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World Journal of Clinical Cases logoLink to World Journal of Clinical Cases
. 2020 Oct 6;8(19):4320–4330. doi: 10.12998/wjcc.v8.i19.4320

Impact of mTOR gene polymorphisms and gene-tea interaction on susceptibility to tuberculosis

Mian Wang 1, Shu-Juan Ma 2, Xin-Yin Wu 3, Xian Zhang 4, Julius Abesig 5, Zheng-Hui Xiao 6, Xin Huang 7, Hai-Peng Yan 8, Jing Wang 9, Meng-Shi Chen 10, Hong-Zhuan Tan 11
PMCID: PMC7559685  PMID: 33083391

Abstract

BACKGROUND

mTOR gene is a key component of the PI3K/Akt/mTOR signaling pathway, and its dysregulation is associated with various diseases. Several studies have demonstrated that tea drinking is a protective factor against tuberculosis (TB). This study was designed to explore five single nucleotide polymorphisms (SNPs) of mTOR in the Han population of China to determine how their interactions with tea drinking affect susceptibility to TB.

AIM

To investigate if the polymorphisms of mTOR gene and the gene-tea interaction are associated with susceptibility to TB.

METHODS

In this case-control study, 503 patients with TB and 494 healthy controls were enrolled by a stratified sampling method. The cases were newly registered TB patients from the county-level centers for disease control and prevention, and the healthy controls were permanent residents from Xin’ansi Community, Changsha city. Demographic data and environmental exposure information including tea drinking were obtained from the study participants. We genotyped five potentially functional SNP sites (rs2295080, rs2024627, rs1057079, rs12137958, and rs7525957) of mTOR gene and assessed their associations with the risk of TB using logistic regression analysis, and marginal structural linear odds models were used to estimate the gene-environment interactions.

RESULTS

The frequencies of four SNPs (rs2295080, rs2024627, rs1057079, and rs7525957) were found to be associated with susceptibility to TB (P < 0.05). Genotypes GT (OR 1.334), GG (OR 2.224), and GT + GG (OR 1.403) at rs2295080; genotypes CT (OR 1.562) and CT + TT (OR 1.578) at rs2024627, genotypes CT (OR 1.597), CC (OR 2.858), and CT + CC (OR 1.682) at rs1057079; and genotypes CT (OR 1.559) and CT + CC (OR 1.568) at rs7525957 of mTOR gene were significantly more prevalent in TB patients than in healthy controls. The relative excess risk of interaction between the four SNPs (rs2295080, rs2024627, rs1057079, and rs7525957) of mTOR genes and tea drinking were found to be -1.5187 (95%CI: -1.9826, -1.0547, P < 0.05), -1.8270 (95%CI: -2.3587, -1.2952, P < 0.05), -2.3246 (95%CI: -2.9417, -1.7076, P < 0.05) and -0.4235 (95%CI: -0.7756, -0.0714, P < 0.05), respectively, which suggest negative interactions.

CONCLUSION

The polymorphisms of mTOR (rs2295080, rs2024627, rs1057079, and rs7525957) are associated with susceptibility to TB, and there is a negative interaction between each of the four SNPs and tea drinking.

Keywords: Tuberculosis, mTOR, Tea drinking, Gene-environment interaction, The relative excess risk of interaction, Single nucleotide polymorphism


Core Tip: Our data demonstrated that genotypes GT, GG, and GT + GG at rs2295080; genotypes CT and CT + TT at rs2024627; genotypes CT, CC and CT + CC at rs1057079; and genotypes CT and CT + CC at rs7525957 of mTOR gene are associated with increased risk of tuberculosis in a Chinese population. In addition, there was a negative interaction between each of the four single nucleotide polymorphism (SNPs) and tea drinking. These findings may be helpful for identifying high-risk populations of tuberculosis, and suggest that promoting tea drinking might be a new way to reduce the risk of tuberculosis for individuals with mutations in the four SNPs.

INTRODUCTION

Tuberculosis (TB) is a serious infectious disease caused by Mycobacterium tuberculosis (MTB) and remains one of the most serious challenges to global health. Approximately 10.0 million new TB cases and 1.451 million TB-associated deaths were reported worldwide in 2018[1]. The severity of national epidemics varies widely among countries. China is one of the 30 countries with the highest TB cases in the world, with an incidence rate of 61/100000[1]. Nearly one-quarter of the world’s population is considered to be latently infected with MTB, whilst only 5%-15% of the infected individuals develop active TB in their lifetime[2]. TB was first discovered in different strains of mice and inbred rabbits, which developed different immune responses after infection with MTB[3]. Since then, a series of studies including candidate gene screening, twin studies, family linkage analysis, and genome-wide association studies have shown that the incidence of TB varies among different races, ethnic groups, and families, indicating that host genetics influence TB susceptibility[4-6].

As one of the downstream effects of innate immune and adaptive immune pathways, autophagy can directly eliminate intracellular MTB, and it plays an indispensable role in the immune responses against MTB infection[7,8]. The formation of autophagy is regulated by a variety of signaling molecules, including the mechanistic target of rapamycin (mTOR), Beclin 1, Ca2+, and p53. Among them, mTOR is at the center of various signaling pathways[9]. It is a serine/threonine protein kinase related to the PI3Ks, which senses fluctuations in intracellular and extracellular nutrients to modulate cellular growth, proliferation, metabolism, autophagy, and survival[10,11]. mTOR mainly achieves negative regulation of autophagy through Atg13. Phosphorylated Atg13 inhibits the formation of ULK-Atg13-FIP200 complexes, which is necessary for the formation of autophagosome, thereby inhibiting autophagy[12]. Moreover, under the action of various growth factors, PI3K/Akt can bind to the tyrosine protein kinase receptor to activate mTOR and inhibit autophagy[13].

