Introduction

Based on the reports of the World Health Organization, neuropsychiatric disorders and substance use are among the leading causes of global disability-adjusted life years (Whiteford et al. 2013). Both single-gene association studies and genome-wide association studies have shown several genomic loci for these conditions (Treutlein and Rietschel 2011; Jensen 2016; Corvin et al. 2012). However, the data provided by these studies are not conclusive. Thus, assessments of the associations in other populations and functional studies are needed to elaborate the mechanisms of neuropsychiatric disorders and substance abuse.

NINJ2 encodes a transmembrane protein that contributes in the interactions between cells as well as interaction between cells and the extracellular matrix. These interactions have crucial roles in different phases of neurodevelopment and regeneration of neurons (Seilheimer and Schachner 1988; Araki and Milbrandt 2000). Expression of this protein has been detected in brain radial glial cell and lymphocytes. Notably, nerve injury has resulted in overexpression of the protein which finally leads to neurite outgrowth (Seilheimer and Schachner 1988). Single-nucleotide polymorphisms (SNPs) within this gene have been associated with decreased risk of Alzheimer’s disease (Lin et al. 2011). Moreover, the rs11833579 and rs3809263 NINJ2 SNPs have been associated with risk of ischemic stroke in Iranian population (Malekzadeh et al. 2019). Furthermore, the rs3809263 has been associated with risk of multiple sclerosis in the same population (Noroozi et al. 2019). While the NINJ2 rs12425791 has been with risk of ischemic stroke in East Asian population, the rs11833579 has not been associated with this condition either in East Asian population or Chinese Han population (Li et al. 2012).

In the present project, we aimed to assess the association between two NINJ2 SNPs (rs11833579 and rs3809263) and risk of neuropsychiatric disorders in Iranian population. The rs11833579 changes Hand1 and Pou3f2 motifs, while the rs3809263 alters Eomes motif. In addition, there are some evidences associating both SNPs with expression quantitative trait loci (eQTL) (Ward and Kellis 2012). So, we genotyped these SNPs in a population of patients with different conditions including major depressive disorder (MDD), bipolar disorder types 1 and 2 (BP I and BP II), schizophrenia (SCZ), and methamphetamine addiction.

Materials and Methods

SNP Characteristics

Table 1 summarizes the features of the selected SNPs in this research project.

Table 1 The characteristics of the selected SNPs
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The genotypes of the mentioned SNPs were determined using the tetra-primer amplification-refractory mutation system (ARMS)-PCR technique. Taq DNA Polymerase Master Mix RED (Amplicon, Denmark) was used for preparation of reactions. Table 2 summarizes the features of forward and reverse (inner/outer) primers, the annealing temperatures, and the expected sizes for different alleles. The PCR was performed using the following conditions: a primary step at 95 °C for 10 min; 35 cycles at 95 °C for 30 s; annealing temperature for 35 s, 72 °C for 40 s, and a final extension at 72 °C for 10 min.

Table 2 Features of primer pairs and PCR protocol (F, forward; R, reverse; i, inner; o, outer)
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Study Participants

A total of 289 persons with methamphetamine addiction, 128 patients with BP I, 86 patients with BP II, 54 patients with MDD, and 189 patients with SCZ were recruited in the current study. Appropriate amounts of age-/sex-matched normal persons were also selected as controls for each category of patients. Patients were selected from those referred to Farshchian Hospital, Hamadan, Iran. Addicted individuals were recruited from Niaz and Atinegar Addiction Treatment Centers, Mashhad, Iran. A psychiatrist assessed all individuals and confirmed the diagnosis based on criteria of the Diagnostic and Statistical Manual of Mental Disorders (5th edition) (Whiteford et al. 2013). Patients with structural or metabolic brain disorders or any condition with neuropsychiatric signs were exempted from the project. Persons with history of substance abuse were exempted from all subgroups rather than the “addiction” group. Individuals enlisted in control groups were selected from volunteers who came for routine health check-up. Control subjects were matched with cases in their ethnicity. These persons were evaluated through a semi-structured interview. Written informed consent forms were signed by all individuals. The study protocol was approved by the ethics committees of Shahid Beheshti Universities of Medical Sciences.

