered at the time of the study. For Delamanid, we identified one high-confidence mutation (fbiC Ala855fs) and seven low-confidence mutations (three in \u003cem\u003efbiC\u003c/em\u003e -Ile406Val, \u003cem\u003eV\u003c/em\u003eal410Gly, Val415Gly- and four in fbiA -Ala30Thr, Gln120Arg, Ile208Val, and one synonymous variant). Regarding Linezolid, we detected two low-confidence synonymous mutations in \u003cem\u003erplC\u003c/em\u003e and one high-confidence mutation (\u003cem\u003erplC\u003c/em\u003e Cys154Arg). No resistance-associated mutations were found for Bedaquiline or Fosfomycin.\u003c/p\u003e \u003cp\u003eIn conclusion, these findings reveal a diverse landscape of resistance mutations among XDR \u003cem\u003eM. tuberculosis\u003c/em\u003e isolates in Argentina, with notable lineage-specific and convergent mutations. Our findings offer a comprehensive view of both established and lesser-known mutations, enriching the understanding of resistance patterns and evolutionary pathways in these isolates.\u003c/p\u003e "},{"header":"Discussion and conclusions","content":"\u003cp\u003eThe study of extensively drug-resistant (XDR) tuberculosis (TB) in Argentina provides critical insights into the evolution, genetic diversity, and complexity of antibiotic resistance in \u003cem\u003eMycobacterium tuberculosis\u003c/em\u003e (\u003cem\u003eMtb\u003c/em\u003e). Despite substantial progress in TB research and the development of drug resistance profiling techniques, XDR-TB remains a significant public health threat worldwide. In Argentina, where TB burden is moderate but concerning, especially with rising drug-resistant strains, our findings contribute to a better understanding of the genetic determinants of XDR-TB in the region and highlight the importance of genomic surveillance in guiding TB control strategies.\u003c/p\u003e \u003cp\u003eThis study characterized the genetic basis of antibiotic resistance to first- and second-line drugs used in tuberculosis treatment, analyzing clinical XDR \u003cem\u003eMycobacterium tuberculosis\u003c/em\u003e isolates from Argentina collected between 2006 and 2015.\u003c/p\u003e \u003cp\u003eThe resistance-associated variants identified in this study remained consistent with those previously reported for drug-resistant isolates in Argentina. For isoniazid, \u003cem\u003ekatG\u003c/em\u003e Ser315Thr and \u003cem\u003efabG1\u003c/em\u003e -15C\u0026thinsp;\u0026gt;\u0026thinsp;T were the most frequent mutations, aligning with previous studies (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e, \u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e73\u003c/span\u003e). For rifampicin resistance, \u003cem\u003erpoB\u003c/em\u003e Ser450Leu, a variant previously reported locally, remained prevalent, while Asp435Val, one of the most frequently identified mutations in other studies, was also commonly detected (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e, \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eFor pyrazinamide (PZA), \u003cem\u003epncA\u003c/em\u003e mutations exhibited expected variability but were mostly contained within specific monophyletic branches, such as the H and LAM5 groups, suggesting phylogenetic constraints on PZA resistance evolution. Ethambutol resistance was primarily associated with the \u003cem\u003eembB\u003c/em\u003e Gly406Ala and Met306Ile variants, consistent with local reports(\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan additionalcitationids=\"CR75\" citationid=\"CR74\" class=\"CitationRef\"\u003e74\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e76\u003c/span\u003e), with the former being exclusive to the H group.\u003c/p\u003e \u003cp\u003eFor second-line aminoglycosides, the \u003cem\u003errs\u003c/em\u003e 1401A\u0026thinsp;\u0026gt;\u0026thinsp;G mutation remained predominant, although it was not conserved throughout the phylogeny. Fluoroquinolone resistance-associated mutations in \u003cem\u003egyrA\u003c/em\u003e exhibited a more varied distribution in the tree compared to other antibiotics. However, the most frequent variant locations, Ala90 and Asp94, known to cause high MIC and found locally in FQL-resistant isolates, were present.\u003c/p\u003e \u003cp\u003eEthionamide resistance variants were found predominantly in the \u003cem\u003efabG1\u003c/em\u003e promoter (also associated with INH resistance), with only one strain having a mutation in its target gene, \u003cem\u003einhA\u003c/em\u003e. The \u003cem\u003efabG1\u003c/em\u003e -15C\u0026thinsp;\u0026gt;\u0026thinsp;T mutation seemed to be fixed in several groups but without a clear common origin.