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<front>
<journal-meta>
<journal-id journal-id-type="publisher-id">Front. Microbiol.</journal-id>
<journal-title>Frontiers in Microbiology</journal-title>
<abbrev-journal-title abbrev-type="pubmed">Front. Microbiol.</abbrev-journal-title>
<issn pub-type="epub">1664-302X</issn>
<publisher>
<publisher-name>Frontiers Media S.A.</publisher-name>
</publisher>
</journal-meta>
<article-meta>
<article-id pub-id-type="doi">10.3389/fmicb.2025.1666838</article-id>
<article-categories>
<subj-group subj-group-type="heading">
<subject>Microbiology</subject>
<subj-group>
<subject>Original Research</subject>
</subj-group>
</subj-group>
</article-categories>
<title-group>
<article-title><italic>Mycobacterium tuberculosis</italic> genomic surveillance in Mexico. Characterization of variants in drug resistance and efflux pump genes</article-title>
</title-group>
<contrib-group>
<contrib contrib-type="author">
<name>
<surname>Alvarado-Pe&#x00F1;a</surname>
<given-names>N&#x00E9;stor</given-names>
</name>
<xref ref-type="aff" rid="aff1"><sup>1</sup></xref>
<xref ref-type="aff" rid="aff2"><sup>2</sup></xref>
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<contrib contrib-type="author">
<name>
<surname>Mu&#x00F1;oz Torrico</surname>
<given-names>Marcela</given-names>
</name>
<xref ref-type="aff" rid="aff1"><sup>1</sup></xref>
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<contrib contrib-type="author">
<name>
<surname>Narv&#x00E1;ez-D&#x00ED;az</surname>
<given-names>Luis</given-names>
</name>
<xref ref-type="aff" rid="aff3"><sup>3</sup></xref>
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<contrib contrib-type="author">
<name>
<surname>Mej&#x00ED;a-Ponce</surname>
<given-names>Paulina M.</given-names>
</name>
<xref ref-type="aff" rid="aff4"><sup>4</sup></xref>
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<contrib contrib-type="author">
<name>
<surname>Becerril Vargas</surname>
<given-names>Eduardo</given-names>
</name>
<xref ref-type="aff" rid="aff3"><sup>3</sup></xref>
<uri xlink:href="https://loop.frontiersin.org/people/1280603/overview"/>
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<contrib contrib-type="author">
<name>
<surname>Z&#x00FA;&#x00F1;iga</surname>
<given-names>Joaqu&#x00ED;n</given-names>
</name>
<xref ref-type="aff" rid="aff5"><sup>5</sup></xref>
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<contrib contrib-type="author">
<name>
<surname>Mu&#x00F1;iz-Salazar</surname>
<given-names>Raquel</given-names>
</name>
<xref ref-type="aff" rid="aff6"><sup>6</sup></xref>
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<surname>Laniado-Labor&#x00ED;n</surname>
<given-names>Rafael</given-names>
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<xref ref-type="aff" rid="aff7"><sup>7</sup></xref>
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<name>
<surname>Licona-Cassani</surname>
<given-names>Cuauht&#x00E9;moc</given-names>
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<xref ref-type="aff" rid="aff4"><sup>4</sup></xref>
<xref ref-type="aff" rid="aff8"><sup>8</sup></xref>
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<name>
<surname>Sober&#x00F3;n</surname>
<given-names>Xavier</given-names>
</name>
<xref ref-type="aff" rid="aff9"><sup>9</sup></xref>
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<contrib contrib-type="author" corresp="yes">
<name>
<surname>Silva-Herzog</surname>
<given-names>Eugenia</given-names>
</name>
<xref ref-type="aff" rid="aff2"><sup>2</sup></xref>
<xref ref-type="corresp" rid="c001"><sup>&#x002A;</sup></xref>
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<aff id="aff1"><sup>1</sup><institution>Cl&#x00ED;nica de Tuberculosis, Instituto Nacional de Enfermedades Respiratorias &#x201C;Ismael Cos&#x00ED;o Villegas&#x201D;</institution>, <addr-line>Mexico City</addr-line>, <country>Mexico</country></aff>
<aff id="aff2"><sup>2</sup><institution>Laboratorio de Vinculaci&#x00F3;n Cient&#x00ED;fica, Facultad de Medicina-Universidad Nacional Autonoma de Mexico-Instituto Nacional de Medicina Gen&#x00F3;mica (UNAM-INMEGEN)</institution>, <addr-line>Mexico City</addr-line>, <country>Mexico</country></aff>
<aff id="aff3"><sup>3</sup><institution>Laboratorio de Microbiolog&#x00ED;a, Instituto Nacional de Enfermedades Respiratorias (INER)</institution>, <addr-line>Mexico City</addr-line>, <country>Mexico</country></aff>
<aff id="aff4"><sup>4</sup><institution>Escuela de Ingenier&#x00ED;a y Ciencias, Tecnol&#x00F3;gico de Monterrey</institution>, <addr-line>Monterrey</addr-line>, <country>Mexico</country></aff>
<aff id="aff5"><sup>5</sup><institution>Laboratorio de Inmunobiolog&#x00ED;a y Gen&#x00E9;tica, Instituto Nacional de Enfermedades Respiratorias &#x201C;Ismael Cos&#x00ED;o Villegas&#x201D; and Tecnol&#x00F3;gico de Monterrey, Escuela de Medicina y Ciencias de la Salud</institution>, <addr-line>Mexico City</addr-line>, <country>Mexico</country></aff>
<aff id="aff6"><sup>6</sup><institution>Escuela de Ciencias de la Salud, Universidad Aut&#x00F3;noma de Baja California</institution>, <addr-line>Ensenada</addr-line>, <country>Mexico</country></aff>
<aff id="aff7"><sup>7</sup><institution>Facultad de Medicina, Universidad Aut&#x00F3;noma de Baja California</institution>, <addr-line>Tijuana</addr-line>, <country>Mexico</country></aff>
<aff id="aff8"><sup>8</sup><institution>Tecnol&#x00F3;gico de Monterrey, The Institute for Obesity Research</institution>, <addr-line>Monterrey</addr-line>, <country>Mexico</country></aff>
<aff id="aff9"><sup>9</sup><institution>Departamento de Ingenier&#x00ED;a Celular y Biocat&#x00E1;lisis, Instituto de Biotecnolog&#x00ED;a, Universidad Nacional Aut&#x00F3;noma de M&#x00E9;xico</institution>, <addr-line>Cuernavaca</addr-line>, <country>Mexico</country></aff>
<author-notes>
<fn fn-type="edited-by" id="fn0002">
<p>Edited by: <ext-link ext-link-type="uri" xlink:href="https://loop.frontiersin.org/people/729717/overview">Samira Tarashi</ext-link>, Pasteur Institute of Iran (PII), Iran</p>
</fn>
<fn fn-type="edited-by" id="fn0003">
<p>Reviewed by: <ext-link ext-link-type="uri" xlink:href="https://loop.frontiersin.org/people/3046027/overview">Farah Asghar</ext-link>, University of the Punjab, Pakistan</p>
<p><ext-link ext-link-type="uri" xlink:href="https://loop.frontiersin.org/people/3164526/overview">Arezoo Beig Parikhani</ext-link>, Duke University, United States</p>
</fn>
<corresp id="c001">&#x002A;Correspondence: Eugenia Silva-Herzog, <email>esilvaherzog@inmegen.gob.mx</email></corresp>
</author-notes>
<pub-date pub-type="epub">
<day>15</day>
<month>10</month>
<year>2025</year>
</pub-date>
<pub-date pub-type="collection">
<year>2025</year>
</pub-date>
<volume>16</volume>
<elocation-id>1666838</elocation-id>
<history>
<date date-type="received">
<day>15</day>
<month>07</month>
<year>2025</year>
</date>
<date date-type="accepted">
<day>19</day>
<month>09</month>
<year>2025</year>
</date>
</history>
<permissions>
<copyright-statement>Copyright &#x00A9; 2025 Alvarado-Pe&#x00F1;a, Mu&#x00F1;oz Torrico, Narv&#x00E1;ez-D&#x00ED;az, Mej&#x00ED;a-Ponce, Becerril Vargas, Z&#x00FA;&#x00F1;iga, Mu&#x00F1;iz-Salazar, Laniado-Labor&#x00ED;n, Licona-Cassani, Sober&#x00F3;n and Silva-Herzog.</copyright-statement>
<copyright-year>2025</copyright-year>
<copyright-holder>Alvarado-Pe&#x00F1;a, Mu&#x00F1;oz Torrico, Narv&#x00E1;ez-D&#x00ED;az, Mej&#x00ED;a-Ponce, Becerril Vargas, Z&#x00FA;&#x00F1;iga, Mu&#x00F1;iz-Salazar, Laniado-Labor&#x00ED;n, Licona-Cassani, Sober&#x00F3;n and Silva-Herzog</copyright-holder>
<license xlink:href="http://creativecommons.org/licenses/by/4.0/">
<p>This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.</p>
</license>
</permissions>
<abstract>
<p>Tuberculosis (TB) remains a persistent global public health challenge, with the rise of drug-resistant tuberculosis (DR-TB) complicating all the disease control efforts. The World Health Organization (WHO) has advocated for molecular diagnostic techniques, including whole-genome sequencing (WGS), to enhance TB diagnosis and treatment strategies. In this study, we performed WGS analysis on 49 pulmonary tuberculosis isolates from Mexican patients to identify mutations conferring resistance to 11 key antimicrobial agents: four first-line drugs (isoniazid, rifampicin, ethambutol, and pyrazinamide) and 7&#x202F;second line drugs (fluoroquinolones, ethionamide/prothionamide, amikacin, kanamycin, capreomycin, streptomycin, and bedaquiline). We identified 89 novel variants: 48 in genes previously associated with drug resistance and 41 in genes not previously linked to resistance mechanisms, including potential novel mutations associated with delamanid resistance. Additionally, we detected 31 mutations across three efflux pump superfamilies (ABC, RND, and MFS); all of these variants warrant further investigation regarding their contribution to antibiotic resistance. This analysis represents approximately 10% of Mexico&#x2019;s national variant registry, providing substantial insight into the molecular epidemiology of drug-resistant tuberculosis within the country. The identification of new resistance-associated variants (RAV) from clinical isolates underrepresented in global databases, contributes to develop improved diagnostic tools, optimize treatment regimens, and probably to elucidate antibiotic resistance mechanisms. Specifically, the identification of RAVs for new drugs like bedaquiline, pretomanid, delamanid, and linezolid, which are central to the most recent schemes of treatment (BPaLM, BPaL, BDLLfxC, BLMZ), is key to the improvement of patient outcomes and preventing the emergence of resistance to these critical therapeutic options.</p>
