Association of PI3K/AKT/mTOR pathway autophagy-related gene polymorphisms with pulmonary tuberculosis susceptibility in a Chinese population

ABSTRACT

Background:  Autophagy can inhibit the survival of intracellular microorganisms including Mycobacterium tuberculosis (Mtb), and the PI3K/AKT/mTOR pathway plays a crucial role. This study investigated the association between PI3K/AKT/mTOR pathway autophagy-related gene polymorphisms and pulmonary tuberculosis (PTB) susceptibility.

Methods:   KEGG pathway and gene ontology (GO) databases were searched for genes belonging to the PI3K/AKT/mTOR and autophagy pathways. Thirty SNPs in nine genes were identified and tested for their associations with tuberculosis in 130 patients with PTB and 271 controls. We constructed genetic risk scores (GRSs) and divided the participants into 3 subgroups based on their GRSs:0-5, 6-10, and 11-16.

Results:  This analysis revealed that the AKT1 (rs12432802), RPTOR (rs11654508, rs12602885, rs2090204, rs2589144, and rs2672897), and TSC2 (rs2074969) polymorphisms were significantly associated with PTB risk. A decreasing trend was observed (P trend 0.020), in which a lower GRS was associated with a higher risk of PTB ([6-10] vs. [0-5]: OR (95%CI) 0.590 (0.374-0.931); [11-16] vs. [0-5]: OR (95%CI) 0.381 (0.160-0.906)).

Conclusions:   Polymorphisms in AKT1, RPTOR, and TSC2 may influence susceptibility to PTB.

Keywords: PI3K/AKT/mTOR; Autophagy; Tuberculosis; SNP; Genetic risk score

INTRODUCTION

Pulmonary tuberculosis (PTB) is a communicable disease caused by Mycobacterium tuberculosis (Mtb). The latest World Health Organization (WHO) figures show that in 2021, there were approximately 10.6 million new TB patients globally¹. Mtb is transmitted through the air, and close contacts of patients with active PTB have a high risk of being infected, although most patients will develop latent TB infections (LTBI). Individuals with LTBI have no clinical symptoms of TB, and nearly one-quarter of the global population harbors latent Mtb infections. About 5-10% of these individuals develop active TB at a later point in their lives when their immune system’s control of the latent infection fails², making them a reservoir for new TB cases. For over thousands of years, Mtb has adapted to the human host and developed the ability to subvert host immune responses, survive within macrophages and cause diseases. Although the immune escape mechanisms of Mtb remain poorly understood, host genetic determinants have been shown to play a vital role in susceptibility to TB³.

Autophagy, which inhibits the survival of intracellular microorganisms such as Mtb, is regulated by various signaling pathways⁴. A critical regulator of autophagy is the PI3K/AKT/mTOR pathway⁵. mTOR functions in two distinct multiprotein complexes: mTORC1 and mTORC2⁶. Activation of the PI3K/AKT pathway promotes Mtb survival by accelerating cell cycle progression and inhibiting apoptosis⁷. Activated AKT directly phosphorylates and stimulates mTORC1, blocking the formation of the unC-51-like autophagy kinase (ULK1) complex, thereby preventing the formation of autophagosomes and allowing Mtb to escape autophagic degradation in the host cell⁷. Mtb can also induce FoxP3+ Tregs to proliferate and be activated by inhibiting the PI3K/AKT/mTOR pathway as part of its immune escape repertoire⁸. Agents that inhibit the mTOR pathway, such as the anti-cancer agents Ibrutinib⁹ and Everolimus¹⁰, have been used as adjuvant host-directed therapeutics (HDT)¹¹. Therefore, by studying this pathway, novel therapeutic targets may be identified.

Genomic techniques¹² can identify single nucleotide polymorphisms (SNPs) that are markers for genes associated with complex diseases. Although polymorphisms in the PI3K/AKT/mTOR pathway have been associated with an increased risk of stomach¹³ and bladder¹⁴ cancers, very few studies have examined the association of this pathway with susceptibility to tuberculosis¹⁵. The present study systematically screened PTB patients for SNPs in autophagy-related genes of the PI3K/AKT/mTOR pathway and then used a case-control study to evaluate their associations with tuberculosis susceptibility.