\u003c/p\u003e \u003cp\u003eThe occurrence of more than one resistance mutation for a given drug was not as uncommon as expected. However, mutations specific to a single strain were also highly prevalent. Resistance-associated mutations were not limited to these known markers. Certain isolates displayed unique mutations or combinations of mutations, which could suggest complex evolutionary mechanisms, such as convergent evolution or homoplasy, where resistance mutations emerge independently in separate lineages. This observation is crucial for understanding the adaptive landscape of \u003cem\u003eMtb\u003c/em\u003e, as different strains may develop resistance through distinct genetic pathways, possibly influenced by local epidemiological and selective pressures.\u003c/p\u003e \u003cp\u003eWe also identified less characterized mutations, such as novel insertions in \u003cem\u003epncA\u003c/em\u003e and \u003cem\u003ekatG\u003c/em\u003e, which warrant further investigation as they could contribute to pyrazinamide and isoniazid resistance, respectively. Some isolates exhibited phenotypic resistance despite lacking known resistance mutations, suggesting the presence of alternative resistance mechanisms (\u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e77\u003c/span\u003e), epigenetic factors influencing drug susceptibility (\u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e78\u003c/span\u003e), or technical limitations such as low sequencing coverage (\u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e78\u003c/span\u003e, \u003cspan citationid=\"CR79\" class=\"CitationRef\"\u003e79\u003c/span\u003e). These undetected cases underscore the importance of comprehensive genetic analysis and the need for updated resistance databases that include less commonly reported mutations, which could enhance diagnostic (\u003cspan citationid=\"CR79\" class=\"CitationRef\"\u003e79\u003c/span\u003e) accuracy for XDR-TB.\u003c/p\u003e \u003cp\u003eWe observed evidence of both lineage-specific resistance mutations and convergent evolution of resistance across different strains. The finding that XDR isolates were not derived from a single transmission event but rather multiple independent resistance acquisitions highlights the complexity of TB control efforts in Argentina. This suggests that strengthening infection control measures and targeted interventions are essential to prevent further spread. Future studies integrating genomic data with epidemiological and clinical data will be critical for designing more effective containment strategies.\u003c/p\u003e \u003cp\u003eFinally, our work provides a detailed genomic characterization of XDR-TB isolates in Argentina, identifying both well-established and lesser-known resistance mutations. Our results highlight the complexity of resistance evolution in \u003cem\u003eM. tuberculosis\u003c/em\u003e and underscore the importance of integrating WGS into routine TB surveillance and diagnosis in developing countries such as Argentina. Continued efforts to expand resistance mutation databases and improve molecular diagnostic tools are essential to combat the growing challenge of XDR-TB worldwide.\u003c/p\u003e"},{"header":"Abbreviations","content":" \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"No\" id=\"Taba\" border=\"1\"\u003e \u003ccolgroup cols=\"2\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003eA\u003c/div\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003eAmikacin\u003c/div\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003e\u003cspan type=\"Bold\" class=\"Bold\" name=\"Emphasis\"\u003eAIDS\u003c/span\u003e\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003eAcquired Immunodeficiency Syndrome\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003e\u003cspan type=\"Bold\" class=\"Bold\" name=\"Emphasis\"\u003eAMK\u003c/span\u003e\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003eAmikacin\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003e\u003cspan type=\"Bold\" class=\"Bold\" name=\"Emphasis\"\u003eANLIS\u003c/span\u003e\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003eAdministraci\u0026oacute;n Nacional de Laboratorios e Institutos de Salud\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003e\u003cspan type=\"Bold\" class=\"Bold\" name=\"Emphasis\"\u003eBACTEC MGIT\u003c/span\u003e\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003eMycobacteria Growth Indicator Tube (BD system for culture and DST)\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003e\u003cspan type=\"Bold\" class=\"Bold\" name=\"Emphasis\"\u003eBD\u003c/span\u003e\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003eBecton Dickinson\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003e\u003cspan type=\"Bold\" class=\"Bold\" name=\"Emphasis\"\u003eBDQ\u003c/span\u003e\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003eBedaquiline\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003e\u003cspan type=\"Bold\" class=\"Bold\" name=\"Emphasis\"\u003eC\u003c/span\u003e\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003eCapreomycin\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003e\u003cspan type=\"Bold\" class=\"Bold\" name=\"Emphasis\"\u003eCAP\u003c/span\u003e\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003eCapreomycin\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003e\u003cspan