Uncovering the Genetic Architecture of Major Depression

Chronic restraint stress induces depression-like behaviors and alterations in the afferent projections of medial prefrontal cortex from multiple brain regions in mice

INTRODUCTION

The medial prefrontal cortex (mPFC) forms output pathways through projection neurons, inversely receiving adjacent and long-range inputs from other brain regions. However, how afferent neurons of mPFC are affected by chronic stress needs to be clarified. In this study, the effects of chronic restraint stress (CRS) on the distribution density of mPFC dendrites/dendritic spines and the projections from the cortex and subcortical brain regions to the mPFC were investigated.

METHODS

In the present study, C57BL/6 J transgenic (Thy1-YFP-H) mice were subjected to CRS to establish an animal model of depression. The infralimbic (IL) of mPFC was selected as the injection site of retrograde AAV using stereotactic technique. The effects of CRS on dendrites/dendritic spines and afferent neurons of the mPFC IL were investigaed by quantitatively assessing the distribution density of green fluorescent (YFP) positive dendrites/dendritic spines and red fluorescent (retrograde AAV recombinant protein) positive neurons, respectively.

RESULTS

The results revealed that retrograde tracing virus labeled neurons were widely distributed in ipsilateral and contralateral cingulate cortex (Cg1), second cingulate cortex (Cg2), prelimbic cortex (PrL), infralimbic cortex, medial orbital cortex (MO), and dorsal peduncular cortex (DP). The effects of CRS on the distribution density of mPFC red fluorescence positive neurons exhibited regional differences, ranging from rostral to caudal or from top to bottom. Simultaneously, CRS resulted a decrease in the distribution density of basal, proximal and distal dendrites, as well as an increase in the loss of dendritic spines of the distal dendrites in the IL of mPFC. Furthermore, varying degrees of red retrograde tracing virus fluorescence signals were observed in other cortices, amygdala, hippocampus, septum/basal forebrain, hypothalamus, thalamus, mesencephalon, and brainstem in both ipsilateral and contralateral brain. CRS significantly reduced the distribution density of red fluorescence positive neurons in other cortices, hippocampus, septum/basal forebrain, hypothalamus, and thalamus. Conversely, CRS significantly increased the distribution density of red fluorescence positive neurons in amygdala.

CONCLUSION

Our results suggest a possible mechanism that CRS leads to disturbances in synaptic plasticity by affecting multiple inputs to the mPFC, which is characterized by a decrease in the distribution density of dendrites/dendritic spines in the IL of mPFC and a reduction in input neurons of multiple cortices to the IL of mPFC as well as an increase in input neurons of amygdala to the IL of mPFC, ultimately causing depression-like behaviors.

Neurodevelopmental signature of a transcriptome-based polygenic risk score for depression

Disentangling the molecular underpinnings of major depressive disorder (MDD) is necessary for identifying new treatment and prevention targets. The functional impact of depression-related transcriptomic changes on the brain remains relatively unexplored. We recently developed a novel transcriptome-based polygenic risk score (tPRS) composed of genes transcriptionally altered in MDD. Here, we sought to investigate effects of tPRS on brain structure in a developmental cohort (Adolescent Brain Cognitive Development study; n = 5124; 2387 female) at baseline (9-10 years) and 2-year follow-up (11-12 years). We tested associations between tPRS and Freesurfer-derived measures of cortical thickness, cortical surface area, and subcortical volume. Across the whole sample, higher tPRS was significantly associated with thicker left posterior cingulate cortex at both baseline and 2-year follow-up. In females only, tPRS was associated with lower right hippocampal volume at baseline and 2-year follow-up, and lower right pallidal volume at baseline. Furthermore, regional subcortical volume significantly mediated an indirect effect of tPRS on depressive symptoms in females at both timepoints. Conversely, tPRS did not have significant effects on cortical surface area. These findings suggest the existence of a sex-specific neurodevelopmental signature associated with shifts towards a more depression-like brain transcriptome, and highlight novel pathways of developmentally mediated MDD risk.

Decoding depression by exploring the exposome-genome edge amidst COVID-19 lockdown

Risk of depression increased in the general population after the COVID-19 pandemic outbreak. By examining the interplay between genetics and individual environmental exposures during the COVID-19 lockdown, we have been able to gain an insight as to why some individuals are more vulnerable to depression, while others are more resilient. This study, conducted on a Spanish cohort of 9218 individuals (COVICAT), includes a comprehensive non-genetic risk analysis, the exposome, complemented by a genomics analysis in a subset of 2442 participants. Depression levels were evaluated using the Hospital Anxiety and Depression Scale. Together with Polygenic Risk Scores (PRS), we introduced a novel score; Poly-Environmental Risk Scores (PERS) for non-genetic risks to estimate the effect of each cumulative score and gene-environment interaction. We found significant positive associations for PERSSoc (Social and Household), PERSLife (Lifestyle and Behaviour), and PERSEnv (Wider Environment and Health) scores across all levels of depression severity, and for PRSB (Broad depression) only for moderate depression (OR 1.2, 95% CI 1.03-1.40). On average OR increased 1.2-fold for PERSEnv and 1.6-fold for PERLife and PERSoc from mild to severe depression level. The complete adjusted model explained 16.9% of the variance. We further observed an interaction between PERSEnv and PRSB showing a potential mitigating effect. In summary, stressors within the social and behavioral domains emerged as the primary drivers of depression risk in this population, unveiling a mitigating interaction effect that should be interpreted with caution.

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.

Antidepressant Exposure and DNA Methylation: Insights from a Methylome-Wide Association Study

Importance

Understanding antidepressant mechanisms could help design more effective and tolerated treatments.

Objective

Identify DNA methylation (DNAm) changes associated with antidepressant exposure.

Design

Case-control methylome-wide association studies (MWAS) of antidepressant exposure were performed from blood samples collected between 2006-2011 in Generation Scotland (GS). The summary statistics were tested for enrichment in specific tissues, gene ontologies and an independent MWAS in the Netherlands Study of Depression and Anxiety (NESDA). A methylation profile score (MPS) was derived and tested for its association with antidepressant exposure in eight independent cohorts, alongside prospective data from GS.

Setting

Cohorts; GS, NESDA, FTC, SHIP-Trend, FOR2107, LBC1936, MARS-UniDep, ALSPAC, E-Risk, and NTR.

Participants

Participants with DNAm data and self-report/prescription derived antidepressant exposure.

Main Outcomes and Measures

Whole-blood DNAm levels were assayed by the EPIC/450K Illumina array (9 studies, N exposed = 661, N unexposed = 9,575) alongside MBD-Seq in NESDA (N exposed = 398, N unexposed = 414). Antidepressant exposure was measured by self- report and/or antidepressant prescriptions.

Results

The self-report MWAS (N = 16,536, N exposed = 1,508, mean age = 48, 59% female) and the prescription-derived MWAS (N = 7,951, N exposed = 861, mean age = 47, 59% female), found hypermethylation at seven and four DNAm sites (p < 9.42x10 -8 ), respectively. The top locus was cg26277237 ( KANK1, p self-report = 9.3x10 -13 , p prescription = 6.1x10 -3 ). The self-report MWAS found a differentially methylated region, mapping to DGUOK-AS1 ( p adj = 5.0x10 -3 ) alongside significant enrichment for genes expressed in the amygdala, the "synaptic vesicle membrane" gene ontology and the top 1% of CpGs from the NESDA MWAS (OR = 1.39, p < 0.042). The MPS was associated with antidepressant exposure in meta-analysed data from external cohorts (N studies = 9, N = 10,236, N exposed = 661, f3 = 0.196, p < 1x10 -4 ).

Conclusions and Relevance

Antidepressant exposure is associated with changes in DNAm across different cohorts. Further investigation into these changes could inform on new targets for antidepressant treatments.

3 Key Points

Question: Is antidepressant exposure associated with differential whole blood DNA methylation?Findings: In this methylome-wide association study of 16,536 adults across Scotland, antidepressant exposure was significantly associated with hypermethylation at CpGs mapping to KANK1 and DGUOK-AS1. A methylation profile score trained on this sample was significantly associated with antidepressant exposure (pooled f3 [95%CI]=0.196 [0.105, 0.288], p < 1x10 -4 ) in a meta-analysis of external datasets. Meaning: Antidepressant exposure is associated with hypermethylation at KANK1 and DGUOK-AS1 , which have roles in mitochondrial metabolism and neurite outgrowth. If replicated in future studies, targeting these genes could inform the design of more effective and better tolerated treatments for depression.

