Systems Approach to Identify Common Genes and Pathways Associated with Response to Selective Serotonin Reuptake Inhibitors and Major Depression Risk

Links among genetic variants and hierarchical brain structural and functional networks for antidepressant treatment: A multivariate study

BACKGROUND

Antidepressant treatment effects are strongly heritable and have substantial effects on brain function and structure, but the underlying mechanisms are still poorly understood. In this research, we aimed to evaluate the factors of single nucleotide polymorphisms (SNPs) and hierarchical brain structural and functional networks that were associated with antidepressant treatment. Moreover, we further explored the correlations and mediation pattern among "brain structure-brain function-gene" in major depressive disorder (MDD).

METHODS

We analysed 405 SNPs and rich club/feeder/local connections of hierarchical structural and functional networks with three-way parallel independent component analysis in 179 MDD patients. The group-discriminative independent components of the three modalities between responders and non-responders of antidepressant treatment were identified. Pearson correlations and mediation analysis were further utilized to investigate the associations among SNPs and connections of the structural and functional networks.

RESULTS

Notably, correlations with antidepressant treatment outcomes were found in structural, functional and SNP modalities simultaneously. The features of group-discriminative independent components included the shared feeder connections of hub regions with the inferior frontal orbital gyrus and amygdala in structural and functional modalities and genes enriched in circadian rhythmic processes and dopaminergic synapse pathways. The structural feeder network displayed close correlations with SNPs and the functional feeder network. Furthermore, the structural feeder network could mediate the association between SNPs and the functional feeder network, implying that genetic variants might influence brain function by affecting brain structure in MDD.

CONCLUSIONS

These findings provide potential biomarkers for antidepressant therapy and provide a better grasp of the associations among SNPs and hierarchical structural and functional networks in MDD.

Genetic Landscape of Major Depressive Disorder: Assessment of Potential Diagnostic and Antidepressant Response Markers

BACKGROUND

The clinical heterogeneity in major depressive disorder (MDD), variable treatment response, and conflicting findings limit the ability of genomics toward the discovery of evidence-based diagnosis and treatment regimen. This study attempts to curate all genetic association findings to evaluate potential variants for clinical translation.

METHODS

We systematically reviewed all candidates and genome-wide association studies for both MDD susceptibility and antidepressant response, independently, using MEDLINE, particularly to identify replicated findings. These variants were evaluated for functional consequences using different in silico tools and further estimated their diagnostic predictability by calculating positive predictive values.

RESULTS

A total of 217 significantly associated studies comprising 1200 variants across 545 genes and 128 studies including 921 variants across 412 genes were included with MDD susceptibility and antidepressant response, respectively. Although the majority of associations were confirmed by a single study, we identified 31 and 18 replicated variants (in at least 2 studies) for MDD and antidepressant response. Functional annotation of these 31 variants predicted 20% coding variants as deleterious/damaging and 80.6% variants with regulatory effect. Similarly, the response-related 18 variants revealed 25% coding variant as damaging and 88.2% with substantial regulatory potential. Finally, we could calculate the diagnostic predictability of 19 and 5 variants whose positive predictive values ranges from 0.49 to 0.66 for MDD and 0.36 to 0.66 for response.

CONCLUSIONS

The replicated variants presented in our data are promising for disease diagnosis and improved response outcomes. Although these quantitative assessment measures are solely directive of available observational evidence, robust homogenous validation studies are required to strengthen these variants for molecular diagnostic application.

The ERICH3 rs11580409 polymorphism is associated with 6-month antidepressant response in depressed patients

INTRODUCTION

Major Depressive Disorder (MDD) is the current leading cause of disability worldwide. The effect of its main treatment option, antidepressant drugs (AD), is influenced by genetic and metabolic factors. The ERICH3 rs11580409(A > C) genetic polymorphism was identified as a factor influencing serotonin (5HT) levels in a pharmacometabolomics-informed genome-wide association study. It was also associated with response following AD treatment in several cohorts of depressed patients.

OBJECTIVE

Our aim was to analyze the association of the ERICH3 rs11580409(A > C) genetic polymorphism with response following AD treatment and plasma 5HT levels in METADAP, a cohort of 6-month AD-treated depressed patients.

METHODS

Clinical (n = 377) and metabolic (n = 150) data were obtained at baseline and after 3 (M3) and 6 months (M6) of treatment. Linear mixed-effects models and generalized logistic mixed-effects models were used to assess the association of the rs11580409 polymorphism with the Hamilton Depression Rating Scale (HDRS) score, response and remission rates, and plasma 5HT levels.

RESULTS

The interaction between the ERICH3 rs11580409 polymorphism and time was an overall significant factor in mixed-effects models of the HDRS score (F_3,870 = 3.35, P = 0.019). At M6, CC homozygotes had a significantly lower HDRS score compared to A allele carriers (coefficient = -3.50, 95%CI [-6.00--0.99], P = 0.019). No association between rs11580409 and 5HT levels was observed.

CONCLUSION

Our results suggest an association of rs11580409 with response following long-term AD treatment. The rs11580409 genetic polymorphism may be a useful biomarker for treatment response in major depression.