Numerous environmental factors are associated with the risk of TB, and the protective effect of tea drinking has been established. Experimental and epidemiologic studies have demonstrated that there is a significantly negative association between tea drinking and TB. Tea polyphenols, especially epigallocatechin-3-gallate (EGCG), protect the immune system from various pathological processes including TB infection due to their antioxidant and free radical scavenging effects[14-16].

Recently, an experimental study confirmed that EGCG could effectively activate PI3K/Akt signaling, leading to the activation of mTOR and inhibition of autophagy[17]. Moreover, previous studies have suggested that polymorphisms of the mTOR gene are associated with susceptibility to various diseases[18-20]. Songane et al[21] observed no significant association between mTOR gene and TB in a multivariate analysis. However, it is still necessary to explore the relationship between mTOR gene and TB in other populations. In this case-control study, we investigated five single nucleotide polymorphisms (SNPs, rs2295080, rs2024627, rs1057079, rs12137958, and rs7525957) of mTOR in the Chinese Han population to clarify the role of mTOR polymorphism and the effect of their interactions with tea drinking on susceptibility to TB.

MATERIALS AND METHODS

Study population

This is a case-control study conducted in 2019. Sample size estimation was based on an estimated C allele of mTOR gene rs7525957 locus frequency of 10% (OR = 1.85, α = 0.05, two-sided, unpaired case-control design; and β = 0.15, two-sided). Based on the above assumptions, at least 490 subjects in each group were needed. A stratified sampling method was used to select cases and controls, and the details were reported in our previous publication[22]. The cases were newly registered TB patients from five randomly selected county-level centers for disease control and prevention (CDCs, Yueyang County CDC, Qidong County CDC, Hongjiang City CDC, Zixing City CDC, and Yueyanglou District CDC) from the 122 CDCs in Hunan Province. Healthy controls were selected from the permanent residents in Xin’ansi Community (a community in Changsha city) using a gender-age frequency matching method. Included criteria for cases and controls were strictly in accordance with the standards that have been previously described[22]. The study protocol was approved by the Medical Ethics Committee of Xiangya School of Public Health, Central South University, No. XYGW-2018-11. The research was carried out in strict accordance with the protocol, and all the included participants (> 18 years old) provided written informed consent.

Information and sample collection

Each participant completed a self-administered questionnaire on baseline characteristics and lifestyle, which included information on sex, age, height, weight, education, marital status, smoking status, tea drinking, alcohol consumption, and Bacillus Calmette–Guérin (BCG) vaccination. All the questionnaires were completed by trained research staffs in accordance with the instructions. Blood sample (5 mL) was collected from each participant by certified nurses in EDTA anticoagulant tube and stored at 4 °C immediately.

Selection of SNPs and genotyping

In this study, candidate SNP sites of mTOR were collected based on the following two points: Firstly, we selected the SNP sites previously associated with susceptibility to TB on PubMed, and secondly, and the SNP sites associated with other infectious diseases. In addition, only loci with a minor allele frequency of at least 5% were included to ensure the statistical efficacy of this study. Using NCBI SNP database (http://www.ncbi.nlm.nih.gov), we searched and learned about the frequencies of corresponding SNP sites of mTOR gene. Finally, five SNPs including rs2295080, rs2024627, rs1057079, rs12137958, and rs7525957 of mTOR gene were selected for analysis in this study.

A Wizard Genomic DNA purification kit (Promega) was used to extract the peripheral white blood cell genome, and the quality-controlled DNA was frozen at -20 °C upon collection. The site sequences of rs2295080, rs2024627, rs1057079, rs12137958, and rs7525957 of mTOR gene were identified in the GenBank and Assay Design 3.1 (Sequenom) was used to design the appropriate primers. The synthesized primers were subjected to quality assessment by matrix-assisted laser desorption ionization time-of-flight mass spectrometry (MALDI-TOF). The polymerase chain reaction (PCR) volume was 5 µL, which included 1.8 µL distilled water (ddH2O), 0.5 µL 10 × PCR buffer, 0.4 µL MgCl2 (25 mmol/L), 0.1 µL dNTP (25 mmol/L), 0.2 µL Hotstar, 1 µL PCR primer mix, and 1 µL gDNA (20-50 ng). The reaction condition was 95 °C predegeneration for 2 min, amplification (95 °C for 30 s, 56 °C for 30 s, and 72 °C for 60 s) for 45 cycles, and 72 °C extension for 5 min. The enzyme digestion reaction system , included 1.53 µL ddH2O, 0.17 µL SAP buffer, and 0.3 µL SAP enzyme; the reaction condition was 37 °C for 40 min and 85 °C for 5 min. Single base extension reaction system was 2 µL, which included 0.619 µL ddH2O, 0.2 µL iplex buffer, 0.2 µL terminator mix, 0.94 µL extension primer mix, and 0.041 µL iplex enzyme; the corresponding reaction condition was 94 °C predegeneration for 30 s, 40 cycles of amplification (five cycles of three temperature settings: 94 °C for 5 s, 52 °C for 5 s, 80 °C for 5 s, and 72 °C extension for 3 min). Subsequently, resin was purified by plating clean resin on a 6-mg resin plate, and the resin-extended product was transferred to a 384-well SpectroCHIP (Sequenom) chip for spotting (MassARRAY Nanodispenser RS1000). Sequenom MassARRAY® SNP assay was used to determine the difference in bases caused by SNP polymorphism as molecular weight difference. MALDI-TOF was used to detect the molecular weight of the extension product, and the analysis was performed using MassArray TYPER 4.0. SNP typing was determined by the difference in molecular weight.

