# Association of Polymorphisms in NHEJ Pathway Genes with HIV-1 Infection and AIDS Progression in a Northern Chinese MSM Population

Authors: Xuelong Zhang; Xi Wang; Han Mo; Yuanting Hu; Yi Yang; Xun Yang; Jiawei Wu; Bangquan Liu; Lidan Xu; Haiming Sun; Xueyuan Jia; Ping Wang; Kaili Wang; Wenjing Sun; Songbin Fu; Yuandong Qiao
Journal: Disease Markers
 (2022)

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## Abstract

Background and Aims. Men who have sex with men (MSM) are at high risk of HIV infection. The nonhomologous end joining (NHEJ) pathway is the main way of double-stranded DNA break (DSB) repair in the higher eukaryotes and can repair the DSB timely at any time in cell cycle. It is also indicated that the NHEJ pathway is associated with HIV-1 infection since the DSB in host genome DNA occurs in the process of HIV-1 integration. The aim of the present investigation was to evaluate associations of single-nucleotide polymorphisms (SNPs) in NHEJ pathway genes with susceptibility to HIV-1 infection and AIDS progression among MSM residing in northern China. Methods. A total of 481 HIV-1 seropositive men and 493 HIV-1 seronegative men were included in this case-control study. Genotyping of 22 SNPs in NHEJ pathway genes was performed using the SNPscan™ Kit. Results. Positive associations were observed between XRCC6 rs132770 and XRCC4 rs1056503 genotypes and the susceptibility to HIV-1 infection. In gene-gene interaction analysis, significant SNP-SNP interactions of XRCC6 and XRCC4 genetic variations were found to play a potential role in the risk of HIV-1 infection. In stratified analysis, XRCC5 rs16855458 was significantly associated with CD4+ T cell counts in AIDS patients, whereas LIG4 rs1805388 was linked to the clinical phases of AIDS patients. Conclusions. NHEJ gene polymorphisms can be considered to be risk factors of HIV-1 infection and AIDS progression in the northern Chinese MSM population.


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## Body

## 1. Introduction

Acquired immune deficiency syndrome (AIDS) due to the infection of human immunodeficiency virus (HIV) is a chronic infectious disease and continues to be a major global public health issue. There were approximately 37.7 million people globally and 1.045 million people in China living with HIV by the end of 2020 [1]. The significant increase in the proportion of behavior spread of men who have sex with men (MSM) is the dominant pathway of all kinds of HIV infection routes. The individuals with different susceptibility to HIV infection and clinical disease progression arise from different genetic backgrounds of the host [2]. The finding of AIDS-related genes with single-nucleotide polymorphisms (SNPs) is an important breakthrough that can help us to explore the role of host genetic background in HIV infection, reveal the pathogenesis of AIDS, predict the disease process, and develop new drugs and vaccines [3].Double-stranded DNA break (DSB) is one of the main reasons for the gene mutation and chromosome break and plays an important role in tumorigenesis and progression of tumors [4]. The nonhomologous end joining (NHEJ) pathway is the main approach of DSB repair (DSBR) in the higher eukaryotes and can repair DSBs timely at any time in cell cycle [5, 6]. There are five core genes (XRCC7, XRCC6, XRCC5, XRCC4, and LIG4) in the NHEJ pathway that encodes five proteins (DNA-PK, Ku70, Ku80, XRCC4, and LIG4), respectively. Studies have shown that NHEJ gene polymorphisms are associated with susceptibility to a wide variety of cancers and disease progression. For instance, XRCC7 gene polymorphisms play an important role in prostate cancer [7], bladder cancer [8], liver cancer [9], thyroid cancer [10], and lung cancer [11]. The other gene polymorphisms such as XRCC4, XRCC5, XRCC6, and LIG4 SNPs are also associated with many different types of cancers [12–15].It has been indicated that the NHEJ pathway is associated with HIV-1 infection because the DSB in host genome DNA occurs in the process of HIV-1 integration [16]. However, the role of SNPs in NHEJ genes and their importance in HIV-1 infection and AIDS progression remain unclear. In this study, we conducted a case-control study in the northern Han Chinese population to investigate associations of 22 SNPs in XRCC7, XRCC6, XRCC5, XRCC4, and LIG4 genes with the risk of HIV-1 infection and the progression of AIDS. Furthermore, a gene-gene interaction analysis was conducted to explore the role of combined effects of SNPs in the risk of HIV-1 infection.


## 2. Materials and Methods

### 2.1. Subjects

A total of 481 HIV-1 seropositive men and 493 health controls were selected for this study. The study participants were all of Han descents and had lived in Harbin, Heilongjiang Province, in North China for at least three generations. All participants were not genetically related within three generations.481 HIV-1 seropositive men were recruited from Heilongjiang Center for Disease Control and Prevention. The age of the subjects ranged from 16 to 75 years (meanage±SD, 35.3±11.55), and the average CD4+ T lymphocyte count at that time point was 335 cells/μl (range, 3-1038 cells/μl). All subjects had acquired HIV-1 infection through male-male homosexual transmission. These patients were categorized as category 1 (T lymphocytes<350 cells/μl) or category 2 (T lymphocytes>350 cells/μl) by the CD4+ T lymphocyte count and as category A (clinical phase III+IV) or category B (clinical phase I+II) by the clinical stage.493 HIV-1 seronegative men age-matched to the HIV-1 patients were randomly selected as the control group from the comprehensive medical examination population of the Second Affiliated Hospital of Harbin Medical University. The age of the uninfected controls ranged from 16 to 75 years (meanage±SD, 35.3±11.59). All participants provided informed consent approved by local ethics review board.