METHODS

Study participants

The 401 study participants were recruited from the Han Chinese population presenting to the Shenzhen Nanshan Center for Chronic Disease Control (Nanshan CCDC, 22° N 113° E) from May 2017 to July 2018. This case-control study included treatment-naïve PTB patients whose sputum cultures were positive for Mtb and a control group with no clinical, radiological, or bacteriological evidence of TB. The controls were tested using interferon-gamma release assays (IGRA)¹⁶ and those that tested positive were considered to have LTBI. Positive sputum cultures were subjected to the p-nitrobenzoic acid (PNB) test, and patients whose cultures contained non-tuberculous mycobacteria were excluded. Participants with HBV infection, HIV infection, concomitant chronic obstructive pulmonary disease, or cancer were excluded.

Ethics approval

The Ethics Review Committee of the Nanshan CCDC approved this study (ll20170018). All study participants provided written informed consent.

DNA extraction

Blood samples were collected from the participants using EDTA tubes. The QIAamp® DNA blood mini kit was used to extract genomic DNA, and the extracted DNA was used for subsequent genotyping.

SNP selection and genotyping

The Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway database was searched for genes related to the PI3K/AKT or mTOR pathways, and the identified genes were grouped into gene set A (Supplementary Figure 1). Autophagy pathway-related genes in the KEGG database were also identified and compared with autophagy-related genes in the Gene Ontology (GO) database to identify genes common to both databases, which were grouped into gene set B. Pooled gene sets A and B were then compared to identify the genes present in both sets.

Genes common to both A and B were entered into STRING to generate a protein-protein interactions (PPI) network. The CytoHubba tool from Cytoscape was used to identify the top ten hub genes ranked by twelve algorithms in the PPI network. Hub genes were defined as those with a high correlation in the candidate modules. From the set of hub genes, those identified using at least six algorithms were included.

The tag SNPs of the selected genes were obtained from the 1000 Genomes Project using HaploView 4.2. We then selected SNPs with minor allele frequencies (MAF) ≥ 0.05 and P > 0.05 in the Hardy-Weinberg Equilibrium (HWE) test. SNPs in genes with predicted functions and SNPs that had been previously reported in the literature were selected. Genotyping was performed using the Illumina HumanOmniZhongHua-8 BeadChip. Five percent of the samples were chosen for repeat analysis.

Statistical analyses

The T-test and chi-square test were used to compare age and sex, respectively. The HWE in controls was evaluated using the SNPassoc package in R 4.0.3¹⁷. Genetic models (codominant, dominant, and recessive)¹⁸ were applied to evaluate the impact of genetic variations on the risk of PTB. The genetic risk score (GRS)¹⁹ was calculated by adding the number of risk alleles (0, 1, or 2) for the PTB-associated SNPs. The generalized multifactor dimensionality reduction (GMDR)²⁰ method was applied to investigate SNP-SNP interactions. Age and sex served as covariates in the interaction analyses. Genotyping data were processed using Genome Studio 2.0 and Plink 1.90²¹. IBM SPSS Statistics (version 22.0) was used for the statistical analyses.

RESULTS

We included 130 PTB patients and 271 controls with a mean age of 31.18 ± 10.85 and 36.52 ± 12.83, respectively. The controls were divided into two groups: 163 healthy controls (HC) and 108 individuals with LTBI. The age distribution of the PTB patients was significantly different from that of the controls (P < 0.05), with more PTB patients in the 15-29 age group and fewer in the 40-89 age group than in either the HC or LTBI groups. This suggests that Mtb is more likely to cause diseases in young and middle-aged populations. No significant differences in sex distribution were observed (Table 1).

TABLE 1:
Characteristics of enrolled subjects.

Characteristics	PTB patients (130)	controls (271)			P PTB patients vs. controls*	P LTBI vs. HC*
		Total	LTBI	HC
			(IGRA+, 108)	(IGRA-, 163)
Age, years (mean ± SD)	31.18 ± 10.85	36.52 ± 12.83	40.09 ± 12.42	34.15 ± 12.57	< 0.001	< 0.001
Age group, years, n (%)
15-29	76 (58.5)	103(38.0)	26 (24.1)	77 (47.2)	< 0.001	< 0.001
30-39	34 (26.2)	83 (30.6)	35 (32.4)	48 (29.4)
40-89	20 (15.3)	85 (31.4)	47 (43.5)	38 (23.4)
Sex, n (%)
Male	74 (56.9)	139 (51.3)	54 (50.0)	85 (52.1)	0.290	0.729
Female	56 (43.1)	132 (48.7)	54 (50.0)	78 (47.9)

• * P < 0.05 is in bold.

