Associations of genetic variants within TYK2 with pulmonary tuberculosis among Chinese population
Mingwu Zhang and Zhengwei Liu contributed equally to the present study.
Abstract
Background
Pulmonary tuberculosis (PTB) is a common infectious disease caused by mycobacterium tuberculosis (MTB) and the present study aims to explore the associations of genetic variants within tyrosine kinases 2 (TYK2) with PTB incidence.
Methods
A population-based case control study including 168 smear-positive PTB cases and 251 controls was conducted. Five single nucleotide polymorphisms (SNPs) including rs280520, rs91755, rs2304256, rs12720270, rs280519 located within TYK2 gene were selected and MassARRAY® MALDI-TOF system was employed for genotyping. SPSS 19.0 was adopted for statistical analysis, non-conditional logistic regression was conducted. Odds ratios (ORs) and 95% confidence intervals (95% CIs) were computed to estimate their contributions to PTB incidence.
Results
In the overall study population, rs91755 TT and rs280519 AA genotypes were found to be associated with reduced PTB risk (OR = 0.34, 95% CI: 0.16–0.72; OR = 0.38, 95% CI: 0.18–0.79, respectively). After stratification for sex, we found that among the male population, rs91755TG/TT, rs12720270AG/GG and rs280519AG/AA genotypes were associated with reduced PTB risk (OR = 0.41, 95% CI: 0.21–0.80; OR = 0.44, 95% CI: 0.21–0.94; OR = 0.42, 95% CI: 0.21–0.82, respectively). After stratification for age, we found that among those aged <60 years, rs91755TT and rs280519AA genotype were associated with reduced PTB risk (OR = 0.29, 95% CI: 0.09–0.90; OR = 0.34, 95% CI: 0.11–1.08, respectively); while rs2304256AC/AA genotype was associated with increased PTB risk (OR = 2.68, 95% CI: 1.05–6.85). Haplotype analysis revealed that AGAAG and ATCGA (Combined with rs280520, rs91755, rs2304256, rs12720270 and rs280519) were associated with increased (OR = 1.54, 95% CI: 1.01–2.37) and decreased PTB risk (OR = 0.70, 95% CI: 0.52–0.94), respectively.
Conclusions
The genetic variants located within TYK2 including rs91755, rs12720270 and rs280519 were found to be associated with modified PTB risk and the SNPs had potential to be the biomarkers to predict PTB incidence risk.
1 BACKGROUND
Tuberculosis (TB) is an infectious disease caused by Mycobacterium tuberculosis (MTB). Global tuberculosis report for 2022 revealed that an estimated 10.6 million people became ill with TB, and 1.6 million people died from TB in 2021 (Bagcchi, 2022). Eight countries including China (8.4%) accounted for two-thirds of the global total (Chakaya et al., 2021). The countries in Asia and North Africa super region experienced a descending trend of TB mortality in the past decades, while the slope of this reduction is quite small (Salehi et al., 2021).
Previous study revealed that the pathogenesis of TB depends on interactions between the host and MTB strains (Smith et al., 2022). So the genetic variants locating within the encoding genes involved in TB infection and development might affect TB incidence. Just as the SNP in tumor necrosis factor (TNF)-receptor-associated factor 1/complement component 5 (TRAF1/C5) gene leads susceptibility to active pulmonary tuberculosis (PTB) by decreasing TNF-alpha expression, conferring increased bacteria load in lung tissues (Souza de Lima et al., 2021). The SNP located within Sirtuin3 (SIRT3), which plays pivotal roles in promoting anti-mycobacterial response of mitochondria and autophagy during MTB infection, was associated with TB incidence (Wu et al., 2020).