The role of serotonin in depression-A historical roundup and future directions

Depression is one of the most common psychiatric disorders worldwide, affecting approximately 280 million people, with probably much higher unrecorded cases. Depression is associated with symptoms such as anhedonia, feelings of hopelessness, sleep disturbances, and even suicidal thoughts. Tragically, more than 700 000 people commit suicide each year. Although depression has been studied for many decades, the exact mechanisms that lead to depression are still unknown, and available treatments only help a fraction of patients. In the late 1960s, the serotonin hypothesis was published, suggesting that serotonin is the key player in depressive disorders. However, this hypothesis is being increasingly doubted as there is evidence for the influence of other neurotransmitters, such as noradrenaline, glutamate, and dopamine, as well as larger systemic causes such as altered activity in the limbic network or inflammatory processes. In this narrative review, we aim to contribute to the ongoing debate on the involvement of serotonin in depression. We will review the evolution of antidepressant treatments, systemic research on depression over the years, and future research applications that will help to bridge the gap between systemic research and neurotransmitter dynamics using biosensors. These new tools in combination with systemic applications, will in the future provide a deeper understanding of the serotonergic dynamics in depression.

Transcriptomic Similarity Informs Neuromorphic Deviations in Depression Biotypes

BACKGROUND

Major depressive disorder (MDD) is complicated by population heterogeneity, motivating the investigation of biotypes through imaging-derived phenotypes. However, neuromorphic heterogeneity in MDD remains unclear, and how the correlated gene expression (CGE) connectome constrains these neuromorphic anomalies in MDD biotypes has not yet been studied.

METHODS

Here, we related cortical thickness deviations in MDD biotypes to a pattern of CGE connectome. Cortical thickness was estimated from 3-dimensional T1-weighted magnetic resonance images in 2 independent cohorts (discovery cohort: N = 425; replication cohort: N = 217). The transcriptional activity was measured according to Allen Human Brain Atlas. A density peak-based clustering algorithm was used to identify MDD biotypes.

RESULTS

We found that patients with MDD were clustered into 2 replicated biotypes based on single-patient regional deviations from healthy control participants across 2 datasets. Biotype 1 mainly exhibited cortical thinning across the brain, whereas biotype 2 mainly showed cortical thickening in the brain. Using brainwide gene expression data, we found that deviations of transcriptionally connected neighbors predicted regional deviation for both biotypes. Furthermore, putative CGE-informed epicenters of biotype 1 were concentrated on the cognitive control circuit, whereas biotype 2 epicenters were located in the social perception circuit. The patterns of epicenter likelihood were separately associated with depression- and anxiety-response maps, suggesting that epicenters of MDD biotypes may be associated with clinical efficacies.

CONCLUSIONS

Our findings linked the CGE connectome and neuromorphic deviations to identify distinct epicenters in MDD biotypes, providing insight into how microscale gene expressions informed MDD biotypes.

Whole-exome sequencing in UK Biobank reveals rare genetic architecture for depression

Nearly two hundred common-variant depression risk loci have been identified by genome-wide association studies (GWAS). However, the impact of rare coding variants on depression remains poorly understood. Here, we present whole-exome sequencing analyses of depression with seven different definitions based on survey, questionnaire, and electronic health records in 320,356 UK Biobank participants. We showed that the burden of rare damaging coding variants in loss-of-function intolerant genes is significantly associated with risk of depression with various definitions. We compared the rare and common genetic architecture across depression definitions by genetic correlation and showed different genetic relationships between definitions across common and rare variants. In addition, we demonstrated that the effects of rare damaging coding variant burden and polygenic risk score on depression risk are additive. The gene set burden analyses revealed overlapping rare genetic variant components with developmental disorder, autism, and schizophrenia. Our study provides insights into the contribution of rare coding variants, separately and in conjunction with common variants, on depression with various definitions and their genetic relationships with neurodevelopmental disorders.

Estimating the impact of transmitted and non-transmitted psychiatric and neurodevelopmental polygenic scores on youth emotional problems

Anxiety and depression (emotional disorders) are familial and heritable, especially when onset is early. However, other cross-generational studies suggest transmission of youth emotional problems is explained by mainly environmental risks. We set out to test the contribution of parental non-transmitted genetic liability, as indexed by psychiatric/neurodevelopmental common polygenic liability, to youth emotional problems using a UK population-based cohort: the Millennium Cohort Study. European (N = 6328) and South Asian (N = 814) ancestries were included, as well as a subset with genomic data from both parents (European: N = 2809; South Asian: N = 254). We examined the association of transmitted (PGST) and non-transmitted polygenic scores (PGSNT) for anxiety, depression, bipolar disorder and neurodevelopmental disorders (attention-deficit/hyperactivity disorder [ADHD], autism spectrum disorder [ASD], schizophrenia) with youth emotional disorder and symptom scores, measured using the parent- and self-reported Strengths and Difficulties Questionnaire emotional subscale at 6 timepoints between ages 3-17 years. In the European sample, PGST for anxiety and depression, but not bipolar disorder, were associated with emotional disorder and symptom scores across all ages, except age 3, with strongest association in adolescence. ADHD and ASD PGST also showed association across ages 11-17 years. In the South Asian sample, evidence for associations between all PGST and outcome measures were weaker. There was weak evidence of association between PGSNT for anxiety and depression and age 17 symptom scores in the South Asian sample, but not in the European sample for any outcome. Overall, PGST for depression, anxiety, ADHD and ASD contributed to youth emotional problems, with stronger associations in adolescence. There was limited support for non-transmitted genetic effects: these findings do not support the hypothesis that parental polygenic psychiatric/neurodevelopmental liability confer risk to offspring emotional problems through non-transmitted rearing/nurture effects.

Phenotype integration improves power and preserves specificity in biobank-based genetic studies of major depressive disorder

Biobanks often contain several phenotypes relevant to diseases such as major depressive disorder (MDD), with partly distinct genetic architectures. Researchers face complex tradeoffs between shallow (large sample size, low specificity/sensitivity) and deep (small sample size, high specificity/sensitivity) phenotypes, and the optimal choices are often unclear. Here we propose to integrate these phenotypes to combine the benefits of each. We use phenotype imputation to integrate information across hundreds of MDD-relevant phenotypes, which significantly increases genome-wide association study (GWAS) power and polygenic risk score (PRS) prediction accuracy of the deepest available MDD phenotype in UK Biobank, LifetimeMDD. We demonstrate that imputation preserves specificity in its genetic architecture using a novel PRS-based pleiotropy metric. We further find that integration via summary statistics also enhances GWAS power and PRS predictions, but can introduce nonspecific genetic effects depending on input. Our work provides a simple and scalable approach to improve genetic studies in large biobanks by integrating shallow and deep phenotypes.

How accurate are self-evaluations of singing ability?

Research has shown that people inaccurately assess their own abilities on self-report measures, including academic, athletic, and music ability. Evidence suggests this is also true for singing, with individuals either overestimating or underestimating their level of singing competency. In this paper, we present the Melbourne Singing Tool Questionnaire (MST-Q), a brief 16-item measure exploring people's self-perceptions of singing ability and engagement with singing. Using a large sample of Australian twins (n = 996), we identified three latent factors underlying MST-Q items and examined whether these factors were related to an objective phenotypic measure of singing ability. The three factors were identified as Personal Engagement, Social Engagement, and Self-Evaluation. All factors were positively associated with objective singing performance, with the Self-Evaluation factor yielding the strongest correlation (r = 0.66). Both the Self-Evaluation factor and a single self-report item of singing ability shared the same predictive strength. Contrary to expectations, our findings suggest that self-evaluation strongly predicts singing ability, and this self-evaluation is of higher predictive value than self-reported engagement with music and singing.

