Scavenger Receptor Genes Polymorphisms Association with Tuberculosis and Latent Tuberculosis Infection in Pakistani population

Scavenger Receptor Genes Polymorphisms Association with Tuberculosis and Latent Tuberculosis Infection in Pakistani 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 Research Article Scavenger Receptor Genes Polymorphisms Association with Tuberculosis and Latent Tuberculosis Infection in Pakistani population Ezza Binte Tariq, Urooj Subhan, Farah Deeba, Riaz Ullah, Zuha Tariq, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3856622/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 Host genetics is pivotal in deciding disease susceptibility and outcome in individuals infected with Mycobacterium tuberculosis (Mtb). Scavenger receptors are PRRs that play a vital role in facilitating molecular interactions between Mtb and the host. This interaction can potentially be modified by polymorphisms in scavenger receptor genes. The role of scavenger receptors in TB or LTBI pathogenesis has not yet been studied. Therefore, we designed a case-control study to investigate the association of polymorphisms in the CD36 gene at rs1761667 (G>A) and rs3211938 (T>G), and SR-B1 gene at rs4238001 (G>A) with TB and LTBI in the Pakistani population using ARMS-PCR. Fisher's exact chi-square test was used to compare genotypes between study groups. We found that rs4238001 (AA, p =0.00) and rs1761667 (AA, p =0.03) were significantly associated with active TB. Furthermore, rs1761667 (GA, p =0.00) and rs3211938 (GG, p <0.00) were significantly associated with LTBI. Our findings suggest that SR-B1 and CD36 gene polymorphisms may contribute to TB pathogenesis in the Pakistani population. Furthermore, different genotypes of a single SNP can have varying effects on the susceptibility to both TB and LTBI. Further studies on polymorphism-associated gene expression will provide insights into their role in TB and LTBI pathogenesis. SR-B1 CD36 rs4238001 (g.5275G > A) rs1761667 (g.18436G > A) rs3211938 (g.73946T > G) single nucleotide polymorphisms Introduction Tuberculosis (TB) is a global health concern caused by Mycobacterium tuberculosis (Mtb), a highly infectious gram-positive bacterium. This bacterium can exist in a latent state or an active disease state resulting in latent TB infection or active disease, respectively. TB primarily spreads via airborne respiratory droplets expelled when infected individuals cough or sneeze. Approximately one-third of the global population is infected with Mtb. Of these, 10% carry a lifetime risk of developing active TB, while the remaining 90% develop a latent infection in which they may not exhibit any symptoms of TB (1). According to the WHO TB Report 2023, in 2022, there were an estimated 1.3 million deaths among HIV-negative people and 0.3 million deaths among HIV-positive people, for a combined total of 1.6 million mortality worldwide. Pakistan is ranked fifth among the countries with the highest TB burden in the world. The mortality rate of TB in Pakistan is 34 deaths per 100,000 people per year. The progression and outcome of TB infection are intricately influenced by the interplay of pathogen, host, and environmental factors. The development of active TB in only a fraction of infected individuals suggests that host genetics is a pivotal determinant of disease development (2). Previous studies have identified a significant association of genetic polymorphisms in human and experimental animal models on the outcomes of Mtb infection (3, 4). Scavenger receptors (SRs) are one of the pattern recognition receptors (PRRs) that recognize mycobacterial pathogen-associated molecular patterns (PAMPs). SRs belong to a class of cell surface transmembrane glycoproteins that play vital roles in the control of macrophages and innate immunity (5). SRs are categorized into 12 classes, with class B comprising SR-B1 , SR-B2 , and CD36 . SRs are one of the various cell surface and intracellular receptors that mediate the uptake of bacteria (6). SRs interact with the lipoglycans and lipoprotein components of mycobacteria and facilitate the direct molecular interaction between the host and Mtb. Inflammatory cytokines are produced by the activation of SRs, which subsequently activate the macrophages. Macrophages kill Mtb through phagocytosis and induce an adaptive immune response. This close interaction suggests the possibility of co-evolution between the host immune system and the pathogen over time. Previous studies have supported the involvement of mutations in SR genes with multiple diseases including infectious diseases (7, 8), cardiovascular diseases (9, 10), metabolic disorders (11, 12), and neurodegenerative disorder (13). There are only two studies on the association between SR gene polymorphisms and TB in the Gambian and Chinese populations (14, 15). The association between CD36 gene polymorphisms and pulmonary TB has been investigated in the Chinese Han population. Furthermore, we did not find any studies on the association of SR gene polymorphisms and latent TB. Therefore, the role of CD36 and SR-B1 polymorphisms in TB and LTBI remains unclear. The association of polymorphisms in SR-B1 and CD36 genes with active or latent TB in the Pakistani population has not been studied yet. SR-B1 and CD36 gene polymorphisms may affect human susceptibility or resistance to mycobacterial disease. Therefore, in the current study, we investigated the association of CD36 gene polymorphisms at rs1761667 (g.18436G > A) and rs3211938 (g.73946T > G) and SR-B1 gene polymorphism rs4238001 (g.5275G > A) with TB and LTBI in the Pakistani population. Material and methods All the experiments in this study were performed following the ethical guidelines and regulations of the NUMS Institutional Review Board. Blood samples were collected after receiving informed consent from patients with GeneXpert-confirmed TB and their contacts at the Nishtar Hospital Multan (NHM). Blood samples of TB contacts were also collected from laboratory workers dealing with TB patients from TEMAR Diagnostics Rawalpindi and Pride Laboratory Lahore. Samples from healthy controls were collected from Raza Laboratory, Nishtar Road Multan. We used Epitool software to calculate the sample size for this case-control study. The calculated sample size was 350 study subjects including 150 healthy controls, 100 active TB patients, and 100 TB contacts. All study subjects were ≥ 16 years of age and those tested positive for HIV infection were excluded from the study. The genomic DNA from blood samples (1-2ml) was extracted using the phenol-chloroform method. SR-B1 and CD36 polymorphisms were identified from the National Center for Biotechnology Information dbSNP database ( https://www.ncbi.nlm.nih.gov/snp ). PRIMER 1 software was used to design primers for the SNPs rs4238001, rs1761667, and rs3211938. All SNPs were genotyped using amplification refractory mutation system PCR (ARMS-PCR). Details of the primers and PCR conditions are given in Table 1 and Table 2 . Table 1 Sequence and properties of primers Gene Mutation Primer Sequence Length (bp) Tm (°C) SR-B1 rs4238001 Outer forward : CTCCCGCCCCAAAACGGAA 19 67.7 Outer reverse : CAGCAGCCCCTCCCGAAGC 19 70.6 Inner forward : CCAGGCGCGCAGACAGGA 18 68.9 Inner reverse : GCGCTTTGGCGGAGCATCC 19 67.0 CD36 rs1761667 Outer forward : AAGGTCTGGTATCCACCTGTTTTCCT 26 64.7 Outer reverse : AAGAGTTTTCATGAAGCTTCCCGC 24 61.9 Inner forward : TTTTATTCATCTTTGCATGCCATCG 25 57.3 Inner reverse : TCATACTCCAGGCTTTGAGCATTGT 25 61.9 rs3211938 Outer forward : GAATAGTTCATGCTTGGCTATTGAGTTT 28 58.2 Outer reverse : CACCATTCTTTCTTCTGCCCTAATTACT 28 60.4 Inner forward : AAAATTATCTCAAAAAATTGTACATCACAG 30 53.5 Inner reverse : TGCATTTGCTGATGTCTAGCACATCA 26 61.4 Table 2 PCR conditions for all polymorphisms. Mutation PCR conditions Initial denaturation Denaturation Annealing (35 cycles) Extension Final extension rs4238001 95°C for 5 min (for all cycles) 95°C for 1 min (for all cycles) 65°C (-1°C for every first 10 cycles) & 55°C (for 25 cycles) 1 min 72°C for 1 min (for all cycles) 72°C for 10 min (last 25 cycles) rs1761667 95°C for 5 min 95°C for 1 min 61°C for 1 min 72°C for 1 min 72°C for 10 min rs3211938 95°C for 5 min 95°C for 1 min 51°C for 1 min 72°C for 1 min 72°C for 10 min GraphPad Prism 10 was used for statistical analysis. The Chi-square test was used to assess the deviation of genotype distribution frequency from Hardy-Weinberg Equilibrium. Independent sample t-test was used to compare mean age and chi-square test was used to compare the gender between study groups. Fisher's exact chi-square test was used for comparison of genotypes. For all the statistical analyses, p values less than 0.05 were considered significant. Results Genotype frequency distribution Genotype patterns of the study groups were examined to investigate the association between the SNPs in the SR-B1 and CD36 genes and susceptibility or resistance to TB. The frequencies of TB patients and TB contacts at rs1761667 and rs3211938 deviated from the Hardy Weinberg Equilibrium (HWE). However, for rs4238001, the frequency distribution for TB patients conformed to HWE, while the frequency distribution for TB contacts did not conform to HWE. Association of CD36 (rs1761667) with active TB and LTBI When the genotypes were compared between healthy controls and TB patients in the co-dominant model a statistically significant p value was obtained ( p = 0.03, OR = 0.73, 95%CI = 0.43–1.24). The frequency of GA was higher in healthy controls (66.89%) as compared to TB patients (55.20%). The genotype frequencies of GG and AA in healthy controls vs. TB patients had minor difference (Healthy controls: GG: 32.41% and AA: 0.01%; TB Patients; 39.58% and 0.05%,). The genotype comparison in the recessive model gave statistically significant p value ( p = 0.02, OR = 0.12, 95%CI = 0.01–0.94), while in the dominant model ( p = 0.25, OR = 0.73, 95%CI = 0.43–1.24) and additive model ( p = 0.76, OR = 0.94, 95%CI = 0.64–1.38) statistically non-significant p values were observed (Table 3 ). Table 3 Genotype comparison of rs1761667 between healthy controls (HC) and TB patients (TP). Genetic models Genotypes HC n (%) TP n (%) OR b (95%CI c ) P value χ 2 a value 145 (100) 96 (100) Co-dominant GG 47 (32.41) 38 (39.58) 0.73 (0.43–1.24) 0.03 6.84 GA 97 (66.89) 53 (55.20) AA 1 (0.01) 5 (0.05) Dominant GG 47 (32.41) 38 (39.58) 0.73 (0.43–1.24) 0.25 1.30 GA + AA 98 (67.59) 58 (60.42) Recessive AA 1 (0.69) 5 (5.21) 0.12 (0.01–0.94) 0.02 4.85 GA + GG 144 (99.31) 91 (94.79) Additive G 191 (65.86) 129 (67.19) 0.94 (0.64–1.38) 0.76 0.09 A 99 (34.14) 63 (32.81) a χ 2 : chi square, b OR: odds ratio c CI: confidence interval. The significant p values are shown in italics. When the genotypes were compared between healthy controls and TB contacts in the co-dominant model statistically significant p value was obtained ( p = 0.00, OR = 3.39 95% CI = 1.73–6.58). The frequency of genotype GG was higher in healthy controls (32.41%) as compared to TB contacts (12.37%) whereas the frequency of GA was higher in TB contacts (86.57%) as compared to healthy controls (66.89%). The frequency of AA was similar in both study groups. Genotype comparison in the dominant model ( p = 0.00, OR = 3.39 95% CI = 1.73–6.58) gave statistically significant p value, while in recessive ( p = 0.77, OR = 0.66, 95%CI = 0.03–12.79) and additive model ( p = 0.05, OR = 1.43, 95%CI = 0.98–2.08) statistically insignificant p values were obtained (Table 4 ). Table 4 Genotype comparison of rs1761667 between healthy controls (HC) and TB contacts (TC). Genetic models Genotypes HC n (%) TC n (%) OR b (95%CI c ) P value χ 2 a value 145 (100) 97 (100) Co-dominant GG 47 (32.41) 12 (12.37) 3.39 (1.73–6.58) 0.00 12.67 GA 97 (66.89) 84 (86.57) AA 1 (0.01) 1 (0.01) Dominant GG 47 (32.41) 12 (12.37) 3.39 (1.73–6.58) 0.00 12.66 GA + AA 98 (67.59) 85 (87.63) Recessive AA 1 (0.69) 1 (1.03) 0.66 (0.03–12.79) 0.77 0.08 GA + GG 144 (99.31) 96 (98.97) Additive G 191 (65.86) 116 (57.43) 1.43 (0.98–2.08) 0.05 3.61 A 99 (34.14) 86 (42.57) a χ 2 : chi square, b OR: odds ratio c CI: confidence interval. The significant p values are shown in italics. Association of CD36 (rs3211938) with active TB and LTBI When the genotypes were compared between healthy controls and TB patients in the co-dominant model, a statistically significant p value was obtained ( p = 0.02, OR = 0.91 95%CI = 0.34–2.20). The frequency of genotype GG was higher in healthy controls (35.29%) compared to TB patients (19.19%), whereas the frequency of genotype GT was higher in TB patients (71.71%) than in healthy controls (56.30%). The frequency of genotype TT was almost similar in both groups. The comparison of healthy controls and TB patients in the recessive model ( p = 0.00, OR = 2.29, 95%CI = 1.22–4.19) exhibited statistically significant p value, while in dominant ( p = 0.85, OR = 0.91, 95%CI = 0.34–2.20) and additive model ( p = 0.07, 0R = 1.97, 95%CI = 1.28–2.98) statistically non-significant p values were observed (Table 5 ). Table 5 Genotype comparison of rs3211938 between healthy controls (HC) and TB patients (TP). Genetic models Genotypes HC n (%) TP n (%) OR b (95%CI c ) P value χ 2 a value 119 (100) 99 (100) Co-dominant TT 10 (8.40) 9 (9.09) 0.91 (0.34–2.20) 0.02 7.06 GT 67 (56.30) 71 (71.71) GG 42 (35.29) 19 (19.19) Dominant TT 10 (8.40) 9 (9.09) 0.91 (0.34–2.20) 0.02 7.06 GT + GG 109 (91.60) 90 (90.91) Recessive GG 42 (35.29) 19 (19.19) 2.29 (1.22–4.19) 0.00 6.95 GT + TT 77 (64.71) 80 (80.81) Additive T 87 (36.55) 89 (44.95) 1.97 (1.28–2.98) 0.07 3.16 G 151 (63.45) 109 (55.05) a χ 2 : chi square, b OR: odds ratio c CI: confidence interval. The significant p values are shown in italics. When the genotypes were compared between healthy controls and TB contacts in the co-dominant model a statistically significant p value was obtained ( p < 0.00, OR = 0.91 95% CI = 0.34–2.20). The frequency of genotype GT was higher in healthy controls (56.30%) compared to TB contacts (26.15%), whereas the frequency of genotype GG was higher in TB contacts (59.09%) compared to healthy controls (35.29%). The frequency of TT was similar in healthy controls and TB contacts. The comparison of genotypes in the dominant ( p = 0.00, OR = 0.91 95% CI = 0.34–2.20) and additive model ( p = 0.00, OR = 1.49, 95%CI = 0.97–2.29) showed statistically significant p values, whereas the recessive model yielded a statistically insignificant p value ( p = 0.26, OR = 0.37, 95%CI = 0.21–0.66) (Table 6 ). Table 6 Genotype comparison of rs3211938 between healthy controls (HC) and TB contacts (TC). Genetic models Genotypes HC n (%) TC n (%) OR b (95%CI c ) P value χ 2 a value 119 (100) 88 (100) Co-dominant TT 10 (8.40) 13 (14.77) 0.52 (0.21–1.30) < 0.00 18.74 GT 67 (56.30) 23 (26.15) GG 42 (35.29) 52 (59.09) Dominant TT 10 (8.40) 13 (14.77) 0.52 (0.21–1.30) 0.14 2.07 GT + GG 109 (91.60) 75 (85.23) Recessive GG 42 (35.29) 52 (59.09) 0.37 (0.21–0.66) 0.00 11.56 GT + TT 77 (64.71) 36 (40.09) Additive T 87 (36.55) 49 (27.84) 1.49 (0.97–2.29) 0.06 3.48 G 151 (63.45) 127 (72.16) a χ 2 : chi square, b OR: odds ratio c CI: confidence interval. The significant p values are shown in italics. Association of SR-B1 (rs4238001) with active TB and LTBI When the genotypes were compared between healthy controls and TB patients in the co-dominant model significant p value was obtained ( p = 0.00, OR = 2.18 95% CI = 1.14–4.33). The frequency of GG was higher in healthy controls (36.36%) compared to TB patients (21.25%), whereas the frequency of genotype AA was higher in TB patients (21.25%) than in healthy controls (3.63%). The frequency of genotype GA was not different between healthy controls (60.00%) and TB patients (57.51%). When genotypes were compared in dominant ( p = 0.00, OR = 2.18 95% CI = 1.14–4.33), recessive ( p = 0.00, OR = 0.13, 95% CI = 0.04–0.40), and additive models ( p = 0.00, OR = 1.97, 95% CI = 1.28–2.98) statistically significant p values were obtained (Table 7 ). Table 7 Genotype comparison of rs4238001 between healthy controls (HC) and TB patients (TP). Genetic models Genotypes HC n (%) TP n (%) OR b (95%CI c ) P value χ2 a value 110 (100) 80 (100) Co-dominant GG 40 (36.36) 17 (21.25) 2.18 (1.14–4.33) 0.00 14.75 GA 66 (60.00) 46 (57.51) AA 4 (3.63) 17 (21.25) Dominant GG 40 (36.36) 17 (20.73) 2.18 (1.14–4.33) 0.01 5.50 GA + AA 70 (63.64) 63 (79.27) Recessive AA 4 (3.63) 17 (20.73) 0.13 (0.04–0.40) 0.00 14.62 GA + GG 106 (96.36) 63 (79.27) Additive G 146 (66.36) 80 (50.00) 1.97 (1.28–2.98) 0.00 10.29 A 74 (33.64) 80 (50.00) a χ 2 : chi square, b OR: odds ratio c CI: confidence interval. The significant p values are shown in italics. When the genotypes were compared between healthy controls and TB contacts in the co-dominant model a statistically significant p value was obtained ( p = 0.00, OR = 0.37 95% CI = 0.20–0.66). The frequency of genotype GG was higher in TB contacts (60.67%) compared to healthy controls (36.36%), whereas the frequency of genotype GA was higher in healthy controls (60.00%) than in TB contacts (38.20%). The difference in frequency of genotype AA between healthy controls (3.63%) and TB contacts (1.12%) was not significant. Genotype comparison in dominant ( p = 0.00, OR = 0.37 95% CI = 0.20–0.66), and additive model ( p = 0.00, OR = 0.50, 95% CI = 0.31–0.78) gave statistically significant p values, while in recessive model ( p = 0.26, OR = 0.30, 95% CI = 0.02–1.87) statistically insignificant p value was obtained (Table 8 ). Table 8 Genotype comparison of rs4238001 between healthy controls (HC) and TB contacts (TC). Genetic models Genotypes HC n (%) TC n (%) OR b (95%CI c ) P value χ 2a value 110 (100) 89 (100) Co-dominant GG 40 (36.36) 54 (60.67) 0.37 (0.20–0.66) 0.00 12.75 GA 66 (60.00) 34 (38.20) AA 4 (3.63) 1 (1.12) Dominant GG 40 (36.36) 54 (60.67) 0.37 (0.20–0.66) 0.00 11.67 GA + AA 70 (63.64) 35 (39.33) Recessive AA 4 (3.64) 1 (1.12) 0.30 (0.02–1.87) 0.26 1.26 GA + GG 106 (96.36) 88 (98.88) Additive G 146 (66.36) 142 (79.78) 0.50 (0.31–0.78) 0.00 8.84 A 74 (33.64) 36 (20.22) a χ 2 : chi square, b OR: odds ratio c CI: confidence interval. The significant p values are shown in italics. Discussion Host genetic factors play a significant role in determining susceptibility or resistance to Mtb infection. In our study, we focused on two scavenger receptors, SR-B1 and CD36 , which are part of the host's innate immune system. We investigated the association between SNPs in these genes, namely rs4238001 in SR-B1 and rs1761667 and rs3211938 in CD36 , and TB in the Pakistani population. Our findings revealed a significant association between rs3211938 and rs1761667 in CD36 and rs4238001 in SR-B1 with active TB and LTBI. These results contribute to our understanding of the genetic factors involved in TB susceptibility and may have implications for future research. We found that the mutant genotype GG in rs3211938 was associated with resistance against active TB (p = 0.02, OR = 0.91 95%CI = 0.34–2.20), as the frequency of mutant genotype was higher in healthy controls (35.29%) compared to TB patients (19.19%). The SNP rs3211938 (T > G), located on Exon 10, introduces an amino acid change from Tyrosine to termination codon at position 325 in CD36 protein which affects the expression and function of the protein. The 'G' allele of rs3211938 is associated with the decrease in expression levels of protein and provides protection against atherogenic profile (16). We hypothesize that a decrease in expression level may result in reduced mycobacterial growth as CD36 is involved in the uptake of surfactant lipids by macrophages which promotes the growth of Mtb within macrophages (17) This mechanism potentially contributes to protection against TB. Our results are consistent with a previous study conducted on the association of CD36 and MARCO with PTB in the Chinese Han population, indicating the significant association of SNPs in the CD36 gene with resistance to PTB (18). Our research findings are further supported by an in vivo study conducted on mice, where mice lacking the CD36 gene showed resistance against mycobacteria. The absence of CD36 led to a reduced ability of mycobacteria to survive within the cells. The study also indicated that CD36 plays a role in cellular processes associated with the formation of granulomas, which aid in the initial growth and spread of bacteria (19). Therefore, we can conclude that the protective role of the GG mutant genotype in rs3211938, which is linked to a decrease in CD36 expression, subsequently contributes to the inhibition of Mtb growth and spread. Conversely, the genotype GG of rs3211938 showed an association with susceptibility to LTBI in comparison of healthy controls vs. TB contacts (p < 0.00, OR = 0.91 95% CI = 0.34–2.20). The frequency of genotype GG was higher in TB contacts (59.09%) compared to healthy controls (35.29%). According to Il'in and Shkurupy, CD36 becomes more abundant in multi-nuclear phagocytes (MP) during periods of Mtb persistence in BCG-infected mice (20), this might be a reason for TB contacts being susceptible to latent infection. These results suggest that a single gene might play different roles in determining the susceptibility or resistance to LTBI and active TB. Our findings also align with the study investigating the association of the SP110 gene with active TB and LTBI in Taiwan, which revealed the differential role of the gene for active TB and LTBI (21). In rs1761667, we found a higher frequency of heterozygous genotype GA in TB contacts (86.57%) than healthy controls (66.89%) suggesting a significant association of rs1761667 GA genotype with risk to LTBI (p = 0.00, OR = 3.39, 95% CI = 1.73–6.58). The SNP rs1761667 is located at Exon 1A, a nucleotide change from G > A results in decreased protein expression (16). These results align with the study conducted in the Chinese Han population to investigate the association of CD36 with carotid atherosclerosis. The results suggested the risk of disease in female patients carrying the GA genotype at rs1761667 (22). Another study suggested a strong association of the GA genotype with an increased risk of coronary heart disease in the Chongqing Han population of China (23). CD36 gene is responsible for mediating the effects of Mannose-capped lipoarabinomannan (ManLAM), leading to the release of TNF-α in peritoneal murine macrophages. Ligands of SRs have similar effects on TNF-α, and NO production as observed with ManLAM (24). CD36 also facilitates lysosomal enzyme transportation and internalizes mycobacteria (25). In accordance with these studies, a decrease in CD36 expression due to mutation might impair the cytokine production and immune response against mycobacteria resulting in susceptibility to disease. We found a weak p value for the comparison of rs1761667 genotypes between healthy controls and active TB patients (p = 0.03, OR = 0.73, 95% CI = 0.43–1.24). The difference in genotype frequency distribution between studied groups may not remain significant by increasing sample size. We conclude that rs1761667 at the CD36 gene may be important in determining the risk of LTBI but not active TB. Interestingly, we found that SR-B1 SNP at rs4238001 was significantly associated with active TB (p = 0 00, OR = 2.18, 95% CI = 1.14–4.33). The frequency of mutant genotype AA was higher in active TB patients (21.25%) compared to healthy controls (3.63%). The SNP rs4238001 (G > A), located at Exon 1, introduces an amino acid change from Glycine to Serine at position 2 in the SR-B1 protein. This amino acid change is associated with a decrease in SR-B1 expression (26). SR-B1 engulfs Mtb in mesenchymal stem cells (MSCs), which exhibit innate control of mycobacterial replication through autophagy. MSCs are found in both human and mouse Mtb granulomas and play an important role in TB pathogenesis (27). This SNP at rs4238001 may affect the phagocytosis