International Clinical Psychopharmacology:
Brain-derived neurotrophic factor gene polymorphisms: influence on treatment response phenotypes of major depressive disorder
Kocabas, Neslihan Ayguna,g; Antonijevic, Irinah; Faghel, Carolea; Forray, Carlosh; Kasper, Siegfriedi; Lecrubier, Yvesj; Linotte, Sylvieb; Massat, Isabellea; Mendlewicz, Juliend; Noro, Magalib; Montgomery, Stuartk; Oswald, Pierref; Snyder, Lenoreh; Zohar, Josephl; Souery, Danielc,e
aLaboratoire de Neurologie Expérimentale
bFonds de la Recherche Scientifique (FNRS)
cLaboratoire de Psychologie Médicale
dUniversité Libre de Bruxelles
eCentre Européen de Psychologie Médicale, Psy-Pluriel, Bruxelles
fPsychiatric center Le Chêne Aux Haies, Mons, Belgium
gDepartment of Toxicology, Faculty of Pharmacy, University of Gazi, Etiler, Ankara, Turkey
hTranslational Research, Lundbeck Research USA, Paramus, New Jersey, USA
iDepartment of General Psychiatry, Medical University of Vienna, Austria
jINSERM U, Hôpital de la Pitié-Salpêtrière, Paris, France
kImperial College School of Medicine, London, UK
lChaim Sheba Medical Center Tel-Hashomer, Israel
Correspondence to Dr Neslihan Aygun Kocabas, MSc, PhD, Université Libre de Bruxelles, Laboratoire de Neurologie Expérimentale, Campus Hôpital Erasme C2.124, 808 Route de Lennik, 1070 Bruxelles, Belgıum Tel: +322 5556408; fax: +322 5554121; e-mail: email@example.com
All supplementary data are available directly from the authors.
Received September 28, 2009
Accepted June 10, 2010
Brain-derived neurotrophic factor (BDNF), a member of the nerve growth factor family of neurotrophins, has pivotal roles in neuronal survival, proliferation, and synaptic plasticity in the brain. Both clinical and pharmacological studies have implicated the common single nucleotide polymorphism (SNP) at position 196, Val66Met in the pathophysiology of major depressive disorder (MDD), and antidepressant response. However, inconsistent results were found between Val66Met (rs6265) polymorphism and treatment response phenotypes in genetic association studies. The functional Val66Met polymorphism and seven other tagging SNP markers selected to capture the major allelic variations across BDNF locus were analyzed in depressed patients, treated with antidepressants, and 76 control patients. Two hundred and six patients with Diagnostic and Statistical Manual of Mental Disorders-IV MDD were recruited for this study and genotyped for eight BDNF tagging SNPs (rs11030096, rs925946, rs10501087, rs6265, rs12273363, rs908867, rs1491850, and rs1491851) to investigate the functional impact of genotypes/haplotypes in the susceptibility of depression and on treatment response. None of the eight SNPs, including the rs6265, were significantly associated with MDD after permutation correction. However, we found an association for rs10501087, rs6265 with nonresponse to antidepressant treatment (corrected permutation P: 0.03599; 0.0399 and power: 0.1420; 0.1492, respectively). Analysis of each two-marker, three-marker, and four-marker sliding window haplotypes showed significance in haplotype combinations. Especially rs10501087 (C), rs6265 (A), and rs1491850 (C) together or with the other SNP haplotypes showed a similar pattern in all treatment response phenotypes. Despite the limited power of analysis, our results suggest that these three SNPs may play a role in antidepressant treatment response phenotypes in MDD.
Major depressive disorder (MDD) is considered as a complex disorder that results from both genetic and environmental influences. It is predicted to be the second leading cause of death and disability worldwide by 2020 (Murray and Lopez, 2006; Laje and McMahon, 2007). Owing to the difficulty in observing pathological changes within the brain and its idiopathic occurrence, knowledge about MDD pathophysiology is rudimentary compared with knowledge of other common multifactorial conditions (Krishnan and Nestler, 2008). Cohort studies indicated that only 50–70% of depressed patients depending on the patients and duration of follow-up, respond to first-line antidepressant treatment and less than 40% achieve remission (Mulder et al., 2006; Palmer and Cardon, 2006).
Polymorphic variations in the genes [single nucleotide polymorphisms (SNPs)] are used in the delineation of genetic influences in multifactorial diseases and as genetic markers to predict response to drugs and adverse drug reactions (Palmer and Cardon, 2006). Such variations may contribute to differences in the susceptibility to MDD among individuals with the same mutations.
Brain-derived neurotrophic factor (BDNF) plays an important role in the development of the nervous system and in the functioning of adult central nervous system. BDNF-mediated activation of TrkB receptor leads to a variety of biological responses, which include cell survival, axonal and dendritic growth, differentiation, morphology and synaptic plasticity (Bath and Lee, 2006). The human BDNF gene located on 11p14.1 has a highly complex genomic structure, spanning approximately 70 kb and containing only one major 3′ exon of its 11 exons encoding the mature BDNF protein. The gene comprises nine functional promoters that are used in tissue and brain-region specifically. Nine alternatively spliced BDNF transcripts leading to different preprotein isoforms have been identified in all examined brain regions (Pruunsild et al., 2007). Okada et al. (2006) described a polymorphic region including 23 novel allelic variants designated as a BDNF-linked complex polymorphic region. They also reported an association with transcriptional activity in an allele-dependent manner related to one of the major alleles with high allele frequency in bipolar patients.