Psychological, endocrine and polygenic predictors of emotional well-being during the COVID-19 pandemic in a longitudinal birth cohort

The COVID-19 pandemic severely affected the lives of families and the well-being of both parents and their children. Various factors, including prenatal stress, dysregulated stress response systems, and genetics may have influenced how the stress caused by the pandemic impacted the well-being of different family members. The present work investigated if emotional well-being during the COVID-19 pandemic could be predicted by developmental stress-related and genetic factors. Emotional well-being of 7-10 year-old children (n = 263) and mothers (n = 241) (participants in a longitudinal German birth cohort (POSEIDON)) was assessed during the COVID-19 pandemic using the CRISIS questionnaire at two time periods (July 2020-October 2020; November 2020-February 2021). Associations of the children's and mothers' well-being with maternal perceived stress, of the children's well-being with their salivary and morning urine cortisol at 45 months, and polygenic risk scores (PRSs) for depression, schizophrenia, loneliness were investigated. Lower emotional well-being was observed in both children and mothers during compared to before the pandemic, with the children's but not the mothers' emotional well-being improving over the course of the pandemic. A positive association between the child and maternal emotional well-being was found. Prenatally assessed maternal perceived stress was associated with a lower well-being in children, but not in mothers. Cortisol measures and PRSs were not significantly associated with the children's emotional well-being. The present study confirms that emotional well-being of children and mothers are linked, and were negatively affected by the COVID-19 pandemic, with differences in development over time.

Metabolomic Investigation of Major Depressive Disorder Identifies a Potentially Causal Association With Polyunsaturated Fatty Acids

BACKGROUND

Metabolic differences have been reported between individuals with and without major depressive disorder (MDD), but their consistency and causal relevance have been unclear.

METHODS

We conducted a metabolome-wide association study of MDD with 249 metabolomic measures available in the UK Biobank (n = 29,757). We then applied two-sample bidirectional Mendelian randomization and colocalization analysis to identify potentially causal relationships between each metabolite and MDD.

RESULTS

A total of 191 metabolites tested were significantly associated with MDD (false discovery rate-corrected p < .05), which decreased to 129 after adjustment for likely confounders. Lower abundance of omega-3 fatty acid measures and a higher omega-6 to omega-3 ratio showed potentially causal effects on liability to MDD. There was no evidence of a causal effect of MDD on metabolite levels. Furthermore, genetic signals associated with docosahexaenoic acid colocalized with loci associated with MDD within the fatty acid desaturase gene cluster. Post hoc Mendelian randomization of gene-transcript abundance within the fatty acid desaturase cluster demonstrated a potentially causal association with MDD. In contrast, colocalization analysis did not suggest a single causal variant for both transcript abundance and MDD liability, but rather the likely existence of two variants in linkage disequilibrium with one another.

CONCLUSIONS

Our findings suggest that decreased docosahexaenoic acid and increased omega-6 to omega-3 fatty acids ratio may be causally related to MDD. These findings provide further support for the causal involvement of fatty acids in MDD.

The Timing Effect of Childhood Maltreatment in Depression: A Systematic Review and meta-Analysis

Although empirical evidence has confirmed the causal relationship between childhood maltreatment and depression, findings are inconsistent on the magnitude of the effect of age of exposure to childhood maltreatment on psychological development. This systematic review with meta-analysis aims to comprehensively synthesize the literature on the relationship between exposure age of maltreatment and depression and to quantitatively compare the magnitude of effect sizes across exposure age groups. Electronic databases and grey literature up to April 6th, 2022, were searched for English-language studies. Studies were included if they: 1) provided the information on exposure age; and 2) provided statistical indicators to examine the relationship between childhood maltreatment and depression. Fifty-eight articles met eligibility criteria and were included in meta-analyses. Subgroup analyses were conducted based on subtypes of maltreatment and measurements of depression. Any kind of maltreatment (correlation coefficient [r] = 0.17, 95% CI = 0.15-0.18), physical abuse (r =0.13, 95% CI = 0.10-0.15), sexual abuse (r = 0.18, 95% CI = 0.15-0.21), emotional abuse (r = 0.17, 95% CI=0.11-0.23), and neglect (r = 0.08, 95% CI=0.06-0.11) were associated with an increased risk of depression. Significant differential effects of maltreatment in depression were found across age groups of exposure to maltreatment (Q = 34.81, p < 0.001). Age of exposure in middle childhood (6-13 years) had the highest risk of depression, followed by late childhood (12-19 years) and early childhood (0-6 years). Implications of the findings provide robust evidence to support targeting victimized children of all ages and paying closer attention to those in middle childhood to effectively reduce the risk of depression.

Individual and Combined Effect of MAO-A/MAO-B Gene Variants and Adverse Childhood Experiences on the Severity of Major Depressive Disorder

BACKGROUND

Major depressive disorder (MDD) is a mood disorder with a high prevalence worldwide that causes disability and, in some cases, suicide. Although environmental factors play a crucial role in this disease, other biological factors may predispose individuals to MDD. Genetic and environmental factors influence mental disorders; therefore, a potential combined effect of MAO-A/MAO-B gene variants may be a target for the study of susceptibility to MDD. This study aimed to evaluate the effects of MAO-A and -B gene variants when combined with adverse childhood experiences (ACEs) on the susceptibility and severity of symptoms in MDD.

METHODS

A case-control study was performed, including 345 individuals, 175 MDD cases and 170 controls. Genotyping was performed using real-time PCR with hydrolysis probes. The analysis of the rs1465107 and rs1799836 gene variants of MAO-A and -B, respectively, was performed either alone or in combination with ACEs on the severity of depression, as determined through specific questionnaires, including DSM-IV diagnostic criteria for MDD.

RESULTS

According to individual effects, the presence of ACEs, as well as the allele G of the rs1465107 of MAO-A, is associated with a higher severity of depression, more significantly in females. Furthermore, the allele rs1799836 G of MAO-B was associated with the severity of depression, even after being adjusted by gene variants and ACEs (IRR = 1.67, p = 0.01). In males, the allele rs1799836 G of MAO-B was shown to interact with SNP with ACEs (IRR = 1.70, p < 0.001). According to combined effect analyses, the severity of depression was associated with ACEs when combined with either allele rs1465107 of MAO-A or allele rs17993836 of MAO-B, whereas SNP risk association was influenced by gender.

CONCLUSIONS

The severity of depression is related to either individual or combined effects of temperamental traits and genetic susceptibility of specific genes such as MAO-A and MAO-B.

Reduced Vrk2 expression is associated with higher risk of depression in humans and mediates depressive-like behaviors in mice

BACKGROUND

Genome-wide association studies (GWAS) have reported single-nucleotide polymorphisms (SNPs) in the VRK serine/threonine kinase 2 gene (VRK2) showing genome-wide significant associations with major depression, but the regulation effect of the risk SNPs on VRK2 as well as their roles in the illness are yet to be elucidated.

METHODS

Based on the summary statistics of major depression GWAS, we conducted population genetic analyses, epigenome bioinformatics analyses, dual luciferase reporter assays, and expression quantitative trait loci (eQTL) analyses to identify the functional SNPs regulating VRK2; we also carried out behavioral assessments, dendritic spine morphological analyses, and phosphorylated 4D-label-free quantitative proteomics analyses in mice with Vrk2 repression.

RESULTS

We identified a SNP rs2678907 located in the 5' upstream of VRK2 gene exhibiting large spatial overlap with enhancer regulatory marks in human neural cells and brain tissues. Using luciferase reporter gene assays and eQTL analyses, the depression risk allele of rs2678907 decreased enhancer activities and predicted lower VRK2 mRNA expression, which is consistent with the observations of reduced VRK2 level in the patients with major depression compared with controls. Notably, Vrk2-/- mice exhibited depressive-like behaviors compared to Vrk2+/+ mice and specifically repressing Vrk2 in the ventral hippocampus using adeno-associated virus (AAV) lead to consistent and even stronger depressive-like behaviors in mice. Compared with Vrk2+/+ mice, the density of mushroom and thin spines in the ventral hippocampus was significantly altered in Vrk2-/- mice, which is in line with the phosphoproteomic analyses showing dysregulated synapse-associated proteins and pathways in Vrk2-/- mice.

CONCLUSIONS

Vrk2 deficiency mice showed behavioral abnormalities that mimic human depressive phenotypes, which may serve as a useful murine model for studying the pathophysiology of depression.