### 2.2. SNP Selection and Genotyping

22 candidate SNPs in NHEJ pathway genes were included in the present study. Among them, two SNPs (rs7830743 and rs7003908) were fromXRCC7, four SNPs (rs132770, rs5751129, rs2267437, and rs132774) were from XRCC6, eight SNPs (rs828907, rs705649, rs16855458, rs3770502, rs9288516, rs3835, rs1051677, and rs2440) were from XRCC5, six SNPs (rs1056503, rs6869366, rs2075685, rs10040363, rs963248, and rs35268) were from XRCC4, and two SNPs (rs1805388 and rs1805389) were from LIG4.Genomic DNA was extracted from 200μl of peripheral blood of all participants using the QIAamp blood kit (Qiagen, Germany) according to the manufacturer’s protocol. All 22 SNPs were genotyped in 481 HIV-1-infected and 493 HIV-1-uninfected individuals using a custom-designed 48-Plex SNPscan™ Kit (supplied by Genesky Bio-technologies Inc., Shanghai, China), according to the method of high-throughput SNP genotyping utilizing double ligation and multiplex fluorescence PCR. For quality control, a 5% random sample of cases and controls was genotyped twice to verify the genotyping accuracy; the reproducibility was 100%.


### 2.3. Statistical Analysis

The genotype and allele frequencies were calculated through directly counting the numbers after the genotypes of the cases and controls were determined. A chi-square test was used for examining the deviation from Hardy-Weinberg’s equilibrium (HWE) for all SNPs of the control group, the association between genotype frequencies and susceptibility to HIV-1 infection, and the association between the genotype frequencies and clinical features (such as the CD4+ T lymphocyte count and clinical stage) of the case group. Odds ratios (ORs) and 95% confidence intervals (95% CI) were estimated as the relative risk associated with SNPs. The generalized multifactor dimensionality reduction (GMDR) software [17] was applied to assess SNP-SNP interactions. SPSS 23.0 software (IBM-SPSS, Inc., Chicago, IL, USA) was used for all statistical analyses. The analyses of linkage disequilibrium (LD) and the haplotype frequencies were performed using the HaploView software [18]. The differences with a P value less than 0.05 were considered statistically significant.


## 2.1. Subjects

A total of 481 HIV-1 seropositive men and 493 health controls were selected for this study. The study participants were all of Han descents and had lived in Harbin, Heilongjiang Province, in North China for at least three generations. All participants were not genetically related within three generations.481 HIV-1 seropositive men were recruited from Heilongjiang Center for Disease Control and Prevention. The age of the subjects ranged from 16 to 75 years (meanage±SD, 35.3±11.55), and the average CD4+ T lymphocyte count at that time point was 335 cells/μl (range, 3-1038 cells/μl). All subjects had acquired HIV-1 infection through male-male homosexual transmission. These patients were categorized as category 1 (T lymphocytes<350 cells/μl) or category 2 (T lymphocytes>350 cells/μl) by the CD4+ T lymphocyte count and as category A (clinical phase III+IV) or category B (clinical phase I+II) by the clinical stage.493 HIV-1 seronegative men age-matched to the HIV-1 patients were randomly selected as the control group from the comprehensive medical examination population of the Second Affiliated Hospital of Harbin Medical University. The age of the uninfected controls ranged from 16 to 75 years (meanage±SD, 35.3±11.59). All participants provided informed consent approved by local ethics review board.


## 2.2. SNP Selection and Genotyping

22 candidate SNPs in NHEJ pathway genes were included in the present study. Among them, two SNPs (rs7830743 and rs7003908) were fromXRCC7, four SNPs (rs132770, rs5751129, rs2267437, and rs132774) were from XRCC6, eight SNPs (rs828907, rs705649, rs16855458, rs3770502, rs9288516, rs3835, rs1051677, and rs2440) were from XRCC5, six SNPs (rs1056503, rs6869366, rs2075685, rs10040363, rs963248, and rs35268) were from XRCC4, and two SNPs (rs1805388 and rs1805389) were from LIG4.Genomic DNA was extracted from 200μl of peripheral blood of all participants using the QIAamp blood kit (Qiagen, Germany) according to the manufacturer’s protocol. All 22 SNPs were genotyped in 481 HIV-1-infected and 493 HIV-1-uninfected individuals using a custom-designed 48-Plex SNPscan™ Kit (supplied by Genesky Bio-technologies Inc., Shanghai, China), according to the method of high-throughput SNP genotyping utilizing double ligation and multiplex fluorescence PCR. For quality control, a 5% random sample of cases and controls was genotyped twice to verify the genotyping accuracy; the reproducibility was 100%.


## 2.3. Statistical Analysis

The genotype and allele frequencies were calculated through directly counting the numbers after the genotypes of the cases and controls were determined. A chi-square test was used for examining the deviation from Hardy-Weinberg’s equilibrium (HWE) for all SNPs of the control group, the association between genotype frequencies and susceptibility to HIV-1 infection, and the association between the genotype frequencies and clinical features (such as the CD4+ T lymphocyte count and clinical stage) of the case group. Odds ratios (ORs) and 95% confidence intervals (95% CI) were estimated as the relative risk associated with SNPs. The generalized multifactor dimensionality reduction (GMDR) software [17] was applied to assess SNP-SNP interactions. SPSS 23.0 software (IBM-SPSS, Inc., Chicago, IL, USA) was used for all statistical analyses. The analyses of linkage disequilibrium (LD) and the haplotype frequencies were performed using the HaploView software [18]. The differences with a P value less than 0.05 were considered statistically significant.