The BDNF gene has received considerable attention as a plausible candidate gene for MDD because of its involvement in the neuroplasticity hypothesis (Krishnan and Nestler, 2008). Despite inconsistent results, two meta-analysis findings have suggested that serum BDNF levels were reduced in depressive patients, that there was a significant correlation between changes in BDNF levels and depression scores, and that BDNF serum levels can be elevated by antidepressant treatment (Brunoni et al., 2008; Sen et al., 2008). Sen et al. (2008) also found significant associations between serum BDNF levels and both depression status and pharmacological antidepressant treatment.
Sequence variations in the BDNF gene, which may lead to variations in gene expression or protein metabolism have been advanced as possible causes of selective neuronal vulnerability (Egan et al., 2003). Activity-dependent regulation of BDNF is modulated by the interactions of proteins in the Golgi apparatus with the prodomain of BDNF. A common G to A polymorphism (rs6265) was identified in the 5′pro-BDNF sequence, which leads to a change of Valine (Val) to Methionine (Met) at codon 66, position 196 of the BDNF gene. This substitution seemed to be of functional significance to affect intracellular trafficking, packaging of proBDNF, and activity dependent secretion of BDNF (Egan et al., 2003). BDNF is implicated in regional neuronal changes which are known to be associated with depression such as decrease of BDNF amount in the hippocampus and increase of similar magnitude in the nucleus accumbens (Krishnan and Nestler, 2008). BDNF Met/Met polymorphism is associated with a reduction in BDNF secretion from nerve terminals. In contrast, the Val allele results in higher activity of the BDNF system than the Met allele (Rybakowski, 2008). However, the different or even opposite effects of BDNF on the behavior depending on the neural circuitry are still in question (Berton et al., 2006).
At present, it is still unclear what the consequences of Val66Met polymorphism on brain function are as both alleles have been associated with different disease processes. Further evidence of BDNF implication in depression comes from preclinical studies investigating genetic disruption of BDNF pathways, which suggest that BDNF mediates antidepressive effects in learned helplessness and forced swim models of depression. Drug treatment with lithium increases mRNA expression of BDNF and its receptor TrkB (Egan et al., 2003; Berton et al., 2006; Chen et al., 2008; De Luca et al., 2008; Krishnan and Nestler, 2008; Wang et al., 2008a). Many preclinical studies have shown that several forms of stress reduce BDNF-mediated signaling in the hippocampus, whereas chronic treatment with antidepressants increases BDNF-mediated signaling (Berton et al., 2006; Krishnan and Nestler, 2008; Wang et al., 2008b).
Genetic studies exploring the potential relation between BDNF SNPs and affective disorders (AD) have produced conflicting results. Mostly, these studies focused on the findings obtained from Val66Met polymorphism and MDD. Moreover, some investigators have also attempted to integrate clinical, cognitive, psychophysiological, neuroimaging, and genetic measures using path modeling to explore the relationship between specific BDNF SNPs and depression (Kemp et al., 2008). A number of clinical studies have reported a lack of association between Val66Met and MDD (Chen et al., 2008; Gratacos et al., 2008; Schumacher et al., 2008, Wray et al., 2008), whereas others have shown the impact of the BDNF Val66Met polymorphism on antidepressant treatment response (Egan et al., 2003; Tsai et al., 2003; Domschke et al., 2009; Matrisciano et al., 2009). An earlier study investigating the role of the BDNF gene polymorphisms in MDD in a Belgian sample by our group, did not find any significant association between Val66Met polymorphism and MDD susceptibility (Oswald et al., 2005). Only one study investigated the tagging SNPs (tSNPs) containing all genomic region of BDNF in a Caucasian sample in relation to AD and antidepressant treatment. Gratacos et al. (2008) found an association between rs908867 and remitter status, whereas the other seven tSNPs were not related to any treatment phenotype.
To unravel differences reported earlier in the literature, we carried out a preliminary-study in 206 MDD patients for eight tSNPs for the BDNF gene to compare the genotype/allele and haplotype frequencies of stratified depressed patients based on treatment response phenotypes. The functional Val66Met (rs6265) polymorphism and seven other tSNP markers (rs11030096, rs925946, rs10501087, rs12273363, rs908867, rs1491850, and rs1491851) covering the entire BDNF genomic region were selected for genotyping based on Gratacos et al. (2008) study. We also analyzed these SNPs in 76 control patients for comparing cases and controls.
Materials and methods
The 206 unrelated MDD patients were recruited from different European centers within the European Framework Program entitled ‘Patterns of treatment resistance and switching strategies in unipolar affective disorders’. The participant centers were the Department of Psychiatry, Erasmus Hospital, Université Libre de Bruxelles, Belgium; the Department of General Psychiatry, Medical University of Vienna, Austria; and the Department of Psychiatry, Chaim Sheba Medical Center Tel-Hashomer, Israel. MDD was diagnosed by experienced psychiatrists according to Diagnostic and Statistical Manual of Mental Disorders-IV classification system criteria and on the basis of a semi-structured diagnostic interview from January 2000 to February 2004. All study participants (patients and controls) were interviewed using the Mini-International Neuropsychiatric Interview (M.I.N.I.) version 5.0.0 Modified for Group for the Study of the Resistant Depression. Each patient was also evaluated for demographic and psychosocial characteristics of the current episode including personal and family history of psychiatric disorders and data on last antidepressant treatment. Inclusion criteria were primary diagnosed MDD receiving at least one adequate antidepressant trial during the current or last episode of depression. The control group was free of any psychiatric disorders. Detailed information on the diagnosis, recruiting method, and treatment response phenotypes of patients have been published by our group (Souery et al., 2007).
