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Mannose-binding lectin 2 gene polymorphisms and their association with tuberculosis in a Chinese population

Infectious Diseases of Poverty volume 9, Article number: 46 (2020) Cite this article

Abstract

Background

Immune- and inflammation-related genes (IIRGs) play an important role in the pathogenesis of tuberculosis (TB). However, the relationship between IIRG polymorphisms and TB risk remains unknown. In this study, the gene polymorphisms and their association with tuberculosis were determined in a Chinese population.

Methods

We performed a case-control study involving 1016 patients with TB and 507 healthy controls of Han Chinese origin. Sixty-four single-nucleotide polymorphisms (SNPs) belonging to 18 IIRGs were genotyped by the PCR-MassArray assay, and the obtained data was analyzed with χ2-test, Bonferroni correction, and unconditional logistic regression analysis.

Results

We observed significant differences in the allele frequency of LTA rs2229094*C (P = 0.015), MBL2 rs2099902*C (P = 0.001), MBL2 rs930507*G (P = 0.004), MBL2 rs10824793*G (P = 0.004), and IL12RB1 rs2305740*G (P = 0.040) between the TB and healthy groups. Increased TB risk was identified in the rs930507 G/G genotype (Padjusted = 0.027) under a codominant genetic model as well as in the rs2099902 (C/T + C/C) vs T/T genotype (Padjusted = 0.020), rs930507 (C/G + G/G) vs C/C genotype (Padjusted = 0.027), and rs10824793 (G/A + G/G) vs A/A genotype (Padjusted = 0.017) under a dominant genetic model after Bonferroni correction in the analysis of the overall TB group rather than the TB subgroups. Furthermore, the rs10824793_rs7916582*GT and rs10824793_rs7916582*GC haplotypes were significantly associated with increased TB risk (P = 0.001, odds ratio [OR] = 1.421, 95% confidence interval [CI]: 1.152–1.753; and P = 0.018, OR = 1.364, 95% CI: 1.055–1.765, respectively). Moreover, the rs10824793_rs7916582*AT/AT or rs10824793_rs7916582*GT/GT diplotype showed a protective (P = 0.003, OR = 0.530, 95% CI: 0.349–0.805) or harmful (P = 0.009, OR = 1.396, 95% CI: 1.087–1.793) effect against the development of TB.

Conclusions

This study indicated that MBL2 polymorphisms, haplotypes, and diplotypes were associated with TB susceptibility in the Han Chinese population. Additionally, larger sample size studies are needed to further confirm these findings in the future.

Background

Tuberculosis (TB) is a global infectious disease in humans. It is a severe and even lethal disease and was responsible for 1.2 million deaths worldwide in 2018 [1]. The main reason worldwide TB eradication is so difficult is that smear-positive TB patients are the most important source of infection. They often transmit the TB bacterium via droplets produced by coughing, sneezing, etc. It was found that a TB patient typically infects 10–15 people from the onset of the disease until diagnosis and treatment and that these infected people can, in turn, become new sources of infection. A healthy person’s chances of being infected with Mycobacterium tuberculosis depend on the number of droplets inhaled and duration as well as the individual’s immune status.

It is well known that approximately one-third of the world’s population is infected with M. tuberculosis [1], whereas only 10% of these infected individuals progress to TB disease [2]. This indicates that the risk of developing TB in humans is strongly associated with host-pathogen interactions, the environment, and genetic background [3]. Recently, a growing number of studies has supported the hypothesis that TB risk is associated with polymorphisms of immune- and inflammation-related genes (IIRGs), including the interleukin-10 (IL-10), IL1A, IL1B, IL6, IL12B, IL27, interleukin 12 receptor beta 1 (IL12RB1), interleukin 18 receptor 1 (IL18R1), signal transducer and activator of transcription 1 (STAT1), natural resistance-associated macrophage protein 1 (SLC11A1 or NRAMP1), SP110, lymphotoxin A (LTA), tumor necrosis factor (TNF), interferon gamma receptor 1 (IFNGR1), IFNGR2, mannose-binding lectin 2 (MBL2), vitamin D receptor (VDR), monocyte chemoattractant protein-1 (MCP-1 or CCL2), and toll-like receptor 8 (TLR8) genes [4,5,6,7,8,9,10,11,12,13]. IIRGs play essential roles in innate and adaptive immunity, which help control M. tuberculosis infection in humans [14]. Therefore, polymorphisms in these genes can alter immunity and lead to genetic susceptibility to TB. Moreover, common genetic variants of IIRGs could be used to predict and evaluate TB risk in the early stages of infection.

