Genetic association and gene expression studies suggest that genetic variants in the SYNE1 and TNF genes are related to menstrual migraine

The population for this research consisted of 437 females recruited by the City of London Migraine Clinic, including both PMM and MRM cases and controls. Migraine diagnosis was in accordance with ICHD-II. The inclusion criteria for cases selection was the occurrence of attacks on day 1 ± 2 of menstruation in at least two out of three menstrual cycles. Diagnosis of PMM (menstrual attacks only) and MRM (additional attacks at other times of the cycle) was confirmed by diary evidence from at least three menstrual cycles. Controls were women with no personal or family history of migraine, age and ethnicity matched to cases, where possible. Biological specimens were collected and transported to the Genomics Research Centre for further processing. Saliva samples were obtained from the 437 females (282 cases: 68 PMM and 214 MRM; and 155 controls (median age 45.0; range 21–60 vs. 39.5; 22–61 years). Saliva samples were collected and DNA isolated using Oragene (Australia) Saliva DNA extraction kits. 74 patients (41 cases and 33 controls (median age 42.0; range 21–49 vs. 35.5; 24–49 years) with paired follicular and luteal phase samples available for 30 cases and 29 controls from the same population, who had not taken any hormonal treatment (including dietary isoflavone supplements) within the previous three months, were asked to donate blood samples at both luteal and follicular menstrual cycle stages. A total number of 134 samples were collected in Paxgene tubes for expression analysis purposes. mRNA extraction was carried out using Qiagen PAXgene blood miRNA kits (Catalog # 763134). Phenotypic data was obtained via a medical questionnaire that surveyed migraine family history, symptoms, triggers, medication use and contraceptive use. The study protocol was originally approved by the Griffith University Human Research Ethics Committee and subsequently by the Queensland University of Technology Human Ethics Committee (Australia), and the East London and the City Research Ethics Committee (UK). All subjects provided signed, informed consent prior to participation.

For this part of the study three different techniques were used. Genotyping of the G594A variant in the ESR1 gene was carried out by using PCR-RFLP. The restriction enzyme Btg I (New England Biolabs, Australia) was used for the determination of the SNP genotype. Variant C325G (rs1801132) was genotyped using TaqMan^® SNP Genotyping Assay (life technologies, Cat. #4351379) and following the manufacturer instructions.

PROGINS insertion/deletion was tested with a standard PCR. Samples were then observed on a 2% agarose gel to detect the presence of the PROGINS insert (details in Additional file 1).

A total of 34 SNPs were selected from 14 different genes on nine chromosomes as the research panel for the study. Selected SNPs were located in genes involved in neuronal, hormonal and immunologic pathways previously associated with migraine. The SNPs were tested by using the Sequenom genotyping platform (Sequenom^®, San Diego, CA, USA), which uses MALDI-TOF mass spectroscopy and MassARRAY technology with an iPlex system. Primers for polymerase chain reaction (PCR) amplification and single base extension were designed by Sequenom Assay Design 3.1 software (Sequenom, San Diego, CA, USA) according to the manufacturer’s instructions (Additional file 2).

Two-step quantitative reverse transcription PCR (qPCR) was performed using a standardized protocol in our laboratory. An amount of 100 ng of RNA was converted into cDNA by adding 9.2 ng/uL Invitrogen Random Hexamers, dNTPs (500 uM), and free-RNAse H2O to a final volume of 32.5ul. The reaction was incubated at 65°C for 10 minutes in a thermocycler to disrupt secondary structures, followed by a second incubation on ice for at least 5 minutes. In the second step of the reaction, 1st Strand Buffer (1X), DTT (2 mM), SuperScript^® III Reverse transcriptase (100U) and RNase- free H2O were added to the previous reaction product for a final volume of 50 ul. The cDNA synthesis reaction was carried out with an initial incubation of 25°C for 5 minutes, followed by a 50°C for 60 minutes and a final step of reaction inactivation at 70°C for 15 min. Stock cDNA was stored at −80°C and 1:2 diluted cDNA working samples were stored at −20°C.

To scan levels of expression of PGR, ESR1, SYNE1 and TNF genes we used SYBR Green PCR Master Mix 1X (Life Technologies, Australia), 200 nM reverse and forward primers (Additional file 2), Rox (reference probe) 2X, 100 ng of cDNA and free RNase- H2O to a final volume of 10 ul. Fifty cycles of 50°C-2 min, 95°C-3 min, 95°C–3 s, and 60°C–30 s were carried out in a 7900HT Fast Real-Time PCR System (Applied Biosystems, Australia). In order to allow for relative quantitation of gene expression, we used the reference genes 18S and GADPH. Efficiency tests for all primers sets to be used (Additional file 3) were performed and amplification efficiencies were found to be comparable.

As part of the quality control process, we first performed a Hardy Weinberg test followed by a standard case–control association test using Chi-square analysis with Plink V1.07 [13]. In order to minimize the effect of having a dissimilar number of cases and controls in our study, we implemented a logistic regression analysis in RStudio (version 0.97.312) [14] for those SNPs with p-values < 0.01 in the previous Chi-square test. A Wald test was applied to fit the logistic regression model.

