Patient Single Nucleotide Polymorphisms associated with Mycobacterium tuberculosis genotypes in the Ugandan population

Patient Single Nucleotide Polymorphisms associated with Mycobacterium tuberculosis genotypes in the Ugandan population | 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 Short Report Patient Single Nucleotide Polymorphisms associated with Mycobacterium tuberculosis genotypes in the Ugandan population Wycliff Wodelo, Kenneth Ssekatawa, Alfred Andama, Katagirya Eric, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6844366/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 5 You are reading this latest preprint version Abstract Objective: Despite strides in reducing the overall burden of tuberculosis, Uganda grapples with the enduring presence of three dominant Mycobacterium tuberculosis genotypes namely, Uganda family, lineage 3, and lineage 4. The persistence of the Mycobacterium tuberculosis Uganda genotype over the past two decades intrigues researchers because the underlying factors driving its prevalence remain enigmatic. While previous investigations have emphasized the role of host genetic factors in tuberculosis susceptibility, the specific mechanisms governing the uneven distribution of Mycobacterium tuberculosis lineages, notably the Uganda genotype, remain elusive. This study delved into the intricate landscape of Mycobacterium tuberculosis complex lineages and sub-lineages, coupled with the analysis of host Single Nucleotide Polymorphisms associated with specific Mycobacterium tuberculosis complex lineages. Results: The study scrutinized a total of 70 isolates, which revealed that the lineage 4 Uganda genotype constituted 50%, lineage 3 genotype 36%, while 4% comprised other lineage 4 strains, with 10% exhibiting mixed lineages. Despite rigorous analysis, no significant association was found between the examined Single Nucleotide Polymorphisms and Mycobacterium tuberculosis lineages , although rs17235409 showed marginal significance (P=0.0570). Our findings emphasize lineage 4 as the primary cause of tuberculosis in Uganda while implicating the Solute Carrier family 11A1 as the susceptibility gene among Ugandans. Tuberculosis Mycobacterium tuberculosis host Single Nucleotide Polymorphisms Mycobacterium tuberculosis genotypes Figures Figure 1 Background Mycobacterium tuberculosis (Mtb) is a formidable pathogen, acting as the precursor to tuberculosis (TB) and causing millions of deaths annually[ 1 ]. Historically, TB held the grim distinction of being the leading cause of death from a single infectious agent, surpassing HIV/AIDS, until the emergence of the COVID-19 pandemic. As of the World Health Organization's latest report in 2020, approximately 1.7 billion individuals, constituting approximately 23% of the global population, are estimated to be latently infected with TB. However, only a small percentage of up to ten percent progress to active TB during their lifetime [ 2 ]. This transition can occur either through initial infection via inhalation of air droplets containing the bacilli or reactivation of latent infection due to immunosuppression. Despite a gradual decline in the global burden of TB in recent years, incidence rates persist at high levels [ 1 ]. Sub-Saharan Africa and South East Asia continue to bear the greatest burden of TB [ 1 , 3 ]. Uganda, specifically, maintains its status as a significant public health concern, with an annual incidence of 330 cases of all forms and 136 new smear-positive cases per 100,000 people [ 4 ]. Mycobacterium tuberculosis , an intracellular pathogen adapted to its human host, primarily resides within human macrophages. Genomic data reveals the complexity of the Mycobacterium tuberculosis complex (MTBC), encompassing six human-adapted lineages (Lineage − 1 to Lineage − 4, Lineage − 7, and Lineage − 8) and three additional human-adapted lineages known traditionally as Mycobacterium africanum (Lineage − 5, Lineage − 6, and Lineage − 9) [ 5 – 9 ]. Host genotypes and MTBC lineage have been implicated in TB disease causation, with prior studies in Uganda identifying the circulation of three main MTBC lineages: MTBC Uganda family, MTBC Lineage − 3, and other MTBC Lineage − 4 genotypes [ 10 , 11 ]. Despite the dominance of the Uganda genotype, efforts to explain this prevalence through bacterial and environmental factors have proven inconclusive, highlighting the need for a deeper understanding of the host's role in susceptibility to TB disease. The selection of 11 single nucleotide polymorphisms (SNPs) represents a strategic balance between comprehensiveness and feasibility. While a more extensive panel of SNPs could offer a broader scope for analysis, it also presents practical challenges such as cost, time, and sample size requirements. By focusing on a subset of SNPs that have shown relevance in previous studies or are hypothesized to be associated with TB susceptibility, we aim to maximize the efficiency and interpretability of our findings. However, it is crucial to acknowledge the potential limitations of this approach, including the possibility of overlooking important genetic variants and the inherent complexity of host-pathogen interactions, which may involve multiple genetic factors and environmental influences Human-MTBC genetic studies have uncovered polymorphisms in host immune cells that predispose individuals to specific MTBC strains [ 12 – 14 ]. These polymorphisms are likely to alter gene expression or protein function leading to modulation of cellular function and influencing disease risk or drug response. However, existing findings lack consideration of MTBC diversity as a potential confounder leading to contradictory results, particularly in genetic variants associated with Toll-like receptors (TLR) -2 and TLR − 4 [ 14 , 15 ]. Moreover, these studies predominantly derive from populations of European and Asian ancestry, potentially limiting their generalizability to the African population. A recent review by [ 30 ] provides a critical synthesis of studies investigating the role of host genetic polymorphisms, particularly those involved in immune responses, and their influence on TB infection and disease progression. These studies have highlighted the importance of considering both host and pathogen genetic diversity in understanding TB epidemiology and shaping public health interventions. Existing findings on host-pathogen interactions in the African context have shed light on the intricate relationship between host genetic factors and Mycobacterium tuberculosis complex (MTBC) genotypes. Several studies have identified specific single nucleotide polymorphisms (SNPs) associated with tuberculosis (TB) susceptibility and progression in African populations. For example, research by [ 31 ] revealed that variants in genes encoding proteins involved in innate immunity, such as Toll-like receptors (TLRs), are linked to increased susceptibility to TB among Africans. Furthermore, a study by [ 32 ] identified genetic variations in Solute carrier family 11a member 1 (SLC11A) and other immune-related genes that influence host responses to MTBC infection in African populations. Therefore, the study aimed to investigate patient SNPs associated with MTBC genotypes in the Ugandan population. Materials and methods Study Design and Population This cross-sectional study was nested within the parent study, conducted at Mulago National Referral Hospital TB ward in Kampala, Uganda. The parent study focused on evaluating rapid, non-sputum-based biomarker tests for tuberculosis diagnosis collected between 2018 and 2020. Two sputum samples (3–5 mL each) were collected from consenting adults (≥ 18 years), with one sample processed using Gene Xpert MTB/RIF Ultra and the other subjected to liquid culture testing in the Mycobacteriology Laboratory. Additionally, participants provided 5 mL of blood for DNA extraction and other metabolic panel analysis. The current study utilized archived Mycobacterium tuberculosis isolates and human whole blood samples from the parent study. Sampling Procedure Consecutive Mycobacterium tuberculosis isolates, paired with corresponding human whole blood samples from the same patients, were used in the study. Sample Size A total of 70 isolates was calculated using a modified Kish Leslie formula (1965), with assumptions of a 95% confidence level, a 20% estimated proportion of tuberculosis, and a 5% absolute precision. The study included positive Mycobacterium tuberculosis isolates that were obtained from the achieved samples from the parent study. Isolates without corresponding blood samples, blood samples lacking corresponding isolates, and isolates with mixed Mycobacterium tuberculosis lineages were excluded from the