However, previous studies have mostly focused on the association between polymorphisms in one or several related genes and susceptibility to TB, rather than multiple IIRGs. It is well known that the interaction between M. tuberculosis and its host leads to a very complex immune response. As such, studies that focus on a single or a small number of sample genes may overlook potential associations between multiple genes: for example, they may ignore linkage disequilibrium between numerous single-nucleotide polymorphisms (SNPs). Therefore, it is imperative to study the association between various SNPs and susceptibility to TB in as many IIRGs as possible.

In this study, 64 SNPs in 18 IIRGs were selected, and the association between these SNPs and TB risk was evaluated using the polymerase chain reaction (PCR)-MassArray method in a large case-control population of Han Chinese origin.

Methods

Patients, controls, and ethics statement

This case-control study was performed in the 8th Medical Center of Chinese PLA General Hospital (Beijing, China) from June 2009 to March 2019 and was approved by the Research Ethics Committee of the 8th Medical Center of the Chinese PLA General Hospital. All DNA samples were extracted from residual blood after a liver function test. Informed consent was obtained from all participants. In total, 1016 patients (597 males and 419 females, mean age 39.5 ± 19.3 years) with a TB diagnosis according to smear, M. tuberculosis culture, radiological examination, and histological examination were randomly included from the patients in the 8th Medical Center of the Chinese PLA General Hospital (Beijing, China). In the same period, 507 healthy volunteers (289 males and 218 females, mean age 51.8 ± 10.6 years) with retrospectively confirmed non-tuberculous diseases were included from the physical examination center of 8th Medical Center of the Chinese PLA General Hospital. All TB and control patients were HIV negative.

DNA extraction

Blood samples (2 ml) from each participant were collected (the residual portion of the blood samples obtained for a liver function test) and stored in citrate-anticoagulated glass tubes at − 40 °C until use. The Whole Blood DNA Extraction Kit (Tiangen Biotech, Co., Ltd., Beijing, China) was used to extract total genomic DNA from 1 ml of the stored blood samples, following the manufacturer’s instructions. Then, the extracted genomic DNA was resuspended in 0.1 × Tris-EDTA buffer (10 mmol/L Tris, 1 mmol/L EDTA, pH 8.0) and stored at − 20 °C.

Screening of target SNPs

Data from the International HapMap Project (http://hapmap.ncbi.nlm.nih.gov) were used to screen potential SNPs using an estimated r2 threshold of > 0.8 for the untyped SNPs as reported in a previous study [15]. The genotype data for the Han Chinese population were obtained from the Haploview 4.2 program (http://www.broad.mit.edu/haploview) and used to select SNPs that have a minor allele frequency (MAF) of > 0.05.

