Frequencies of genetic polymorphisms related to triptans metabolism in chronic migraine
- First Online:
- Cite this article as:
- Gentile, G., Missori, S., Borro, M. et al. J Headache Pain (2010) 11: 151. doi:10.1007/s10194-010-0202-7
- 163 Downloads
Chronic migraine (CM) prevalence ranges around 1–5%. Most of these patients usually treat their acute attacks with triptans, whose efficacy is extremely variable. A genetic basis for migraine is evident and many susceptibility genes have been described, as well as gene polymorphisms possibly implied in therapy response. Several factors could be involved in the evolution of episodic migraine into a chronic form, such as natural history, psychiatric comorbidity, and the individual’s response to therapy. During a study aimed at detecting connections between genotype and response to triptans administration, we characterized a CM population for polymorphisms in the genes coding for monoamine oxidase A, g-protein beta 3 and the cytochromes CYP3A4 and CYP1A2. Alleles and genotypes distributions were compared with known frequencies of healthy Caucasian populations. A significant association with CM was found for the long allele of monoamine oxidase A 30 bp VNTR and CYP1A2*1F variant. Such genomic analysis is part of an integrated platform able to evaluate different levels of metabolic pathways of drugs in CM and their influence in the chronicization process.
KeywordsChronic migraineTriptansPyrosequencingPharmacogenomicsGenetic liability
The definition of chronic migraine (CM) is still debated [1, 2], although its prevalence ranges around 1–5%  and its management requires specific skills, being headaches that are most difficult to treat.
Triptans are mainly used for the acute treatment of CM attacks and share a similar mechanism of action, based on the stimulation of serotonin receptors (5HT1B/1D), yet their therapeutic benefits are largely variable among different subjects. Different metabolic rates may probably account for different levels of drug response. Enzymes belonging to the cytochrome P450 superfamily are the central metabolizer of eletriptan, frovatriptan and naratriptan, while the monoamine oxidase MAO A is the main metabolizing enzyme for sumatriptan and rizatriptan. The two systems cooperate in metabolism of almotriptan (MAO A and CYP 3A4) and zolmitriptan (MAO A, CYP 1A2 and CYP3A4) . Several SNPs in these genes are related to alteration of enzymatic activity and genotypization can thus contribute to select the most adequate class of triptan to be administered.
Polymorphisms in genes involved in transduction signal via the 5HT1B/1D receptor, as the C825T SNP in the gene coding the G protein β3 subunit (GNB3), can also be important determinants of triptan therapy outcome .
In the framework of a study aimed at identifying a correlation between triptans response and individual genetic profile, we characterized a panel of gene SNPs involved in triptans pharmacokinetics and pharmacodynamics in a CM population.
Patients and methods
Description of analyzed populations
A total of 104 patients (12 males 92 females) affected with CM, aged 25–81, were enrolled in the study. All patients were referred to the Regional Referral Headache Centre of 2nd School of Medicine of Sapienza University at Sant’Andrea Hospital in Rome, Italy. The diagnosis of CM was performed accordingly to 2006 ICHD-II revised rules . The study received the approval of the University Ethic Committee. All patients signed a written consent before the enrolment in the study.
We studied the following DNA polymorphisms: an untranslated variable number of tandem repeats (uVNTR) of 30 basepairs located about 1.1 kb upstream of the ATG initiation codon of monoamine oxidase A (MAO A) gene; a A → C substitution and at position −163 and a G → A transition at position −3860 in the 5′noncoding region of the CYP1A2 gene, indicated as *1F allele (rs762551) and *1C allele (rs2069514), respectively; an A → G transition at position −392 in the promoter region of CYP3A4 gene, indicated as *1B allele (rs2740574) and a C → T transition at nucleotide 825 (rs5443) in the coding sequence of G protein β3 subunit (GNB3) gene which produces a truncated form of the protein.
Genomic DNA was isolated from peripheral blood using the X-tractor Gene system (Corbett Life Science, Australia).
The MAO A promoter region polymorphism was genotyped on the basis of previously described method . Identification of the amplified fragments size was performed by microchannel electrophoresis on chip, using the Agilent 2100 Bioanalyzer system (Agilent Technologies, Santa Clara, CA, USA).
Primers and Mg2+ concentrations for PCR amplification and pyrosequencing
Forward primer (5′–3′)
Reverse primer (5′–3′)
Sequencing primer (5′–3′)
Mg2+ concentration (mM)
Briefly, regions covering the SNP of interest were amplified as follows: after initial denaturation (95°C, 10 min), a thermal cycler protocol (35 cycles) was employed cycling 20 s at 95°C, 20 s at 48°C, followed by 30 s extension at 72°C; a final extension of 5 min at 72°C was added. All PCR reactions were performed in a final volume of 50 μl containing 40 ng of genomic DNA, 10 pmol of each primer, 0.2 mΜ dNTPs, the appropriate concentration of MgCl2 (Table 1), PCR buffer and 1 U of Taq DNA polymerase (Takara Bio Inc., Otsu, Japan).