type=\"Bold\" class=\"Bold\" name=\"Emphasis\"\u003eCARD RGI\u003c/span\u003e\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003eComprehensive Antibiotic Resistance Database \u0026ndash; Resistance Gene Identifier\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003e\u003cspan type=\"Bold\" class=\"Bold\" name=\"Emphasis\"\u003eCONICET\u003c/span\u003e\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003eConsejo Nacional de Investigaciones Cient\u0026iacute;ficas y T\u0026eacute;cnicas\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003e\u003cspan type=\"Bold\" class=\"Bold\" name=\"Emphasis\"\u003eCRISPR\u003c/span\u003e\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003eClustered Regularly Interspaced Short Palindromic Repeats\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003e\u003cspan type=\"Bold\" class=\"Bold\" name=\"Emphasis\"\u003eDNA\u003c/span\u003e\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003eDeoxyribonucleic Acid\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003e\u003cspan type=\"Bold\" class=\"Bold\" name=\"Emphasis\"\u003eDST\u003c/span\u003e\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003eDrug Susceptibility Testing\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003e\u003cspan type=\"Bold\" class=\"Bold\" name=\"Emphasis\"\u003eE\u003c/span\u003e\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003eEthambutol\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003e\u003cspan type=\"Bold\" class=\"Bold\" name=\"Emphasis\"\u003eEMB\u003c/span\u003e\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003eEthambutol\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003e\u003cspan type=\"Bold\" class=\"Bold\" name=\"Emphasis\"\u003eETH\u003c/span\u003e\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003eEthionamide\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003e\u003cspan type=\"Bold\" class=\"Bold\" name=\"Emphasis\"\u003eF\u003c/span\u003e\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003eFluoroquinolones\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003e\u003cspan type=\"Bold\" class=\"Bold\" name=\"Emphasis\"\u003eFLQ\u003c/span\u003e\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003eFluoroquinolones\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003e\u003cspan type=\"Bold\" class=\"Bold\" name=\"Emphasis\"\u003eH\u003c/span\u003e\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003eHaarlem (a spoligotype family)\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003e\u003cspan type=\"Bold\" class=\"Bold\" name=\"Emphasis\"\u003eHIV\u003c/span\u003e\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003eHuman Immunodeficiency Virus\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003e\u003cspan type=\"Bold\" class=\"Bold\" name=\"Emphasis\"\u003eI\u003c/span\u003e\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003eIsoniazid\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003e\u003cspan type=\"Bold\" class=\"Bold\" name=\"Emphasis\"\u003eIGV\u003c/span\u003e\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003eIntegrative Genomics Viewer\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003e\u003cspan type=\"Bold\" class=\"Bold\" name=\"Emphasis\"\u003eINH\u003c/span\u003e\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003eIsoniazid\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003e\u003cspan type=\"Bold\" class=\"Bold\" name=\"Emphasis\"\u003eIQR\u003c/span\u003e\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003eInterquartile Range\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003e\u003cspan type=\"Bold\" class=\"Bold\" name=\"Emphasis\"\u003eIS\u003c/span\u003e\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003eInsertion Sequence\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003e\u003cspan type=\"Bold\" class=\"Bold\" name=\"Emphasis\"\u003eKAN\u003c/span\u003e\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003eKanamycin\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003e\u003cspan type=\"Bold\" class=\"Bold\" name=\"Emphasis\"\u003eL\u003c/span\u003e\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003eLAM (Latin American-Mediterranean) lineage\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003e\u003cspan type=\"Bold\" class=\"Bold\" name=\"Emphasis\"\u003eLAM\u003c/span\u003e\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003eLatin American-Mediterranean (a spoligotype family)\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003e\u003cspan type=\"Bold\" class=\"Bold\" name=\"Emphasis\"\u003eLJ\u003c/span\u003e\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003eL\u0026ouml;wenstein-Jensen medium\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003e\u003cspan type=\"Bold\" class=\"Bold\" name=\"Emphasis\"\u003eLZD\u003c/span\u003e\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003eLinezolid\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003e\u003cspan