Targeted exome sequencing identifies five novel loci at genome-wide significance for modulating antidepressant response in patients with major depressive disorder

Uncommon Protein-Coding Variants Associated With Suicide Attempt in a Diverse Sample of U.S. Army Soldiers

BACKGROUND

Suicide is a societal and public health concern of global scale. Identifying genetic risk factors for suicide attempt can characterize underlying biology and enable early interventions to prevent deaths. Recent studies have described common genetic variants for suicide-related behaviors. Here, we advance this search for genetic risk by analyzing the association between suicide attempt and uncommon variation exome-wide in a large, ancestrally diverse sample.

METHODS

We sequenced whole genomes of 13,584 soldiers from the Army STARRS (Army Study to Assess Risk and Resilience in Servicemembers), including 979 individuals with a history of suicide attempt. Uncommon, nonsilent protein-coding variants were analyzed exome-wide for association with suicide attempt using gene-collapsed and single-variant analyses.

RESULTS

We identified 19 genes with variants enriched in individuals with history of suicide attempt, either through gene-collapsed or single-variant analysis (Bonferroni padjusted < .05). These genes were CIB2, MLF1, HERC1, YWHAE, RCN2, VWA5B1, ATAD3A, NACA, EP400, ZNF585A, LYST, RC3H2, PSD3, STARD9, SGMS1, ACTR6, RGS7BP, DIRAS2, and KRTAP10-1. Most genes had variants across multiple genomic ancestry groups. Seventeen of these genes were expressed in healthy brain tissue, with 9 genes expressed at the highest levels in the brain versus other tissues. Brains from individuals deceased from suicide aberrantly expressed RGS7BP (padjusted = .035) in addition to nominally significant genes including YWHAE and ACTR6, all of which have reported associations with other mental disorders.

CONCLUSIONS

These results advance the molecular characterization of suicide attempt behavior and support the utility of whole-genome sequencing for complementing the findings of genome-wide association studies in suicide research.

Mutational landscape of risk variants in comorbid depression and obesity: a next-generation sequencing approach

Major depression (MD) and obesity are complex genetic disorders that are frequently comorbid. However, the study of both diseases concurrently remains poorly addressed and therefore the underlying genetic mechanisms involved in this comorbidity remain largely unknown. Here we examine the contribution of common and rare variants to this comorbidity through a next-generation sequencing (NGS) approach. Specific genomic regions of interest in MD and obesity were sequenced in a group of 654 individuals from the PISMA-ep epidemiological study. We obtained variants across the entire frequency spectrum and assessed their association with comorbid MD and obesity, both at variant and gene levels. We identified 55 independent common variants and a burden of rare variants in 4 genes (PARK2, FGF21, HIST1H3D and RSRC1) associated with the comorbid phenotype. Follow-up analyses revealed significantly enriched gene-sets associated with biological processes and pathways involved in metabolic dysregulation, hormone signaling and cell cycle regulation. Our results suggest that, while risk variants specific to the comorbid phenotype have been identified, the genes functionally impacted by the risk variants share cell biological processes and signaling pathways with MD and obesity phenotypes separately. To the best of our knowledge, this is the first study involving a targeted sequencing approach toward the study of the comorbid MD and obesity. The framework presented here allowed a deep characterization of the genetics of the co-occurring MD and obesity, revealing insights into the mutational and functional profile that underlies this comorbidity and contributing to a better understanding of the relationship between these two disabling disorders.

Adjunctive cariprazine as a novel effective strategy for treating major depressive disorder: A systematic review and meta-analysis

BACKGROUND

Cariprazine has been approved by the Food and Drug Administration for treating bipolar depression and as an adjunctive treatment for Major Depressive Disorder (MDD). However, it remains unclear about its pharmacological efficacy in treating MDD. Therefore, a meta-analysis was conducted to investigate the adjunctive use of cariprazine in MDD.

METHODS

Electronic databases were searched for eligible studies evaluating the efficacy and safety of cariprazine in patients with MDD up to November 15, 2023. The changes in Montgomery-Asberg Depression Rating Scale (MADRS) score and incidence of adverse events (AEs), which represents of efficacy and tolerability, are considered as the main outcomes.

RESULTS

A total of 3066 patients with MDD included in all across 5 RCTs. With regard to MADRS score, cariprazine group showed better results than control group (SMD = -0.12, 95% CI -0.19 to -0.04, P = 0.002, 5 RCTs, n = 3066). Cariprazine, meanwhile, improved the MADRS response (RR = 1.19, 95% CI 1.08 to 1.31, P = 0.0004, 5 RCTs, n = 3066). For safety outcomes, statistical difference was observed in AEs (RR = 1.26, 95% CI 1.18 to 1.35, P < 0.00001, 5 RCTs, n = 3077). The suicide ideation and SAEs showed no statistical difference between two groups.

CONCLUSION

Cariprazine demonstrated antidepressant effect as an augmentation therapy in treating MDD. Meanwhile, the tolerability of it was acceptable as an adjunctive treatment. However, studies with larger sample sizes are still needed to explore the optimal dosage.

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.