Genotyping

In total, 64 SNPs of IL-10, IL18R1, IL1A, IL1B, STAT1, SLC11A1, SP110, IL12B, LTA, TNF, IFNGR1, MBL2, VDR, IL27, CCL2, IL12RB1, IFNGR2, and TLR8 were genotyped in samples from both TB patients and controls using the iPLEX assay on a MassArray system (Sequenom Inc., San Diego, United States) according to a previously published protocol [16]. The main particularities are listed below: (1) PCR reactions: Genomic DNA (10 ng), Taq DNA polymerase (0.5 U, HotStarTaq, Qiagen, Shanghai, China), dNTPs (500 μmol), and PCR primers (100 nmol) in a 5-μl reaction volume were added into a 384-well plate. Then, PCR thermal cycling was performed at 94 °C, followed by 45 cycles of 20 s at 94 °C, 30 s at 56 °C, and 60 s at 72 °C using an ABI-9700 instrument (Thermo Fisher Scientific Inc., Waltham, United States). Finally, the PCR products were examined by 2.0% agarose gel electrophoresis. (2) Purification: After the PCR reaction, 2 μl of shrimp alkaline phosphatase (0.3 U) was mixed with the PCR products, incubated at 37 °C for 20 min, and then inactivated at 85 °C for 5 min. (3) Extension: The concentrations of the extension primers were adjusted to equilibrate the signal-to-noise ratios. Then, termination mix (100 μmol), DNA polymerase (0.05 U, Sequenom, Inc., San Diego, United States), and extension primers (625 to 1250 nmol/L) in a final volume of 9 μl were pooled together and detected using an iPLEX Gold Kit (Sequenom, Inc., San Diego, United States) at 94 °C for 30 s, followed by 5 s at 94 °C and 5 cycles of 5 s at 52 °C and 5 s at 80 °C. An additional 40 annealing and extension cycles were then performed, with 5 s at 94 °C and 5 cycles of 5 s at 52 °C and 5 s at 80 °C. The final extension was carried out at 72 °C for 3 min; then, the sample was cooled to 4 °C. (4) MALDI-TOF-MS: The samples were then manually desalted using 6 mg of clean resin and a dimple plate and subsequently transferred to a 384-well Spectro-CHIP (Sequenom, Inc., San Diego, United States) using a nano-dispenser. The mass spectra were acquired using the Compact Mass Spectrometer and analyzed via the MassArray Typer 4.0 Software (Sequenom, Inc., San Diego, United States). The PCR assay was performed with two no-template controls and four duplicated samples in each 384-well format as quality controls. Each genotyping result was generated and analyzed by laboratory staff who were unaware of the patient’s status.

Statistical analyses

All statistical analyses were performed using the Stata statistical package (version 10.0; StataCorp LP, College Station, TX, USA), and all P values were two-tailed. The statistical differences in allele and genotype frequencies between the TB and control groups were evaluated using the χ2-test. In the χ2-test, P values with a Bonferroni correction of < 0.05 were considered significant. The Hardy-Weinberg Equilibrium (HWE) was tested via the χ2-test for goodness of fit using a web program (http://ihg.gsf.de/cgi-bin/hw/hwa1.pl). Moreover, Akaike’s information criterion was used to select the genetic model with maximum parsimony for each SNP. Odds ratios (ORs) as well as 95% confidence intervals (CIs) were calculated via unconditional logistic regression analysis with adjustment for age and gender.

The pairwise linkage disequilibrium (LD) among the SNPs was determined using Lewontin’s standardized coefficient D’ and LD coefficient r2 as described in a previous study [17], whereas haplotype blocks were defined in Haploview 4.2 (https://www.broadinstitute.org/haploview/haploview) with default settings following the criteria published in a previous study [18]. In addition, the haplotype frequencies were estimated using the PHASE 2.1 Bayesian algorithm [19] and HAPLO.STATS [20]. The haplotypes were then pooled into a combined group if their frequency was less than 0.03. Empirical P values, based on 100 000 simulations, were computed for the global score test and each of the haplotype-specific score tests. The diplotype (haplotype dosage, an estimate of the number of copies of the haplotype) was the most probable haplotype pair for each individual. Unconditional logistic regression analysis was used to evaluate the ORs and 95% CIs for participants carrying 1 to 2 copies versus 0 copies of each common haplotype for the dichotomized diplotypes.

Results

The distribution of 64 SNP alleles in TB patients and healthy controls

One thousand and sixteen patients with a TB diagnosis and 507 healthy controls were recruited. Among the TB patients, 680 (66.9%) had total pulmonary TB (TPTB), including 388 with simple PTB and 74 with simple TB pleurisy (TBP), 166 (16.3%) had extrapulmonary TB (EPTB), and 170 (16.7%) had concomitant PTB and EPTB (PTB + EPTB).

Sixty-four SNPs from 18 IIRGs were selected and genotyped, and all allele distributions in the control group were consistent with those from the HWE (P > 0.01, Table 1). The results showed that the allele distributions of LTA rs2229094*C (P = 0.015), MBL2 rs2099902*C (P = 0.001), MBL2 rs930507*G (P = 0.004), MBL2 rs10824793*G (P = 0.004), and IL12RB1 rs2305740*G (P = 0.040) were significantly different between the TB patients and healthy controls (Table 1), whereas the allele distributions of the other SNPs were not.