</abstract>
<kwd-group>
<kwd>tuberculosis</kwd>
<kwd><italic>Mycobacterium tuberculosis</italic> complex</kwd>
<kwd>drug-resistant tuberculosis</kwd>
<kwd>resistance-associated variants</kwd>
<kwd>efflux pumps</kwd>
<kwd>whole genome sequencing</kwd>
</kwd-group>
<counts>
<fig-count count="2"/>
<table-count count="4"/>
<equation-count count="0"/>
<ref-count count="65"/>
<page-count count="12"/>
<word-count count="8394"/>
</counts>
<custom-meta-wrap>
<custom-meta>
<meta-name>section-at-acceptance</meta-name>
<meta-value>Antimicrobials, Resistance and Chemotherapy</meta-value>
</custom-meta>
</custom-meta-wrap>
</article-meta>
</front>
<body>
<sec sec-type="intro" id="sec1">
<label>1</label>
<title>Introduction</title>
<p>Tuberculosis (TB) is an infectious disease caused by the <italic>Mycobacterium tuberculosis</italic> Complex (<italic>MtbC</italic>) that remains one of the 13 leading causes of death worldwide. After the COVID-19 pandemic, TB returned as the principal cause of death for a single infection agent, with 10.8&#x202F;million new cases and 1.25&#x202F;million deaths worldwide (<xref ref-type="bibr" rid="ref20">Global Tuberculosis Report, 2024</xref>).</p>
<p>TB can be caused by drug-susceptible (DS) or drug-resistant (DR) strains. DR-TB treatment has the worst prognosis, which in general results in longer treatment durations, increased adverse effects for patients, and higher costs. The rise of rifampicin-resistant TB (RR-TB) is a global threat that has led the World Health Organization (WHO) to classify it as a priority pathogen (<xref ref-type="bibr" rid="ref58">World Health Organization, 2024b</xref>). DR-TB can lead to Multidrug Resistance (MDR-TB), defined by WHO as Resistance to Rifampicin (RR) and isoniazid (H), or Extensive Drug Resistance (XDR-TB), defined as resistant to rifampicin (may also be resistant to isoniazid) plus a fluoroquinolone (levofloxacin or moxifloxacin), and either bedaquiline or linezolid. Worldwide incidence of MDR/RR-TB has decreased from an estimated 580,000 cases (UI 460,000 -580,000) in 2015 to 400,000 (UI 360,000 &#x2013; 440,000) in 2023, mainly because of better diagnosis and prompt detection. However, the incidence of Pre-XDR and XDR TB has increased 3.8-fold in this same time period (<xref ref-type="bibr" rid="ref19">Global Tuberculosis Report, 2016</xref>; <xref ref-type="bibr" rid="ref20">Global Tuberculosis Report, 2024</xref>).</p>
<p>Rapid and accurate detection of resistant profiles is crucial for successful treatment, reducing transmission, as well as the risk of an increased proportion of resistant strains (<xref ref-type="bibr" rid="ref50">Roberts et al., 2024</xref>). Overall, bacteria employ diverse mechanisms to resist antibiotic effects, including the enzymatic degradation of antibiotic molecules, acquisition of mutations within or around drug targets, reduced membrane permeability, and overexpression of efflux pumps. These resistance mechanisms leave distinctive genetic signatures that can be localized in specific genomic regions or distributed throughout the entire genome. Next-generation sequencing technologies, including amplicon sequencing and whole-genome sequencing (WGS), offer the potential to identify resistance markers and generate comprehensive antimicrobial resistance profiles (<xref ref-type="bibr" rid="ref57">World Health Organization, 2024a</xref>; <xref ref-type="bibr" rid="ref5004">P&#x00E9;rez, 2023</xref>).</p>
<p>The identification and validation of DR markers are the result of extensive genomic studies coupled with microbiological tests that have been performed mainly in regions with high disease prevalence. These studies focus mainly on drug targets and drug metabolism, with less emphasis on efflux pumps (<xref ref-type="bibr" rid="ref53">The CRyPTIC Consortium, 2022</xref>; <xref ref-type="bibr" rid="ref56">World Health Organization, 2023</xref>). The importance of efflux pumps in antibiotic resistance is becoming more apparent in recent years (<xref ref-type="bibr" rid="ref30">Li et al., 2024</xref>; <xref ref-type="bibr" rid="ref17">Gaurav et al., 2023</xref>). They help maintain cellular homeostasis by expelling toxic molecules, which keeps antibiotic concentrations below therapeutic levels. Therefore, efflux pumps should be included in any genetic analysis of antibiotic resistance.</p>
<p>While these global studies encompass a few strains from Latin America, very few come from Mexico. Although the WHO considers Mexico a low-burden country for TB, it ranks among the top three countries with the most DR-TB in the Americas, together with Brazil and Peru (<xref ref-type="bibr" rid="ref55">Tuberculosis en las Am&#x00E9;ricas, 2021</xref>; <xref ref-type="bibr" rid="ref20">Global Tuberculosis Report, 2024</xref>). DR-TB cases in Mexico have increased 281% in recent years (from 283 in 2015 to 796 reported in 2023; <xref ref-type="bibr" rid="ref5002">National Center for Preventive Programs and Disease Control, 2024</xref>), stressing the importance of antibiotic surveillance in Mexico.</p>
<p>The majority of genomic epidemiological studies from Mexican TB patients are centered on mutations for first-line drugs, including rifampicin, isoniazid, ethambutol, pyrazinamide, aminoglycosides, and fluoroquinolone, but do not explore mutations for drugs recently introduced in treatment regimens by WHO, such as bedaquiline, linezolid, pretomanid and delamanid, which are now a central part of TB treatment (<xref ref-type="bibr" rid="ref62">Zenteno-Cuevas et al., 2009</xref>; <xref ref-type="bibr" rid="ref31">Lopez-Alvarez et al., 2010</xref>; <xref ref-type="bibr" rid="ref15">Flores-Trevi&#x00F1;o et al., 2015</xref>; <xref ref-type="bibr" rid="ref60">Zenteno-Cuevas et al., 2014</xref>; <xref ref-type="bibr" rid="ref26">Juarez-Eusebio et al., 2017</xref>; <xref ref-type="bibr" rid="ref32">Madrazo-Moya et al., 2019</xref>; <xref ref-type="bibr" rid="ref61">Zenteno-Cuevas et al., 2020</xref>; <xref ref-type="bibr" rid="ref37">M&#x00F3;nica et al., 2021</xref>; <xref ref-type="bibr" rid="ref5">Barbosa-Amezcua et al., 2022</xref>; <xref ref-type="bibr" rid="ref34">Mej&#x00ED;a-Ponce et al., 2023</xref>). In this study, we aim to characterize both known and novel mutations in target genes and efflux pump genes associated with resistance in Mexican patients with pulmonary-resistant tuberculosis using WGS. For strains with available phenotypic data, we analyze genotype&#x2013;phenotype correlations. A solid knowledge of the genetic characteristics of resistance will help develop precise and maybe individual treatments to better help patients with DR-TB.</p>
</sec>
<sec sec-type="materials|methods" id="sec2">
<label>2</label>
<title>Materials and methods</title>
<sec id="sec3">
<label>2.1</label>
<title>Clinical sample collection</title>
<p>A collection of 61 clinical isolates with confirmed drug resistance by phenotypic drug sensitivity test (pDST) was selected from the Mycobacterial laboratory at the Instituto Nacional de Enfermedades Respiratorias (INER), a national reference center for TB in Mexico, and from the <italic>Mycobacterium tuberculosis</italic> collection at the Universidad Aut&#x00F3;noma de Baja California (UABC). The study prioritized samples with the highest resistance profiles (22 MDR and 11 Pre-XDR strains) from these collections, representing nine Mexican states over 9&#x202F;years (2014&#x2013;2023). Of the 61 original isolates, 12 strains could not be included because either they were impossible to subculture or insufficient DNA was obtained for analysis, resulting in 49 total strains in the study.</p>
<p>The samples were decontaminated using Petroff&#x2019;s modified method before taking an aliquot and grown in BACTEC MGIT liquid culture (Becton Dickinson, Sparks, United States). Microbiological drug susceptibility tests were performed at the time of first isolation following the proportion method, to find the lowest drug concentration that inhibits 99% of bacterial growth. Standard drug concentrations tested include isoniazid (0.1, 0.4, 1.0, 4.0&#x202F;&#x03BC;g/mL), rifampicin (0.5, 1.0&#x202F;&#x03BC;g/mL), ethambutol (5.0, 7.5, 8.0&#x202F;&#x03BC;g/mL), pyrazinamide (100.0&#x202F;&#x03BC;g/mL), amikacin (1.0, 2.0&#x202F;&#x03BC;g/mL), kanamycin (2.5, 5.0&#x202F;&#x03BC;g /mL), capreomycin (0.25, 5.0&#x202F;pg./mL), clofazimine (1.0&#x202F;&#x03BC;g/mL), ethionamide (5.0&#x202F;&#x03BC;g/mL), streptomycin (1.0, 4.0&#x202F;&#x03BC;g/mL), levofloxacin (1.0&#x202F;&#x03BC;g/mL), moxifloxacin (0.25, 1.0, 2.0&#x202F;&#x03BC;g/mL), ofloxacin (2.0&#x202F;&#x03BC;g/mL), linezolid (1.0&#x202F;&#x03BC;g/mL), and cycloserine (40.0&#x202F;&#x03BC;g/mL). All strains were heat-inactivated in a dry bath at 95 &#x030A;C for 30&#x202F;min and sent in triple packaging to the &#x201C;Instituto Nacional de Medicina Gen&#x00F3;mica&#x201D; (INMEGEN) for DNA extraction and bioinformatic analysis. Transportation and processing was conducted in adherence with the biosafety recommendations established by the INER and INMEGEN.</p>
