Genome-wide analyses of Mycobacterium tuberculosis complex isolates reveal insights into circulating lineages and drug resistance mutations in The Gambia

Genome-wide analyses of Mycobacterium tuberculosis complex isolates reveal insights into circulating lineages and drug resistance mutations in The Gambia | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Genome-wide analyses of Mycobacterium tuberculosis complex isolates reveal insights into circulating lineages and drug resistance mutations in The Gambia Leopold Tientcheu, Fatou Faal, Naffie Top, Olimatou Jobe, Sang Marie Colley, and 18 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5913893/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Tuberculosis (TB), caused by the Mycobacterium tuberculosis complex (MTBC), remains a pressing global health challenge, with the West African region, including The Gambia, experiencing a substantial burden. This study explores the genetic diversity of MTBC strains circulating in The Gambia for nearly two decades (2002–2021) to enhance understanding of drug resistance dynamics and inform targeted diagnostic and treatment strategies. Using whole-genome sequencing (WGS) data from 1,803 TB isolates, we identified the predominance of lineage 4 (L4, 67.2%) and lineage 6 (L6, 26.6%) strains, with L4 showing more significant genetic variability over time. Drug susceptibility analysis of these isolates revealed that 78% (1421 isolates) were drug-susceptible, while 6.5% (119 isolates) exhibited resistance, primarily to isoniazid, rifampicin, and their combination. Additionally, 15.5% (282 isolates) were classified as Other, having potential drug-resistance mutations of uncertain significance by the WHO catalogue. Interestingly, our resistance-associated analysis showed the lineage 6 specific ethambutol uncertain significance (by WHO catalogue) mutation (embC Ala307Thr) more prevalent in The Gambia than in West Africa and globally. Structural analysis showed that first-line drug resistance mutations frequently occur in solvent-inaccessible and conserved regions of proteins, often impacting protein stability and reflecting a balance between resistance, fitness, and evolutionary adaptation. This study highlights the coexistence of globally prevalent and regionally restricted MTBC lineages, underscoring the importance of region-specific TB control measures. Integrating bioinformatic and structural analyses revealed many uncertain significant mutations by the WHO catalogue in The Gambian isolates compared to West Africa and globally. These findings reinforce the necessity of continuous genomic surveillance to address the evolving challenges of TB in high-burden settings like West Africa. Biological sciences/Microbiology/Microbial genetics/Bacterial genetics Biological sciences/Microbiology/Infectious-disease diagnostics Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Introduction Tuberculosis (TB) caused by bacilli of the Mycobacterium tuberculosis complex (MTBC) remains a significant public health problem worldwide. In 2022, an estimated 10.6 million people developed TB, with 1.3 million deaths reported. West Africa accounted for 10% of global TB deaths in 2022, including 3,900 cases and 580 fatalities in The Gambia. TB disproportionately affects resource-limited communities and low-income countries 1 . Whole genome sequencing (WGS) advances have greatly enhanced our understanding of MTBC lineage diversity and phylogeographical distribution 2 , 3 . Of the ten known MTBC lineages (L1-L10) (Guyeux et al., 2024), all are found in Africa. In West Africa, TB is driven by M. tuberculosis sensu stricto ( Mtb ) and M. africanum (Maf) lineages, which co-exist in affected populations 4 . While geographically restricted lineages like M. africanum (L5, L6) are endemic to West Africa, globally disseminated lineages such as Mtb -Beijing (L2) and Mtb -Europe-America (L4) are also prevalent (Galagan et al., 2014). West Africa follows the World Health Organization (WHO) guidelines for TB treatment, involving a prolonged multi-drug regimen 1 . TB patients’ treatment encompasses two regimens: drug-susceptible (DS) and -resistant (DR). DS-TB patients undergo a six-month treatment course comprising an intensive phase with rifampicin (RIF), isoniazid (INH), ethambutol (EMB), and pyrazinamide (PZA) for two months, followed by a four-month continuation phase with RIF and INH 5 . For DR-TB cases, treatment regimens are adjusted based on resistance profiles, often requiring second-line drugs and extended treatment durations, which complicate patient management and result in poorer treatment outcomes (Seaworth, 2017). Previous studies have shown that TB patient responses to standard anti-TB treatment vary depending on the infecting MTBC lineages, notably their immune response 6 , 7 , 8 , 9 , 10 . Moreover, hypervirulent strains can dampen immune defences, leading to accelerated disease progression and increased transmission rates 11 . The mutation rates of lineages have been found to vary significantly, with some lineages exhibiting a higher propensity for developing mutations that confer resistance to primary TB drugs 12 . For example, L2 has been noted for its increased mutation rates, particularly in drug-resistant (DR) strains, contributing to intrinsic treatment challenges and the emergence of multidrug-resistant (MDR) TB 13 , 14 , 15 . The persistence and proliferation of resistant strains during treatment can lead to therapeutic failures and complicate TB control efforts 16 . This study investigates the genetic diversity of MTBC strains circulating in The Gambia over nearly two decades (2002 to 2021) and explores their implications for effective TB management. By integrating structural bioinformatics and computational approaches, we analysed the most extensive WGS collection of MTBC isolates in a West African country. We present the abundance, distribution and effects of genetic mutations on drug-target protein stability and conservation properties. Understanding these genetic variations is crucial for designing new drugs and developing effective TB treatment strategies, particularly in regions like West Africa, where multiple MTBC lineages coexist. Methodology Ethical statement This study received ethical approval from the Medical Research Council Unit, The Gambia, at the London School of Hygiene and Tropical Medicine (MRCG@LSHTM)/The Gambian Government joint ethical committee. All recruited study participants or guardians provided written informed consent. Sequencing and epidemiological data The whole genome sequences (WGS) and epidemiological data used in this analysis (n=1803) were sourced from consecutive TB projects hosted by the TB case contact platform at the MRCG@LSHTM between 2002 and 2020. These projects include the following with their respective number of WGS samples: PRJEB53138 (Enhance Case Finding; n=1302), SCC 1289 (Childhood TB Program; n=234), SCC 1523 (TB Sequel; n=216) and Recurrent TB (n=52). The isolate’s metadata used in this study included age, sex and collection year. Microbiology and DNA Extraction Briefly, stored MTBC isolates from archived stocks or directly from a microbiology growth indicator (MGIT TM ) positive tube were subcultured into Middlebrook 7H9 broth or Lowenstein-Jensen (LJ) slopes to multiply the colonies. Genomic DNA was extracted using the cetyltrimethylammonium bromide (CTAB) method, as previously described 17 . The extracted DNA underwent WGS on the Illumina HiSeqX platform at the Forschungszentrum Research Center Borstel, Germany. For the PRJEB53138 isolates, the DNA was extracted using Maxwell® 16 Viral Total Nucleic Acid Purification Kit (Promega Corporation, Fitchburg, WI, USA) following the manufacturer’s instructions and sequenced in MicrobsNG in the United Kingdom. Bioinformatic and phylogenetic analysis Raw sequence data (approximately 2000 samples) was processed and analysed to ensure data quality and accuracy. The Kraken2 database tool was used to filter contaminated sequences and exclude non-MTBC strains. Poor-quality reads were trimmed using Trimmomatic (v0.39) with the following parameters: LEADING:3 TRAILING:3 SLIDINGWINDOW:4:20 MINLEN:36. The quality of the processed reads were reassessed using FastQC. For each sample, trimmed reads were aligned to the Mycobacterium tuberculosis H37Rv reference genome (accession: NC_000962.3) using BWA-MEM software 18 . Single nucleotide polymorphisms (SNPs) and insertions/deletions (indels) were identified through the application of the Genome Analysis Toolkit (GATK) 19 and Sequence Alignment Map (SAM) 20 tools. Genomic VCF files from all the samples were merged, and multi-FASTA alignments were generated using BEDTools software. Phylogenetic relationships between the samples were inferred by constructing a phylogenetic tree with IQ-TREE software 21 . The tree was visualised and annotated using the Interactive Tree of Life (iTOL) v6 software platform 22 . The tree was visualised and annotated using the iTOL software platform. MTBC lineages and genotypic drug resistance profiles for each isolate were determined using the TB-Profiler pipeline (v4.4.0; database version: e25540b) 23 . Variants, including missense and frameshift mutations within known drug resistance loci, were analysed and compared to established databases such as TB-Profiler and the WHO catalogue to identify reported and potential unreported polymorphisms. Mutations in Tier1 genes for The Gambian dataset were compared with global mutation data (>100K mutations) and specific datasets from other West African countries 24, 25 . The analysis included detailed information for each mutation, including the gene names, nucleotide changes, mutation frequency, and count for each country, identified by their country codes. This comprehensive approach provided insight into regional and global genetic diversity and drug resistance dynamics in MTBC strains. Protein structural modelling and mutant stability prediction Missense mutations associated with first-line drugs (RIF, INH, PZA, and EMB) were filtered using a frequency cutoff of 80% to ensure accuracy and relevance. Redundant mutations within the same gene were identified and removed to clarify the dataset and eliminate duplication. The final dataset comprised 943 isolates and their associated missense mutations, of which 614 were classified as susceptible and 329 as resistant. This curated dataset offers a comprehensive overview of the genetic basis of drug susceptibility and resistance. Protein structures for the target genes were obtained from the Protein Data Bank (PDB), a key repository of experimentally determined macromolecular structures critical for biomedical research and drug discovery 26, 27 . This dataset included four PDB files derived from experimentally determined crystal structures and twenty predicted structures using AlphaFold, a protein structure prediction tool 28 . To predict the stability changes caused by mutations, the Delta Delta G (ΔΔG), representing the difference in free energy between wild-type and mutant protein forms, was calculated using PyRosetta, FoldX, and site-directed mutator (SDM) (https://compbio.medschl.cam.ac.uk/sdm2/) 29, 30, 31 . ConSurf (https://consurf.tau.ac.il/consurf_index.php) was employed to evaluate the evolutionary conservation of amino acids at mutation sites. Conservation grades, ranging from 1 (highly variable) to 9 (highly conserved), were assigned based on the evolutionary significance of specific residues 32 . Highly conserved residues are often critical for structural integrity or functional roles in proteins. The conservation grades for mutation positions were extracted and compared between mutations classified as resistant and susceptible. This comparison offered insights into the mutations' evolutionary importance and potential functional consequences. Results Study Demography This study analysed 1803 MTBC isolates with whole-genome sequencing (WGS) data from TB patients residing in the Greater Banjul Area, which accounts for 80% of all TB cases in The Gambia. Metadata, including either age, sex, or year of sample collection, was available for 1713 isolates (95%) ( Table 1 ). The majority of isolates (1145/1585, 72.2%) were from male patients, with the highest representation from individuals aged 18-29 (498, 32%) and 30-44 (409, 26.3%) years. Most samples were from patients diagnosed between 2012 and 2015 (1433/1713, 83.6% ) ( S1 figure ). MTBC lineages Phylogenetic analysis revealed the clustering of isolates by lineage, confirming the dominance of specific MTBC lineages in the population ( Figure 1 ). Most isolates (94%) belonged to Mtb Lineage 4 (L4; 1214/1804, 67.2%) and Maf Lineage 6 (L6; 480/1804, 26.6%) (Table 1). L4 has remained the predominant lineage throughout the study period ( S1 figure ). Among the sub-lineages, L4.1 (410 isolates) and L4.3 (LAM; 323 isolates) were the most abundant within L4, while L6.1 was the most common sub-lineage of L6 ( S2 figure ). Drug resistance The genotypic resistance profile revealed that 1421 (78%) were drug-susceptible (DS). Among the drug-resistant (DR) isolates, 90 (5.0%) were resistant to INH alone, 10 (0.6%) were resistant to RIF alone, and 19 (1.1%) were multi-drug resistant (MDR). Additionally, 282 isolates (15.3%) were classified as other, having potential drug-resistance mutations, identified by the TB-Profiler pipeline 23 . The most frequent mutation underlying resistance to INH was katG Ser315Thr, observed in 71 isolates (71/90, 78.8%) ( Table 2 ). Lineage-specific mutations were also detected in known drug-resistance genes. For instance, embC Ala307Thr, a mutation associated with ethambutol but classified as uncertain significant by the WHO catalogue, was specific to L6, with a frequency of 56.34% (270/480). Another ethambutol-associated uncertain significant mutation, embA Thr113Arg, was also unique to L6 with a frequency of 22.61% (106/480) ( Table 2). According to the drug resistance classification, INH resistance (HR-TB) and MDR-TB were predominantly observed in lineage 4. In contrast, lineage 6 exhibited the highest number of resistances classified as "Other," including mutations of uncertain significance defined by the WHO catalogue. INH and these "Other" resistances were mainly detected between 2012 and 2019 ( S3 figure). Genetic variability To investigate the specificity of genetic mutations in MTBC isolates from The Gambia, we compared missense mutation frequencies in Gambian isolates to those in the rest of West Africa and global datasets. Among over 100,000 mutations identified across 100 countries, 12,411 mutations in all drug resistance genes (11.6%) originated from The Gambia, underscoring the country’s notable contribution to the global mutation pool. Mutations in genes associated with drug resistance were found at significantly higher frequencies in The Gambia compared to global averages, particularly in key genes such as rpoB (RIF resistance), inhA (INH resistance), and embB (EMB resistance). For example, the rpoB Thr350Ile uncertain significant mutation by the WHO catalogue was observed in 26.9% of Gambian isolates compared to 0.99% globally ( Figure 2A ). The embC Ala307Thr uncertain significant mutation appeared in 15.7% of Gambian isolates but was rare globally ( Figure 2C ). These mutations were steadily identified in MTBC isolates over the study period ( S4 figure ). In contrast, uncertain significant mutations associated with resistance to second-line drugs, such as moxifloxacin, were more common in The Gambia and West Africa than the global average. For example, gyrB Ala403Sep and gyrA Leu398Phe were both found at higher frequencies in The Gambia (34% and 29%) and West Africa (41% and 31%), respectively, compared to the global frequency ( S5 figure ). Some non-resistance mutations, like c.