Treatment response phenotypes
Recruited patients were characterized for clinical response to adequate (administered at least 4 weeks and at adequate dose) antidepressant treatment as measured by the 17-item Hamilton Depression Rating Scale (HAMD-17) score. The patients who had an HAMD-17 score of less than 17 after 4 weeks or more of adequate antidepressant treatment, were defined as ‘responder’. The treatment outcome phenotype was defined as ‘resistant’ when the patient failed to reach an HAMD-17 score of less than 17, after at least two adequate antidepressant treatments within the current episode. Moreover, ‘remission’ was considered while achieving an HAMD-17 score of less than 7, after adequate antidepressant treatment. Patients were treated in a naturalistic setting with a variety of antidepressants according to the psychiatrists' choice [selective serotonin reuptake inhibitors (40.8%), serotonin/norepinephrine reuptake inhibitors (25.2%), noradrenergic antidepressants (11.7%), tricyclic antidepressants (8.7%), and others]. One hundred and seventeen patients were nonresponders and 180 patients were nonremitters. Seventy-five patients were classified as resistant (Table 1). Seventy-six control patients did not have any personal or familial history of psychiatric disorders. The research ethical committees of all participating centers approved the study. A written informed consent was obtained from all the study participants.
Genomic DNA was purified from whole blood using standard phenol–chloroform extraction procedure. All patients and controls were screened for the BDNF gene polymorphisms.
Genotypes were obtained using the Sequenom iPLEX assay (Sequenom, Cambridge, Massachusetts, USA) by Cogenics (Morrisville, North Carolina, USA). Locus-specific PCR primers and allele-specific detection primers were designed using the MassARRAY Assay Design software (Sequenom). The sequences of all primers are listed in the Supplementary data Tables 1 and 2. The sample DNAs were amplified in a 3-PLEX PCR reaction and labeled using a locus-specific single base extension reaction. Amplification reactions were performed with HotstarTaq DNA polymerase (Qiagen, New Jersey, USA), in the 5 μl reaction mixtures containing 0.5 U/μl of DNA polymerase, 1 μl DNA sample (approximately 10 ng/μl), 500 nmol/l of each primer, 2.5 mmol/l dNTPmix (Qiagen), and 0.625 μl of 10X PCR buffer that contained 15 mmol/l MgCl2, 0.325 μl 25 mmol/l MgCl2. After an initial melting step at 95°C for 15 min, amplification was carried out for 45 cycles by denaturing at 95°C for 20 s, annealing at 56°C for 30 s, extending at 72°C for 1 min, and a final extention at 72°C for 6 min for one cycle. Amplified samples were incubated with 1.7 U/μl shrimp alkaline phosphatase at 37°C for 40 min and 85°C for 5 min to obtain purified samples. iPLEX primer extend reactions were performed on these purified samples containing iPLEX extend cocktail [iPLEX terminator mix, extend primer mix (based on mass of the extend primer, see Supplementary data Table 2), enzyme]. To extend primer with the target complementary template, thermal cycling of 200 short cycle programs of 40 cycles at 94°C for 5 s, 5 cycles at 52°C for 5 s, 80°C for 5 s, and after initiation at 94°C for 30 s and finishing at 72°C for 3 min was conducted. The resulting products were desalted by resin treatment and transferred to a 384-element SpectroCHIP array (Sequenom). Allele detection was performed using matrix-assisted laser desorption/ionization–time-of-flight mass spectrometry. The mass spectrograms were analyzed by the MassARRAY TYPER 4.0 software (Sequenom) (Gabriel et al., 2009). The call rates of samples and assays are presented in the Supplementary data Table 3.
Statistical data analysis
We selected eight tSNPs covering all bins of 87.2 kb region of the BDNF gene based on Gratacos et al. (2008) study. The tSNPs and their locations, including rs6265 corresponding to the functional coding variant of Val66Met, are indicated in Table 2 according to their order on the chromosome.
Hardy–Weinberg equilibrium, allele and genotype frequency, and odds ratio (OR) were evaluated by gPLINK v1.05 (http://pngu.mgh.harvard.edu/~purcell/plink/download.shtml). The χ2-test was used to compare the genotype frequency between patients and controls, and stratified group of patients according to their phenotypes. Permutation procedures provide a computationally intensive approach to generating significance levels empirically, which is dealing with rare alleles and small sample sizes, providing a framework for correction for multiple testing. The empirical P value was calculated by comparing each observed test statistic against the maximum of all permuted statistics (over all eight tSNPs) for each single replicates. In addition to the single-locus analysis, haplotype associations and haplotype frequencies for marker combinations were calculated using the program haplo.stats v1.4.0 R package (http://mayoresearch.mayo.edu/mayo/research/schaidlab/software.cfm). Haplotype analysis was carried out using two-adjacent, three-adjacent, and four-adjacent SNPs sliding windows. To avoid false positive findings owing to multiple testing, empirical simulated P values were calculated for the single marker allelic and genotypic tests and using 5000 random permutations of patient and control labels for sliding window, global haplotype, and haplotype-specific P value. Haplotypes with frequencies lower than 1% were excluded from the test. We assessed the significance of each haplotype individually by 5000 permutations, producing empirical P values, and also a global measure of association. Post-hoc comparisons were performed using Bonferroni multiple comparison test. All P values were multiplied 10 times to be considered as significant according to Bonferrroni correction. The power analysis was carried out using G*Power2 (http://www.psycho.uni-duesseldorf.de/aap/projects/gpower).
Single nucleotide polymorphism association analyses
We found differences between cases and controls in terms of age (55.7±17.78 and 39.64±13.12-years old) and sex (79 and 74.6% females), respectively (P<0.001 and <0.05).
We performed a genetic association study in 206 patients with MDD and 76 controls by genotyping eight tSNPs covering whole the genomic region of BDNF. All genotype frequencies of eight SNPs in controls were in Hardy–Weinberg equilibrium (P>0.05 by χ2). When we compared the genotype and allele frequencies of patients and controls, we found that rs6265 G (Val) allele was more frequent among patients than the controls [OR: 1.554 (95% confidence interval; CI: 1.003–2.410), P: 0.0475]. However, this association did not remain after permutation correction for multiple analyses (permutation P: 0.067). There was no significant difference in either the allele or the genotype frequency of patients and controls for the other seven tSNPs. We did not find any significant associations between age, sex, and smoking status of individuals and genotype distribution in patients and controls (data not shown).