Tyrosine kinase 2 (TYK2, OMIM accession number: 176941) is a Janus kinase (JAK) involved in various signaling pathways, including responses to interleukin12 (IL-12), IL-23, interferon alpha/beta (IFN-α/β) and IL-10 (Strobl et al., 2011; Yamaoka et al., 2004). The homozygosity for TYK2 P1104A underlies TB in about 1% of patients in a cohort of European ancestry (Kerner et al., 2019), which means TYK2 might play key roles in TB incidence. However, few studies have focused on the associations of TYK2 polymorphisms with PTB in China. In the present study, single nucleotide polymorphisms (SNPs) of TYK2 were selected and their associations with PTB risk were analyzed.
2 METHODS
2.1 Ethical compliance
The study was approved by the ethics committee of Zhejiang Provincial Center for Disease Control and Prevention, and a signed informed consent form was obtained from each participant.
2.2 Study population
Just as reported in our previous study (Zhang et al., 2021), a population-based case–control study was conducted in Zhejiang province, China, and 168 newly diagnosed smear-positive PTB patients and 251 clinically diagnosed non-TB patients, and healthy subjects were enrolled from the same hospitals meanwhile. All the recruited controls declared they had no history of TB.
All the participants were required to complete a structured questionnaire, and information of the demographic factors including age, sex, occupation, smoking, alcohol drinking, family income, etc. was collected. As well as the other information about health condition and clinical history was asked to provide.
2.3 Genotyping
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Polymerase chain reaction (PCR)
PCR reactions were performed and each 5 μL PCR volume containing 1 μL (30 ng) DNA template.
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SPA reaction
Two microliter of SPA reaction buffer was added into each PCR well, incubating at 37°C for 40 min, at 85°C for 5 min, and at 4°C forever.
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Single base extension
Two microliter of iPlex reaction reagent was added into each PCR well and the reactions were performed according to the given parameter.
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Purification and measurement
Purified reaction products were spotted on sample plates. Draft data generated with TYPER software was used for genotyping.
| SNP no. | Primersa | Sequences (5′-3′) |
|---|---|---|
| rs280520 | P1 | ACGTTGGATGTGCTTACTGCTCCCACCTCT |
| P2 | ACGTTGGATGTGCTCAATGGGGAACTTTCG | |
| P3 | ACCTCTGCCACCTCC | |
| rs91755 | P1 | ACGTTGGATGGGCACACACCTGCTTTTTTG |
| P2 | ACGTTGGATGGCAATAAGCTGAGATCACGC | |
| P3 | GGGTGTTTGTATTTTTTAGACGGA | |
| rs2304256 | P1 | ACGTTGGATGTGTTTGGGAAGAAGGCCAAG |
| P2 | ACGTTGGATGTCACAGAAGTAGGCCCACAG | |
| P3 | AAGGCTCACAAGGCA | |
| rs12720270 | P1 | ACGTTGGATGTGCCACTAGAACCCTGAACA |
| P2 | ACGTTGGATGAGTGCAGGAGGTATAAACGG | |
| P3 | TGGCAGAGGATGGAGAG | |
| rs280519 | P1 | ACGTTGGATGGATGACTGCTTCTCTCTGCG |
| P2 | ACGTTGGATGCCCGCATGATGATGAGATTG | |
| P3 | GCCCCAACCAGGAGGTACGA |
- a P1 and P2 were adopted for PCR reaction and P3 for single base extension.
In total, 10% of the DNA samples were selected randomly and rechecked with same methods.
2.4 Statistical analysis
SPSS software version 19.0 was employed in the study for data analysis. Distributions of age, sex, smoking, alcohol drinking, and family income between case and control groups were compared with Chi-square test. Contributions of the selected genetic variants were estimated with non-conditional logistic regression. The factors including age, sex, smoking, alcohol drinking, and family income were included in the regression models as covariant, and odds ratios (ORs) and 95% confidence intervals (95% CIs) were computed. Haplotype analyses were performed with online software (http://analysis.bio-x.cn/myAnalysis.php) and those of frequency <0.03 were excluded. ORs and 95% CIs of the recruited haplotypes were computed to estimate their contribution to PTB incidence. A two-sided p-value <0.05 was considered to be significant.
