Analysis of rs1864182 and rs1864183 variants in ATG10 gene and antineutrophil cytoplasmic autoantibody‐associated vasculitis in Chinese Guangxi population

Abstract Objectives To investigate the association of autophagy‐associated gene 10 (ATG10) gene polymorphisms (rs1864182 and rs1864183) with antineutrophil cytoplasmic autoantibody (ANCA)‐associated vasculitis (AAV) in Chinese Guangxi population. Methods The single nucleotide polymorphisms (SNPs) of ATG10 rs1864182 and rs1864183 in 395 participants (195 AAVs and 200 healthy controls) were genotyped. Generalized multiple dimensionality reduction (GMDR) was used to analyze the SNP‐SNP interactions among two SNPs of ATG10 gene and other SNPs of autophagy gene previously studied by our research team. Results In this study, we found that the two ATG10 SNPs were not associated with AAV risk in Chinese Guangxi population. However, there were statistically significant differences in the incidence of hemoptysis, hematuria, and proteinuria among the three genotypes of ATG10 rs1864182 and rs1864183 (p < 0.05). Moreover, permutation test of GMDR suggested that immunity‐related GTPase M(IRGM) rs4958847, autophagy‐associated gene 7 (ATG7) rs6442260, ATG7 rs2594966, ATG10 rs1864183, protein kinase B(AKT2) rs3730051, and AKT2 rs11552192 might interact with each other in the process of developing AAV (p < 0.05). Conclusions Our results indicated that there existed no association between ATG10 SNPs and AAV, and SNP‐SNP interactions among IRGM rs4958847, ATG7 rs6442260, ATG7 rs2594966, ATG10 rs1864183, AKT2 rs3730051, and AKT2 rs11552192 may confer AAV risk in the Chinese Guangxi population.

GPA is associated with major histocompatibility complex, class II, DP 1 (HLA-DP1), EGPA with major histocompatibility complex, class II, and DR beta 4 (HLA-DRB4), while MPA is closely related to HLA-DQ. 8 Autophagy is a complex cellular mechanism, which mainly maintains the homeostasis and integrity of cells and tissues through misfolded proteins and the degradation of infectious factors. An increasing number of studies [9][10][11] have shown that autophagy is involved in various immune processes, including the clearance of intracellular bacteria, the presentation of autoantigen and the production of cytokines, and the survival of lymphocytes, which indicates that autophagy plays an obvious and important role in the pre-adaptive and adaptive immune responses. Moreover, genome-wide association studies (GWAS) identified that autophagy-related gene polymorphisms have been implicated in the pathogenesis of a variety of autoimmune and inflammatory diseases, such as systemic lupus erythematosus (SLE), rheumatoid arthritis (RA), inflammatory bowel disease (IBD), and multiple sclerosis (MS). [12][13][14] However, the role of autophagy-associated gene 10 (ATG10) gene polymorphism in AAV still lacks related research. In our study, two single nucleotide polymorphisms (SNPs) of ATG10 gene were selected to preliminarily explore the relationship between gene polymorphism and AAV patients in Guangxi population. Besides, we used generalized multiple dimensionality reduction (GMDR) to analyze the interaction between autophagy genes and the pathogenesis of AAV, so as to provide new ideas for the prevention and treatment of AAV.

| Patients and controls
In this study, a total of 195 AAV patients hospitalized in the Second

| DNA extraction
Peripheral venous blood (5 ml) from all subjects was collected by EDTA anticoagulant blood sampling vessel. Total DNA was extracted by DNA extraction kit provided by Tiangen Biochemical Technology. All operations were conducted in strict accordance with the instructions. DNA samples with absorbance value (A260/280 nm) ranging from 1.5 to 2.0 at a concentration >50 ng/μl were stored in a refrigerator at −80℃ for subsequent experiments.

| SNP selection
The locus information of ATG10 gene was downloaded from 1000 genomes (http://grch37.ensem ble.org/), and the SNP was screened by

| SNP genotyping
In this study, multiplex PCR combined with high-throughput sequencing technology (Sangon Biotech) was used to detect the genotype of SNPs. Briefly, we design and synthesize a primer pool (containing two SNP loci of ATG10 gene), and then, we use a two-step PCR to amplify the target SNPs sequence and prepare a compatible Illumina sequencing library. The first round of PCR system included: DNA template (10 ng/μl, 2 μl); upstream primer pool (10 μmol/L, 1 μl); downstream primer pool (10 μmol/L, 1 μl); and 2×PCR Ready Mix 15 μl (total volume 25 μl; Kapa HiFi Ready Mix). The reaction steps were performed on the PCR instrument (Bio-Rad, T100TM) using the prepared reaction system. PCR product size was detected using 1% agarose gel electrophoresis, and AMPure XP magnetic beads were used to purify and recycle PCR products. The method of obtaining a library with molecular tags was to perform a second PCR using the first PCR product as a template. The reaction system was performed according to the following procedure: The first PCR product was used as template (10 ng/μl, 2 μl), universal P7 primer (including molecular label, 10 μmol/L, 1 μl); universal P5 primer (10 μmol/L, 1 μl), and PCR Ready Mix 15 μl (total volume 30 μl). A new round of PCR was conducted using the prepared reaction system. AMPure XP magnetic beads are used to purify and recycle the final product. The products were mixed in equal quantities and sequenced by using a HiSeq Xten sequencer (Illumina).