The genetic basis of major depressive disorder

The genetic dissection of major depressive disorder (MDD) ranks as one of the success stories of psychiatric genetics, with genome-wide association studies (GWAS) identifying 178 genetic risk loci and proposing more than 200 candidate genes. However, the GWAS results derive from the analysis of cohorts in which most cases are diagnosed by minimal phenotyping, a method that has low specificity. I review data indicating that there is a large genetic component unique to MDD that remains inaccessible to minimal phenotyping strategies and that the majority of genetic risk loci identified with minimal phenotyping approaches are unlikely to be MDD risk loci. I show that inventive uses of biobank data, novel imputation methods, combined with more interviewer diagnosed cases, can identify loci that contribute to the episodic severe shifts of mood, and neurovegetative and cognitive changes that are central to MDD. Furthermore, new theories about the nature and causes of MDD, drawing upon advances in neuroscience and psychology, can provide handles on how best to interpret and exploit genetic mapping results.

The interplay of family history of depression and early trauma: associations with lifetime and current depression in the German national cohort (NAKO)

Introduction

Family history of depression and childhood maltreatment are established risk factors for depression. However, how these factors are interrelated and jointly influence depression risk is not well understood. The present study investigated (i) if childhood maltreatment is associated with a family history of depression (ii) if family history and childhood maltreatment are associated with increased lifetime and current depression, and whether both factors interact beyond their main effects, and (iii) if family history affects lifetime and current depression via childhood maltreatment.

Methods

Analyses were based on a subgroup of the first 100,000 participants of the German National Cohort (NAKO), with complete information (58,703 participants, mean age = 51.2 years, 53% female). Parental family history of depression was assessed via self-report, childhood maltreatment with the Childhood Trauma Screener (CTS), lifetime depression with self-reported physician's diagnosis and the Mini-International Neuropsychiatric Interview (MINI), and current depressive symptoms with the depression scale of the Patient Health Questionnaire (PHQ-9). Generalized linear models were used to test main and interaction effects. Mediation was tested using causal mediation analyses.

Results

Higher frequencies of the childhood maltreatment measures were found in subjects reporting a positive family history of depression. Family history and childhood maltreatment were independently associated with increased depression. No statistical interactions of family history and childhood maltreatment were found for the lifetime depression measures. For current depressive symptoms (PHQ-9 sum score), an interaction was found, with stronger associations of childhood maltreatment and depression in subjects with a positive family history. Childhood maltreatment was estimated to mediate 7%-12% of the effect of family history on depression, with higher mediated proportions in subjects whose parents had a depression onset below 40 years. Abuse showed stronger associations with family history and depression, and higher mediated proportions of family history effects on depression than neglect.

Discussion

The present study confirms the association of childhood maltreatment and family history with depression in a large population-based cohort. While analyses provide little evidence for the joint effects of both risk factors on depression beyond their individual effects, results are consistent with family history affecting depression via childhood maltreatment to a small extent.

Designing multi-target drugs for the treatment of major depressive disorder

INTRODUCTION

Major depressive disorders (MDD) pose major health burdens globally. Currently available medications have their limitations due to serious adverse effects, long latency periods as well as resistance. Considering the highly complicated pathological nature of this disorder, it has been suggested that multitarget drugs or multi-target-directed ligands (MTDLs) may provide long-term therapeutic solutions for the treatment of MDD.

AREAS COVERED

In the current review, recent lead design and lead modification strategies have been covered. Important investigations reported in the last ten years (2013-2022) for the pre-clinical development of MTDLs (through synthetic medicinal chemistry and biological evaluation) for the treatment of MDD were discussed as case studies to focus on the recent design strategies. The discussions are categorized based on the pharmacological targets. On the basis of these important case studies, the challenges involved in different design strategies were discussed in detail.

EXPERT OPINION

Even though large variations were observed in the selection of pharmacological targets, some potential biological targets (NMDA, melatonin receptors) are required to be explored extensively for the design of MTDLs. Similarly, apart from structure activity relationship (SAR), in silico techniques such as multitasking cheminformatic modelling, molecular dynamics simulation and virtual screening should be exploited to a greater extent.

Genetic propensity, socioeconomic status, and trajectories of depression over a course of 14 years in older adults

Depression is one of the leading causes of disability worldwide and is a major contributor to the global burden of disease among older adults. The study aimed to investigate the interplay between socio-economic markers (education and financial resources) and polygenic predisposition influencing individual differences in depressive symptoms and their change over time in older adults, which is of central relevance for preventative strategies. The sample encompassing n = 6202 adults aged ≥50 years old with a follow-up period of 14 years was utilised from the English Longitudinal Study of Ageing. Polygenic scores for depressive symptoms were calculated using summary statistics for (1) single-trait depressive symptoms (PGS-DS_single), and (2) multi-trait including depressive symptoms, subjective well-being, neuroticism, loneliness, and self-rated health (PGS-DS_multi-trait). The depressive symptoms over the past week were measured using the eight-item Centre for Epidemiologic Studies Depression Scale. One standard deviation increase in each PGS was associated with a higher baseline score in depressive symptoms. Each additional year of completed schooling was associated with lower baseline depression symptoms (β = -0.06, 95%CI = -0.07 to -0.05, p < 0.001); intermediate and lower wealth were associated with a higher baseline score in depressive symptoms. Although there was a weak interaction effect between PGS-DSs and socio-economic status in association with the baseline depressive symptoms, there were no significant relationships of PGS-DSs, socio-economic factors, and rate of change in the depressive symptoms during the 14-year follow-up period. Common genetic variants for depressive symptoms are associated with a greater number of depressive symptoms onset but not with their rate of change in the following 14 years. Lower socio-economic status is an important factor influencing individual levels of depressive symptoms, independently from polygenic predisposition to depressive symptoms.

Labor epidural analgesia and subsequent risk of offspring autism spectrum disorder and attention-deficit/hyperactivity disorder: a cross-national cohort study of 4.5 million individuals and their siblings

BACKGROUND

A recent study has suggested that labor epidural analgesia may be associated with increased rates of offspring autism spectrum disorder. Subsequent replication attempts have lacked sufficient power to confidently exclude the possibility of a small effect, and the causal nature of this association remains unknown.

OBJECTIVE

This study aimed to investigate the extent to which exposure to labor epidural analgesia is associated with offspring autism spectrum disorder and attention-deficit/hyperactivity disorder following adjustments for unmeasured familial confounding.

STUDY DESIGN

We identified 4,498,462 singletons and their parents using the Medical Birth Registers in Finland (cohorts born from 1987-2005), Norway (1999-2015), and Sweden (1987-2011) linked with population and patient registries. These cohorts were followed from birth until they either had the outcomes of interest, emigrated, died, or reached the end of the follow-up (at mean ages 13.6-16.8 years), whichever occurred first. Cox regression models were used to estimate country-specific associations between labor epidural analgesia recorded at birth and outcomes (eg, at least 1 secondary care diagnosis of autism spectrum disorder and attention-deficit/hyperactivity disorder or at least 1 dispensed prescription of medication used for the treatment of attention-deficit/hyperactivity disorder). The models were adjusted for sex, birth year, birth order, and unmeasured familial confounders via sibling comparisons. Pooled estimates across all the 3 countries were estimated using inverse variance weighted fixed-effects meta-analysis models.

RESULTS

A total of 4,498,462 individuals (48.7% female) were included, 1,091,846 (24.3%) of which were exposed to labor epidural analgesia. Of these, 1.2% were diagnosed with autism spectrum disorder and 4.0% with attention-deficit/hyperactivity disorder. On the population level, pooled estimates showed that labor epidural analgesia was associated with increased risk of offspring autism spectrum disorder (adjusted hazard ratio, 1.12; 95% confidence interval, 1.10-1.14, absolute risks, 1.20% vs 1.07%) and attention-deficit/hyperactivity disorder (adjusted hazard ratio, 1.20; 95% confidence interval, 1.19-1.21; absolute risks, 3.95% vs 3.32%). However, when comparing full siblings who were differentially exposed to labor epidural analgesia, the associations were fully attenuated for both conditions with narrow confidence intervals (adjusted hazard ratio [autism spectrum disorder], 0.98; 95% confidence interval, 0.93-1.03; adjusted hazard ratio attention-deficit/hyperactivity disorder, 0.99; 95% confidence interval, 0.96-1.02).

