Influence and interaction of genetic, cognitive, neuroendocrine and personalistic markers to antidepressant response in Chinese patients with major depression

Machine learning for antidepressant treatment selection in depression

Finding the right antidepressant for the individual patient with major depressive disorder can be a difficult endeavor and is mostly based on trial-and-error. Machine learning (ML) is a promising tool to personalize antidepressant prescription. In this review, we summarize the current evidence of ML in the selection of antidepressants and conclude that its value for clinical practice is still limited. Apart from the current focus on effectiveness, several other factors should be taken into account to make ML-based prediction models useful for clinical application.

The antidepressive mechanism of Longya Lilium combined with Fluoxetine in mice with depression-like behaviors

Traditional Chinese medicine is one of the most commonly used complementary and alternative medicine therapies for depression. Integrated Chinese-western therapies have been extensively applied in numerous diseases due to their superior efficiency in individual treatment. We used the meta-analysis, network pharmacology, and bioinformatics studies to identify the putative role of Longya Lilium combined with Fluoxetine in depression. Depression-like behaviors were mimicked in mice after exposure to the chronic unpredictable mild stress (CUMS). The underlying potential mechanism of this combination therapy was further explored based on in vitro and in vivo experiments to analyze the expression of COX-2, PGE2, and IL-22, activation of microglial cells, and neuron viability and apoptosis in the hippocampus. The antidepressant effect was noted for the combination of Longya Lilium with Fluoxetine in mice compared to a single treatment. COX-2 was mainly expressed in hippocampal CA1 areas. Longya Lilium combined with Fluoxetine reduced the expression of COX-2 and thus alleviated depression-like behavior and neuroinflammation in mice. A decrease of COX-2 curtailed BV-2 microglial cell activation, inflammation, and neuron apoptosis by blunting the PGE2/IL-22 axis. Therefore, a combination of Longya Lilium with Fluoxetine inactivates the COX-2/PGE2/IL-22 axis, consequently relieving the neuroinflammatory response and the resultant depression.

Genetic contributions to brain serotonin transporter levels in healthy adults

The serotonin transporter (5-HTT) critically shapes serotonin neurotransmission by regulating extracellular brain serotonin levels; it remains unclear to what extent 5-HTT levels in the human brain are genetically determined. Here we applied [11C]DASB positron emission tomography to image brain 5-HTT levels and evaluated associations with five common serotonin-related genetic variants that might indirectly regulate 5-HTT levels (BDNF rs6265, SLC6A4 5-HTTLPR, HTR1A rs6295, HTR2A rs7333412, and MAOA rs1137070) in 140 healthy volunteers. In addition, we explored whether these variants could predict in vivo 5-HTT levels using a five-fold cross-validation random forest framework. MAOA rs1137070 T-carriers showed significantly higher brain 5-HTT levels compared to C-homozygotes (2-11% across caudate, putamen, midbrain, thalamus, hippocampus, amygdala and neocortex). We did not observe significant associations for the HTR1A rs6295 and HTR2A rs7333412 genotypes. Our previously observed lower subcortical 5-HTT availability for rs6265 met-carriers remained in the presence of these additional variants. Despite this significant association, our prediction models showed that genotype moderately improved prediction of 5-HTT in caudate, but effects were not statistically significant after adjustment for multiple comparisons. Our observations provide additional evidence that serotonin-related genetic variants modulate adult human brain serotonin neurotransmission.