3 RESULTS
3.1 Distributions of demographic factors and lifestyle-related factors
Distributions of demographic factors and lifestyle-related factors in case and control groups were presented in Table 2. In the study, significant differences were detected in terms of age, alcohol drinking, and family income between case and control groups, while the differences in terms of sex and smoking showed no significance.
| Variables | Control (n, %) | Case (n, %) | χ 2 | p-Value |
|---|---|---|---|---|
| Age | 4.690 | 0.030 | ||
| <60 years | 117 (46.6) | 60 (35.9) | ||
| ≥ 60 years | 134 (53.4) | 107 (64.1) | ||
| Sex | 0.039 | 0.844 | ||
| Male | 162 (64.5) | 110 (65.5) | ||
| Female | 89 (35.5) | 58 (34.5) | ||
| Smoking | 0.046 | 0.830 | ||
| No | 156 (62.2) | 91 (61.1) | ||
| Yes | 95 (37.8) | 58 (38.9) | ||
| Drinking | 7.828 | 0.005 | ||
| No | 155 (62.2) | 113 (75.8) | ||
| Yes | 94 (37.8) | 36 (24.2) | ||
| Family average income | 5.985 | 0.014 | ||
| <10,000 RMB | 66 (32.5) | 60 (45.8) | ||
| ≥10,000 RMB | 137 (67.5) | 71 (54.2) | ||
3.2 Association of SNPs with PTB risk
From Table 3, it can be seen in the overall study population that rs91755 TT genotype was associated with reduced PTB risk (OR = 0.34, 95% CI: 0.16–0.72, p = 0.005), while a marginal association was detected among those carrying rs91755 GT/TT genotype (OR = 0.61, 95% CI: 0.36–1.02, p = 0.061). Similarly, the SNP at rs280519 AA genotype was associated with reduced PTB risk (OR = 0.38, 95% CI: 0.18–0.79, p = 0.010), and marginal association was detected among those carrying AG/GG genotype (OR = 0.60, 95% CI: 0.36–1.01, p = 0.054). A marginal association was detected among those carrying rs2304256 AA genotype (OR = 1.90, 95% CI: 0.93–3.89, p = 0.079). Neither of the SNPs rs280520 and rs12720270 was associated with PTB risk.
| Variables | Control (n, %) | Case (n, %) | Adjusted ORs (95% CIs)a | p-Value |
|---|---|---|---|---|
| rs280520 | ||||
| AA | 79 (38.0) | 60 (36.4) | 1.00 (Reference) | |
| AG | 94 (45.2) | 72 (43.6) | 1.04 (0.61–1.76) | 0.888 |
| GG | 35 (16.8) | 33 (20.0) | 1.16 (0.58–2.32) | 0.676 |
| AG/GG | 129 (62.0) | 105 (63.6) | 1.05 (0.67–1.65) | 0.830 |
| rs91755 | ||||
| GG | 59 (28.4) | 59 (35.8) | 1.00 (Reference) | |
| GT | 96 (46.2) | 84 (50.9) | 0.75 (0.44–1.30) | 0.309 |
| TT | 53 (25.4) | 22 (13.3) | 0.34 (0.16–0.72) | 0.005 |
| GT/TT | 149 (71.6) | 106 (64.2) | 0.61 (0.36–1.02) | 0.061 |
| rs2304256 | ||||
| CC | 61 (29.5) | 37 (22.6) | 1.00 (Reference) | |
| AC | 103 (49.8) | 86 (52.4) | 1.54 (0.85–2.80) | 0.157 |
| AA | 43 (20.8) | 41 (25.0) | 1.90 (0.93–3.89) | 0.079 |
| AC/AA | 146 (70.5) | 127 (77.4) | 1.64 (0.93–2.89) | 0.090 |
| rs12720270 | ||||
| GG | 67 (32.4) | 43 (26.2) | 1.00 (Reference) | |
| AG | 101 (48.8) | 83 (50.6) | 0.87 (0.46–1.66) | 0.681 |
| AA | 39 (18.8) | 38 (23.2) | 0.61 (0.30–1.23) | 0.167 |
| AG/AA | 140 (67.6) | 121 (73.8) | 0.77 (0.42–1.40) | 0.385 |
| rs280519 | ||||
| GG | 59 (28.5) | 59 (36.0) | 1.00 (Reference) | |
| AG | 100 (48.3) | 82 (50.0) | 0.71 (0.41–1.22) | 0.212 |
| AA | 48 (23.2) | 23 (14.0) | 0.38 (0.18–0.79) | 0.010 |
| AG/GG | 148 (71.5) | 105 (64.0) | 0.60 (0.36–1.01) | 0.054 |
- a Adjusted for age, sex, smoking, drinking, and family income.