-100C>T, were uniquely prominent in The Gambia, often co-occurring with ethambutol-resistance ( S6 figure ). Structural and conservation properties of resistance and susceptible mutations The distributions and structural properties of mutant sites were analysed based on residue depth, occluded surface packing (OSP), and relative solvent accessibility (RSA). A significant difference was observed between resistance and susceptible mutations in all categories of the structural properties (two-tailed Mann-Whitney test, p 0.4) and less tightly packed (OSP < 0.4) environments in the protein structure, respectively ( Figure 3A ). In terms of residue depth, susceptible mutations were observed more often at shallow residue depths (< 4 Å), while resistant mutations were concentrated at greater depths (8-9 Å) ( Figure 3B ). Similar trends were also observed for RSA, where the resistance mutations were found at higher frequency in solvent-inaccessible regions (RSA < 20%). In contrast, susceptible mutations were more frequently located in solvent-accessible regions ( Figure 3C ). Additionally, the conservation levels of resistance and susceptible mutations were analysed. Resistance mutations exhibited a significantly higher conservation level than background and susceptible mutations. Specifically, at the conservation grade from 5 to 9, the density of resistant mutations surpassed that of background and susceptible mutations. Conversely, at conservation grades of 1 to 4, the density of susceptible mutations was more prevalent than background and resistant mutations ( Figure 3D ). Overall, resistant mutations displayed more significant conservation than susceptible mutations (one-tailed Wilcoxon matched-pair signed rank test, p<0.05), highlighting their potential functional and evolutionary significance. Mutant stability prediction The values of the ΔΔG (change in free energy) were analysed to predict the impact of resistant and susceptible mutations on protein stability. No significant differences in ΔΔG were observed between resistance and susceptible mutation using FoldX and SDM ( Figure 4 ). SDM predicted median ΔΔG values of -0.21 for resistance and -0.18 for susceptible mutations. On the other hand, FoldX predicted median ΔΔG values of 0.31 for resistance and 0.24 for susceptible mutations. Both tools suggested that these mutations could severely destabilise or stabilise the protein (absolute ΔΔG > 0.5). In contrast, PyRosetta analysis revealed significant differences in ΔΔG values (two-tailed Mann-Whitney test, p<0.05). Resistant mutations showed a relatively more significant destabilising effect (median ΔΔG=-52.74) compared to susceptible mutations (median ΔΔG=-62.28) ( Figure 4 ). To further investigate ΔΔG differences between resistant and susceptible mutations, mutations were grouped by first-line anti-tuberculosis drugs (RIF, INH, EMB, and PZA) and categorised by MTBC lineages. No significant differences between resistant and susceptible mutations were observed for any of the four drugs using SDM ( S7 figure ). In contrast, PyRosetta analysis revealed statistically significant differences (two-tailed Mann-Whitney test) for INH in Lineage 6 and EMB in Lineage 4 ( Figure 5A ). FoldX analysis also identified substantial differences in ΔΔG values for RIF in Lineage 6 and EMB and PZA in Lineage 2 ( Figure 5B ). Discussion This study provides insights into the genomic landscape of Mycobacterium tuberculosis complex (MTBC) isolates in The Gambia for nearly two decades, shedding light on the country's epidemiology, lineage diversity and drug resistance (DR) patterns. Consistent with prior studies in West Africa (De Jong et al., 2010), MTBC Lineage 4 (L4) and 6 (L6) were found to dominate, collectively accounting for 94% of TB cases. L4, the most prevalent lineage globally, represented 67.2% of isolates, likely due to its high virulence 33 , rapid progression to active TB 34 , and adaptability to diverse environments 35 . Within L4, the overrepresentation of sub-lineages L4.3 (LAM) and L4.1 further supports the role of these genetic clusters in driving its success in this population. LAM is a well-known lineage associated with increased virulence and transmission potential, which may explain its widespread distribution in this region 36 . In contrast, L6, which accounted for 26.6% of the isolates, remains mainly geographically restricted to West Africa. This highlights the importance of region-specific interventions considering the local strains' genetic makeup 37 . Despite its limited global prevalence, this study's significant presence of L6 aligns with its known confinement to the region. The predominance of sub-lineage L6.1 highlights its evolutionary adaptation and persistence within this population, likely shaped by unique host-pathogen interactions in the region 38 . The demographic trends observed are consistent with global TB patterns. Most cases were identified in male patients (72.5%), reflecting the higher incidence of TB among men worldwide 1 . The patient’s age distribution, with the highest burden among individuals aged 18-29 and 30-44, underscores the significant impact of TB on economically and socially critical population segments in The Gambia. These findings emphasise the importance of targeted interventions to mitigate the disease’s socioeconomic impact in The Gambia. The detection of 19 multidrug-resistant (MDR) isolates highlights the pressing challenge of TB in The Gambia, given the limited treatment options and increased risk of treatment failure associated with mistreated MDR TB. The higher prevalence of isoniazid (INH) mono-resistance (90isolates) comparedto rifampicin (RIF) mono-resistance (10 isolates) suggests that INH resistance may be an emerging issue in The Gambia. This finding underscores the need for continuous surveillance and updated treatment protocols that reflect the evolving resistance patterns. Furthermore, the study’s reliance on genotypic data to infer DR highlights the importance of integrating molecular diagnostics into routine TB control programs, which can facilitate the timely and accurate detection of DR strains. The high frequency of specific mutations, such as rpoB Thr350Ile and embC Ala307Thr (EMB), highlights the importance of region-specific genomic surveillance to guide TB control efforts effectively. Identifying mutations with uncertain significance in the WHO catalogue, particularly those with higher frequencies in The Gambia, highlights a critical knowledge gap. Investigating these mutations’ clinical relevance, fitness effects, and contribution to DR through in vitro and in vivo studies will enhance our understanding of DR mechanisms. Analysis of structural properties revealed key differences between resistant and susceptible mutations. Resistant mutations were more frequently located in tightly packed regions of the protein structure (OSP>0.4) and solvent-inaccessible regions (RSA < 20%), suggesting that they may disrupt proteins’ core structural stability 39 . In contrast, susceptible mutations were more often in less tightly packed, solvent-accessible regions. Additionally, resistance mutations exhibited higher conservation grades (5-9) than susceptible and background mutations, indicating their critical role in maintaining essential protein functions. These findings align with the hypothesis that resistance mutations often occur at functionally or structurally critical residues under strong evolutionary constraints 40 . Understanding these distinctions could inform drug design by prioritising highly conserved and structurally significant target sites. The stability analysis demonstrated variations in the predictive capabilities of different computational tools. While SDM and FoldX did not identify significant differences in ΔΔG values between resistant and susceptible mutations, PyRosetta revealed substantial differences. Resistant mutations showed a relatively more destabilising effect (median ΔΔG= -52.74) compared to susceptible mutations (median ΔΔG= -62.28), suggesting that PyRosetta may be more sensitive. These destabilising effects may reflect structural alterations that disrupt drug-binding interactions or enable conformational changes, thereby reducing drug efficacy. Grouping mutations by first-line anti-TB drugs and MTBC lineages provided further resolution. For example, PyRosetta detected significant ΔΔG differences for NIH mutations in lineage 6 and EMB for Lineage 4, while FoldX highlighted significant differences for RIF (Lineage 6) and EMB and PZA (Lineage 2). These lineage-specific findings underscore the importance of considering genetic background and selective pressures when studying resistance mechanisms and designing treatment strategies. While this study offers valuable insights into the genomic landscape of MTBC isolates in The Gambia, several limitations must be acknowledged. First, there is a potential underestimation of lineage 6 prevalence, as genotyping was primarily conducted from subcultured isolates, which may introduce culture bias against the slow-growing L6 isolation. Future studies could consider directly genotyping from sputum samples, minimising the culture bias associated with subculturing from freezer-stored isolates. Second, the functional significance of many identified mutations, particularly those classified as having "uncertain significance," requires further investigation to clarify their clinical relevance. Experimental validation through in vitro and in vivo studies is essential. Thirdly, while tools like AlphaFold provided valuable insights, their accuracy in predicting the effect of mutations on protein stability (ΔΔG) remains limited 41 . Integrating multiple predictive methods with experimental validation will enhance our understanding of mutation impacts. Lastly, only some of the WGS isolates had phenotypic resistance data, which limits our ability to confirm these mutations' importance in drug resistance pathways. Despite these limitations, this study highlights the importance of lineage-specific mutations providing a robust framework for future research. Addressing these gaps will improve our understanding of the genomic and structural mechanisms underlying DR and inform more effective TB control strategies. Conclusion Our genome-wide analysis of Mycobacterium tuberculosis complex (MTBC) strains in The Gambia reveals a diverse landscape of circulating lineages and drug resistance profiles. L4 and L6 dominate the region, with L4 exhibiting global adaptability and virulence, while L6 remains regionally confined to West Africa. The coexistence of global and local lineages underscores the need for tailored, region-specific TB control strategies. Drug resistance mutations, such as katG Ser315Thr and embC Ala307Thr, were notably frequent. Lineage-specific variants like embC Ala307Thr in L6 warrant further investigation for their roles in ethambutol resistance and bacterial fitness. Structural analyses highlighted that resistance mutations often occur in solvent-inaccessible, highly conserved regions, impacting protein stability and evolutionary fitness. These findings underscore the complexity of resistance mechanisms and their implications for treatment outcomes. Finally, our study emphasises the importance of integrating genomic surveillance with functional validation to bridge the gap between genomic data and clinical outcomes. By refining our understanding of MTBC evolution, epidemiology, and resistance mechanisms, these efforts will inform tailored strategies to combat TB in The Gambia and contribute to global efforts to end TB. Declarations Acknowledgements FF is funded by the MRCG at LSHTM Training and Career Development Department PhD studentship. LDT is funded by FIC|NIH (K43TW011125), The Royal Society/Africa Academy of Sciences (FLR\R1\191166) and Crick Africa Network/LifeArc (PRJ_20762). MTBC strain sequences were supported by TB sequel grant number 66.3010.7-002.00 and the Childhood TB program grant awarded to BK (MR/K011944/1) . TGC and SC are funded by the UKRI (BBSRC BB/X018156/1; MRC MR/X005895/1; EPSRC EP/Y018842/1)). APP thanks Professor Andres Floto for support through the UK CF Trust (Innovation Hub Award 001; Strategic Research Centre SRC010). HL thanks Gonville and Caius College, University of Cambridge, for summer research funding. The authors wish to acknowledge the MRCG at LSHTM TB Group field, clinic and laboratory teams who facilitated the study participants’ recruitment, screening and diagnosis. We are particularly grateful to The Gambia National Leprosy and TB Control Programme and all those who participated in these studies over the years. Author contributions LDT, APP, and TGC conceived and directed the project. TDL, FF, VD, SN, SC, APP, TGC, JEP, NT, OJ, SMC, BJ, MA, BK, JSS, BS, AM, AA, AR and TLB contributed bioinformatic tools and sequence data. FF, JEP, and HI performed bioinformatic and statistical analyses under the supervision of TDL, APP, and TGC. FF, TDL, JEP, SC, APP, and TGC interpreted results. FF, TDL, and JEP wrote the first draft of the manuscript, which included contributions from APP and TGC. All authors commented and edited draft versions and approved the final manuscript. FF and TDL compiled the final manuscript. Competing interest . All authors declare no conflict of interest. Data availability All the data used in the analysis presented will be uploaded into a dedicated space, the link of which will be published online together with the manuscript for open access to other researchers. References World Health, O. Global tuberculosis report 2023 . World Health Organization: Geneva, 2023. Ngabonziza, J.C.S. et al. A sister lineage of the Mycobacterium tuberculosis complex discovered in the African Great Lakes region. Nat Commun 11 ,2917 (2020). Napier, G. et al. Robust barcoding and identification of Mycobacterium tuberculosis lineages for epidemiological and clinical studies. Genome medicine 12 ,1-10 (2020). De Jong, B.C., Antonio, M. & Gagneux, S. Mycobacterium africanum—review of an important cause of human tuberculosis in West Africa. PLoS neglected tropical diseases 4 ,e744 (2010). Fattorini, L. & Iacobino, A. Drug Resistance Mechanisms In Tuberculosis | Encyclopedia. 