Fifty genes were identified by screening for autophagy-related genes in the PI3K/AKT/mTOR pathway and introduced into STRING to generate a PPI network (Figure 1). Nine hub genes were selected (AKT1, AKT2, mTOR, PIK3CA, PTEN, RHEB, RPS6KB1, RPTOR, and TSC2), and 30 SNPs found in these genes were analyzed for their associations with PTB susceptibility. The distributions of the allele frequencies for the rs11654508, rs2090204, rs2589144, rs2672897, and rs7503807 SNPs in the RPTOR gene and the rs2074969 SNP in the TSC2 gene had significantly different frequencies in the PTB and control groups (P = 0.016, 0.038, 0.001, 0.001, 0.044, and 0.015, respectively, using the chi-square test) (Supplementary Table 1). In the control group, the allele frequencies of all selected SNPs did not deviate from HWE (P < 0.05).

FIGURE 1:
PPI network generated from STRING. The joint genes of gene set A and B (N=50) were put into STRING to generate a protein-protein interactions (PPI) network.

Among the 30 SNPs, one in AKT1 (rs12432802), five in RPTOR (rs11654508, rs12602885, rs2589144, rs2672897, and rs7503807), and one in TSC2 (rs2074969) were associated with PTB risk in the genetic models (P < 0.05) (Table 2). The frequencies of the remaining 23 SNPs were not significantly different (P > 0.05) (Supplementary Table 2).

TABLE 2:
Positive results of genetic model associations between PTB patients and controls.

Gene	SNP	Genotypes	PTB patients	controls1	Codominant		Dominant		Recessive
					(MM/Mm/mm)		(MM/Mm + mm)		(MM + Mm/mm)
	(M/m)		N (%)		OR (95% CI)	P*	OR (95% CI)	P*	OR (95% CI)	P*
AKT1	rs12432802	AA	43(33.1)	69(25.5)	Rf		Rf		Rf
	A/G	GA	57(43.8)	144(53.1)	0.560(0.323-0.970)	0.038	0.651(0.389-1.091)	0.103	1.341(0.754-2.384)	0.318
		GG	30(23.1)	58(21.4)	0.931(0.476-1.822)	0.836
RPTOR	rs11654508	AA	44(33.8)	105(38.7)	Rf		Rf		Rf
	A/G	GA	51(39.3)	128(47.2)	1.111(0.655-1.884)	0.697	1.366(0.840-2.219)	0.209	2.001(1.110-3.605)	0.021
		GG	35(26.9)	38(14.1)	2.116(1.101-4.068)	0.025
	rs12602885	GG	76(58.5)	132(48.7)	Rf		Rf		Rf
	G/A	AG	49(37.8)	123(45.4)	0.630(0.387-1.025)	0.063	0.611(0.380-0.981)	0.041	0.581(0.192-1.757)	0.336
		AA	5(3.8)	16(5.9)	0.467(0.151-1.446)	0.187
	rs2589144	GG	95(73.1)	152(56.1)	Rf		Rf		Rf
	G/A	AG	30(23.1)	99(36.5)	0.540(0.317-0.920)	0.023	0.527(0.318-0.873)	0.013	0.558(0.187-1.664)	0.295
		AA	5(3.8)	20(7.4)	0.459(0.152-1.383)	0.166
	rs2672897	AA	59(45.4)	87(32.1)	Rf		Rf		Rf
	A/G	GA	57(43.8)	127(46.9)	0.776(0.465-1.296)	0.332	0.632(0.391-1.021)	0.061	0.410(0.210-0.800)	0.009
		GG	14(10.8)	57(21.0)	0.358(0.174-0.737)	0.005
	rs7503807	AA	69(53.0)	119(43.9)	Rf		Rf		Rf
	A/C	CA	53(40.8)	122(45.0)	0.722(0.441-1.183)	0.196	0.651(0.406-1.045)	0.075	0.458(0.194-1.085)	0.076
		CC	8(6.2)	30(11.1)	0.391(0.160-0.956)	0.040
TSC2	rs2074969	GG	100(76.9)	175(64.6)	Rf		Rf		Rf
	G/A	AG	26(20.0)	83(30.6)	0.442(0.253-0.771)	0.004	0.424(0.249-0.719)	0.001	0.426(0.129-1.405)	0.161
		AA	4(3.1)	13(4.8)	0.332(0.099-1.110)	0.073

• M: Major allele. m: minor allele. ¹ control = LTBI + HC. For all results see Supplementary Table 2. * Adjusted for sex and age (P < 0.05) is in bold.