Depression among Patients with an Implanted Left Ventricular Assist Device: Uncovering Pathophysiological Mechanisms and Implications for Patient Care

Depression is a common and devastating mental illness associated with increased morbidity and mortality, partially due to elevated rates of suicidal attempts and death. Select patients with end-stage heart failure on a waiting-list for a donor heart undergo left ventricular assist device (LVAD) implantation. The LVAD provides a circulatory flow of oxygenated blood to the body, mimicking heart functionality by operating on a mechanical technique. LVAD improves functional capacity and survivability among patients with end-stage heart failure. However, accumulating data suggests that LVAD recipients suffer from an increased incidence of depression and suicide attempts. There is scarce knowledge regarding the pathological mechanism and appropriate treatment approach for depressed LVAD patients. This article summarizes the current evidence on the association between LVAD implantation and occurrence of depression, suggesting possible pathological mechanisms underlying the device-associated depression and reviewing the current treatment strategies. The summarized data underscores the need for a rigorous pre-(LVAD)-implantation psychiatric evaluation, continued post-implantation mental health assessment, and administration of antidepressant treatment as necessary.

The Role of Pharmacogenetics in Personalizing the Antidepressant and Anxiolytic Therapy

Pharmacotherapy for neuropsychiatric disorders, such as anxiety and depression, has been characterized by significant inter-individual variability in drug response and the development of side effects. Pharmacogenetics, as a key part of personalized medicine, aims to optimize therapy according to a patient's individual genetic signature by targeting genetic variations involved in pharmacokinetic or pharmacodynamic processes. Pharmacokinetic variability refers to variations in a drug's absorption, distribution, metabolism, and elimination, whereas pharmacodynamic variability results from variable interactions of an active drug with its target molecules. Pharmacogenetic research on depression and anxiety has focused on genetic polymorphisms affecting metabolizing cytochrome P450 (CYP) and uridine 5'-diphospho-glucuronosyltransferase (UGT) enzymes, P-glycoprotein ATP-binding cassette (ABC) transporters, and monoamine and γ-aminobutyric acid (GABA) metabolic enzymes, transporters, and receptors. Recent pharmacogenetic studies have revealed that more efficient and safer treatments with antidepressants and anxiolytics could be achieved through genotype-guided decisions. However, because pharmacogenetics cannot explain all observed heritable variations in drug response, an emerging field of pharmacoepigenetics investigates how epigenetic mechanisms, which modify gene expression without altering the genetic code, might influence individual responses to drugs. By understanding the epi(genetic) variability of a patient's response to pharmacotherapy, clinicians could select more effective drugs while minimizing the likelihood of adverse reactions and therefore improve the quality of treatment.

Machine Learning and Pharmacogenomics at the Time of Precision Psychiatry

Traditional medicine and biomedical sciences are reaching a turning point because of the constantly growing impact and volume of Big Data. Machine Learning (ML) techniques and related algorithms play a central role as diagnostic, prognostic, and decision-making tools in this field. Another promising area becoming part of everyday clinical practice is personalized therapy and pharmacogenomics. Applying ML to pharmacogenomics opens new frontiers to tailored therapeutical strategies to help clinicians choose drugs with the best response and fewer side effects, operating with genetic information and combining it with the clinical profile. This systematic review aims to draw up the state-of-the-art ML applied to pharmacogenomics in psychiatry. Our research yielded fourteen papers; most were published in the last three years. The sample comprises 9,180 patients diagnosed with mood disorders, psychoses, or autism spectrum disorders. Prediction of drug response and prediction of side effects are the most frequently considered domains with the supervised ML technique, which first requires training and then testing. The random forest is the most used algorithm; it comprises several decision trees, reduces the training set's overfitting, and makes precise predictions. ML proved effective and reliable, especially when genetic and biodemographic information were integrated into the algorithm. Even though ML and pharmacogenomics are not part of everyday clinical practice yet, they will gain a unique role in the next future in improving personalized treatments in psychiatry.