2020; 2020. Diarra, B. et al. Mycobacterium africanum (Lineage 6) shows slower sputum smear conversion on tuberculosis treatment than Mycobacterium tuberculosis (Lineage 4) in Bamako, Mali. PLoS One 13 ,e0208603 (2018). Tientcheu, L. et al. Host Immune Responses Differ between M. africanum- and M. tuberculosis-Infected Patients following Standard Anti-tuberculosis Treatment. PLOS Neglected Tropical Diseases 10 ,e0004701 (2016). Click, E.S., Winston, C.A., Oeltmann, J.E., Moonan, P.K. & Mac Kenzie, W.R. Association between Mycobacterium tuberculosis lineage and time to sputum culture conversion. Int J Tuberc Lung Dis 17 ,878-884 (2013). Tientcheu, L.D. et al. Immunological consequences of strain variation within the Mycobacterium tuberculosis complex. Eur J Immunol (2017). Tarekegn, B.G. et al. Host and pathogen factors that influence variability of Mycobacterium tuberculosis lipid body content in sputum from patients with tuberculosis: an observational study. Lancet Microbe 5 ,100885 (2024). Jia, X. et al. The Bioinformatics Analysis of Comparative Genomics of Mycobacterium tuberculosis Complex (MTBC) Provides Insight into Dissimilarities between Intraspecific Groups Differing in Host Association, Virulence, and Epitope Diversity. Frontiers in Cellular and Infection Microbiology 7 (2017). Yuen, C.M., Kurbatova, E.V., Click, E.S., Cavanaugh, J.S. & Cegielski, J.P. Association between mycobacterium tuberculosis complex phylogenetic lineage and acquired drug resistance. PLoS One 8 ,e83006 (2013). Al-Saeedi, M. & Al-Hajoj, S. Diversity and evolution of drug resistance mechanisms in Mycobacterium tuberculosis. Infect Drug Resist 10 ,333-342 (2017). Ford, C.B. et al. Mycobacterium tuberculosis mutation rate estimates from different lineages predict substantial differences in the emergence of drug-resistant tuberculosis. Nat Genet 45 ,784-790 (2013). Loiseau, C. et al. The relative transmission fitness of multidrug-resistant Mycobacterium tuberculosis in a drug resistance hotspot. Nat Commun 14 ,1988 (2023). Senghore, M. et al. Evolution of Mycobacterium tuberculosis complex lineages and their role in an emerging threat of multidrug resistant tuberculosis in Bamako, Mali. Sci Rep 10 ,327 (2020). Bjorn-Mortensen, K. et al. Direct DNA Extraction from Mycobacterium tuberculosis Frozen Stocks as a Reculture-Independent Approach to Whole-Genome Sequencing. J Clin Microbiol 53 ,2716-2719 (2015). Li, H. Aligning sequence reads, clone sequences and assembly contigs with BWA-MEM. arXiv: Genomics (2013). McKenna, A. et al. The Genome Analysis Toolkit: a MapReduce framework for analyzing next-generation DNA sequencing data. Genome research 20 9 ,1297-1303 (2010). Li, H. et al. The Sequence Alignment/Map format and SAMtools. Bioinformatics 25 ,2078 - 2079 (2009). Minh, B.Q. et al. IQ-TREE 2: New Models and Efficient Methods for Phylogenetic Inference in the Genomic Era. Molecular Biology and Evolution 37 ,1530 - 1534 (2019). Letunic, I. & Bork, P. Interactive Tree of Life (iTOL) v6: recent updates to the phylogenetic tree display and annotation tool. Nucleic Acids Res 52 ,W78-w82 (2024). Phelan, J.E. et al. Integrating informatics tools and portable sequencing technology for rapid detection of resistance to anti-tuberculous drugs. Genome Medicine 11 ,41 (2019). Napier, G. et al. Comparison of in silico predicted Mycobacterium tuberculosis spoligotypes and lineages from whole genome sequencing data. Sci Rep 13 ,11368 (2023). Pruthi, S.S. et al. Leveraging large-scale Mycobacterium tuberculosis whole genome sequence data to characterise drug-resistant mutations using machine learning and statistical approaches. Sci Rep 14 ,27091 (2024). Burley, S.K. Impact of structural biologists and the Protein Data Bank on small-molecule drug discovery and development. The Journal of Biological Chemistry 296 (2021). Goodsell, D.S. et al. RCSB Protein Data Bank: Enabling biomedical research and drug discovery. Protein Science 29 ,52 - 65 (2019). Jumper, J. et al. Highly accurate protein structure prediction with AlphaFold. Nature 596 ,583-589 (2021). Chaudhury, S., Lyskov, S. & Gray, J.J. PyRosetta: a script-based interface for implementing molecular modeling algorithms using Rosetta. Bioinformatics 26 ,689-691 (2010). Delgado, J., Radusky, L.G., Cianferoni, D. & Serrano, L. FoldX 5.0: working with RNA, small molecules and a new graphical interface. Bioinformatics 35 ,4168-4169 (2019). Pandurangan, A.P., Ochoa-Montaño, B., Ascher, D.B. & Blundell, T.L. SDM: a server for predicting effects of mutations on protein stability. Nucleic Acids Res 45 ,W229-w235 (2017). Ashkenazy, H. et al. ConSurf 2016: an improved methodology to estimate and visualize evolutionary conservation in macromolecules. Nucleic Acids Res 44 ,W344-350 (2016). Via, L.E. et al. Differential virulence and disease progression following Mycobacterium tuberculosis complex infection of the common marmoset (Callithrix jacchus). Infect Immun 81 ,2909-2919 (2013). de Jong, B.C. et al. Progression to active tuberculosis, but not transmission, varies by Mycobacterium tuberculosis lineage in The Gambia. J Infect Dis 198 ,1037-1043 (2008). Galagan, J.E. Genomic insights into tuberculosis. Nature reviews. Genetics 15 ,307-320 (2014). Senghore, M. et al. Evolution of Mycobacterium tuberculosis complex lineages and their role in an emerging threat of multidrug resistant tuberculosis in Bamako, Mali. Scientific reports 10 ,327 (2020). Osei-Wusu, S. et al. Relevance of genomic diversity of Mycobacterium tuberculosis complex in Africa. Int J Infect Dis 124 Suppl 1 ,S47-S49 (2022). Balamurugan, M., Banerjee, R., Kasibhatla, S.M., Achalere, A. & Joshi, R. Understanding the Genetic Diversity of Mycobacterium africanum Using Phylogenetics and Population Genomics Approaches. Frontiers in Genetics 13 (2022). Pandurangan, A.P. & Blundell, T.L. Prediction of impacts of mutations on protein structure and interactions: SDM, a statistical approach, and mCSM, using machine learning. Protein Sci 29 ,247-257 (2020). Camps, M., Herman, A., Loh, E. & Loeb, L.A. Genetic constraints on protein evolution. Crit Rev Biochem Mol Biol 42 ,313-326 (2007). Pak, M.A. et al. Using AlphaFold to predict the impact of single mutations on protein stability and function. PLoS One 18 ,e0282689 (2023). Tables Table 1 Characteristics of the MTBC strains and underlying patients (N = 1803) Characteristics Number of isolates (N) Percentage (%) Year of diagnosis 2002–2011 38 2.11 2012 338 18.75 2013 471 26.12 2014 585 32.45 2015 39 2.16 2016 26 1.44 2017 23 1.28 2018 94 5.21 2019 93 5.16 2020 6 0.33 NA 90 4.99 Age groups(yrs) Under 18 18 0.9 18–29 696 38.6 30–44 551 30.5 45 and above 287 15.9 NA 251 13.9 Sex Female 440 24.4 Male 1145 63.5 NA 218 12 Lineage L4 1214 67.2 L6 480 26.6 L2 59 3.2 L1 25 1.3 L3 21 1.1 L5 4 0.2 Genotypic drug res. Pan sensitive 1421 78 RIF 10 0.5 INH 90 5 MDR 19 1 Other 282 15.5 RIF = Rifampicin, INH = Isoniazid, MDR = multi-drug resistance, Other = Known drug resistance mutation but classified as uncertain significant by the WHO catalogue Table 2 Known resistance markers in the first-line drugs Drug Gene Mutation Frequencies in The Gambia (%) West-African frequencies (%) Global frequencies (%) Lineage (L) Rifampicin rpoB Thr350Ile 26.9 50.9 0.99 6 c.-199C > T 1.79 NA 6.75 4 Ser450Leu 0.44 28.4 34.5 2,3,4 Asp435Val 0.44 7.43 3.01 4 Ile770Val 1.6 NA 0.11 4,6 Isoniazid katG Ser315Thr 3.84 48.34 39.34 2,3,4,6 c.-497delA 4.07 NA 0.02 inhA c.-777C > T 0.72 6.32 13.33 c.-154G > A 0.33 9.23 1.86 4,6 ahpC Pro44Arg 1.79 11.7 12.17 4 Ethambutol embA Thr113Arg 6.02 50.74 0.45 6 embC Ala307Thr 15.73 NA 0.05 6 embB Met306Val 0.19 3.66 13.85 2,4 Met306Ile 0.14 7.02 12.32 4 Pyrazinamide clpC1 Pro766Leu 1.65 2.74 7.60 4 pncA Leu172Pro 0.19 2.22 0.61 Asp63Ala 0.14 NA 0.97 His57Asp 0.04 NA 10 Additional Declarations There is NO Competing Interest. Supplementary Files SupplementaryFigures.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-5913893","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":409091839,"identity":"82408ce8-dfe3-412d-a36f-e74039f35faf","order_by":0,"name":"Leopold Tientcheu","email":"data:image/png;base64,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","orcid":"https://orcid.org/0000-0002-2189-3501","institution":"MRC Unit The Gambia at the London School of Hygiene \u0026 Tropical Medicine","correspondingAuthor":true,"prefix":"","firstName":"Leopold","middleName":"","lastName":"Tientcheu","suffix":""},{"id":409091840,"identity":"06b39efe-dad4-4cfe-95d1-23b0a9b6c79e","order_by":1,"name":"Fatou Faal","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Fatou","middleName":"","lastName":"Faal","suffix":""},{"id":409091841,"identity":"23fb999a-3001-42ee-afc6-0d577105e4de","order_by":2,"name":"Naffie Top","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Naffie","middleName":"","lastName":"Top","suffix":""},{"id":409091842,"identity":"4152c0e8-ccc2-4695-ac9e-a568167c0a3b","order_by":3,"name":"Olimatou Jobe","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Olimatou","middleName":"","lastName":"Jobe","suffix":""},{"id":409091843,"identity":"2d80fa47-30f2-4df6-a962-110eceb0bbab","order_by":4,"name":"Sang Marie Colley","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Sang","middleName":"Marie","lastName":"Colley","suffix":""},{"id":409091844,"identity":"cc2efc5c-6c1f-4a9d-bd8e-d0e304b7a73c","order_by":5,"name":"Abigail Ayorinde","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Abigail","middleName":"","lastName":"Ayorinde","suffix":""},{"id":409091845,"identity":"d9506f9e-27d9-4cb7-a673-31413b959f05","order_by":6,"name":"Alieu Mendy","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Alieu","middleName":"","lastName":"Mendy","suffix":""},{"id":409091846,"identity":"23a30b5e-553e-46b3-871f-ec737e580a62","order_by":7,"name":"Binta Sarr-Kuyateh","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Binta","middleName":"","lastName":"Sarr-Kuyateh","suffix":""},{"id":409091847,"identity":"0e33483e-04a9-4f4c-9667-ffe69574352a","order_by":8,"name":"Simon Donkor","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Simon","middleName":"","lastName":"Donkor","suffix":""},{"id":409091848,"identity":"896da746-0562-4779-9bc1-2b0a79717d55","order_by":9,"name":"Martin Antonio","email":"","orcid":"","institution":"Medical Research Council The Gambia Unit","correspondingAuthor":false,"prefix":"","firstName":"Martin","middleName":"","lastName":"Antonio","suffix":""},{"id":409091849,"identity":"a0e0ea63-6f2e-4a83-980f-bcbf44bc77e2","order_by":10,"name":"Bouke de Jong","email":"","orcid":"https://orcid.org/0000-0002-1017-4675","institution":"Institute of Tropical Medicine","correspondingAuthor":false,"prefix":"","firstName":"Bouke","middleName":"","lastName":"de Jong","suffix":""},{"id":409091850,"identity":"edc5beea-95cb-4ad0-9c6f-8e5c11cdeb80","order_by":11,"name":"Andrea Rachow","email":"","orcid":"","institution":"LMU Munich","correspondingAuthor":false,"prefix":"","firstName":"Andrea","middleName":"","lastName":"Rachow","suffix":""},{"id":409091851,"identity":"a0f6c84c-d9b5-496e-b4db-46e58350e010","order_by":12,"name":"Beate Kampmann","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Beate","middleName":"","lastName":"Kampmann","suffix":""},{"id":409091852,"identity":"980d90cf-b11d-42d1-b7cc-0dbb6b645dbd","order_by":13,"name":"Jayne S. Sutherland","email":"","orcid":"","institution":"The Gambia at the London School of Hygiene and Tropical Medicine, Vaccines \u0026 Immunity Theme","correspondingAuthor":false,"prefix":"","firstName":"Jayne","middleName":"S.","lastName":"Sutherland","suffix":""},{"id":409091853,"identity":"ed4246d9-4669-48b5-941d-68fedb5c277d","order_by":14,"name":"Hongwei Li","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Hongwei","middleName":"","lastName":"Li","suffix":""},{"id":409091854,"identity":"3f42a765-08af-47d0-bdf7-01aa81e00a1b","order_by":15,"name":"Tom Blundell","email":"","orcid":"https://orcid.org/0000-0002-2708-8992","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Tom","middleName":"","lastName":"Blundell","suffix":""},{"id":409091855,"identity":"545d576e-7412-46d0-8368-ee3dc61b1761","order_by":16,"name":"Susana