For the AKT1 gene, the GA genotype of rs12432802 was less prevalent in the PTB group than in controls (GA vs. AA: OR (95%CI) 0.560 (0.323-0.970); P = 0.038). Similarly, the TSC2 rs2074969 polymorphism was associated with a reduced risk of PTB in the codominant (AG vs. GG: OR (95%CI) 0.442 (0.253-0.771); P = 0.004) and dominant models (AG + AA vs. GG: OR (95%CI) 0.424 (0.249-0.719); P = 0.001). For the RPTOR rs12602885 polymorphism, subjects with the AA + AG genotypes had a relatively lower risk of PTB compared with those carrying the GG genotype in the dominant model (AG + AA vs. GG: OR (95%CI) 0.611 (0.380-0.981); P = 0.041). Furthermore, the RPTOR rs2589144, rs2672897, and rs7503807 polymorphisms were associated with a significantly reduced risk of PTB in co-dominant models (Table 2). Patients with these three RPTOR SNPs had a significantly reduced risk of PTB with the rs2589144 polymorphism in the dominant model (AA + AG vs. GG: OR (95%CI) 0.527 (0.318-0.873); P = 0.013) and the rs2672897 polymorphism in the recessive model (GG vs. AA + GA: OR (95%CI) 0.410 (0.210-0.800); P = 0.009). In contrast, an increased risk of PTB was associated with the RPTOR rs11654508 polymorphism in both the codominant (GG vs. AA: OR (95%CI) 2.116 (1.101-4.068); P = 0.025) and recessive models (GG vs. AA + GA: OR (95%CI) 2.001 (1.110-3.605); P = 0.021).

When comparing the LTBI and PTB groups, we also found significant associations between the risk of PTB and SNPs in RPTOR (rs10871489, rs2090204, rs2589144, and rs2672897) and TSC2 (rs2074969) (P < 0.05) (Supplementary Table 3). For the RPTOR (rs10871489, rs2090204, and rs2589144) and TSC2 (rs2074969) polymorphisms, the Mm genotype was significantly less frequent in the PTB patients with the codominant model (Mm vs. MM: rs10871489, OR (95%CI) 0.535 (0.295-0.970), P = 0.039; rs2090204, OR (95%CI) 0.465(0.252-0.858), P = 0.014; rs2589144, OR (95%CI) 0.541 (0.293-0.998), P = 0.049; rs2074969, OR (95%CI) 0.499 (0.260-0.958), P = 0.037, respectively) and also with the dominant model. In addition, when the PTB group was compared with the LTBI group, the RPTOR rs2672897 polymorphism was associated with a reduced risk of PTB in the dominant model (GA + GG vs. AA: OR (95%CI) 0.539 (0.302-0.963); P = 0.037) (Table 3).

TABLE 3:
Positive results of genetic model associations between PTB patients and LTBI.

Gene	SNP	Genotypes	PTB patients	LTBI	Codominant		Dominant		Recessive
					(MM/Mm/mm)		(MM/Mm + mm)		(MM + Mm/mm)
	(M/m)		N (%)		OR (95% CI)	P *	OR (95% CI)	P *	OR (95% CI)	P *
RPTOR	rs10871489	AA	90(69.3)	61(56.5)	Rf		Rf		Rf
	A/G	GA	35(26.9)	41(38.0)	0.535(0.295-0.970)	0.039	0.564(0.319-0.997)	0.049	1.020(0.272-3.819)	0.977
		GG	5(3.8)	6(5.5)	0.823(0.216-3.136)	0.775
	rs2090204	CC	95(73.1)	65(60.2)	Rf		Rf		Rf
	C/A	AC	30(23.1)	39(36.1)	0.465(0.252-0.858)	0.014	0.511(0.284-0.920)	0.025	1.433(0.326-6.300)	0.634
		AA	5(3.8)	4(3.7)	1.124(0.251-5.038)	0.878
	rs2589144	GG	95(73.1)	58(53.7)	Rf		Rf		Rf
	G/A	AG	30(23.1)	41(38.0)	0.541(0.293-0.998)	0.049	0.523(0.293-0.932)	0.028	0.543(0.168-1.757)	0.308
		AA	5(3.8)	9(8.3)	0.442(0.134-1.452)	0.178
	rs2672897	AA	59(45.4)	32(29.6)	Rf		Rf		Rf
	A/G	GA	57(43.8)	57(52.8)	0.562(0.306-1.032)	0.063	0.539(0.302-0.963)	0.037	0.646(0.289-1.442)	0.286
		GG	14(10.8)	19(17.6)	0.462(0.191-1.114)	0.086
TSC2	rs2074969	GG	100(76.9)	75(69.4)	Rf		Rf		Rf
	G/A	AG	26(20.0)	30(27.8)	0.499(0.260-0.958)	0.037	0.512(0.273-0.960)	0.037	0.796(0.155-4.084)	0.785
		AA	4(3.1)	3(2.8)	0.648(0.124-3.395)	0.608

• M: Major allele. m: minor allele. For all results see Supplementary Table 3. * Adjusted for sex and age (P < 0.05) is in bold.