For expression analysis the ΔΔCt method was used. The ΔΔCt calculations were executed using Microsoft Excel software. The statistical significance of differentially expressed genes between cases and controls was determined by a standard t-test. Logistic regression was also performed using genotype and gene expression levels as predicting variables for migraine. The Pearson’s correlation was used to test correlations between expression levels of PGR, ESR1, TNF and SYNE1. The statistical significance was assessed by comparing the observed p values to an alpha threshold of 0.01. All these analyses were performed by using RStudio.

The initial quality control process allowed us to identify and subsequently to delete 6 SNPs for violating the Hardy/Weinberg equilibrium (rs1805087, rs1584243, rs1800683, rs1800629, rs1519480 and rs7127507), 2 SNPs (rs1800630, rs363314) for having a low genotyping calling rate (<80%), 1 SNP with 3 alleles (rs12273363) and 3 SNPs for showing a unique allele (rs146806052, rs113352055 and 5965992). Table 1 shows Chi-square and p-values for the studied SNPs that passed all the quality control checks. The analysis was completed for all cases grouped together (PMM and MRM), and individually for each subgroup. SNPs rs3093664 (TNF) and rs9371601 (SYNE1) were both significantly associated with migraine in the combined PMM-MRM sample and the MRM sample. Association after exclusion of PMM individuals from the analysis lead to a increased of the level of significance, suggesting a stronger effect of TNF and SYNE1 variants in MRM patients. Significance was detected in two SNPs (rs2229741 and rs4986938), located in NRIP1 and ESR2 in PMM individuals. Table 2 shows allelic and genotypic frequencies and counts for associated SNPs.

Table 3 presents logistic regression analysis results. Both allelic and genotypic odds ratios are shown Genotype GA in SNP rs3093664 showed a protective effect on migraine risk in the total population (OR = 0.46, 95% CI = 0.24-0.86) and in the MRM sub- population (OR = 0.46, 95% CI 0.24-0.89). This effect is likely based on the presence of the G allele, which showed a significant p-value of 0.009 for the odds ratio test in the total population (OR = 0.48, 95% CI = 0.28-0.84) and 0.007 in the MRM sub-population (OR = 0.45, 95% CI = 0.25-0.81), which suggests an important protective effect of this variant on the MRM phenotype. While the model for the GG genotype was not significant and the 95% CI straddled 1, this was likely caused by the extreme rarity of the GG genotype in both case and control populations.

Additionally, allele T in SNP rs9371601 showed a significant OR in MRM (OR = 0.60, 95% CI = 0.43-0.84) and not the PMM sub-population (OR = 0.93, 95% CI = 0.60-1.43) (see Table 2), indicating that the effects of this SNP may be largely confined to the MRM sub-population. Interestingly, the genotypic odds ratio indicated that this protective effect was limited to TT genotypes and that heterozygotes did not show confident protection compared to GG homozygotes (OR = 0.83, 95% CI = 0.50-1.36). Allele G in SNP rs4986938 (OR = 1.90, 95% CI = 1.05-3.47) and allele A in SNP rs2229741 (OR = 1.61, 95% CI = 1.01-2.56) seemed to be a risk factor for migraine in the PMM sub-population. However, the effect is probably not strong due to the small size of the population.

Our analysis of gene expression in our migraine populations indicated no significant difference in levels of expression in the studied genes between cases and controls and across the sub-populations (Table 4). However, we have detected significant positive correlations in the follicular phase between the expression of TNF and SYNE1 (Rho = 0.371, p = 0.005), ESR1 and PGR (Rho = 0.435, p = 0.006) and ESR1 and SYNE1 (Rho = 0.412, p = 0.002), which would indicate some interaction between them. In contrast, in luteal phase, we found a correlation between SYNE1 and PGR (Rho = 0.444, p = 0.006) and TNF and SYNE1 (Rho = 0.345, p = 0. 015). More interestingly, we found differences in the correlations between gene expression in cases and controls. For ESR1 and SYNE1, these differences were small, with both cases and controls maintaining similar levels of correlation (cases: Rho = 0.48 p < 0,001; controls: Rho = 0.4 p = 0.001). For other gene correlations however, cases maintained significant relationships while the controls had weakened or non-significant relationships. For TNF and SYNE1, the relationship was weaker, but still significant in controls (cases: Rho = 0.449 p < 0,001; controls: Rho = 0.27 p = 0.03). For ESR1 and PGR controls ceased to maintain correlation of expression (cases: Rho = 0.511 p < 0,001; controls: Rho = 0.091 p = 0.56) and a similar loss of correlation occurred between PGR and SYNE1 (cases: Rho = 0.393 p = 0.005; controls: Rho = 0.243 p = 0.12) (Table 5).

The results of our expression analysis suggest that although there are not significant changes in gene expression of genes that may influence migraine in PMM and MRM cases compared to controls, these genes interact in a different fashion both in the luteal and follicular stages of the menstrual cycle and in cases compared to controls.