Statistical analysis

All the statistical analyses were performed using SPSS 23.0 software (SPSS Inc., Chicago, IL, United States). Continuous variables were presented as mean ± SD and categorical variables were presented as proportions. The independent-sample t test was used for the analysis of continuous variables. The Chi-square test (χ2) was conducted for the comparison of categorical data and Hardy-Weinberg equilibrium detection. Odds ratios (ORs) and the corresponding 95%CIs were calculated to measure the association between each SNP and TB susceptibility using an unconditional logistic regression model, with the adjustment of possible confounders, such as age and sex. The interaction of additive effects between SNP and tea drinking was analyzed and the relative excess risk of interaction (RERI) was used to estimate if the main effect on TB was meaningful. Point estimation and interval estimation of RERI were calculated using Marginal Structural Linear Odds Models[23]. RERI > 0 indicates positive interactions. All statistical tests were two-sided and a P value < 0.05 indicated statistical significance.

Biostatistics statement

The statistical methods of this study were reviewed by Meng-Shi Chen from the Department of Epidemiology and Health Statistics, Xiangya School of Public Health, Central South University.

RESULTS

A total of 503 TB patients and 494 healthy controls were included in the study. There was no statistically significant difference (P > 0.05) in terms of sex, age, marital status, educational background, and alcohol consumption between the two groups. Differences in body mass index (BMI), history of BCG vaccination, smoking status, and tea drinking were statistically significant (P < 0.05) (Table 1).

Table 1.

Demographic characteristics and associated risk factors in tuberculosis patients vs. healthy controls

TB patients (n = 503)
Healthy controls (n = 494)
χ2 P value
n % n %
Sex
Male 369 73.36 348 70.45 1.048 0.306
Female 134 26.64 146 29.55
Age, yr
18-30 68 13.52 78 15.79 1.908 0.592
31-50 190 37.77 178 36.03
51-70 167 32.20 153 30.97
> 70 78 15.51 85 17.21
Marital status
Married 345 68.59 330 66.80 0.364 0.546
Other 158 31.41 164 33.20
Educational background
Primary school or below 198 39.36 220 44.53 2.824 0.244
Junior high school 161 32.01 148 29.96
Senior high school or above 144 28.63 126 25.51
BMI, kg/m2
< 18.5 180 35.79 169 34.21 9.310 0.010a
18.5-24.9 297 59.05 274 55.47
≥ 25.0 26 5.17 51 10.32
History of BCG vaccination
Yes 105 20.87 141 28.54 7.884 0.005a
No 398 79.13 353 71.46
Smoking
Yes 298 59.24 240 48.58 11.403 < 0.001a
No 205 40.76 254 51.42
Alcohol drinking
Yes 91 18.09 77 15.59 1.116 0.291
No 412 81.91 417 84.41
Tea drinking
Yes 247 49.11 293 59.31 10.457 0.001a
No 256 50.89 201 40.69
a

P < 0.05. TB: Tuberculosis; BMI: Body mass index; BCG: Bacillus Calmette–Guérin.

The distribution of SNPs at the selected five sites of the mTOR gene in each group is shown in Table 2. The genotypic distributions in TB patients and healthy controls were tested separately for Hardy-Weinberg equilibrium. No significant deviation was observed with all the five polymorphism sites (P > 0.05).

Table 2.

Genotypes of the mTOR gene in the two groups

TB patients
Healthy controls
χ2 P value
n % n %
rs2295080 TT 286 56.86 323 65.38 9.621 0.008a
GT 191 37.97 158 31.98
GG 26 5.17 13 2.63
HWE-P 0.220
rs2024627 CC 406 80.72 429 86.84 6.989 0.030a
CT 93 18.49 63 12.75
TT 4 0.80 2 0.40
HWE-P 0.847
rs1057079 TT 271 53.88 330 66.80 19.708 < 0.001a
CT 204 40.56 152 30.77
CC 28 5.57 12 2.43
HWE-P 0.259
rs12137958 GG 414 82.31 419 84.82 1.626 0.526
AG 87 17.30 72 14.57
AA 2 0.40 3 0.61
HWE-P 0.961
rs7525957 TT 368 73.16 402 81.38 9.568 0.008a
CT 126 25.05 86 17.41
CC 9 1.79 6 1.21
HWE-P 0.566
a

P < 0.05. TB: Tuberculosis; HWE-P: Hardy-Weinberg equilibrium-P value.

The univariate analysis showed that the genotypes of rs2295080 (χ2 = 9.621, P < 0.05), genotypes of rs2024627 (χ2 = 6.989, P < 0.05), genotypes of rs1057079 (χ2 = 19.708, P < 0.001), and genotypes of rs7525957 (χ2 = 9.568, P < 0.05) were closely associated with TB incidence. In addition, no statistically significant difference was observed in the genotypes of rs12137958 between the two groups (P > 0.05). Genotype TT at rs2295080, genotype CC at rs2024627, genotype TT at rs1057079, and genotype TT at rs7525957 were all less prevalent in TB patients. Moreover, genotype TT at rs2024627 and genotype AA at rs12137958 were rare in the participants (Table 2).