| GMDR analysis
We evaluated the interactions among susceptible SNPs of autophagy gene family by using GMDR 0.7 software. The test level α = 0.05, and p < 0.05 was considered statistically significant.

| Statistical analysis
Chi-squared test was used to estimate the deviations from Hardy-Weinberg equilibrium (HWE) of selected ATG10 SNPs. SPSS 23.0 statistical software (IBM) was performed for statistical analysis, and p < 0.05 was considered statistically significant. The enumeration data are expressed in percentage, and the measurement data are expressed as mean (SD). Odds ratios (ORs) and 95% confidence intervals (CIs) were carried out. Linkage disequilibrium (LD) test and haplotype analysis were conducted by SHEsis online software (http://analy sis.bio-x. cn/myAna lysis.php; Shi and He 2005). 16

| Demographic characteristics and comparison of participants
In this study, a total of 395 subjects were involved, of which 195 were AAV cases and 200 were healthy controls. There was no statistically significant difference in the average age and gender composition between the two groups (p > 0.05), and they were comparable.
The demographic characteristics of each participant are summarized in Table 1.

| Allele frequency, genotype distribution, and HWE analysis
The genotype distributions of the two ATG10 SNPs in controls were in HWE (all p > 0.05). As shown in Table 2, the alleles and genotypes did Note: This data type is enumeration data. p-value: comparisons were made between AAV patients and controls using chi-squared (χ 2 ) test. p < 0.05 was considered statistically significant.
not show any significant difference between AAV patients and controls. We also demonstrated that ATG10 rs1864182 and rs1864183 were in complete linkage disequilibrium, as shown in Figure 1 (D′ = 0.99, R 2 = 0.73).

| Correlations between genotypes and clinical characteristics
As shown in Table 3, there were statistically significant differences in the incidence of hemoptysis, hematuria, and proteinuria among the three genotypes of ATG10 (rs1864182 and rs1864183; p < 0.05). No remarkable association was observed between the two SNPs and white blood cell (WBC), neutrophil count, hemoglobin, creatinine, IgG, IgA, and IgM (p > 0.05), which is displayed in Table 4.

| GMDR analysis
We detected a significant six-locus model including ATG10 Note: This data type is enumeration data. *p < 0.05: the comparisons were made among two SNPs of ATG10 gene and clinical symptoms in AAVs using chi-squared (χ 2 ) test. p < 0.05 was considered statistically significant.

| DISCUSS ION
Autophagy  GMDR is a nonparametric analysis method without specifying genetic model and interaction model. 23 It is a powerful tool for studying multi-gene diseases. It is necessary to combine the gene or the interaction between gene and environment for analysis in order to reveal the real correlation between ATG10 and AAV accurately and comprehensively. Therefore, we used GMDR software to analyze the relationship between the interaction of two ATG10 gene SNPs and other autophagy-related gene SNPs studied by our research team, and the incidence of AAV. The results showed that IRGM rs4958847, ATG7 rs6442260, ATG7 rs2594966, ATG10 rs1864183, AKT2 rs3730051, and AKT2 rs11552192 have interactions, suggesting that mutations in ATG10, IRGM, ATG7, and AKT2 genes may interact and increase the risk of individual susceptibility to AAV. As one of the core processes of autophagy, the formation of autophagosomes is similar to ubiquitination. ATG10 is an autophagic E2 enzyme that interacts with ATG7 to receive the ubiquitin-like molecule ATG12. Previous study suggested that IRGM may promote the nucleation and/or elongation of autophagy vesicles by interacting with one or more of ATG10, ATG5, microtubule-associated protein 1 light chain 3 gamma (MAP1LC3C), and SH3 domain-containing GRB2 like, endophilin B1 (Sh3GLB1). 24 Therefore, we speculate that the correlation between IRGM and ATG10 may be enhanced in AAV patients, thereby increasing individual susceptibility to AAV. However, we did not perform studies to explore how these six SNPs interact with each other and the molecular mechanisms of their interaction to increase the susceptibility to AAV, and future experimental studies will be needed.
In conclusion, there are polymorphisms in rs1864182 and rs1864183 of ATG10 gene in Guangxi population. Although we did not report the association between ATG10 rs1864182 and rs1864183 gene polymorphisms and AAV in our population, SNP-SNP interactions in autophagy genes family may increase the susceptibility risk of individuals to AAV. We do believe that our results partially fill this gap, which may be useful for our understanding of the pathogenesis of AAV. Future studies are needed to investigate more autophagy genes family SNPs in a large sample to demonstrate their associations.

CO N FLI C T O F I NTE R E S T
The authors declare no conflict of interests.

DATA AVA I L A B I L I T Y S TAT E M E N T
The data that support the findings of the current study are available from the corresponding author upon reasonable request. Note: The best interaction combination model: ATG10 rs1864183, IRGM rs4958847, ATG7 rs6442260, ATG7 rs2594966, AKT2 rs3730051, and AKT2 rs11552192. *p < 0.001: we evaluated the interactions among susceptible SNPs of autophagy gene family by using GMDR 0.7 software. p < 0.05 was considered statistically significant.

O RCI D
Abbreviations: 95% CI, 95% confidence interval; GMDR, generalized multiple dimensionality reduction; OR, odds ratios. TA B L E 5 GMDR analysis for the best interaction combination model