After stratification by sex, associations of the genetic variants with PTB incidence among male and female population are determined and shown in Table 4.
| Male | Female | |||||||
|---|---|---|---|---|---|---|---|---|
| Variables | Control (n, %) | Case (n, %) | Adjusted ORs (95% CIs)a | p-Value | Control (n, %) | Case (n, %) | Adjusted ORs (95% CIs)a | p-Value |
| rs280520 | ||||||||
| AA | 46 (37.4) | 36 (33.6) | 1.00 (Reference) | 33 (38.8) | 24 (41.4) | 1.00 (Reference) | ||
| AG | 58 (47.2) | 50 (46.7) | 1.09 (0.55–2.16) | 0.798 | 36 (42.4) | 22 (37.9) | 1.02 (0.43–2.40) | 0.965 |
| GG | 19 (15.4) | 21 (19.7) | 1.50 (0.62–3.67) | 0.371 | 16 (18.8) | 12 (20.7) | 0.97 (0.30–3.15) | 0.954 |
| AG/GG | 77 (62.6) | 71 (66.4) | 1.19 (0.63–2.25) | 0.586 | 52 (61.2) | 34 (58.6) | 1.01 (0.45–2.24) | 0.989 |
| rs91755 | ||||||||
| GG | 33 (26.8) | 41 (38.3) | 1.00 (Reference) | 26 (30.6) | 18 (31.0) | 1.00 (Reference) | ||
| TG | 55 (44.7) | 52 (48.6) | 0.52 (0.26–1.07) | 0.075 | 41 (48.2) | 32 (55.2) | 1.25 (0.51–3.10) | 0.626 |
| TT | 35 (28.5) | 14 (13.1) | 0.23 (0.09–0.58) | 0.002 | 18 (21.2) | 8 (13.8) | 0.62 (0.18–2.19) | 0.459 |
| TG/TT | 90 (73.2) | 66 (61.7) | 0.41 (0.21–0.80) | 0.009 | 59 (69.4) | 40 (69.0) | 1.07 (0.45–2.55) | 0.888 |
| rs2304256 | ||||||||
| CC | 36 (29.3) | 26 (24.3) | 1.00 (Reference) | 25 (29.8) | 11 (19.3) | 1.00 (Reference) | ||
| AC | 62 (50.4) | 48 (44.9) | 1.20 (0.56–2.55) | 0.643 | 41 (48.8) | 38 (66.7) | 2.41 (0.89–6.54) | 0.084 |
| AA | 25 (20.3) | 33 (30.8) | 2.63 (1.10–6.34) | 0.031 | 18 (21.4) | 8(14.0) | 0.88 (0.22–3.63) | 0.864 |
| AC/AA | 87 (70.7) | 81 (75.7) | 1.55 (0.77–3.15) | 0.223 | 59 (70.2) | 46 (80.7) | 1.98 (0.74–5.24) | 0.172 |
| rs12720270 | ||||||||
| AA | 21 (17.1) | 30 (28.0) | 1.00 (Reference) | 18 (21.4) | 8 (14.0) | 1.00 (Reference) | ||
| AG | 63 (51.2) | 48 (44.9) | 0.46 (0.20–1.04) | 0.062 | 38 (45.2) | 35 (61.4) | 2.80 (0.81–9.66) | 0.103 |
| GG | 39 (31.7) | 29 (27.1) | 0.41 (0.17–0.99) | 0.048 | 28 (33.4) | 14 (24.6) | 1.27 (0.32–5.03) | 0.729 |
| AG/GG | 102 (82.9) | 77 (72.0) | 0.44 (0.21–0.94) | 0.035 | 76 (78.6) | 49 (86.0) | 2.16 (0.65–7.19) | 0.209 |
| rs280519 | ||||||||
| GG | 34 (27.6) | 41 (38.3) | 1.00 (Reference) | 25 (29.8) | 18 (31.6) | 1.00 (Reference) | ||
| AG | 59 (48.0) | 51 (47.7) | 0.50 (0.25–1.02) | 0.056 | 41 (48.8) | 31 (54.4) | 1.13 (0.45–2.84) | 0.788 |