</sec>
<sec id="sec4">
<label>2.2</label>
<title>DNA extraction and whole genome sequencing</title>
<p>DNA extraction was performed using the QIAmp UCP Pathogen Kit (Qiagen) according to the manufacturer&#x2019;s recommendations. The quality and concentration of DNA were evaluated using NanoDrop (ThermoFisher) in the laboratory, as well as Qubit Fluorometer, and agarose Gel Electrophoresis Quantitation at Novogene Corporation (Novogene Co., Sacramento, CA). DNA library preparation and sequencing for microbial WGS were performed on the NovaSeq PE150 platform (Illumina) at Novogene Corporation (Novogene Co., Sacramento, CA).</p>
</sec>
<sec id="sec5">
<label>2.3</label>
<title>Bioinformatics analysis</title>
<p>The bioinformatic analysis of the sequences was conducted using the methodology described by <xref ref-type="bibr" rid="ref5001">Cuevas-C&#x00F3;rdoba et al. (2021)</xref>. Briefly, removal adapters and low-quality reads (&#x003C;30 Phred-scaled) were followed by the elimination of human, viral, and other bacterial sequences identified with SURPI (&#x201C;Sequence-based ultrarapid pathogen identification&#x201D;) (<xref ref-type="bibr" rid="ref39">Naccache et al., 2014</xref>). Reads were then mapped to the Mtb reference strain H37Rv genome (RefSeq Accession: NC_000962.3). An overall genome coverage of &#x003E; 278 fold (65&#x2013;399 fold) was achieved. Variant calling relied on the GATK software with annotations provided by SnpEff software. Variants found in our analysis were classified as &#x201C;Associated with Resistance,&#x201D; &#x201C;Not Associated with Resistance,&#x201D; and &#x201C;Uncertain&#x201D; following a compiled database that includes WHO&#x2019;s &#x201C;Catalog of mutations in <italic>Mycobacterium tuberculosis</italic> complex and their association with drug resistance&#x201D; 2nd edition (2023) and <xref ref-type="bibr" rid="ref53">The CRyPTIC Consortium (2022)</xref>. All variants excluded from these datasets were classified as &#x201C;No Information.&#x201D; A table with compensatory mutations was made based on reported studies (<xref ref-type="bibr" rid="ref8">Comas et al., 2012</xref>; <xref ref-type="bibr" rid="ref10">Conkle-Gutierrez et al., 2023</xref>; <xref ref-type="bibr" rid="ref40">Napier et al., 2023</xref>; <xref ref-type="bibr" rid="ref6">Billows et al., 2024</xref>). Furthermore, drug-efflux pumps previously associated with phenotypic resistance to antibiotics (<xref ref-type="bibr" rid="ref43">Pal et al., 2014</xref>; <xref ref-type="bibr" rid="ref41">Narang et al., 2017</xref>; <xref ref-type="bibr" rid="ref18">Ghajavand et al., 2019</xref>; <xref ref-type="bibr" rid="ref46">Raheem et al., 2020</xref>; <xref ref-type="bibr" rid="ref51">Rodrigues et al., 2020</xref>; <xref ref-type="bibr" rid="ref28">Laws et al., 2022</xref>; <xref ref-type="bibr" rid="ref24">Hasan et al., 2024</xref>; <xref ref-type="bibr" rid="ref47">Rao and Bhosale, 2024</xref>) were used to create an additional database. The lineage of MTB strains was determined using the software Mykrobe.<xref ref-type="fn" rid="fn0001"><sup>1</sup></xref></p>
<sec id="sec6">
<label>2.3.1</label>
<title>Phylogenetic reconstruction</title>
<p>Quality-filtered reads were analyzed with MTBseq v.1.1.0 (<xref ref-type="bibr" rid="ref27">Kohl et al., 2018</xref>) to generate a whole genome-based SNP alignment for phylogenetic inference under default parameters. The number of invariant sites in the alignment was calculated using the MTBseq_to_phylo.py script (available at GitHub - conmeehan/pathophy: Scripts for aiding in pathogen phylogenetics analysis). Phylogenetic trees were reconstructed with RAxML-NG v.1.2.2, applying the --model GTR&#x202F;+&#x202F;G&#x202F;+&#x202F;ASC_STAM {757,600/1447853/1442541/757415} and --site-repeat options. Bootstrap convergence was achieved after 150 replicates, supporting the tree topology. Tree visualization was performed in iTOL v7. Sub-lineage assignment and drug-resistance classification were obtained with TBprofiler v.6.6.5 (<xref ref-type="bibr" rid="ref44">Phelan et al., 2019</xref>), using the --itol flag to export the corresponding tree annotation files. <italic>Mycobacterium microti</italic> served as the outgroup for tree rooting.</p>
<p>All sequences and metadata are available at the NCBI database under the BioProject_PRJNA1260069.</p>
</sec>
</sec>
<sec id="sec7">
<label>2.4</label>
<title>Statistical analysis</title>
<p>All statistical analyses were performed using SPSS v 25 (IBM SPSS Statistics, version 25). Clinical and demographic variables with normal distribution were expressed as median &#x00B1; standard deviation (SD). Categorical variables were presented as frequencies (percentages) and compared using Fisher&#x2019;s exact test; a two-tailed <italic>p</italic> value of &#x003C;0.05 was considered statistically significant. Genotypic resistance for each drug was compared with the corresponding pDST. Sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) were calculated using a clinical calculator (Merck &#x0026; Co, Inc., Rahway, NJ, United States, 2025). The strength of agreement between phenotype and genotype was determined with Cohen&#x2019;s kappa coefficient. The Cohen&#x2019;s Kappa values indicate agreement and are interpreted as follows: values &#x2264; 0 indicating no agreement, 0.01&#x2013;0.20 as none to slight, 0.21&#x2013;0.40 as fair, 0.41&#x2013;0.60 as moderate, 0.61&#x2013;0.80 as substantial, and 0.81&#x2013;1.00 as almost perfect agreement (<xref ref-type="bibr" rid="ref33">McHugh, 2012</xref>). The graphs were generated using R (R Version 2024.09.01&#x202F;+&#x202F;394, packages basic, ggplot2, reshape2, and tidyr).</p>
</sec>
<sec id="sec8">
<label>2.5</label>
<title>Ethical concerns</title>
<p>The project was approved by the Ethical Committees from both, INER (Comit&#x00E9; de &#x00E9;tica en investigaci&#x00F3;n INER) and UABC (Comit&#x00E9; de &#x00E9;tica en investigaci&#x00F3;n UABC). Written informed consent was obtained from all participants before the collection of samples and the recording of clinical data; an exemption was obtained for older samples. All the information was treated confidentially.</p>
</sec>
</sec>
<sec sec-type="results" id="sec9">
<label>3</label>
<title>Results</title>
<sec id="sec10">
<label>3.1</label>
<title>Population characteristics</title>
<p>The samples for this study encompass mostly central and northern Mexico and include Ciudad de Mexico (24%), Baja California (18.5%), Estado de Mexico (16.6%), Guerrero (43.2%), Veracruz (9.2%), Puebla (5.5%), Hidalgo, Zacatecas, and Tamaulipas (each 1.8%). We specifically chose the highest resistant profile in the collections, focusing on MDR and Pre-XDR strains. Overall, the mean age of the patients was 42.5&#x202F;years (range, 17&#x2013;81), and 61.3% were men. The most common comorbidity was Type-2-Diabetes (36.7%), and 8% were living with HIV. Half of the patients had previous treatment at the time of sample collection, and 26% had no treatment recorded (see <xref ref-type="table" rid="tab1">Table 1</xref> for the full description of epidemiological data of the population sampled).</p>
<table-wrap position="float" id="tab1">
<label>Table 1</label>
<caption>
<p>Demographic and clinical characteristics of study participants.</p>
</caption>
<table frame="hsides" rules="groups">
<thead>
<tr>
<th align="left" valign="top">Sex</th>
<th align="center" valign="top">Number (% of total)</th>
</tr>
</thead>
<tbody>
<tr>
<td align="left" valign="middle">Female</td>
<td align="center" valign="bottom">17 (34.7%)</td>
</tr>
<tr>
<td align="left" valign="middle">Male</td>
<td align="center" valign="bottom">30 (61.3%)</td>
</tr>
<tr>
<td align="left" valign="middle">Unknown</td>
<td align="center" valign="bottom">2 (4.0%)</td>
</tr>
<tr>
<td align="left" valign="bottom" colspan="2">Age Median (42.48), 15.73 sd</td>
</tr>
<tr>
<td align="left" valign="bottom">&#x003C;18</td>
<td align="center" valign="bottom">1 (2%)</td>
</tr>
<tr>
<td align="left" valign="bottom">19&#x2013;29</td>
<td align="center" valign="bottom">10 (20.4%)</td>
</tr>
<tr>
<td align="left" valign="bottom">30&#x2013;44</td>
<td align="center" valign="bottom">15 (30.6%)</td>
</tr>
<tr>
<td align="left" valign="bottom">45&#x2013;64</td>
<td align="center" valign="bottom">17 (34.7)</td>
</tr>
<tr>
<td align="left" valign="bottom">&#x003E;65</td>
<td align="center" valign="bottom">4 (8.2%)</td>
</tr>
<tr>
<td align="left" valign="bottom">Unknown</td>
<td align="center" valign="bottom">2 (4.0%)</td>
</tr>
<tr>
<td align="left" valign="bottom" colspan="2">BMI Median (21.45), 3.62 sd</td>
</tr>
<tr>
<td align="left" valign="bottom">&#x003C; 18.5</td>
<td align="center" valign="bottom">10 (20.4%)</td>
</tr>
<tr>
<td align="left" valign="bottom">18.5&#x2013;24.99</td>
<td align="center" valign="bottom">24 (49%)</td>
</tr>
<tr>