We compared the HC and LTBI groups and found that only AKT1(rs11848899) and RHEB (rs3789817) were associated with a reduced risk of LTBI (Supplementary Table 4). For the AKT1 rs11848899 polymorphism, the LTBI group had a significantly lower frequency of the AC genotype (AC vs. CC: OR (95%CI) 0.558 (0.318-0.979); P = 0.042) and the AC + AA genotypes (AC + AA vs. CC: OR (95%CI) 0.539 (0.312-0.931); P = 0.027) than the CC genotype. For the RHEB rs3789817 polymorphism, the AG + AA genotypes were also significantly less common in the LTBI group than in the HC group.

The 11 SNPs (AKT: rs12432802 and rs11848899; RHEB: rs3789817; RPTOR: rs11654508, rs12602885, rs2589144, rs2672897, rs2090204, rs10871489, and rs7503807; TSC2: rs2074969) with significantly different group frequencies were included in the calculation of GRS. Individual genetic risk scores, ranging from a minimum of 0 to a maximum of 16, were combined into subgroups of 0-5, 6-10, and 11-16 (Table 4). The mean GRS was lower in the PTB group than in the control group (mean GRS:5.88 vs. 6.94, P < 0.001), and there was a positive trend (P trend = 0.020). Compared to individuals with a GRS of 0-5, those with a higher GRS were less likely to have PTB (GRS 6-10, OR (95%CI) 0.590 (0.374-0.931), P = 0.023; GRS 11-16, OR (95%CI) 0.381 (0.160-0.906), P = 0.029).

TABLE 4:
The relationship between the GRS and tuberculosis susceptibility.

GRS a	PTB patients (130)	controls (271)	PTB patients vs. controls
			T or OR (95% CI)	P*	P trend
Mean ± SD	5.88 ± 2.597	6.94 ± 2.822	3.613	< 0.001
Subgroup, n (%)
0-5	59 (45.4)	86 (31.7)	1.00		0.020
6-10	63 (48.5)	156 (57.6)	0.590 (0.374-0.931)	0.023
11-16	8 (6.1)	29 (10.7)	0.381 (0.160-0.906)	0.029

• GRS: genetic risk score. ^a A total of 11 SNPS (AKT: rs12432802 and rs11848899; RHEB: rs3789817; RPTOR: rs11654508, rs12602885, rs2589144, rs2672897, rs2090204, rs10871489, and rs7503807; TSC2: rs2074969) were included in the calculation of GRS. * Adjusted for sex and age (P < 0.05) is in bold.

We then analyzed SNP-SNP interactions with or without a diagnosis of PTB as the outcome of GMDR. There was a significant difference in only one SNP (rs11848899) in the interaction model (P = 0.0107), with a testing accuracy of 50.45% (Supplementary Table 5). No significant differences were found in the interaction models with two or more SNPs.

DISCUSSION

This study investigated the associations of 30 SNPs in autophagy-related genes of the PI3K/AKT/mTOR pathway with the risk of PTB in a Han Chinese population. The results revealed that AKT1 (rs12432802), RPTOR (rs12602885, rs2589144, rs2672897, rs7503807), and TSC2 (rs2074969) polymorphisms were associated with a significantly lower risk of developing PTB, whereas the RPTOR (rs11654508) polymorphism was associated with a significantly higher risk of PTB. The results also showed that a lower GRS based on the significant SNPs identified in our study was associated with a higher risk of PTB.

AKT1, RPTOR, and TSC2 were identified as hub genes in our study. AKT1 activation contributes to the transition of macrophages into alternatively activated phenotypes (M2)²². The transition of host macrophages from classically activated phenotypes (M1) to M2 phenotypes is one way by which Mtb can avoid hostile environments²³. Among the five AKT1 SNPs, the AKT1 rs12432802 polymorphism was associated with a decreased risk of PTB, whereas the rs11848899 polymorphism was more frequent in the LTBI group. We found no previous publications on these two SNPs. Wang et al.²⁴ found that the AKT1 rs3730358 and rs1130233 polymorphisms were associated with susceptibility to PTB, but the association with rs1130233 was not confirmed in our study, perhaps due to differences in allele frequencies between Han populations in northern and southern China or inadequate sample sizes in either or both studies. AKT1 polymorphisms were associated with clinical outcomes in patients with lung²⁵ and gastric cancers²⁶; however, a separate study found that AKT1 polymorphisms are rare and not valuable as biomarkers for bladder cancer management²⁷.