of Mtb in MSCs and result in an impaired control of mycobacterium. This SNP has not been studied in TB, LTBI, or any other lung disease, however, two independent research groups reported significant association of rs4238001 with coronary heart disease and low progesterone level (28, 29). In the comparison of SR-B1 SNP at rs4238001 between healthy controls vs. TB contacts, we found that heterozygous GA genotype was significantly associated with protection against LTBI (p = 0 00, OR = 0.37, 95% CI = 0.20–0.66). SR-B1 on microfold cells (M cells) interact with Mtb EsxA enabling it to cross airway mucosa and initiate infection. Disruption in SR-B1 genes decreases Mtb binding and translocation across M cells (30). Overexpression of SR-B1 in macrophages increases Mtb and BCG binding (31). We hypothesize that changes in SR-B1 gene expression due to mutation may affect Mtb binding, resulting in protection against LTBI. Our results are consistent with the study conducted by Acton et al., in which the SNP rs4238001 was associated with protection towards atherogenic lipid profile in white men (32). Our study possesses several noteworthy strengths. The sample size utilized in our study was calculated using scientific methods and was adequate to establish a correlation between genetic mutations and TB within the specific population. We examined two variations in each of the targeted genes that were known to impact gene expression. It is worth noting that our study is the first of its kind to explore the association between CD36 and SR-B1 gene polymorphisms with TB and LTBI. However, there were a few limitations to our study that should be acknowledged. We did not validate latent TB infection in TB contacts using Interferon Gamma Release Assay (IGRA) or QuantiFERON (QFT). Additionally, ARMS-PCR does not serve as a definitive technique for genotyping as it may fail to detect mutations at low levels and is prone to producing false positive and false negative results. The findings of this research can be confirmed by categorizing the study participants with similar genetic variations and subsequently measuring protein levels to establish the cumulative impact of these genetic variations. In conclusion, conducting follow-up studies with a larger sample size and validating the genotypes through sequencing will contribute to the verification of the current study's results. Declarations Ethics approval and consent to participate: This study was performed in line with the principles of the NUMS Institutional Review Board. Approval was granted by the Ethics Committee of NUMS (Date.18 Oct 2022 / No. 06/IRB & EC/NUMS/24) (S1). Informed consent was obtained from all individual participants included in the study (S2). Consent for publication: Not applicable Availability of data and materials: Not applicable Competing Interests: Authors declare NO conflict of interest. Funding: This project was financially supported by the National University of Medical Sciences, Pakistan, and the Researchers Supporting Project Number (RSP2024R110) at King Saud University, Riyadh, Saudi Arabia. Author contributions: Ezza Binte Tariq: Data curation, Formal analysis, Investigation, and Writing original draft. Urooj Subhan: Formal analysis, and Investigation. Riaz Ullah: Resources, and Editing Zuha Tariq: Investigation Dr. Farah Deeba: Writing-review, and Editing. Dr. Afrose Liaquat: Writing-review, and Editing Dr. Sidra Younis: Conceptualization, Project administration, Methodology, Supervision, Visualization, Validation, Writing-review, and Editing. Acknowledgements: We acknowledge the study subjects for providing their samples, the authors of the manuscript for their efforts, and the National University of Medical Sciences, Pakistan and the researchers supporting project number (RSP2024R110) at King Saud University Riyadh Saudi Arabia for financial support and research facilitation. References Luo F, Zou P, Liao Y, Luo J, Luo D, Hu K, et al. Association between TAP gene polymorphisms and tuberculosis susceptibility in a Han Chinese population in Guangdong. Molecular Genetics and Genomics. 2022;297(3):779-90. O'Garra A, Redford PS, McNab FW, Bloom CI, Wilkinson RJ, Berry MP. The immune response in tuberculosis. Annual review of immunology. 2013;31:475-527. Apt A, Kramnik I. Man and mouse TB: contradictions and solutions. Tuberculosis (Edinburgh, Scotland). 2009;89(3):195. Fortin A, Abel L, Casanova J, Gros P. Host genetics of mycobacterial diseases in mice and men: forward genetic studies of BCG-osis and tuberculosis. 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Juárez-Meavepena M, Carreón-Torres E, López-Osorio C, García-Sánchez C, Gamboa R, Torres-Tamayo M, et al. The Srb1+ 1050T allele is associated with metabolic syndrome in children but not with cholesteryl ester plasma concentrations of high-density lipoprotein subclasses. Metabolic Syndrome and Related Disorders. 2012;10(2):110-6. Gautam S, Agrawal CG, Banerjee M. CD36 gene variants in early prediction of type 2 diabetes mellitus. Genetic Testing and Molecular Biomarkers. 2015;19(3):144-9. Marquez BTM, Garcia FS, Del Mercado MV, García EAM, Meraz FIC, Rayas ALF, et al. Contribution of rs3211938 polymorphism at CD36 to glucose levels, oxidized low-density lipoproteins, insulin resistance, and body mass index in Mexican mestizos with type-2 diabetes from western Mexico. Nutrición hospitalaria: Organo oficial de la Sociedad española de nutrición parenteral y enteral. 2021;38(4):742-8. Šerý O, Zeman T, Sheardová K, Vyhnálek M, Marková H, Laczó J, et al. Six genetically linked mutations in the CD36 gene significantly delay the onset of Alzheimer's disease. Scientific Reports. 2022;12(1):10994. Ma M-J, Wang H-B, Li H, Yang J-H, Yan Y, Xie L-P, et al. Genetic variants in MARCO are associated with the susceptibility to pulmonary tuberculosis in Chinese Han population. PLoS One. 2011;6(8):e24069. Bowdish DM, Sakamoto K, Lack NA, Hill PC, Sirugo G, Newport MJ, et al. Genetic variants of MARCO are associated with susceptibility to pulmonary tuberculosis in a Gambian population. BMC medical genetics. 2013;14(1):1-7. Love-Gregory L, Sherva R, Schappe T, Qi J-S, McCrea J, Klein S, et al. Common CD36 SNPs reduce protein expression and may contribute to a protective atherogenic profile. Human molecular genetics. 2011;20(1):193-201. Dodd CE, Pyle CJ, Glowinski R, Rajaram MV, Schlesinger LS. CD36-mediated uptake of surfactant lipids by human macrophages promotes intracellular growth of Mycobacterium tuberculosis. The Journal of Immunology. 2016;197(12):4727-35. Lao W, Kang H, Jin G, Chen L, Chu Y, Sun J, et al. Evaluation of the relationship between MARCO and CD36 single-nucleotide polymorphisms and susceptibility to pulmonary tuberculosis in a Chinese Han population. BMC Infectious Diseases. 2017;17:1-9. Hawkes M, Li X, Crockett M, Diassiti A, Finney C, Min-Oo G, et al. CD36 deficiency attenuates experimental mycobacterial infection. BMC infectious diseases. 2010;10(1):1-17. Il'in D, Shkurupy V. In vitro analysis of the expression of CD11, CD29, CD36, and DC-STAMP molecules during the formation of multinuclear macrophages in BCG-infected mice. Bulletin of Experimental Biology and Medicine. 2019;167(5):653-5. Chang S-Y, Chen M-L, Lee M-R, Liang Y-C, Lu T-P, Wang J-Y, et al. SP110 polymorphisms are genetic markers for vulnerability to latent and active tuberculosis infection in Taiwan. Disease markers. 2018;2018. Chu Y, Lao W, Jin G, Dai D, Chen L, Kang H. Evaluation of the relationship between CD36 and MARCO single-nucleotide polymorphisms and susceptibility to carotid atherosclerosis in a Chinese Han population. Gene. 2017;633:66-70. Zhang Y, Ling Z, Deng S, Du H, Yin Y, Yuan J, et al. Associations between CD36 gene polymorphisms and susceptibility to coronary artery heart disease. Brazilian Journal of Medical and Biological Research. 2014;47:895-903. Józefowski S, Sobota A, Hamasur B, Pawłowski A, Kwiatkowska K. Mycobacterium tuberculosis lipoarabinomannan enhances LPS-induced TNF-α production and inhibits NO secretion by engaging scavenger receptors. Microbial pathogenesis. 2011;50(6):350-9. Sattler N, Bosmani C, Barisch C, Guého A, Gopaldass N, Dias M, et al. Functions of the Dictyostelium LIMP-2 and CD36 homologues in bacteria uptake, phagolysosome biogenesis and host cell defence. Journal of cell science. 2018;131(17):jcs218040. West M, Greason E, Kolmakova A, Jahangiri A, Asztalos B, Pollin TI, et al. Scavenger receptor class B type I protein as an independent predictor of high-density lipoprotein cholesterol levels in subjects with hyperalphalipoproteinemia. The Journal of Clinical Endocrinology & Metabolism. 2009;94(4):1451-7. Khan A, Mann L, Papanna R, Lyu M-A, Singh CR, Olson S, et al. Mesenchymal stem cells internalize Mycobacterium tuberculosis through scavenger receptors and restrict bacterial growth through autophagy. Scientific reports. 2017;7(1):15010. Manichaikul A, Wang X-Q, Musani SK, Herrington DM, Post WS, Wilson JG, et al. Association of the lipoprotein receptor SCARB1 common missense variant rs4238001 with incident coronary heart disease. PLoS One. 2015;10(5):e0125497. Yates M, Kolmakova A, Zhao Y, Rodriguez A. Clinical impact of scavenger receptor class B type I gene polymorphisms on human female fertility. Human reproduction. 2011;26(7):1910-6. Khan HS, Nair VR, Ruhl CR, Alvarez-Arguedas S, Galvan Rendiz JL, Franco LH, et al. Identification of scavenger receptor B1 as the airway microfold cell receptor for Mycobacterium tuberculosis. Elife. 2020;9:e52551. Schäfer G, Guler R, Murray G, Brombacher F, Brown GD. The role of scavenger receptor B1 in infection with Mycobacterium tuberculosis in a murine model. PloS one. 2009;4(12):e8448. Acton S, Osgood D, Donoghue M, Corella D, Pocovi M, Cenarro A, et al. Association of polymorphisms at the SR-BI gene locus with plasma lipid levels and body mass index in a white population. Arteriosclerosis, thrombosis, and vascular biology. 1999;19(7):1734-43. Additional Declarations No competing interests reported. Supplementary Files SupplementarymaterialSRandTB.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. 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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-3856622","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":267812358,"identity":"c0e9fa51-6f6f-43cd-bcab-a923afb04581","order_by":0,"name":"Ezza Binte Tariq","email":"","orcid":"","institution":"National University of Medical Sciences","correspondingAuthor":false,"prefix":"","firstName":"Ezza","middleName":"Binte","lastName":"Tariq","suffix":""},{"id":267812359,"identity":"41d9ad32-1ee9-43cc-bfb6-8e69015d97aa","order_by":1,"name":"Urooj Subhan","email":"","orcid":"","institution":"The Women University Multan","correspondingAuthor":false,"prefix":"","firstName":"Urooj","middleName":"","lastName":"Subhan","suffix":""},{"id":267812360,"identity":"6fe5fb71-f73a-4996-9707-9a1100ae5084","order_by":2,"name":"Farah Deeba","email":"","orcid":"","institution":"The Women University Multan","correspondingAuthor":false,"prefix":"","firstName":"Farah","middleName":"","lastName":"Deeba","suffix":""},{"id":267812361,"identity":"c2b48976-f1d4-4dc6-84f2-42d094be93fd","order_by":3,"name":"Riaz Ullah","email":"","orcid":"","institution":"King Saud University","correspondingAuthor":false,"prefix":"","firstName":"Riaz","middleName":"","lastName":"Ullah","suffix":""},{"id":267812362,"identity":"f2261b62-ad55-445e-a948-1d53dd27426b","order_by":4,"name":"Zuha Tariq","email":"","orcid":"","institution":"COMSATS University Islamabad","correspondingAuthor":false,"prefix":"","firstName":"Zuha","middleName":"","lastName":"Tariq","suffix":""},{"id":267812363,"identity":"8dc31daf-1222-4787-9475-2dd465650750","order_by":5,"name":"Afrose Liaquat","email":"","orcid":"","institution":"Shifa College of Medicine","correspondingAuthor":false,"prefix":"","firstName":"Afrose","middleName":"","lastName":"Liaquat","suffix":""},{"id":267812364,"identity":"e844da8f-ecfd-463a-8132-0d3d9bde2c07","order_by":6,"name":"Sidra Younis","email":"data:image/png;base64,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","orcid":"","institution":"National University of Medical Sciences","correspondingAuthor":true,"prefix":"","firstName":"Sidra","middleName":"","lastName":"Younis","suffix":""}],"badges":[],"createdAt":"2024-01-12 10:44:17","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3856622/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3856622/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":51694700,"identity":"3c43abcd-ff6f-4a4a-b06e-38e44b74fa2a","added_by":"auto","created_at":"2024-02-27 11:55:07","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":364719,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3856622/v1/9c463e66-bc7a-416f-8b94-6ce1de7e21d3.pdf"},{"id":49866106,"identity":"3b064dcf-9de4-4ce9-a1e0-ea36c34d84ba","added_by":"auto","created_at":"2024-01-19 10:25:03","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":752125,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementarymaterialSRandTB.docx","url":"https://assets-eu.researchsquare.com/files/rs-3856622/v1/91e8ae1bd4eda32e1b241d31.