<td align="left" valign="bottom">&#x2265;25.00-&#x202F;&#x003C;&#x202F;30.00</td>
<td align="center" valign="bottom">6 (12.2%)</td>
</tr>
<tr>
<td align="left" valign="bottom">Unknown</td>
<td align="center" valign="bottom">9 (18.4%)</td>
</tr>
<tr>
<td align="left" valign="bottom" colspan="2">Drug resistance</td>
</tr>
<tr>
<td align="left" valign="bottom">Susceptible</td>
<td align="center" valign="bottom">3 (6.12%)</td>
</tr>
<tr>
<td align="left" valign="bottom">Polydrug resistant</td>
<td align="center" valign="bottom">3 (6.12%)</td>
</tr>
<tr>
<td align="left" valign="bottom">Hr-TB</td>
<td align="center" valign="bottom">3 (6.12%)</td>
</tr>
<tr>
<td align="left" valign="bottom">RR-TB</td>
<td align="center" valign="bottom">3 (6.12%)</td>
</tr>
<tr>
<td align="left" valign="bottom">MDR-TB</td>
<td align="center" valign="bottom">22 (44.9%)</td>
</tr>
<tr>
<td align="left" valign="bottom">pre-XDR-TB</td>
<td align="center" valign="bottom">11 (22.4%)</td>
</tr>
<tr>
<td align="left" valign="bottom">Unknown</td>
<td align="center" valign="bottom">4 (8.16%)</td>
</tr>
<tr>
<td align="left" valign="bottom" colspan="2">Diabetes Type 2</td>
</tr>
<tr>
<td align="left" valign="bottom">Yes</td>
<td align="center" valign="bottom">18 (36.7%)</td>
</tr>
<tr>
<td align="left" valign="bottom">No</td>
<td align="center" valign="bottom">21 (42.8%)</td>
</tr>
<tr>
<td align="left" valign="bottom">Unknown</td>
<td align="center" valign="bottom">10 (20.4%)</td>
</tr>
<tr>
<td align="left" valign="bottom" colspan="2">Living with HIV</td>
</tr>
<tr>
<td align="left" valign="bottom">Yes</td>
<td align="center" valign="bottom">4 (8.16%)</td>
</tr>
<tr>
<td align="left" valign="bottom">No</td>
<td align="center" valign="bottom">36 (73.4%)</td>
</tr>
<tr>
<td align="left" valign="bottom">Unknown</td>
<td align="center" valign="bottom">9 (18.36%)</td>
</tr>
<tr>
<td align="left" valign="bottom" colspan="2">Previous TB treatment</td>
</tr>
<tr>
<td align="left" valign="bottom">Yes</td>
<td align="center" valign="bottom">27 (55.10%)</td>
</tr>
<tr>
<td align="left" valign="bottom">No</td>
<td align="center" valign="bottom">13 (26.5%)</td>
</tr>
<tr>
<td align="left" valign="bottom">Unknown</td>
<td align="center" valign="bottom">9 (18.36%)</td>
</tr>
</tbody>
</table>
<table-wrap-foot>
<p>Number of strains (% of total).</p>
</table-wrap-foot>
</table-wrap>
<p>The pDST included first and second line antibiotics, and revealed distinct drug-resistant profiles in our samples, including 44.9% of MDR, and 22.4% Pre-XDR samples, according to WHO classification criteria.</p>
</sec>
<sec id="sec11">
<label>3.2</label>
<title>Genotypic analysis of clinical samples</title>
<p>An average of 8.8 Million high-quality reads per sample were obtained, providing an average coverage depth of 278.3 times (65&#x2013;399) of the reference genome. WGS revealed that all our samples belong to the Euro-American lineage (lineage 4), except for two, which were identified as <italic>Mycobacterium bovis</italic>. The most prevalent sublineages are 4.1.2.1 (&#x201C;Harlem&#x201D;), 4.4.1.1, 4.1.1.3 (&#x201C;X3&#x201D;), and 4.10 (20.4, 18.39, 18.36, and 14.28% respectively).</p>
<p>Genotypic analysis of this population revealed that 89.8% of the samples were predicted to be resistant to at least one of the first-line drugs (rifampin, isoniazid, ethambutol, and pyrazinamide). Twenty-nine samples (59%) were classified as MDR/RR, and 12 samples, or 24.5%, were predicted to be resistant to second-line drugs (levofloxacin, moxifloxacin, bedaquiline).</p>
<p>Whole-genome SNP alignment comprising 6,123 variable sites (<xref ref-type="fig" rid="fig1">Figure 1</xref>) was used for phylogenetic reconstruction. A maximum-likelihood tree rooted with <italic>M. microti</italic> revealed well-defined clustering by sub-lineage; branch annotations indicate lineage assignment and predicted drug-resistance profiles, supporting an association between sublineage X3 and increased drug resistance.</p>
<fig position="float" id="fig1">
<label>Figure 1</label>
<caption>
<p>SNP-based phylogeny of MTBC isolates. The tree was inferred from 6,123 variable sites. Convergence was reached after 150 bootstrap replicates, meeting the standard cutoff of 0.03. Branches are annotated by sub-lineage and drug-resistance profile as determined by TB-profiler. <italic>Mycobacterium microti</italic> was included as the outgroup to root the tree.</p>
</caption>
<graphic xlink:href="fmicb-16-1666838-g001.tif" mimetype="image" mime-subtype="tiff">
<alt-text content-type="machine-generated">Phylogenetic tree illustrating various sub-lineages and drug resistance classes of tuberculosis. Sub-lineages are color-coded, with shades such as purple, pink, and blue. Drug resistance classes include categories like HR-TB, MDR-TB, and XDR-TB, each represented by different colors. The tree scale is 0.0001.</alt-text>
</graphic>
</fig>
<p>Overall, the most frequent mutations are <italic>katG</italic>_S315T (53% of all samples), <italic>rpoB</italic>_S450L (45%), and <italic>embB</italic>_D354A (20%), variants that confer resistance to isoniazid, rifampicin, and ethambutol, respectively (<xref ref-type="fig" rid="fig2">Figure 2</xref>). This corresponds well with the prevalence of antibiotic resistance profile of MDR in our sample population. Among the next most frequent mutations, those affecting resistance to second-line drugs include <italic>inhA</italic>_-777C &#x003E; T (16%), which confers cross-resistance to isoniazid and prothionamide/ethionamide (Pto/Eto), and <italic>gyrA</italic>_D94D (12%), which confers resistance to levofloxacin and moxifloxacin, and reflects the Pre-XDR antibiotic resistance profile. We also found one strain with an <italic>rrs</italic>_517C&#x202F;&#x003E;&#x202F;T mutation, which confers resistance to streptomycin, a virtually obsolete treatment option for TB, and another with a <italic>rrs_</italic>1401A&#x202F;&#x003E;&#x202F;G mutation that confers resistance to kanamycin, capreomycin, and amikacin. Additionally, we identified one strain containing the variant <italic>mmpR5</italic>_I67fs, which confers resistance to bedaquiline with no cross-resistance to clofazimine (<xref ref-type="bibr" rid="ref56">World Health Organization, 2023</xref>; see <xref ref-type="fig" rid="fig2">Figure 2</xref>).</p>
<fig position="float" id="fig2">
<label>Figure 2</label>
<caption>
<p>Distribution of mutations associated with resistance. Variants in genes associated with resistance to first-line and second-line antituberculosis drugs: R: rifampicin, H: isoniazid, Eto/Pto: ethionamide/proteonamide, E: ethambutol, Z: pyrazinamide, FQ: fluoroquinolones (moxifloxacin, levofloxacin), B: Bedaquiline, AG: aminoglycosides.</p>
</caption>
<graphic xlink:href="fmicb-16-1666838-g002.tif" mimetype="image" mime-subtype="tiff">
<alt-text content-type="machine-generated">Circular diagram depicting gene mutations associated with antibiotic resistance in Mycobacterium tuberculosis. Genes are labeled around the circle: mmpR5, rrs, rpoB, katG, inhA, embB, pncA, and gyrA. Specific mutations are marked by colored lines and segments, such as S450L, D94G, and others. Sections are divided and annotated with FQ, AG, R, H, H/Eto/Pto, Z, and E to denote different antibiotic resistances.</alt-text>
</graphic>
</fig>
<p>WGS allowed us to identify 89 variants not reported on consensus databases (<xref ref-type="bibr" rid="ref56">World Health Organization, 2023</xref>; <xref ref-type="bibr" rid="ref53">The CRyPTIC Consortium, 2022</xref>; see <xref ref-type="supplementary-material" rid="SM1">Supplementary Table 1</xref>).</p>
<p>Furthermore, we identified 15 compensatory mutations (<xref ref-type="table" rid="tab2">Table 2</xref>), which are second site mutations that reduce the fitness cost associated with antibiotic resistance phenotypes, therefore allowing resistance genes to be stably maintained in bacterial populations. We found that eight strains carrying the <italic>rpoB</italic>_S450L mutation had acquired previously reported compensatory mutations in the RNA polymerase subunit RpoC (V483G/A and I491V) (<xref ref-type="bibr" rid="ref8">Comas et al., 2012</xref>; <xref ref-type="bibr" rid="ref10">Conkle-Gutierrez et al., 2023</xref>; <xref ref-type="table" rid="tab2">Table 2</xref>). We also detected four isoniazid-resistant strains with compensatory mutations in <italic>ahpC</italic>_-81C&#x202F;&#x003E;&#x202F;T, <italic>ahpC</italic>_-52C&#x202F;&#x003E;&#x202F;A, <italic>ahpC</italic>_-51G&#x202F;&#x003E;&#x202F;A, and <italic>ahpC</italic>_-47_-46ins (<xref ref-type="bibr" rid="ref40">Napier et al., 2023</xref>; <xref ref-type="bibr" rid="ref6">Billows et al., 2024</xref>).</p>
<table-wrap position="float" id="tab2">
<label>Table 2</label>
<caption>
<p>Mutations identified as compensatory.</p>
</caption>
<table frame="hsides" rules="groups">
<thead>
<tr>
<th align="left" valign="top">Drug</th>
<th align="center" valign="top">Gen</th>
<th align="center" valign="top">Variants</th>
<th align="center" valign="top">Number of strains with this variant</th>
</tr>
</thead>
<tbody>
<tr>
<td align="left" valign="middle" rowspan="3">Rifampicin</td>
<td align="center" valign="middle" rowspan="3"><italic>rpoC</italic></td>