Campino","email":"","orcid":"https://orcid.org/0000-0003-1403-6138","institution":"London School of Hygiene and Tropical Medicine","correspondingAuthor":false,"prefix":"","firstName":"Susana","middleName":"","lastName":"Campino","suffix":""},{"id":409091856,"identity":"bc264c0c-9471-40ac-9c29-4143869a1199","order_by":17,"name":"Thomas Kohl","email":"","orcid":"https://orcid.org/0000-0002-1126-6803","institution":"Research Center Borstel - Leibniz-Center for Medicine and Biosciences","correspondingAuthor":false,"prefix":"","firstName":"Thomas","middleName":"","lastName":"Kohl","suffix":""},{"id":409091857,"identity":"ae4ca28d-790e-492c-abaf-ae64e6a08bc3","order_by":18,"name":"Viola Dreyer","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Viola","middleName":"","lastName":"Dreyer","suffix":""},{"id":409091858,"identity":"3958c5f4-f02f-44f5-80c0-d1992a2b5835","order_by":19,"name":"Stefan Niemann","email":"","orcid":"","institution":"Research Center Borstel - Leibniz-Center for Medicine and Biosciences","correspondingAuthor":false,"prefix":"","firstName":"Stefan","middleName":"","lastName":"Niemann","suffix":""},{"id":409091859,"identity":"9895b140-a20a-474c-90d6-902a7fc8b3a2","order_by":20,"name":"Arun Pandurangan","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Arun","middleName":"","lastName":"Pandurangan","suffix":""},{"id":409091860,"identity":"f9fd5c8f-1dff-47e2-bef1-d3a7123d1108","order_by":21,"name":"Taane Clark","email":"","orcid":"https://orcid.org/0000-0001-8985-9265","institution":"London School of Hygiene \u0026 Tropical Medicine","correspondingAuthor":false,"prefix":"","firstName":"Taane","middleName":"","lastName":"Clark","suffix":""},{"id":409091861,"identity":"be815e0d-c98a-4cfe-bbc1-a85c5a52b2de","order_by":22,"name":"Jody Phelan","email":"","orcid":"https://orcid.org/0000-0001-8323-7019","institution":"London School of Hygiene and Tropical Medicine","correspondingAuthor":false,"prefix":"","firstName":"Jody","middleName":"","lastName":"Phelan","suffix":""}],"badges":[],"createdAt":"2025-01-27 16:51:13","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5913893/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5913893/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":77315125,"identity":"2e6f6f10-704d-4b67-b967-b5a3268dc5f3","added_by":"auto","created_at":"2025-02-27 10:36:31","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":603134,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ePhylogenetic relationship and drug resistance profile of 1803 MTBC isolates.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe outer circle represents drug susceptibility output based on genotyping: drug-sensitive isolates (Green), Rifampicin mono-resistant (RR-TB; blue), Isoniazid mono-resistant (HR-TB; Pink), multi-drug-resistant (MDR; Yellow), and isolates resistant to other drugs (Other; White). The inner circle consists of MTBC lineages: Lineage 1 (Dark blue), Lineage 2 (Brown), Lineage 3 (Green), Lineage 4 (Orange), Lineage 5 (Light blue), Lineage 6 (Yellow) and Other (Black).\u003c/p\u003e\n\u003cp\u003eThe branches represent a clustering of isolates based on the SNP’s differences between them.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-5913893/v1/a30f7f098215e75500d15d8b.png"},{"id":77313024,"identity":"41575013-b3d4-41fb-b835-bece8d0599cf","added_by":"auto","created_at":"2025-02-27 10:12:31","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":99820,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eDiversity and prevalence of the WHO catalogue’s mutations, with uncertain significant mutations in the first-line drug-targeted genes across The Gambia, West Africa, and the global dataset.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn each figure, the Y-axis shows the frequency of each mutation displayed on the X-axis in the three regions of interest: The Gambia (Blue), West Africa (Yellow), and the rest of the globe (Red) for the first-line drugs, including (A) Rifampicin, (B) Isoniazid, (C) Ethambutol, and (D) Pyrazinamide.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-5913893/v1/46ed1548ba2d2ef59ce487b5.png"},{"id":77313028,"identity":"a09fb35e-9f51-47d5-b18d-df9e8ca8af9e","added_by":"auto","created_at":"2025-02-27 10:12:31","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":82755,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eStructural and conservation properties for resistant, susceptible and background mutations\u003c/strong\u003e. (A) Residue occluded packing density, (B) Residue depth, (C) Relative side chain solvent accessibility and (D) Residue conservation. The line pattern shows the density of each mutation type for the different parameters evaluated.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-5913893/v1/8c05afce3ec4dfe3160883b1.png"},{"id":77314903,"identity":"799ae8a7-9881-4287-b7a9-56cd93184f94","added_by":"auto","created_at":"2025-02-27 10:28:31","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":85557,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ePredicted ΔΔG of overall resistant and susceptible mutations in first-line drugs.\u003c/strong\u003e Comparison of the distributions of ΔΔG for SDM (A), FoldX (B) and PyRosetta (C) between resistant and susceptible mutations.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-5913893/v1/bf554ff2372d293a77494d46.png"},{"id":77313093,"identity":"db4e0080-2faa-4b39-8a3c-b3e36fa12889","added_by":"auto","created_at":"2025-02-27 10:12:35","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":114132,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ePredicted ΔΔG of resistant and susceptible mutations by first-line drug and MTBC lineages.\u003c/strong\u003e Comparison of the distributions for (A) PyRosetta and (B) FoldX between resistant and susceptible mutations in lineage 2 (Brown), L4 (Orange) and L6 (Yellow).\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-5913893/v1/d26617e3e52b8f10766b4a30.png"},{"id":77315127,"identity":"89eedf83-48e8-4093-a31d-82999b3b6eca","added_by":"auto","created_at":"2025-02-27 10:36:37","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2124752,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5913893/v1/0a2bb505-818e-4860-ad54-818e5ad97ac6.pdf"},{"id":77313031,"identity":"ba181689-009c-4aa3-ac32-ab2510b21e64","added_by":"auto","created_at":"2025-02-27 10:12:32","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":1048153,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryFigures.docx","url":"https://assets-eu.researchsquare.com/files/rs-5913893/v1/812d950981360a920064c2be.docx"}],"financialInterests":"There is \u003cb\u003eNO\u003c/b\u003e Competing Interest.","formattedTitle":"Genome-wide analyses of Mycobacterium tuberculosis complex isolates reveal insights into circulating lineages and drug resistance mutations in The Gambia","fulltext":[{"header":"Introduction","content":"\u003cp\u003eTuberculosis (TB) caused by bacilli of the \u003cem\u003eMycobacterium tuberculosis\u003c/em\u003e complex (MTBC) remains a significant public health problem worldwide. In 2022, an estimated 10.6\u0026nbsp;million people developed TB, with 1.3\u0026nbsp;million deaths reported. West Africa accounted for 10% of global TB deaths in 2022, including 3,900 cases and 580 fatalities in The Gambia. TB disproportionately affects resource-limited communities and low-income countries \u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eWhole genome sequencing (WGS) advances have greatly enhanced our understanding of MTBC lineage diversity and phylogeographical distribution \u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e. Of the ten known MTBC lineages (L1-L10) (Guyeux et al., 2024), all are found in Africa. In West Africa, TB is driven by \u003cem\u003eM. tuberculosis\u003c/em\u003e sensu stricto (\u003cem\u003eMtb\u003c/em\u003e) and \u003cem\u003eM. africanum (Maf)\u003c/em\u003e lineages, which co-exist in affected populations \u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e. While geographically restricted lineages like \u003cem\u003eM. africanum\u003c/em\u003e (L5, L6) are endemic to West Africa, globally disseminated lineages such as \u003cem\u003eMtb\u003c/em\u003e-Beijing (L2) and \u003cem\u003eMtb\u003c/em\u003e-Europe-America (L4) are also prevalent (Galagan et al., 2014).\u003c/p\u003e \u003cp\u003eWest Africa follows the World Health Organization (WHO) guidelines for TB treatment, involving a prolonged multi-drug regimen \u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e. TB patients\u0026rsquo; treatment encompasses two regimens: drug-susceptible (DS) and -resistant (DR). DS-TB patients undergo a six-month treatment course comprising an intensive phase with rifampicin (RIF), isoniazid (INH), ethambutol (EMB), and pyrazinamide (PZA) for two months, followed by a four-month continuation phase with RIF and INH \u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e. For DR-TB cases, treatment regimens are adjusted based on resistance profiles, often requiring second-line drugs and extended treatment durations, which complicate patient management and result in poorer treatment outcomes (Seaworth, 2017).\u003c/p\u003e \u003cp\u003ePrevious studies have shown that TB patient responses to standard anti-TB treatment vary depending on the infecting MTBC lineages, notably their immune response \u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e. Moreover, hypervirulent strains can dampen immune defences, leading to accelerated disease progression and increased transmission rates \u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThe mutation rates of lineages have been found to vary significantly, with some lineages exhibiting a higher propensity for developing mutations that confer resistance to primary TB drugs \u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e. For example, L2 has been noted for its increased mutation rates, particularly in drug-resistant (DR) strains, contributing to intrinsic treatment challenges and the emergence of multidrug-resistant (MDR) TB \u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e. The persistence and proliferation of resistant strains during treatment can lead to therapeutic failures and complicate TB control efforts \u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThis study investigates the genetic diversity of MTBC strains circulating in The Gambia over nearly two decades (2002 to 2021) and explores their implications for effective TB management. By integrating structural bioinformatics and computational approaches, we analysed the most extensive WGS collection of MTBC isolates in a West African country. We present the abundance, distribution and effects of genetic mutations on drug-target protein stability and conservation properties. Understanding these genetic variations is crucial for designing new drugs and developing effective TB treatment strategies, particularly in regions like West Africa, where multiple MTBC lineages coexist.\u003c/p\u003e"},{"header":"Methodology","content":"\u003cp\u003e\u003cstrong\u003e\u003cem\u003eEthical statement\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study received ethical approval from the\u0026nbsp;Medical Research Council Unit, The Gambia, at the London School of Hygiene and Tropical Medicine (MRCG@LSHTM)/The Gambian Government joint ethical committee. All recruited study participants or guardians provided written informed consent.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eSequencing and epidemiological data\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe whole genome sequences (WGS) and epidemiological data used in this analysis (n=1803) were sourced from consecutive TB projects hosted by the TB case contact platform at the MRCG@LSHTM between 2002 and 2020. These projects include the following with their respective number of WGS samples: PRJEB53138 (Enhance Case Finding; n=1302), SCC 1289 (Childhood TB Program; n=234), SCC 1523 (TB Sequel; n=216)\u0026nbsp;and Recurrent TB (n=52). The isolate’s\u0026nbsp;metadata used in this study included age, sex and collection year.\u0026nbsp;\u003c/p\u003e\n\u003ch2\u003e\u003cstrong\u003e\u003cem\u003eMicrobiology and DNA Extraction\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003c/h2\u003e\n\u003cp\u003eBriefly, stored MTBC isolates from archived stocks or directly from a microbiology growth indicator (MGIT\u003csup\u003eTM\u003c/sup\u003e) positive tube were subcultured into Middlebrook 7H9 broth or Lowenstein-Jensen (LJ) slopes to multiply the colonies. Genomic DNA was extracted using the cetyltrimethylammonium bromide (CTAB) method, as previously described \u003csup\u003e17\u003c/sup\u003e. The extracted DNA underwent WGS on the Illumina HiSeqX platform at the Forschungszentrum Research Center Borstel, Germany.\u0026nbsp;For the PRJEB53138 isolates, the DNA was extracted using Maxwell® 16 Viral Total Nucleic Acid Purification Kit (Promega Corporation, Fitchburg, WI, USA) following the manufacturer’s instructions and sequenced in MicrobsNG in the United Kingdom.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eBioinformatic and phylogenetic analysis\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eRaw sequence data (approximately 2000 samples) was processed and analysed to ensure data quality and accuracy. The Kraken2 database tool was used to filter contaminated sequences and exclude non-MTBC strains. Poor-quality reads were trimmed using Trimmomatic (v0.39) with the following parameters: LEADING:3 TRAILING:3 SLIDINGWINDOW:4:20 MINLEN:36. The quality of the processed reads were reassessed using FastQC.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFor each sample, trimmed reads were aligned to the \u003cem\u003eMycobacterium tuberculosis\u003c/em\u003e H37Rv reference genome (accession: NC_000962.3) using BWA-MEM software \u003csup\u003e18\u003c/sup\u003e. Single nucleotide polymorphisms (SNPs) and insertions/deletions (indels) were identified through the application of the Genome Analysis Toolkit (GATK) \u003csup\u003e19\u003c/sup\u003e and Sequence Alignment Map (SAM) \u003csup\u003e20\u003c/sup\u003e tools. Genomic VCF files from all the samples were merged, and multi-FASTA alignments were generated using BEDTools software.