## 3. Results

### 3.1. Hardy-Weinberg Equilibrium Test

The success rates of genotyping were >98% for all SNPs. As shown in Table1, all 22 SNPs did not deviate from the Hardy-Weinberg equilibrium in the control group (P>0.05).Table 1
Hardy-Weinberg equilibrium test for 22 NHEJ gene SNPs in controls.
Gene
Chra
SNPs
Major/minor allele
P for HWETb
XRCC7
8
rs7830743
A/G
0.248
8
rs7003908
A/C
0.780
XRCC6
22
rs5751129
T/C
0.677
22
rs2267437
C/G
0.178
22
rs132770
G/A
0.468
22
rs132774
G/C
0.568
XRCC5
2
rs828907
G/T
0.307
2
rs705649
G/A
0.185
2
rs16855458
C/A
0.762
2
rs3770502
C/T
0.501
2
rs9288516
T/A
0.504
2
rs3835
G/A
0.529
2
rs1051677
T/C
0.920
2
rs2440
A/G
0.055
XRCC4
5
rs6869366
T/G
0.936
5
rs2075685
G/T
0.476
5
rs10040363
A/G
0.247
5
rs963248
C/T
0.127
5
rs35268
T/C
0.397
5
rs1056503
G/T
0.051
LIG4
13
rs1805388
G/A
0.810
13
rs1805389
G/A
0.994
aChr: chromosome; bHardy-Weinberg equilibrium test.


### 3.2. Associations of NHEJ Gene Polymorphisms with HIV-1 Infection

To explore the possible associations, the genotype distribution of 22 SNPs was investigated. Then, differences of genotype frequencies between cases and controls were analyzed under three genetic models (codominant model, dominant model, and recessive model). As shown in Figure1, a significant association was found for XRCC6 rs132770 under codominant (P=0.005, OR=10.51, 95% CI: 2.000-55.251) and recessive (P=0.006, OR=10.45, 95% CI: 1.986-54.933) genetic models. In addition, the genotype TT of XRCC4 rs1056503 showed significant association with increased susceptibility of HIV-1 infection in the codominant model (TT vs. GG, P=0.035, OR=1.698, 95% CI: 1.037-2.779) and recessive model (TT vs. TG+GG, P=0.028, OR=1.707, 95% CI: 1.060-2.750). However, no association with HIV-1 infection was observed in any genetic model for the remaining 20 SNPs (P>0.05).Figure 1
The genotype distribution map of NHEJ gene polymorphisms and association analysis of HIV-1 infection risk. The bar marked by the letters a and b corresponds to the ordinate of the minimum value of 0.2%. Codominant 1, the first column homozygote versus the third column homozygote. Codominant 2, heterozygote versus the third column homozygote. Bold italic values indicate statistical significance.


### 3.3. Analysis of the SNP-SNP Interaction

The GMDR method was used to study the association of 10 SNPs inXRCC6 and XRCC4 genes with high-order interactions on HIV-1 infection. Through a 10-fold cross-validation, the best four-locus model involving XRCC6 (rs2267437) and XRCC4 (rs10040363, rs963248, and rs1056503) was identified (Figure 2). In order to obtain the ORs for joint effects of the four SNPs on HIV-1 infection, traditional statistical methods were applied to this four-locus model to aid in interpretation, which identified three significant genotype combinations from all possible high-risk genotype combinations. In this four-locus (rs1056503-rs2267437-rs10040363-rs963248) model, the ORs for three significant high-risk genotype combinations (TT)-(CC)-(AG/GG)-(TC/CC), (TT)-(CC)-(AA)-(TC/CC), and (TT)-(CC)-(AA)-(TT) were 6.667 (P=0.035), 7.333 (P=0.026), and 6.667 (P=0.035), respectively (Table 2).Figure 2
The best four-locus SNP-SNP interaction model identified by the generalized multifactor dimensionality reduction method. High-risk cells are in dark, low-risk cells are in grey, and empty cells are indicated by no shading. In each cell, the left bar represents case while the right bar represents control. The heights of the bars are proportional to the sum of samples in each group. Note that the patterns of high-risk and low-risk cells differ across each of the different multilocus dimensions, presenting evidence of SNP-SNP interaction or epistasis.Table 2
Combined effects of rs1056503, rs2267437, rs10040363, and rs963248 on HIV-1 infection.
rs1056503
rs2267437
rs10040363
rs963248
P value
OR (95% CI)
TT
CG+GG
AG+GG
TC+CC
—
1.000
TT
CC
AG+GG
TC+CC
0.035
6.667 (1.145-38.833)
TT
CG+GG
AA
TC+CC
0.848
1.200 (0.185-7.770)
TT
CG+GG
AG+GG
TT
1.000
1.000 (0.125-7.995)
TT
CC
AA
TC+CC
0.026
7.333 (1.272-42.294)
TT
CC
AG+GG
TT
0.756
0.667 (0.051-8.639)
TT
CG+GG
AA
TT
0.642
24.556 (1.991-302.866)
TT
CC
AA
TT
0.035
6.667 (1.145-38.833)
Italicized values indicate statistical significance.