Large-scale whole-exome sequencing association study identifies FOXH1 gene and sphingolipid metabolism pathway influencing major depressive disorder

In the present study, we performed an exome-wide investigation of the burden of rare disease-causing variants for major depressive disorder (MDD) using 16,702 samples from UK biobank. Gene-based association analysis and candidate gene prioritization analysis indicated that FOXH1 have significant association with MDD. In addition, sphingolipid metabolism pathway was found to be less enriched with rare disease-causing variants in the MDD group, suggesting that this gene set may be involved in the pathophysiology of MDD.

Antidepressants and Risk of Sudden Cardiac Death: A Network Meta-Analysis and Systematic Review

Background: Antidepressants are one of the most prescribed medications, particularly for patients with mental disorders. Nevertheless, there are still limited data regarding the risk of ventricular arrhythmia (VA) and sudden cardiac death (SCD) associated with these medications. Thus, we performed systemic review and meta-analysis to characterize the risks of VA and SCD among patients who used common antidepressants. Methods: A literature search for studies that reported risk of ventricular arrhythmias and sudden cardiac death in antidepressant use from MEDLINE, EMBASE, and Cochrane Database from inception through September 2020. A random-effects model network meta-analysis model was used to analyze the relation between antidepressants and VA/SCD. Surface Under Cumulative Ranking Curve (SUCRA) was used to rank the treatment for each outcome. Results: The mean study sample size was 355,158 subjects. Tricyclic antidepressant (TCA) patients were the least likely to develop ventricular arrhythmia events/sudden cardiac deaths at OR 0.24, 0.028–1.2, OR 0.32 (95% CI 0.038–1.6) for serotonin and norepinephrine reuptake inhibitors (SNRI), and OR 0.36 (95% CI 0.043, 1.8) for selective serotonin reuptake inhibitors (SSRI), respectively. According to SUCRA analysis, TCA was on a higher rank compared to SNRI and SSRI considering the risk of VA/SCD. Conclusion: Our network meta-analysis demonstrated the low risk of VA/SCD among patients using antidepressants for SNRI, SSRI and especially, TCA. Despite the relatively lowest VA/SCD in TCA, drug efficacy and other adverse effects should be taken into account in patients with mental disorders.

Prospects for personalization of depression treatment with genome sequencing

The effectiveness of antidepressants in the treatment of major depressive disorder varies considerably between patients. With these interindividual differences and a number of antidepressants to choose from, the first choice of treatment often fails to produce improvement in the patient's condition. A substantial part of the variation in response to antidepressants can be explained by genetic factors. Accordingly, variants related to drug metabolism in two pharmacogenes, CYP2D6 and CYP2C19, have already been translated into guidelines for antidepressant prescriptions. The role of variants in other genes that influence antidepressant responses is not yet understood. Furthermore, rare and individual variants account for a substantial part of genetic differences in antidepressant efficacy. Recent years have brought a tremendous increase in the accessibility of genome sequencing in terms of data availability and its clinical use. In this review, we summarize recent developments and current issues in the personalization of major depressive disorder treatment through pharmacogenomics.

Whole-genome sequencing reveals KRTAP1-1 as a novel genetic variant associated with antidepressant treatment outcomes

Achieving remission following initial antidepressant therapy in patients with major depressive disorder (MDD) is an important clinical result. Making predictions based on genetic markers holds promise for improving the remission rate. However, genetic variants found in previous genetic studies do not provide robust evidence to aid pharmacogenetic decision-making in clinical settings. Thus, the objective of this study was to perform whole-genome sequencing (WGS) using genomic DNA to identify genetic variants associated with the treatment outcomes of selective serotonin reuptake inhibitors (SSRIs). We performed WGS on 100 patients with MDD who were treated with escitalopram (discovery set: 36 remitted and 64 non-remitted). The findings were applied to an additional 553 patients with MDD who were treated with SSRIs (replication set: 185 remitted and 368 non-remitted). A novel loss-of-function variant (rs3213755) in keratin-associated protein 1-1 (KRTAP1-1) was identified in this study. This rs3213755 variant was significantly associated with remission following antidepressant treatment (p = 0.0184, OR 3.09, 95% confidence interval [CI] 1.22-7.80 in the discovery set; p = 0.00269, OR 1.75, 95% CI 1.22-2.53 in the replication set). Moreover, the expression level of KRTAP1-1 in surgically resected human temporal lobe samples was significantly associated with the rs3213755 genotype. WGS studies on a larger sample size in various ethnic groups are needed to investigate genetic markers useful in the pharmacogenetic prediction of remission following antidepressant treatment.

Rare Functional Variants Associated with Antidepressant Remission in Mexican-Americans: Short title: Antidepressant remission and pharmacogenetics in Mexican-Americans

INTRODUCTION

Rare genetic functional variants can contribute to 30-40% of functional variability in genes relevant to drug action. Therefore, we investigated the role of rare functional variants in antidepressant response.

METHOD

Mexican-American individuals meeting the Diagnostic and Statistical Manual-IV criteria for major depressive disorder (MDD) participated in a prospective randomized, double-blind study with desipramine or fluoxetine. The rare variant analysis was performed using whole-exome genotyping data. Network and pathway analyses were carried out with the list of significant genes.