\u003c/p\u003e\n\u003cp\u003ePhylogenetic relationships between the samples were inferred by constructing a phylogenetic tree with IQ-TREE software \u003csup\u003e21\u003c/sup\u003e. The tree was visualised and annotated using the Interactive Tree of Life (iTOL) v6 software platform \u003csup\u003e22\u003c/sup\u003e. The tree was visualised and annotated using the iTOL software platform. MTBC lineages and genotypic drug resistance profiles for each isolate were determined using the TB-Profiler pipeline (v4.4.0; database version: e25540b) \u003csup\u003e23\u003c/sup\u003e. Variants, including missense and frameshift mutations within known drug resistance loci, were analysed and compared to established databases such as TB-Profiler and the WHO catalogue to identify reported and potential unreported polymorphisms. Mutations in Tier1 genes for The Gambian dataset were compared with global mutation data (\u0026gt;100K mutations) and specific datasets from other West African countries \u003csup\u003e24, 25\u003c/sup\u003e.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe analysis included detailed information for each mutation,\u0026nbsp;including the gene names, nucleotide changes, mutation frequency, and count for each country, identified by their country codes. This comprehensive approach provided insight into regional and global genetic diversity and drug resistance dynamics in MTBC strains.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eProtein structural modelling and mutant stability prediction\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eMissense mutations associated with first-line drugs (RIF, INH, PZA, and EMB) were filtered using a frequency cutoff of 80% to ensure accuracy and relevance. Redundant mutations within the same gene were identified and removed to clarify the dataset and eliminate duplication. The final dataset comprised 943 isolates and their associated missense mutations, of which 614 were classified as susceptible and 329 as resistant. This curated dataset offers a comprehensive overview of the genetic basis of drug susceptibility and resistance. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eProtein structures for the target genes were obtained from the Protein Data Bank (PDB), a key repository of experimentally determined macromolecular structures critical for biomedical research and drug discovery \u003csup\u003e26, 27\u003c/sup\u003e. This dataset included four PDB files derived from experimentally determined crystal structures and twenty predicted structures using AlphaFold, a protein structure prediction tool \u003csup\u003e28\u003c/sup\u003e. To predict the stability changes caused by mutations, the Delta Delta G (ΔΔG), representing the difference in free energy between wild-type and mutant protein forms, was calculated using PyRosetta, FoldX, and site-directed mutator (SDM) (https://compbio.medschl.cam.ac.uk/sdm2/) \u003csup\u003e29, 30, 31\u003c/sup\u003e.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eConSurf (https://consurf.tau.ac.il/consurf_index.php) was employed to evaluate the evolutionary conservation of amino acids at mutation sites. Conservation grades, ranging from 1 (highly variable) to 9 (highly conserved), were assigned based on the evolutionary significance of specific residues \u003csup\u003e32\u003c/sup\u003e. Highly conserved residues are often critical for structural integrity or functional roles in proteins. The conservation grades for mutation positions were extracted and compared between mutations classified as resistant and susceptible. This comparison offered insights into the mutations' evolutionary importance and potential functional consequences.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003e\u003cem\u003eStudy Demography\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study analysed 1803 MTBC isolates with whole-genome sequencing (WGS) data from TB patients residing in the Greater Banjul Area, which accounts for 80% of all TB cases in The Gambia. Metadata, including either age, sex, or year of sample collection, was available for 1713 isolates (95%) (\u003cstrong\u003eTable 1\u003c/strong\u003e). The majority of isolates (1145/1585, 72.2%) were from male patients, with the highest representation from individuals aged 18-29 (498, 32%) and 30-44 (409, 26.3%) years. Most samples were from patients diagnosed between \u003cstrong\u003e2012 and 2015\u003c/strong\u003e (1433/1713, \u003cstrong\u003e83.6%\u003c/strong\u003e) (\u003cstrong\u003eS1 figure\u003c/strong\u003e).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eMTBC lineages\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePhylogenetic analysis revealed the clustering of isolates by lineage, confirming the dominance of specific MTBC lineages in the population (\u003cstrong\u003eFigure 1\u003c/strong\u003e). Most isolates (94%) belonged to \u003cem\u003eMtb\u003c/em\u003e Lineage 4 (L4; 1214/1804, 67.2%) and \u003cem\u003eMaf\u0026nbsp;\u003c/em\u003eLineage 6 (L6; 480/1804, 26.6%) (Table 1). L4 has remained the predominant lineage throughout the study period (\u003cstrong\u003eS1 figure\u003c/strong\u003e). Among the sub-lineages, L4.1 (410 isolates) and L4.3 (LAM; 323 isolates) were the most abundant within L4, while L6.1 was the most common sub-lineage of L6 (\u003cstrong\u003eS2 figure\u003c/strong\u003e).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eDrug resistance\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe genotypic resistance profile revealed that 1421 (78%) were drug-susceptible (DS). Among the drug-resistant (DR) isolates, 90 (5.0%) were resistant to INH alone, 10 (0.6%) were resistant to RIF alone, and 19 (1.1%) were multi-drug resistant (MDR). Additionally, 282 isolates (15.3%) were classified as other, having potential drug-resistance mutations, identified by the TB-Profiler pipeline \u003csup\u003e23\u003c/sup\u003e. The most frequent mutation underlying resistance to INH was \u003cem\u003ekatG\u003c/em\u003e Ser315Thr, observed in 71 isolates (71/90, 78.8%) (\u003cstrong\u003eTable 2\u003c/strong\u003e). Lineage-specific mutations were also detected in known drug-resistance genes. For instance, \u003cem\u003eembC\u0026nbsp;\u003c/em\u003eAla307Thr, a mutation associated with ethambutol but classified as uncertain significant by the WHO catalogue, was specific to L6, with a frequency of 56.34% (270/480). Another ethambutol-associated uncertain significant mutation, \u003cem\u003eembA\u0026nbsp;\u003c/em\u003eThr113Arg, was also unique to L6 with a frequency of 22.61% (106/480) (\u003cstrong\u003eTable 2).\u0026nbsp;\u003c/strong\u003eAccording to the drug resistance classification, INH resistance (HR-TB) and MDR-TB were predominantly observed in lineage 4. In contrast, lineage 6 exhibited the highest number of resistances classified as \"Other,\" including mutations of uncertain significance defined by the WHO catalogue. INH and these \"Other\" resistances were mainly detected between 2012 and 2019 \u003cstrong\u003e(\u003c/strong\u003e\u003cstrong\u003eS3 figure).\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eGenetic variability\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo investigate the specificity of genetic mutations in MTBC isolates from The Gambia, we compared missense mutation frequencies in Gambian isolates to those in the rest of West Africa and global datasets. Among over 100,000 mutations identified across 100 countries, 12,411 mutations in all drug resistance genes (11.6%) originated from The Gambia, underscoring the country’s notable contribution to the global mutation pool. Mutations in genes associated with drug resistance were found at significantly higher frequencies in The Gambia compared to global averages, particularly in key genes such as rpoB (RIF resistance), inhA (INH resistance), and embB (EMB resistance). For example, the rpoB Thr350Ile uncertain significant mutation by the WHO catalogue was observed in 26.9% of Gambian isolates compared to 0.99% globally (\u003cstrong\u003eFigure 2A\u003c/strong\u003e). The embC Ala307Thr uncertain significant mutation appeared in 15.7% of Gambian isolates but was rare globally (\u003cstrong\u003eFigure 2C\u003c/strong\u003e). These mutations were steadily identified in MTBC isolates over the study period (\u003cstrong\u003eS4 figure\u003c/strong\u003e). In contrast, uncertain significant mutations associated with resistance to second-line drugs, such as moxifloxacin, were more common in The Gambia and West Africa than the global average. For example, gyrB Ala403Sep and gyrA Leu398Phe were both found at higher frequencies in The Gambia (34% and 29%) and West Africa (41% and 31%), respectively, compared to the global frequency (\u003cstrong\u003eS5 figure\u003c/strong\u003e). Some non-resistance mutations, like c.-100C\u0026gt;T, were uniquely prominent in The Gambia, often co-occurring with ethambutol-resistance (\u003cstrong\u003eS6 figure\u003c/strong\u003e).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eStructural and conservation properties of resistance and susceptible mutations\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe distributions and structural properties of mutant sites were analysed based on residue depth, occluded surface packing (OSP), and relative solvent accessibility (RSA). A significant difference was observed between resistance and susceptible mutations in all categories of the structural properties (two-tailed Mann-Whitney test, p\u0026lt;0.05). It is interesting to note that the resistance and susceptible mutations occur more frequently in tightly (OSP \u0026gt; 0.4) and less tightly packed (OSP \u0026lt; 0.4) environments in the protein structure, respectively (\u003cstrong\u003eFigure 3A\u003c/strong\u003e). In terms of residue depth, susceptible mutations were observed more often at shallow residue depths (\u0026lt; 4 Å), while resistant mutations were concentrated at greater depths (8-9 Å) (\u003cstrong\u003eFigure 3B\u003c/strong\u003e). Similar trends were also observed for RSA, where the resistance mutations were found at higher frequency in solvent-inaccessible regions (RSA \u0026lt; 20%). In contrast, susceptible mutations were more frequently located in solvent-accessible regions (\u003cstrong\u003eFigure 3C\u003c/strong\u003e).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAdditionally, the conservation levels of resistance and susceptible mutations were analysed. Resistance mutations exhibited a significantly higher conservation level than background and susceptible mutations. Specifically, at the conservation grade from 5 to 9, the density of resistant mutations surpassed that of background and susceptible mutations. Conversely, at conservation grades of 1 to 4, the density of susceptible mutations was more prevalent than background and resistant mutations (\u003cstrong\u003eFigure 3D\u003c/strong\u003e). Overall, resistant mutations displayed more significant conservation than susceptible mutations (one-tailed Wilcoxon matched-pair signed rank test, p\u0026lt;0.05), highlighting their potential functional and evolutionary significance.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eMutant stability prediction\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe values of the ΔΔG (change in free energy) were analysed to predict the impact of resistant and susceptible mutations on protein stability. No significant differences in ΔΔG were observed between resistance and susceptible mutation using FoldX and SDM (\u003cstrong\u003eFigure 4\u003c/strong\u003e). SDM predicted median\u0026nbsp;ΔΔG values of\u0026nbsp;-0.21 for resistance and -0.18 for susceptible mutations. On the other hand, FoldX predicted median\u0026nbsp;ΔΔG values of\u0026nbsp;0.31 for resistance and 0.24 for susceptible mutations. Both tools suggested that these mutations could severely destabilise or stabilise the protein (absolute\u0026nbsp;ΔΔG \u0026gt; 0.5).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIn contrast, PyRosetta analysis revealed significant differences in ΔΔG values\u0026nbsp;(two-tailed Mann-Whitney test, p\u0026lt;0.05). Resistant mutations showed a relatively more significant destabilising effect (median\u0026nbsp;ΔΔG=-52.74) compared to susceptible mutations (median\u0026nbsp;ΔΔG=-62.28) (\u003cstrong\u003eFigure 4\u003c/strong\u003e).\u003c/p\u003e\n\u003cp\u003eTo further investigate ΔΔG differences between resistant and susceptible mutations, mutations were grouped by first-line anti-tuberculosis drugs (RIF, INH, EMB, and PZA) and categorised by MTBC lineages. No significant differences between resistant and susceptible mutations were observed for any of the four drugs using SDM (\u003cstrong\u003eS7 figure\u003c/strong\u003e). In contrast, PyRosetta analysis revealed statistically significant differences (two-tailed Mann-Whitney test) for INH in Lineage 6 and EMB in Lineage 4 (\u003cstrong\u003eFigure 5A\u003c/strong\u003e). FoldX analysis also identified substantial differences in ΔΔG values for RIF in Lineage 6 and EMB and PZA in Lineage 2 (\u003cstrong\u003eFigure 5B\u003c/strong\u003e).\u003c/p\u003e"},{"header":"Discussion ","content":"\u003cp\u003eThis study provides insights into the genomic landscape of \u003cem\u003eMycobacterium tuberculosis\u003c/em\u003e complex (MTBC) isolates in The Gambia for nearly two decades, shedding light on the country's epidemiology, lineage diversity and drug resistance (DR) patterns. Consistent with prior studies in West Africa (De Jong et al., 2010), MTBC Lineage 4 (L4) and 6 (L6) were found to dominate, collectively accounting for 94% of TB cases.