### 3.4. Analysis of Haplotype Associations

LD between SNPs in NHEJ genes was analyzed using HaploView software. There was strong LD among four SNPs inXRCC6 gene, eight SNPs in XRCC5 gene, six SNPs in XRCC4 gene, and two SNPs in LIG4 gene, respectively. There were no significant differences in frequencies of all haplotypes between HIV-1-infected cases and healthy controls (P>0.05). Table 3 shows all blocks and haplotypes identified and the frequencies of these haplotypes.Table 3
The frequencies of haplotypes of NHEJ genes in cases and controls.
Gene
Haplotype
Frequency
Haplotype frequencies in the cases
Haplotype frequencies in the controls
Chi-square
P
XRCC6
TCGG
0.671
0.665
0.677
0.309
0.578
TGGG
0.237
0.233
0.241
0.175
0.675
CCAC
0.070
0.081
0.060
3.181
0.075
CCGC
0.021
0.021
0.022
0.012
0.912
XRCC5
Block 1
GG
0.771
0.777
0.765
0.335
0.563
TA
0.223
0.219
0.227
0.169
0.681
Block 2
TG
0.468
0.470
0.466
0.040
0.841
AG
0.452
0.448
0.456
0.149
0.699
TA
0.080
0.082
0.078
0.115
0.735
Block 3
TA
0.693
0.705
0.683
1.101
0.294
CG
0.165
0.164
0.166
0.009
0.923
TG
0.142
0.132
0.152
1.649
0.199
XRCC4
Block 1
TG
0.806
0.806
0.805
0.007
0.931
TT
0.141
0.135
0.147
0.597
0.440
GT
0.053
0.059
0.048
1.095
0.295
Block 2
CT
0.555
0.536
0.573
2.552
0.110
TT
0.308
0.327
0.290
3.095
0.079
CC
0.133
0.132
0.134
0.020
0.888
LIG4
GG
0.814
0.815
0.812
0.035
0.852
AA
0.102
0.101
0.104
0.030
0.863
AG
0.084
0.084
0.084
0.005
0.941


### 3.5. Associations of NHEJ Gene SNPs with CD4+ T Cell Count and Clinical Phase in AIDS Patients

To investigate the relationship between NHEJ gene polymorphisms and AIDS progression, differences in allele frequencies were analyzed between subgroups of HIV-1-infected cases which were divided using CD4+ T lymphocyte count and clinical stage as indicators, respectively. The CD4+ T cell counts of HIV-1-infected cases ranged from 3 to 1038 cells/μl (mean±SD, 335.57±198.79). The associations between SNPs and CD4+ T cell counts were used to assess the influence of gene polymorphisms on the immunity status of patients. As shown in Table 4, there were significant differences in genotype frequencies between different subgroups of cases for XRCC5 rs16855458 and LIG4 rs1805388 (P<0.05). In detail, the subjects with AA or AC of rs16855458 have a significantly lower CD4+ T lymphocyte count, compared to subjects with CC genotype (P=0.025, OR=1.538, 95% CI: 1.054-2.243). The subjects with AA or AG of rs1805388 have a later clinical stage of AIDS, compared to subjects with GG genotype (P=0.036, OR=1.506, 95% CI: 1.027-2.209). However, other SNPs were not associated with the CD4+ T lymphocyte count and clinical stages (P>0.05). These results suggested that rs16855458 and rs1805388 were associated with the clinical features and progression of AIDS in the northern Chinese population.Table 4
Associations between 22 SNPs in NHEJ genes and clinical features of AIDS.
Gene
Gene polymorphisms
Genotype
CD4+ T lymphocyte counta
P
OR (95% CI)
Clinical stageb
P
OR (95% CI)
<350 cells/μl
>350 cells/μl
Phase III+IV
Phase I+II
XRCC7
rs7830743
GG+AG
37
35
0.550
0.858 (0.519-1.418)
29
43
0.438
0.817 (0.491-1.361)
AA
223
181
184
223
rs7003908
CC+CA
125
92
0.232
1.248 (0.868-1.795)
100
118
0.572
1.110 (0.773-1.594)
AA
135
124
113
148
XRCC6
rs5751129
CC+CT
49
39
0.825
1.054 (0.662-1.679)
40
48
0.837
1.050 (0.660-1.671)
TT
211
177
173
218
rs2267437
GG+CG
118
82
0.103
1.358 (0.940-1.961)
95
107
0.335
1.196 (0.831-1.723)
CC
142
134
118
159
rs132770
AA+AG
38
31
0.908
1.031 (0.617-1.722)
27
43
0.282
0.752 (0.448-1.264)
GG
220
185
187
224
rs132774
CC+CG
50
39
0.743
1.081 (0.679-1.719)
40
49
0.920
1.024 (0.645-1.627)
GG
210
177
173
217
XRCC5
rs828907
TT+GT
104
86
0.967
1.008 (0.697-1.457)
92
99
0.185
1.283 (0.888-1.853)
GG
156
130
121
167
rs705649
AA+GA
105
85
0.819
1.044 (0.722-1.509)
92
99
0.185
1.283 (0.888-1.853)
GG
155
131
121
167
rs16855458
AA+AC
109
69
0.025
1.538 (1.054-2.243)
87
92
0.160
1.306 (0.900-1.895)
CC
151
147
126
174
rs3770502
TT+CT
81
61
0.489
1.150 (0.774-1.708)
66
76
0.499
1.146 (0.772-1.700)
CC
179
155
144
190
rs9288516
AA+TA
183
145
0.445
1.164 (0.789-1.717)
144
185
0.649
0.914 (0.620-1.347)
TT
77
71
69
81
rs3835
AA+AG
36
35
0.495
0.839 (0.506-1.390)
33
39
0.804
1.066 (0.645-1.763)
GG
222
181
181
228
rs1051677
CC+TC
83
61
0.384
1.192 (0.803-1.768)
68
77
0.481
1.151 (0.778-1.703)
TT
177
155
145
189
rs2440
GG+AG
127
113
0.451
0.870 (0.607-1.249)
96
136
0.767
0.947 (0.660-1.358)
AA
133
103
107
130
XRCC4
rs6869366
GG+GT
36
20
0.124
1.575 (0.883-2.811)
23
33
0.587
0.855 (0.485-1.505)
TT
224
196
190
233
rs2075685
TT+GT
77
80
0.087
0.715 (0.487-1.050)
62
95
0.126
0.739 (0.502-1.089)
GG
183
136
151
171
rs10040363
GG+AG
72
66
0.510
0.875 (0.588-1.302)
59
79
0.613
0.902 (0.605-1.345)
AA
187
150
154
186
rs963248
TT+TC
136
132
0.054
0.698 (0.484-1.007)
114
155
0.298
0.825 (0.574-1.186)
CC
124
84
99
111
rs35268
CC+TC
70
51
0.409
1.192 (0.786-1.808)
60
61
0.191
1.318 (0.872-1.992)
TT
190
165
153
205
rs1056503
TT+TG
128
118
0.241
0.805 (0.561-1.156)
107
140
0.602
0.909 (0.633-1.303)
GG
132
98
106
126
LIG4
rs1805388
AA+AG
87
69
0.726
1.071 (0.729-1.574)
81
77
0.036
1.506 (1.027-2.209)
GG
173
147
132
189
rs1805389
AA+AG
52
39
0.591
1.135 (0.716-1.799)
48
45
0.124
1.429 (0.907-2.249)
GG
208
177
165
221
Italicized values indicate statistical significance.aThe CD4+ T lymphocyte counts were divided into two groups: category 1, <350 cells/μl, and category 2, >350 cells/μl. bClinical stage: category A, clinical phase III+IV, and category B, clinical phase I+II.