The significant allele frequencies of the BDNF gene polymorphisms in treatment response phenotypes are presented in Table 3. C allele for rs10501087, A allele for rs6265, and C allele for rs1491850 were found to be associated with treatment response and nonresistance to the treatment. Significant associations were found among rs10501087, rs6265, rs1491850 SNPs, and response and resistance to antidepressant treatment phenotypes. These three SNPs were significantly less frequent in the nonresponder and the resistant groups. We converted to the corresponding other alleles (T–G–C) with positive associations to make the results more understandable (Table 3 presents the frequencies of T–G–C of the three SNPs). T allele for rs10501087 [OR: 2.058 (95% CI: 1.227–3.448)], G allele for rs6265 [OR: 2.0704 (95% CI: 1.22–3.506)], and T allele for rs1491850 [OR: 1.883 (95% CI: 1.229–2.882)] were significantly more frequent in nonresponders than responders. However, these associations did not remain significant after Bonferroni corrections for multiple analyses.
The allele frequencies of these SNPs in responders were not different from controls, whereas they were significantly more frequent in nonresponders compared with controls. The associations between nonresistant phenotypes and SNPs of rs10501087 and rs6265 still remained significant after Bonferroni corrections [corrected permutation (emp): 0.03599; 0.0399, and power=0.1420; 0.1492, respectively]. The frequencies of T, G, and T alleles for rs10501087, rs6265, and rs1491850 SNPs, respectively were more common in resistant patients than nonresistant group. The difference in rs1491850 allele frequency of resistant patients compared with nonresistant ones was statistically significant [OR: 0.5814 (95% CI: 0.355–0.96), P: 0.03308], but this significance did not remain after the permutation correction (emp: 0.05119). The same three alleles were more frequent in resistant patients when compared with controls [OR: 2.013 (95% CI: 1.134–3.573), emp: 0.0242; OR: 2.078 (95% CI: 1.165–3.706), emp: 0.0168; OR: 1.661 (95% CI: 1.040–2.653), emp: 0.03959, respectively]. However, these associations did not remain significant, after Bonferroni corrections.
None of the SNPs were associated to remission in comparison with controls. The allele frequencies of rs10501087 [OR: 1.597 (95% CI: 1.019–2.505)] and rs6265 SNPs [OR: 1.621 (95% CI: 1.034–2.542)] were significantly different in nonremitters versus the controls. However, these associations did not remain significant after Bonferroni corrections.
Haplotype association study
We used haplo.stats program to estimate multimarker haplotype frequencies and to test each haplotype for differences in frequency among our groups. The significant results from sliding window analyses are represented in Table 4.
The results obtained from these analyses showed significance in several haplotype combinations related to response phenotypes. However, after Bonferroni correction, several associations disappeared.
When we examined the details of significant two-marker, three-marker, and four-marker haplotypes analyses, especially rs10501087, rs6265, and rs1491850 haplotypes, it showed the same pattern in all combinations of treatment response phenotypes. Haplotype combinations of T haplotype for rs10501087, G haplotype for rs6265, and T haplotype for rs1491850 together or with the other SNP haplotypes were more frequent in nonresponders than responders (Table 4). The same SNPs were significantly more frequent in nonresponders compared with controls, whereas no difference was found between responders and controls. Also, these three SNP haplotypes (T–G–T, respectively) together or with the other SNPs, were found to be significantly more frequent in the resistant group compared with the controls.
Pharmacogenetics and personalized medicine in psychiatry aims toward characterizing individual genetic differences in the molecular processes underlying disease pathogenesis, progression, and the response to therapeutics. Patients with MDD show substantial unexplained individual variability in their clinical response to treatment. Response rates of little more than 50% are common for the treatment of depression (Reynolds, 2007; Kemp et al., 2008).
In this study, a significant difference in the BDNF allele frequency of rs6265 was detected between patients and controls (P: 0.0475). However, after permutation correction, we could not find any single-marker associations between BDNF tSNPs including Val66Met (rs6265) and MDD. Our study is however lacking the necessary power to exclude the absence of association.
We focused mainly on the genetic predictors of treatment response phenotypes (response, resistance, and remission to antidepressant treatment). Among the 206 MDD patients recruited, 75 patients were considered as resistant, whereas 117 patients were nonresponders. Our results showed that rs10501087, rs6265, and rs1491850 tSNPs of BDNF may be related to treatment response phenotypes. One of three related SNPs was rs6265 which is the functional exonic SNP, whereas rs10501087 and rs1491850 are located in 3′ downstream and 5′ upstream region of BDNF, respectively, which are not considered to have an effect on the BDNF gene expression. After Bonferroni correction, the allele frequencies of these SNPs in responders/treatment resistant patients were not different from nonresponders/nonresistant ones. When comparing nonresponders and controls, we found an allelic association between T allele for rs10501087 [OR: 2.085 (95% CI: 1.2509–3.475)] and G allele for rs6265 [OR: 2.1753 (95% CI: 1.299–3.643)], despite limited power.