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eL4, the most prevalent lineage globally, represented 67.2% of isolates, likely due to its high virulence \u003csup\u003e33\u003c/sup\u003e, rapid progression to active TB \u003csup\u003e34\u003c/sup\u003e, and adaptability to diverse environments \u003csup\u003e35\u003c/sup\u003e. Within L4, the overrepresentation of sub-lineages L4.3 (LAM) and L4.1 further supports the role of these genetic clusters in driving its success in this population. LAM is a well-known lineage associated with increased virulence and transmission potential, which may explain its widespread distribution in this region \u003csup\u003e36\u003c/sup\u003e.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIn contrast, L6, which accounted for 26.6% of the isolates, remains mainly geographically restricted to West Africa. This highlights the importance of region-specific interventions considering the local strains' genetic makeup \u003csup\u003e37\u003c/sup\u003e. Despite its limited global prevalence, this study's significant presence of L6 aligns with its known confinement to the region. The predominance of sub-lineage L6.1 highlights its evolutionary adaptation and persistence within this population, likely shaped by unique host-pathogen interactions in the region \u003csup\u003e38\u003c/sup\u003e.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe demographic trends observed are consistent with global TB patterns. Most cases were identified in male patients (72.5%), reflecting the higher incidence of TB among men worldwide \u003csup\u003e1\u003c/sup\u003e. The patient’s age distribution, with the highest burden among individuals aged 18-29 and 30-44, underscores the significant impact of TB on economically and socially critical population segments in The Gambia. These findings emphasise the importance of targeted interventions to mitigate the disease’s socioeconomic impact in The Gambia.\u003c/p\u003e\n\u003cp\u003eThe detection of 19 multidrug-resistant (MDR) isolates highlights the pressing challenge of TB in The Gambia, given the limited treatment options and increased risk of treatment failure associated with mistreated MDR TB. The higher prevalence of isoniazid (INH) mono-resistance (90isolates) comparedto rifampicin (RIF) mono-resistance (10 isolates) suggests that INH resistance may be an emerging issue in The Gambia. This finding underscores the need for continuous surveillance and updated treatment protocols that reflect the evolving resistance patterns. Furthermore, the study’s reliance on genotypic data to infer DR highlights the importance of integrating molecular diagnostics into routine TB control programs, which can facilitate the timely and accurate detection of DR strains. The high frequency of specific mutations, such as \u003cem\u003erpoB\u0026nbsp;\u003c/em\u003eThr350Ile and embC Ala307Thr (EMB), highlights the importance of region-specific genomic surveillance to guide TB control efforts effectively. Identifying mutations with uncertain significance in the WHO catalogue, particularly those with higher frequencies in The Gambia, highlights a critical knowledge gap. Investigating these mutations’ clinical relevance, fitness effects, and contribution to DR through in vitro and in vivo studies will enhance our understanding of DR mechanisms.\u003c/p\u003e\n\u003cp\u003eAnalysis of\u0026nbsp;structural properties revealed key differences between resistant and susceptible mutations. Resistant mutations were more frequently located in tightly packed regions of the protein structure (OSP\u0026gt;0.4) and solvent-inaccessible regions (RSA \u0026lt; 20%), suggesting that they may disrupt proteins’ core structural stability \u003csup\u003e39\u003c/sup\u003e. In contrast, susceptible mutations were more often in less tightly packed, solvent-accessible regions. \u0026nbsp;Additionally, resistance mutations exhibited higher conservation grades (5-9) than susceptible and background mutations, indicating their critical role in maintaining essential protein functions. These findings align with the hypothesis that resistance mutations often occur at functionally or structurally critical residues under strong evolutionary constraints \u003csup\u003e40\u003c/sup\u003e. Understanding these distinctions could inform drug design by prioritising highly conserved and structurally significant target sites.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cbr\u003e\u0026nbsp;The stability analysis demonstrated variations in the predictive capabilities of different computational tools. While SDM and FoldX did not identify significant differences in ΔΔG values between resistant and susceptible mutations, PyRosetta revealed substantial differences. Resistant mutations showed a relatively more destabilising effect (median ΔΔG= -52.74) compared to susceptible mutations (median ΔΔG= -62.28), suggesting that PyRosetta may be more sensitive. These destabilising effects may reflect structural alterations that disrupt drug-binding interactions or enable conformational changes, thereby reducing drug efficacy.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eGrouping mutations by first-line anti-TB drugs and MTBC lineages provided further resolution. For example, PyRosetta detected significant ΔΔG differences for NIH mutations in lineage 6 and EMB for Lineage 4, while FoldX highlighted significant differences for RIF (Lineage 6) and EMB and PZA (Lineage 2). These lineage-specific findings underscore the importance of considering genetic background and selective pressures when studying resistance mechanisms and designing treatment strategies.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eWhile this study offers valuable insights into the genomic landscape of MTBC isolates in The Gambia, several limitations must be acknowledged. First, there is a potential underestimation of lineage 6 prevalence, as genotyping was primarily conducted from subcultured isolates, which may introduce culture bias against the slow-growing L6 isolation. Future studies could consider directly genotyping from sputum samples, minimising the culture bias associated with subculturing from freezer-stored isolates. Second, the functional significance of many identified mutations, particularly those classified as having \"uncertain significance,\" requires further investigation to clarify their clinical relevance. Experimental validation through in vitro and in vivo studies is essential. Thirdly, while tools like AlphaFold provided valuable insights, their accuracy in predicting the effect of mutations on protein stability (ΔΔG) remains limited \u0026nbsp;\u003csup\u003e41\u003c/sup\u003e. Integrating multiple predictive methods with experimental validation will enhance our understanding of mutation impacts. Lastly, only some of the WGS isolates had phenotypic resistance data, which limits our ability to confirm these mutations' importance in drug resistance pathways.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eDespite these limitations, this study highlights the importance of lineage-specific mutations providing a robust framework for future research. Addressing these gaps will improve our understanding of the genomic and structural mechanisms underlying DR and inform more effective TB control strategies.\u003c/p\u003e"},{"header":" Conclusion","content":"\u003cp\u003eOur genome-wide analysis of \u003cem\u003eMycobacterium tuberculosis\u003c/em\u003e complex (MTBC) strains in The Gambia reveals a diverse landscape of circulating lineages and drug resistance profiles. L4 and L6 dominate the region, with L4 exhibiting global adaptability and virulence, while L6 remains regionally confined to West Africa. The coexistence of global and local lineages underscores the need for tailored, region-specific TB control strategies.\u003c/p\u003e\n\u003cp\u003eDrug resistance mutations, such as katG Ser315Thr and embC Ala307Thr, were notably frequent. Lineage-specific variants like embC Ala307Thr in L6 warrant further investigation for their roles in ethambutol resistance and bacterial fitness. Structural analyses highlighted that resistance mutations often occur in solvent-inaccessible, highly conserved regions, impacting protein stability and evolutionary fitness. These findings underscore the complexity of resistance mechanisms and their implications for treatment outcomes.\u003c/p\u003e\n\u003cp\u003eFinally, our study emphasises the importance of integrating genomic surveillance with functional validation to bridge the gap between genomic data and clinical outcomes. By refining our understanding of MTBC evolution, epidemiology, and resistance mechanisms, these efforts will inform tailored strategies to combat TB in The Gambia and contribute to global efforts to end TB.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFF is funded by the MRCG at LSHTM Training and Career Development Department PhD studentship. LDT is funded by FIC|NIH (K43TW011125), \u0026nbsp;The Royal Society/Africa Academy of Sciences (FLR\\R1\\191166) and Crick Africa Network/LifeArc (PRJ_20762). MTBC strain sequences were supported by TB sequel grant number 66.3010.7-002.00 and the Childhood TB program \u003cem\u003egrant awarded to BK (MR/K011944/1)\u003c/em\u003e.\u0026nbsp;TGC and SC are funded by the UKRI (BBSRC BB/X018156/1; MRC MR/X005895/1; EPSRC EP/Y018842/1)). APP thanks Professor Andres Floto for support through the UK CF Trust (Innovation Hub Award 001; Strategic Research Centre SRC010). HL thanks Gonville and Caius College, University of Cambridge, for summer research funding. The authors wish to acknowledge the MRCG at LSHTM TB Group field, clinic and laboratory teams who facilitated the study participants\u0026rsquo; recruitment, screening and diagnosis. We are particularly grateful to The Gambia National Leprosy and TB Control Programme and all those who participated in these studies over the years.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eLDT, APP, and TGC conceived and directed the project. TDL, FF, VD, SN, SC, APP, TGC, JEP, NT, OJ, SMC, BJ, MA, BK, JSS, BS, AM, AA, AR and TLB contributed bioinformatic tools and sequence data. \u0026nbsp;FF, JEP, and HI performed bioinformatic and statistical analyses under the supervision of TDL, APP, and TGC. FF, TDL, JEP, SC, APP, and TGC interpreted results. FF, TDL, and JEP wrote the first draft of the manuscript, which included contributions from APP and TGC. All authors commented and edited draft versions and approved the final manuscript. FF and TDL compiled the final manuscript.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interest\u003c/strong\u003e.\u003c/p\u003e\n\u003cp\u003eAll authors declare no conflict of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll the data used in the analysis presented will be uploaded into a dedicated space, the link of which will be published online together with the manuscript for open access to other researchers.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eWorld Health, O. \u003cem\u003eGlobal tuberculosis report 2023\u003c/em\u003e. World Health Organization: Geneva, 2023.\u003c/li\u003e\n\u003cli\u003eNgabonziza, J.C.S.\u003cem\u003e et al.\u003c/em\u003e A sister lineage of the Mycobacterium tuberculosis complex discovered in the African Great Lakes region. \u003cem\u003eNat Commun\u003c/em\u003e \u003cstrong\u003e11\u003c/strong\u003e,2917 (2020).\u003c/li\u003e\n\u003cli\u003eNapier, G.\u003cem\u003e et al.\u003c/em\u003e Robust barcoding and identification of Mycobacterium tuberculosis lineages for epidemiological and clinical studies. \u003cem\u003eGenome medicine\u003c/em\u003e \u003cstrong\u003e12\u003c/strong\u003e,1-10 (2020).\u003c/li\u003e\n\u003cli\u003eDe Jong, B.C., Antonio, M. \u0026amp; Gagneux, S. Mycobacterium africanum\u0026mdash;review of an important cause of human tuberculosis in West Africa. \u003cem\u003ePLoS neglected tropical diseases\u003c/em\u003e \u003cstrong\u003e4\u003c/strong\u003e,e744 (2010).\u003c/li\u003e\n\u003cli\u003eFattorini, L. \u0026amp; Iacobino, A. Drug Resistance Mechanisms In Tuberculosis | Encyclopedia. 2020; 2020.\u003c/li\u003e\n\u003cli\u003eDiarra, B.\u003cem\u003e et al.\u003c/em\u003e Mycobacterium africanum (Lineage 6) shows slower sputum smear conversion on tuberculosis treatment than Mycobacterium tuberculosis (Lineage 4) in Bamako, Mali. \u003cem\u003ePLoS One\u003c/em\u003e \u003cstrong\u003e13\u003c/strong\u003e,e0208603 (2018).\u003c/li\u003e\n\u003cli\u003eTientcheu, L.\u003cem\u003e et al.\u003c/em\u003e Host Immune Responses Differ between M. africanum- and M. tuberculosis-Infected Patients following Standard Anti-tuberculosis Treatment. \u003cem\u003ePLOS Neglected Tropical Diseases\u003c/em\u003e \u003cstrong\u003e10\u003c/strong\u003e,e0004701 (2016).\u003c/li\u003e\n\u003cli\u003eClick, E.S., Winston, C.A., Oeltmann, J.E., Moonan, P.K. \u0026amp; Mac Kenzie, W.R. Association between Mycobacterium tuberculosis lineage and time to sputum culture conversion. \u003cem\u003eInt J Tuberc Lung Dis\u003c/em\u003e \u003cstrong\u003e17\u003c/strong\u003e,878-884 (2013).\u003c/li\u003e\n\u003cli\u003eTientcheu, L.D.\u003cem\u003e et al.\u003c/em\u003e Immunological consequences of strain variation within the Mycobacterium tuberculosis complex. \u003cem\u003eEur J Immunol\u003c/em\u003e (2017).\u003c/li\u003e\n\u003cli\u003eTarekegn, B.G.\u003cem\u003e et al.\u003c/em\u003e Host and pathogen factors that influence variability of Mycobacterium tuberculosis lipid body content in sputum from patients with tuberculosis: an observational study. \u003cem\u003eLancet Microbe\u003c/em\u003e \u003cstrong\u003e5\u003c/strong\u003e,100885 (2024).\u003c/li\u003e\n\u003cli\u003eJia, X.\u003cem\u003e et al.\u003c/em\u003e The Bioinformatics Analysis of Comparative Genomics of Mycobacterium tuberculosis Complex (MTBC) Provides Insight into Dissimilarities between Intraspecific Groups Differing in Host Association, Virulence, and Epitope Diversity. \u003cem\u003eFrontiers in Cellular and Infection Microbiology\u003c/em\u003e \u003cstrong\u003e7\u003c/strong\u003e (2017).