## 3.1. Hardy-Weinberg Equilibrium Test

The success rates of genotyping were >98% for all SNPs. As shown in Table1, all 22 SNPs did not deviate from the Hardy-Weinberg equilibrium in the control group (P>0.05).Table 1
Hardy-Weinberg equilibrium test for 22 NHEJ gene SNPs in controls.
Gene
Chra
SNPs
Major/minor allele
P for HWETb
XRCC7
8
rs7830743
A/G
0.248
8
rs7003908
A/C
0.780
XRCC6
22
rs5751129
T/C
0.677
22
rs2267437
C/G
0.178
22
rs132770
G/A
0.468
22
rs132774
G/C
0.568
XRCC5
2
rs828907
G/T
0.307
2
rs705649
G/A
0.185
2
rs16855458
C/A
0.762
2
rs3770502
C/T
0.501
2
rs9288516
T/A
0.504
2
rs3835
G/A
0.529
2
rs1051677
T/C
0.920
2
rs2440
A/G
0.055
XRCC4
5
rs6869366
T/G
0.936
5
rs2075685
G/T
0.476
5
rs10040363
A/G
0.247
5
rs963248
C/T
0.127
5
rs35268
T/C
0.397
5
rs1056503
G/T
0.051
LIG4
13
rs1805388
G/A
0.810
13
rs1805389
G/A
0.994
aChr: chromosome; bHardy-Weinberg equilibrium test.


## 3.2. Associations of NHEJ Gene Polymorphisms with HIV-1 Infection

To explore the possible associations, the genotype distribution of 22 SNPs was investigated. Then, differences of genotype frequencies between cases and controls were analyzed under three genetic models (codominant model, dominant model, and recessive model). As shown in Figure1, a significant association was found for XRCC6 rs132770 under codominant (P=0.005, OR=10.51, 95% CI: 2.000-55.251) and recessive (P=0.006, OR=10.45, 95% CI: 1.986-54.933) genetic models. In addition, the genotype TT of XRCC4 rs1056503 showed significant association with increased susceptibility of HIV-1 infection in the codominant model (TT vs. GG, P=0.035, OR=1.698, 95% CI: 1.037-2.779) and recessive model (TT vs. TG+GG, P=0.028, OR=1.707, 95% CI: 1.060-2.750). However, no association with HIV-1 infection was observed in any genetic model for the remaining 20 SNPs (P>0.05).Figure 1
The genotype distribution map of NHEJ gene polymorphisms and association analysis of HIV-1 infection risk. The bar marked by the letters a and b corresponds to the ordinate of the minimum value of 0.2%. Codominant 1, the first column homozygote versus the third column homozygote. Codominant 2, heterozygote versus the third column homozygote. Bold italic values indicate statistical significance.