Further multivariate unconditional logistic regression analysis confirmed that rs2295080, rs2024627, rs1057079, and rs7525957 of mTOR gene were associated with susceptibility to TB. Genotypes GT, GG, and GT + GG at rs2295080; genotypes CT and CT + TT at rs2024627; genotypes CT, CC, and CT + CC at rs1057079; and genotypes CT and CT + CC at rs7525957 of mTOR gene were significantly more prevalent in TB patients than in healthy controls (Table 3).

Table 3.

mTOR gene polymorphism vs. tuberculosis incidence

TB patients
Healthy controls
ORc (95%CI) ORa1 (95%CI)
n % n %
rs2295080 TT 286 56.86 323 65.38 1 1
GT 191 37.97 158 31.98 1.365 (1.048-1.778) 1.334 (1.018-1.749)a
GG 26 5.17 13 2.63 2.259 (1.139-4.479) 2.224 (1.110-4.458)a
GT + GG 217 43.14 171 34.61 1.433 (1.110-1.851) 1.403 (1.080-1.823)a
rs2024627 CC 406 80.72 429 86.84 1 1
CT 93 18.49 63 12.75 1.560 (1.102-2.208) 1.562 (1.096-2.226)a
TT 4 0.80 2 0.40 2.113 (0.385-11.600) 2.069 (0.369-11.607)
CT + TT 97 19.29 65 13.15 1.577 (1.120-2.220) 1.578 (1.113-2.237)a
rs1057079 TT 271 53.88 330 66.80 1 1
CT 204 40.56 152 30.77 1.634 (1.255-2.129) 1.597 (1.216-2.097)a
CC 28 5.57 12 2.43 2.841 (1.418-5.694) 2.858 (1.404-5.818)a
CT + CC 232 46.13 164 33.20 1.723 (1.333-2.226) 1.682 (1.290-2.194)a
rs12137958 GG 414 82.31 419 84.82 1 1
AG 87 17.30 72 14.57 1.223 (0.870-1.719) 1.187 (0.840-1.678)
AA 2 0.40 3 0.61 0.675 (0.112-4.059) 0.879 (0.144-5.379)
AG + AA 89 17.70 75 15.18 1.201 (0.858-1.680) 1.176 (0.836-1.656)
rs7525957 TT 368 73.16 402 81.38 1 1
CT 126 25.05 86 17.41 1.600 (1.176-2.179) 1.559 (1.138-2.136)a
CC 9 1.79 6 1.21 1.639 (0.578-4.648) 1.686 (0.588-4.831)
CT + CC 135 26.84 92 18.62 1.603 (1.187-2.165) 1.568 (1.154-2.130)a
a

P < 0.05.

1

Adjusted for the covariates of sex, age, marital status, educational background, body mass index, smoking status, alcohol drinking, tea drinking and Bacillus Calmette–Guérin vaccination. TB: Tuberculosis.

Marginal structural linear odds models were used to examine the interactions between the selected five sites of the mTOR gene and tea drinking. After adjusting for the covariates of sex, age, marital status, educational background, BMI, alcohol drinking, smoking status, and history of BCG vaccination, the RERI between rs2295080 of mTOR genes and tea drinking was -1.5187, which suggests negative interactions (Table 4). Similarly, negative interactions were also observed between each of the other three sites (rs2024627, rs1057079 and rs7525957) of the mTOR gene and tea drinking, with the adjusted RERI of -1.8270, -2.3246, and -0.4235, respectively (Table 4).

Table 4.

Impact of interactions between genotypes of mTOR gene and tea drinking on incidence of tuberculosis

Tea drinking
RERIc
RERIa1 (95%CI)
No Yes
rs2295080 TT 245 364 -2.0341 -1.5187 (-1.9826, -1.0547)a
GT + GG 212 176
rs2024627 CC 367 468 -1.6561 -1.8270 (-2.3587, -1.2952)a
CT + TT 90 72
rs1057079 TT 228 373 -2.7054 -2.3246 (-2.9417, -1.7076)a
CT + CC 229 167
rs7525957 TT 326 444 -1.3684 -0.4235 (-0.7756, -0.0714)a
CT + CC 131 96
1

Adjusted for the covariates of sex, age, marital status, educational background, body mass index, smoking status, alcohol drinking and Bacillus Calmette–Guérin vaccination.

a

P < 0.05, RERI < 0 suggests negative interactions. RERI: Relative excess risk of interaction.