CONCLUSION

In this large cross-national study, we found no support for the hypothesis that exposure to labor epidural analgesia causes either offspring autism spectrum disorder or attention-deficit/hyperactivity disorder.

AI-based dimensional neuroimaging system for characterizing heterogeneity in brain structure and function in major depressive disorder: COORDINATE-MDD consortium design and rationale

BACKGROUND

Efforts to develop neuroimaging-based biomarkers in major depressive disorder (MDD), at the individual level, have been limited to date. As diagnostic criteria are currently symptom-based, MDD is conceptualized as a disorder rather than a disease with a known etiology; further, neural measures are often confounded by medication status and heterogeneous symptom states.

METHODS

We describe a consortium to quantify neuroanatomical and neurofunctional heterogeneity via the dimensions of novel multivariate coordinate system (COORDINATE-MDD). Utilizing imaging harmonization and machine learning methods in a large cohort of medication-free, deeply phenotyped MDD participants, patterns of brain alteration are defined in replicable and neurobiologically-based dimensions and offer the potential to predict treatment response at the individual level. International datasets are being shared from multi-ethnic community populations, first episode and recurrent MDD, which are medication-free, in a current depressive episode with prospective longitudinal treatment outcomes and in remission. Neuroimaging data consist of de-identified, individual, structural MRI and resting-state functional MRI with additional positron emission tomography (PET) data at specific sites. State-of-the-art analytic methods include automated image processing for extraction of anatomical and functional imaging variables, statistical harmonization of imaging variables to account for site and scanner variations, and semi-supervised machine learning methods that identify dominant patterns associated with MDD from neural structure and function in healthy participants.

RESULTS

We are applying an iterative process by defining the neural dimensions that characterise deeply phenotyped samples and then testing the dimensions in novel samples to assess specificity and reliability. Crucially, we aim to use machine learning methods to identify novel predictors of treatment response based on prospective longitudinal treatment outcome data, and we can externally validate the dimensions in fully independent sites.

CONCLUSION

We describe the consortium, imaging protocols and analytics using preliminary results. Our findings thus far demonstrate how datasets across many sites can be harmonized and constructively pooled to enable execution of this large-scale project.

Gender differences in the association of polygenic risk and divergent depression trajectories from mid to late life: a national longitudinal study

Our research fills a critical gap in the depression literature by utilizing a life course perspective to examine gender-gene interactions in association with depression trajectories over time. Using data from the Health and Retirement Study, we estimated multi-level negative binomial and logistic mixed models to analyze gender-specific trajectories of depressive symptoms (CESD-8) and potential clinical depression risk from middle to late adulthood in relation to gender-by-polygenic-risk (PRS) interactions. We found increasingly greater female-male gaps in the CESD-8 scale and a higher probability of clinical depression risk with increasing polygenic risk scores. Furthermore, females' higher genetic vulnerabilities to depressive conditions than males vary from ages 51 to 90 years, with most salient larger differences at oldest old ages at 76-85 (e.g. 0.28 higher CESD-8 scale for females at ages 76-85 years than for similar-aged males; higher 3.44% probability of depression risk for females at ages 81-85 compared to similar-aged males) followed by old ages at 61-70  years (e.g. about 2.40% higher probability of depression risk for females at ages 61-70 years than for similar-aged males) in comparison to younger ages during middle adulthood. This study contributes to new knowledge of how gender-by-polygenic-risk interactions are associated with depression trajectories across the life course.

Symptom dimensions of major depression in a large community-based cohort

BACKGROUND

Our understanding of major depression is complicated by substantial heterogeneity in disease presentation, which can be disentangled by data-driven analyses of depressive symptom dimensions. We aimed to determine the clinical portrait of such symptom dimensions among individuals in the community.

METHODS

This cross-sectional study consisted of 25 261 self-reported White UK Biobank participants with major depression. Nine questions from the UK Biobank Mental Health Questionnaire encompassing depressive symptoms were decomposed into underlying factors or 'symptom dimensions' via factor analysis, which were then tested for association with psychiatric diagnoses and polygenic risk scores for major depressive disorder (MDD), bipolar disorder and schizophrenia. Replication was performed among 655 self-reported non-White participants, across sexes, and among 7190 individuals with an ICD-10 code for MDD from linked inpatient or primary care records.

RESULTS

Four broad symptom dimensions were identified, encompassing negative cognition, functional impairment, insomnia and atypical symptoms. These dimensions replicated across ancestries, sexes and individuals with inpatient or primary care MDD diagnoses, and were also consistent among 43 090 self-reported White participants with undiagnosed self-reported depression. Every dimension was associated with increased risk of nearly every psychiatric diagnosis and polygenic risk score. However, while certain psychiatric diagnoses were disproportionately associated with specific symptom dimensions, the three polygenic risk scores did not show the same specificity of associations.

CONCLUSIONS

An analysis of questionnaire data from a large community-based cohort reveals four replicable symptom dimensions of depression with distinct clinical, but not genetic, correlates.

Leveraging Machine Learning for Gaining Neurobiological and Nosological Insights in Psychiatric Research

Much attention is currently devoted to developing diagnostic classifiers for mental disorders. Complementing these efforts, we highlight the potential of machine learning to gain biological insights into the psychopathology and nosology of mental disorders. Studies to this end have mainly used brain imaging data, which can be obtained noninvasively from large cohorts and have repeatedly been argued to reveal potentially intermediate phenotypes. This may become particularly relevant in light of recent efforts to identify magnetic resonance imaging-derived biomarkers that yield insight into pathophysiological processes as well as to refine the taxonomy of mental illness. In particular, the accuracy of machine learning models may be used as dependent variables to identify features relevant to pathophysiology. Moreover, such approaches may help disentangle the dimensional (within diagnosis) and often overlapping (across diagnoses) symptomatology of psychiatric illness. We also point out a multiview perspective that combines data from different sources, bridging molecular and system-level information. Finally, we summarize recent efforts toward a data-driven definition of subtypes or disease entities through unsupervised and semisupervised approaches. The latter, blending unsupervised and supervised concepts, may represent a particularly promising avenue toward dissecting heterogeneous categories. Finally, we raise several technical and conceptual aspects related to the reviewed approaches. In particular, we discuss common pitfalls pertaining to flawed input data or analytic procedures that would likely lead to unreliable outputs.

Scoping Review on the Multimodal Classification of Depression and Experimental Study on Existing Multimodal Models

Depression is a prevalent comorbidity in patients with severe physical disorders, such as cancer, stroke, and coronary diseases. Although it can significantly impact the course of the primary disease, the signs of depression are often underestimated and overlooked. The aim of this paper was to review algorithms for the automatic, uniform, and multimodal classification of signs of depression from human conversations and to evaluate their accuracy. For the scoping review, the PRISMA guidelines for scoping reviews were followed. In the scoping review, the search yielded 1095 papers, out of which 20 papers (8.26%) included more than two modalities, and 3 of those papers provided codes. Within the scope of this review, supported vector machine (SVM), random forest (RF), and long short-term memory network (LSTM; with gated and non-gated recurrent units) models, as well as different combinations of features, were identified as the most widely researched techniques. We tested the models using the DAIC-WOZ dataset (original training dataset) and using the SymptomMedia dataset to further assess their reliability and dependency on the nature of the training datasets. The best performance was obtained by the LSTM with gated recurrent units (F1-score of 0.64 for the DAIC-WOZ dataset). However, with a drop to an F1-score of 0.56 for the SymptomMedia dataset, the method also appears to be the most data-dependent.

Genetic similarities and differences among distinct definitions of depression

Depression is a common and complex psychiatric illness with considerable heritability. Genome-wide association studies (GWAS) have been conducted among different definitions of depression based on different diagnostic criteria. However, the heritability explained by different depression GWAS and the identified loci varied widely. To understand the genetic architectures of different definitions of depression, we conducted a series of genetic analyses including linkage disequilibrium score regression (LDSC), Mendelian randomization, and polygenic overlap quantification and identification. Different definitions of depression and other common psychiatric traits were included in this analysis. We found that although genetic correlations between different definitions of depression were relatively high, they showed substantially different genetic correlation and causality with other psychiatric traits. Using bivariate causal mixture mode (MiXeR) and conjunctional false discovery rate (conjFDR) approach, we observed both shared and unique risk loci across different definitions of depression. Further functional mapping with expression quantitative trait loci (eQTL) information from multiple brain tissues and single cell types indicated distinct genes underlying different definitions of depression, and pathways associated with synapses were significantly enriched in the illness. Our study showed that the genetic architectures of different definitions of depression were distinct and genetic studies of depression should be conducted more cautious.