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Scavenger Receptor Genes Polymorphisms Association with Tuberculosis and Latent Tuberculosis Infection in Pakistani population","fulltext":[{"header":"Introduction","content":"\u003cp\u003eTuberculosis (TB) is a global health concern caused by \u003cem\u003eMycobacterium tuberculosis\u003c/em\u003e (Mtb), a highly infectious gram-positive bacterium. This bacterium can exist in a latent state or an active disease state resulting in latent TB infection or active disease, respectively. TB primarily spreads via airborne respiratory droplets expelled when infected individuals cough or sneeze. Approximately one-third of the global population is infected with Mtb. Of these, 10% carry a lifetime risk of developing active TB, while the remaining 90% develop a latent infection in which they may not exhibit any symptoms of TB (1). According to the WHO TB Report 2023, in 2022, there were an estimated 1.3\u0026nbsp;million deaths among HIV-negative people and 0.3\u0026nbsp;million deaths among HIV-positive people, for a combined total of 1.6\u0026nbsp;million mortality worldwide. Pakistan is ranked fifth among the countries with the highest TB burden in the world. The mortality rate of TB in Pakistan is 34 deaths per 100,000 people per year.\u003c/p\u003e \u003cp\u003eThe progression and outcome of TB infection are intricately influenced by the interplay of pathogen, host, and environmental factors. The development of active TB in only a fraction of infected individuals suggests that host genetics is a pivotal determinant of disease development (2). Previous studies have identified a significant association of genetic polymorphisms in human and experimental animal models on the outcomes of Mtb infection (3, 4).\u003c/p\u003e \u003cp\u003eScavenger receptors (SRs) are one of the pattern recognition receptors (PRRs) that recognize mycobacterial pathogen-associated molecular patterns (PAMPs). SRs belong to a class of cell surface transmembrane glycoproteins that play vital roles in the control of macrophages and innate immunity (5). SRs are categorized into 12 classes, with class B comprising \u003cem\u003eSR-B1\u003c/em\u003e, \u003cem\u003eSR-B2\u003c/em\u003e, and \u003cem\u003eCD36\u003c/em\u003e. SRs are one of the various cell surface and intracellular receptors that mediate the uptake of bacteria (6). SRs interact with the lipoglycans and lipoprotein components of mycobacteria and facilitate the direct molecular interaction between the host and Mtb. Inflammatory cytokines are produced by the activation of SRs, which subsequently activate the macrophages. Macrophages kill Mtb through phagocytosis and induce an adaptive immune response. This close interaction suggests the possibility of co-evolution between the host immune system and the pathogen over time.\u003c/p\u003e \u003cp\u003ePrevious studies have supported the involvement of mutations in SR genes with multiple diseases including infectious diseases (7, 8), cardiovascular diseases (9, 10), metabolic disorders (11, 12), and neurodegenerative disorder (13). There are only two studies on the association between SR gene polymorphisms and TB in the Gambian and Chinese populations (14, 15). The association between \u003cem\u003eCD36\u003c/em\u003e gene polymorphisms and pulmonary TB has been investigated in the Chinese Han population. Furthermore, we did not find any studies on the association of SR gene polymorphisms and latent TB. Therefore, the role of \u003cem\u003eCD36\u003c/em\u003e and \u003cem\u003eSR-B1\u003c/em\u003e polymorphisms in TB and LTBI remains unclear. The association of polymorphisms in \u003cem\u003eSR-B1\u003c/em\u003e and \u003cem\u003eCD36\u003c/em\u003e genes with active or latent TB in the Pakistani population has not been studied yet. \u003cem\u003eSR-B1\u003c/em\u003e and \u003cem\u003eCD36\u003c/em\u003e gene polymorphisms may affect human susceptibility or resistance to mycobacterial disease. Therefore, in the current study, we investigated the association of \u003cem\u003eCD36\u003c/em\u003e gene polymorphisms at rs1761667 (g.18436G\u0026thinsp;\u0026gt;\u0026thinsp;A) and rs3211938 (g.73946T\u0026thinsp;\u0026gt;\u0026thinsp;G) and \u003cem\u003eSR-B1\u003c/em\u003e gene polymorphism rs4238001 (g.5275G\u0026thinsp;\u0026gt;\u0026thinsp;A) with TB and LTBI in the Pakistani population.\u003c/p\u003e"},{"header":"Material and methods","content":"\u003cp\u003e All the experiments in this study were performed following the ethical guidelines and regulations of the NUMS Institutional Review Board. Blood samples were collected after receiving informed consent from patients with GeneXpert-confirmed TB and their contacts at the Nishtar Hospital Multan (NHM). Blood samples of TB contacts were also collected from laboratory workers dealing with TB patients from TEMAR Diagnostics Rawalpindi and Pride Laboratory Lahore. Samples from healthy controls were collected from Raza Laboratory, Nishtar Road Multan. We used Epitool software to calculate the sample size for this case-control study. The calculated sample size was 350 study subjects including 150 healthy controls, 100 active TB patients, and 100 TB contacts. All study subjects were \u0026ge;\u0026thinsp;16 years of age and those tested positive for HIV infection were excluded from the study.\u003c/p\u003e \u003cp\u003eThe genomic DNA from blood samples (1-2ml) was extracted using the phenol-chloroform method. \u003cem\u003eSR-B1\u003c/em\u003e and \u003cem\u003eCD36\u003c/em\u003e polymorphisms were identified from the National Center for Biotechnology Information dbSNP database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.ncbi.nlm.nih.gov/snp\u003c/span\u003e\u003cspan address=\"https://www.ncbi.nlm.nih.gov/snp\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). PRIMER 1 software was used to design primers for the SNPs rs4238001, rs1761667, and rs3211938. All SNPs were genotyped using amplification refractory mutation system PCR (ARMS-PCR). Details of the primers and PCR conditions are given in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e and Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\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\u003eSequence and properties of primers\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\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=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGene\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMutation\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePrimer Sequence\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLength (bp)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eTm (\u0026deg;C)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e\u003cem\u003eSR-B1\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003ers4238001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eOuter forward\u003c/b\u003e: CTCCCGCCCCAAAACGGAA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e67.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eOuter reverse\u003c/b\u003e: CAGCAGCCCCTCCCGAAGC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e70.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eInner forward\u003c/b\u003e: CCAGGCGCGCAGACAGGA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e68.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eInner reverse\u003c/b\u003e: GCGCTTTGGCGGAGCATCC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e67.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"7\" rowspan=\"8\"\u003e \u003cp\u003e\u003cem\u003eCD36\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003ers1761667\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eOuter forward\u003c/b\u003e: AAGGTCTGGTATCCACCTGTTTTCCT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e64.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eOuter reverse\u003c/b\u003e: AAGAGTTTTCATGAAGCTTCCCGC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e61.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eInner forward\u003c/b\u003e: TTTTATTCATCTTTGCATGCCATCG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e57.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eInner reverse\u003c/b\u003e: TCATACTCCAGGCTTTGAGCATTGT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e61.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003ers3211938\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eOuter forward\u003c/b\u003e: GAATAGTTCATGCTTGGCTATTGAGTTT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e58.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eOuter reverse\u003c/b\u003e: CACCATTCTTTCTTCTGCCCTAATTACT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e60.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eInner forward\u003c/b\u003e: AAAATTATCTCAAAAAATTGTACATCACAG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e53.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eInner reverse\u003c/b\u003e: TGCATTTGCTGATGTCTAGCACATCA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e61.4\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 \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\u003ePCR conditions for all polymorphisms.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eMutation\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"5\" nameend=\"c6\" namest=\"c2\"\u003e \u003cp\u003ePCR conditions\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eInitial denaturation\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDenaturation\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAnnealing (35 cycles)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eExtension\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eFinal extension\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ers4238001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e95\u0026deg;C for 5 min (for all cycles)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e95\u0026deg;C for 1 min (for all cycles)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e65\u0026deg;C (-1\u0026deg;C for every first 10 cycles) \u0026amp; 55\u0026deg;C (for 25 cycles) 1 min\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e72\u0026deg;C for 1 min (for all cycles)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e72\u0026deg;C for 10 min\u003c/p\u003e \u003cp\u003e(last 25 cycles)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ers1761667\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e95\u0026deg;C for 5 min\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e95\u0026deg;C for 1 min\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e61\u0026deg;C for 1 min\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e72\u0026deg;C for 1 min\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e72\u0026deg;C for 10 min\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ers3211938\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e95\u0026deg;C for 5 min\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e95\u0026deg;C for 1 min\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e51\u0026deg;C for 1 min\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e72\u0026deg;C for 1 min\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e72\u0026deg;C for 10 min\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\u003eGraphPad Prism 10 was used for statistical analysis. The Chi-square test was used to assess the deviation of genotype distribution frequency from Hardy-Weinberg Equilibrium. Independent sample t-test was used to compare mean age and chi-square test was used to compare the gender between study groups. Fisher's exact chi-square test was used for comparison of genotypes. For all the statistical analyses, p values less than 0.05 were considered significant.