DISCUSSION

This case-control study examined the association between mTOR polymorphisms and TB susceptibility as well as their interactions with tea drinking. It is noteworthy that the frequencies of four SNPs (rs2295080, rs2024627, rs1057079, and rs7525957) of mTOR gene were associated with susceptibility to TB. However, no association was observed between the genotypes of rs12137958 and TB incidence.

mTOR gene is a key component of the PI3K/Akt/mTOR signaling pathway, and its dysregulation is associated with the pathogenesis and progression of various cancers[20,24]. Although the reason for this anomaly is still controversial, it is biologically plausible that functional SNPs of the mTOR gene may contribute to cancer susceptibility[25]. In vitro studies have revealed a higher transcription activity of mTOR in the presence of rs2295080 T allele in 786-O, HEK293, GES-1, and HeLa cell lines. Similarly, individuals with TT genotypes have higher mTOR mRNA level[18,26]. These indicate that the rs2295080 T allele could probably increase the affinity of special transcription factors to the mTOR promoter region and subsequently contribute to the enhanced mTOR activity in humans[26]. Multiple population studies have confirmed that mTOR polymorphisms affect the susceptibility of various kinds of cancers. In previous studies, individuals with TG/GG genotype displayed a significantly decreased susceptibility to gastric cancer, colorectal cancer, and breast cancer, compared with those carrying rs2295080 TT genotype[26-28]. For SNP rs1057079, the G allele carriers are at higher risk of developing esophageal squamous cell carcinoma, colon cancer, and breast cancer[29-31]. In addition, rs2024627, located in the intron, affects the expression of mTOR gene[32], and it is recommended as a genetic marker of pharmacogenetics of kidney transplant[33]. Our results showed that the frequencies of our SNPs (including rs2295080, rs1057079, and rs2024627) of mTOR gene were associated with TB susceptibility, suggesting that these functional SNPs of mTOR gene might play a critical role in the prediction of susceptibility to TB.

To our knowledge, no report has been made on the association of rs12137958 or rs7525957 of mTOR gene with TB susceptibility. Although we speculate that these SNPs with unknown functional effects on exons or introns may affect the binding capacity of transcription factors and subsequent gene transcription, an explanation for the correlation should be determined in future mechanistic biological studies. Several studies have demonstrated that mTOR targeted therapies could be designed to block the induction of the prosurvival, proliferative, and oncogenic functions of mTOR[34]. Therefore, this finding is important not only for the understanding of the pathogenesis of TB, but also for the identification of high-risk populations of TB and the development of appropriate population-specific prevention measures to control the spread of TB.

Moreover, marginal structural linear odds model analysis showed that there were negative interactions between rs2295080, rs2024627, rs1057079, and rs7525957 of mTOR genes and tea drinking, which suggests that when mutations occur in these four SNPs of mTOR genes, the risk for TB will decrease for individuals that drink tea regularly. Additionally, our previous study confirmed that increasing tea consumption is associated with a decreased risk of TB[15]. Moreover, some studies have demonstrated that catechin, an important antioxidant extracted from tea leaves, can protect cells from damage by inducing antioxidant enzymes, inhibiting oxidase, and scavenging free radicals[14,16]. EGCG, which is the most potent component in catechins, plays an important role in arresting the growth of tubercle bacillus. Previous studies demonstrated that EGCG could inhibit the transcription of tryptophan-aspartate containing coat protein (TACO) gene, which is essential in the entry and intracellular survival of mycobacteria[35]. Anand et al[16] found that EGCG could inhibit Sp1 transcription factor, a DNA-binding protein located in the promoter region of TACO gene, and block the binding of Sp1 binding sequence to the promoter region of fatty acid synthase gene, thus down-regulating the expression of TACO gene. Genetic mutations cannot be altered, and hence, our findings suggest that promoting tea drinking may be considered a new way to reduce the risk of developing TB for individuals with mutations in these four SNPs.

This study had some limitations. Firstly, TB is a complex disease, and the genetic background of the study population or the difference in environmental exposure led to an inevitable heterogeneity between studies and a limited control of confounding factors in the study. Hence, the analyses of gene-gene, gene-environment, and environment-environment interactions are required. Secondly, only five potentially functional SNPs of mTOR gene were investigated in the present study, which did not cover all the variants in mTOR gene, and the importance of combining SNPs was neglected. Hence, the results could not fully represent the role of mTOR genetic susceptibility factors in TB. A joint analysis of multiple genes or multiple sites of the same gene will facilitate the discovery of true positive associations and the elucidation of the mechanism of these genetic variants.

CONCLUSION

The present study provides evidence of the association between mTOR polymorphism and TB susceptibility. Rs2295080, rs2024627, rs1057079, and rs7525957 of mTOR gene were associated with significant increased risk of TB in a Chinese population. In addition, there was a negative interaction between each of the four SNPs and tea drinking. Nevertheless, these findings should be verified by larger independent population-based studies.

ARTICLE HIGHLIGHTS

Research background

Tuberculosis (TB) is a serious infectious disease caused by Mycobacterium tuberculosis. The incidence of TB has been shown to vary among different races, ethnic groups, and families, indicating that host genetics influence TB susceptibility. mTOR gene is a key component of the PI3K/Akt/mTOR signaling pathway, and its dysregulation is associated with various diseases. In addition, several studies have demonstrated that tea is a protective factor against TB due to its antioxidant and free radical scavenging effects.

Research motivation

Investigations have suggested that polymorphisms of the mTOR gene are associated with susceptibility to various diseases. And epigallocatechin-3-gallate, the major component of tea catechins, could effectively activate PI3K/Akt signaling, leading to the activation of mTOR and inhibition of autophagy. The role of mTOR polymorphisms in TB is still inconclusive. Moreover, whether there is any interaction on TB risk between tea drinking and polymorphisms of mTOR gene has not been reported.

Research objectives

This study aimed to investigate five single nucleotide polymorphisms (SNPs) of mTOR in the Han population of China to determine how their interactions with tea drinking affect susceptibility to TB.