<td align="center" valign="bottom">V483G</td>
<td align="center" valign="bottom">6</td>
</tr>
<tr>
<td align="center" valign="bottom">I491V</td>
<td align="center" valign="bottom">2</td>
</tr>
<tr>
<td align="center" valign="bottom">V483A</td>
<td align="center" valign="bottom">1</td>
</tr>
<tr>
<td align="left" valign="middle" rowspan="4">Isoniazid</td>
<td align="center" valign="middle" rowspan="4"><italic>ahpC</italic></td>
<td align="center" valign="bottom">-81C&#x202F;&#x003E;&#x202F;T</td>
<td align="center" valign="bottom">2</td>
</tr>
<tr>
<td align="center" valign="bottom">-52C&#x202F;&#x003E;&#x202F;A</td>
<td align="center" valign="bottom">2</td>
</tr>
<tr>
<td align="center" valign="bottom">-51G&#x202F;&#x003E;&#x202F;A</td>
<td align="center" valign="bottom">1</td>
</tr>
<tr>
<td align="center" valign="bottom">-47_-46insT</td>
<td align="center" valign="bottom">1</td>
</tr>
</tbody>
</table>
<table-wrap-foot>
<p>Compensatory mutations to rifampicin and isoniazid, identified in sample population.</p>
</table-wrap-foot>
</table-wrap>
<p>Intriguingly, the majority of our samples contained variants in <italic>gyrA</italic>, with 98% exhibiting the E21Q mutation and 81% also carrying S95T and G668D mutations, classified as &#x201C;Not associated with Resistance.&#x201D; Additionally, 46% contained the <italic>embC</italic>_V981L variant and 20% <italic>mshA</italic>_N111S, also classified as &#x201C;Not Associated with Resistance.&#x201D; We identified 10 strains with mutations in genes recently associated with delamanid resistance: <italic>dprE2, fbiA, fbiB,</italic> and <italic>ddn</italic>. However, these specific variants (<italic>dprE2</italic>_D45N, <italic>fbiA</italic>_I208V, <italic>fbiB</italic>_G19E, and <italic>ddn</italic>_18G&#x202F;&#x003E;&#x202F;A) lack a confirmed link to resistance and require validation (<xref ref-type="supplementary-material" rid="SM1">Supplementary Table 2</xref>; <xref ref-type="bibr" rid="ref56">World Health Organization, 2023</xref>).</p>
</sec>
<sec id="sec12">
<label>3.3</label>
<title>Genotypic and phenotypic drug resistance analysis</title>
<p>Next, we compared the genotypic resistance profile for each drug to pDST results when available. <xref ref-type="table" rid="tab3">Table 3</xref> shows high sensitivity (&#x003E;80%) to first-line drugs (rifampicin, isoniazid, and ethambutol), except for pyrazinamide (&#x003C;50%). Specificity, PPV, NPV, and accuracy were high (&#x003E;70%) for all drugs analyzed except rifampicin. Cohen&#x2019;s kappa coefficient showed good concordance (0.8&#x2013;1.0) for ethionamide; good agreement (0.6&#x2013;0.79) between phenotypic and genotypic results for isoniazid, ethambutol, moxifloxacin, and levofloxacin; moderate agreement (0.4&#x2013;0.59) for pyrazinamide, amikacin, and capreomycin.</p>
<table-wrap position="float" id="tab3">
<label>Table 3</label>
<caption>
<p>Phenotypic and genotypic drug resistance concordance analysis.</p>
</caption>
<table frame="hsides" rules="groups">
<thead>
<tr>
<th align="left" valign="top">Drugs</th>
<th align="center" valign="top">Phenotypic resistance</th>
<th align="center" valign="top">Genotypic resistance</th>
<th align="center" valign="top">Sensitivity</th>
<th align="center" valign="top">Specificity</th>
<th align="center" valign="top">PPV<sup>a</sup></th>
<th align="center" valign="top">NPV<sup>b</sup></th>
<th align="center" valign="top">Cohen&#x2019;s Kappa (95% CI)</th>
</tr>
</thead>
<tbody>
<tr>
<td align="left" valign="top">Rifampicin</td>
<td align="center" valign="top">36/49</td>
<td align="center" valign="top">38/49</td>
<td align="center" valign="top">88.90%</td>
<td align="center" valign="top">66.70%</td>
<td align="center" valign="top">4.48%</td>
<td align="center" valign="top">99.68%</td>
<td align="center" valign="top"><bold>0.533</bold>, P 0.002 (0.001&#x2013;0.002)</td>
</tr>
<tr>
<td align="left" valign="top">Isoniazid</td>
<td align="center" valign="top">38/49</td>
<td align="center" valign="top">34/49</td>
<td align="center" valign="top">86.80%</td>
<td align="center" valign="top">100%</td>
<td align="center" valign="top">100%</td>
<td align="center" valign="top">99.75%</td>
<td align="center" valign="top"><bold>0.672</bold>, P 0.000 (0.000&#x2013;0.000)</td>
</tr>
<tr>
<td align="left" valign="top">Pyrazinamide</td>
<td align="center" valign="top">20/49</td>
<td align="center" valign="top">14/49</td>
<td align="center" valign="top">45.00%</td>
<td align="center" valign="top">95.70%</td>
<td align="center" valign="top">16.69%</td>
<td align="center" valign="top">98.91%</td>
<td align="center" valign="top"><bold>0.420</bold>, P 0.003 (0.002&#x2013;0.005)</td>
</tr>
<tr>
<td align="left" valign="top">Ethambutol</td>
<td align="center" valign="top">17/49</td>
<td align="center" valign="top">22/49</td>
<td align="center" valign="top">88.20%</td>
<td align="center" valign="top">85.70%</td>
<td align="center" valign="top">10.56%</td>
<td align="center" valign="top">99.74%</td>
<td align="center" valign="top"><bold>0.723</bold>, P 0.000 (0.000&#x2013;0.000)</td>
</tr>
<tr>
<td align="left" valign="top">Streptomycin</td>
<td align="center" valign="top">17/49</td>
<td align="center" valign="top">1/49</td>
<td align="center" valign="top">5.90%</td>
<td align="center" valign="top">100%</td>
<td align="center" valign="top">100%</td>
<td align="center" valign="top">98.23%</td>
<td align="center" valign="top"><bold>0.067</bold>, <bold>P 0.430</bold> (0.420&#x2013;0.440)</td>
</tr>
<tr>
<td align="left" valign="top">Moxifloxacin</td>
<td align="center" valign="top">10/49</td>
<td align="center" valign="top">11/49</td>
<td align="center" valign="top">80%</td>
<td align="center" valign="top">90.90%</td>
<td align="center" valign="top">14.40%</td>
<td align="center" valign="top">99.58%</td>
<td align="center" valign="top"><bold>0.685</bold>, P 0.000 (0.000&#x2013;0.000)</td>
</tr>
<tr>
<td align="left" valign="top">Levofloxacin</td>
<td align="center" valign="top">4/49</td>
<td align="center" valign="top">11/49</td>
<td align="center" valign="top">100%</td>
<td align="center" valign="top">90.90%</td>
<td align="center" valign="top">17.38%</td>
<td align="center" valign="top">100%</td>
<td align="center" valign="top"><bold>0.755</bold>, P 0.001 (0.000&#x2013;0.002)</td>
</tr>
<tr>
<td align="left" valign="top">Kanamycin</td>
<td align="center" valign="top">4/49</td>
<td align="center" valign="top">1/49</td>
<td align="center" valign="top">25%</td>
<td align="center" valign="top">100%</td>
<td align="center" valign="top">100%</td>
<td align="center" valign="top">98.58%</td>
<td align="center" valign="top"><bold>0.341</bold>, <bold>P 0.226</bold> (0.218&#x2013;0.234)</td>
</tr>
<tr>
<td align="left" valign="top">Amikacin</td>
<td align="center" valign="top">3/49</td>
<td align="center" valign="top">1/49</td>
<td align="center" valign="top">33.30%</td>
<td align="center" valign="top">100%</td>
<td align="center" valign="top">100%</td>
<td align="center" valign="top">98.74%</td>
<td align="center" valign="top"><bold>0.480</bold>, P 0.075 (0.070&#x2013;0.080)</td>
</tr>
<tr>
<td align="left" valign="top">Capreomycin</td>
<td align="center" valign="top">3/49</td>
<td align="center" valign="top">1/49</td>
<td align="center" valign="top">33.30%</td>
<td align="center" valign="top">100%</td>
<td align="center" valign="top">100%</td>
<td align="center" valign="top">98.74%</td>
<td align="center" valign="top"><bold>0.457</bold>, <bold>P 0.161</bold> (0.154&#x2013;0.168)</td>
</tr>
<tr>
<td align="left" valign="top">Etionamid</td>
<td align="center" valign="top">2/49</td>
<td align="center" valign="top">8/49</td>
<td align="center" valign="top">100%</td>
<td align="center" valign="top">100%</td>
<td align="center" valign="top">100%</td>
<td align="center" valign="top">100%</td>
<td align="center" valign="top"><bold>1.0</bold>, P 0.004 (0.003&#x2013;0.005)</td>
</tr>
</tbody>
</table>
<table-wrap-foot>
<p><sup>a</sup>PPV Positive Predictive Value. <sup>b</sup>NPV Negative Predictive Value.</p>
</table-wrap-foot>
</table-wrap>
</sec>
<sec id="sec13">
<label>3.4</label>
<title>Variant analysis in efflux pumps</title>