| AA | 30 (24.4) | 15 (14.0) | 0.27 (0.10–0.68) | 0.006 | 18 (21.4) | 8 (14.0) | 0.58 (0.16–2.07) | 0.405 |
| AG/AA | 89 (82.4) | 66 (61.7) | 0.42 (0.21–0.82) | 0.011 | 59 (70.2) | 39 (68.4) | 0.97 (0.40–2.34) | 0.947 |
- a Adjusted for age, family income, smoking, and drinking.
Among the male population, those carrying rs91755 TT (OR = 0.23, 95%CI: 0.09–0.58, p = 0.002) and TG/TT (OR = 0.41, 95%CI: 0.21–0.80, p = 0.009); those carrying rs12720270 GG (OR = 0.41, 95% CI: 0.17–0.99, p = 0.048) and AG/GG (OR = 0.44, 95% CI: 0.21–0.94, p = 0.035) also showed the reduced PTB risk; for those carrying rs280519AA (OR = 0.27, 95% CI: 0.10–0.68, p = 0.006) and AG/AA (OR = 0.42, 95% CI: 0.21–0.82, p = 0.011), reduced risk was detected. No significant association was detected in terms of the SNPs at rs280520 and rs2304256. Among the female population, none of the SNPs were found to be associated with PTB risk.
After stratification by age, associations of the genetic variants with PTB risk among those aged <60 years and ≥60 years were determined and are shown in Table 5.
| Age < 60 years | Age ≥ 60 years | |||||||
|---|---|---|---|---|---|---|---|---|
| Variables | Control (n, %) | Case (n, %) | Adjusted ORs (95% CIs)a | p-Value | Control (n, %) | Case (n, %) | Adjusted ORs (95% CIs)a | p-Value |
| rs280520 | ||||||||
| AA | 38 (35.8) | 20 (33.9) | 1.00 (Reference) | 41 (40.2) | 40 (38.1) | 1.00 (Reference) | ||
| AG | 50 (47.2) | 26 (44.1) | 0.82 (0.38–1.80) | 0.623 | 44 (43.1) | 45 (42.9) | 1.04 (0.50–2.19) | 0.917 |
| GG | 18 (17.0) | 13 (22.0) | 0.34 (0.11–1.08) | 0.066 | 17 (16.7) | 20 (19.0) | 0.94 (0.34–2.56) | 0.895 |
| AG/GG | 68 (64.2) | 39 (66.1) | 0.66 (0.31–1.39) | 0.276 | 61 (59.8) | 65 (61.9) | 1.01 (0.50–2.03) | 0.975 |
| rs91755 | ||||||||
| GG | 32 (30.2) | 22 (37.3) | 1.00 (Reference) | 27 (26.5) | 36 (34.3) | 1.00 (Reference) | ||
| TG | 48 (45.3) | 32 (54.2) | 0.87 (0.40–1.89) | 0.716 | 48 (47.0) | 52 (49.5) | 0.71 (0.33–1.57) | 0.400 |
| TT | 26 (24.5) | 5 (8.5) | 0.29 (0.09–0.90) | 0.033 | 27 (26.5) | 17 (16.2) | 0.37 (0.13–1.03) | 0.057 |
| TG/TT | 74 (69.8) | 37 (62.7) | 0.65 (0.31–1.36) | 0.252 | 75 (73.5) | 69 (65.7) | 0.60 (0.28–1.26) | 0.174 |
| rs2304256 | ||||||||
| CC | 31 (29.2) | 7 (12.1) | 1.00 (Reference) | 30 (29.7) | 30 (28.6) | 1.00 (Reference) | ||