\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eGenotype frequency distribution\u003c/h2\u003e \u003cp\u003eGenotype patterns of the study groups were examined to investigate the association between the SNPs in the \u003cem\u003eSR-B1\u003c/em\u003e and \u003cem\u003eCD36\u003c/em\u003e genes and susceptibility or resistance to TB. The frequencies of TB patients and TB contacts at rs1761667 and rs3211938 deviated from the Hardy Weinberg Equilibrium (HWE). However, for rs4238001, the frequency distribution for TB patients conformed to HWE, while the frequency distribution for TB contacts did not conform to HWE.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eAssociation of CD36 (rs1761667) with active TB and LTBI\u003c/h2\u003e \u003cp\u003eWhen the genotypes were compared between healthy controls and TB patients in the co-dominant model a statistically significant \u003cem\u003ep\u003c/em\u003e value was obtained (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.03, OR\u0026thinsp;=\u0026thinsp;0.73, 95%CI\u0026thinsp;=\u0026thinsp;0.43\u0026ndash;1.24). The frequency of GA was higher in healthy controls (66.89%) as compared to TB patients (55.20%). The genotype frequencies of GG and AA in healthy controls \u003cem\u003evs.\u003c/em\u003e TB patients had minor difference (Healthy controls: GG: 32.41% and AA: 0.01%; TB Patients; 39.58% and 0.05%,). The genotype comparison in the recessive model gave statistically significant \u003cem\u003ep\u003c/em\u003e value (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.02, OR\u0026thinsp;=\u0026thinsp;0.12, 95%CI\u0026thinsp;=\u0026thinsp;0.01\u0026ndash;0.94), while in the dominant model (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.25, OR\u0026thinsp;=\u0026thinsp;0.73, 95%CI\u0026thinsp;=\u0026thinsp;0.43\u0026ndash;1.24) and additive model (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.76, OR\u0026thinsp;=\u0026thinsp;0.94, 95%CI\u0026thinsp;=\u0026thinsp;0.64\u0026ndash;1.38) statistically non-significant \u003cem\u003ep\u003c/em\u003e values were observed (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eGenotype comparison of rs1761667 between healthy controls (HC) and TB patients (TP).\u003c/p\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=\"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=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eGenetic models\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eGenotypes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHC\u003c/p\u003e \u003cp\u003en (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTP\u003c/p\u003e \u003cp\u003en (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eOR\u003csup\u003eb\u003c/sup\u003e (95%CI\u003csup\u003ec\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eχ\u003csup\u003e2 a\u003c/sup\u003e value\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e145 (100)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e96 (100)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eCo-dominant\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e47 (32.41)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e38 (39.58)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e0.73 (0.43\u0026ndash;1.24)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e\u003cem\u003e0.03\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e6.84\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e97 (66.89)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e53 (55.20)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1 (0.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5 (0.05)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eDominant\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e47 (32.41)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e38 (39.58)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.73 (0.43\u0026ndash;1.24)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e1.30\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGA\u0026thinsp;+\u0026thinsp;AA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e98 (67.59)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e58 (60.42)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eRecessive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1 (0.69)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5 (5.21)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.12 (0.01\u0026ndash;0.94)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cem\u003e0.02\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e4.85\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGA\u0026thinsp;+\u0026thinsp;GG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e144 (99.31)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e91 (94.79)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eAdditive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e191 (65.86)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e129 (67.19)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.94 (0.64\u0026ndash;1.38)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.09\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e99 (34.14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e63 (32.81)\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 \u003csup\u003ea\u003c/sup\u003eχ\u003csup\u003e2\u003c/sup\u003e: chi square, \u003csup\u003e\u003cb\u003eb\u003c/b\u003e\u003c/sup\u003eOR: odds ratio \u003csup\u003ec\u003c/sup\u003eCI: confidence interval. The significant \u003cem\u003ep\u003c/em\u003e values are shown in italics.\u003c/p\u003e \u003cp\u003eWhen the genotypes were compared between healthy controls and TB contacts in the co-dominant model statistically significant \u003cem\u003ep\u003c/em\u003e value was obtained (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.00, OR\u0026thinsp;=\u0026thinsp;3.39 95% CI\u0026thinsp;=\u0026thinsp;1.73\u0026ndash;6.58). The frequency of genotype GG was higher in healthy controls (32.41%) as compared to TB contacts (12.37%) whereas the frequency of GA was higher in TB contacts (86.57%) as compared to healthy controls (66.89%). The frequency of AA was similar in both study groups. Genotype comparison in the dominant model (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.00, OR\u0026thinsp;=\u0026thinsp;3.39 95% CI\u0026thinsp;=\u0026thinsp;1.73\u0026ndash;6.58) gave statistically significant \u003cem\u003ep\u003c/em\u003e value, while in recessive (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.77, OR\u0026thinsp;=\u0026thinsp;0.66, 95%CI\u0026thinsp;=\u0026thinsp;0.03\u0026ndash;12.79) and additive model (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.05, OR\u0026thinsp;=\u0026thinsp;1.43, 95%CI\u0026thinsp;=\u0026thinsp;0.98\u0026ndash;2.08) statistically insignificant \u003cem\u003ep\u003c/em\u003e values were obtained (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eGenotype comparison of rs1761667 between healthy controls (HC) and TB contacts (TC).\u003c/p\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=\"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=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eGenetic models\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eGenotypes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHC\u003c/p\u003e \u003cp\u003en (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTC\u003c/p\u003e \u003cp\u003en (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eOR\u003csup\u003eb\u003c/sup\u003e (95%CI\u003csup\u003ec\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eχ\u003csup\u003e2 a\u003c/sup\u003e value\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e145 (100)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e97 (100)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eCo-dominant\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e47 (32.41)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e12 (12.37)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e3.39 (1.73\u0026ndash;6.58)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e\u003cem\u003e0.00\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e12.67\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e97 (66.89)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e84 (86.57)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1 (0.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1 (0.01)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eDominant\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e47 (32.41)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e12 (12.37)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e3.39 (1.73\u0026ndash;6.58)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cem\u003e0.00\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e12.66\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGA\u0026thinsp;+\u0026thinsp;AA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e98 (67.59)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e85 (87.63)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eRecessive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1 (0.69)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1 (1.03)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.66 (0.03\u0026ndash;12.79)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.08\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGA\u0026thinsp;+\u0026thinsp;GG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e144 (99.31)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e96 (98.97)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eAdditive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e191 (65.86)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e116 (57.43)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e1.43 (0.98\u0026ndash;2.08)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e3.61\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e99 (34.14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e86 (42.57)\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 \u003csup\u003ea\u003c/sup\u003eχ\u003csup\u003e2\u003c/sup\u003e: chi square, \u003csup\u003e\u003cb\u003eb\u003c/b\u003e\u003c/sup\u003eOR: odds ratio \u003csup\u003ec\u003c/sup\u003eCI: confidence interval. The significant \u003cem\u003ep\u003c/em\u003e values are shown in italics.