Depression in young people

Depression rates in young people have risen sharply in the past decade, especially in females, which is of concern because adolescence is a period of rapid social, emotional, and cognitive development and key life transitions. Adverse outcomes associated with depression in young people include depression recurrence; the onset of other psychiatric disorders; and wider, protracted impairments in interpersonal, social, educational, and occupational functioning. Thus, prevention and early intervention for depression in young people are priorities. Preventive and early intervention strategies typically target predisposing factors, antecedents, and symptoms of depression. Young people who have a family history of depression, exposure to social stressors (eg, bullying, discordant relationships, or stressful life events), and belong to certain subgroups (eg, having a chronic physical health problem or being a sexual minority) are at especially high risk of depression. Clinical antecedents include depressive symptoms, anxiety, and irritability. Evidence favours indicated prevention and targeted prevention to universal prevention. Emerging school-based and community-based social interventions show some promise. Depression is highly heterogeneous; therefore, a stepwise treatment approach is recommended, starting with brief psychosocial interventions, then a specific psychological therapy, and then an antidepressant medication.

Functional Genomics Analysis to Disentangle the Role of Genetic Variants in Major Depression

Understanding the molecular basis of major depression is critical for identifying new potential biomarkers and drug targets to alleviate its burden on society. Leveraging available GWAS data and functional genomic tools to assess regulatory variation could help explain the role of major depression-associated genetic variants in disease pathogenesis. We have conducted a fine-mapping analysis of genetic variants associated with major depression and applied a pipeline focused on gene expression regulation by using two complementary approaches: cis-eQTL colocalization analysis and alteration of transcription factor binding sites. The fine-mapping process uncovered putative causally associated variants whose proximal genes were linked with major depression pathophysiology. Four colocalizing genetic variants altered the expression of five genes, highlighting the role of SLC12A5 in neuronal chlorine homeostasis and MYRF in nervous system myelination and oligodendrocyte differentiation. The transcription factor binding analysis revealed the potential role of rs62259947 in modulating P4HTM expression by altering the YY1 binding site, altogether regulating hypoxia response. Overall, our pipeline could prioritize putative causal genetic variants in major depression. More importantly, it can be applied when only index genetic variants are available. Finally, the presented approach enabled the proposal of mechanistic hypotheses of these genetic variants and their role in disease pathogenesis.

Epigenetic signatures in antidepressant treatment response: a methylome-wide association study in the EMC trial

Although the currently available antidepressants are well established in the treatment of the major depressive disorder (MDD), there is strong variability in the response of individual patients. Reliable predictors to guide treatment decisions before or in an early stage of treatment are needed. DNA-methylation has been proven a useful biomarker in different clinical conditions, but its importance for mechanisms of antidepressant response has not yet been determined. 80 MDD patients were selected out of >500 participants from the Early Medication Change (EMC) cohort with available genetic material based on their antidepressant response after four weeks and stratified into clear responders and age- and sex-matched non-responders (N = 40, each). Early improvement after two weeks was analyzed as a secondary outcome. DNA-methylation was determined using the Illumina EPIC BeadChip. Epigenome-wide association studies were performed and differentially methylated regions (DMRs) identified using the comb-p algorithm. Enrichment was tested for hallmark gene-sets and in genome-wide association studies of depression and antidepressant response. No epigenome-wide significant differentially methylated positions were found for treatment response or early improvement. Twenty DMRs were associated with response; the strongest in an enhancer region in SORBS2, which has been related to cardiovascular diseases and type II diabetes. Another DMR was located in CYP2C18, a gene previously linked to antidepressant response. Results pointed towards differential methylation in genes associated with cardiac function, neuroticism, and depression. Linking differential methylation to antidepressant treatment response is an emerging topic and represents a step towards personalized medicine, potentially facilitating the prediction of patients' response before treatment.

Digital tools for the assessment of pharmacological treatment for depressive disorder: State of the art

Depression is an invalidating disorder, marked by phenotypic heterogeneity. Clinical assessments for treatment adjustments and data-collection for pharmacological research often rely on subjective representations of functioning. Better phenotyping through digital applications may add unseen information and facilitate disentangling the clinical characteristics and impact of depression and its pharmacological treatment in everyday life. Researchers, physicians, and patients benefit from well-understood digital phenotyping approaches to assess the treatment efficacy and side-effects. This review discusses the current possibilities and pitfalls of wearables and technology for the assessment of the pharmacological treatment of depression. Their applications in the whole spectrum of treatment for depression, including diagnosis, treatment of an episode, and monitoring of relapse risk and prevention are discussed. Multiple aspects are to be considered, including concerns that come with collecting sensitive data and health recordings. Also, privacy and trust are addressed. Available applications range from questionnaire-like apps to objective assessment of behavioural patterns and promises in handling suicidality. Nonetheless, interpretation and integration of this high-resolution information with other phenotyping levels, remains challenging. This review provides a state-of-the-art description of wearables and technology in digital phenotyping for monitoring pharmacological treatment in depression, focusing on the challenges and opportunities of its application in clinical trials and research.

The molecular pathophysiology of mood disorders: From the analysis of single molecular layers to multi-omic integration

Next-generation sequencing now enables the rapid and affordable production of reliable biological data at multiple molecular levels, collectively referred to as "omics". To maximize the potential for discovery, computational biologists have created and adapted integrative multi-omic analytical methods. When applied to diseases with traceable pathophysiology such as cancer, these new algorithms and statistical approaches have enabled the discovery of clinically relevant molecular mechanisms and biomarkers. In contrast, these methods have been much less applied to the field of molecular psychiatry, although diagnostic and prognostic biomarkers are similarly needed. In the present review, we first briefly summarize main findings from two decades of studies that investigated single molecular processes in relation to mood disorders. Then, we conduct a systematic review of multi-omic strategies that have been proposed and used more recently. We also list databases and types of data available to researchers for future work. Finally, we present the newest methodologies that have been employed for multi-omics integration in other medical fields, and discuss their potential for molecular psychiatry studies.

Integrative PheWAS analysis in risk categorization of major depressive disorder and identifying their associations with genetic variants using a latent topic model approach

Major depressive disorder (MDD) is the most prevalent mental disorder that constitutes a major public health problem. A tool for predicting the risk of MDD could assist with the early identification of MDD patients and targeted interventions to reduce the risk. We aimed to derive a risk prediction tool that can categorize the risk of MDD as well as discover biologically meaningful genetic variants. Data analyzed were from the fourth and fifth data collections of a longitudinal community-based cohort from Southwest Montreal, Canada, between 2015 and 2018. To account for high dimensional features, we adopted a latent topic model approach to infer a set of topical distributions over those studied predictors that characterize the underlying meta-phenotypes of the MDD cohort. MDD probability derived from 30 MDD meta-phenotypes demonstrated superior prediction accuracy to differentiate MDD cases and controls. Six latent MDD meta-phenotypes we inferred via a latent topic model were highly interpretable. We then explored potential genetic variants that were statistically associated with these MDD meta-phenotypes. The genetic heritability of MDD meta-phenotypes was 0.126 (SE = 0.316), compared to 0.000001 (SE = 0.297) for MDD diagnosis defined by the structured interviews. We discovered a list of significant MDD - related genes and pathways that were missed by MDD diagnosis. Our risk prediction model confers not only accurate MDD risk categorization but also meaningful associations with genetic predispositions that are linked to MDD subtypes. Our findings shed light on future research focusing on these identified genes and pathways for MDD subtypes.