Our results suggest that rs10501087, rs6265, and rs1491850 tSNPs may be related to the treatment response phenotypes investigated, especially phenotype of nonresponse to antidepressant treatment. The association between these SNPs and antidepressant response may be through strong linkage disequilibrium with functional rs6265 and other functional variants of the BDNF gene or nearby gene. In the BDNF gene, the selected SNPs were within two blocks with high linkage disequilibrium a short one of 1 kb and a large one of 79 kb (Gratacos et al., 2008). rs10501087, rs6265, and rs1491850 haplotypes (T–G–T, respectively) using two-adjacent, three-adjacent, and four-adjacent SNPs sliding windows analysis showed significant associations among subgroups stratified by treatment response phenotype. In addition, these haplotype results showed the similar pattern as the one for single SNP association analysis. Our results showed that the haplotype frequencies of combinations in the resistant group had nearly the same haplotype frequencies as that of nonresponsive and nonremitter groups. However, most of these associations disappeared after multiple test corrections.
With respect to the neurotrophin hypothesis of depression, BDNF is of major importance, because it modulates hippocampal plasticity and hippocampal related learning in animals. Recent human studies have indicated that Val66Met polymorphism has an impact on episodic memory, hippocampal morphology, and memory-related hippocampal activity (Egan et al., 2003; Hariri et al., 2003; Hashimoto, 2007). Frodl et al. (2007) showed that the Met allele may be a risk factor for developing smaller hippocampal volumes in depressive patients, whereas Hashimoto et al. (2008) found no genotype effect on episodic memory function, but a negative correlation between the Met allele and encoding related brain activity in the bilateral hippocampi and right parahippocampal gyrus). There is indeed considerable evidence for antidepressant-like effects of BDNF in hippocampal circuits and also growing evidence for prodepression-like effects of BDNF in the mesolimbic dopamine system (Frodl et al., 2007). Although Berton et al. (2006) suggested that BDNF inactivation may lead to antidepressant effects; there are also opposite data claiming BDNF may have antidepressant effects (Kalueff et al., 2006). The forced swim test is widely used for animal model of depression and of antidepressant action (Chen et al., 2008). Adachi et al. (2008) showed that BDNF deletion within the dentate gyrus (DG), (a subregion of the hippocampus), was associated with an attenuated response to desipramine and citalopram in the forced swim test using DG knockout mice lacking either BDNF or TrkB. The DG deletion of BDNF will block the antidepressant induced effects on maturation, survival, and synaptic plasticity of newborn neurons (Wang et al., 2008a, 2008b). The Val66Met polymorphism is also modulated by stressful life events. The knockin mice given equivalent response in forced swim test showed more anxiety-like behavior compared with wildtype mice (Laje and McMahon, 2007). Chen et al. (2008) showed that Met homozygous mice showed equivalent BDNF expression to that of wild mice, but 30% deficit in the activity dependent release of BDNF Met from neurons. In view of the above studies, the mechanistic hypothesis of BDNF in response to antidepressant treatment could be explained by the following cascade. As a consequence of increased BDNF in DG, the enhanced plasticity may allow for increase in cognitive flexibility, which may enable the individual to better adapt to stressful life situations and may be a component of the antidepressant response (Berton et al., 2006). It must be taken into account that the BDNF-mediated signaling is involved in neuroplastic responses to stress and antidepressants, but these effects are both region-specific and antidepressant-specific (Krishnan and Nestler, 2008).
Findings from the genetic association studies exploring the potential relation between BDNF SNPs, mainly Val66Met and MDD have been inconsistent. Duncan et al. (2009) and Sen et al. (2008), found an association between Val/Val genotype and higher levels of depression. However, one large study obtained a positive haplotype association of rs988748, dinucleotide repeat (GTn) and rs6265, and MDD (Schumacher et al., 2008). Our earlier negative findings on rs6265 are consistent with other studies on unipolar patients. The mutant allele frequency of rs6265 in unipolar patients (18.9%) was not significantly different from the allele frequency of control group (20.3%) (Oswald et al., 2005). In addition, no significant association was found between bipolar AD and rs6265 in an other case–control study (Oswald et al., 2004). In two recent meta-analyses, no significant associations between Val66Met polymorphism and MDD were found (Chen et al., 2008; Verhafen et al., 2008). However, when stratifying for sex, meta-analyses showed significant effects in both the allelic and genotypic analyses of Met allele in men with an OR: 1.67 (95% CI: 1.19–2.36) (Chen et al., 2008;). Neither genotype nor allelic distribution of our study showed any significant associations when patients and controls were stratified according to sex and age.
Only one study so far has investigated the same tSNPs containing all genomic regions of BDNF and the treatment response phenotype of AD (Gratacos et al., 2008). We selected the tSNPs based on this study, however, we found that the tagging variations for the BDNF gene in Caucasian populations under the assumption of r2=0.85 and minor allele frequency=0.01 were completely different. They found an association between rs908867 and remitter status, whereas the other seven tSNPs were not related to any phenotypes of treatment. They also showed a T–A–T haplotype association between rs12273363, rs908867, and rs1491850 and remission status of patients. Unlike their findings, we could not find any significant associations between rs908867 and any treatment response phenotypes. The reason for the discrepancy between this study and our study, may be because of our patients being unipolar depressive patients and not bipolar patients. The other reasons may be the differences of designing the study including ethnic differences of patient and control populations (even though their patient origins were Spanish–Caucasian), sample sizes, treatment type and dose, definition of phenotypes, and outcome measures.
In a recent study, Licinio et al. (2009) resequenced the DNA genomic region of BDNF in Mexican–Americans. They investigated the effects of novel SNPs including Val66Met in relation to antidepressant treatment response. Six SNPs (rs12273539, rs11030103, rs6265, rs28722151, rs41282918, and rs11030101) were associated with MDD, however, only one SNP in 5′ untranslated region, rs61888800, was associated with antidepressant response after adjustments. The other studies in Asian populations have only explored the impact of BDNF Val66Met polymorphism on treatment response phenotype (Tsai et al., 2003; Choi et al., 2006). Both of these studies did not show any significant differences in allele and genotype frequency between patients and controls. However, Choi et al. (2006) found an association between Met allele and better response in 8 weeks of citalopram treatment, whereas Tsai et al. (2003) detected a trend towards an association of heterozygous genotype with improved 4 weeks fluoxetine response (P: 0.086). In the study of Choi et al. (2006) the Met allele carriers were significantly higher in the responder group than in nonresponder ones with an OR: 4.375 (95% CI: 1.609–11.892). Our results are consistent with these two Asian population study findings and suggest a better response of antidepressants in patients with a lower activity of the BDNF system.