Table 1 Information about 64 genotyped SNPs in the IL-10, IL18R1, IL1A, IL1B, STAT1, SLC11A1, SP110, IL12B, LTA, TNF, IFNGR1, MBL2, VDR, IL27, CCL2, IL12RB1, IFNGR2, and TLR8
Full size table

The genotypic frequencies of SNPs and their associations with TB risk

When investigating the TB group, the unconditional logistic regression analysis showed that 14 SNPs of IL18R1, IL1A, STAT1, LTA, IFNGR1, MBL2, VDR, and IL12RB1 were associated with TB risk under a codominant model (Table S1) and under a dominant and recessive genetic model (Table S2). However, after adjusting for the Bonferroni correction, only SNPs in the MBL2 gene were found to still be associated with TB risk. Therefore, we next focused on the MBL2 gene. Our results showed that: 1) Under a codominant genetic model (Table 2), the rs2099902 C/T and C/C genotypes, rs930507 C/G genotype, rs10824793 G/A and G/G genotypes, and rs7916582 T/C genotype were associated with increased risk of TB. After the Bonferroni correction, increased TB risk was still observed in patients with a rs930507 G/G genotype (Padjusted = 0.027). 2) Under a dominant and recessive genetic model (Table 3), the rs2099902 (C/T + C/C) vs T/T and C/C vs (T/T + C/T) genotypes, rs930507 (C/G + G/G) vs C/C genotype, rs10824793 (G/A + G/G) vs A/A as well as G/G vs (A/A + G/A) genotypes, and rs7916582 (T/C + C/C) vs T/T genotype were associated with increased risk of TB. Interestingly, increased TB risk was still observed for the rs2099902 (Padjusted = 0.020), rs930507 (Padjusted = 0.027), and rs10824793 (Padjusted = 0.017) SNPs under a dominant genetic model after the Bonferroni correction.

Table 2 Genotype frequencies of SNPs in the MBL2 gene among cases and controls and their associations with tuberculosis risk under a codominant genetic model
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Table 3 Association analysis of SNPs in the MBL2 gene under a dominant and recessive genetic model
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The distribution of the MBL2 SNP genotype frequency

To further confirm the differences in the distribution of the MBL2 SNP genotype frequency between the TB subgroups (TPTB, PTB, EPTB, and PTB + EPTB) and healthy controls, we performed unconditional logistic regression analysis under codominant, dominant, and recessive genetic models. The results indicated that the rs2099902 C/T and C/C genotypes, rs930507 C/G genotype, rs10824793 G/A and G/G genotypes were associated with increased TB risk in the TB subgroups (Table S3). However, these statistically significant differences disappeared after applying the Bonferroni correction under a codominant genetic model (Padjusted > 0.05, Table S3). A similar result was observed under the dominant and recessive genetic model (Padjusted > 0.05, Table S4).

The distribution of the MBL2 haplotypes and diplotypes

To investigate the associations regarding LD patterns between these four SNPs, we used Haploview to plot their haplotype blocks. We identified one haplotype block composed of rs10824793 and rs7916582 (r2 = 0.98). However, the rs2099902 and rs930507 SNPs in the MBL2 gene were outside this haplotype block (Fig. 1). In the haplotype analysis, three common haplotypes (rs10824793_rs7916582*AT, GT, and GC) were observed among the participants; the total percentage of these common haplotypes was as high as 99.85% in the TB group or 99.9% in the control group. Conversely, the total percentage of other haplotypes was only 0.15% or 0.10% in the TB or control group, respectively (Table 4). The global score test indicated that the frequency of the haplotypes from the block between the TB and control groups was significantly different (global P = 0.00222, Psim = 0.00207). Interestingly, statistical differences were observed in the frequency of the rs10824793_rs7916582*AT (P = 0.00014) or rs10824793_rs7916582*GT (P = 0.003) haplotype between the TB and control groups. Moreover, this difference remained significant after the Bonferroni correction (rs10824793_rs7916582*AT, Padjusted = 0.00042; rs10824793_rs7916582*GT, Padjusted = 0.009). Furthermore, the rs10824793_rs7916582*GT or rs10824793_rs7916582*GC haplotype was significantly associated with increased TB risk (P = 0.001, OR: 1.421, 95% CI: 1.152–1.753; or P = 0.018, OR: 1.364, 95% CI: 1.055–1.765) in the logistic regression analysis when compared to the rs10824793_rs7916582*AT haplotype (Table 4).