type=\"Bold\" class=\"Bold\" name=\"Emphasis\"\u003eM\u003c/span\u003e\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003eM strain of Mycobacterium tuberculosis\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003e\u003cspan type=\"Bold\" class=\"Bold\" name=\"Emphasis\"\u003eMAS-PCR\u003c/span\u003e\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003eMultiplex Allele-Specific Polymerase Chain Reaction\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003e\u003cspan type=\"Bold\" class=\"Bold\" name=\"Emphasis\"\u003eMAFFT\u003c/span\u003e\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003eMultiple Alignment using Fast Fourier Transform\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003e\u003cspan type=\"Bold\" class=\"Bold\" name=\"Emphasis\"\u003eMDR\u003c/span\u003e\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003eMultidrug-Resistant\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003e\u003cspan type=\"Bold\" class=\"Bold\" name=\"Emphasis\"\u003eMDR-TB\u003c/span\u003e\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003eMultidrug-Resistant Tuberculosis\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003e\u003cspan type=\"Bold\" class=\"Bold\" name=\"Emphasis\"\u003eMIC\u003c/span\u003e\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003eMinimum Inhibitory Concentration\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003e\u003cspan type=\"Bold\" class=\"Bold\" name=\"Emphasis\"\u003eMIRU-VNTR\u003c/span\u003e\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003eMycobacterial Interspersed Repetitive Unit - Variable Number Tandem Repeat\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003e\u003cspan type=\"Bold\" class=\"Bold\" name=\"Emphasis\"\u003eML\u003c/span\u003e\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003eMaximum Likelihood\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003e\u003cspan type=\"Bold\" class=\"Bold\" name=\"Emphasis\"\u003eMtb\u003c/span\u003e\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003eMycobacterium tuberculosis\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003e\u003cspan type=\"Bold\" class=\"Bold\" name=\"Emphasis\"\u003eNCBI\u003c/span\u003e\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003eNational Center for Biotechnology Information\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003e\u003cspan type=\"Bold\" class=\"Bold\" name=\"Emphasis\"\u003eNRL\u003c/span\u003e\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003eNational Reference Laboratory\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003e\u003cspan type=\"Bold\" class=\"Bold\" name=\"Emphasis\"\u003eP\u003c/span\u003e\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003ePyrazinamide\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003e\u003cspan type=\"Bold\" class=\"Bold\" name=\"Emphasis\"\u003ePCR\u003c/span\u003e\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003ePolymerase Chain Reaction\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003e\u003cspan type=\"Bold\" class=\"Bold\" name=\"Emphasis\"\u003ePfam\u003c/span\u003e\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003eProtein Family database\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003e\u003cspan type=\"Bold\" class=\"Bold\" name=\"Emphasis\"\u003ePZA\u003c/span\u003e\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003ePyrazinamide\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003e\u003cspan type=\"Bold\" class=\"Bold\" name=\"Emphasis\"\u003eRAxML\u003c/span\u003e\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003eRandomized Axelerated Maximum Likelihood\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003e\u003cspan type=\"Bold\" class=\"Bold\" name=\"Emphasis\"\u003eR\u003c/span\u003e\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003eRifampicin\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003e\u003cspan type=\"Bold\" class=\"Bold\" name=\"Emphasis\"\u003eRIF\u003c/span\u003e\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003eRifampicin\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003e\u003cspan type=\"Bold\" class=\"Bold\" name=\"Emphasis\"\u003errs\u003c/span\u003e\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003eRibosomal RNA gene commonly associated with aminoglycoside resistance\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003e\u003cspan type=\"Bold\" class=\"Bold\" name=\"Emphasis\"\u003eS\u003c/span\u003e\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003eStreptomycin\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003e\u003cspan type=\"Bold\" class=\"Bold\" name=\"Emphasis\"\u003eSNP\u003c/span\u003e\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003eSingle-Nucleotide Polymorphism\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003e\u003cspan type=\"Bold\" class=\"Bold\" name=\"Emphasis\"\u003eSRA\u003c/span\u003e\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003eSequence Read Archive\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003e\u003cspan