## 3.3. Analysis of the SNP-SNP Interaction

The GMDR method was used to study the association of 10 SNPs inXRCC6 and XRCC4 genes with high-order interactions on HIV-1 infection. Through a 10-fold cross-validation, the best four-locus model involving XRCC6 (rs2267437) and XRCC4 (rs10040363, rs963248, and rs1056503) was identified (Figure 2). In order to obtain the ORs for joint effects of the four SNPs on HIV-1 infection, traditional statistical methods were applied to this four-locus model to aid in interpretation, which identified three significant genotype combinations from all possible high-risk genotype combinations. In this four-locus (rs1056503-rs2267437-rs10040363-rs963248) model, the ORs for three significant high-risk genotype combinations (TT)-(CC)-(AG/GG)-(TC/CC), (TT)-(CC)-(AA)-(TC/CC), and (TT)-(CC)-(AA)-(TT) were 6.667 (P=0.035), 7.333 (P=0.026), and 6.667 (P=0.035), respectively (Table 2).Figure 2
The best four-locus SNP-SNP interaction model identified by the generalized multifactor dimensionality reduction method. High-risk cells are in dark, low-risk cells are in grey, and empty cells are indicated by no shading. In each cell, the left bar represents case while the right bar represents control. The heights of the bars are proportional to the sum of samples in each group. Note that the patterns of high-risk and low-risk cells differ across each of the different multilocus dimensions, presenting evidence of SNP-SNP interaction or epistasis.Table 2
Combined effects of rs1056503, rs2267437, rs10040363, and rs963248 on HIV-1 infection.
rs1056503
rs2267437
rs10040363
rs963248
P value
OR (95% CI)
TT
CG+GG
AG+GG
TC+CC
—
1.000
TT
CC
AG+GG
TC+CC
0.035
6.667 (1.145-38.833)
TT
CG+GG
AA
TC+CC
0.848
1.200 (0.185-7.770)
TT
CG+GG
AG+GG
TT
1.000
1.000 (0.125-7.995)
TT
CC
AA
TC+CC
0.026
7.333 (1.272-42.294)
TT
CC
AG+GG
TT
0.756
0.667 (0.051-8.639)
TT
CG+GG
AA
TT
0.642
24.556 (1.991-302.866)
TT
CC
AA
TT
0.035
6.667 (1.145-38.833)
Italicized values indicate statistical significance.


## 3.4. Analysis of Haplotype Associations

LD between SNPs in NHEJ genes was analyzed using HaploView software. There was strong LD among four SNPs inXRCC6 gene, eight SNPs in XRCC5 gene, six SNPs in XRCC4 gene, and two SNPs in LIG4 gene, respectively. There were no significant differences in frequencies of all haplotypes between HIV-1-infected cases and healthy controls (P>0.05). Table 3 shows all blocks and haplotypes identified and the frequencies of these haplotypes.Table 3
The frequencies of haplotypes of NHEJ genes in cases and controls.
Gene
Haplotype
Frequency
Haplotype frequencies in the cases
Haplotype frequencies in the controls
Chi-square
P
XRCC6
TCGG
0.671
0.665
0.677
0.309
0.578
TGGG
0.237
0.233
0.241
0.175
0.675
CCAC
0.070
0.081
0.060
3.181
0.075
CCGC
0.021
0.021
0.022
0.012
0.912
XRCC5
Block 1
GG
0.771
0.777
0.765
0.335
0.563
TA
0.223
0.219
0.227
0.169
0.681
Block 2
TG
0.468
0.470
0.466
0.040
0.841
AG
0.452
0.448
0.456
0.149
0.699
TA
0.080
0.082
0.078
0.115
0.735
Block 3
TA
0.693
0.705
0.683
1.101
0.294
CG
0.165
0.164
0.166
0.009
0.923
TG
0.142
0.132
0.152
1.649
0.199
XRCC4
Block 1
TG
0.806
0.806
0.805
0.007
0.931
TT
0.141
0.135
0.147
0.597
0.440
GT
0.053
0.059
0.048
1.095
0.295
Block 2
CT
0.555
0.536
0.573
2.552
0.110
TT
0.308
0.327
0.290
3.095
0.079
CC
0.133
0.132
0.134
0.020
0.888
LIG4
GG
0.814
0.815
0.812
0.035
0.852
AA
0.102
0.101
0.104
0.030
0.863
AG
0.084
0.084
0.084
0.005
0.941