Research methods

In this case-control study, 503 TB patients and 494 healthy controls were enrolled by a stratified sampling method. The cases were newly registered TB patients from the county-level centers for disease control and prevention, and the healthy controls were permanent residents from Xin’ansi Community, Changsha city. Demographic data and environmental exposure information including tea drinking were obtained from the study participants. We genotyped five potentially functional SNP sites (rs2295080, rs2024627, rs1057079, rs12137958, and rs7525957) of mTOR gene and assessed their associations with the risk of TB using logistic regression analysis, and marginal structural linear odds models were used to estimate the gene-environment interactions.

Research results

The frequencies of four SNPs (rs2295080, rs2024627, rs1057079, and rs7525957) were found to be associated with susceptibility to TB (P < 0.05). Genotypes GT (OR 1.334), GG (OR 2.224), and GT + GG (OR 1.403) at rs2295080; genotypes CT (OR 1.562) and CT + TT (OR 1.578) at rs2024627, genotypes CT (OR 1.597), CC (OR 2.858), and CT + CC (OR 1.682) at rs1057079; and genotypes CT (OR 1.559) and CT + CC (OR 1.568) at rs7525957 of mTOR gene were significantly more prevalent in TB patients than in healthy controls. The relative excess risk of interaction between the four SNPs of mTOR genes and tea drinking was found to be -1.5187 (95%CI -1.9826, -1.0547, P < 0.05), -1.8270 (95%CI -2.3587, -1.2952, P < 0.05), -2.3246 (95%CI -2.9417, -1.7076, P < 0.05) and -0.4235 (95%CI -0.7756, -0.0714, P < 0.05), respectively, which suggest negative interactions.

Research conclusions

The polymorphisms of mTOR (rs2295080, rs2024627, rs1057079, and rs7525957) are associated with susceptibility to TB, and there is a negative interaction between each of the four SNPs and tea drinking. These findings are significant for identifying populations with high risk of developing TB, and suggest that preventive measures through promoting the consumption of tea should be emphasized in the high-risk populations.

Research perspectives

Since TB is a complex disease involving various factors including heredity, biology and environment, the genetic background of the study population or the difference in environmental exposure may lead to an inevitable heterogeneity between studies. Hence, larger independent population-based studies in different countries or ethnic groups are required to validate our initial findings.

ACKNOWLEDGEMENTS

We thank Dr. Li-Qiong Bai and Dr. Zu-Hui Xu (Hunan Institute of Tuberculosis Prevention and Treatment) for their input into this work.

Footnotes

Institutional review board statement: The study was reviewed and approved by the Medical Ethical Committee of Xiangya School of Public Health Central South University, No. XYGW-2018-11.

Informed consent statement: All study participants gave informed written consent prior to study enrollment.

Conflict-of-interest statement: The authors declare that they have no competing interests.

STROBE statement: The authors have read the STROBE Statement-checklist of items, and the manuscript was prepared and revised according to the STROBE Statement-checklist of items.

Manuscript source: Unsolicited manuscript

Peer-review started: June 4, 2020

First decision: July 25, 2020

Article in press: August 29, 2020

Specialty type: Medicine, research and experimental

Country/Territory of origin: China

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P-Reviewer: García-Elorriaga G S-Editor: Huang P L-Editor: MedE-Ma JY P-Editor: Xing YX

Contributor Information

Mian Wang, Hunan Provincial Key Laboratory of Clinical Epidemiology, Xiangya School of Public Health, Central South University, Changsha 410078, Hunan Province, China.

Shu-Juan Ma, Department of Epidemiology and Health Statistics, Xiangya School of Public Health, Central South University, Changsha 410078, Hunan Province, China.

Xin-Yin Wu, Department of Epidemiology and Health Statistics, Xiangya School of Public Health, Central South University, Changsha 410078, Hunan Province, China.

Xian Zhang, Department of Occupational and Environmental Hygiene, Xiangya School of Public Health, Central South University, Changsha 410078, Hunan Province, China.

Julius Abesig, Department of Epidemiology and Health Statistics, Xiangya School of Public Health, Central South University, Changsha 410078, Hunan Province, China.

Zheng-Hui Xiao, Hunan Provincial Key Laboratory of Pediatric Emergency, Hunan Children’s Hospital, Changsha 410007, Hunan Province, China.

Xin Huang, Department of Epidemiology and Health Statistics, Hunan Normal University, Changsha 410008, Hunan Province, China.

Hai-Peng Yan, Hunan Provincial Key Laboratory of Pediatric Emergency, Hunan Children’s Hospital, Changsha 410007, Hunan Province, China.

Jing Wang, Department of Epidemiology and Health Statistics, Xiangya School of Public Health, Central South University, Changsha 410078, Hunan Province, China.

Meng-Shi Chen, Hunan Provincial Key Laboratory of Clinical Epidemiology, Xiangya School of Public Health, Central South University, Changsha 410078, Hunan Province, China. 121444639@qq.com.

Hong-Zhuan Tan, Hunan Provincial Key Laboratory of Clinical Epidemiology, Xiangya School of Public Health, Central South University, Changsha 410078, Hunan Province, China.

Data sharing statement

No additional data are available.