During immunization or infection, RPTOR is involved in initiating T-follicular regulatory cell differentiation²⁸, and hypomethylation of the RPTOR gene is thought to be associated with TB involving the pleura²⁹. Of the ten RPTOR SNPs included in our study, five (rs12602885, rs2589144, rs2672897, rs7503807, and rs11654508) were significantly associated with the risk of TB. The rs12602885 polymorphism has been extensively studied for its association with cancer risk; however, a meta-analysis found no association with cancer in the Chinese population³⁰. The association of the rs12602885 with a decreased risk of PTB has not been previously reported. Polymorphisms in the RPTOR gene have been associated with an increased risk of pancreatic cancer³¹ and obesity³², but the studies of these associations have been conflicting and inconclusive.

The TSC2 (tuberous sclerosis 2) gene is essential for maintaining macrophage quiescence to prevent the development of granulomatous disease via an mTOR-dependent pathway³³. Mtb infections classically cause granulomas within the lungs, and the early formation of granulomas is thought to favor the development of LTBI³⁴ rather than PTB. We found that the TSC2 rs2074969 polymorphism was associated with decreased susceptibility to PTB compared to the LTBI group. However, when the LTBI group was compared to the HC group, the association was not significant. Similar to a previous study using TSC2 conditional knockout mice³³, we suggest that TSC2 may be involved in the development of TB and therefore TSC2 polymorphisms could be biomarkers for disease progression. Mutations in the TSC1 and TSC2 genes have been reported as the genetic basis for the tuberous sclerosis disease complex, and TSC2 gene variation has been associated with status epilepticus in Chinese children³⁵.

Ras homologue enriched in brain (RHEB) is a target gene of microRNA-155 and contributes to a reduction in the survival of intracellular mycobacteria in macrophages³⁶. RHEB gene polymorphisms may be associated with susceptibility to Mtb infection when compared with the HC group but not when compared with the PTB group. The RHEB rs1109089 polymorphism has been associated with suicide in a historically high-risk isolated population from Northeast India³⁷; however, there are few other published studies on RHEB gene polymorphisms.

Using a simple GRS based on our SNPs with statistically significant associations, we found that lower GRS scores were associated with an increased risk of PTB compared with controls, but there was no association of GRS scores with LTBI. Our GMDR analysis revealed no obvious interactions among the 11 significantly associated SNPs, and the highest testing accuracy was 50.45%. However, the failure to identify interactions could be due to the limited number of loci examined and the relatively small sample size.

This study had some limitations. First, we included only individuals from Nanshan's Han Chinese population, and the number of participants may not have been sufficiently large to detect small effects. Therefore, the identified associations should be confirmed in larger studies involving different ethnicities. Second, healthy controls were evaluated for LTBI using IGRA but not the Mantoux tuberculin skin test (TST). Although both IGRA and TST are recommended by the WHO¹⁶, we employed IGRA because all participants were vaccinated with Bacillus Calmette-Guerin (BCG). Third, only a limited number of SNPs were identified. The key role of the PI3K/AKT/mTOR pathway in PTB pathology suggests that studies identifying additional risk loci and haplotypes in this pathway are worthwhile.

In summary, this study of PTB and control groups drawn from the Han Chinese population of Nanshan, Shenzhen found significant associations between the risk of developing PTB and polymorphisms in genes related to the PI3K/AKT/mTOR pathway. Based on the known functions of the encoded proteins, we suggest that polymorphisms in AKT1, RPTOR, and TSC2 may alter PTB susceptibility by modifying autophagy-mediated immune responses to Mtb. Additionally, a reduction in the GRS was associated with an increased risk of PTB. Studies with larger sample sizes and ethnically diverse populations are required to confirm these associations.