The pharmacogenomics of selective serotonin reuptake inhibitors

Antidepressant medications are frequently used as the first line of treatment for depression. However, their effectiveness is highly variable and influenced by genetic factors. Recently, pharmacogenetic studies, including candidate-gene, genome-wide association studies or polygenic risk scores, have attempted to uncover the genetic architecture of antidepressant response. Genetic variants in at least 27 genes are linked to antidepressant treatment response in both coding and non-coding genomic regions, but evidence is largely inconclusive due to the high polygenicity of the trait and limited cohort sizes in published studies. Future studies should increase the number and diversity of participants to yield sufficient statistical power to characterize the genetic underpinnings and biological mechanisms of treatment response, improve results generalizability and reduce racial health-related inequities.

Effects of Meranzin Hydrate On the LncRNA-miRNA-mRNA Regulatory Network in the Hippocampus of a Rat Model of Depression

Meranzin hydrate (MH) is a frequently used antidepressant drug in China; however it underlying mechanism remains unknown. In this study, we aimed to explore whether MH could ameliorate depression-like behavior in rats by regulating the competitive endogenous RNA (ceRNA) network. We developed a depression-like rat model using an unpredictable chronic mild stress (UCMS) protocol, and the differentially expressed lncRNAs, miRNAs, and mRNAs were identified between the model group and MH group. Then, a ceRNA network responding to MH treatment was constructed by their corresponding relationships in the databases. Finally, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were conducted to explore molecular mechanisms associated with MH treatment. The study indicated that rats in the model group showed loss of weight and deteriorated behavior in behavior tests compared with rats in the normal group. A total of 826 lncRNAs, 121 miRNAs, and 954 mRNAs were differentially expressed in the hippocampus of UCMS rats after MH treatment. In addition, 13 miRNAs were selected, and 12 of them were validated in the hippocampus by qRT-PCR. Then, we predicted upstream lncRNAs and downstream mRNAs of the validated miRNAs and interacted with the results of microarrays. Eventually, a lncRNA-miRNA-mRNA regulatory network, responding to MH treatment, was constructed based on the 314 lncRNAs, 11 miRNAs, and 221 mRNAs. KEGG pathways suggested that these genes may be highly related to Wnt signaling, axon guidance, and MAPK signaling pathways. All these results suggest that MH may be a potential representative compound for the treatment of depression, and its mechanism of action is related to the ceRNA modification.

An Investigation into the Association Between Dopamine Receptor D1 Multilocus Genetic Variation, Multiparametric Magnetic Resonance Imaging, and Antidepressant Treatment

BACKGROUND

Combining genetic variants with neuroimaging phenotypes may facilitate understanding of the biological mechanisms for the etiology and pharmacology of antidepressant treatment of major depressive disorder (MDD).

PURPOSE

To explore the latent pathway of dopamine gene-hierarchical brain network-antidepressant treatment.

STUDY TYPE

Retrospective.

POPULATION

One hundred and sixty-eight MDD inpatients divided into responders (N = 98) or nonresponders (N = 70) based on the treatment outcome of antidepressant.

FIELD STRENGTH/SEQUENCE

Diffusion tensors imaging and resting-state functional magnetic resonance imaging at 3.0T using echo-planar sequence.

ASSESSMENT

Four genetic variations of the dopamine receptor D1 (DRD1) were genotyped. Strengths of rich-club, feeder, and local connections were calculated based on the rich-club organizations of structural and functional brain networks at baseline and following 4 weeks of selective serotonin reuptake inhibitor (SSRI) therapy.

STATISTICAL TESTS

Logistic and linear regressions were used to analyze the impact of DRD1 multilocus genetic profile score on the treatment response of SSRI, and their associations with strengths of rich-club, feeder, and local connections. Mediation models were developed to explore the mediation role of rich-club organizations on the relationship between DRD1 and SSRI therapy response. A P value <0.05 was considered to be statistically significant.

RESULTS

Multiple genetic variations of DRD1 were significantly related to the strengths of feeder connections both in structural and functional networks, and to the treatment response of SSRI. Furthermore, the strength of the structural feeder connection significantly modulated the effect of DRD1 variants on SSRI treatment outcome.

DATA CONCLUSION

DRD1 displayed close connections both with SSRI treatment outcome and rich-club organizations of structural and functional data. Moreover, structural feeder connection played a mediating role in the relationship between DRD1 and antidepressant therapy.