In contrast to our results, some studies found different associations with Val66Met allele and different SNPs combinations. Domschke et al. (2009) detected a significant relation between rs7103411 C allele and rs6265 A allele and a poor treatment response over 6 weeks of treatment in melancholic depression subtype). Antilla et al. (2007) found an increased risk of treatment-resistant depression in patients with BDNF Met allele and with the combination of serotonin 1A receptor genotype. It has also been shown that BDNF Val66Met SNP interacts with the serotonin transporter polymorphism (5-HTTLPR) on molecular and system level (Uher, 2008). Uher (2008) on the basis of the findings of 5-HTTLPR hypothesized that environmentally sensitive genotype (short 5-HTTLPR and/or Met BDNF) might be associated with worse response to pharmacological treatment and better response to psychological treatment. However, a significant association was reported between SNPs of 5-HTTLPR, BDNF Val66Met, and response to lithium prophylaxis (Rybakowski et al., 2007).
There is consistent evidence suggesting that serum BDNF levels were significantly lower in naive depressed patients than in controls and that antidepressant treatment elevates BDNF levels in depressive patients (Sen et al., 2008). Moreover, the findings may suggest that changes in serum BDNF levels are associated with the pathophysiology of MDD and pharmacological treatment response. However, it needs to be clarified by further studies whether the reduction of serum BDNF levels results from decreased levels of BDNF in the brain or the correlation between serum BDNF levels and the Val66Met genotype. In addition, it must be considered that different antidepressant drugs may have variable effects on serum BDNF levels (Sen et al., 2008). A recent study indicates that venlafaxine increases serum BDNF levels after 6 months therapy, whereas escitalopram did not affect them (Matrisciano et al., 2009).
Moreover, the meta-analysis of clinical studies suggests that different antidepressant treatments and nonpharmacological interventions are associated with an increase in serum BDNF levels (Brunoni et al., 2008). Another recent study confirming earlier studies suggested that the serum BDNF levels could be a potential marker for the response to electroconvulsive therapy and maybe used as a predictor of treatment response (Piccinni et al., 2009). Bocchio-Chiavetto et al. (2008) investigated the role of BDNF SNP combinations and repetitive transcranial magnetic stimulation response in treatment resistant patients group. Their results showed that repetitive transcranial magnetic stimulation treatment significantly improved depression symptomatology (P<0.0001), and the response was significantly greater in Val allele compared with Met allele carrier (P: 0.024). Another association study between Val66Met and depression trait suggested that BDNF Met allele predicted elevated working memory commission errors and altered resting electroencephalographic band activity (Gatt et al., 2008).
The main limitations of our study were its retrospective design, genetic heterogeneity of study group, limited power, and the absence of placebo-controlled study. Another limitation was that the sample size of controls was not comparable with the number of patients. However, we examined the controls for any possible risk of psychiatric disorders, so that our control group included healthy individuals. Even if we applied the Bonferroni corrections to our data to mitigate false positive results, there remains a risk of false positive findings of genetic studies (Sullivan, 2007).
A number of studies have been carried out to examine whether genetic polymorphisms were associated with antidepressant treatment response (Murray and Lopez, 2006). From these the most obvious conclusion is that no single marker seems to account for differences in treatment response of antidepressant. The completion of Sequenced Treatment Alternatives to Relieve Depression study has provided a considerable impulse for understanding genetic factors that may be associated with treatment response phenotypes (Henn, 2008). However, an overall assessment of Sequenced Treatment Alternatives to Relieve Depression study indicated that among the 768 SNPs including BDNF SNPs of rs12273363, rs6265, rs1491850, only serotonin 2A receptor SNP was associated with antidepressant response (McMahon et al., 2006).
In conclusion, our results suggest that rs10501087, rs6265, and rs1491850 tSNPs of BDNF, despite low power, may be associated with a better response to antidepressant treatment. The haplotype results are consistent with the results of single SNP association analyses. Further large prospectives controlled pharmacogenetic studies addressing the involvement of other SNPs of the BDNF gene, and their interaction with other functional genes are warranted to better understand the molecular role of the BDNF gene in relation to antidepressant reponse.
The authors are grateful to all study participants. This study was funded by an unrestricted Grant of the Group for the Study of the Resistant Depression (GSRD), by Lundbeck A/S and by the Belgian National Fund for Scientific Research (FNRS; 3.4.530.07 F).
Financial disclosure: Professor S.M. has been a consultant/speaker for: AstraZeneca, Bristol Myers Squibb, Eli Lilly, GlaxoSmithKline, Johnson & Johnson, Lundbeck, Merck, Merz, Neurim, Pierre Fabre, Pfizer, Sanofi, Servier, Shire, Sepracor, Takeda, Targacept, Wyeth. Dr S.K. has received Grant/Research support from Eli Lilly, Lundbeck, Bristol-Myers Squibb, GlaxoSmithKline, Organon, Sepracor and Servier; has served as a consultant or on advisory boards for AstraZeneca, Bristol-Myers Squibb, GlaxoSmithKline, Eli Lilly, Lundbeck, Pfizer, Organon, Schwabe, Sepracor, Servier, Janssen, and Novartis; and has served on speakers' Bureaus for AstraZeneca, Eli Lily, Lundbeck, Schwabe, Sepracor, Servier, Pierre Fabre, and Janssen. Dr Y.L. has received Honoraria from Pierre Fabre, Lundbeck, Servier, and Pfizer and is a member of the Speakers/Advisory Boards of Eli Lilly, Sanofi, and Novartis. Professor J.M. and Drs N.A.K., I.A., C.F., S.L., I.M., M.N., P.O., L.S., J.Z., and D.S. report no financial or other relationship relevant to the subject of this article.