References

  • 1.World Health Organization. Geneva: WHO, 2019. [accessed; 2020. Global tuberculosis report 2019; p. February 2]. Available from: https://apps.who.int/iris/bitstream/handle/10665/329368/9789241565714-eng.pdf. [Google Scholar]
  • 2.Ganmaa D, Uyanga B, Zhou X, Gantsetseg G, Delgerekh B, Enkhmaa D, Khulan D, Ariunzaya S, Sumiya E, Bolortuya B, Yanjmaa J, Enkhtsetseg T, Munkhzaya A, Tunsag M, Khudyakov P, Seddon JA, Marais BJ, Batbayar O, Erdenetuya G, Amarsaikhan B, Spiegelman D, Tsolmon J, Martineau AR. Vitamin D Supplements for Prevention of Tuberculosis Infection and Disease. N Engl J Med. 2020;383:359–368. doi: 10.1056/NEJMoa1915176. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 3.Smith CM, Sassetti CM. Modeling Diversity: Do Homogeneous Laboratory Strains Limit Discovery? Trends Microbiol. 2018;26:892–895. doi: 10.1016/j.tim.2018.08.002. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 4.Aravindan PP. Host genetics and tuberculosis: Theory of genetic polymorphism and tuberculosis. Lung India. 2019;36:244–252. doi: 10.4103/lungindia.lungindia_146_15. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 5.Lawn SD, Zumla AI. Tuberculosis. Lancet. 2011;378:57–72. doi: 10.1016/S0140-6736(10)62173-3. [DOI] [PubMed] [Google Scholar]
  • 6.Dallmann-Sauer M, Correa-Macedo W, Schurr E. Human genetics of mycobacterial disease. Mamm Genome. 2018;29:523–538. doi: 10.1007/s00335-018-9765-4. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 7.Wu YW, Li F. Bacterial interaction with host autophagy. Virulence. 2019;10:352–362. doi: 10.1080/21505594.2019.1602020. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 8.Xiao Y, Cai W. Autophagy and Bacterial Infection. Adv Exp Med Biol. 2020;1207:413–423. doi: 10.1007/978-981-15-4272-5_29. [DOI] [PubMed] [Google Scholar]
  • 9.Yang Z, Klionsky DJ. Mammalian autophagy: core molecular machinery and signaling regulation. Curr Opin Cell Biol. 2010;22:124–131. doi: 10.1016/j.ceb.2009.11.014. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 10.Liu C, Chapman NM, Karmaus PW, Zeng H, Chi H. mTOR and metabolic regulation of conventional and regulatory T cells. J Leukoc Biol. 2015;97:837–847. doi: 10.1189/jlb.2RI0814-408R. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 11.Rabanal-Ruiz Y, Otten EG, Korolchuk VI. mTORC1 as the main gateway to autophagy. Essays Biochem. 2017;61:565–584. doi: 10.1042/EBC20170027. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 12.Jung CH, Jun CB, Ro SH, Kim YM, Otto NM, Cao J, Kundu M, Kim DH. ULK-Atg13-FIP200 complexes mediate mTOR signaling to the autophagy machinery. Mol Biol Cell. 2009;20:1992–2003. doi: 10.1091/mbc.E08-12-1249. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 13.Mizushima N, Klionsky DJ. Protein turnover via autophagy: implications for metabolism. Annu Rev Nutr. 2007;27:19–40. doi: 10.1146/annurev.nutr.27.061406.093749. [DOI] [PubMed] [Google Scholar]
  • 14.Maiolini M, Gause S, Taylor J, Steakin T, Shipp G, Lamichhane P, Deshmukh B, Shinde V, Bishayee A, Deshmukh RR. The War against Tuberculosis: A Review of Natural Compounds and Their Derivatives. Molecules. 2020:25. doi: 10.3390/molecules25133011. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 15.Chen M, Deng J, Li W, Lin D, Su C, Wang M, Li X, Abuaku BK, Tan H, Wen SW. Impact of tea drinking upon tuberculosis: a neglected issue. BMC Public Health. 2015;15:515. doi: 10.1186/s12889-015-1855-6. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 16.Anand PK, Kaul D, Sharma M. Green tea polyphenol inhibits Mycobacterium tuberculosis survival within human macrophages. Int J Biochem Cell Biol. 2006;38:600–609. doi: 10.1016/j.biocel.2005.10.021. [DOI] [PubMed] [Google Scholar]
  • 17.Ding ML, Ma H, Man YG, Lv HY. Protective effects of a green tea polyphenol, epigallocatechin-3-gallate, against sevoflurane-induced neuronal apoptosis involve regulation of CREB/BDNF/TrkB and PI3K/Akt/mTOR signalling pathways in neonatal mice. Can J Physiol Pharmacol. 2017;95:1396–1405. doi: 10.1139/cjpp-2016-0333. [DOI] [PubMed] [Google Scholar]
  • 18.Cao Q, Ju X, Li P, Meng X, Shao P, Cai H, Wang M, Zhang Z, Qin C, Yin C. A functional variant in the MTOR promoter modulates its expression and is associated with renal cell cancer risk. PLoS One. 2012;7:e50302. doi: 10.1371/journal.pone.0050302. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 19.Bonnet S, Falkowski S, Deppenweiler M, Monchaud C, Arnion H, Picard N, Woillard JB. Effect of genetic polymorphisms in CYP3A4, CYP3A5, and m-TOR on everolimus blood exposure and clinical outcomes in cancer patients. Pharmacogenomics J. 2020 doi: 10.1038/s41397-020-0152-7. [DOI] [PubMed] [Google Scholar]
