Dissecting the molecular mechanisms underlying the antidepressant activities of herbal medicines through the comprehensive review of the recent literatures

Depression is clinically defined as a mood disorder with persistent feeling of sadness, despair, fatigue, and loss of interest. The pathophysiology of depression is tightly regulated by the biosynthesis, transport and signaling of neurotransmitters [e.g., serotonin, norepinephrine, dopamine, or γ-aminobutyric acid (GABA)] in the central nervous system. The existing antidepressant drugs mainly target the dysfunctions of various neurotransmitters, while the efficacy of antidepressant therapeutics is undermined by different adverse side-effects. The present review aimed to dissect the molecular mechanisms underlying the antidepressant activities of herbal medicines toward the development of effective and safe antidepressant drugs. Our strategy involved comprehensive review and network pharmacology analysis for the active compounds and associated target proteins. As results, 45 different antidepressant herbal medicines were identified from various in vivo and in vitro studies. The antidepressant mechanisms might involve multiple signaling pathways that regulate neurotransmitters, neurogenesis, anti-inflammation, antioxidation, endocrine, and microbiota. Importantly, herbal medicines could modulate broader spectrum of the cellular pathways and processes to attenuate depression and avoid the side-effects of synthetic antidepressant drugs. The present review not only recognized the antidepressant potential of herbal medicines but also provided molecular insights for the development of novel antidepressant drugs.

Association of polymorphic variants of <i>GRIN2A</i> and <i>GRIN2B</i> genes with alcohol and tobacco abuse in patients with schizophrenia

Aim. To compare the frequency of genotypes for polymorphic variants of GRIN2A and GRIN2B genes in patients with schizophrenia and addictive behavior (alcohol / tobacco abuse) and in patients with schizophrenia without addictive behavior in the Slavic population of the Tomsk region.

Materials and methods. The study included 219 inpatients with the established diagnosis of schizophrenia who received treatment in the clinics of Mental Health Research Institute and Tomsk Clinical Psychiatric Hospital. A history of alcohol / tobacco abuse was identified during a clinical interview and objective data collection. DNA was isolated from peripheral blood leukocytes by standard phenol – chloroform extraction.

15 single nucleotide polymorphisms (SNPs) in the GRIN2A gene and 9 polymorphisms in the GRIN2B gene were selected for genotyping. Allelic variants were determined by real-time polymerase chain reaction (PCR) with specific primers. The SPSS 17.0 software package was used for statistical data processing. The distribution of genotype frequency was assessed using the Pearson’s χ2 test with the Yates’ correction and the Fisher’s exact test.

Results. Significant differences in the allele frequency for the rs9788936 polymorphism in the GRIN2A gene (χ2 = 4.23, p = 0.04) and for the rs10845838 polymorphism in the GRIN2B gene (χ2 = 4.27, p = 0.04) were reveled between the groups of patients with and without alcohol abuse. It was found that the polymorphic variant rs8049651 of the GRIN2A gene had a clear association (F = 8.06, p = 0.029) with the development of tobacco addiction in patients with schizophrenia.

Conclusion. The study identified the association between alcohol abuse and the rs9788936 polymorphism in the GRIN2A gene and the rs10845838 polymorphism in the GRIN2B gene in patients with schizophrenia. The association between the rs8049651 and rs7190619 polymorphisms in the GRIN2A gene and the development of tobacco abuse in patients with schizophrenia was revealed.

Development of mass spectrometry-based relatively quantitative targeted method for amino acids and neurotransmitters: Applications in the diagnosis of major depression

Targeted metabolomics analysis based on triple quadrupole (QQQ) MS coupled with multiple reaction monitoring mode (MRM) is the gold standard for metabolite quantification and it is widely applied in metabolomics. However, standard compounds for each metabolite and the corresponding analogs are necessary for quantitative measurements. To identify the differentially present metabolites in various groups, determining the relative concentration of metabolites would be more efficient than accurate quantification. In this study, a relatively quantitative targeted method was established for metabonomics research, on the basis of hydrophilic interaction liquid chromatography (HILIC)/QQQ MS operated in MRM mode. The quality control-base random forest signal correction algorithm (QC-RFSC algorithm) was applied for quality control instead of the internal standard method. High quality relative quantification was achieved without internal standards, and integrated peak areas were successfully used for statistical and pathway analyses. Amino acids and neurotransmitters (dopamine, kynurenic acid, urocanic acid, tryptophan, kynurenine, tyrosine, valine, threonine, serine, alanine, glycine, glutamine, citrulline, GABA, glutamate, aspartate, arginine, ornithine and histidine) in serum samples were simultaneously determined with the newly developed method. To demonstrate the applicability of this method in large-scale analyses, we analyzed the above metabolites in serum from patients with major depression. The serum levels of glutamate, aspartate, threonine, glycine and alanine were significantly higher, and those of citrulline, kynurenic acid and urocanic acid were significantly lower, in patients with major depression than in controls. This is the first report of the difference in urocanic acid, a compound reported to improve glutamate biosynthesis and release in the central nervous system, between healthy controls and patients with major depression.