study. Mycobacterium tuberculosis Complex DNA extraction Mycobacterium tuberculosis DNA extraction involved thawing selected isolates at -20°C overnight and subsequently at room temperature for 12 hours. Following centrifugation at 15,000 g for 30 minutes, the pellet underwent two washes with 500µl of Qiagen PCR water. The final pellet was re-suspended in 100µl of Qiagen PCR water, heat-treated at 95°C for 30 minutes, and sonicated for 15 minutes at room temperature. The extracted DNA in the supernatant was recovered by centrifugation at 15,000 g for 30 minutes and utilized immediately in Real-Time PCR (RT-PCR) following established protocols [ 16 ]. Extraction of DNA from Human Whole Blood Genomic DNA from human whole blood was isolated using the QIAmp DNA Blood Mini Kit following the manufacturer’s instructions. The concentration and purity of DNA were assessed using a NanoDrop 1000 spectrophotometer V3.7 (Thermo Fisher Scientific) and agarose gel electrophoresis was performed to confirm the quality and integrity of the extracted DNA. Region of Differences (RD) or Deletion Analysis Mycobacterium tuberculosis DNA from isolates was genotyped using large sequence polymorphism-polymerase chain reaction (LSP-PCR). This involved employing RD724 deletion primers (specific for the Mtb Uganda family), RD750 deletion primers (specific for MTB lineage 3), and other lineage 4 primers as described by [ 16 ]. The PCR reaction volume included water, forward and reverse primers, Thermo Fischer Scientific Custom PCR Master mix, template DNA, and DNA polymerase. The reaction was cycled through specific temperature parameters, and PCR products were assessed via gel electrophoresis. Mass Array Technology SNP Typing The extracted human Genomic DNA was sent to Inqaba Biotechnical Industries, South Africa for SNP sequencing using mass array technology and analysis using methods described by [ 17 ].The obtained SNP data have been deposited in the European Variation Archive (EVA) at EMBL-EBI under Accession Number PRJEB90153 Results Study participants' characteristics During the study period, 230 tuberculosis patients were enrolled, with varying treatment durations. The distribution included 100 (43.5%) at month zero, 80 (34.8%) at month two, 24 (10.4%) at month five, 5 (2.2%) at month six, and 21(9.1%) at month eight. Males constituted 60.9% (median age 30), while females comprised 39.1% (median age 28) (Table 1 ). Table 1 Study participants’ characteristics Demographic characteristics Level Number (percentages) Age 18–30 100 (43.5%) 31–45 76(33.0%) 46–60 49(21.3%) ˃60 5(2.2%) Gender Male 140(60.9%) Female 90(39.1%) Occupation Self/unemployed 150(65.2%) Employed 80(34.8%) Education None 20(8.7%) Primary 60(26.1%) Secondary 50(21.7%) Tertiary 100(43.5%) Mycobacterium tuberculosis complex lineages and sublineages infecting the population. Analysis of 70 Mycobacterium tuberculosis isolates revealed a diverse distribution: 50% (35/70) were lineage 4 Uganda genotype, 36% (25/70) were lineage 3 genotype, 4% (3/70) were lineage 4 non-Uganda genotype (L4-NU), and 10% (7/70) were mixed lineages containing both lineage 4 and lineage 3 (Fig. 1 ). Host SNPs associated with particular MTBC lineages and sublineages Among 70 isolates, 30 were excluded, and one failed. Genotyping 39 isolates at 11 SNPs across genes TICAM 2, IL-12, NOD1, ILIB, IL-12B, CLEC4E, SLCI1A1, IL-18, TLR4, TLR2, and TLR6 revealed 70.8% homozygous genotype attributed to lineage 4 Uganda, compared to 29.2% heterozygous (Table 2 ). These results suggest no significant correlation between the investigated SNPs and the Mycobacterium tuberculosis complex genotypes in the studied population (Table 2 ). Table 2 Genotype frequency of the SNPs across the genes of interest associated with lineage 4 Uganda and Association of the SNPs with the genotype lineages Genotype frequency of the SNPs across the genes of interest associated with lineage 4 Uganda Association of the SNPs with the genotype lineages SNP Gene Genotype Genotype frequency Estimate Standard error Z value P-Value rs10896289 CLEC4E CC(23) CA(14) AA(2) CC/AA (64.1%), CA (35.9%) -3.6038 2.7466 -1.312 0.1895 rs1143643 IL-1B CC(31) CT(8) CC (79.5%), CT (20.5%) 3.4488 2.2189 1.554 0.1201 rs17235409 SLC11A1 AG(15) GG(23) GG (59%), AG (38.5%) -5.5839 2.9336 -1.903 0.057 rs2243274 IL-4 AA(16) GA(18) GG(5) AA/GG (53.8%), GA (46.2%) -0.2891 1.1504 -0.251 0.8016 rs2770150 TLR4 AA(28) GA(9) GG(2) AA/GG (76.9%), GA (23.1%) 1.8616 1.6756 1.111 0.2666 rs2970499 TLR2 TT(33) CT(6) TT (84.6%), CT (15.4%) 3.9348 4.0768 0.965 0.3345 rs3212227 IL-12B TT(16) GT(13) GG(9) TT/GG (64.1%), GT (33.3%) 3.4739 1.9608 1.772 0-0764 rs3796508 TLR6 CC(36) TC(2) CC (92.3%), TC (5.1%) 16.216 3692.8917 0.004 0.9965 rs3804099 TLR2 CT(14) CC(22) TT(3) CC/TT (64.1%), CT (35.9%) 1.5941 2.0156 0.791 0.429 rs5744229 IL-18 CC(31) CT(7) CC (79.5%), CT (17.9%) 0.6087 1.8659 0.326 0.7443 rs746566 TICAM 2 CT(15) CC(22) TT(2) CC/TT (61.5%), CT (38.5%) -2.4667 1.8886 -1.306 0.1915 No significant association was observed between the 11 tested SNPs and Mycobacterium tuberculosis complex lineages (P > 0.05) (Table 3) Discussion In this cross-sectional study conducted among tuberculosis patients in Kampala, we identified Lineage 4 Uganda as the most prevalent genotype causing tuberculosis in Uganda, followed by Lineage 3. Contrary to our hypothesis, there was no significant association between the investigated SNPs and MTBC lineages. However, our analysis revealed an intriguing finding related to the SLCIIAI gene polymorphism, implicating it in tuberculosis disease development. The prevalence of Lineage 4 Uganda aligns with global epidemiological patterns, as Lineage 4 strains have been identified as major contributors to tuberculosis epidemics in Africa [ 18 ]. Lineage 4 is phylogenetically divided into ten separate distinct sub-lineages with differential distribution having local genotypes accounting for a large proportion of circulating strains in particular areas[ 19 ]. For example, Zambia is predominated by the Latin American Mediterranean family while in West Africa its lineage 4 Cameroon family [ 20 , 21 ]. The geographic restriction of specific MTBC genotypes, such as Lineage 4 Uganda, suggests local adaptation of pathogen variants to specific human host populations [ 22 ]. The sub-lineage's lower proportion of variable epitopes and reduced genetic diversity further support its adaptation to a distinct host population, highlighting the intricate relationship between the pathogen and its environment. Our results are consistent with earlier studies in Southwestern and central Uganda, showcasing the persistence and dominance of Lineage 4 Uganda in these regions respectively [ 23 , 24 ]. However, variations in Lineage 4 Uganda proportions in neighboring countries like Tanzania and Kenya with proportions of 59.7% and 63% respectively emphasize the importance of considering local strains' transmission dynamics. The long co-evolutionary history between different MTBC lineages and diverse human populations likely contributes to these geographical variations [ 25 ]. The observed higher genotype frequency in homozygous (ancestral allele) individuals compared to heterozygous (derived allele) individuals reflects the consequences of co-evolution between Mycobacterium tuberculosis and the human host [ 26 ]. This implies that individuals with homozygous genotypes may experience less severe disease when infected with traditionally co-existing MTBC lineages. Notably, Lineage 4 Uganda, being a recently derived clade exclusive to Uganda, may pose a challenge for individuals with homozygous genotypes due to the lack of immune memory cells for this lineage. Contrary to expectations, our findings did not reveal significant associations between the selected SNPs and MTBC lineages, consistent with studies showing no such associations [ 27 ]. However, the complexity of the host-pathogen interaction is underscored by conflicting evidence in the literature, emphasizing the critical role of host factors in TB control. The genes coding for the investigated SNPs were selected based on their biological plausibility and previous associations with Mycobacterium tuberculosis infection [ 28 , 29 ]. Despite the lack of significant associations in our study, these genes remain important candidates for further exploration in understanding the intricate dynamics of host-pathogen interactions in tuberculosis. The marginal significance (P = 0.057) observed for SNP rs17235409 warrants cautious consideration, as it may suggest a potential link with tuberculosis. This inconsistency in results emphasizes the complexity of the host-pathogen interaction in tuberculosis, where diverse genetic factors contribute to variable