\u003c/li\u003e\n\u003cli\u003eYuen, C.M., Kurbatova, E.V., Click, E.S., Cavanaugh, J.S. \u0026amp; Cegielski, J.P. Association between mycobacterium tuberculosis complex phylogenetic lineage and acquired drug resistance. \u003cem\u003ePLoS One\u003c/em\u003e \u003cstrong\u003e8\u003c/strong\u003e,e83006 (2013).\u003c/li\u003e\n\u003cli\u003eAl-Saeedi, M. \u0026amp; Al-Hajoj, S. Diversity and evolution of drug resistance mechanisms in Mycobacterium tuberculosis. \u003cem\u003eInfect Drug Resist\u003c/em\u003e \u003cstrong\u003e10\u003c/strong\u003e,333-342 (2017).\u003c/li\u003e\n\u003cli\u003eFord, C.B.\u003cem\u003e et al.\u003c/em\u003e Mycobacterium tuberculosis mutation rate estimates from different lineages predict substantial differences in the emergence of drug-resistant tuberculosis. \u003cem\u003eNat Genet\u003c/em\u003e \u003cstrong\u003e45\u003c/strong\u003e,784-790 (2013).\u003c/li\u003e\n\u003cli\u003eLoiseau, C.\u003cem\u003e et al.\u003c/em\u003e The relative transmission fitness of multidrug-resistant Mycobacterium tuberculosis in a drug resistance hotspot. \u003cem\u003eNat Commun\u003c/em\u003e \u003cstrong\u003e14\u003c/strong\u003e,1988 (2023).\u003c/li\u003e\n\u003cli\u003eSenghore, M.\u003cem\u003e et al.\u003c/em\u003e Evolution of Mycobacterium tuberculosis complex lineages and their role in an emerging threat of multidrug resistant tuberculosis in Bamako, Mali. \u003cem\u003eSci Rep\u003c/em\u003e \u003cstrong\u003e10\u003c/strong\u003e,327 (2020).\u003c/li\u003e\n\u003cli\u003eBjorn-Mortensen, K.\u003cem\u003e et al.\u003c/em\u003e Direct DNA Extraction from Mycobacterium tuberculosis Frozen Stocks as a Reculture-Independent Approach to Whole-Genome Sequencing. \u003cem\u003eJ Clin Microbiol\u003c/em\u003e \u003cstrong\u003e53\u003c/strong\u003e,2716-2719 (2015).\u003c/li\u003e\n\u003cli\u003eLi, H. Aligning sequence reads, clone sequences and assembly contigs with BWA-MEM. \u003cem\u003earXiv: Genomics\u003c/em\u003e (2013).\u003c/li\u003e\n\u003cli\u003eMcKenna, A.\u003cem\u003e et al.\u003c/em\u003e The Genome Analysis Toolkit: a MapReduce framework for analyzing next-generation DNA sequencing data. \u003cem\u003eGenome research\u003c/em\u003e \u003cstrong\u003e20 9\u003c/strong\u003e,1297-1303 (2010).\u003c/li\u003e\n\u003cli\u003eLi, H.\u003cem\u003e et al.\u003c/em\u003e The Sequence Alignment/Map format and SAMtools. \u003cem\u003eBioinformatics\u003c/em\u003e \u003cstrong\u003e25\u003c/strong\u003e,2078 - 2079 (2009).\u003c/li\u003e\n\u003cli\u003eMinh, B.Q.\u003cem\u003e et al.\u003c/em\u003e IQ-TREE 2: New Models and Efficient Methods for Phylogenetic Inference in the Genomic Era. \u003cem\u003eMolecular Biology and Evolution\u003c/em\u003e \u003cstrong\u003e37\u003c/strong\u003e,1530 - 1534 (2019).\u003c/li\u003e\n\u003cli\u003eLetunic, I. \u0026amp; Bork, P. Interactive Tree of Life (iTOL) v6: recent updates to the phylogenetic tree display and annotation tool. \u003cem\u003eNucleic Acids Res\u003c/em\u003e \u003cstrong\u003e52\u003c/strong\u003e,W78-w82 (2024).\u003c/li\u003e\n\u003cli\u003ePhelan, J.E.\u003cem\u003e et al.\u003c/em\u003e Integrating informatics tools and portable sequencing technology for rapid detection of resistance to anti-tuberculous drugs. \u003cem\u003eGenome Medicine\u003c/em\u003e \u003cstrong\u003e11\u003c/strong\u003e,41 (2019).\u003c/li\u003e\n\u003cli\u003eNapier, G.\u003cem\u003e et al.\u003c/em\u003e Comparison of in silico predicted Mycobacterium tuberculosis spoligotypes and lineages from whole genome sequencing data. \u003cem\u003eSci Rep\u003c/em\u003e \u003cstrong\u003e13\u003c/strong\u003e,11368 (2023).\u003c/li\u003e\n\u003cli\u003ePruthi, S.S.\u003cem\u003e et al.\u003c/em\u003e Leveraging large-scale Mycobacterium tuberculosis whole genome sequence data to characterise drug-resistant mutations using machine learning and statistical approaches. \u003cem\u003eSci Rep\u003c/em\u003e \u003cstrong\u003e14\u003c/strong\u003e,27091 (2024).\u003c/li\u003e\n\u003cli\u003eBurley, S.K. Impact of structural biologists and the Protein Data Bank on small-molecule drug discovery and development. \u003cem\u003eThe Journal of Biological Chemistry\u003c/em\u003e \u003cstrong\u003e296\u003c/strong\u003e (2021).\u003c/li\u003e\n\u003cli\u003eGoodsell, D.S.\u003cem\u003e et al.\u003c/em\u003e RCSB Protein Data Bank: Enabling biomedical research and drug discovery. \u003cem\u003eProtein Science\u003c/em\u003e \u003cstrong\u003e29\u003c/strong\u003e,52 - 65 (2019).\u003c/li\u003e\n\u003cli\u003eJumper, J.\u003cem\u003e et al.\u003c/em\u003e Highly accurate protein structure prediction with AlphaFold. \u003cem\u003eNature\u003c/em\u003e \u003cstrong\u003e596\u003c/strong\u003e,583-589 (2021).\u003c/li\u003e\n\u003cli\u003eChaudhury, S., Lyskov, S. \u0026amp; Gray, J.J. PyRosetta: a script-based interface for implementing molecular modeling algorithms using Rosetta. \u003cem\u003eBioinformatics\u003c/em\u003e \u003cstrong\u003e26\u003c/strong\u003e,689-691 (2010).\u003c/li\u003e\n\u003cli\u003eDelgado, J., Radusky, L.G., Cianferoni, D. \u0026amp; Serrano, L. FoldX 5.0: working with RNA, small molecules and a new graphical interface. \u003cem\u003eBioinformatics\u003c/em\u003e \u003cstrong\u003e35\u003c/strong\u003e,4168-4169 (2019).\u003c/li\u003e\n\u003cli\u003ePandurangan, A.P., Ochoa-Monta\u0026ntilde;o, B., Ascher, D.B. \u0026amp; Blundell, T.L. SDM: a server for predicting effects of mutations on protein stability. \u003cem\u003eNucleic Acids Res\u003c/em\u003e \u003cstrong\u003e45\u003c/strong\u003e,W229-w235 (2017).\u003c/li\u003e\n\u003cli\u003eAshkenazy, H.\u003cem\u003e et al.\u003c/em\u003e ConSurf 2016: an improved methodology to estimate and visualize evolutionary conservation in macromolecules. \u003cem\u003eNucleic Acids Res\u003c/em\u003e \u003cstrong\u003e44\u003c/strong\u003e,W344-350 (2016).\u003c/li\u003e\n\u003cli\u003eVia, L.E.\u003cem\u003e et al.\u003c/em\u003e Differential virulence and disease progression following Mycobacterium tuberculosis complex infection of the common marmoset (Callithrix jacchus). \u003cem\u003eInfect Immun\u003c/em\u003e \u003cstrong\u003e81\u003c/strong\u003e,2909-2919 (2013).\u003c/li\u003e\n\u003cli\u003ede Jong, B.C.\u003cem\u003e et al.\u003c/em\u003e Progression to active tuberculosis, but not transmission, varies by Mycobacterium tuberculosis lineage in The Gambia. \u003cem\u003eJ Infect Dis\u003c/em\u003e \u003cstrong\u003e198\u003c/strong\u003e,1037-1043 (2008).\u003c/li\u003e\n\u003cli\u003eGalagan, J.E. Genomic insights into tuberculosis. \u003cem\u003eNature reviews. Genetics\u003c/em\u003e \u003cstrong\u003e15\u003c/strong\u003e,307-320 (2014).\u003c/li\u003e\n\u003cli\u003eSenghore, M.\u003cem\u003e et al.\u003c/em\u003e Evolution of Mycobacterium tuberculosis complex lineages and their role in an emerging threat of multidrug resistant tuberculosis in Bamako, Mali. \u003cem\u003eScientific reports\u003c/em\u003e \u003cstrong\u003e10\u003c/strong\u003e,327 (2020).\u003c/li\u003e\n\u003cli\u003eOsei-Wusu, S.\u003cem\u003e et al.\u003c/em\u003e Relevance of genomic diversity of Mycobacterium tuberculosis complex in Africa. \u003cem\u003eInt J Infect Dis\u003c/em\u003e \u003cstrong\u003e124 Suppl 1\u003c/strong\u003e,S47-S49 (2022).\u003c/li\u003e\n\u003cli\u003eBalamurugan, M., Banerjee, R., Kasibhatla, S.M., Achalere, A. \u0026amp; Joshi, R. Understanding the Genetic Diversity of Mycobacterium africanum Using Phylogenetics and Population Genomics Approaches. \u003cem\u003eFrontiers in Genetics\u003c/em\u003e \u003cstrong\u003e13\u003c/strong\u003e (2022).\u003c/li\u003e\n\u003cli\u003ePandurangan, A.P. \u0026amp; Blundell, T.L. Prediction of impacts of mutations on protein structure and interactions: SDM, a statistical approach, and mCSM, using machine learning. \u003cem\u003eProtein Sci\u003c/em\u003e \u003cstrong\u003e29\u003c/strong\u003e,247-257 (2020).\u003c/li\u003e\n\u003cli\u003eCamps, M., Herman, A., Loh, E. \u0026amp; Loeb, L.A. Genetic constraints on protein evolution. \u003cem\u003eCrit Rev Biochem Mol Biol\u003c/em\u003e \u003cstrong\u003e42\u003c/strong\u003e,313-326 (2007).\u003c/li\u003e\n\u003cli\u003ePak, M.A.\u003cem\u003e et al.\u003c/em\u003e Using AlphaFold to predict the impact of single mutations on protein stability and function. \u003cem\u003ePLoS One\u003c/em\u003e \u003cstrong\u003e18\u003c/strong\u003e,e0282689 (2023).\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":" \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cdiv class=\"SimplePara\"\u003eCharacteristics of the MTBC strains and underlying patients (N\u0026thinsp;=\u0026thinsp;1803)\u003c/div\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003eCharacteristics\u003c/div\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003eNumber of isolates (N)\u003c/div\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003ePercentage (%)\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\"\u003eYear of diagnosis\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003e2002\u0026ndash;2011\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003e38\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e2.11\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003e2012\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003e338\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e18.75\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003e2013\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003e471\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e26.12\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003e2014\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003e585\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e32.45\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003e2015\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003e39\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e2.16\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003e2016\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003e26\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e1.44\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003e2017\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003e23\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e1.28\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003e2018\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003e94\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e5.21\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003e2019\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003e93\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e5.16\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003e2020\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003e6\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e0.33\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003eNA\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003e90\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e4.99\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003eAge groups(yrs)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003eUnder 18\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003e18\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e0.9\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003e18\u0026ndash;29\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003e696\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e38.6\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003e30\u0026ndash;44\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003e551\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e30.5\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003e45 and above\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003e287\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e15.9\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003eNA\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003e251\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e13.9\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003eSex\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003eFemale\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003e440\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e24.4\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003eMale\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003e1145\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e63.5\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003eNA\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003e218\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e12\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003eLineage\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003eL4\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003e1214\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e67.2\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003eL6\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003e480\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e26.6\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003eL2\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003e59\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e3.2\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003eL1\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003e25\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e1.3\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003eL3\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003e21\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e1.1\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003eL5\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003e4\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e0.2\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003eGenotypic drug res.