## 3.5. Associations of NHEJ Gene SNPs with CD4+ T Cell Count and Clinical Phase in AIDS Patients

To investigate the relationship between NHEJ gene polymorphisms and AIDS progression, differences in allele frequencies were analyzed between subgroups of HIV-1-infected cases which were divided using CD4+ T lymphocyte count and clinical stage as indicators, respectively. The CD4+ T cell counts of HIV-1-infected cases ranged from 3 to 1038 cells/μl (mean±SD, 335.57±198.79). The associations between SNPs and CD4+ T cell counts were used to assess the influence of gene polymorphisms on the immunity status of patients. As shown in Table 4, there were significant differences in genotype frequencies between different subgroups of cases for XRCC5 rs16855458 and LIG4 rs1805388 (P<0.05). In detail, the subjects with AA or AC of rs16855458 have a significantly lower CD4+ T lymphocyte count, compared to subjects with CC genotype (P=0.025, OR=1.538, 95% CI: 1.054-2.243). The subjects with AA or AG of rs1805388 have a later clinical stage of AIDS, compared to subjects with GG genotype (P=0.036, OR=1.506, 95% CI: 1.027-2.209). However, other SNPs were not associated with the CD4+ T lymphocyte count and clinical stages (P>0.05). These results suggested that rs16855458 and rs1805388 were associated with the clinical features and progression of AIDS in the northern Chinese population.Table 4
Associations between 22 SNPs in NHEJ genes and clinical features of AIDS.
Gene
Gene polymorphisms
Genotype
CD4+ T lymphocyte counta
P
OR (95% CI)
Clinical stageb
P
OR (95% CI)
<350 cells/μl
>350 cells/μl
Phase III+IV
Phase I+II
XRCC7
rs7830743
GG+AG
37
35
0.550
0.858 (0.519-1.418)
29
43
0.438
0.817 (0.491-1.361)
AA
223
181
184
223
rs7003908
CC+CA
125
92
0.232
1.248 (0.868-1.795)
100
118
0.572
1.110 (0.773-1.594)
AA
135
124
113
148
XRCC6
rs5751129
CC+CT
49
39
0.825
1.054 (0.662-1.679)
40
48
0.837
1.050 (0.660-1.671)
TT
211
177
173
218
rs2267437
GG+CG
118
82
0.103
1.358 (0.940-1.961)
95
107
0.335
1.196 (0.831-1.723)
CC
142
134
118
159
rs132770
AA+AG
38
31
0.908
1.031 (0.617-1.722)
27
43
0.282
0.752 (0.448-1.264)
GG
220
185
187
224
rs132774
CC+CG
50
39
0.743
1.081 (0.679-1.719)
40
49
0.920
1.024 (0.645-1.627)
GG
210
177
173
217
XRCC5
rs828907
TT+GT
104
86
0.967
1.008 (0.697-1.457)
92
99
0.185
1.283 (0.888-1.853)
GG
156
130
121
167
rs705649
AA+GA
105
85
0.819
1.044 (0.722-1.509)
92
99
0.185
1.283 (0.888-1.853)
GG
155
131
121
167
rs16855458
AA+AC
109
69
0.025
1.538 (1.054-2.243)
87
92
0.160
1.306 (0.900-1.895)
CC
151
147
126
174
rs3770502
TT+CT
81
61
0.489
1.150 (0.774-1.708)
66
76
0.499
1.146 (0.772-1.700)
CC
179
155
144
190
rs9288516
AA+TA
183
145
0.445
1.164 (0.789-1.717)
144
185
0.649
0.914 (0.620-1.347)
TT
77
71
69
81
rs3835
AA+AG
36
35
0.495
0.839 (0.506-1.390)
33
39
0.804
1.066 (0.645-1.763)
GG
222
181
181
228
rs1051677
CC+TC
83
61
0.384
1.192 (0.803-1.768)
68
77
0.481
1.151 (0.778-1.703)
TT
177
155
145
189
rs2440
GG+AG
127
113
0.451
0.870 (0.607-1.249)
96
136
0.767
0.947 (0.660-1.358)
AA
133
103
107
130
XRCC4
rs6869366
GG+GT
36
20
0.124
1.575 (0.883-2.811)
23
33
0.587
0.855 (0.485-1.505)
TT
224
196
190
233
rs2075685
TT+GT
77
80
0.087
0.715 (0.487-1.050)
62
95
0.126
0.739 (0.502-1.089)
GG
183
136
151
171
rs10040363
GG+AG
72
66
0.510
0.875 (0.588-1.302)
59
79
0.613
0.902 (0.605-1.345)
AA
187
150
154
186
rs963248
TT+TC
136
132
0.054
0.698 (0.484-1.007)
114
155
0.298
0.825 (0.574-1.186)
CC
124
84
99
111
rs35268
CC+TC
70
51
0.409
1.192 (0.786-1.808)
60
61
0.191
1.318 (0.872-1.992)
TT
190
165
153
205
rs1056503
TT+TG
128
118
0.241
0.805 (0.561-1.156)
107
140
0.602
0.909 (0.633-1.303)
GG
132
98
106
126
LIG4
rs1805388
AA+AG
87
69
0.726
1.071 (0.729-1.574)
81
77
0.036
1.506 (1.027-2.209)
GG
173
147
132
189
rs1805389
AA+AG
52
39
0.591
1.135 (0.716-1.799)
48
45
0.124
1.429 (0.907-2.249)
GG
208
177
165
221
Italicized values indicate statistical significance.aThe CD4+ T lymphocyte counts were divided into two groups: category 1, <350 cells/μl, and category 2, >350 cells/μl. bClinical stage: category A, clinical phase III+IV, and category B, clinical phase I+II.