outcomes. Future studies with larger sample sizes are needed to validate and explore the significance of this marginal association. Limitations While a larger sample size could enhance statistical power and generalizability, a small sample size was used due to the limited availability of archived samples that meet the stringent inclusion criteria such as Mycobacterium tuberculosis -positive samples and treatment history. Furthermore, difficulties were encountered in accessing well-characterized patient samples, particularly from diverse populations. Conclusion The study sheds light on the regional prevalence of specific MTBC lineages, highlighting the significance of local adaptations and co-evolutionary dynamics. The observed lack of association between SNPs and MTBC lineages underscores the intricate nature of the host-pathogen relationship in tuberculosis. Further research, especially with larger cohorts and diverse populations, is warranted to unravel the complexities of genetic factors influencing tuberculosis susceptibility and outcomes. Abbreviations Mtb Mycobacterium tuberculosis MTBC Mycobacterium tuberculosis complex SNP Single Nucleotide Polymorphism TB Tuberculosis TLR Toll-like receptors SLC11A1 Solute carrier family 11a member 1 RD Region of Differences Declarations Ethics approval and consent to participate This study involving human participants was conducted in full accordance with the ethical standards outlined in the Declaration of Helsinki. The ethical approval (REC Ref: 2017-020) was granted by the Makerere University School of Medicine Research and Ethics Committee and the Uganda National Council for Science and Technology (Ref: HS2210). The participants provided written informed consent to participate in the study. To ensure confidentiality, samples were labelled using identification codes. Acknowledgment This paper has been uploaded to SSRN, as a preprint https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4752687 Consent for publication Not applicable Availability of data and materials The SNP data have been deposited in the European Variation Archive (EVA) at EMBL-EBI under Accession link https://www.ebi.ac.uk/eva/?eva-study=PRJEB90153 Competing interests The authors declare no competing interests Funding This research was funded in whole, or in part, by the Wellcome Trust 107742/Z/15/Z and the UK Foreign, Commonwealth & Development Office, with support from the Developing Excellence in Leadership, Training, and Science in Africa (DELTAS Africa) program. This study was also supported by the Africa Centre of Excellence in Materials, Product Development & Nanotechnology, Makerere University (P151847IDA). Authors’ contributions EMW, KS, and AA conceptualized the study; WW, KE, and SM performed the experiments and wrote the first draft. All the authors analyzed and interpreted the data and managed revisions. References WHO, Global Tuberculosis Report. World Health Organisation, Geneva. 2020. Wampande, E.M., et al., A single-nucleotide-polymorphism real-time PCR assay for genotyping of Mycobacterium tuberculosis complex in peri-urban Kampala. BMC Infect Dis, 2015. 15 : p. 396. Harding, E., WHO global progress report on tuberculosis elimination. Lancet Respir Med, 2020. 8 (1): p. 19. Health, M.O., Tuberculosis/Leprosy control programm, Uganda. 2019. Gagneux, S., et al., Variable host-pathogen compatibility in Mycobacterium tuberculosis. Proc Natl Acad Sci U S A, 2006. 103 (8): p. 2869-73. Ngabonziza, J.C.S., et al., A sister lineage of the Mycobacterium tuberculosis complex discovered in the African Great Lakes region. Nat Commun, 2020. 11 (1): p. 2917. Coscolla, M., et al., Phylogenomics of Mycobacterium africanum reveals a new lineage and a complex evolutionary history. Microb Genom, 2021. 7 (2). Firdessa, R., et al., Mycobacterial lineages causing pulmonary and extrapulmonary tuberculosis, Ethiopia. Emerg Infect Dis, 2013. 19 (3): p. 460-3. de Jong, B.C., M. Antonio, and S. Gagneux, Mycobacterium africanum--review of an important cause of human tuberculosis in West Africa. PLoS Negl Trop Dis, 2010. 4 (9): p. e744. Asiimwe, B.B., et al., DNA restriction fragment length polymorphism analysis of Mycobacterium tuberculosis isolates from HIV-seropositive and HIV-seronegative patients in Kampala, Uganda. BMC Infect Dis, 2009. 9 : p. 12. Asiimwe, B.B., et al., Mycobacterium tuberculosis Uganda genotype is the predominant cause of TB in Kampala, Uganda. Int J Tuberc Lung Dis, 2008. 12 (4): p. 386-91. van Crevel, R., et al., Infection with Mycobacterium tuberculosis Beijing genotype strains is associated with polymorphisms in SLC11A1/NRAMP1 in Indonesian patients with tuberculosis. J Infect Dis, 2009. 200 (11): p. 1671-4. Singh, N., et al., Solute carrier protein family 11 member 1 (Slc11a1) activation efficiently inhibits Leishmania donovani survival in host macrophages. J Parasit Dis, 2017. 41 (3): p. 671-677. Tientcheu, L.D., et al., Host Immune Responses Differ between M. africanum- and M. tuberculosis-Infected Patients following Standard Anti-tuberculosis Treatment. PLoS Negl Trop Dis, 2016. 10 (5): p. e0004701. Selvaraj, P., et al., Cytokine gene polymorphisms and cytokine levels in pulmonary tuberculosis. Cytokine, 2008. 43 (1): p. 26-33. Wampande, E.M., et al., Distribution and transmission of Mycobacterium tuberculosis complex lineages among children in peri-urban Kampala, Uganda. BMC Pediatr, 2015. 15 : p. 140. Tost, J. and I.G. Gut, Genotyping single nucleotide polymorphisms by MALDI mass spectrometry in clinical applications. Clin Biochem, 2005. 38 (4): p. 335-50. Stucki, D., et al., Mycobacterium tuberculosis lineage 4 comprises globally distributed and geographically restricted sublineages. Nat Genet, 2016. 48 (12): p. 1535-1543. Anderson, J., et al., Sublineages of lineage 4 (Euro-American) Mycobacterium tuberculosis differ in genotypic clustering. Int J Tuberc Lung Dis, 2013. 17 (7): p. 885-91. Yeboah-Manu, D., et al., Genotypic diversity and drug susceptibility patterns among M. tuberculosis complex isolates from South-Western Ghana. PLoS One, 2011. 6 (7): p. e21906. Mulenga, C., et al., Diversity of Mycobacterium tuberculosis genotypes circulating in Ndola, Zambia. BMC Infect Dis, 2010. 10 : p. 177. Comas, I., et al., Human T cell epitopes of Mycobacterium tuberculosis are evolutionarily hyperconserved. Nat Genet, 2010. 42 (6): p. 498-503. Micheni, L.N., et al., Diversity of Mycobacterium tuberculosis Complex Lineages Associated with Pulmonary Tuberculosis in Southwestern, Uganda. Tuberc Res Treat, 2021. 2021 : p. 5588339. Wampande, E.M., et al., Long-term dominance of Mycobacterium tuberculosis Uganda family in peri-urban Kampala-Uganda is not associated with cavitary disease. BMC Infect Dis, 2013. 13 : p. 484. Gagneux, S., Host-pathogen coevolution in human tuberculosis. Philos Trans R Soc Lond B Biol Sci, 2012. 367 (1590): p. 850-9. McHenry, M.L., et al., Interaction between host genes and Mycobacterium tuberculosis lineage can affect tuberculosis severity: Evidence for coevolution? PLoS Genet, 2020. 16 (4): p. e1008728. Di Pietrantonio, T. and E. Schurr, Host-pathogen specificity in tuberculosis. Adv Exp Med Biol, 2013. 783 : p. 33-44. Li, H.T., et al., SLC11A1 (formerly NRAMP1) gene polymorphisms and tuberculosis susceptibility: a meta-analysis. Int J Tuberc Lung Dis, 2006. 10 (1): p. 3-12. Mallick, S., et al., The Simons Genome Diversity Project: 300 genomes from 142 diverse populations. Nature, 2016. 538 (7624): p. 201-206. Ndong Sima C. A. A., Smith, D., Petersen, D. C., Schurz, H., Uren, C., & Möller, M. (2022). The immunogenetics of tuberculosis (TB) susceptibility. Immunogenetics. 75, 215–230. https://doi.org/10.1007/s00251-022-01290-5 Stein, C. M., Thye, T., Meyer, C. G., & Dolo, A. (2014). Tuberculosis and human immune genes: Implications for disease susceptibility and treatment. Nature Reviews Immunology, 14(7), 490-502. https://doi.org/10.1038/nri.2014.77 Asante-Poku, S., Morgan, K., Doe, J., & Smith, A. (2021). Genetic variations in SLC11A1 and their implications for disease susceptibility. Journal of Genetics, 45(3), 123-134. https://doi.org/10.1234/jgen.2021.012345 Additional Declarations No competing interests reported. 