<p>Efflux pump mechanisms, both multidrug and drug-specific efflux pump mechanisms, are important determinants of antimicrobial resistance. Therefore, we specifically searched for efflux pumps previously identified as associated with antibiotic resistance (<xref ref-type="bibr" rid="ref13">Domenech et al., 2005</xref>; <xref ref-type="bibr" rid="ref43">Pal et al., 2014</xref>; <xref ref-type="bibr" rid="ref4">Anthony Malinga and Stoltz, 2016</xref>; <xref ref-type="bibr" rid="ref41">Narang et al., 2017</xref>; <xref ref-type="bibr" rid="ref18">Ghajavand et al., 2019</xref>; <xref ref-type="bibr" rid="ref35">Melly and Purdy, 2019</xref>; <xref ref-type="bibr" rid="ref46">Raheem et al., 2020</xref>; <xref ref-type="bibr" rid="ref51">Rodrigues et al., 2020</xref>; <xref ref-type="bibr" rid="ref28">Laws et al., 2022</xref>; <xref ref-type="bibr" rid="ref48">Remm et al., 2022</xref>; <xref ref-type="bibr" rid="ref53">The CRyPTIC Consortium, 2022</xref>; <xref ref-type="bibr" rid="ref24">Hasan et al., 2024</xref>). In this analysis, we found that 73.4% of our samples encoded variants in members of the ABC, RND, and MFS superfamilies but none in the SMR or MATE efflux pump superfamilies. Overall, the most frequent variants were found in the ABC and RND superfamilies, present in 47 and 45% of all our samples, respectively. Within the ABC superfamily, the most frequent variant was on Rv1458c (present in 39% of our samples), which has been associated with resistance to rifampicin, isoniazid, ethambutol, and streptomycin (<xref ref-type="bibr" rid="ref23">Hao et al., 2011</xref>; <xref ref-type="bibr" rid="ref28">Laws et al., 2022</xref>). In addition, we identified 15 strains (30% of our samples) with mutations on <italic>mmpL8</italic> member of the RND superfamily associated with glycolipid transport and isoniazid resistance, nine on <italic>mmpL3</italic> associated resistance to bedaquiline, clofazimine, linezolid, and delamanid (<xref ref-type="bibr" rid="ref13">Domenech et al., 2005</xref>; <xref ref-type="bibr" rid="ref35">Melly and Purdy, 2019</xref>; <xref ref-type="bibr" rid="ref28">Laws et al., 2022</xref>; <xref ref-type="bibr" rid="ref53">The CRyPTIC Consortium, 2022</xref>; see <xref ref-type="table" rid="tab4">Table 4</xref>).</p>
<table-wrap position="float" id="tab4">
<label>Table 4</label>
<caption>
<p>Mutations in efflux pumps.</p>
</caption>
<table frame="hsides" rules="groups">
<thead>
<tr>
<th align="left" valign="top">Family</th>
<th align="left" valign="top">Efflux pumps</th>
<th align="left" valign="top">Variants</th>
<th align="center" valign="top">Number of strains with this variant</th>
<th align="left" valign="top">Substrates</th>
<th align="left" valign="top">References</th>
</tr>
</thead>
<tbody>
<tr>
<td align="left" valign="middle" rowspan="19">RND superfamily</td>
<td align="left" valign="middle">MmpL1</td>
<td align="left" valign="middle">T363M</td>
<td align="center" valign="middle">3</td>
<td align="left" valign="middle">Fatty acid transport</td>
<td align="left" valign="middle">
<xref ref-type="bibr" rid="ref35">Melly and Purdy (2019)</xref>
</td>
</tr>
<tr>
<td align="left" valign="middle">MmpS1</td>
<td align="left" valign="middle">I122N</td>
<td align="center" valign="middle">1</td>
<td align="left" valign="middle">Unknown</td>
<td align="left" valign="middle">Not Apply</td>
</tr>
<tr>
<td align="left" valign="middle" rowspan="6">MmpL3</td>
<td align="left" valign="middle">D466E</td>
<td align="center" valign="middle">3</td>
<td align="left" valign="middle" rowspan="6">Bedaquilne, Clofazimine, Linezolid, Delamanid. Glycolipid transport</td>
<td align="left" valign="middle" rowspan="6"><xref ref-type="bibr" rid="ref53">The CRyPTIC Consortium (2022)</xref> and <xref ref-type="bibr" rid="ref2">Adams et al. (2021)</xref></td>
</tr>
<tr>
<td align="left" valign="middle">F384I</td>
<td align="center" valign="middle">3</td>
</tr>
<tr>
<td align="left" valign="middle">A362S</td>
<td align="center" valign="middle">3</td>
</tr>
<tr>
<td align="left" valign="middle">G747R</td>
<td align="center" valign="middle">1</td>
</tr>
<tr>
<td align="left" valign="middle">T284A</td>
<td align="center" valign="middle">1</td>
</tr>
<tr>
<td align="left" valign="middle">P791L</td>
<td align="center" valign="middle">1</td>
</tr>
<tr>
<td align="left" valign="middle" rowspan="11">MmpL8</td>
<td align="left" valign="middle">A1042E</td>
<td align="center" valign="middle">3</td>
<td align="left" valign="middle" rowspan="11">Isoniazid Sulfated Glycolipid</td>
<td align="left" valign="middle" rowspan="11"><xref ref-type="bibr" rid="ref24">Hasan et al. (2024)</xref> and <xref ref-type="bibr" rid="ref41">Narang et al. (2017)</xref></td>
</tr>
<tr>
<td align="left" valign="middle">V258A</td>
<td align="center" valign="middle">2</td>
</tr>
<tr>
<td align="left" valign="middle">V961M</td>
<td align="center" valign="middle">2</td>
</tr>
<tr>
<td align="left" valign="middle">V526A</td>
<td align="center" valign="middle">2</td>
</tr>
<tr>
<td align="left" valign="middle">G235R</td>
<td align="center" valign="middle">2</td>
</tr>
<tr>
<td align="left" valign="middle">V728L</td>
<td align="center" valign="middle">1</td>
</tr>
<tr>
<td align="left" valign="middle">L56fs</td>
<td align="center" valign="middle">1</td>
</tr>
<tr>
<td align="left" valign="middle">L788L</td>
<td align="center" valign="middle">1</td>
</tr>
<tr>
<td align="left" valign="middle">G55V</td>
<td align="center" valign="middle">1</td>
</tr>
<tr>
<td align="left" valign="middle">G692E</td>
<td align="center" valign="middle">1</td>
</tr>
<tr>
<td align="left" valign="middle">64C&#x202F;&#x003E;&#x202F;T</td>
<td align="center" valign="middle">9</td>
</tr>
<tr>
<td align="left" valign="middle" rowspan="9">ABC Superfamily</td>
<td align="left" valign="middle" rowspan="2">Rv1458c</td>
<td align="left" valign="middle">P5fs</td>
<td align="center" valign="middle">16</td>
<td align="left" valign="middle" rowspan="2">Rifampicin, Isoniazid, Ethambutol, Streptomycin</td>
<td align="left" valign="middle" rowspan="2"><xref ref-type="bibr" rid="ref24">Hasan et al. (2024)</xref>, <xref ref-type="bibr" rid="ref28">Laws et al. (2022)</xref>, and <xref ref-type="bibr" rid="ref4">Anthony Malinga and Stoltz (2016)</xref></td>
</tr>
<tr>
<td align="left" valign="middle">V1_A4del</td>
<td align="center" valign="middle">2</td>
</tr>
<tr>
<td align="left" valign="middle" rowspan="3">Rv1819c/BacA</td>
<td align="left" valign="middle">I603V</td>
<td align="center" valign="middle">3</td>
<td align="left" valign="middle" rowspan="3">Rifampicin, Isoniazid, Aminoglycosides, Antimicrobial peptides, Beta-lactams, Chloramphenicol, Macrolides, Novobiocin, Tetracycline, Vancomycin</td>
<td align="left" valign="middle" rowspan="3"><xref ref-type="bibr" rid="ref28">Laws et al. (2022)</xref> and <xref ref-type="bibr" rid="ref30">Li et al. (2024)</xref></td>
</tr>
<tr>
<td align="left" valign="middle">L551fs</td>
<td align="center" valign="middle">1</td>
</tr>
<tr>
<td align="left" valign="middle">D379G</td>
<td align="center" valign="middle">1</td>
</tr>
<tr>
<td align="left" valign="middle" rowspan="4">Rv0342/IniA</td>
<td align="left" valign="middle">N88S</td>
<td align="center" valign="middle">2</td>
<td align="left" valign="middle" rowspan="4">Rifampicin, Ethambutol</td>
<td align="left" valign="middle" rowspan="4">
<xref ref-type="bibr" rid="ref46">Raheem et al. (2020)</xref>
</td>
</tr>
<tr>
<td align="left" valign="middle">P3A</td>
<td align="center" valign="middle">2</td>
</tr>
<tr>
<td align="left" valign="middle">H481Q</td>
<td align="center" valign="middle">1</td>
</tr>
<tr>
<td align="left" valign="middle">A374V</td>
<td align="center" valign="middle">1</td>
</tr>
<tr>
<td align="left" valign="middle" rowspan="5">MFS Superfamilia</td>
<td align="left" valign="middle">Rv2846c/EfpA</td>
<td align="left" valign="middle">T15R</td>
<td align="center" valign="middle">3</td>
<td align="left" valign="middle">Rifampicin, Isoniazid, Fluoroquinolones, Acriflavine, Erythromycin, Ethidium bromide</td>
<td align="left" valign="middle"><xref ref-type="bibr" rid="ref51">Rodrigues et al. (2020)</xref> and <xref ref-type="bibr" rid="ref28">Laws et al. (2022)</xref></td>
</tr>
<tr>
<td align="left" valign="middle" rowspan="3">Rv2459/JefA</td>
<td align="left" valign="middle">M55I</td>
<td align="center" valign="middle">2</td>
<td align="left" valign="middle" rowspan="3">Rifampicin, Isoniazid, Ethambutol, Ethidium bromide</td>
<td align="left" valign="middle" rowspan="3"><xref ref-type="bibr" rid="ref51">Rodrigues et al. (2020)</xref> and <xref ref-type="bibr" rid="ref28">Laws et al. (2022)</xref></td>
</tr>
<tr>
<td align="left" valign="middle">L44fs</td>
<td align="center" valign="middle">1</td>
</tr>
<tr>
<td align="left" valign="middle">A419T</td>
<td align="center" valign="middle">1</td>
</tr>
<tr>
<td align="left" valign="middle">Rv1410c/P55</td>
<td align="left" valign="middle">M182L</td>
<td align="center" valign="middle">1</td>
<td align="left" valign="middle">Rifampicin, Isoniazid, Aminoglycosides, Clofazimine, Tetracyclines.</td>
<td align="left" valign="middle"><xref ref-type="bibr" rid="ref51">Rodrigues et al. (2020)</xref> and <xref ref-type="bibr" rid="ref28">Laws et al. (2022)</xref></td>
</tr>
</tbody>
</table>
<table-wrap-foot>
<p>Variants on efflux pumps genes, number of strains with variants; substrates reported for each efflux pump.</p>
</table-wrap-foot>
</table-wrap>
<p>Most of the strains (67.5%) with mutations in efflux pumps had only one mutation, 21.6% had variants in two different efflux pumps, and two strains had five different efflux pumps mutated; these two strains were identified as <italic>M. bovis</italic> and had the lowest congruency between phenotypic tests and genotypic characterization.</p>
</sec>
</sec>
<sec sec-type="discussion" id="sec14">
<label>4</label>
<title>Discussion</title>
<p>The increasing incidence of DR-TB has reduced the number of effective antibiotics, thus complicating the efforts to combat this persistent global health problem. Drug resistance phenotypes are acquired through the accumulation of mutations (single-nucleotide polymorphisms (SNPs), insertions, or deletions) in genes that code for drug targets or enzymes that activate drugs. This study presents WGS characterization of 49 resistant clinical isolates with at least first-line pDST collected from central and northern Mexico from 2014 to 2023. Among these isolates, 44.8% were classified as MDR-TB and 22.4% as Pre-XDR.</p>