## 4. Discussion

According to the molecular mechanism of HIV-1 infection, viral DNA is inserted into the host genomic DNA in the process of HIV-1 integration. The integration process was equivalent to genomic DNA with DSBs in host cells under the action of HIV-1, and then, the signal of damage repair would start the NHEJ pathway. For example, the DNA-PK protein interacts with HIV-1 Tat to regulate HIV-1 replication and transcription [19, 20]. Therefore, we believed that the NHEJ genes were involved in HIV-1 infection and the disease progression. To the best of our knowledge, this comprehensive study is the first to systematically evaluate the association between the polymorphisms in NHEJ genes and the susceptibility to HIV-1 infection and the progression of AIDS.In this study, the differences of genotype frequencies ofXRCC6 rs132770 and XRCC4 rs1056503 were found between the cases and the controls under different genetic models. Our results implied a positive association of SNPs in NEHJ genes with the susceptibility to HIV-1 infection in the northern Chinese MSM population. The XRCC6 gene encodes Ku70 protein, which functions as a single-stranded DNA- and ATP-dependent helicase and may be involved in the repair of nonhomologous DNA ends such as that required for DSB repair. The Ku70 protein also interacts with HIV-1 integrase and is beneficial to virus integration and replication in the process of the HIV-1 infection [21, 22]. Given that rs132770 locates close to the translation starting point in the XRCC6 promoter, one of the possible reasons for the positive association is that rs132770 affects the expression of Ku70 mRNA; or rs132770 may be in high linkage with some functional variants conferring the etiology of HIV-1 infection. Similar to our findings, it has been reported that different XRCC6 genotypes may contribute to susceptibility to another disease related to virus infection, namely, hepatocellular carcinoma (HCC) [23–25].TheXRCC4 gene encodes XRCC4 protein, which can activate and enhance the activity of LIG4 protein and play an important role in NEHJ repair pathway [26]. Recently, XRCC4 SNPs have been reported to be associated with the risk of a variety of diseases. For example, one study found that XRCC4 mutations may lead to the occurrence of small head dwarfism [27]. Several other studies have shown that SNPs in XRCC4 gene could affect the susceptibility and progression of virus-related HCC [28–30]. Our study implicated that XRCC4 rs1056503 was associated with HIV-1 infection, which was consistent with the above reports. Rs1056503 is located in the 5′ regulatory region of XRCC4 gene, which may cause changes in mRNA expression level and XRCC4 protein function. Then, functional changes in XRCC4 protein may affect NHEJ biological processes in DSBR. Further experimental assay should be performed to solidify our speculations. In addition, in the analysis of SNP-SNP interaction, our results provide evidence for a four-locus interaction between XRCC6 and XRCC4 variants in the risk of HIV-1 infection and further highlight the role of multilocus effects in the genetic component of HIV-1 infection.As an indicator of AIDS clinical characteristics, CD4+ T cell count reflects the number of immune cells in patients. The AIDS patients with CD4+ T cell count less than 350 cells/μl should be given antiretroviral therapy or other treatments according to the World Health Organization (WHO) [31–33]. In the present study, we found a significant difference in frequencies of XRCC5 rs16855458 genotypes between the two subgroups of cases, where genotypes AA and AC were associated with lower numbers of CD4+ T cells. These results suggest that XRCC5 rs16855458 is involved in the progression of AIDS. The XRCC5 gene encodes Ku80 protein which forms a Ku heterodimer with Ku70 protein. Functional studies showed that changes in expression levels of Ku80 protein are the main reason of tumor development and can be used as a predictor of patient survival as well as treatment outcome [34, 35]. In the process of HIV-1 infection, the XRCC5 gene is closely related to HIV-1 integration and translation [36–38]. We propose that the rs16855458 in XRCC5 intron may regulate the transcription and expression of the XRCC5 by alternative splicing, which interacts with HIV-1 to promote its integration and translation, leading to the decrease in the CD4+ T lymphocyte count and the AIDS acceleration. Similar to our findings, the polymorphisms of XRCC5 gene have also been reported to be associated with virus-related HCC [24].In this study, the HIV-1 seropositive cases were divided into two subgroups based on clinical stage, which is a clinical feature of AIDS and directly reflects the disease progression. The clinical symptoms of patients in phases I and II are mild and just show HIV-1 antibody positive. On the contrary, patients in phases III and IV have serious clinical symptoms such as nervous system lesions, continuous fever and diarrhea, sepsis, and various kinds of tumors caused by the loss of immune functions and should be timely given the antiretroviral therapy or other treatments. The results of our study revealed that there was a significant difference in genotype frequencies ofLIG4 rs1805388 between MSM cases in clinical phase I+II and those in clinical phase III+IV, and AA/AG genotypes could significantly promote the disease progression of AIDS. The LIG4 gene encodes LIG4 protein, which connects the DSB end and completes NHEJ repair. Previous studies have shown that LIG4 gene polymorphisms are associated with many clinical features of lung and ovarian cancer, such as treatment outcome, progression-free survival, and overall survival [39, 40]. Mutations in the LIG4 gene can not only lead to abnormal development of immune defects but also cause severe combined immunodeficiency disease in normal individuals [41]. The rs1805388 is located in the exon region of LIG4 gene, which is a missense mutation of threonine and isoleucine. Here, we propose that the reason for this association was the functional changes of LIG4 protein resulting from the genetic variant directly affecting the clinical stage of AIDS.Several limitations of this study should be considered. First, there is a lack of information on critical factors in MSM cases, including history of injection drug use, clinical data on viral loads, and other clinical manifestations. Second, cases and controls were not exposed to the same conditions, because we could not collect samples of healthy MSM controls due to privacy regulations.For future studies, we recommend that the findings of this study should be expanded to other ethnic groups in different regions in the world, beyond the northern Chinese Han population.


## 5. Conclusions

The study confirmed that NHEJ gene polymorphisms played an important role in HIV-1 infection and AIDS progression among MSM populations in northern China. Our study opens a new field for further investigation of underlying functional mechanisms of the association between NHEJ gene polymorphisms and HIV-1/AIDS.



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*Source: 5126867-2022-10-19.xml*