1. Adachi M, Barrot M, Autry AE, Theobald D, Monteggia LM. Selective loss of brain-derived neurotrophic factor in dentate gyrus attenuates antidepressant efficacy. Biol Psychiatry 2008;63:642–649
2. Antilla S, Huuhka K, Huuhka M, Rontu R, Hurme M, Leinonen E, et al. Interaction between 5-HT1A and BDNF genotypes increases the risk of treatment-resistant depression. J Neural Transm 2007;114:1065–1068
3. Bath KG, Lee FS. Variant BDNF (Val66Met) impact on brain structure and function. Cogn Affect Behav Neurosci 2006;6:79–85
4. Berton O, Krishnan V, Nestler EJ. BDNF in anxiety and depression. Science 2006;312:1598–1599
5. Bocchio-Chiavetto C, Miniussi C, Zanardini R, Gazzoli A, Bignotti S, Specchia C, Gennarelli M. 5-HTTLPR and BDNF Val66Met polymorphisms and response to rTMS treatment in drug resistant depression. Neurosci Lett 2008;437:130–134
6. Brunoni AR, Lopes M, Fregni F. A systematic review and meta-analysis of clinical studies on major depression and BDNF levels: implications for the role of neuroplasticity in depression. Int J Neuropsychopharmacol 2008;11:1169–1180
7. Chen L, Lawlor DA, Lewis SJ, Yuan W, Abdollahi MR, Timpson NJ, et al. Genetic association study of BDNF in depression: finding from two cohort studies and a meta-analysis. Am J Med Genet B Neuropsychiatr Genet 2008;147:814–821
8. Choi MY, Kang RH, Lim SW, Oh KS, Lee MS. Brain-derived neurotrophic factor (BDNF) gene polymorphism and citalopram response in major depressive disorders. Brain Res 2006;1118:176–182
9. De Luca V, Strauss J, Semeralul M, Huang S, Li PP, Warsh JJ, et al. Analysis of BDNF Val66Met allele-specific mRNA levels in bipolar disorder. Neurosci Lett 2008;441:229–232
10. Domschke K, Lawford B, Laje G, Berger K, Young R, Morris P, et al. Brain-derived neurotrophic factor (BDNF) gene: no major impact on antidepressant treatment response. Int J Neuropsychopharmacol 2009;23:1–9
11. Duncan LE, Hutchison KE, Carey G, Craighead WE. Variation brain-derived neurotrophic factor (BDNF) gene is associated with the symptoms of depression. J Affect Disord 2009;115:215–219
12. Egan MF, Kojima M, Callicott JH, Goldberg TE, Kolachana BS, Bertolino A, et al. The BDNF Val66Met polymorphism affects activity-dependent secretion of BDNF and human memory and hippocampal function. Cell 2003;112:257–269
13. Frodl T, Schule C, Schmitt G, Born C, Baghai T, Zill P, et al. Association of brain-derived neurotrophic factor Val66Met polymorphism with reduced hippocampal volumes in major depression. Arch Gen Psychiatry 2007;64:410–416
14. Gabriel S, Ziaugra L, Tabbaa D, Gabriel S, Ziaugra L, Tabbaa D 2009 SNP genotyping using Sequenom MassARRAY iPLEX platform. Curr Protoc Hum Genet Chapter 2. Unit 2.12.
15. Gatt JM, Kuan SA, Dobson-Stone C, Paul RH, Joffe RT, Kemp AH, et al. Association between BDNF Val66Met polymorphism and trait depression is mediated via resting EEG alpha band activity. Biol Psychol 2008;79:275–284
16. Gratacos M, Soria V, Urretavizcaya M, Gonzalez JR, Crespo JM, Bayes M, et al. A brain-derived neurotrophic factor (BDNF) haplotype is associated with antidepressant treatment outcome in mood disorders. Pharmacogenomics J 2008;8:101–112
17. Hariri AR, Goldberg TE, Mattay VS, Kolachana BS, Callicot JH, Ehan MF, et al. Brain-derived neurotrophic factor (BDNF) Val66Met polymorphism affects human memory-related hippocampal activity and predicts memory performance. J Neurosci 2003;23:6690–6694
18. Hashimoto K. BDNF variant linked to anxiety related behaviors. Bioassays 2007;29:116–119
19. Hashimoto R, Moriguchi Y, Yamashita F, Mori T, Nemoto K, Okada T, et al. Dose-dependent effect of the Val66met polymorphism of the brain-derived neurotrophic factor gene on memory-related hippocampal activity. Neurosci Lett 2008;61:360–367
20. Henn FA. Pharmacogenetic studies of depression. Biol Psychiatry 2008;63:1101–1102
21. Kalueff AV, Avgustinovich DF, Kudryavtseva NN, Murphy DL, Berton O, Krishnan V, Nestler EJ. BDNF in anxiety and depression. Science 2006;312:1598–1599
22. Kemp AH, Gordon E, Rush AJ, Williams LM. Improving the prediction of the treatment response in depression: integration of clinical, cognitive, psychophysiological, neuroimaging, and genetic measures. CNS Spectr 2008;13:1066–1086
23. Krishnan V, Nestler EJ. The molecular neurobiology of depression. Nature 2008;455:894–902
24. Laje G, McMahon FJ. The pharmacogenetics of major depression: past, present and future. Biol Psychiatry 2007;62:1205–1207
25. Licinio J, Dong C, Wong ML. Novel sequence variations in the brain-derived neurotrophic factor (BDNF) gene and association with major depression and antidepressant treatment response. Arch Gen Psychiatry 2009;66:488–497