Morphological Biomarkers in the Amygdala and Hippocampus of Children and Adults at High Familial Risk for Depression

Major Depressive Disorder (MDD) is highly familial, and the hippocampus and amygdala are important in the pathophysiology of MDD. Whether morphological markers of risk for familial depression are present in the hippocampus or amygdala is unknown. We imaged the brains of 148 individuals, aged 6 to 54 years, who were members of a three-generation family cohort study and who were at either high or low familial risk for MDD. We compared surface morphological features of the hippocampus and amygdala across risk groups and assessed their associations with depression severity. High- compared with low-risk individuals had inward deformations of the head of both hippocampi and the medial surface of the left amygdala. The hippocampus findings persisted in analyses that included only those participants who had never had MDD, suggesting that these are true endophenotypic biomarkers for familial MDD. Posterior extension of the inward deformations was associated with more severe depressive symptoms, suggesting that a greater spatial extent of this biomarker may contribute to the transition from risk to the overt expression of symptoms. Significant associations of these biomarkers with corresponding biomarkers for cortical thickness suggest that these markers are components of a distributed cortico-limbic network of familial vulnerability to MDD.

Complex trait methylation scores in the prediction of major depressive disorder

BACKGROUND

DNA methylation (DNAm) is associated with time-varying environmental factors that contribute to major depressive disorder (MDD) risk. We sought to test whether DNAm signatures of lifestyle and biochemical factors were associated with MDD to reveal dynamic biomarkers of MDD risk that may be amenable to lifestyle interventions.

METHODS

Here, we calculated methylation scores (MS) at multiple p-value thresholds for lifestyle (BMI, smoking, alcohol consumption, and educational attainment) and biochemical (high-density lipoprotein (HDL) and total cholesterol) factors in Generation Scotland (GS) (N=9,502) and in a replication cohort (ALSPAC_adults, N=565), using CpG sites reported in previous well-powered methylome-wide association studies. We also compared their predictive accuracy for MDD to a MDD MS in an independent GS sub-sample (N=4,432).

FINDINGS

Each trait MS was significantly associated with its corresponding phenotype in GS (β_range=0.089-1.457) and in ALSPAC (β_range=0.078-2.533). Each MS was also significantly associated with MDD before and after adjustment for its corresponding phenotype in GS (β_range=0.053-0.145). After accounting for relevant lifestyle factors, MS for educational attainment (β=0.094) and alcohol consumption (MS_p-value<0.01-0.5; β_range=-0.069-0.083) remained significantly associated with MDD in GS. Smoking (AUC=0.569) and educational attainment (AUC=0.585) MSs could discriminate MDD from controls better than the MDD MS (AUC=0.553) in the independent GS sub-sample. Analyses implicating MDD did not replicate across ALSPAC, although the direction of effect was consistent for all traits when adjusting for the MS corresponding phenotypes.

INTERPRETATION

We showed that lifestyle and biochemical MS were associated with MDD before and after adjustment for their corresponding phenotypes (p_nominal<0.05), but not when smoking, alcohol consumption, and BMI were also included as covariates. MDD results did not replicate in the smaller, female-only independent ALSPAC cohort (N_ALSPAC=565; N_GS=9,502), potentially due to demographic differences or low statistical power, but effect sizes were consistent with the direction reported in GS. DNAm scores for modifiable MDD risk factors may contribute to disease vulnerability and, in some cases, explain additional variance to their observed phenotypes.

FUNDING

Wellcome Trust.

Major Depressive Disorder: Existing Hypotheses about Pathophysiological Mechanisms and New Genetic Findings

Major depressive disorder (MDD) is a common mental disorder generally characterized by symptoms associated with mood, pleasure and effectiveness in daily life activities. MDD is ranked as a major contributor to worldwide disability. The complex pathogenesis of MDD is not yet understood, and this is a major cause of failure to develop new therapies and MDD recurrence. Here we summarize the literature on existing hypotheses about the pathophysiological mechanisms of MDD. We describe the different approaches undertaken to understand the molecular mechanism of MDD using genetic data. Hundreds of loci have now been identified by large genome-wide association studies (GWAS). We describe these studies and how they have provided information on the biological processes, cell types, tissues and druggable targets that are enriched for MDD risk genes. We detail our understanding of the genetic correlations and causal relationships between MDD and many psychiatric and non-psychiatric disorders and traits. We highlight the challenges associated with genetic studies, including the complexity of MDD genetics in diverse populations and the need for a study of rare variants and new studies of gene-environment interactions.

A new paradigm shift in antidepressant therapy? From dual-action to multitarget-directed ligands

Major Depressive Disorder is a chronic, recurring, and potentially fatal disease affecting up to 20% of the global population. Since the monoamine hypothesis was proposed more than 60 years ago, only a few relevant advances have been achieved, with very little disease course changing, from a pharmacological perspective. Moreover, since negative efficacy studies with novel molecules are frequent, many pharmaceutical companies have put new studies on hold. Fortunately, relevant clinical studies are currently being performed, and extensive striving is being developed by universities, research centers, and other public and private institutions. Depression is no longer considered a simple disease but a multifactorial one. New research fields are emerging in what could be a paradigm shift: the multitarget approach beyond monoamines. In this review, we summarize the present and the past of antidepressant drug discovery, with the aim to shed some light on the current state of the art in clinical and preclinical advances to face this increasingly devastating disease.

Subgrouping time-dependent prescribing patterns of first-onset major depressive episodes by psychotropics dissection

BACKGROUND

Subgrouping patients with major depressive disorder is a promising solution for the issue of heterogeneity. However, the link between available subtypes and distinct pathological mechanisms is weak and yields disappointing results in clinical application.

AIM

To develop a novel approach for classification of patients with time-dependent prescription patterns at first onset in real-world settings.

METHODS

Drug-naive patients experiencing their first major depressive episode (n = 105) participated in this study. Psychotropic agents prescribed in the first 24 mo following disease onset were recorded monthly and categorized as antidepressants, augmentation agents, and hypnosedatives. Monthly cumulative doses of agents in each category were converted into relevant equivalents. Four parameters were used to summarize the time-dependent prescription patterns for each psychotropic load: Stability, amount, frequency, and the time trend of monthly prescriptions. A K-means cluster analysis was used to derive subgroups of participants based on these input parameters of psychotropic agents across 24 mo. Clinical validity of the resulting data-driven clusters was compared using relevant severity indicators.

RESULTS

Four distinct clusters were derived from K-means analysis, which matches experts’ consent: "Short-term antidepressants use", "long-term antidepressants use", "long-term antidepressants and sedatives use", and "long-term antidepressants, sedatives, and augmentation use". At the first 2 years of disease course, the four clusters differed on the number of antidepressants used at adequate dosage and duration, frequency of outpatient service use, and number of psychiatric admissions. After the first 2 years following disease onset, depression severity was differed in the four subgroups.

CONCLUSION

Our findings suggested a new approach to optimize the subgrouping of patients with major depressive disorder, which may assist future etiological and treatment response studies.

The miRNome of Depression

Depression is an effect of complex interactions between genetic, epigenetic and environmental factors. It is well established that stress responses are associated with multiple modest and often dynamic molecular changes in the homeostatic balance, rather than with a single genetic factor that has a strong phenotypic penetration. As depression is a multifaceted phenotype, it is important to study biochemical pathways that can regulate the overall allostasis of the brain. One such biological system that has the potential to fine-tune a multitude of diverse molecular processes is RNA interference (RNAi). RNAi is an epigenetic process showing a very low level of evolutionary diversity, and relies on the posttranscriptional regulation of gene expression using, in the case of mammals, primarily short (17–23 nucleotides) noncoding RNA transcripts called microRNAs (miRNA). In this review, our objective was to examine, summarize and discuss recent advances in the field of biomedical and clinical research on the role of miRNA-mediated regulation of gene expression in the development of depression. We focused on studies investigating post-mortem brain tissue of individuals with depression, as well as research aiming to elucidate the biomarker potential of miRNAs in depression and antidepressant response.

The genetic basis of major depression

Major depressive disorder (MDD) is a common, debilitating, phenotypically heterogeneous disorder with heritability ranges from 30% to 50%. Compared to other psychiatric disorders, its high prevalence, moderate heritability, and strong polygenicity have posed major challenges for gene-mapping in MDD. Studies of common genetic variation in MDD, driven by large international collaborations such as the Psychiatric Genomics Consortium, have confirmed the highly polygenic nature of the disorder and implicated over 100 genetic risk loci to date. Rare copy number variants associated with MDD risk were also recently identified. The goal of this review is to present a broad picture of our current understanding of the epidemiology, genetic epidemiology, molecular genetics, and gene-environment interplay in MDD. Insights into the impact of genetic factors on the aetiology of this complex disorder hold great promise for improving clinical care.