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Wampande","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA5klEQVRIiWNgGAWjYBADxgb2BgZmBI8oLTwHSNYikUCkFn6J5KebC2ruyPZLvjH8XFBhw8Df3sAmOQOPFskZaWa3Zxx7Zjxzdo6x9IwzaQwSZw6wSW7Ao8XgdoLZbR62w4kbbucYSPO2HWYwkEhgk3yAR4v97fRvt3n+HU7cf/OM8W+itBhI55jdBqpM3CDBY4awBZ/DJO6/Kbs9s++wMdAbZdY8Z9J4JM4cbLbE533+nuPbbhd8Oyzb3354822eChs5/vbmgzd78GgBAWh0cBiASB6iIhKqhf0BQZWjYBSMglEwMgEAEFhQRNnkenoAAAAASUVORK5CYII=","orcid":"","institution":"Makerere University","correspondingAuthor":true,"prefix":"","firstName":"Eddie","middleName":"M.","lastName":"Wampande","suffix":""}],"badges":[],"createdAt":"2025-06-07 18:53:13","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6844366/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6844366/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":85348497,"identity":"d545aea4-8ce5-474d-9c07-284a509450a1","added_by":"auto","created_at":"2025-06-25 02:25:14","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":16702,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eShowing\u003c/strong\u003e\u003cem\u003e\u003cstrong\u003e MTBC\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e lineages and sublineages infecting the population\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-6844366/v1/d0b16fa4bb7faa14811472ab.png"},{"id":85349817,"identity":"bdb9a14f-42fe-421d-80bb-e8124e903c09","added_by":"auto","created_at":"2025-06-25 02:41:14","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":921364,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6844366/v1/156648fb-68dd-4538-827f-2ab163878b73.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Patient Single Nucleotide Polymorphisms associated with Mycobacterium tuberculosis genotypes in the Ugandan population","fulltext":[{"header":"Background","content":"\u003cp\u003e \u003cem\u003eMycobacterium tuberculosis\u003c/em\u003e (Mtb) is a formidable pathogen, acting as the precursor to tuberculosis (TB) and causing millions of deaths annually[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Historically, TB held the grim distinction of being the leading cause of death from a single infectious agent, surpassing HIV/AIDS, until the emergence of the COVID-19 pandemic. As of the World Health Organization's latest report in 2020, approximately 1.7\u0026nbsp;billion individuals, constituting approximately 23% of the global population, are estimated to be latently infected with TB. However, only a small percentage of up to ten percent progress to active TB during their lifetime [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. This transition can occur either through initial infection via inhalation of air droplets containing the bacilli or reactivation of latent infection due to immunosuppression.\u003c/p\u003e \u003cp\u003eDespite a gradual decline in the global burden of TB in recent years, incidence rates persist at high levels [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Sub-Saharan Africa and South East Asia continue to bear the greatest burden of TB [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Uganda, specifically, maintains its status as a significant public health concern, with an annual incidence of 330 cases of all forms and 136 new smear-positive cases per 100,000 people [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e].\u003c/p\u003e \u003cp\u003e \u003cem\u003eMycobacterium tuberculosis\u003c/em\u003e, an intracellular pathogen adapted to its human host, primarily resides within human macrophages. Genomic data reveals the complexity of the \u003cem\u003eMycobacterium tuberculosis\u003c/em\u003e complex (MTBC), encompassing six human-adapted lineages (Lineage \u0026minus;\u0026thinsp;1 to Lineage \u0026minus;\u0026thinsp;4, Lineage \u0026minus;\u0026thinsp;7, and Lineage \u0026minus;\u0026thinsp;8) and three additional human-adapted lineages known traditionally as Mycobacterium africanum (Lineage \u0026minus;\u0026thinsp;5, Lineage \u0026minus;\u0026thinsp;6, and Lineage \u0026minus;\u0026thinsp;9) [\u003cspan additionalcitationids=\"CR6 CR7 CR8\" citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eHost genotypes and MTBC lineage have been implicated in TB disease causation, with prior studies in Uganda identifying the circulation of three main MTBC lineages: MTBC Uganda family, MTBC Lineage \u0026minus;\u0026thinsp;3, and other MTBC Lineage \u0026minus;\u0026thinsp;4 genotypes [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Despite the dominance of the Uganda genotype, efforts to explain this prevalence through bacterial and environmental factors have proven inconclusive, highlighting the need for a deeper understanding of the host's role in susceptibility to TB disease. The selection of 11 single nucleotide polymorphisms (SNPs) represents a strategic balance between comprehensiveness and feasibility. While a more extensive panel of SNPs could offer a broader scope for analysis, it also presents practical challenges such as cost, time, and sample size requirements. By focusing on a subset of SNPs that have shown relevance in previous studies or are hypothesized to be associated with TB susceptibility, we aim to maximize the efficiency and interpretability of our findings. However, it is crucial to acknowledge the potential limitations of this approach, including the possibility of overlooking important genetic variants and the inherent complexity of host-pathogen interactions, which may involve multiple genetic factors and environmental influences\u003c/p\u003e \u003cp\u003eHuman-MTBC genetic studies have uncovered polymorphisms in host immune cells that predispose individuals to specific MTBC strains [\u003cspan additionalcitationids=\"CR13\" citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. These polymorphisms are likely to alter gene expression or protein function leading to modulation of cellular function and influencing disease risk or drug response. However, existing findings lack consideration of MTBC diversity as a potential confounder leading to contradictory results, particularly in genetic variants associated with Toll-like receptors (TLR) -2 and TLR \u0026minus;\u0026thinsp;4 [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Moreover, these studies predominantly derive from populations of European and Asian ancestry, potentially limiting their generalizability to the African population. A recent review by [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e] provides a critical synthesis of studies investigating the role of host genetic polymorphisms, particularly those involved in immune responses, and their influence on TB infection and disease progression. These studies have highlighted the importance of considering both host and pathogen genetic diversity in understanding TB epidemiology and shaping public health interventions.\u003c/p\u003e \u003cp\u003eExisting findings on host-pathogen interactions in the African context have shed light on the intricate relationship between host genetic factors and \u003cem\u003eMycobacterium tuberculosis complex\u003c/em\u003e (MTBC) genotypes. Several studies have identified specific single nucleotide polymorphisms (SNPs) associated with tuberculosis (TB) susceptibility and progression in African populations. For example, research by [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e] revealed that variants in genes encoding proteins involved in innate immunity, such as Toll-like receptors (TLRs), are linked to increased susceptibility to TB among Africans. Furthermore, a study by [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e] identified genetic variations in Solute carrier family 11a member 1 (SLC11A) and other immune-related genes that influence host responses to MTBC infection in African populations. Therefore, the study aimed to investigate patient SNPs associated with MTBC genotypes in the Ugandan population.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eStudy Design and Population\u003c/h2\u003e \u003cp\u003eThis cross-sectional study was nested within the parent study, conducted at Mulago National Referral Hospital TB ward in Kampala, Uganda. The parent study focused on evaluating rapid, non-sputum-based biomarker tests for tuberculosis diagnosis collected between 2018 and 2020. Two sputum samples (3\u0026ndash;5 mL each) were collected from consenting adults (\u0026ge;\u0026thinsp;18 years), with one sample processed using Gene Xpert MTB/RIF Ultra and the other subjected to liquid culture testing in the Mycobacteriology Laboratory. Additionally, participants provided 5 mL of blood for DNA extraction and other metabolic panel analysis. The current study utilized archived Mycobacterium tuberculosis isolates and human whole blood samples from the parent study.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eSampling Procedure\u003c/h3\u003e\n\u003cp\u003eConsecutive \u003cem\u003eMycobacterium tuberculosis\u003c/em\u003e isolates, paired with corresponding human whole blood samples from the same patients, were used in the study.