26. Matrisciano F, Bonaccorso S, Ricciardi A, Scaccianoce S, Panaccione I, Wang L, et al. Changes in BDNF serum levels in patients with major depression disorder (MDD) after 6 months treatment with sertraline, escitalopram, or venlafaxine. J Psychiatr Res 2009;43:247–254
27. McMahon FJ, Buervenich S, Charney D, Lipsky R, Rush AJ, Wilson AF, et al. Variation in the gene encoding the Serotonin 2A receptor is associated with outcome of antidepressant treatment. Am J Hum Genet 2006;78:804–814
28. Mulder RT, Joyce PR, Frampton CMA, Luty SE, Sullivan PF. Six months of treatment for depression: outcome and predictors of course of illness. Am J Psychiatry 2006;166:95–100
29. Murray CJ, Lopez AD. Evidence-based health policy-lessons from the Global Burden of Disease study. Science 2006;274:740–743
30. Okada T, Hashimoto R, Numakawa T, Lijima Y, Kosuga A, Tatsumi M, et al. Complex polymorphic region in the Brain-derived neurotrophic factor (BDNF) gene confers susceptibility to bipolar disorder and affects transcriptional activity. Mol Psychiatry 2006;11:695–703
31. Oswald P, Del-Favero J, Massat I, Souery D, Claes S, Van Broeckhoven C, Mendlewicz J. Non-replication of the brain-derived neurotrophic factor (BDNF) association in bipolar affective disorder: a Belgian patient-control study. Am J Med Genet B Neuropsychiatr Genet 2004;129:34–35
32. Oswald P, Del-Favero J, Massat I, Souery D, Claes S, Van Broeckhoven C, Mendlewicz J. No implication of brain-derived neurotrophic factor (BDNF) gene in unipolar affective disorder: evidence from Belgian first and replication patient-control studies. E Neuropsychopharmacol 2005;15:491–495
33. Palmer L, Cardon L. Shaking the tree: mapping complex disease genes with linkage disequilibrium. Lancet 2006;366:1223–1234
34. Piccinni A, Debbio AD, Medda P, Bianchi C, Roncaglia I, Veltri A, et al. Plasma brain-derived neurotrophic factor (BDNF) in treatment-resistant depressed patients receiving electroconvulsive therapy. E Neuropsychopharmacol 2009;15:491–495
35. Pruunsild P, Kazantseva A, Aid T, Palm K, Timmusk T. Dissecting the human BDNF locus: bidirectional transcription, complex splicing and multiple promoters. Genomics 2007;90:397–406
36. Reynolds GP. The impact of pharmacogenetics on the development and use of antipsychotic drugs. Drug Discov Today 2007;12:953–959
38. Rybakowski JK, Suwalska A, Skibinska M, Dmitrzak-Weglarz M, Leszczynska-Rodziewicz A, Hauser J. Response to lithium prophylaxis: interaction between serotonin transporter and BDNF genes. Am J Med Genet B Neuropsychiatr Genet 2007;144:820–823
37. Rybakowski JK. BDNF gene: functional Val66Met polymorphism in mood disorders and schizophrenia. Pharmacogenomics 2008;9:1589–1593
39. Schumacher J, Jamra RA, Becker T, Ohlraun S, Kloop N, Binder EB, et al. Evidence for a relationship between genetic variants at the brain-derived neurotrophic factor (BDNF) locus and major depression. Biol Psychiatry 2008;58:307–314
40. Sen S, Duman R, Sanacora G. Serum brain-derived neurotrophic factor, depression, and antidepressant medications: meta-analyses and implications. Biol Psychiatry 2008;64:527–532
41. Souery D, Oswald P, Massat I, Bailer U, Bollen J, Demyttenaere K, et al. Clinical factors associated with treatment resistance in major depressive disorder: results from a European multicenter study. J Clin Psychiatry 2007;68:1062–1070
42. Sullivan PF. Spurious genetic associations. Biol Psychiatry 2007;61:1121–1126
43. Tsai SJ, Cheng CY, Yu YW, Chen TJ, Hong CJ. Association study of a brain-derived neurotrophic factor genetic polymorphism and major depressive disorders, symptomatology, and antidepressant response. Am J Med Genet B Neuropsychiatr Genet 2003;123:19–22
44. Uher R. The implications of gene-environment interactions in depression: will cause inform cure?. Mol Psychiatry 2008;13:1070–1078
45. Verhafen M, Van der Meij A, Van Deurzen P, Janzing JGE, Arias-Vasquez A, Buitelaar JK, et al. Meta-analysis of BDNF Val66Met polymorphisms in major depressive disorder: effects of gender and ethnicity. Mol Psychiatry 2008;1:1–12
46. Wang JW, Dranovsky A, Hen R. The when and where of BDNF and the antidepressant response. Biol Psychiatry 2008a;63:640–641
47. Wang JW, Dranovsky A, Monckton JE, Battaglia F, Hen R. Chronic fluoxetine stimulates maturation and synaptic plasticity of adult-born hippocampal granule cells. J Neurosci 2008b;28:1374–1384
48. Wray NR, James MR, Handoko HY, Dumenil T, Lind PA, Montgomery GW, Martin NG. Association study of candidate variants from a brain-derived neurotrophic factor and dystrobrevin-binding protein 1 with neuroticism, anxiety and depression. Psychiatr Genet 2008;18:219–225
antidepressant; brain-derived neurotrophic factor; haplotype; major depressive disorder; tagging SNPs; treatment response phenotype
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