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eAssociation of CD36 (rs3211938) with active TB and LTBI\u003c/h2\u003e \u003cp\u003eWhen the genotypes were compared between healthy controls and TB patients in the co-dominant model, a statistically significant \u003cem\u003ep\u003c/em\u003e value was obtained (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.02, OR\u0026thinsp;=\u0026thinsp;0.91 95%CI\u0026thinsp;=\u0026thinsp;0.34\u0026ndash;2.20). The frequency of genotype GG was higher in healthy controls (35.29%) compared to TB patients (19.19%), whereas the frequency of genotype GT was higher in TB patients (71.71%) than in healthy controls (56.30%). The frequency of genotype TT was almost similar in both groups. The comparison of healthy controls and TB patients in the recessive model (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.00, OR\u0026thinsp;=\u0026thinsp;2.29, 95%CI\u0026thinsp;=\u0026thinsp;1.22\u0026ndash;4.19) exhibited statistically significant \u003cem\u003ep\u003c/em\u003e value, while in dominant (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.85, OR\u0026thinsp;=\u0026thinsp;0.91, 95%CI\u0026thinsp;=\u0026thinsp;0.34\u0026ndash;2.20) and additive model (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.07, 0R\u0026thinsp;=\u0026thinsp;1.97, 95%CI\u0026thinsp;=\u0026thinsp;1.28\u0026ndash;2.98) statistically non-significant \u003cem\u003ep\u003c/em\u003e values were observed (Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eGenotype comparison of rs3211938 between healthy controls (HC) and TB patients (TP).\u003c/p\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=\"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=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eGenetic models\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eGenotypes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHC\u003c/p\u003e \u003cp\u003en (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTP\u003c/p\u003e \u003cp\u003en (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eOR\u003csup\u003eb\u003c/sup\u003e (95%CI\u003csup\u003ec\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eχ\u003csup\u003e2 a\u003c/sup\u003e value\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e119 (100)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e99 (100)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eCo-dominant\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10 (8.40)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9 (9.09)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e0.91 (0.34\u0026ndash;2.20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e\u003cem\u003e0.02\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e7.06\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e67 (56.30)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e71 (71.71)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e42 (35.29)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e19 (19.19)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eDominant\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10 (8.40)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9 (9.09)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.91 (0.34\u0026ndash;2.20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cem\u003e0.02\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e7.06\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGT\u0026thinsp;+\u0026thinsp;GG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e109 (91.60)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e90 (90.91)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eRecessive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e42 (35.29)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e19 (19.19)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e2.29 (1.22\u0026ndash;4.19)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cem\u003e0.00\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e6.95\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGT\u0026thinsp;+\u0026thinsp;TT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e77 (64.71)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e80 (80.81)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eAdditive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e87 (36.55)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e89 (44.95)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e1.97 (1.28\u0026ndash;2.98)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e3.16\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e151 (63.45)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e109 (55.05)\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 \u003csup\u003ea\u003c/sup\u003eχ\u003csup\u003e2\u003c/sup\u003e: chi square, \u003csup\u003e\u003cb\u003eb\u003c/b\u003e\u003c/sup\u003eOR: odds ratio \u003csup\u003ec\u003c/sup\u003eCI: confidence interval. The significant \u003cem\u003ep\u003c/em\u003e values are shown in italics.\u003c/p\u003e \u003cp\u003eWhen the genotypes were compared between healthy controls and TB contacts in the co-dominant model a statistically significant \u003cem\u003ep\u003c/em\u003e value was obtained (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.00, OR\u0026thinsp;=\u0026thinsp;0.91 95% CI\u0026thinsp;=\u0026thinsp;0.34\u0026ndash;2.20). The frequency of genotype GT was higher in healthy controls (56.30%) compared to TB contacts (26.15%), whereas the frequency of genotype GG was higher in TB contacts (59.09%) compared to healthy controls (35.29%). The frequency of TT was similar in healthy controls and TB contacts. The comparison of genotypes in the dominant (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.00, OR\u0026thinsp;=\u0026thinsp;0.91 95% CI\u0026thinsp;=\u0026thinsp;0.34\u0026ndash;2.20) and additive model (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.00, OR\u0026thinsp;=\u0026thinsp;1.49, 95%CI\u0026thinsp;=\u0026thinsp;0.97\u0026ndash;2.29) showed statistically significant \u003cem\u003ep\u003c/em\u003e values, whereas the recessive model yielded a statistically insignificant \u003cem\u003ep\u003c/em\u003e value (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.26, OR\u0026thinsp;=\u0026thinsp;0.37, 95%CI\u0026thinsp;=\u0026thinsp;0.21\u0026ndash;0.66) (Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eGenotype comparison of rs3211938 between healthy controls (HC) and TB contacts (TC).\u003c/p\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=\"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=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eGenetic models\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eGenotypes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHC\u003c/p\u003e \u003cp\u003en (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTC\u003c/p\u003e \u003cp\u003en (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eOR\u003csup\u003eb\u003c/sup\u003e (95%CI\u003csup\u003ec\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eχ\u003csup\u003e2 a\u003c/sup\u003e value\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e119 (100)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e88 (100)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eCo-dominant\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10 (8.40)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e13 (14.77)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e0.52 (0.21\u0026ndash;1.30)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e\u003cem\u003e\u0026lt;\u0026thinsp;0.00\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e18.74\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e67 (56.30)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e23 (26.15)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e42 (35.29)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e52 (59.09)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eDominant\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10 (8.40)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e13 (14.77)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.52 (0.21\u0026ndash;1.30)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e2.07\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGT\u0026thinsp;+\u0026thinsp;GG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e109 (91.60)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e75 (85.23)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eRecessive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e42 (35.29)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e52 (59.09)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.37 (0.21\u0026ndash;0.66)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cem\u003e0.00\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e11.56\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGT\u0026thinsp;+\u0026thinsp;TT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e77 (64.71)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e36 (40.09)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eAdditive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e87 (36.55)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e49 (27.84)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e1.49 (0.97\u0026ndash;2.29)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e3.48\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e151 (63.45)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e127 (72.16)\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 \u003csup\u003ea\u003c/sup\u003e χ\u003csup\u003e2\u003c/sup\u003e: chi square, \u003csup\u003e\u003cb\u003eb\u003c/b\u003e\u003c/sup\u003eOR: odds ratio \u003csup\u003ec\u003c/sup\u003eCI: confidence interval. The significant \u003cem\u003ep\u003c/em\u003e values are shown in italics.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eAssociation of SR-B1 (rs4238001) with active TB and LTBI\u003c/h2\u003e \u003cp\u003eWhen the genotypes were compared between healthy controls and TB patients in the co-dominant model significant \u003cem\u003ep\u003c/em\u003e value was obtained (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.00, OR\u0026thinsp;=\u0026thinsp;2.18 95% CI\u0026thinsp;=\u0026thinsp;1.14\u0026ndash;4.33). The frequency of GG was higher in healthy controls (36.36%) compared to TB patients (21.25%), whereas the frequency of genotype AA was higher in TB patients (21.25%) than in healthy controls (3.63%). The frequency of genotype GA was not different between healthy controls (60.00%) and TB patients (57.51%). When genotypes were compared in dominant (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.00, OR\u0026thinsp;=\u0026thinsp;2.18 95% CI\u0026thinsp;=\u0026thinsp;1.14\u0026ndash;4.33), recessive (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.00, OR\u0026thinsp;=\u0026thinsp;0.13, 95% CI\u0026thinsp;=\u0026thinsp;0.04\u0026ndash;0.40), and additive models (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.00, OR\u0026thinsp;=\u0026thinsp;1.97, 95% CI\u0026thinsp;=\u0026thinsp;1.28\u0026ndash;2.98) statistically significant \u003cem\u003ep\u003c/em\u003e values were obtained (Table\u0026nbsp;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e7\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab7\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 7\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eGenotype comparison of rs4238001 between healthy controls (HC) and TB patients (TP).\u003c/p\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=\"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=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eGenetic models\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eGenotypes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHC\u003c/p\u003e \u003cp\u003en (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTP\u003c/p\u003e \u003cp\u003en (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eOR\u003csup\u003eb\u003c/sup\u003e (95%CI\u003csup\u003ec\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eP value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eχ2 \u003csup\u003ea\u003c/sup\u003e value\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e110 (100)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e80 (100)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eCo-dominant\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e40 (36.36)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e17 (21.25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e2.18 (1.14\u0026ndash;4.33)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e\u003cem\u003e0.00\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e14.75\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e66 (60.