| AC | 52 (49.1) | 35 (60.3) | 2.68 (1.01–7.10) | 0.048 | 51 (50.5) | 51 (48.6) | 1.20 (0.53–2.74) | 0.665 |
| AA | 23 (21.7) | 16 (27.6) | 2.70 (0.88–8.24) | 0.082 | 20 (19.8) | 24 (22.8) | 1.53 (0.57–4.11) | 0.402 |
| AC/AA | 75 (70.8) | 51 (87.9) | 2.68 (1.05–6.85) | 0.039 | 71 (70.3) | 75 (71.4) | 1.29 (0.59–2.82) | 0.519 |
| rs12720270 | ||||||||
| AA | 21 (19.8) | 15 (25.9) | 1.00 (Reference) | 18 (17.8) | 22 (21.0) | 1.00 (Reference) | ||
| AG | 52 (49.1) | 34 (58.6) | 1.03 (0.41–2.57) | 0.946 | 49 (48.5) | 49 (46.7) | 0.87 (0.34–2.19) | 0.760 |
| GG | 33 (31.1) | 9 (15.5) | 0.42 (0.14–1.26) | 0.122 | 34 (33.7) | 34 (32.3) | 0.78 (0.29–2.07) | 0.612 |
| AG/GG | 85 (80.2) | 43 (74.1) | 0.77 (0.32–1.84) | 0.561 | 83 (82.2) | 83 (79.0) | 0.83 (0.35–1.97) | 0.668 |
| rs280519 | ||||||||
| GG | 33 (31.1) | 22 (37.9) | 1.00 (Reference) | 26 (25.7) | 36 (34.3) | 1.00 (Reference) | ||
| AG | 50 (47.2) | 31 (53.4) | 0.82 (0.38–1.80) | 0.623 | 50 (49.5) | 51 (48.6) | 0.66 (0.30–1.45) | 0.303 |
| AA | 23 (21.7) | 5 (8.7) | 0.34 (0.11–1.08) | 0.066 | 25 (24.8) | 18 (17.1) | 0.40 (0.14–1.12) | 0.080 |
| AG/AA | 73 (68.9) | 36 (91.3) | 0.66 (0.31–1.39) | 0.276 | 75 (74.3) | 69 (65.7) | 0.58 (0.27–1.22) | 0.150 |
- a Adjusted for sex, family income, smoking, and drinking.
Among those aged <60 years, a reduced PTB risk was detected for the rs91755 TT genotype carriers (OR = 0.29, 95% CI: 0.09–0.90, p = 0.033), while increased risk was detected among those carrying rs2304256 AC genotype (OR = 2.68, 95% CI: 1.01–7.10, p = 0.048) and rs2304256 AC/AA genotype (OR = 2.68, 95% CI: 1.05–6.85, p = 0.039). No significant associations were detected about the other SNPs with PTB risk among the population.
Among those aged ≥60 years, no significant association was detected in terms of the five SNPs.
3.3 Associations of haplotypes with PTB risk
Associations of PTB risk with the recruited haplotypes are summarized in Table 6.
| Haplotypea | Case (%) | Control (%) | OR (95% CI) | p-Value |
|---|---|---|---|---|
| AGAAG | 52.6 (16.0) | 45.22 (10.9) | 1.54 (1.01–2.37) | 0.046 |
| ATCGA | 127.0 (38.7) | 193.7 (46.8) | 0.70 (0.52–0.94) | 0.019 |
| GGAAG | 106.4 (32.5) | 127.78 (30.9) | 1.06 (0.78–1.46) | 0.706 |
| GGCGG | 30.56 (9.3) | 29.00 (7.0) | 1.35 (0.81–2.30) | 0.265 |
- a The haplotypes were combined with rs280520, rs91755, rs2304256, rs12720270, and rs280519.
