Preclinical studies have shown that brain-derived neurotrophic factor (BDNF) may be involved in both antidepressant action and the pathophysiology of major depressive disorder (MDD). The present study tested the hypothesis that the BDNF-gene Val66Met polymorphism is associated with MDD, its clinical manifestations, and antidepressant response. To elucidate a genetic predisposition of MDD, we studied BDNF-gene Val66Met polymorphism in 152 MDD patients in 255 normal controls. We also examined the association of this polymorphism and fluoxetine therapeutic response in 110 MDD patients who received a 4-week fluoxetine treatment. No significant differences were demonstrated for the genotype or allele frequency of the BDNF polymorphism comparing the MDD and control groups. Further, no significant differences were noted comparing the three-genotype groups for depressive-cluster symptoms. However, a trend (P = 0.086) to improved 4-week-fluoxetine antidepressant response was demonstrated for heterozygous patients in comparison to homozygous analogs. This finding suggests the BDNF polymorphism investigated plays no major role in the pathogenesis of MDD.

Monoamine oxidase A (MAOA), a mitochondrial enzyme involved in the degradation of biogenic amines, may be implicated in the pathogenesis of major depressive disorder (MDD) and be related to the therapeutic effects of antidepressants. To elucidate a genetic predisposition of MDD, we studied a variable-number-tandem-repeat (VNTR) polymorphism in the promoter region of the MAOA gene in a Chinese population of 230 MDD patients and 217 controls. We also examined the association of this polymorphism and antidepressant therapeutic response in the MDD patients who underwent a 4-week fluoxetine treatment. Our results showed a significantly increased frequency of 4-repeat (4R) allele in MDD patients, especially in the female population. Furthermore, MDD female patients who were 3R homozygotes had a significantly better response to 4-week fluoxetine treatment when compared to 4R carriers (p ¼ 0.024), but there was a nonsignificant difference found in male patients (p ¼ 0.081). Since the 4R allele transcribed 2-10 times more efficiently than those with 3R allele, our findings suggest that the MAOA-uVNTR may be involved in the pathogenesis of MDD and the antidepressant therapeutic mechanisms in Chinese population, and that there may be a gender effect in this association.

In the wake of recent advances in scientific research, personalized medicine using deep learning techniques represents a new paradigm. In this work, our goal was to establish deep learning models which distinguish responders from non-responders, and also to predict possible antidepressant treatment outcomes in major depressive disorder (MDD). To uncover relationships between the responsiveness of antidepressant treatment and biomarkers, we developed a deep learning prediction approach resulting from the analysis of genetic and clinical factors such as single nucleotide polymorphisms (SNPs), age, sex, baseline Hamilton Rating Scale for Depression score, depressive episodes, marital status, and suicide attempt status of MDD patients. The cohort consisted of 455 patients who were treated with selective serotonin reuptake inhibitors (treatment-response rate = 61.0%; remission rate = 33.0%). By using the SNP dataset that was original to a genome-wide association study, we selected 10 SNPs (including ABCA13 rs4917029, BNIP3 rs9419139, CACNA1E rs704329, EXOC4 rs6978272, GRIN2B rs7954376, LHFPL3 rs4352778, NELL1 rs2139423, NUAK1 rs2956406, PREX1 rs4810894, and SLIT3 rs139863958) which were associated with antidepressant treatment response. Furthermore, we pinpointed 10 SNPs (including ARNTL rs11022778, CAMK1D rs2724812, GABRB3 rs12904459, GRM8 rs35864549, NAALADL2 rs9878985, NCALD rs483986, PLA2G4A rs12046378, PROK2 rs73103153, RBFOX1 rs17134927, and ZNF536 rs77554113) in relation to remission. Then, we employed multilayer feedforward neural networks (MFNNs) containing 1–3 hidden layers and compared MFNN models with logistic regression models. Our analysis results revealed that the MFNN model with 2 hidden layers (area under the receiver operating characteristic curve (AUC) = 0.8228 ± 0.0571; sensitivity = 0.7546 ± 0.0619; specificity = 0.6922 ± 0.0765) performed maximally among predictive models to infer the complex relationship between antidepressant treatment response and biomarkers. In addition, the MFNN model with 3 hidden layers (AUC = 0.8060 ± 0.0722; sensitivity = 0.7732 ± 0.0583; specificity = 0.6623 ± 0.0853) achieved best among predictive models to predict remission. Our study indicates that the deep MFNN framework may provide a suitable method to establish a tool for distinguishing treatment responders from non-responders prior to antidepressant therapy.

Serotonin systems appear to play a key role in the pathogenesis of major depression and the therapeutic mechanisms of antidepressants. The firing rate of dorsal raphe serotonergic neurons is controlled by somatodendritic 5-hydroxytryptamine 1A (HTR1A) autoreceptors, and desensitization of these receptors is implicated in the antidepressant mechanism of selective serotonin reuptake inhibitors. We tested whether a functional polymorphism (C-1019G) in the promoter region of the HTR1A gene and serotonin-related genetic variants are related to fluoxetine antidepressant effect. We genotyped the HTR1A C-1019G polymorphism as well as polymorphisms in the serotonin transporter gene-linked polymorphic region (SERTPR), variable-number tandem-repeat polymorphisms in intron 2 (STin2) of the serotonin transporter gene, serotonin 2A receptor (T102C), tryptophan hydroxylase (A218C), and G-protein beta3 subunit (C825T) in 224 Chinese patients from southern Taiwan with major depression, who accepted 4-week fluoxetine treatment and therapeutic evaluation. Our results demonstrated that the HTR1A À1019C/C carriers (P ¼ 0.009) and SERTPR l/l carriers (Po0.001) showed a better response to fluoxetine, while other polymorphisms were not associated with fluoxetine therapeutic response. The major limitation of this study is the lack of a placebo control. Future prospective study with placebo control may help to predict and individualize antidepressant treatment.

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