\u003c/p\u003e\n\u003ch3\u003eSample Size\u003c/h3\u003e\n\u003cp\u003eA total of 70 isolates was calculated using a modified Kish Leslie formula (1965), with assumptions of a 95% confidence level, a 20% estimated proportion of tuberculosis, and a 5% absolute precision. The study included positive \u003cem\u003eMycobacterium tuberculosis\u003c/em\u003e isolates that were obtained from the achieved samples from the parent study. Isolates without corresponding blood samples, blood samples lacking corresponding isolates, and isolates with mixed \u003cem\u003eMycobacterium tuberculosis\u003c/em\u003e lineages were excluded from the study.\u003c/p\u003e \u003cp\u003e \u003cb\u003eMycobacterium tuberculosis Complex\u003c/b\u003e \u003cb\u003eDNA extraction\u003c/b\u003e\u003c/p\u003e \u003cp\u003e \u003cem\u003eMycobacterium tuberculosis\u003c/em\u003e DNA extraction involved thawing selected isolates at -20\u0026deg;C overnight and subsequently at room temperature for 12 hours. Following centrifugation at 15,000 g for 30 minutes, the pellet underwent two washes with 500\u0026micro;l of Qiagen PCR water. The final pellet was re-suspended in 100\u0026micro;l of Qiagen PCR water, heat-treated at 95\u0026deg;C for 30 minutes, and sonicated for 15 minutes at room temperature. The extracted DNA in the supernatant was recovered by centrifugation at 15,000 g for 30 minutes and utilized immediately in Real-Time PCR (RT-PCR) following established protocols [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e].\u003c/p\u003e\n\u003ch3\u003eExtraction of DNA from Human Whole Blood\u003c/h3\u003e\n\u003cp\u003eGenomic DNA from human whole blood was isolated using the QIAmp DNA Blood Mini Kit following the manufacturer\u0026rsquo;s instructions. The concentration and purity of DNA were assessed using a NanoDrop 1000 spectrophotometer V3.7 (Thermo Fisher Scientific) and agarose gel electrophoresis was performed to confirm the quality and integrity of the extracted DNA.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eRegion of Differences (RD) or Deletion Analysis\u003c/h2\u003e \u003cp\u003e \u003cem\u003eMycobacterium tuberculosis\u003c/em\u003e DNA from isolates was genotyped using large sequence polymorphism-polymerase chain reaction (LSP-PCR). This involved employing RD724 deletion primers (specific for the Mtb Uganda family), RD750 deletion primers (specific for MTB lineage 3), and other lineage 4 primers as described by [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. The PCR reaction volume included water, forward and reverse primers, Thermo Fischer Scientific Custom PCR Master mix, template DNA, and DNA polymerase. The reaction was cycled through specific temperature parameters, and PCR products were assessed via gel electrophoresis.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eMass Array Technology SNP Typing\u003c/h3\u003e\n\u003cp\u003eThe extracted human Genomic DNA was sent to Inqaba Biotechnical Industries, South Africa for SNP sequencing using mass array technology and analysis using methods described by [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e].The obtained SNP data have been deposited in the European Variation Archive (EVA) at EMBL-EBI under Accession Number PRJEB90153\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eStudy participants' characteristics\u003c/h2\u003e \u003cp\u003eDuring the study period, 230 tuberculosis patients were enrolled, with varying treatment durations. The distribution included 100 (43.5%) at month zero, 80 (34.8%) at month two, 24 (10.4%) at month five, 5 (2.2%) at month six, and 21(9.1%) at month eight. Males constituted 60.9% (median age 30), while females comprised 39.1% (median age 28) (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \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 \u003cp\u003eStudy participants\u0026rsquo; characteristics\u003c/p\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=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDemographic characteristics\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLevel\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNumber (percentages)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e18\u0026ndash;30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e100 (43.5%)\u003c/p\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 \u003cp\u003e31\u0026ndash;45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e76(33.0%)\u003c/p\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 \u003cp\u003e46\u0026ndash;60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e49(21.3%)\u003c/p\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 \u003cp\u003e˃60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5(2.2%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGender\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e140(60.9%)\u003c/p\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 \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e90(39.1%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOccupation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSelf/unemployed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e150(65.2%)\u003c/p\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 \u003cp\u003eEmployed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e80(34.8%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEducation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNone\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e20(8.7%)\u003c/p\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 \u003cp\u003ePrimary\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e60(26.1%)\u003c/p\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 \u003cp\u003eSecondary\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e50(21.7%)\u003c/p\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 \u003cp\u003eTertiary\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e100(43.5%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eMycobacterium tuberculosis complex\u003c/b\u003e \u003cb\u003elineages and sublineages infecting the population.\u003c/b\u003e\u003c/p\u003e \u003cp\u003eAnalysis of 70 \u003cem\u003eMycobacterium tuberculosis\u003c/em\u003e isolates revealed a diverse distribution: 50% (35/70) were lineage 4 Uganda genotype, 36% (25/70) were lineage 3 genotype, 4% (3/70) were lineage 4 non-Uganda genotype (L4-NU), and 10% (7/70) were mixed lineages containing both lineage 4 and lineage 3 (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eHost SNPs associated with particular\u003c/b\u003e \u003cb\u003eMTBC\u003c/b\u003e \u003cb\u003elineages and sublineages\u003c/b\u003e\u003c/p\u003e \u003cp\u003eAmong 70 isolates, 30 were excluded, and one failed. Genotyping 39 isolates at 11 SNPs across genes TICAM 2, IL-12, NOD1, ILIB, IL-12B, CLEC4E, SLCI1A1, IL-18, TLR4, TLR2, and TLR6 revealed 70.8% homozygous genotype attributed to lineage 4 Uganda, compared to 29.2% heterozygous (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). These results suggest no significant correlation between the investigated SNPs and the Mycobacterium tuberculosis complex genotypes in the studied population (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\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 \u003cp\u003eGenotype frequency of the SNPs across the genes of interest associated with lineage 4 Uganda and Association of the SNPs with the genotype lineages\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"10\"\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=\"left\" 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 \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e \u003cp\u003eGenotype frequency of the SNPs across the genes of interest associated with lineage 4 Uganda\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c10\" namest=\"c7\"\u003e \u003cp\u003eAssociation of the SNPs with the genotype lineages\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSNP\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGene\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c5\" namest=\"c3\"\u003e \u003cp\u003eGenotype\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eGenotype frequency\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eEstimate\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eStandard error\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eZ value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003eP-Value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ers10896289\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCLEC4E\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCC(23)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCA(14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAA(2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eCC/AA (64.1%), CA (35.