RESULTS

The Kernel-Based Adaptive Cluster method identified functional rare variants in 35 genes significantly associated with treatment remission (False discovery rate, FDR <0.01). Pathway analysis of these genes supports the involvement of the following gene ontology processes: olfactory/sensory transduction, regulation of response to cytokine stimulus, and meiotic cell cycleprocess.

LIMITATIONS

Our study did not have a placebo arm. We were not able to use antidepressant blood level as a covariate. Our study is based on a small sample size of only 65 Mexican-American individuals. Further studies using larger cohorts are warranted.

CONCLUSION

Our data identified several rare functional variants in antidepressant drug response in MDD patients. These have the potential to serve as genetic markers for predicting drug response.

TRIAL REGISTRATION

ClinicalTrials.gov NCT00265291.

Potential of Antithrombin III as a Biomarker of Antidepressive Effect in Major Depressive Disorder

Background: The evaluation of treatment response to antidepressant therapy commonly depends on neuropsychologic assessments, as there are currently no suitable biomarkers. Previous research has identified a panel of increased proteins in patients with major depressive disorder (MDD), including antithrombin III (ATIII), as potential biomarkers of depression. Methods: A total of 90 MDD patients were recruited. Of these, 74 patients received occipital repetitive transcranial magnetic stimulation (rTMS) as individualized, standard, or sham treatment for 5 days, and underwent the complete procedure, including clinical assessments, blood collection, and protein measurement. Results: After treatment, ATIII was significantly decreased in both the individualized and standard groups (both p < 0.001) relative to the sham group. In the individualized group, reduction in ATIII was associated with improvements in several neuropsychological assessments. Furthermore, ATIII at baseline in the standard group and after individualized rTMS showed good performance for evaluating or predicting the response to five-day treatment (AUC = 0.771, 95% CI, 0.571-0.971; AUC = 0.875, 95% CI, 0.714-1.000, respectively) and remission at follow-up (AUC = 0.736, 95% CI, 0.529-0.943; AUC = 0.828, 95% CI, 0.656-1.000, respectively). Lastly, both baseline ATIII and change in ATIII showed good predictive value for the 24-item Hamilton Depression Rating Scale at follow-up (p = 0.024 and 0.023, respectively). Conclusion: Our study revealed a reduction in ATIII after occipital rTMS in MDD patients and a relationship between change in ATIII and therapeutic response. Taken together, these findings provide evidence for the potential of ATIII as a biomarker for the evaluation and prediction of antidepressive effects.

Radiomic Analysis of Native T1 Mapping Images Discriminates Between MYH7 and MYBPC3-Related Hypertrophic Cardiomyopathy

BACKGROUND

The phenotype via conventional cardiac MRI analysis of MYH7 (β-myosin heavy chain)- and MYBPC3 (β-myosin-binding protein C)-associated hypertrophic cardiomyopathy (HCM) groups is similar. Few studies exist on the genotypic-phenotypic association as assessed by machine learning in HCM patients.

PURPOSE

To explore the phenotypic differences based on radiomics analysis of T₁ mapping images between MYH7 and MYBPC3-associated HCM subgroups.

STUDY TYPE

Prospective observational study.

SUBJECTS

In all, 102 HCM patients with pathogenic, or likely pathogenic mutation, in MYH7 (n = 68) or MYBPC3 (n = 34) genes.

FIELD STRENGTH/SEQUENCE

Cardiac MRI was performed at 3.0T with balanced steady-state free precession (bSSFP), phase-sensitive inversion recovery (PSIR) late gadolinium enhancement (LGE), and modified Look-Locker inversion recovery (MOLLI) T₁ mapping sequences.

ASSESSMENT

All patients underwent next-generation sequencing and Sanger genetic sequencing. Left ventricular native T₁ and LGE were analyzed. One hundred and fifty-seven radiomic features were extracted and modeled using a support vector machine (SVM) combined with principal component analysis (PCA). Each subgroup was randomly split 4:1 (feature selection / test validation).

STATISTICAL TESTS

Mann-Whitney U-tests and Student's t-tests were performed to assess differences between subgroups. A receiver operating characteristic (ROC) curve was used to assess the model's ability to stratify patients based on radiomic features.

RESULTS

There were no significant differences between MYH7- and MYBPC3-associated HCM subgroups based on traditional native T₁ values (global, basal, and middle short-axis slice native T₁ ; P = 0.760, 0.914, and 0.178, respectively). However, the SVM model combined with PCA achieved an accuracy and area under the curve (AUC) of 92.0% and 0.968 (95% confidence interval [CI]: 0.968-0.971), respectively. For the test validation dataset, the accuracy and AUC were 85.5% and 0.886 (95% CI: 0.881-0.901), respectively.

DATA CONCLUSION

Radiomic analysis of native T₁ mapping images may be able to discriminate between MYH7- and MYBPC3-associated HCM patients, exceeding the performance of conventional native T₁ values.

LEVEL OF EVIDENCE

3 TECHNICAL EFFICACY STAGE: 2 J. MAGN. RESON. IMAGING 2020;52:1714-1721.