From Table 6, we can see that, while all the five SNPs were included, haplotypes (containing the SNPs of rs280520, rs91755, rs2304256, rs12720270, and rs280519) of AGAAG and ATCGA were associated with modified PTB risk (OR = 1.54, 95% CI: 1.01–2.37, p = 0.046; 0.70, 95% CI: 0.52–0.94, p = 0.019, respectively).
4 DISCUSSION
TYK2 is a vital activator of STAT1/2 associated with interleukin-6 (IL-6), IL-10, IL-12, and IL-23 receptors, playing key roles in the activation of these cytokine pathways and immunity regulation (Ghoreschi et al., 2009; O'Shea & Plenge, 2012). In the present study, contributions of some genetic variants within TYK2 gene to PTB risk were analyzed. We found that rs91755 TT (OR = 0.34, p = 0.005) and rs280519 AA (OR = 0.38, p = 0.010) were associated with reduced PTB risk in overall population. After stratification by sex and age, the genotypes of rs91755TT (OR = 0.23, p = 0.002) and TG/TT (OR = 0.41, p = 0.009), rs12720270GG (OR = 0.41, p = 0.048) and AG/GG (OR = 0.44, p = 0.035), rs280519AA (OR = 0.27, p = 0.006) and AG/AA (OR = 0.42, p = 0.011) were found to be associated with reduced PTB risk in male population, while among those aged <60 years, the SNP of rs280520 GG (OR = 0.34, p = 0.066) and rs91755TT (OR = 0.29, p = 0.033) were associated with reduced PTB risk, while rs2304256AC (OR = 2.68, p = 0.048) and AC/AA (OR = 2.68, p = 0.039) were associated with increased risk. Haplotype AGAAG was associated with increased (OR = 1.54, p = 0.046), while ATCGA was associated with reduced PTB risk (OR = 0.70, p = 0.019). These findings indicated the importance of TYK2 on PTB incidence and confirmed that the SNPs might have functions on TYK2 protein. It was helpful to provide much more clues for understanding the potential molecular mechanism of PTB incidence.
Family income was an important factor that relate to social-economic status, which is confirmed to be implicated in TB incidence, development, treatment, and prognosis (Jeong et al., 2023; Silva et al., 2021). The families with higher income means the members of the family have more opportunities to achieve social, economic, and medical support from society. Alcohol drinking and smoking were common lifestyle-related factors that relate to health (Soh et al., 2017; Sureshchandra et al., 2019). So these two factors together with age and sex, were taken as covariant and included in the regression models, and stratification for sex and age was conducted while the logistic regression was performed.
TYK2 was found to be involved in some immune-regulation-related diseases and responsible for the disease incidence and development, including rheumatoid arthritis, systemic sclerosis, and type 1 diabetes mellitus, etc. (Pellenz, Dieter, Lemos, et al., 2021). In our present study, both rs280519 and rs91755 showed protective effect on PTB, and the AA and TT genotype carriers of the two SNPs had almost 60% of reduced risk, respectively. The SNP of rs280519 was detected to be significantly associated with susceptibility to SLE both in Caucasians and Asians (Lee & Bae, 2016). These studies indicated that the SNPs might be functional ones that implicated in these diseases.
As biological factors, age and gender were validated to be implicated in TB incidence (Peer et al., 2022). In addition, it was reported that approximately 70% of PTB inpatients had at least one type of comorbidity, and age and gender were involved in comorbidity of PTB (Kang et al., 2020). So in the present study, stratifications for age and sex were conducted. We found that after stratification for sex, protective effects of rs91755, rs12720270, and rs280519 were detected to be focused on male, but not on female population. The findings might be derived from the internal or external differences between different genders, just as it was reported that frequency of cigarette smoking among men was higher than that in women, and smoking was regarded to be a determinant factor for gender differences in TB incidence (Abedi et al., 2019).