Fig. 1
figure 1

Location and linkage disequilibrium structure for the four SNPs in the MBL2 gene. The SNP distribution and haplotype block structure across four SNPs in the MBL2 gene are shown, respectively. The figure was composed of chromosome-scale (the top line with even division), the transcription string (the thick bar represents exon (yellow), or UTR (grey), and the thin line represent intron), SNP scale (the hollow bar with scales representing SNPs location), and graphic of LD (black-and-green) or block definition (flammulated). Correlation coefficients (r2, × 100) are shown in the individual boxes; the color from white to red denotes r2 from 0 to 1. LD, linkage disequilibrium; UTR, Untranslated Regions

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Table 4 Haplotypes analysis of MBL2 gene polymorphisms with tuberculosis risk
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Moreover, the association between the diplotypes of the MBL2 gene polymorphisms and TB risk was also analyzed. As shown in Table 5, the diplotype composed of the rs10824793_rs7916582*AT haplotypes had a considerably decreased TB risk in a 2-copy logistic regression analysis compared with 0-copy (P = 0.003, OR = 0.530, 95% CI: 0.349–0.805). Moreover, this significant protective effect was still observed after Bonferroni correction (Padjusted = 0.009). In contrast, increased TB risk was found in the diplotype composed of the rs10824793_rs7916582*GT (P = 0.009, OR = 1.396, 95% CI: 1.087–1.793) or rs10824793_rs7916582*GC (P = 0.05, OR = 1.330, 95% CI: 1.000–1.768) haplotypes in 1-copy logistic regression analysis compared with 0-copy. However, this significant difference was only observed in the diplotype composed of the rs10824793_rs7916582*GT haplotype after Bonferroni correction (Padjusted = 0.027).

Table 5 Diplotypes analysis of MBL2 gene polymorphisms with tuberculosis risk
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Discussion

In this study, we genotyped 64 SNPs from 18 IIRGs in a Han Chinese population. We first showed that the rs930507 G/G, rs2099902 [(C/T + C/C) vs T/T], rs930507 [(C/G + G/G) vs C/C], and rs10824793 [(G/A + G/G) vs A/A] genotypes were risk factors for TB under a codominant or dominant genetic model in TB patients and healthy controls (Fig. 2). Interestingly, these significant associations were not observed under any genetic model between subgroups of the TB patients and controls. This may be attributed to the low number of patients included in each tuberculosis subgroup. Therefore, to further improve the accuracy of the study, the sample size of each tuberculosis subgroup should be increased in the future.

Fig. 2
figure 2

Neural network diagram of 64 SNPs in the 18 immune- and inflammation-related genes (IIRGs). The neural network diagram was plotted using an open-source graph visualization and manipulation software termed Gehpi. In the present figure, 18 genes and their SNPs were represented by solid dots. The circle size and color of the dot represent the number of connection degree, red represents the maximum connection degree, and blue represents the minimum connection degree. Three genes (MBL2, LTA, and IL12RB1) and their significant SNPs were showed as red color

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The MBL protein is encoded by the MBL2 gene and is secreted in the liver, where it activates the complement system via the lectin pathway to combat pathogens during host infection [21]. Although the mechanisms by which the MBL2 mutations regulate TB progression remain unclear, there is no doubt that MBL2 plays a vital role in the pathophysiology of TB. To our knowledge, this is the first study to report that the rs930507, rs2099902, and rs10824793 polymorphisms can affect TB development in a population of Han Chinese origin. It is worth mentioning that several meta-analysis studies have reported that five MBL2 SNPs (rs1800450, rs1800451, rs5030737, rs7095891, and rs7096206) are associated with an increased or decreased TB risk [22,23,24,25,26]. However, there is insufficient data regarding the role of rs930507, rs2099902, and rs10824793 in TB susceptibility. Several studies on diseases other than TB have revealed that rs930507 was associated with an increased risk of invasive pneumococcal disease (IPD) in African Americans [27] and otitis media in children younger than 2 years of age [28]. Moreover, it was also shown to be associated with sodium-lithium countertransport (SLC) and systolic blood pressure [29]. Previous studies have found no association between rs2099902 and recurrent vulvovaginal infections risk [30] or severe dengue [31]; however, another study performed by Zanetti et al. suggested that rs2099902 was associated with increased risk of colon cancer in African Americans [32]. These data indicate that the susceptibility and pathogenicity of the same SNP were different in various diseases. As such, the mechanisms that underlie these differences might deserve further investigation.