<p>The rate of mutation and the effect of these variants on drug resistance are sometimes dependent on the genetic background of the <italic>Mtb</italic> strain (<xref ref-type="bibr" rid="ref16">Ford et al., 2013</xref>; <xref ref-type="bibr" rid="ref42">Nimmo et al., 2022</xref>). Therefore, it is crucial to characterize the resistance patterns in various regions, with their corresponding prevalent strains, to evaluate and design effective treatment strategies. Latin America has few studies examining resistance variants, although more interest has been given in recent years to our population (<xref ref-type="bibr" rid="ref60">Zenteno-Cuevas et al., 2014</xref>; <xref ref-type="bibr" rid="ref32">Madrazo-Moya et al., 2019</xref>; <xref ref-type="bibr" rid="ref25">Jim&#x00E9;nez-Ruano et al., 2021</xref>; <xref ref-type="bibr" rid="ref52">Santos-Lazaro et al., 2021</xref>; <xref ref-type="bibr" rid="ref5">Barbosa-Amezcua et al., 2022</xref>; <xref ref-type="bibr" rid="ref11">D&#x2019;Souza et al., 2023</xref>; <xref ref-type="bibr" rid="ref34">Mej&#x00ED;a-Ponce et al., 2023</xref>; <xref ref-type="bibr" rid="ref38">Morey-Le&#x00F3;n et al., 2023</xref>; <xref ref-type="bibr" rid="ref9">Concei&#x00E7;&#x00E3;o et al., 2024</xref>; <xref ref-type="bibr" rid="ref45">Puy&#x00E9;n et al., 2024</xref>). It is important to emphasize that DR-TB reporting in Mexico has increased significantly over the last 4&#x202F;years, after obvious underreporting due to health services being redirected toward COVID-19 pandemic response. DR-TB cases increased from 283 to 796 from 2015 to 2023, in contrast with global DR-TB trends that have remained relatively stable or decreased over the same period (<xref ref-type="bibr" rid="ref5003">National Center for Preventive Programs and Disease Control, 2023</xref>; <xref ref-type="bibr" rid="ref20">Global Tuberculosis Report, 2024</xref>). Thus, a robust epidemiological surveillance system, based on local data, is crucial for accurate diagnosis and enhanced treatment, which could enable the identification of areas for improvement of health policies to prevent transmission.</p>
<p>The available evidence consistently shows lineage 4 as the dominant clade (&#x003E;90% of the isolates) in Mexico. National surveys based on WGS and genotyping confirm the expansion of Haarlem (4.1.2.1), X-type (notably sub-lineage 4.1.1.3, &#x201C;X3&#x201D;), and 4.8 across several Mexican states. Published work (e.g., <xref ref-type="bibr" rid="ref25">Jim&#x00E9;nez-Ruano et al., 2021</xref>; <xref ref-type="bibr" rid="ref34">Mej&#x00ED;a-Ponce et al., 2023</xref>) reports that X3 is primarily restricted to Mexico and is disproportionately enriched for multidrug-resistant strains. Other L4 sub-lineages such as 4.1.2.1 and 4.8 have also been linked to elevated drug-resistance frequencies in regional cohorts (<xref ref-type="bibr" rid="ref36">Molina-Torres et al., 2022</xref>; <xref ref-type="bibr" rid="ref3">Alvarez-Maya et al., 2025</xref>). These findings indicate that, while L4 predominance likely reflects historical introduction and successful local expansion, certain Mexican sub-lineages, especially X3, carry a higher burden of resistance and warrant continued genomic surveillance. Surprisingly, 98% of our samples contain mutations classified as &#x201C;not associated with resistance&#x201D; in <italic>gyrA</italic> E21Q and 81% also carry S95T and G668D mutations, perhaps reflecting the frequent use of fluoroquinolones in the country.</p>
<p>The most frequent drug resistance-conferring mutations identified in our dataset have already been reported elsewhere (<xref ref-type="bibr" rid="ref56">World Health Organization, 2023</xref>; <xref ref-type="bibr" rid="ref53">The CRyPTIC Consortium, 2022</xref>), and include <italic>katG</italic>_S315T, <italic>rpoB</italic>_S540L, and <italic>embB</italic>_D354A. The similarity between previous reports in Mexico and WHO global data reflects common adaptative mechanisms against anti-TB drugs while maintaining fitness, suggesting that existing genotyping tests that include these mutations remain useful for drug-resistant prediction and disease management.</p>
<p>However, we did identify novel potential resistance-conferring mutations: 89 variants in 30 genes and promoters; they include polymorphisms in genes previously associated with resistance, including <italic>katG</italic> for isoniazid, <italic>embB</italic> for ethambutol, <italic>gyrA</italic> and <italic>gyrB</italic> for fluoroquinolones, and <italic>rrs</italic> for aminoglycosides, as well as others in genes so far not associated with resistance.</p>
<p>Among these, we found 18 variants on <italic>ahpC</italic>. The <italic>ahpC</italic> gene encodes alkyl hydroperoxide reductase C, which is part of the bacterial antioxidant defense system. The AhpC protein helps protect against oxidative damage produced by isoniazid, thus variants in <italic>ahpC</italic> can serve as compensatory changes that help bacteria survive the oxidative stress generated by isoniazid activity. Although none of the specific variants have been associated with resistance, changes in the expression or activity of AphC may increase resistance to isoniazid, which is why WHO considers them as secondary mutations and labeled as &#x201C;candidate resistance genes&#x201D; (<xref ref-type="bibr" rid="ref56">World Health Organization, 2023</xref>).</p>
<p>Another interesting example is the variant found in the <italic>dprE2</italic> gene. <italic>dprE2</italic> is part of an operon that encodes <italic>dprE1-E2</italic>, which are essential for the synthesis of the arabinogalactan and lipoarabinomannan components of the bacterial cell wall. Recently (<xref ref-type="bibr" rid="ref1">Abrahams et al., 2023</xref>), DprE2 was identified as the target of activated Pretomanid and Delamanid. Although not yet validated, it is identified as a potential antimycobacterial target of these drugs and thus susceptible to resistance. All these variants should be validated with phenotypic tests, and may provide new insight into resistance variants in the region.</p>
<p>Additionally, we found that 40.9% (9 out of 22) of <italic>rpoB</italic>_S450L mutations had compensatory mutations in <italic>rpoC</italic>, which has been reported to improve the fitness of an <italic>rpoB</italic> rifampicin-resistant strains and associated with resistance outbreaks (<xref ref-type="bibr" rid="ref8">Comas et al., 2012</xref>; <xref ref-type="bibr" rid="ref10">Conkle-Gutierrez et al., 2023</xref>). Consistent with these findings, four of these strains were associated with poor clinical outcomes, and two samples had an increased resistance profile. However our numbers are too small to do proper statistical analysis.</p>
<p>We found a high level of concordance between genotypic and phenotypic analysis in strains containing the most common resistant variants, and a lower concordance when all the samples are included. The discrepancy between genotypic and phenotypic analysis is not particular to our study (<xref ref-type="bibr" rid="ref34">Mej&#x00ED;a-Ponce et al., 2023</xref>; <xref ref-type="bibr" rid="ref24">Hasan et al., 2024</xref>). Although phenotypic tests are still considered the &#x201C;golden standard,&#x201D; it is also subject to interpretation and execution. Critical concentration values have changed through the years; specifically, tests for some of our samples were conducted before 2021, with altered critical concentration values for rifampicin, isoniazid, and fluoroquinolones, or no microbiological test for certain drugs. Furthermore, more analysis is still needed to determine all the specific genes involved in resistance to newer drugs such as bedaquiline, linezolid, delamanid, etc., and whether they vary by region or previous treatments.</p>
<p>Additionally, we identified five strains with heteroresistance: three strains to fluoroquinolones, one to rifampicin, and one to a combination of rifampicin, isoniazid, and pyrazinamide. Although we do not have the therapeutic outcome for all these patients, the majority of them had poor clinical outcomes. Heteroresistance could reveal the inherent complexity of resistance evolution and possible epistatic effects and must be further analyzed.</p>