  • 20.Zining J, Lu X, Caiyun H, Yuan Y. Genetic polymorphisms of mTOR and cancer risk: a systematic review and updated meta-analysis. Oncotarget. 2016;7:57464–57480. doi: 10.18632/oncotarget.10805. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 21.Songane M, Kleinnijenhuis J, Alisjahbana B, Sahiratmadja E, Parwati I, Oosting M, Plantinga TS, Joosten LA, Netea MG, Ottenhoff TH, van de Vosse E, van Crevel R. Polymorphisms in autophagy genes and susceptibility to tuberculosis. PLoS One. 2012;7:e41618. doi: 10.1371/journal.pone.0041618. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 22.Chen M, Liang Y, Li W, Wang M, Hu L, Abuaku BK, Huang X, Tan H, Wen SW. Impact of MBL and MASP-2 gene polymorphism and its interaction on susceptibility to tuberculosis. BMC Infect Dis. 2015;15:151. doi: 10.1186/s12879-015-0879-y. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 23.VanderWeele TJ, Vansteelandt S. A weighting approach to causal effects and additive interaction in case-control studies: marginal structural linear odds models. Am J Epidemiol. 2011;174:1197–1203. doi: 10.1093/aje/kwr334. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 24.Wang FM, Zhang X, Lan L, Ji JM, Tang HB, Yao XJ, Jiang Y, Qian J, Xu XG, Li Q, Yao P, Li JH, Shen YP. [Association of PD-1, TIM-3 and TREM-1 single nucleotide polymorphisms with pulmonary tuberculosis susceptibility] Zhonghua Yi Xue Za Zhi. 2017;97:3301–3305. doi: 10.3760/cma.j.issn.0376-2491.2017.42.006. [DOI] [PubMed] [Google Scholar]
  • 25.Qi GH, Wang CH, Zhang HG, Yu JG, Ding F, Song ZC, Xia QH. Comprehensive analysis of the effect of rs2295080 and rs2536 polymorphisms within the mTOR gene on cancer risk. Biosci Rep. 2020:40. doi: 10.1042/BSR20191825. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 26.Xu M, Tao G, Kang M, Gao Y, Zhu H, Gong W, Wang M, Wu D, Zhang Z, Zhao Q. A polymorphism (rs2295080) in mTOR promoter region and its association with gastric cancer in a Chinese population. PLoS One. 2013;8:e60080. doi: 10.1371/journal.pone.0060080. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 27.Xu M, Gao Y, Yu T, Wang J, Cheng L, Cheng L, Cheng D, Zhu B. Functional promoter rs2295080 T>G variant in MTOR gene is associated with risk of colorectal cancer in a Chinese population. Biomed Pharmacother. 2015;70:28–32. doi: 10.1016/j.biopha.2014.12.045. [DOI] [PubMed] [Google Scholar]
  • 28.Zhao Y, Diao Y, Wang X, Lin S, Wang M, Kang H, Yang P, Dai C, Liu X, Liu K, Li S, Zhu Y, Dai Z. Impacts of the mTOR gene polymorphisms rs2536 and rs2295080 on breast cancer risk in the Chinese population. Oncotarget. 2016;7:58174–58180. doi: 10.18632/oncotarget.11272. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 29.Zhu J, Wang M, Zhu M, He J, Wang JC, Jin L, Wang XF, Xiang JQ, Wei Q. Associations of PI3KR1 and mTOR polymorphisms with esophageal squamous cell carcinoma risk and gene-environment interactions in Eastern Chinese populations. Sci Rep. 2015;5:8250. doi: 10.1038/srep08250. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 30.Slattery ML, Herrick JS, Lundgreen A, Fitzpatrick FA, Curtin K, Wolff RK. Genetic variation in a metabolic signaling pathway and colon and rectal cancer risk: mTOR, PTEN, STK11, RPKAA1, PRKAG2, TSC1, TSC2, PI3K and Akt1. Carcinogenesis. 2010;31:1604–1611. doi: 10.1093/carcin/bgq142. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 31.Slattery ML, John EM, Torres-Mejia G, Lundgreen A, Herrick JS, Baumgartner KB, Hines LM, Stern MC, Wolff RK. Genetic variation in genes involved in hormones, inflammation and energetic factors and breast cancer risk in an admixed population. Carcinogenesis. 2012;33:1512–1521. doi: 10.1093/carcin/bgs163. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 32.Slattery ML, Lundgreen A, Mullany LE, Penney RB, Wolff RK. Influence of CHIEF pathway genes on gene expression: a pathway approach to functionality. Int J Mol Epidemiol Genet. 2014;5:100–111. [PMC free article] [PubMed] [Google Scholar]
  • 33.Pouché L, Stojanova J, Marquet P, Picard N. New challenges and promises in solid organ transplantation pharmacogenetics: the genetic variability of proteins involved in the pharmacodynamics of immunosuppressive drugs. Pharmacogenomics. 2016;17:277–296. doi: 10.2217/pgs.15.169. [DOI] [PubMed] [Google Scholar]
  • 34.Harwood FC, Klein Geltink RI, O'Hara BP, Cardone M, Janke L, Finkelstein D, Entin I, Paul L, Houghton PJ, Grosveld GC. ETV7 is an essential component of a rapamycin-insensitive mTOR complex in cancer. Sci Adv. 2018;4:eaar3938. doi: 10.1126/sciadv.aar3938. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 35.Anand PK, Kaul D. Downregulation of TACO gene transcription restricts mycobacterial entry/survival within human macrophages. FEMS Microbiol Lett. 2005;250:137–144. doi: 10.1016/j.femsle.2005.06.056. [DOI] [PubMed] [Google Scholar]

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Data Availability Statement

No additional data are available.


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