In our present study, just a marginal significance was detected among the overall population between the SNP of rs2304256 and PTB incidence risk. However, after stratification for age, significant association was detected among those aged <60 years, and the rs2304256 AC/AA genotype carriers had an increased PTB risk, while the rs91755 TT genotype carriers showed a reduced incidence risk. In another study conducted among Chinese Han population, association of rs2304256 with systemic sclerosis showed no significance (Liu et al., 2021). While the study in Brazilian population revealed that rs2304256 AA genotype SNP was associated with protection against type 1 diabetes mellitus (Pellenz, Dieter, Duarte, et al., 2021). About the SNP of rs280520, no significant association was detected among the overall population in our present study, while a marginal significance was detected among those aged <60 years but not among those aged more than 60 years.
Peng et al. discovered that a series of SNPs in TYK2 including rs280500, rs280521, and rs8108236 were associated with rheumatoid arthritis among Chinese population (Peng et al., 2021) and verified that TYK2 gene makes functional roles in the physiological and pathological processes of some diseases. Another study on peripheral blood mononuclear cells revealed that the SNP rs280520 in TYK2 was associated with increased IFN-gamma production (van de Schoor et al., 2022). These studies provide much more information for us to understand the contributions as well as the potential molecular mechanism of the genetic variants to the disease incidence.
Haplotype analysis was a useful and effective tool to select multi-SNP markers and predict incidence risk of some diseases. In our present study, an online tool was employed, and contributions of some haplotypes to PTB risk were analyzed. We found that the haplotypes (containing the SNPs of rs280520, rs91755, rs2304256, rs12720270, and rs280519) of AGAAG and ATCGA were associated with modified PTB risk.
The findings in our study supported that TYK2 was a key regulator that related to TB incidence, and the previous studies also indicated the relation might be derived from the resistance of host to MTB infection. Just as the study that Wang et al. reported, low constitutive STAT3 derived from the TT/AA genotype/T-A haplotype (rs1053004-rs1053005) acts to down-regulate STAT3, leads to an enhanced mycobacterial infection or TB in high-risk individuals (Wang et al., 2021).
In a word, our present study revealed that some of the SNPs located within TYK2 gene were associated with PTB incidence and have potential to be biomarkers in predicting PTB risk. However, the small sample size might be a major limitation, so the findings of the present study needed to be validated in the further study with larger sample size. In addition, we think the detailed potential molecular roles that TYK2 gene and the SNPs implicated in PTB incidence should be a key point for us to achieve much more to fully understand the relation of TYK2 to PTB.
AUTHOR CONTRIBUTIONS
X.M.W., S.H.C., J.H.P., and L.Z. were responsible for study design and implementation; M.W.Z., Z.W.L., and K.Y.W. were responsible for sample collection and laboratory testing; Y.L.Z., B.C., and Y.P. were responsible for data analysis. All authors have read and approved the manuscript.
ACKNOWLEDGMENTS
Not Applicable.
FUNDING INFORMATION
The study was supported by Natural Science Foundation of Zhejiang Province in China (LTGY23H190002), and the Zhejiang Medical and Health Zhejiang M Project: Study on Molecular Mechanism and Risk Prediction of Pulmonary Tuberculosis Based on microRNA Regulation (2020keyan512), and Quality of life and its influencing factors of MDR-TB patient research (2015KYA056).
CONFLICT OF INTEREST STATEMENT
No conflict of interest to declare.
DECLARATION
A questionnaire was developed for this study and never published elsewhere.
ETHICS STATEMENT
This study was approved by the ethics committee of Zhejiang Provincial Center for Disease Control and Prevention.
CONSENT TO PARTICIPATE
A signed consent form was completed by each study participant.
CONSENT FOR PUBLICATION
All the authors were consent for publication.
Open Research
DATA AVAILABILITY STATEMENT
Supporting data can be acquired via correspondence with the corresponding author.