The above-mentioned evidence indicated an association between the MBL2 gene and TB risk genotypes. Herein, we found an association between them by linkage disequilibrium, haplotype, and diplotype analyses. In this study (Fig. 2), the rs7916582 polymorphism was not found to be significantly associated with TB susceptibility. However, when the rs10824793 and rs7916582 SNPs were combined in haplotypes, the rs10824793*G/rs7916582*T and rs10824793*G/rs7916582*C alleles were found to be significantly associated with TB risk, which is similar to the haplotype block rs7095891*G/rs1800450*C/rs1800451*C/rs4935047*A/rs930509*G/rs2120131*G/rs2099902*C yielded by LD analysis in a previous study [31]. LD is the non-random combination of alleles at different loci and is influenced by several factors, such as selection, genetic drift, recombination rate, mutation rate, and population structure as well as genetic linkage. A haplotype is a group of genes in an organism that are inherited together from a single parent. Haplotypes are critical for investigating the genetics of common diseases, which have been studied in humans through the International HapMap Project [33].

Analyses of polymorphism data based on LD and haplotype structure are becoming increasingly important; both have been successfully used to determine the association between MBL2 polymorphisms and TB susceptibility. A previous study indicated that MBL2 gene diplotypes might be significantly more common in TB patients than in the control group [24]. It is well known that the haplotype or genotype information can be statistically defined as complete or incomplete data because the genotype data can be extracted from the haplotype data, but the reverse is not true. Consequently, it seems more important to determine the association between polymorphism and phenotype based on the configuration of haplotypes and diplotypes compared with alleles and genotypes. Recently, some studies have indicated that reactions to drugs and phenotypes are associated with the arrangement of haplotypes or diplotype rather than genotypes [34], which is consistent with the results of our present study. Although there were no significant differences in the MBL2 alleles observed between the TB and control groups, haplotype or diplotype configuration analysis found that the rs10824793_rs7916582*AT/AT diplotype had a significantly decreased TB risk in 1-copy logistic regression analysis compared with 0-copy, but the rs10824793_rs7916582*GT/GT diplotype had a considerably increased TB risk.

However, the limitation of the present study is that we did not analyze the relationship between MBL levels and TB risk. It has been reported that serum MBL levels were significantly higher in patients with active TB than in healthy controls [35], which may protect against the early development of pulmonary TB after infection [36].

Conclusions

This case-control study showed, for the first time, that the rs930507 G/G, rs2099902 (C/T + C/C) vs T/T, rs930507 (C/G + G/G) vs C/C, and rs10824793 (G/A + G/G) vs A/A genotypes were associated with an increased risk of TB in the Han Chinese population. Interestingly, our findings also showed that the rs10824793_rs7916582*GT and rs10824793_rs7916582*AT haplotypes or diplotypes were significantly associated with TB risk. These findings provide new insights into the association between SNPs in IIRGs and susceptibility to TB. However, it is necessary to confirm the findings of our study by performing further multi-centric clinical and extensive sample studies on different populations in China.

Availability of data and materials

All data generated or analyzed during this study are included in this published article [and its supplementary information files].

Abbreviations

CIs:

Confidence intervals

EPTB:

Extrapulmonary tuberculosis

HWE:

Hardy-Weinberg Equilibrium

IFNGR1:

Interferon gamma receptor 1

IL-10:

Interleukin-10

IL12RB1:

Interleukin 12 receptor beta 1

IL18R1:

Interleukin 18 receptor 1

IIRGs:

Immune- and Inflammatory-Related Genes

LD:

Linkage Disequilibrium

LTA:

Lymphotoxin A

MAF:

Minor Allele Frequency

MBL:

Mannose-Binding Lectin

MCP-1 or CCL2:

Monocyte chemoattractant protein-1

NRAMP1 or SLC11A1:

Natural resistance-associated macrophage protein 1

ORs:

Odds ratios

PCR:

Polymerase Chain Reaction

PTB:

Pulmonary Tuberculosis

SLC:

Sodium-Lithium Countertransport

SNPs:

Single-Nucleotide Polymorphisms

STAT1:

Signal transducer and activator of transcription 1

TB:

Tuberculosis

TLR8:

Toll-like receptor 8

TNF:

Tumor necrosis factor

TPTB:

Total pulmonary tuberculosis

VDR:

Vitamin D receptor

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Acknowledgments

We would like to thank the tireless contributions of the staff in the Physical Examination Center and the Institute for Tuberculosis Research, and Editage (www.editage.cn) for English language editing.