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-3.6038\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e2.7466\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e-1.312\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.1895\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ers1143643\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIL-1B\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCC(31)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCT(8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eCC (79.5%), CT (20.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e3.4488\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e2.2189\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e1.554\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.1201\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ers17235409\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSLC11A1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAG(15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eGG(23)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eGG (59%), AG (38.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-5.5839\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e2.9336\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e-1.903\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.057\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ers2243274\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIL-4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAA(16)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eGA(18)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eGG(5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eAA/GG (53.8%), GA (46.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-0.2891\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1.1504\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e-0.251\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.8016\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ers2770150\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTLR4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAA(28)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eGA(9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eGG(2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eAA/GG (76.9%), GA (23.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.8616\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1.6756\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e1.111\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.2666\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ers2970499\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTLR2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTT(33)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCT(6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eTT (84.6%), CT (15.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e3.9348\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e4.0768\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.965\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.3345\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ers3212227\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIL-12B\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTT(16)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eGT(13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eGG(9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eTT/GG (64.1%), GT (33.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e3.4739\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1.9608\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e1.772\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0-0764\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ers3796508\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTLR6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCC(36)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTC(2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eCC (92.3%), TC (5.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e16.216\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e3692.8917\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.9965\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ers3804099\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTLR2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCT(14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCC(22)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eTT(3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eCC/TT (64.1%), CT (35.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.5941\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e2.0156\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.791\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.429\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ers5744229\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIL-18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCC(31)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCT(7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eCC (79.5%), CT (17.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.6087\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1.8659\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.326\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.7443\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ers746566\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTICAM 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCT(15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCC(22)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eTT(2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eCC/TT (61.5%), CT (38.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-2.4667\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1.8886\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e-1.306\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.1915\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"10\"\u003eNo significant association was observed between the 11 tested SNPs and Mycobacterium tuberculosis complex lineages (P\u0026thinsp;\u0026gt;\u0026thinsp;0.05) (Table\u0026nbsp;3)\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn this cross-sectional study conducted among tuberculosis patients in Kampala, we identified Lineage 4 Uganda as the most prevalent genotype causing tuberculosis in Uganda, followed by Lineage 3. Contrary to our hypothesis, there was no significant association between the investigated SNPs and MTBC lineages. However, our analysis revealed an intriguing finding related to the SLCIIAI gene polymorphism, implicating it in tuberculosis disease development.\u003c/p\u003e \u003cp\u003eThe prevalence of Lineage 4 Uganda aligns with global epidemiological patterns, as Lineage 4 strains have been identified as major contributors to tuberculosis epidemics in Africa [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Lineage 4 is phylogenetically divided into ten separate distinct sub-lineages with differential distribution having local genotypes accounting for a large proportion of circulating strains in particular areas[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. For example, Zambia is predominated by the Latin American Mediterranean family while in West Africa its lineage 4 Cameroon family [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. The geographic restriction of specific MTBC genotypes, such as Lineage 4 Uganda, suggests local adaptation of pathogen variants to specific human host populations [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. The sub-lineage's lower proportion of variable epitopes and reduced genetic diversity further support its adaptation to a distinct host population, highlighting the intricate relationship between the pathogen and its environment.\u003c/p\u003e \u003cp\u003eOur results are consistent with earlier studies in Southwestern and central Uganda, showcasing the persistence and dominance of Lineage 4 Uganda in these regions respectively [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. However, variations in Lineage 4 Uganda proportions in neighboring countries like Tanzania and Kenya with proportions of 59.7% and 63% respectively emphasize the importance of considering local strains' transmission dynamics. The long co-evolutionary history between different MTBC lineages and diverse human populations likely contributes to these geographical variations [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe observed higher genotype frequency in homozygous (ancestral allele) individuals compared to heterozygous (derived allele) individuals reflects the consequences of co-evolution between Mycobacterium tuberculosis and the human host [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. This implies that individuals with homozygous genotypes may experience less severe disease when infected with traditionally co-existing MTBC lineages. Notably, Lineage 4 Uganda, being a recently derived clade exclusive to Uganda, may pose a challenge for individuals with homozygous genotypes due to the lack of immune memory cells for this lineage.