The MAOA rs979605 Genetic Polymorphism Is Differentially Associated with Clinical Improvement Following Antidepressant Treatment between Male and Female Depressed Patients

Major depressive disorder (MDD) is the leading cause of disability worldwide. Treatment with antidepressant drugs (ATD), which target monoamine neurotransmitters including serotonin (5HT), are only modestly effective. Monoamine oxidase (MAO) metabolizes 5HT to 5-hydroxy indoleacetic acid (5HIAA). Genetic variants in the X-chromosome-linked MAO-encoding genes, MAOA and MAOB, have been associated with clinical improvement following ATD treatment in depressed patients. Our aim was to analyze the association of MAOA and MAOB genetic variants with (1) clinical improvement and (2) the plasma 5HIAA/5HT ratio in 6-month ATD-treated depressed individuals. Clinical (n = 378) and metabolite (n = 148) data were obtained at baseline and up to 6 months after beginning ATD treatment (M6) in patients of METADAP. Mixed-effects models were used to assess the association of variants with the Hamilton Depression Rating Scale (HDRS) score, response and remission rates, and the plasma 5HIAA/5HT ratio. Variant × sex interactions and dominance terms were included to control for X-chromosome-linked factors. The MAOA rs979605 and MAOB rs1799836 polymorphisms were analyzed. The sex × rs979605 interaction was significantly associated with the HDRS score (p = 0.012). At M6, A allele-carrying males had a lower HDRS score (n = 24, 10.9 ± 1.61) compared to AA homozygous females (n = 14, 18.1 ± 1.87; p = 0.0067). The rs1799836 polymorphism was significantly associated with the plasma 5HIAA/5HT ratio (p = 0.018). Overall, CC/C females/males had a lower ratio (n = 44, 2.18 ± 0.28) compared to TT/T females/males (n = 60, 2.79 ± 0.27; p = 0.047). The MAOA rs979605 polymorphism, associated with the HDRS score in a sex-dependent manner, could be a useful biomarker for the response to ATD treatment.

Interplay between Serotonin, Immune Response, and Intestinal Dysbiosis in Inflammatory Bowel Disease

Inflammatory Bowel Disease (IBD) is a chronic gastrointestinal disorder characterized by periods of activity and remission. IBD includes Crohn's disease (CD) and ulcerative colitis (UC), and even though IBD has not been considered as a heritable disease, there are genetic variants associated with increased risk for the disease. 5-Hydroxytriptamine (5-HT), or serotonin, exerts a wide range of gastrointestinal effects under both normal and pathological conditions. Furthermore, Serotonin Transporter (SERT) coded by Solute Carrier Family 6 Member 4 (SLC6A4) gene (located in the 17q11.1-q12 chromosome), possesses genetic variants, such as Serotonin Transporter Gene Variable Number Tandem Repeat in Intron 2 (STin2-VNTR) and Serotonin-Transporter-linked promoter region (5-HTTLPR), which have an influence over the functionality of SERT in the re-uptake and bioavailability of serotonin. The intestinal microbiota is a crucial actor in normal human gut physiology, exerting effects on serotonin, SERT function, and inflammatory processes. As a consequence of abnormal serotonin signaling and SERT function under these inflammatory processes, the use of selective serotonin re-uptake inhibitors (SSRIs) has been seen to improve disease activity and extraintestinal manifestations, such as depression and anxiety. The aim of this study is to integrate scientific data linking the intestinal microbiota as a regulator of gut serotonin signaling and re-uptake, as well as its role in the pathogenesis of IBD. We performed a narrative review, including a literature search in the PubMed database of both review and original articles (no date restriction), as well as information about the SLC6A4 gene and its genetic variants obtained from the Ensembl website. Scientific evidence from in vitro, in vivo, and clinical trials regarding the use of selective serotonin reuptake inhibitors as an adjuvant therapy in patients with IBD is also discussed. A total of 194 articles were used between reviews, in vivo, in vitro studies, and clinical trials.