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e46 (57.51)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4 (3.63)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e17 (21.25)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eDominant\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e40 (36.36)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e17 (20.73)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e2.18 (1.14\u0026ndash;4.33)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cem\u003e0.01\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e5.50\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGA\u0026thinsp;+\u0026thinsp;AA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e70 (63.64)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e63 (79.27)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eRecessive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4 (3.63)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e17 (20.73)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.13 (0.04\u0026ndash;0.40)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cem\u003e0.00\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e14.62\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGA\u0026thinsp;+\u0026thinsp;GG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e106 (96.36)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e63 (79.27)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eAdditive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e146 (66.36)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e80 (50.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e1.97 (1.28\u0026ndash;2.98)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cem\u003e0.00\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e10.29\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e74 (33.64)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e80 (50.00)\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 \u003csup\u003ea\u003c/sup\u003eχ\u003csup\u003e2\u003c/sup\u003e: chi square, \u003csup\u003e\u003cb\u003eb\u003c/b\u003e\u003c/sup\u003eOR: odds ratio \u003csup\u003ec\u003c/sup\u003eCI: confidence interval. The significant \u003cem\u003ep\u003c/em\u003e values are shown in italics.\u003c/p\u003e \u003cp\u003eWhen the genotypes were compared between healthy controls and TB contacts in the co-dominant model a statistically significant \u003cem\u003ep\u003c/em\u003e value was obtained (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.00, OR\u0026thinsp;=\u0026thinsp;0.37 95% CI\u0026thinsp;=\u0026thinsp;0.20\u0026ndash;0.66). The frequency of genotype GG was higher in TB contacts (60.67%) compared to healthy controls (36.36%), whereas the frequency of genotype GA was higher in healthy controls (60.00%) than in TB contacts (38.20%). The difference in frequency of genotype AA between healthy controls (3.63%) and TB contacts (1.12%) was not significant. Genotype comparison in dominant (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.00, OR\u0026thinsp;=\u0026thinsp;0.37 95% CI\u0026thinsp;=\u0026thinsp;0.20\u0026ndash;0.66), and additive model (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.00, OR\u0026thinsp;=\u0026thinsp;0.50, 95% CI\u0026thinsp;=\u0026thinsp;0.31\u0026ndash;0.78) gave statistically significant \u003cem\u003ep\u003c/em\u003e values, while in recessive model (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.26, OR\u0026thinsp;=\u0026thinsp;0.30, 95% CI\u0026thinsp;=\u0026thinsp;0.02\u0026ndash;1.87) statistically insignificant \u003cem\u003ep\u003c/em\u003e value was obtained (Table\u0026nbsp;\u003cspan refid=\"Tab8\" class=\"InternalRef\"\u003e8\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab8\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 8\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eGenotype comparison of rs4238001 between healthy controls (HC) and TB contacts (TC).\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\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=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eGenetic models\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eGenotypes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHC\u003c/p\u003e \u003cp\u003en (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eTC\u003c/p\u003e \u003cp\u003en (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eOR\u003csup\u003eb\u003c/sup\u003e (95%CI\u003csup\u003ec\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eχ\u003csup\u003e2a\u003c/sup\u003e value\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e110 (100)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e89 (100)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eCo-dominant\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e40 (36.36)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e54 (60.67)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e0.37 (0.20\u0026ndash;0.66)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e\u003cem\u003e0.00\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e12.75\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e66 (60.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e34 (38.20)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e4 (3.63)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1 (1.12)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eDominant\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e40 (36.36)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e54 (60.67)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.37 (0.20\u0026ndash;0.66)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cem\u003e0.00\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e11.67\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGA\u0026thinsp;+\u0026thinsp;AA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e70 (63.64)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e35 (39.33)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eRecessive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e4 (3.64)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1 (1.12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.30 (0.02\u0026ndash;1.87)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e1.26\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGA\u0026thinsp;+\u0026thinsp;GG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e106 (96.36)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e88 (98.88)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eAdditive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e146 (66.36)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e142 (79.78)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.50 (0.31\u0026ndash;0.78)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cem\u003e0.00\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e8.84\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e74 (33.64)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e36 (20.22)\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 \u003csup\u003ea\u003c/sup\u003eχ\u003csup\u003e2\u003c/sup\u003e: chi square, \u003csup\u003e\u003cb\u003eb\u003c/b\u003e\u003c/sup\u003eOR: odds ratio \u003csup\u003ec\u003c/sup\u003eCI: confidence interval. The significant \u003cem\u003ep\u003c/em\u003e values are shown in italics.\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eHost genetic factors play a significant role in determining susceptibility or resistance to Mtb infection. In our study, we focused on two scavenger receptors, \u003cem\u003eSR-B1\u003c/em\u003e and \u003cem\u003eCD36\u003c/em\u003e, which are part of the host's innate immune system. We investigated the association between SNPs in these genes, namely rs4238001 in \u003cem\u003eSR-B1\u003c/em\u003e and rs1761667 and rs3211938 in \u003cem\u003eCD36\u003c/em\u003e, and TB in the Pakistani population. Our findings revealed a significant association between rs3211938 and rs1761667 in \u003cem\u003eCD36\u003c/em\u003e and rs4238001 in \u003cem\u003eSR-B1\u003c/em\u003e with active TB and LTBI. These results contribute to our understanding of the genetic factors involved in TB susceptibility and may have implications for future research.\u003c/p\u003e \u003cp\u003eWe found that the mutant genotype GG in rs3211938 was associated with resistance against active TB (p\u0026thinsp;=\u0026thinsp;0.02, OR\u0026thinsp;=\u0026thinsp;0.91 95%CI\u0026thinsp;=\u0026thinsp;0.34\u0026ndash;2.20), as the frequency of mutant genotype was higher in healthy controls (35.29%) compared to TB patients (19.19%). The SNP rs3211938 (T\u0026thinsp;\u0026gt;\u0026thinsp;G), located on Exon 10, introduces an amino acid change from Tyrosine to termination codon at position 325 in \u003cem\u003eCD36\u003c/em\u003e protein which affects the expression and function of the protein. The 'G' allele of rs3211938 is associated with the decrease in expression levels of protein and provides protection against atherogenic profile (16). We hypothesize that a decrease in expression level may result in reduced mycobacterial growth as \u003cem\u003eCD36\u003c/em\u003e is involved in the uptake of surfactant lipids by macrophages which promotes the growth of Mtb within macrophages (17) This mechanism potentially contributes to protection against TB. Our results are consistent with a previous study conducted on the association of \u003cem\u003eCD36\u003c/em\u003e and \u003cem\u003eMARCO\u003c/em\u003e with PTB in the Chinese Han population, indicating the significant association of SNPs in \u003cem\u003ethe CD36\u003c/em\u003e gene with resistance to PTB (18). Our research findings are further supported by an \u003cem\u003ein vivo\u003c/em\u003e study conducted on mice, where mice lacking the \u003cem\u003eCD36\u003c/em\u003e gene showed resistance against mycobacteria. The absence of \u003cem\u003eCD36\u003c/em\u003e led to a reduced ability of mycobacteria to survive within the cells. The study also indicated that \u003cem\u003eCD36\u003c/em\u003e plays a role in cellular processes associated with the formation of granulomas, which aid in the initial growth and spread of bacteria (19). Therefore, we can conclude that the protective role of the GG mutant genotype in rs3211938, which is linked to a decrease in \u003cem\u003eCD36\u003c/em\u003e expression, subsequently contributes to the inhibition of Mtb growth and spread.\u003c/p\u003e \u003cp\u003eConversely, the genotype GG of rs3211938 showed an association with susceptibility to LTBI in comparison of healthy controls \u003cem\u003evs.\u003c/em\u003e TB contacts (p\u0026thinsp;\u0026lt;\u0026thinsp;0.00, OR\u0026thinsp;=\u0026thinsp;0.91 95% CI\u0026thinsp;=\u0026thinsp;0.34\u0026ndash;2.20). The frequency of genotype GG was higher in TB contacts (59.09%) compared to healthy controls (35.29%). According to Il'in and Shkurupy, \u003cem\u003eCD36\u003c/em\u003e becomes more abundant in multi-nuclear phagocytes (MP) during periods of Mtb persistence in BCG-infected mice (20), this might be a reason for TB contacts being susceptible to latent infection. These results suggest that a single gene might play different roles in determining the susceptibility or resistance to LTBI and active TB. Our findings also align with the study investigating the association of the \u003cem\u003eSP110\u003c/em\u003e gene with active TB and LTBI in Taiwan, which revealed the differential role of the gene for active TB and LTBI (21).