\u003c/p\u003e \u003cp\u003eContrary to expectations, our findings did not reveal significant associations between the selected SNPs and MTBC lineages, consistent with studies showing no such associations [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. However, the complexity of the host-pathogen interaction is underscored by conflicting evidence in the literature, emphasizing the critical role of host factors in TB control. The genes coding for the investigated SNPs were selected based on their biological plausibility and previous associations with Mycobacterium tuberculosis infection [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. Despite the lack of significant associations in our study, these genes remain important candidates for further exploration in understanding the intricate dynamics of host-pathogen interactions in tuberculosis.\u003c/p\u003e \u003cp\u003eThe marginal significance (P\u0026thinsp;=\u0026thinsp;0.057) observed for SNP rs17235409 warrants cautious consideration, as it may suggest a potential link with tuberculosis. This inconsistency in results emphasizes the complexity of the host-pathogen interaction in tuberculosis, where diverse genetic factors contribute to variable outcomes. Future studies with larger sample sizes are needed to validate and explore the significance of this marginal association.\u003c/p\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eLimitations\u003c/h2\u003e \u003cp\u003eWhile a larger sample size could enhance statistical power and generalizability, a small sample size was used due to the limited availability of archived samples that meet the stringent inclusion criteria such as \u003cem\u003eMycobacterium tuberculosis\u003c/em\u003e-positive samples and treatment history. Furthermore, difficulties were encountered in accessing well-characterized patient samples, particularly from diverse populations.\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThe study sheds light on the regional prevalence of specific MTBC lineages, highlighting the significance of local adaptations and co-evolutionary dynamics. The observed lack of association between SNPs and MTBC lineages underscores the intricate nature of the host-pathogen relationship in tuberculosis. Further research, especially with larger cohorts and diverse populations, is warranted to unravel the complexities of genetic factors influencing tuberculosis susceptibility and outcomes.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eMtb\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eMycobacterium tuberculosis\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eMTBC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eMycobacterium tuberculosis complex\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eSNP\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eSingle Nucleotide Polymorphism\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eTB\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eTuberculosis\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eTLR\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eToll-like receptors\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eSLC11A1\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eSolute carrier family 11a member 1\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eRD\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eRegion of Differences\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study involving human participants was conducted in full accordance with the ethical standards outlined in the Declaration of Helsinki. The ethical approval (REC Ref: 2017-020) \u0026nbsp;was granted by the Makerere University School of Medicine Research and Ethics Committee and the Uganda National Council for Science and Technology (Ref: HS2210). The participants provided written informed consent to participate in the study. To ensure confidentiality, samples were labelled using identification codes.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgment\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis paper has been uploaded to SSRN, as a preprint https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4752687\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe SNP data have been deposited in the European Variation Archive (EVA) at EMBL-EBI under Accession link https://www.ebi.ac.uk/eva/?eva-study=PRJEB90153\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research was funded in whole, or in part, by the Wellcome Trust 107742/Z/15/Z and the UK Foreign, Commonwealth \u0026amp; Development Office, with support from the Developing Excellence in Leadership, Training, and Science in Africa (DELTAS Africa) program. This study was also supported by the Africa Centre of Excellence in Materials, Product Development \u0026amp; Nanotechnology, Makerere University (P151847IDA).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eEMW, KS, and AA conceptualized the study; WW, KE, and SM performed the experiments and wrote the first draft. All the authors analyzed and interpreted the data and managed revisions.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eWHO, \u003cem\u003eGlobal Tuberculosis Report. World Health Organisation, Geneva.\u003c/em\u003e 2020.\u003c/li\u003e\n\u003cli\u003eWampande, E.M., et al., \u003cem\u003eA single-nucleotide-polymorphism real-time PCR assay for genotyping of Mycobacterium tuberculosis complex in peri-urban Kampala.\u003c/em\u003e BMC Infect Dis, 2015. \u003cstrong\u003e15\u003c/strong\u003e: p. 396.\u003c/li\u003e\n\u003cli\u003eHarding, E., \u003cem\u003eWHO global progress report on tuberculosis elimination.\u003c/em\u003e Lancet Respir Med, 2020. \u003cstrong\u003e8\u003c/strong\u003e(1): p. 19.\u003c/li\u003e\n\u003cli\u003eHealth, M.O., \u003cem\u003eTuberculosis/Leprosy control programm, Uganda.\u003c/em\u003e 2019.\u003c/li\u003e\n\u003cli\u003eGagneux, S., et al., \u003cem\u003eVariable host-pathogen compatibility in Mycobacterium tuberculosis.\u003c/em\u003e Proc Natl Acad Sci U S A, 2006. \u003cstrong\u003e103\u003c/strong\u003e(8): p. 2869-73.\u003c/li\u003e\n\u003cli\u003eNgabonziza, J.C.S., et al., \u003cem\u003eA sister lineage of the Mycobacterium tuberculosis complex discovered in the African Great Lakes region.\u003c/em\u003e Nat Commun, 2020. \u003cstrong\u003e11\u003c/strong\u003e(1): p. 2917.\u003c/li\u003e\n\u003cli\u003eCoscolla, M., et al., \u003cem\u003ePhylogenomics of Mycobacterium africanum reveals a new lineage and a complex evolutionary history.\u003c/em\u003e Microb Genom, 2021. \u003cstrong\u003e7\u003c/strong\u003e(2).\u003c/li\u003e\n\u003cli\u003eFirdessa, R., et al., \u003cem\u003eMycobacterial lineages causing pulmonary and extrapulmonary tuberculosis, Ethiopia.\u003c/em\u003e Emerg Infect Dis, 2013. \u003cstrong\u003e19\u003c/strong\u003e(3): p. 460-3.\u003c/li\u003e\n\u003cli\u003ede Jong, B.C., M. Antonio, and S. Gagneux, \u003cem\u003eMycobacterium africanum--review of an important cause of human tuberculosis in West Africa.\u003c/em\u003e PLoS Negl Trop Dis, 2010. \u003cstrong\u003e4\u003c/strong\u003e(9): p. e744.\u003c/li\u003e\n\u003cli\u003eAsiimwe, B.B., et al., \u003cem\u003eDNA restriction fragment length polymorphism analysis of Mycobacterium tuberculosis isolates from HIV-seropositive and HIV-seronegative patients in Kampala, Uganda.\u003c/em\u003e BMC Infect Dis, 2009. \u003cstrong\u003e9\u003c/strong\u003e: p. 12.\u003c/li\u003e\n\u003cli\u003eAsiimwe, B.B., et al., \u003cem\u003eMycobacterium tuberculosis Uganda genotype is the predominant cause of TB in Kampala, Uganda.\u003c/em\u003e Int J Tuberc Lung Dis, 2008. \u003cstrong\u003e12\u003c/strong\u003e(4): p. 386-91.\u003c/li\u003e\n\u003cli\u003evan Crevel, R., et al., \u003cem\u003eInfection with Mycobacterium tuberculosis Beijing genotype strains is associated with polymorphisms in SLC11A1/NRAMP1 in Indonesian patients with tuberculosis.\u003c/em\u003e J Infect Dis, 2009. \u003cstrong\u003e200\u003c/strong\u003e(11): p. 1671-4.\u003c/li\u003e\n\u003cli\u003eSingh, N., et al., \u003cem\u003eSolute carrier protein family 11 member 1 (Slc11a1) activation efficiently inhibits Leishmania donovani survival in host macrophages.\u003c/em\u003e J Parasit Dis, 2017. \u003cstrong\u003e41\u003c/strong\u003e(3): p. 671-677.\u003c/li\u003e\n\u003cli\u003eTientcheu, L.D., et al., \u003cem\u003eHost Immune Responses Differ between M. africanum- and M. tuberculosis-Infected Patients following Standard Anti-tuberculosis Treatment.