type=\"Bold\" class=\"Bold\" name=\"Emphasis\"\u003eSTR\u003c/span\u003e\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003eStreptomycin\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003e\u003cspan type=\"Bold\" class=\"Bold\" name=\"Emphasis\"\u003eSITVITWEB\u003c/span\u003e\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003eInternational database for M. tuberculosis spoligotypes\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003e\u003cspan type=\"Bold\" class=\"Bold\" name=\"Emphasis\"\u003eTB\u003c/span\u003e\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003eTuberculosis\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003e\u003cspan type=\"Bold\" class=\"Bold\" name=\"Emphasis\"\u003eTBProfiler\u003c/span\u003e\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003eA bioinformatics tool for predicting TB resistance from WGS data\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003e\u003cspan type=\"Bold\" class=\"Bold\" name=\"Emphasis\"\u003eTGS-TB\u003c/span\u003e\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003eTotal Genotyping Solution for Tuberculosis\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003e\u003cspan type=\"Bold\" class=\"Bold\" name=\"Emphasis\"\u003eT\u003c/span\u003e\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003eT lineage (a spoligotype family)\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003e\u003cspan type=\"Bold\" class=\"Bold\" name=\"Emphasis\"\u003eVNTR\u003c/span\u003e\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003eVariable Number Tandem Repeat\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003e\u003cspan type=\"Bold\" class=\"Bold\" name=\"Emphasis\"\u003eVCF\u003c/span\u003e\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003eVariant Call Format\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003e\u003cspan type=\"Bold\" class=\"Bold\" name=\"Emphasis\"\u003eWGS\u003c/span\u003e\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003eWhole-Genome Sequencing\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003e\u003cspan type=\"Bold\" class=\"Bold\" name=\"Emphasis\"\u003eWHO\u003c/span\u003e\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003eWorld Health Organization\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003e\u003cspan type=\"Bold\" class=\"Bold\" name=\"Emphasis\"\u003eXDR\u003c/span\u003e\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003eExtensively Drug-Resistant\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003e\u003cspan type=\"Bold\" class=\"Bold\" name=\"Emphasis\"\u003eXDR-TB\u003c/span\u003e\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003eExtensively Drug-Resistant Tuberculosis\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003cbr/\u003e"},{"header":"Declarations","content":"\u003cp\u003eEthics approval and consent to participate\u003c/p\u003e\n\u003cp\u003eThis research has been approved by the INEI ANLIS research review board.\u003c/p\u003e\n\u003cp\u003eClinical Trial\u003c/p\u003e\n\u003cp\u003eClinical trial number: not applicable.\u003c/p\u003e\n\u003cp\u003eConsent for publication\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003eAvailability of data and material\u003c/p\u003e\n\u003cp\u003eData is provided within the manuscript. The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request. Sequence data will be available in the European Nucleotide Archive (ENA) at the time of manuscript acceptance.\u003c/p\u003e\n\u003cp\u003eCompeting interests\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003eFunding\u003c/p\u003e\n\u003cp\u003eAgencia Nacional de Promoci\u0026oacute;n Cient\u0026iacute;fica y Tecnol\u0026oacute;gica [ANPCyT, PICT START UP: PICT-2018-04663 to D.F.D.P.]. CONICET membership of the research career [D.F.D.P., M.M., A.T.], CONICET doctoral fellowship and support staff [M.C.P., F.S., F.A.C. and E.J.S.]. The funders had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication.\u003c/p\u003e\n\u003cp\u003eAuthors\u0026apos; contributions\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eD.F.D.P., M.M., A.T., \u0026nbsp;B.L., J.C. and N.S. \u0026nbsp;conceived and designed the study. F.A.C. and D.F.D.P. wrote the main manuscript text. F.A.C. and E.J.S. prepared the figures with input from the other authors. J.C., J.Mo., T.P., N.S., R.P., B.L., M.M.M., and M.M.P. collected the samples and performed the wet lab experimental work. D.F.D.P., E.J.S., J.Me., L.G.G., M.C.P., F.S. \u0026nbsp; and F.A.C. carried out the bioinformatic analyses. All authors reviewed and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003eAcknowledgements\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eWorld Health Organization. Global tuberculosis report 2023. World Health Organization; 2023. p. 75.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHarding E. WHO global progress report on tuberculosis elimination. Lancet Respir Med. 2020;8(1):19.