Funding

This study was funded by the Beijing Municipal Science & Technology Commission (Grant No. Z181100001718005 and 19 L2152); the National Natural Science Foundation of China (Grant No. 81801643); the Army “Twelfth Five” Scientific Research Foundation (Grant No. BWS11J050); and the Chinese PLA General Hospital (Grant No. QNC19047).

Author information

Author notes

  1. Jun-Xian Zhang and Wen-Ping Gong contributed equally to this work.

Authors and Affiliations

  1. Army Tuberculosis Prevention and Control Key Laboratory/Beijing Key Laboratory of New Techniques of Tuberculosis Diagnosis and Treatment, Institute for Tuberculosis Research, the 8th Medical Center of Chinese PLA General Hospital, 17# Heishanhu Road, Haidian District, Beijing, 100091, China

    Jun-Xian Zhang, Wen-Ping Gong, Dong-Lin Zhu, Hui-Ru An, You-Rong Yang, Yan Liang, Jie Wang & Xue-Qiong Wu

  2. Laboratory of Animal Experiment, the 8th Medical Center of Chinese PLA General Hospital, 17# Heishanhu Road, Haidian District, Beijing, 100091, China

    Jun-Xian Zhang

  3. Physical Examination Center, the 8th Medical Center of Chinese PLA General Hospital, 17# Heishanhu Road, Haidian District, Beijing, 100091, China

    Jing Tang

  4. Department of Respiration, the 8th Medical Center of Chinese PLA General Hospital, 17# Heishanhu Road, Haidian District, Beijing, 100091, China

    Wei-guo Zhao

Authors
  1. Jun-Xian Zhang
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Contributions

XQW was responsible for the initial conception, study design, implementation of this study, data management, and quality control. WPG was responsible for literature search, figures making, and writing for the original draft. JXZ was accountable for the implementation of this study, data management and quality control, and data analysis. HRA, WGZ, DLZ, YRY, YL, JW, and JT were responsible for organizing investigation at the study sites, data collection and data management, and comments for this draft. WPG and XQW were responsible for funding acquisition, supervision, critical revision for the original draft, and the final decision for submission. All authors contributed to reviewing and have seen and approved this manuscript for submission.

Corresponding author

Correspondence to Xue-Qiong Wu.

Ethics declarations

Ethics approval and consent to participate

The study protocol was approved by the Research Ethics Committee of the 8th Medical Center of Chinese PLA General Hospital. The signed informed consent was obtained from all participants before the investigation.

Consent for publication

Not applicable.

Competing interests

The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.

Supplementary information

Additional file 1: Table S1.

Genotype frequencies of 64 genotyped SNPs in the IL-10, IL18R1, IL1A, IL1B, STAT1, SLC11A1, SP110, IL12B, LTA, TNF, IFNGR1, MBL2, VDR, IL27, CCL2, IL12RB1, IFNGR2 and TLR8 genes among cases and controls and their associations with tuberculosis risk under a codominant genetic model.

Additional file 2: Table S2.

. Association analysis of 64 SNPs in the IL-10, IL18R1, IL1A, IL1B, STAT1, SLC11A1, SP110, IL12B, LTA, TNF, IFNGR1, MBL2, VDR, IL27, CCL2, IL12RB1, IFNGR2 and TLR8 genes under a dominant and recessive genetic model.

Additional file 3: Table S3.

Association analysis of MBL2 SNPs between TB subgroups and healthy controls under a codominant genetic model.

Additional file 4: Table S4.

Association analysis of MBL2 SNPs between TB subgroups and healthy controls under a dominant and recessive genetic model.

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Zhang, JX., Gong, WP., Zhu, DL. et al. Mannose-binding lectin 2 gene polymorphisms and their association with tuberculosis in a Chinese population. Infect Dis Poverty 9, 46 (2020). https://doi.org/10.1186/s40249-020-00664-9

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Keywords

Infectious Diseases of Poverty

ISSN: 2049-9957

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