\u003c/p\u003e \u003cp\u003eIn rs1761667, we found a higher frequency of heterozygous genotype GA in TB contacts (86.57%) than healthy controls (66.89%) suggesting a significant association of rs1761667 GA genotype with risk to LTBI (p\u0026thinsp;=\u0026thinsp;0.00, OR\u0026thinsp;=\u0026thinsp;3.39, 95% CI\u0026thinsp;=\u0026thinsp;1.73\u0026ndash;6.58). The SNP rs1761667 is located at Exon 1A, a nucleotide change from G\u0026thinsp;\u0026gt;\u0026thinsp;A results in decreased protein expression (16). These results align with the study conducted in the Chinese Han population to investigate the association of \u003cem\u003eCD36\u003c/em\u003e with carotid atherosclerosis. The results suggested the risk of disease in female patients carrying the GA genotype at rs1761667 (22). Another study suggested a strong association of the GA genotype with an increased risk of coronary heart disease in the Chongqing Han population of China (23). \u003cem\u003eCD36\u003c/em\u003e gene is responsible for mediating the effects of Mannose-capped lipoarabinomannan (ManLAM), leading to the release of TNF-α in peritoneal murine macrophages. Ligands of SRs have similar effects on TNF-α, and NO production as observed with ManLAM (24). \u003cem\u003eCD36\u003c/em\u003e also facilitates lysosomal enzyme transportation and internalizes mycobacteria (25). In accordance with these studies, a decrease in \u003cem\u003eCD36\u003c/em\u003e expression due to mutation might impair the cytokine production and immune response against mycobacteria resulting in susceptibility to disease. We found a weak \u003cem\u003ep\u003c/em\u003e value for the comparison of rs1761667 genotypes between healthy controls and active TB patients (p\u0026thinsp;=\u0026thinsp;0.03, OR\u0026thinsp;=\u0026thinsp;0.73, 95% CI\u0026thinsp;=\u0026thinsp;0.43\u0026ndash;1.24). The difference in genotype frequency distribution between studied groups may not remain significant by increasing sample size. We conclude that rs1761667 at \u003cem\u003ethe CD36\u003c/em\u003e gene may be important in determining the risk of LTBI but not active TB.\u003c/p\u003e \u003cp\u003eInterestingly, we found that \u003cem\u003eSR-B1\u003c/em\u003e SNP at rs4238001 was significantly associated with active TB (p\u0026thinsp;=\u0026thinsp;0 00, OR\u0026thinsp;=\u0026thinsp;2.18, 95% CI\u0026thinsp;=\u0026thinsp;1.14\u0026ndash;4.33). The frequency of mutant genotype AA was higher in active TB patients (21.25%) compared to healthy controls (3.63%). The SNP rs4238001 (G\u0026thinsp;\u0026gt;\u0026thinsp;A), located at Exon 1, introduces an amino acid change from Glycine to Serine at position 2 in \u003cem\u003ethe SR-B1\u003c/em\u003e protein. This amino acid change is associated with a decrease in \u003cem\u003eSR-B1\u003c/em\u003e expression (26). \u003cem\u003eSR-B1\u003c/em\u003e engulfs Mtb in mesenchymal stem cells (MSCs), which exhibit innate control of mycobacterial replication through autophagy. MSCs are found in both human and mouse Mtb granulomas and play an important role in TB pathogenesis (27). This SNP at rs4238001 may affect the phagocytosis of Mtb in MSCs and result in an impaired control of mycobacterium. This SNP has not been studied in TB, LTBI, or any other lung disease, however, two independent research groups reported significant association of rs4238001 with coronary heart disease and low progesterone level (28, 29).\u003c/p\u003e \u003cp\u003eIn the comparison of \u003cem\u003eSR-B1\u003c/em\u003e SNP at rs4238001 between healthy controls \u003cem\u003evs.\u003c/em\u003e TB contacts, we found that heterozygous GA genotype was significantly associated with protection against LTBI (p\u0026thinsp;=\u0026thinsp;0 00, OR\u0026thinsp;=\u0026thinsp;0.37, 95% CI\u0026thinsp;=\u0026thinsp;0.20\u0026ndash;0.66). \u003cem\u003eSR-B1\u003c/em\u003e on microfold cells (M cells) interact with Mtb EsxA enabling it to cross airway mucosa and initiate infection. Disruption in \u003cem\u003eSR-B1\u003c/em\u003e genes decreases Mtb binding and translocation across M cells (30). Overexpression of \u003cem\u003eSR-B1\u003c/em\u003e in macrophages increases Mtb and BCG binding (31). We hypothesize that changes in \u003cem\u003eSR-B1\u003c/em\u003e gene expression due to mutation may affect Mtb binding, resulting in protection against LTBI. Our results are consistent with the study conducted by Acton et al., in which the SNP rs4238001 was associated with protection towards atherogenic lipid profile in white men (32).\u003c/p\u003e \u003cp\u003eOur study possesses several noteworthy strengths. The sample size utilized in our study was calculated using scientific methods and was adequate to establish a correlation between genetic mutations and TB within the specific population. We examined two variations in each of the targeted genes that were known to impact gene expression. It is worth noting that our study is the first of its kind to explore the association between \u003cem\u003eCD36\u003c/em\u003e and \u003cem\u003eSR-B1\u003c/em\u003e gene polymorphisms with TB and LTBI. However, there were a few limitations to our study that should be acknowledged. We did not validate latent TB infection in TB contacts using Interferon Gamma Release Assay (IGRA) or QuantiFERON (QFT). Additionally, ARMS-PCR does not serve as a definitive technique for genotyping as it may fail to detect mutations at low levels and is prone to producing false positive and false negative results. The findings of this research can be confirmed by categorizing the study participants with similar genetic variations and subsequently measuring protein levels to establish the cumulative impact of these genetic variations. In conclusion, conducting follow-up studies with a larger sample size and validating the genotypes through sequencing will contribute to the verification of the current study's results.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was performed in line with the principles of the NUMS Institutional Review Board. Approval was granted by the Ethics Committee of NUMS (Date.18 Oct 2022 / No. 06/IRB \u0026amp; EC/NUMS/24) (S1). Informed consent was obtained from all individual participants included in the study (S2).\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\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting Interests:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAuthors declare NO conflict of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis project was financially supported by the National University of Medical Sciences, Pakistan, and the Researchers Supporting Project Number (RSP2024R110) at King Saud University, Riyadh, Saudi Arabia.\u003c/p\u003e\n\u003ch1\u003eAuthor contributions:\u003c/h1\u003e\n\u003cp\u003eEzza Binte Tariq: Data curation, Formal analysis, Investigation, and Writing original draft.\u003c/p\u003e\n\u003cp\u003eUrooj Subhan: Formal analysis, and Investigation.\u003c/p\u003e\n\u003cp\u003eRiaz Ullah: Resources, and Editing\u003c/p\u003e\n\u003cp\u003eZuha Tariq: Investigation\u003c/p\u003e\n\u003cp\u003eDr. Farah Deeba: Writing-review, and Editing.\u003c/p\u003e\n\u003cp\u003eDr. Afrose Liaquat: Writing-review, and Editing\u003c/p\u003e\n\u003cp\u003eDr. Sidra Younis: Conceptualization, Project administration, Methodology, Supervision, Visualization, Validation, Writing-review, and Editing.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe acknowledge the study subjects for providing their samples, the authors of the manuscript for their efforts, and the National University of Medical Sciences, Pakistan and the researchers supporting project number (RSP2024R110) at King Saud University Riyadh Saudi Arabia for financial support and research facilitation.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eLuo F, Zou P, Liao Y, Luo J, Luo D, Hu K, et al. Association between TAP gene polymorphisms and tuberculosis susceptibility in a Han Chinese population in Guangdong. Molecular Genetics and Genomics. 2022;297(3):779-90.\u003c/li\u003e\n\u003cli\u003eO\u0026apos;Garra A, Redford PS, McNab FW, Bloom CI, Wilkinson RJ, Berry MP. The immune response in tuberculosis. Annual review of immunology. 2013;31:475-527.\u003c/li\u003e\n\u003cli\u003eApt A, Kramnik I. Man and mouse TB: contradictions and solutions. Tuberculosis (Edinburgh, Scotland). 2009;89(3):195.\u003c/li\u003e\n\u003cli\u003eFortin A, Abel L, Casanova J, Gros P. Host genetics of mycobacterial diseases in mice and men: forward genetic studies of BCG-osis and tuberculosis. Annu Rev Genomics Hum Genet. 2007;8:163-92.\u003c/li\u003e\n\u003cli\u003ePeiser L, Gordon S. The function of scavenger receptors expressed by macrophages and their role in the regulation of inflammation. Microbes and Infection. 2001;3(2):149-59.\u003c/li\u003e\n\u003cli\u003eSia JK, Rengarajan J. 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Identification of scavenger receptor B1 as the airway microfold cell receptor for Mycobacterium tuberculosis. Elife. 2020;9:e52551.\u003c/li\u003e\n\u003cli\u003eSch\u0026auml;fer G, Guler R, Murray G, Brombacher F, Brown GD. The role of scavenger receptor B1 in infection with Mycobacterium tuberculosis in a murine model. PloS one. 2009;4(12):e8448.\u003c/li\u003e\n\u003cli\u003eActon S, Osgood D, Donoghue M, Corella D, Pocovi M, Cenarro A, et al. Association of polymorphisms at the SR-BI gene locus with plasma lipid levels and body mass index in a white population. Arteriosclerosis, thrombosis, and vascular biology. 1999;19(7):1734-43.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"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":"SR-B1, CD36, rs4238001 (g.5275G \u003e A), rs1761667 (g.18436G \u003e A), rs3211938 (g.73946T \u003e G), single nucleotide polymorphisms","lastPublishedDoi":"10.21203/rs.3.rs-3856622/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3856622/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eHost genetics is pivotal in deciding disease susceptibility and outcome in individuals infected with \u003cem\u003eMycobacterium tuberculosis\u003c/em\u003e (Mtb). Scavenger receptors are PRRs that play a vital role in facilitating molecular interactions between Mtb and the host. This interaction can potentially be modified by polymorphisms in scavenger receptor genes. The role of scavenger receptors in TB or LTBI pathogenesis has not yet been studied. Therefore, we designed a case-control study to investigate the association of polymorphisms in \u003cem\u003ethe CD36\u003c/em\u003e gene at rs1761667 (G\u0026gt;A) and rs3211938 (T\u0026gt;G), and \u003cem\u003eSR-B1\u003c/em\u003e gene at rs4238001 (G\u0026gt;A) with TB and LTBI in the Pakistani population using ARMS-PCR. Fisher's exact chi-square test was used to compare genotypes between study groups. We found that rs4238001 (AA, \u003cem\u003ep\u003c/em\u003e=0.00) and rs1761667 (AA, \u003cem\u003ep\u003c/em\u003e=0.03) were significantly associated with active TB. Furthermore, rs1761667 (GA, \u003cem\u003ep\u003c/em\u003e=0.00) and rs3211938 (GG, \u003cem\u003ep\u003c/em\u003e\u0026lt;0.00) were significantly associated with LTBI. Our findings suggest that \u003cem\u003eSR-B1\u003c/em\u003e and \u003cem\u003eCD36\u003c/em\u003e gene polymorphisms may contribute to TB pathogenesis in the Pakistani population. Furthermore, different genotypes of a single SNP can have varying effects on the susceptibility to both TB and LTBI. Further studies on polymorphism-associated gene expression will provide insights into their role in TB and LTBI pathogenesis.\u003c/p\u003e","manuscriptTitle":"Scavenger Receptor Genes Polymorphisms Association with Tuberculosis and Latent Tuberculosis Infection in Pakistani population","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-01-19 10:24:58","doi":"10.21203/rs.3.rs-3856622/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":"c471202a-c766-49fb-a717-98a07cd800a9","owner":[],"postedDate":"January 19th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-02-27T11:46:57+00:00","versionOfRecord":[],"versionCreatedAt":"2024-01-19 10:24:58","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-3856622","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3856622","identity":"rs-3856622","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}