Statistical Analyses

R 3.2.2 software was used for statistical assessments. Agreement with Hardy-Weinberg equilibrium (HWE) was judged using Chi-square test. Associations between the neuropsychiatric conditions and the rs11833579/rs3809263 alleles/genotypes were judged in codominant, dominant, recessive, and multiplicative inheritance models. Haplotype frequencies were compared between each subgroup of patients and the corresponding controls. Odds ratios (OR), 95% confidence intervals (95% CI), and P values were quantified to judge the statistical significance. P values less than 0.05 were considered significant. P values were corrected by multiplying the original P value by the number of comparisons.

Results

General Data of Study Participants

General demographic data of study participants are summarized in Table 3.

Table 3 Demographic data of study participants (total number of control subjects was 801. Cases and controls were sex-matched except for the MDD cohort)
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Allele Frequencies

Table 4 shows the allele frequencies of the rs11833579 and rs3809263 in study subgroups.

Table 4 The allele frequencies in the study subgroups
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Genotype Frequencies

Genotype frequencies of the rs11833579 and rs3809263 were in accordance with the HWE supposition in all subgroup patients and controls (Tables 5 and 6).

Table 5 Compliance of genotype frequencies of the rs11833579 with HWE
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Table 6 Compliance of genotype frequencies of the rs3809263 with HWE
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Either SNP was associated with risk of psychiatric conditions in any inheritance model. Moreover, there was no significant difference in the frequencies of the estimated haplotypes between patients and controls (Tables 7, 8, 9, 10, 11, 12, 13, 14, and 15). The assessed SNPs were in linkage disequilibrium (D′ statistic = 0.95716, r2 = 0.24554) (Table 16).

Table 7 Associations between genotypes and substance addiction in four inheritance models
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Table 8 Haplotype analysis in addiction group
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Table 9 Associations between genotypes and BP1 in four inheritance models
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Table 10 Haplotype analysis in BP1 group
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Table 11 Associations between genotypes and BP2 in four inheritance models
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Table 12 Haplotype analysis in BP2 group
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Table 13 Associations between genotypes and MDD in four inheritance models
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Table 14 Haplotype analysis in MDD group
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Table 15 Associations between genotypes and SCZ in four inheritance models
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Table 16 Haplotype analysis in SCZ group
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Discussion

In the current project, we genotyped two NINJ2 SNPs in a large cohort of Iranian patients with diverse psychiatric conditions to unravel their possible role in conferring risk of these disorders. Some recent studies have shown shared genetic loci for a number of psychiatric conditions (Smeland et al. 2019), so we hypothesized that this NINJ2 locus might be one of the shared loci in these disorders. However, we could not find any associations between NINJ2 SNPs and the mentioned disorders. Upregulation of NINJ2 has been detected in Schwann cells adjacent to the distal part of an injured nerve. This protein can enhance neurite outgrowth, probably through homophilic cellular adhesion (Araki and Milbrandt 2000). NINJ2 protein also participates in the regulation of interactions between cells and the extracellular matrix, so it is possibly involved in the neurodevelopmental processes and regeneration of neurons (Seilheimer and Schachner 1988; Araki and Milbrandt 2000). Consequently, NINJ2 polymorphisms are putative candidates for psychiatric disorders. The associations between these SNPs and human disorders have been evaluated in different populations. However, a previous meta-analysis showed no association between rs11833579 and ischemic stroke risk (Lian et al. 2012). Homozygosity for rs11833579 SNP was significantly associated with lower susceptibility to Alzheimer’s disease in Chinese population (Lin et al. 2011). The rs3809263 is a functional polymorphism in the NINJ2 promoter and has been associated with large artery atherosclerotic stroke in Chinese population. Moreover, the AA genotype of this SNP has been associated with higher levels of NINJ2 transcripts (Zhang et al. 2016). Both selected SNPs have been associated with ischemic stroke in Iranian population (Malekzadeh et al. 2019). Based on the results of in silico analyses, the selected SNPs could affect the expression of NINJ2 (Ward and Kellis 2012). Moreover, the results of previous studies implied their association with some human disorders at least in some populations. However, we did not report any association between these SNPs and neuropsychiatric disorders. This might be explained by different etiology of neuropsychiatric disorders and their independence from a “vascular susceptibility gene” or small sample size of current study. Assessment of expression levels of NINJ2 in peripheral blood of patients with neuropsychiatric disorders and their comparison with matched controls is needed to evaluate contribution of NINJ2 in the pathogenesis of these disorders.

Taken together, the current study exclude association between rs11833579 and rs3809263 and the mentioned neuropsychiatric disorders. Future studies are needed to appraise our results.