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDuc PT, Nhat BS, Luyen LT. Establishing population pharmacokinetic model for pyrazinamide in pulmonary tuberculosis patients. 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Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://dx.doi.org/10.1099/mgen.0.000724\u003c/span\u003e\u003cspan address=\"10.1099/mgen.0.000724\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAlexander DC, Ma JH, Guthrie JL, Blair J, Chedore P, Jamieson FB. Gene sequencing for routine verification of pyrazinamide resistance in Mycobacterium tuberculosis: a role for pncA but not rpsA. J Clin Microbiol. 2012;50(11):3726\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRamaswamy SV, Amin AG, G\u0026ouml;ksel S, Stager CE, Dou SJ, El Sahly H, et al. Molecular genetic analysis of nucleotide polymorphisms associated with ethambutol resistance in human isolates of Mycobacterium tuberculosis. Antimicrob Agents Chemother. 2000;44(2):326\u0026ndash;36.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAlcaide F, Pfyffer GE, Telenti A. Role of embB in natural and acquired resistance to ethambutol in mycobacteria. Antimicrob Agents Chemother. 1997;41(10):2270\u0026ndash;3.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDeng L, Mikusov\u0026aacute; K, Robuck KG, Scherman M, Brennan PJ, McNeil MR. Recognition of multiple effects of ethambutol on metabolism of mycobacterial cell envelope. Antimicrob Agents Chemother. 1995;39(3):694\u0026ndash;701.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eIwainsky H. EMB: mode of action, biotransformation and pharmacokinetics. In: Bartman K, editor. Antituberculosis drugs. Berlin, Germany: Springer-; 1988. pp. 533\u0026ndash;40.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRamaswamy S, Musser JM. Molecular genetic basis of antimicrobial agent resistance inMycobacterium tuberculosis: 1998 update. Tuber Lung Dis. 1998;79(1):3\u0026ndash;29.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBrossier F, Sougakoff W, Bernard C, Petrou M, Adeyema K, Pham A, et al. Molecular analysis of the embCAB locus and embR gene involved in ethambutol resistance in clinical isolates of Mycobacterium tuberculosis in France. Antimicrob Agents Chemother. 2015;59(8):4800\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLuo D, Chen Q, Xiong G, Peng Y, Liu T, Chen X, et al. Prevalence and molecular characterization of multidrug-resistant M. tuberculosis in Jiangxi province, China. Sci Rep. 2019;9(1):7315.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eImperiale B, Di Giulio \u0026Aacute;, Mancino M, Zum\u0026aacute;rraga M, Morcillo N. Surveillance and characterization of drug-resistant Mycobacterium tuberculosis isolated in a reference hospital from Argentina during 8 years\u0026rsquo; period. Int J Mycobacteriol. 2019;8(3):223.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eImperiale BR, Zum\u0026aacute;rraga MJ, Di Giulio AB, Cataldi AA, Morcillo NS. Molecular and phenotypic characterisation of Mycobacterium tuberculosis resistant to anti-tuberculosis drugs. Int J Tuberc Lung Dis. 2013;17(8):1088\u0026ndash;93.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHiebert M, Sharma MK, Rabb M, Karlowsky L, Bergman K, Soualhine H. Mutations in embB406 are associated with low-level ethambutol resistance in Canadian Mycobacterium tuberculosis isolates. Antibiot (Basel). 2024;13(7):624.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCancino-Mu\u0026ntilde;oz I, Moreno-Molina M, Furi\u0026oacute; V, Goig GA, Torres-Puente M, Chiner-Oms \u0026Aacute;, et al. Cryptic Resistance Mutations Associated With Misdiagnoses of Multidrug-Resistant Tuberculosis. J Infect Dis. 2019;220(2):316\u0026ndash;20.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMarimani M, Ahmad A, Duse A. The role of epigenetics, bacterial and host factors in progression of Mycobacterium tuberculosis infection. Tuberculosis (Edinb). 2018;113:200\u0026ndash;14.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCohen KA, Manson AL, Desjardins CA, Abeel T, Earl AM. Deciphering drug resistance in Mycobacterium tuberculosis using whole-genome sequencing: progress, promise, and challenges. Genome Med. 2019;11(1):45.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-infectious-diseases","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"infd","sideBox":"Learn more about [BMC Infectious Diseases](http://bmcinfectdis.