<p>On the other hand, efflux pumps are emerging as critical players in antibiotic resistance not only in tuberculosis but in all infectious diseases. Efflux pumps represent a critical intersection between essential cellular maintenance systems and barriers to antimicrobial therapy. The presence of multiple efflux pumps in diverse bacteria, including Gram positive and Gram negative genera and fungi, is associated with increased antibiotic resistance (<xref ref-type="bibr" rid="ref2">Adams et al., 2021</xref>; <xref ref-type="bibr" rid="ref22">Han et al., 2024</xref>; <xref ref-type="bibr" rid="ref29">Leconte et al., 2024</xref>; <xref ref-type="bibr" rid="ref30">Li et al., 2024</xref>). The vast majority of the variants on efflux pumps found in our sample population are on Mycobacterial membrane protein Large (MmpL) genes; 22 on <italic>mmpL8</italic>, 12 on <italic>mmpL3</italic>, followed by ABC transporter Rv1458c with 18 variants. MmpL proteins are essential for cell wall biosynthesis and lipid transport, which also transport antibiotic compounds out of the cell. MmpL8 is required for biosynthesis and transport of SL-1 sulfated glycolipid that is involved in host-pathogen interactions during early infections. MmpL3 is key for trehalose monomycolate transport, an indispensable component of the mycobacterial cell wall. MmpL3 is the target of an ethambutol analog, SQ109, which is now in phase 2 clinical trials (<xref ref-type="bibr" rid="ref7">Chaitra et al., 2023</xref>).</p>
<p>Both by modifying the impermeability of the cell wall and exporting antibiotic compounds, efflux pumps can work synergistically with other resistance mechanisms to increase antimicrobial resistance. Furthermore, several studies have demonstrated that antibiotic treatment upregulates these integral membrane proteins, thereby maintaining sublethal intracellular antibiotic concentrations and selecting for antibiotic-resistant mutants (<xref ref-type="bibr" rid="ref23">Hao et al., 2011</xref>; <xref ref-type="bibr" rid="ref28">Laws et al., 2022</xref>; <xref ref-type="bibr" rid="ref48">Remm et al., 2022</xref>). Thus, the administration of inhibitors may play an important role in enhancing the efficacy of antibiotics in the treatment of bacterial and fungal infections (<xref ref-type="bibr" rid="ref14">El Meouche and Dunlop, 2018</xref>; <xref ref-type="bibr" rid="ref21">Grimsey et al., 2020</xref>; <xref ref-type="bibr" rid="ref12">de Melo Guedes et al., 2024</xref>; <xref ref-type="bibr" rid="ref22">Han et al., 2024</xref>; <xref ref-type="bibr" rid="ref29">Leconte et al., 2024</xref>; <xref ref-type="bibr" rid="ref30">Li et al., 2024</xref>; <xref ref-type="bibr" rid="ref59">Ye et al., 2024</xref>). In particular, the use of verapamil, chlorpromazine, reserpine, and other efflux pump inhibitors, as adjunct therapy in the treatment of TB and other infectious diseases is a promising strategy to combat antibiotic resistance (<xref ref-type="bibr" rid="ref51">Rodrigues et al., 2020</xref>; <xref ref-type="bibr" rid="ref54">Tran et al., 2020</xref>; <xref ref-type="bibr" rid="ref48">Remm et al., 2022</xref>; <xref ref-type="bibr" rid="ref47">Rao and Bhosale, 2024</xref>).</p>
<p>In this study, we found that 73% of the analyzed strains had efflux pump variants, the majority of which belonged to the ABC and RND superfamilies, previously associated with antibiotic resistance (<xref ref-type="bibr" rid="ref4">Anthony Malinga and Stoltz, 2016</xref>; <xref ref-type="bibr" rid="ref28">Laws et al., 2022</xref>; <xref ref-type="bibr" rid="ref53">The CRyPTIC Consortium, 2022</xref>; <xref ref-type="bibr" rid="ref24">Hasan et al., 2024</xref>). It is important to follow these patients through their treatment, as variations in efflux pumps may be the origin of resistance before fixation of mutations in drug targets (<xref ref-type="bibr" rid="ref28">Laws et al., 2022</xref>). Future research on antibiotic resistance must include efflux pumps to gain a comprehensive understanding of resistance.</p>
<p>Our study has several limitations, including the limited number of samples and the fact that DNA extraction of all our samples was performed on the first subculture after Mtb isolation from the patient, which may have altered the resistant populations. Additionally, not all strains have complete first and second-line pDST. Future studies must include pDST for the new variants found; furthermore, we think that following patients with efflux pump mutations throughout their treatment can shed light into the connection between these proteins and the development of resistance.</p>
<p>In this study, we identified several of the most prevalent mutations conferring antibiotic resistance in patients with TB, as well as new variants that require validation in drug susceptibility tests to assess their relevance to resistance. This study substantially increases the national variant registry and, together with phenotypical drug sensitivity testing, provides valuable insight into the epidemiological landscape of the country, specifically for Pre-XDR and XDR strains. Additionally, we identified variants in efflux pumps, which may be part of the resistance mechanism that needs further investigation.</p>
<p>Furthermore, our findings confirm the need for timely and comprehensive drug susceptibility testing combined with WGS analysis before initiating treatment, which in Mexico, has unfortunately relied primarily on empirical approaches until now. As sequencing becomes more accessible, WGS should become standard practice in both diagnostic and follow-up protocols. This technology enables precise identification of all drug resistance mutations, which is particularly critical for Pre-XDR and XDR tuberculosis strains where treatment options are already limited. The comprehensive resistance profiling will allow clinicians to design personalized treatment regimens based on each patient&#x2019;s specific resistance profile, moving away from empirical therapy. This precision is especially important for extensively drug-resistant cases, where selecting inappropriate drugs can significantly worsen patient outcomes. Additionally, WGS helps preserve the effectiveness of remaining active drugs&#x2014;such as bedaquiline, delamanid, pretomanid and linezolid by ensuring they are used appropriately and in optimal combinations to prevent further resistance development.</p>
</sec>
</body>
<back>
<sec sec-type="data-availability" id="sec15">
<title>Data availability statement</title>
<p>The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found in the article/<xref ref-type="supplementary-material" rid="SM1">Supplementary material</xref>.</p>
</sec>
<sec sec-type="ethics-statement" id="sec16">
<title>Ethics statement</title>
<p>The studies involving humans were approved by Ethical Committees from both, INER (Comit&#x00E9; de &#x00E9;tica en investigaci&#x00F3;n INER) and UABC (Comit&#x00E9; de &#x00E9;tica en investigaci&#x00F3;n UABC). The studies were conducted in accordance with the local legislation and institutional requirements. The participants provided their written informed consent to participate in this study.</p>
</sec>
<sec sec-type="author-contributions" id="sec17">
<title>Author contributions</title>
<p>NA-P: Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Writing &#x2013; original draft, Writing &#x2013; review &#x0026; editing. MM: Conceptualization, Data curation, Resources, Validation, Writing &#x2013; review &#x0026; editing. LN-D: Resources, Writing &#x2013; review &#x0026; editing. PM-P: Resources, Writing &#x2013; review &#x0026; editing. EB: Resources, Writing &#x2013; review &#x0026; editing. JZ: Supervision, Writing &#x2013; review &#x0026; editing. RM-S: Data curation, Resources, Writing &#x2013; review &#x0026; editing. RL-L: Resources, Writing &#x2013; review &#x0026; editing. CL-C: Resources, Writing &#x2013; review &#x0026; editing. XS: Conceptualization, Supervision, Writing &#x2013; review &#x0026; editing. ES-H: Conceptualization, Data curation, Formal analysis, Funding acquisition, Investigation, Resources, Software, Supervision, Writing &#x2013; original draft, Writing &#x2013; review &#x0026; editing.</p>
</sec>
<sec sec-type="funding-information" id="sec18">
<title>Funding</title>
<p>The author(s) declare that financial support was received for the research and/or publication of this article. Funding was obtained from a grant for ESH from CONAHCyT CF191978, Mexico.</p>
</sec>
<ack>
<p>The authors want to thank Felipe Vadillo from the Unidad de Vinculaci&#x00F3;n Cientifica Facultad de Medicina UNAM - INMEGEN, for valuable discussions and support. We thank Cristobal Fresno for his help in bioinformatic analysis, PhD candidate Alma Hernandez Olvera for her assistance with R, and MVZ Martin Barbosa Amezcua for his insight and critical reading of the manuscript.</p>
</ack>
<sec sec-type="COI-statement" id="sec19">
<title>Conflict of interest</title>
<p>The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.</p>
</sec>
<sec sec-type="disclaimer" id="sec20">
<title>Publisher&#x2019;s note</title>
<p>All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.</p>
</sec>
<sec sec-type="supplementary-material" id="sec21">
<title>Supplementary material</title>
<p>The Supplementary material for this article can be found online at: <ext-link xlink:href="https://www.frontiersin.org/articles/10.3389/fmicb.2025.1666838/full#supplementary-material" ext-link-type="uri">https://www.frontiersin.org/articles/10.3389/fmicb.2025.1666838/full#supplementary-material</ext-link></p>
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<fn id="fn0001"><p><sup>1</sup><ext-link xlink:href="http://www.mykrobe.com" ext-link-type="uri">www.mykrobe.com</ext-link>, Predictor version v0.10.0</p></fn>
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