Predicting the Treatment Outcomes of Antidepressants Using a Deep Neural Network of Deep Learning in Drug-Naïve Major Depressive Patients

Predicting the treatment response to antidepressants by pretreatment features would be useful, as up to 70–90% of patients with major depressive disorder (MDD) do not respond to treatment as expected. Therefore, we aim to establish a deep neural network (DNN) model of deep learning to predict the treatment outcomes of antidepressants in drug-naïve and first-diagnosis MDD patients during severe depressive stage using different domains of signature profiles of clinical features, peripheral biochemistry, psychosocial factors, and genetic polymorphisms. The multilayer feedforward neural network containing two hidden layers was applied to build models with tenfold cross-validation. The areas under the curve (AUC) of the receiver operating characteristic curves were used to evaluate the performance of the models. The results demonstrated that the AUCs of the model ranged between 0.7 and 0.8 using a combination of different domains of categorical variables. Moreover, models using the extracted variables demonstrated better performance, and the best performing model was characterized by an AUC of 0.825, using the levels of cortisol and oxytocin, scales of social support and quality of life, and polymorphisms of the OXTR gene. A complex interactions model developed through DNN could be useful at the clinical level for predicting the individualized outcomes of antidepressants.

Integrated co-expression network analysis uncovers novel tissue-specific genes in major depressive disorder and bipolar disorder

Tissue-specific gene expression has been found to be associated with multiple complex diseases including cancer, metabolic disease, aging, etc. However, few studies of brain-tissue-specific gene expression patterns have been reported, especially in psychiatric disorders. In this study, we performed joint analysis on large-scale transcriptome multi-tissue data to investigate tissue-specific expression patterns in major depressive disorder (MDD) and bipolar disorder (BP). We established the strategies of identifying tissues-specific modules, annotated pathways for elucidating biological functions of tissues, and tissue-specific genes based on weighted gene co-expression network analysis (WGCNA) and robust rank aggregation (RRA) with transcriptional profiling data from different human tissues and genome wide association study (GWAS) data, which have been expanded into overlapping tissue-specific modules and genes sharing with MDD and BP. Nine tissue-specific modules were identified and distributed across the four tissues in the MDD and six modules in the BP. In general, the annotated biological functions of differentially expressed genes (DEGs) in blood were mainly involved in MDD and BP progression through immune response, while those in the brain were in neuron and neuroendocrine response. Tissue-specific genes of the prefrontal cortex (PFC) in MDD-, such as IGFBP2 and HTR1A, were involved in disease-related functions, such as response to glucocorticoid, taste transduction, and tissue-specific genes of PFC in BP-, such as CHRM5 and LTB4R2, were involved in neuroactive ligand-receptor interaction. We also found PFC tissue-specific genes including SST and CRHBP were shared in MDD-BP, SST was enriched in neuroactive ligand-receptor interaction, and CRHBP shown was related to the regulation of hormone secretion and hormone transport.

Pharmacogenetic-based management of depression: Role of traditional Persian medicine

Depression is one of the most common mental disorders worldwide. The genetic factors are linked to depression and anti-depressant outcomes. Traditional Persian medicine (TPM) manuscripts have provided various anti-depressant remedies, which may be useful in depression management. This review has studied the bioactive compounds, underlying mechanisms, and treatment outcomes of the medicinal plants traditionally mentioned effective for depression from "The storehouse of medicament" (a famous pharmacopeia of TPM) to merge those with the novel genetics science and serve new scope in depression prevention and management. This review paper has been conducted in two sections: (1) Collecting medicinal plants and their bioactive components from "The storehouse of medicament," "Physician's Desk Reference (PDR) for Herbal Medicines," and "Google scholar" database. (2) The critical key factors and genes in depression pathophysiology, prevention, and treatment were clarified. Subsequently, the association between bioactive components' underlying mechanism and depression treatment outcomes via considering polymorphisms in related genes was derived. Taken together, α-Mangostin, β-carotene, β-pinene, apigenin, caffeic acid, catechin, chlorogenic acid, citral, ellagic acid, esculetin, ferulic acid, gallic acid, gentiopicroside, hyperoside, kaempferol, limonene, linalool, lycopene, naringin, protocatechuic acid, quercetin, resveratrol, rosmarinic acid, and umbelliferone are suitable for future pharmacogenetics-based studies in the management of depression.