\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003ePan sensitive\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003e1421\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e78\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003eRIF\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003e10\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e0.5\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003eINH\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003e90\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e5\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003eMDR\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003e19\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e1\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003eOther\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003e282\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e15.5\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"3\"\u003eRIF\u0026thinsp;=\u0026thinsp;Rifampicin, INH\u0026thinsp;=\u0026thinsp;Isoniazid, MDR\u0026thinsp;=\u0026thinsp;multi-drug resistance, Other\u0026thinsp;=\u0026thinsp;Known drug resistance mutation but classified as uncertain significant by the WHO catalogue\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003cbr/\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cdiv class=\"SimplePara\"\u003eKnown resistance markers in the first-line drugs\u003c/div\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\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 \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003eDrug\u003c/div\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003eGene\u003c/div\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003eMutation\u003c/div\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cdiv class=\"SimplePara\"\u003eFrequencies in The Gambia (%)\u003c/div\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cdiv class=\"SimplePara\"\u003eWest-African frequencies (%)\u003c/div\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cdiv class=\"SimplePara\"\u003eGlobal frequencies\u003c/div\u003e \u003cdiv class=\"SimplePara\"\u003e(%)\u003c/div\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cdiv class=\"SimplePara\"\u003eLineage\u003c/div\u003e \u003cdiv class=\"SimplePara\"\u003e(L)\u003c/div\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e \u003cdiv class=\"SimplePara\"\u003eRifampicin\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"4\" rowspan=\"5\"\u003e \u003cdiv class=\"SimplePara\"\u003e\u003cspan type=\"Italic\" class=\"Italic\" name=\"Emphasis\"\u003erpoB\u003c/span\u003e\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003eThr350Ile\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cdiv class=\"SimplePara\"\u003e26.9\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cdiv class=\"SimplePara\"\u003e50.9\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cdiv class=\"SimplePara\"\u003e0.99\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cdiv class=\"SimplePara\"\u003e6\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003ec.-199C\u0026thinsp;\u0026gt;\u0026thinsp;T\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cdiv class=\"SimplePara\"\u003e1.79\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cdiv class=\"SimplePara\"\u003eNA\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cdiv class=\"SimplePara\"\u003e6.75\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cdiv class=\"SimplePara\"\u003e4\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003eSer450Leu\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cdiv class=\"SimplePara\"\u003e0.44\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cdiv class=\"SimplePara\"\u003e28.4\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cdiv class=\"SimplePara\"\u003e34.5\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cdiv class=\"SimplePara\"\u003e2,3,4\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003eAsp435Val\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cdiv class=\"SimplePara\"\u003e0.44\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cdiv class=\"SimplePara\"\u003e7.43\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cdiv class=\"SimplePara\"\u003e3.01\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cdiv class=\"SimplePara\"\u003e4\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003eIle770Val\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cdiv class=\"SimplePara\"\u003e1.6\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cdiv class=\"SimplePara\"\u003eNA\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cdiv class=\"SimplePara\"\u003e0.11\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cdiv class=\"SimplePara\"\u003e4,6\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003eIsoniazid\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003e\u003cspan type=\"Italic\" class=\"Italic\" name=\"Emphasis\"\u003ekatG\u003c/span\u003e\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003eSer315Thr\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cdiv class=\"SimplePara\"\u003e3.84\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cdiv class=\"SimplePara\"\u003e48.34\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cdiv class=\"SimplePara\"\u003e39.34\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cdiv class=\"SimplePara\"\u003e2,3,4,6\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003ec.-497delA\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cdiv class=\"SimplePara\"\u003e4.07\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cdiv class=\"SimplePara\"\u003eNA\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cdiv class=\"SimplePara\"\u003e0.02\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003e\u003cspan type=\"Italic\" class=\"Italic\" name=\"Emphasis\"\u003einhA\u003c/span\u003e\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003ec.-777C\u0026thinsp;\u0026gt;\u0026thinsp;T\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cdiv class=\"SimplePara\"\u003e0.72\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cdiv class=\"SimplePara\"\u003e6.32\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cdiv class=\"SimplePara\"\u003e13.33\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003ec.-154G\u0026thinsp;\u0026gt;\u0026thinsp;A\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cdiv class=\"SimplePara\"\u003e0.33\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cdiv class=\"SimplePara\"\u003e9.23\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cdiv class=\"SimplePara\"\u003e1.86\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cdiv class=\"SimplePara\"\u003e4,6\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003e\u003cspan type=\"Italic\" class=\"Italic\" name=\"Emphasis\"\u003eahpC\u003c/span\u003e\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003ePro44Arg\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cdiv class=\"SimplePara\"\u003e1.79\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cdiv class=\"SimplePara\"\u003e11.7\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cdiv class=\"SimplePara\"\u003e12.17\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cdiv class=\"SimplePara\"\u003e4\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003eEthambutol\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003e\u003cspan type=\"Italic\" class=\"Italic\" name=\"Emphasis\"\u003eembA\u003c/span\u003e\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003eThr113Arg\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cdiv class=\"SimplePara\"\u003e6.02\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cdiv class=\"SimplePara\"\u003e50.74\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cdiv class=\"SimplePara\"\u003e0.45\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cdiv class=\"SimplePara\"\u003e6\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003e\u003cspan type=\"Italic\" class=\"Italic\" name=\"Emphasis\"\u003eembC\u003c/span\u003e\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003eAla307Thr\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cdiv class=\"SimplePara\"\u003e15.73\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cdiv class=\"SimplePara\"\u003eNA\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cdiv class=\"SimplePara\"\u003e0.05\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cdiv class=\"SimplePara\"\u003e6\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003e\u003cspan type=\"Italic\" class=\"Italic\" name=\"Emphasis\"\u003eembB\u003c/span\u003e\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003eMet306Val\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cdiv class=\"SimplePara\"\u003e0.19\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cdiv class=\"SimplePara\"\u003e3.66\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cdiv class=\"SimplePara\"\u003e13.85\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cdiv class=\"SimplePara\"\u003e2,4\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003eMet306Ile\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cdiv class=\"SimplePara\"\u003e0.14\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cdiv class=\"SimplePara\"\u003e7.02\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cdiv class=\"SimplePara\"\u003e12.32\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cdiv class=\"SimplePara\"\u003e4\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003ePyrazinamide\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003eclpC1\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003ePro766Leu\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cdiv class=\"SimplePara\"\u003e1.65\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cdiv class=\"SimplePara\"\u003e2.74\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cdiv class=\"SimplePara\"\u003e7.60\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cdiv class=\"SimplePara\"\u003e4\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003e\u003cspan type=\"Italic\" class=\"Italic\" name=\"Emphasis\"\u003epncA\u003c/span\u003e\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003eLeu172Pro\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cdiv class=\"SimplePara\"\u003e0.19\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cdiv class=\"SimplePara\"\u003e2.22\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cdiv class=\"SimplePara\"\u003e0.61\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003eAsp63Ala\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cdiv class=\"SimplePara\"\u003e0.14\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cdiv class=\"SimplePara\"\u003eNA\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cdiv class=\"SimplePara\"\u003e0.97\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003eHis57Asp\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cdiv class=\"SimplePara\"\u003e0.04\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cdiv class=\"SimplePara\"\u003eNA\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cdiv class=\"SimplePara\"\u003e10\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003cbr/\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-5913893/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5913893/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eTuberculosis (TB), caused by the \u003cem\u003eMycobacterium tuberculosis\u003c/em\u003e complex (MTBC), remains a pressing global health challenge, with the West African region, including The Gambia, experiencing a substantial burden. This study explores the genetic diversity of MTBC strains circulating in The Gambia for nearly two decades (2002\u0026ndash;2021) to enhance understanding of drug resistance dynamics and inform targeted diagnostic and treatment strategies. Using whole-genome sequencing (WGS) data from 1,803 TB isolates, we identified the predominance of lineage 4 (L4, 67.2%) and lineage 6 (L6, 26.6%) strains, with L4 showing more significant genetic variability over time. Drug susceptibility analysis of these isolates revealed that 78% (1421 isolates) were drug-susceptible, while 6.5% (119 isolates) exhibited resistance, primarily to isoniazid, rifampicin, and their combination. Additionally, 15.5% (282 isolates) were classified as Other, having potential drug-resistance mutations of uncertain significance by the WHO catalogue. Interestingly, our resistance-associated analysis showed the lineage 6 specific ethambutol uncertain significance (by WHO catalogue) mutation (embC Ala307Thr) more prevalent in The Gambia than in West Africa and globally. Structural analysis showed that first-line drug resistance mutations frequently occur in solvent-inaccessible and conserved regions of proteins, often impacting protein stability and reflecting a balance between resistance, fitness, and evolutionary adaptation.\u003c/p\u003e \u003cp\u003eThis study highlights the coexistence of globally prevalent and regionally restricted MTBC lineages, underscoring the importance of region-specific TB control measures. Integrating bioinformatic and structural analyses revealed many uncertain significant mutations by the WHO catalogue in The Gambian isolates compared to West Africa and globally. These findings reinforce the necessity of continuous genomic surveillance to address the evolving challenges of TB in high-burden settings like West Africa.\u003c/p\u003e","manuscriptTitle":"Genome-wide analyses of Mycobacterium tuberculosis complex isolates reveal insights into circulating lineages and drug resistance mutations in The Gambia","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-02-27 10:12:24","doi":"10.21203/rs.3.rs-5913893/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"6f631d3a-4e68-4b2b-a2b2-aa3aa3d7d150","owner":[],"postedDate":"February 27th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":43622680,"name":"Biological sciences/Microbiology/Microbial genetics/Bacterial genetics"},{"id":43622681,"name":"Biological sciences/Microbiology/Infectious-disease diagnostics"}],"tags":[],"updatedAt":"2025-02-27T10:12:24+00:00","versionOfRecord":[],"versionCreatedAt":"2025-02-27 10:12:24","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5913893","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5913893","identity":"rs-5913893","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}