Single-stranded DNA was isolated from the PCR reaction using the Pyrosequencing Vacuum Prep Workstation (Biotage) and Streptavidin Sepharose TM High Performance beads (Amersham Biosciences, Uppsala, Sweden) that bind to the biotinylated primer. After washing in 70% ethanol, incubation in denaturing buffer and flushing with wash buffer, the beads were then released into a 96-well plate containing annealing buffer and the specific sequencing primer. Annealing was performed at 80°C for 2 min followed by cooling at room temperature. Then real-time sequencing was performed.
Chi-square Hardy–Weinberg equilibrium (HWE) test calculator for biallelic markers available at http://www.oege.org/software/hwe-mr-calc.shtml  was used to test for deviations of genotype frequencies from HWE. The genotype and allelic distributions in the migraine population were compared with those reported in literature for healthy Caucasian subjects.
In the case of the X-linked MAO A uVNTR polymorphism, male patients (12 out of 104 total subjects) were excluded from statistical analysis.
Genotypic and allelic frequencies among the different groups were compared using Chi-square analysis. Values of P < 0.05 were considered significant.
All the polymorphisms analyzed were in Hardy–Weinberg equilibrium. Frequencies of GNB3 C825T, CYP3A4*1B and CYP1A2*1C SNPs in CM population do not differ from those reported in literature for healthy Caucasian subjects [8–10]. Pertaining to GNB3 C825T SNP, there are 50 (48%) homozygous noncarriers of the mutated 825T allele, 42 (40.4%) heterozygous carriers and 12 (11.5%) homozygous carriers of the mutated 825T allele in our study population.
As regards CYP3A4 −392A > G SNP, only 4 (3.8%) heterozygous carriers of the mutated G allele were found; the remaining 100 CM patients (96.2%) were homozygous noncarriers of the mutated variant. About CYP1A2, there are 99 (95.2%) homozygous noncarriers of *1C allele and 5 (4.8%) heterozygous carriers of the mutated −3860A allele.
Allelic frequencies of MAO A uVNTR polymorphism in controls (Data from Deckert et al. ) and chronic migraine
Mao A uVNTR alleles
Migraine female patients (n = 92)
Healthy female subjectsa (n = 70)
8.39 (0.039) df = 3
Genotypic and allelic frequencies of CYP1A2 −163A > C SNP in controls (Data from Skarke et al. ) and chronic migraine
−163A > C (CYP1A2*1F)
Migraine patients (n = 104)
Healthy subjectsa (n = 495)
df = 2
df = 1
We designed a study to characterize potential association between genotype and triptans response in CM, aimed to assign a targeted therapy according to the metabolic class (based on genotype) of the CM patients. Here, we present the results of the preliminary genetic characterization of a population of 104 CM patients.
On the basis of our analysis, neither GNB3 and CYP3A4 SNPs nor CYP1A2*1C variant seems to be implicated in the genetic liability to CM, whereas an association was found for MAO A and CYP1A2*1F polymorphisms.
The cytochrome P450 enzyme CYP1A2 plays a main role in the metabolism of a variety of structurally unrelated compounds, including a broad range of different drugs and its activity is widely modulated by a plethora of inducer/inhibitor molecules. As the other members of the cytochrome P450 superfamily, CYP1A2 gene is highly polymorphic and the *1C and the *1F alleles are associated with reduced metabolic activity [14, 15]. We found a significant over-representation of the *1F variant in migraineurs. An involvement of *1F allele has been described in various unrelated pathologies [16, 17] and its effect on the enzymatic activity and inducibility of cytochrome has been mainly studied in relation with smoking, a main known inducer of cytochrome.
A more comprehensive analysis is required to elucidate the effective functional link between *1F variant and migraine, taking into account physiological, environmental and lifestyle factors affecting both CYP P450 phenotype and the manifestation of migraine pain.
The potential of MAO A and CYP 1A2 polymorphisms as susceptibility factors for migraine should be further addressed in light of the known implication of these genes in metabolism of triptans, especially in the case of zolmitriptan, which undergoes a two-step catabolism: an active metabolite is produced by CYP1A2 (more stable and active of the parent compound) and successively degraded by the MAO A activity . Therefore, the study of functional interaction between CYP and MAO A SNPs might be of striking impact for tailored drug selection . The possibility to define a specific metabolic background responsible for drug’s efficacy (triptans) in migraine could reduce the use (misuse → abuse) of such drugs in a pre-defined nonresponders’ population of migraine patients, avoiding one of the most important factors in the chronicization progress towards CM, such as triptans abuse. The next steps in the research area of CM determinants should include pharmacogenomics for the definition of the responders’ patients to specific known drugs, such as triptans [20–22] as well as the application of these methods in testing new active compounds for migraine [23, 24].
Conflict of interest