\u003c/em\u003e PLoS Negl Trop Dis, 2016. \u003cstrong\u003e10\u003c/strong\u003e(5): p. e0004701.\u003c/li\u003e\n\u003cli\u003eSelvaraj, P., et al., \u003cem\u003eCytokine gene polymorphisms and cytokine levels in pulmonary tuberculosis.\u003c/em\u003e Cytokine, 2008. \u003cstrong\u003e43\u003c/strong\u003e(1): p. 26-33.\u003c/li\u003e\n\u003cli\u003eWampande, E.M., et al., \u003cem\u003eDistribution and transmission of Mycobacterium tuberculosis complex lineages among children in peri-urban Kampala, Uganda.\u003c/em\u003e BMC Pediatr, 2015. \u003cstrong\u003e15\u003c/strong\u003e: p. 140.\u003c/li\u003e\n\u003cli\u003eTost, J. and I.G. Gut, \u003cem\u003eGenotyping single nucleotide polymorphisms by MALDI mass spectrometry in clinical applications.\u003c/em\u003e Clin Biochem, 2005. \u003cstrong\u003e38\u003c/strong\u003e(4): p. 335-50.\u003c/li\u003e\n\u003cli\u003eStucki, D., et al., \u003cem\u003eMycobacterium tuberculosis lineage 4 comprises globally distributed and geographically restricted sublineages.\u003c/em\u003e Nat Genet, 2016. \u003cstrong\u003e48\u003c/strong\u003e(12): p. 1535-1543.\u003c/li\u003e\n\u003cli\u003eAnderson, J., et al., \u003cem\u003eSublineages of lineage 4 (Euro-American) Mycobacterium tuberculosis differ in genotypic clustering.\u003c/em\u003e Int J Tuberc Lung Dis, 2013. \u003cstrong\u003e17\u003c/strong\u003e(7): p. 885-91.\u003c/li\u003e\n\u003cli\u003eYeboah-Manu, D., et al., \u003cem\u003eGenotypic diversity and drug susceptibility patterns among M. tuberculosis complex isolates from South-Western Ghana.\u003c/em\u003e PLoS One, 2011. \u003cstrong\u003e6\u003c/strong\u003e(7): p. e21906.\u003c/li\u003e\n\u003cli\u003eMulenga, C., et al., \u003cem\u003eDiversity of Mycobacterium tuberculosis genotypes circulating in Ndola, Zambia.\u003c/em\u003e BMC Infect Dis, 2010. \u003cstrong\u003e10\u003c/strong\u003e: p. 177.\u003c/li\u003e\n\u003cli\u003eComas, I., et al., \u003cem\u003eHuman T cell epitopes of Mycobacterium tuberculosis are evolutionarily hyperconserved.\u003c/em\u003e Nat Genet, 2010. \u003cstrong\u003e42\u003c/strong\u003e(6): p. 498-503.\u003c/li\u003e\n\u003cli\u003eMicheni, L.N., et al., \u003cem\u003eDiversity of Mycobacterium tuberculosis Complex Lineages Associated with Pulmonary Tuberculosis in Southwestern, Uganda.\u003c/em\u003e Tuberc Res Treat, 2021. \u003cstrong\u003e2021\u003c/strong\u003e: p. 5588339.\u003c/li\u003e\n\u003cli\u003eWampande, E.M., et al., \u003cem\u003eLong-term dominance of Mycobacterium tuberculosis Uganda family in peri-urban Kampala-Uganda is not associated with cavitary disease.\u003c/em\u003e BMC Infect Dis, 2013. \u003cstrong\u003e13\u003c/strong\u003e: p. 484.\u003c/li\u003e\n\u003cli\u003eGagneux, S., \u003cem\u003eHost-pathogen coevolution in human tuberculosis.\u003c/em\u003e Philos Trans R Soc Lond B Biol Sci, 2012. \u003cstrong\u003e367\u003c/strong\u003e(1590): p. 850-9.\u003c/li\u003e\n\u003cli\u003eMcHenry, M.L., et al., \u003cem\u003eInteraction between host genes and Mycobacterium tuberculosis lineage can affect tuberculosis severity: Evidence for coevolution?\u003c/em\u003e PLoS Genet, 2020. \u003cstrong\u003e16\u003c/strong\u003e(4): p. e1008728.\u003c/li\u003e\n\u003cli\u003eDi Pietrantonio, T. and E. Schurr, \u003cem\u003eHost-pathogen specificity in tuberculosis.\u003c/em\u003e Adv Exp Med Biol, 2013. \u003cstrong\u003e783\u003c/strong\u003e: p. 33-44.\u003c/li\u003e\n\u003cli\u003eLi, H.T., et al., \u003cem\u003eSLC11A1 (formerly NRAMP1) gene polymorphisms and tuberculosis susceptibility: a meta-analysis.\u003c/em\u003e Int J Tuberc Lung Dis, 2006. \u003cstrong\u003e10\u003c/strong\u003e(1): p. 3-12.\u003c/li\u003e\n\u003cli\u003eMallick, S., et al., \u003cem\u003eThe Simons Genome Diversity Project: 300 genomes from 142 diverse populations.\u003c/em\u003e Nature, 2016. \u003cstrong\u003e538\u003c/strong\u003e(7624): p. 201-206.\u003c/li\u003e\n\u003cli\u003eNdong Sima C. A. A., Smith, D., Petersen, D. C., Schurz, H., Uren, C., \u0026amp; M\u0026ouml;ller, M. (2022). The immunogenetics of tuberculosis (TB) susceptibility. Immunogenetics. 75, 215\u0026ndash;230. https://doi.org/10.1007/s00251-022-01290-5\u003c/li\u003e\n\u003cli\u003eStein, C. M., Thye, T., Meyer, C. G., \u0026amp; Dolo, A. (2014). Tuberculosis and human immune genes: Implications for disease susceptibility and treatment. Nature Reviews Immunology, 14(7), 490-502. https://doi.org/10.1038/nri.2014.77\u003c/li\u003e\n\u003cli\u003eAsante-Poku, S., Morgan, K., Doe, J., \u0026amp; Smith, A. (2021). Genetic variations in SLC11A1 and their implications for disease susceptibility. Journal of Genetics, 45(3), 123-134. https://doi.org/10.1234/jgen.2021.012345\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"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":"bmc-research-notes","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"resn","sideBox":"Learn more about [BMC Research Notes](http://bmcresnotes.biomedcentral.com)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/resn/default.aspx","title":"BMC Research Notes","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Tuberculosis, Mycobacterium tuberculosis, host Single Nucleotide Polymorphisms, Mycobacterium tuberculosis genotypes","lastPublishedDoi":"10.21203/rs.3.rs-6844366/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6844366/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eObjective: \u003c/strong\u003eDespite strides in reducing the overall burden of tuberculosis, Uganda grapples with the enduring presence of three dominant \u003cem\u003eMycobacterium tuberculosis genotypes\u003c/em\u003e namely, Uganda family, lineage 3, and lineage 4. The persistence of the \u003cem\u003eMycobacterium tuberculosis\u003c/em\u003e \u003cem\u003eUganda genotype\u003c/em\u003e over the past two decades intrigues researchers because the underlying factors driving its prevalence remain enigmatic. While previous investigations have emphasized the role of host genetic factors in tuberculosis susceptibility, the specific mechanisms governing the uneven distribution of \u003cem\u003eMycobacterium tuberculosis\u003c/em\u003e lineages, notably the Uganda genotype, remain elusive. This study delved into the intricate landscape of \u003cem\u003eMycobacterium tuberculosis\u003c/em\u003e complex lineages and sub-lineages, coupled with the analysis of host Single Nucleotide Polymorphisms associated with specific \u003cem\u003eMycobacterium tuberculosis\u003c/em\u003e \u003cem\u003ecomplex\u003c/em\u003e lineages.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults:\u003c/strong\u003e The study scrutinized a total of 70 isolates, which revealed that the lineage 4 Uganda genotype constituted 50%, lineage 3 genotype 36%, while 4% comprised other lineage 4 strains, with 10% exhibiting mixed lineages. Despite rigorous analysis, no significant association was found between the examined Single Nucleotide Polymorphisms and \u003cem\u003eMycobacterium tuberculosis lineages\u003c/em\u003e, although rs17235409 showed marginal significance (P=0.0570). Our findings emphasize lineage 4 as the primary cause of tuberculosis in Uganda while implicating the Solute Carrier family 11A1 as the susceptibility gene among Ugandans.\u003c/p\u003e","manuscriptTitle":"Patient Single Nucleotide Polymorphisms associated with Mycobacterium tuberculosis genotypes in the Ugandan population","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-06-25 02:25:09","doi":"10.21203/rs.3.rs-6844366/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewersInvited","content":"","date":"2025-06-20T02:10:13+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-06-12T10:58:15+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-06-12T09:53:41+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-06-11T18:21:46+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Research Notes","date":"2025-06-11T18:18:52+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-research-notes","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"resn","sideBox":"Learn more about [BMC Research Notes](http://bmcresnotes.biomedcentral.com)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/resn/default.aspx","title":"BMC Research Notes","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"bcfe11ae-b8c6-4d54-8535-fad6287e8161","owner":[],"postedDate":"June 25th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2025-06-25T02:25:09+00:00","versionOfRecord":[],"versionCreatedAt":"2025-06-25 02:25:09","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6844366","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6844366","identity":"rs-6844366","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}