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/infd","title":"BMC Infectious Diseases","twitterHandle":"#bmcinfectdis","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"multiresistance, genomics, tuberculosis, drugs, XDR","lastPublishedDoi":"10.21203/rs.3.rs-6456461/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6456461/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground:\u003c/h2\u003e \u003cp\u003eTuberculosis (TB), caused by the intracellular bacterium \u003cem\u003eMycobacterium tuberculosis\u003c/em\u003e (Mtb), remains a significant global health challenge, with Mtb being the second leading infectious killer worldwide, following COVID-19. Despite over a century of research, the disease continues to pose a major threat, with an estimated one-fourth of the global population latently infected. According to the World Health Organization (WHO), approximately 1.25\u0026nbsp;million deaths were attributed to TB in 2023 alone. The emergence of multidrug-resistant (MDR) strains, resistant to isoniazid and rifampin, and extensively drug-resistant (XDR) strains, resistant to isoniazid, rifampin, a fluoroquinolone, and a second-line injectable aminoglycoside, further complicates the situation, posing significant challenges for healthcare systems. In Argentina, TB burden is moderate compared to other countries, with approximately 10,500 new cases and 1,000 deaths reported annually. While standard therapy is generally effective, XDR Mtb infections require prolonged and costly treatment and are often associated with a guarded prognosis.\u003c/p\u003e\u003ch2\u003eMethods:\u003c/h2\u003e \u003cp\u003eIn this work, we applied whole-genome sequencing analysis to investigate XDR strains circulating in Argentina between 2006 and 2015. Genotypic variants of each isolate were compared against resistance-associated variant databases and subjected to local and global phylogenetic analyses.\u003c/p\u003e\u003ch2\u003eResults:\u003c/h2\u003e \u003cp\u003eThe analysis revealed no common origins for the most frequently observed resistance mutations. Notable variants associated with resistance to first-line drugs included \u003cem\u003ekatG\u003c/em\u003e Ser315Thr and \u003cem\u003efabG1\u003c/em\u003e -15C\u0026thinsp;\u0026lt;\u0026thinsp;T for isoniazid, \u003cem\u003erpoB\u003c/em\u003e Ser450Leu and Asp435Val for rifampin, \u003cem\u003eembB\u003c/em\u003e Gly406Ala, and Met306Ile for ethambutol, as well as multiple variants in the \u003cem\u003epncA\u003c/em\u003e gene linked to pyrazinamide resistance.\u003c/p\u003e\u003ch2\u003eConclusions:\u003c/h2\u003e \u003cp\u003eThis study provides valuable insights into the molecular mechanisms of antibiotic resistance in \u003cem\u003eM. tuberculosis\u003c/em\u003e, specifically focusing on XDR strains circulating in Argentina. The findings highlight the genetic diversity and complexity of resistance-associated variants, emphasizing the need for continued research and surveillance efforts to address this pressing global health threat.\u003c/p\u003e","manuscriptTitle":"Genomic Characterization of XDR Mycobacterium tuberculosis Isolates in Argentina (2006-2015)","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-05-13 12:42:22","doi":"10.21203/rs.3.rs-6456461/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-06-30T07:29:43+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-06-20T13:18:27+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-06-03T05:33:45+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-05-21T17:14:26+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"106671953629538421281633062986280914542","date":"2025-05-20T06:31:09+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-05-19T09:59:30+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"144618706037956927856484954655903829300","date":"2025-05-17T16:13:41+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"57431263448478658042033874745006005131","date":"2025-05-08T17:37:19+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"57989390075426004906778084642095765919","date":"2025-05-08T12:56:48+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-05-08T11:46:48+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-04-17T12:59:00+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-04-17T00:47:29+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-04-17T00:47:00+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Infectious Diseases","date":"2025-04-15T15:42:55+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-infectious-diseases","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"infd","sideBox":"Learn more about [BMC Infectious Diseases](http://bmcinfectdis.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/infd","title":"BMC Infectious Diseases","twitterHandle":"#bmcinfectdis","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"d4bcf872-f7b9-46e4-b66c-7e831e526b03","owner":[],"postedDate":"May 13th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-11-24T16:01:26+00:00","versionOfRecord":{"articleIdentity":"rs-6456461","link":"https://doi.org/10.1186/s12879-025-11913-3","journal":{"identity":"bmc-infectious-diseases","isVorOnly":false,"title":"BMC Infectious Diseases"},"publishedOn":"2025-11-17 15:57:39","publishedOnDateReadable":"November 17th, 2025"},"versionCreatedAt":"2025-05-13 12:42:22","video":"","vorDoi":"10.1186/s12879-025-11913-3","vorDoiUrl":"https://doi.org/10.1186/s12879-025-11913-3","workflowStages":[]},"version":"v1","identity":"rs-6456461","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6456461","identity":"rs-6456461","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}