LEVEL OF EVIDENCE

3 TECHNICAL EFFICACY STAGE: 4.

Molecular Mechanisms Associated with Antidepressant Treatment on Major Depression

Major depressive disorder (MDD) is a complex and multifactorial psychiatric disorder that causes serious health, social, and economic concerns worldwide. The main treatment of the symptoms is through antidepressant (AD) drugs. However, not all patients respond properly to these drugs. Omic sciences are widely used to analyze not only biomarkers for the AD response but also their molecular mechanism. In this review, we aimed to focus on omics data to better understand the molecular mechanisms involving AD effects on MDD. We consistently found, from preclinical to clinical data, that glutamatergic transmission, immune/inflammatory processes, energy metabolism, oxidative stress, and lipid metabolism were associated with traditional and potential new ADs. Despite efforts of studies investigating biomarkers of response to ADs, which could contribute to personalized treatment, there is no biomarker panel available for clinical application. From clinical genomic studies, we found that the main findings contribute to the development of pharmacogenomic tests for AD efficacy for each patient. Several studies pointed at DRD2, PXDNL, CACNA1E, and CACNA2D1 genes as potential targets for MDD treatment and the efficacy and rapid-antidepressant effect of ketamine. Finally, more in-depth studies of the molecular targets pointed here are needed to determine the clinical relevance and provide further evidence for precision MDD treatment.

Gastric Serotonin Biosynthesis and Its Functional Role in L-Arginine-Induced Gastric Proton Secretion

Among mammals, serotonin is predominantly found in the gastrointestinal tract, where it has been shown to participate in pathway-regulating satiation. For the stomach, vascular serotonin release induced by gastric distension is thought to chiefly contribute to satiation after food intake. However, little information is available on the capability of gastric cells to synthesize, release and respond to serotonin by functional changes of mechanisms regulating gastric acid secretion. We investigated whether human gastric cells are capable of serotonin synthesis and release. First, HGT-1 cells, derived from a human adenocarcinoma of the stomach, and human stomach specimens were immunostained positive for serotonin. In HGT-1 cells, incubation with the tryptophan hydroxylase inhibitor p-chlorophenylalanine reduced the mean serotonin-induced fluorescence signal intensity by 27%. Serotonin release of 147 ± 18%, compared to control HGT-1 cells (set to 100%) was demonstrated after treatment with 30 mM of the satiating amino acid L-Arg. Granisetron, a 5-HT3 receptor antagonist, reduced this L-Arg-induced serotonin release, as well as L-Arg-induced proton secretion. Similarly to the in vitro experiment, human antrum samples released serotonin upon incubation with 10 mM L-Arg. Overall, our data suggest that human parietal cells in culture, as well as from the gastric antrum, synthesize serotonin and release it after treatment with L-Arg via an HTR3-related mechanism. Moreover, we suggest not only gastric distension but also gastric acid secretion to result in peripheral serotonin release.

Insights into the genomics of affective disorders

Affective disorders, or mood disorders, are a group of neuropsychiatric illnesses that are characterized by a disturbance of mood or affect. Most genetic research in this field to date has focused on bipolar disorder and major depression. Symptoms of major depression include a depressed mood, reduced energy, and a loss of interest and enjoyment. Bipolar disorder is characterized by the occurrence of (hypo)manic episodes, which generally alternate with periods of depression. Formal and molecular genetic studies have demonstrated that affective disorders are multifactorial diseases, in which both genetic and environmental factors contribute to disease development. Twin and family studies have generated heritability estimates of 58–85 % for bipolar disorder and 40 % for major depression.

Large genome-wide association studies have provided important insights into the genetics of affective disorders via the identification of a number of common genetic risk factors. Based on these studies, the estimated overall contribution of common variants to the phenotypic variability (single-nucleotide polymorphism [SNP]-based heritability) is 17–23 % for bipolar disorder and 9 % for major depression. Bioinformatic analyses suggest that the associated loci and implicated genes converge into specific pathways, including calcium signaling. Research suggests that rare copy number variants make a lower contribution to the development of affective disorders than to other psychiatric diseases, such as schizophrenia or the autism spectrum disorders, which would be compatible with their less pronounced negative impact on reproduction. However, the identification of rare sequence variants remains in its infancy, as available next-generation sequencing studies have been conducted in limited samples. Future research strategies will include the enlargement of genomic data sets via innovative recruitment strategies; functional analyses of known associated loci; and the development of new, etiologically based disease models. Researchers hope that deeper insights into the biological causes of affective disorders will eventually lead to improved diagnostics and disease prediction, as well as to the development of new preventative, diagnostic, and therapeutic strategies. Pharmacogenetics and the application of polygenic risk scores represent promising initial approaches to the future translation of genomic findings into psychiatric clinical practice.

Brain Proteomic Analysis on the Effects of the Antidepressant Fluoxetine

Psychiatric disorders such as major depression are linked to early mortality, and patients affected by these conditions are at an increased risk of developing other diseases that are characteristic of the old and very old. Antidepressants are prescribed in the treatment of depression, although the mechanism of how they exert their therapeutic effects is only partly understood. To shed further light on their mode of action, this chapter presents a protocol using two-dimensional differential in-gel electrophoresis (2D-DIGE) combined with matrix-assisted laser desorption/ionization time-of-flight (MALDI-TOF) mass spectrometry for identifying a proteomic signature in guinea pig brains after treatment with the antidepressant, fluoxetine. As this signature pointed toward changes in synaptic structure, we also present a protocol for Western blot analysis targeting selected proteins identified by the combined 2D-DIGE-MALDI-TOF mass spectrometry procedure. Such validation experiments are critical for the translation of putative biomarkers into preclinical and clinical studies.