REFERENCES

• ¹ World Health Organization (WHO). Global tuberculosis programme. Global tuberculosis report 2022. Geneva: WHO; 2022. 13 p.
• ² Vynnycky E, Fine PE. Lifetime risks, incubation period, and serial interval of tuberculosis. Am J Epidemiol. 2000;152(3):247-63.
• ³ Takiff HE. Host Genetics and Susceptibility. In: Palomino JC, Leão SC, Ritacco V, editors. Tuberculosis 2007: From basic science to patient care. 1st ed. Brazil: Flying Publisher; 2007. p. 207-62.
• ⁴ Pellegrini JM, Tateosian NL, Morelli MP, García VE. Shedding Light on Autophagy During Human Tuberculosis. A Long Way to Go. Front Cell Infect Microbiol. 2021;11:820095.
• ⁵ Xu Z, Han X, Ou D, Liu T, Li Z, Jiang G, et al. Targeting PI3K/AKT/mTOR-mediated autophagy for tumor therapy. Appl Microbiol Biotechnol. 2020;104(2):575-87.
• ⁶ Balsat M, Cornillon J. m-TOR inhibitors: biology and use in the treatment of haematological diseases. Bull Cancer. 2011;98(8):935-43.
• ⁷ Cao Y, Luo Y, Zou J, Ouyang J, Cai Z, Zeng X, et al. Autophagy and its role in gastric cancer. Clin Chim Acta. 2019;489:10-20.
• ⁸ Zhang X, Huang T, Wu Y, Peng W, Xie H, Pan M, et al. Inhibition of the PI3K-Akt-mTOR signaling pathway in T lymphocytes in patients with active tuberculosis. Int J Infect Dis IJID Off Publ Int Soc Infect Dis. 2017;59:110-7.
• ⁹ Hu Y, Wen Z, Liu S, Cai Y, Guo J, Xu Y, et al. Ibrutinib suppresses intracellular mycobacterium tuberculosis growth by inducing macrophage autophagy. J Infect. 2020;80(6):e19-26.
• ¹⁰ Cerni S, Shafer D, To K, Venketaraman V. Investigating the Role of Everolimus in mTOR Inhibition and Autophagy Promotion as a Potential Host-Directed Therapeutic Target in Mycobacterium tuberculosis Infection. J Clin Med. 2019;8(2):232.
• ¹¹ Singh P, Subbian S. Harnessing the mTOR Pathway for Tuberculosis Treatment. Front Microbiol. 2018;9:70.
• ¹² Ding C, Jin S. High-throughput methods for SNP genotyping. Methods Mol Biol. 2009;578:245-54.
• ¹³ Ge Y, Liu H, Qiu X, Ma G, Wang H, Du M, et al. Genetic variants in PI3K/Akt/mTOR pathway genes contribute to gastric cancer risk. Gene. 2018;670:130-5.
• ¹⁴ Chen M, Cassidy A, Gu J, Delclos GL, Zhen F, Yang H, et al. Genetic variations in PI3K-AKT-mTOR pathway and bladder cancer risk. Carcinogenesis. 2009;30(12):2047-52.
• ¹⁵ Wang M, Ma SJ, Wu XY, Zhang X, Abesig J, Xiao ZH, et al. Impact of mTOR gene polymorphisms and gene-tea interaction on susceptibility to tuberculosis. World J Clin cases. 2020;8(19):4320-30.
• ¹⁶ World Health Organization (WHO). Global tuberculosis programme. Guidelines on the Management of Latent Tuberculosis Infection. Geneva: WHO ; 2015. 8 p.
• ¹⁷ González JR, Armengol L, Solé X, Guinó E, Mercader JM, Estivill X, et al. SNPassoc: an R package to perform whole genome association studies. Bioinformatics. 2007;23(5):644-5.
• ¹⁸ Tian C, Fang S, Du X, Jia C. Association of the C47T polymorphism in SOD2 with diabetes mellitus and diabetic microvascular complications: a meta-analysis. Diabetologia. 2011;54(4):803-11.
• ¹⁹ Fujii R, Hishida A, Nakatochi M, Furusyo N, Murata M, Tanaka K, et al. Association of genetic risk score and chronic kidney disease in a Japanese population. Nephrology (Carlton) . 2019;24(6):670-3.
• ²⁰ Xu HM, Xu LF, Hou TT, Luo LF, Chen GB, Sun XW, et al. GMDR: Versatile Software for Detecting Gene-Gene and Gene-Environ- ment Interactions Underlying Complex Traits. Curr Genomics. 2016;17(5):396-402.
• ²¹ Purcell S, Neale B, Todd-Brown K, Thomas L, Ferreira MA, Bender D, et al. PLINK: a tool set for whole-genome association and population-based linkage analyses. Am J Hum Genet. 2007;81(3):559-75.
• ²² Arranz A, Doxaki C, Vergadi E, Martinez de la Torre Y, Vaporidi K, Lagoudaki ED, et al. Akt1 and Akt2 protein kinases differentially contribute to macrophage polarization. Proc Natl Acad Sci U S A. 2012;109(24):9517-22.
• ²³ Sha S, Shi Y, Tang Y, Jia L, Han X, Liu Y, et al. Mycobacterium tuberculosis Rv1987 protein induces M2 polarization of macrophages through activating the PI3K/Akt1/mTOR signaling pathway. Immunol Cell Biol. 2021;99(6):570-85.
• ²⁴ Wang X, Cao Z, Jiang J, Zhu Y, Dong M, Tong A, et al. AKT1 polymorphisms are associated with tuberculosis in the Chinese population. Int J Immunogenet. 2010;37(2):97-101.
• ²⁵ Pu X, Hildebrandt MAT, Lu C, Lin J, Stewart DJ, Ye Y, et al. PI3K/PTEN/AKT/mTOR pathway genetic variation predicts toxicity and distant progression in lung cancer patients receiving platinum-based chemotherapy. Lung Cancer. 2011;71(1):82-8.
• ²⁶ Li H, Guo J, Cheng G, Wei Y, Liu S, Qi Y, et al. Identification and Validation of SNP-Containing Genes With Prognostic Value in Gastric Cancer via Integrated Bioinformatics Analysis. Front Oncol. 2021;11:564296.
• ²⁷ El Ahanidi H, El Azzouzi M, Arrouchi H, Alaoui CH, Tetou M, Bensaid M, et al. AKT1 and PIK3CA activating mutations in Moroccan bladder cancer patients´ biopsies and matched urine. Pan Afr Med J. 2022;41:59.
• ²⁸ Xu L, Huang Q, Wang H, Hao Y, Bai Q, Hu J, et al. The Kinase mTORC1 Promotes the Generation and Suppressive Function of Follicular Regulatory T Cells. Immunity. 2017;47(3):538-551.e5.
• ²⁹ Chen YC, Hsiao CC, Chen TW, Wu CC, Chao TY, Leung SY, et al. Whole Genome DNA Methylation Analysis of Active Pulmonary Tuberculosis Disease Identifies Novel Epigenotypes: PARP9/miR-505/RASGRP4/GNG12 Gene Methylation and Clinical Phenotypes. Int J Mol Sci. 2020;21(9):3180.
• ³⁰ Lu X, Liu M, Liao Y, Huang C, Chai L, Jin Y, et al. Meta-analysis of the association between mTORC1-related genes polymorphisms and cancer risk. Pathol Res Pract. 2022;229:153696.
• ³¹ Xu X, Qian D, Liu H, Cruz D, Luo S, Walsh KM, et al. Genetic variants in the liver kinase B1-AMP-activated protein kinase pathway genes and pancreatic cancer risk. Mol Carcinog. 2019;58(8):1338-48.
• ³² Morris BJ, Carnes BA, Chen R, Donlon TA, He Q, Grove JS, et al. Genetic variation in the raptor gene is associated with overweight but not hypertension in American men of Japanese ancestry. Am J Hypertens. 2015;28(4):508-17.
• ³³ Linke M, Pham HTT, Katholnig K, Schnöller T, Miller A, Demel F, et al. Chronic signaling via the metabolic checkpoint kinase mTORC1 induces macrophage granuloma formation and marks sarcoidosis progression. Nat Immunol. 2017;18(3):293-302.
• ³⁴ Racanelli AC, Kikkers SA, Choi AMK, Cloonan SM. Autophagy and inflammation in chronic respiratory disease. Autophagy. 2018;14(2):221-32.
• ³⁵ Wang T, Wang J, Ma Y, Zhou H, Ding D, Li C, et al. High genetic burden in 163 Chinese children with status epilepticus. Seizure. 2021;84:40-6.
• ³⁶ Wang J, Yang K, Zhou L, Minhaowu, Wu Y, Zhu M, et al. MicroRNA-155 promotes autophagy to eliminate intracellular mycobacteria by targeting Rheb. PLoS Pathog. 2013;9(10):e1003697.
• ³⁷ Gupta G, Deval R, Mishra A, Upadhyay S, Singh PK, Rao VR. Re-testing reported significant SNPs related to suicide in a historical high-risk isolated population from north east India. Hereditas. 2020;157(1):31.

• *Juan He and Shengyuan Liu contributed equally to this work.
• All authors read and approved the final version of the manuscript.
• Financial Support: This work was supported by the Sanming project of Medicine in Shenzhen (grant number SZSM201603029), the Natural Science Foundation of Guangdong Province (grant number 2018A030313123), the Nanshan Science and Technology Innovation Bureau (grant number NS2022100) and the Key Disciplines of Medicine in Nanshan District.

Publication Dates

• Publication in this collection
  24 July 2023
• Date of issue
  2023

History

• Received
  04 May 2023
• Accepted
  19 May 2023