Genetics of eating disorders in the genome-wide era

Enabled by advances in high throughput genomic sequencing and an unprecedented level of global data sharing, molecular genetic research is beginning to unlock the biological basis of eating disorders. This invited review provides an overview of genetic discoveries in eating disorders in the genome-wide era. To date, five genome-wide association studies on eating disorders have been conducted - all on anorexia nervosa (AN). For AN, several risk loci have been detected, and ~11-17% of the heritability has been accounted for by common genetic variants. There is extensive genetic overlap between AN and psychological traits, especially obsessive-compulsive disorder, and intriguingly, with metabolic phenotypes even after adjusting for body mass index (BMI) risk variants. Furthermore, genetic risk variants predisposing to lower BMI may be causal risk factors for AN. Causal genes and biological pathways of eating disorders have yet to be elucidated and will require greater sample sizes and statistical power, and functional follow-up studies. Several studies are underway to recruit individuals with bulimia nervosa and binge-eating disorder to enable further genome-wide studies. Data collections and research labs focused on the genetics of eating disorders have joined together in a global effort with the Psychiatric Genomics Consortium. Molecular genetics research in the genome-wide era is improving knowledge about the biology behind the established heritability of eating disorders. This has the potential to offer new hope for understanding eating disorder etiology and for overcoming the therapeutic challenges that confront the eating disorder field.

Genetic Overlap Profiles of Cognitive Ability in Psychotic and Affective Illnesses: A Multisite Study of Multiplex Pedigrees

BACKGROUND

Cognitive impairment is a key feature of psychiatric illness, making cognition an important tool for exploring of the genetics of illness risk. It remains unclear which measures should be prioritized in pleiotropy-guided research. Here, we generate profiles of genetic overlap between psychotic and affective disorders and cognitive measures in Caucasian and Hispanic groups.

METHODS

Data were from 4 samples of extended pedigrees (N = 3046). Coefficient of relationship analyses were used to estimate genetic overlap between illness risk and cognitive ability. Results were meta-analyzed.

RESULTS

Psychosis was characterized by cognitive impairments on all measures with a generalized profile of genetic overlap. General cognitive ability shared greatest genetic overlap with psychosis risk (average endophenotype ranking value [ERV] across samples from a random-effects meta-analysis = 0.32), followed by verbal memory (ERV = 0.24), executive function (ERV = 0.22), and working memory (ERV = 0.21). For bipolar disorder, there was genetic overlap with processing speed (ERV = 0.05) and verbal memory (ERV = 0.11), but these were confined to select samples. Major depressive disorder was characterized by enhanced working and face memory performance, as reflected in significant genetic overlap in 2 samples.

CONCLUSIONS

There is substantial genetic overlap between risk for psychosis and a range of cognitive abilities (including general intelligence). Most of these effects are largely stable across of ascertainment strategy and ethnicity. Genetic overlap between affective disorders and cognition, on the other hand, tends to be specific to ascertainment strategy, ethnicity, and cognitive test battery.

KCNQ variants and pain modulation: a missense variant in Kv7.3 contributes to pain resilience

There is a pressing need for understanding of factors that confer resilience to pain. Gain-of-function mutations in sodium channel Nav1.7 produce hyperexcitability of dorsal root ganglion neurons underlying inherited erythromelalgia, a human genetic model of neuropathic pain. While most individuals with erythromelalgia experience excruciating pain, occasional outliers report more moderate pain. These differences in pain profiles in blood-related erythromelalgia subjects carrying the same pain-causative Nav1.7 mutation and markedly different pain experience provide a unique opportunity to investigate potential genetic factors that contribute to inter-individual variability in pain. We studied a patient with inherited erythromelalgia and a Nav1.7 mutation (c.4345T>G, p. F1449V) with severe pain as is characteristic of most inherited erythromelalgia patients, and her mother who carries the same Nav1.7 mutation with a milder pain phenotype. Detailed six-week daily pain diaries of pain episodes confirmed their distinct pain profiles. Electrophysiological studies on subject-specific induced pluripotent stem cell-derived sensory neurons from each of these patients showed that the excitability of these cells paralleled their pain phenotype. Whole-exome sequencing identified a missense variant (c.2263C>T, p. D755N) in KCNQ3 (Kv7.3) in the pain resilient mother. Voltage-clamp recordings showed that co-expression of Kv7.2-wild type (WT)/Kv7.3-D755N channels produced larger M-currents than that of Kv7.2-WT/Kv7.3-WT. The difference in excitability of the patient-specific induced pluripotent stem cell-derived sensory neurons was mimicked by modulating M-current levels using the dynamic clamp and a model of the mutant Kv7.2-WT/Kv7.3-D755N channels. These results show that a 'pain-in-a-dish' model can be used to explicate genetic contributors to pain, and confirm that KCNQ variants can confer pain resilience via an effect on peripheral sensory neurons.

Self-reported medication use as an alternate phenotyping method for anxiety and depression in the UK Biobank

The requirement for large sample sizes for psychiatric genetic analyses necessitates novel approaches to derive cases. Anxiety and depression show substantial genetic overlap and share pharmacological treatments. Data on prescribed medication could be effective for inferring case status when other indicators of mental health are unavailable. We investigated self-reported current medication use in UK Biobank participants of European ancestry. Medication Status cases reported using antidepressant or anxiolytic medication (n = 22,218), controls did not report psychotropic medication use (n = 168,959). A subset, "Medication Only," additionally did not meet criteria for any other mental health indicator (case n = 2,643, control n = 107,029). We assessed genetic overlap between these phenotypes and two published genetic association studies of anxiety and depression, and an internalizing disorder trait derived from symptom-based questionnaires in UK Biobank. Genetic correlations between Medication Status and the three anxiety and depression phenotypes were significant (r_g  = 0.60-0.73). In the Medication Only subset, the genetic correlation with depression was significant (r_g  = 0.51). The three polygenic scores explained 0.33% - 0.80% of the variance in Medication Status and 0.07% - 0.19% of the variance in Medication Only. This study provides evidence that self-reported current medication use offers an alternate or supplementary anxiety or depression phenotype in genetic studies where diagnostic information is sparse or unavailable.

[Modern approaches to the genetics of depression: scopes and limitations]

SUMMARY

Recent findings in candidate genes for depression showed significant replication failures and thus appeared irrelevant. Much of the earlier studies' limitations can be overcome by the strategy of genome-wide association studies (GWAS), which aims to identify associations between different genomic variants and phenotypic traits without pathophysiological hypotheses application. With the use of such studies, it seems possible to calculate polygenic risk scores (PRS) as a promising approach for predicting depression risk. The aim of this review is to analyze modern approaches of genetic research used to assess the risk of depression in a population.

What Do the Animal Studies of Stress Resilience Teach Us?

Long-lasting stress factors, both biological and psychological, are commonly accepted as the main cause of depressive disorders. Several animal models, using various stressful stimuli, have been used to find biochemical and molecular alterations that could help us understand the etiopathogenesis of depression. However, recent sophisticated studies indicate that the most frequently used animal models of stress only capture a portion of the molecular features associated with complex human disorders. On the other hand, some of these models generate groups of animals resilient to stress. Studies of the mechanisms of stress resilience bring us closer to understanding the process of adapting to aversive stimuli and the differences between stress-susceptible vs. resilient phenotypes. Especially interesting in this context is the chronic mild stress (CMS) experimental paradigm, most often using rats. Studies using this animal model have revealed that biochemical (e.g., the dopamine D2 receptor) and molecular (e.g., microRNA) alterations are dynamic (i.e., depend on stress duration, 2 vs. 7 weeks) and much more pronounced in stress-resilient than stress-susceptible groups of animals. We strongly suggest that studies aimed at understanding the molecular and biochemical mechanisms of depression must consider these dynamics. A good candidate to serve as a biomarker in such studies might be serum microRNA, since it can be obtained relatively easily from living individuals at various time points.

Major Depression: One Brain, One Disease, One Set of Intertwined Processes

Major depressive disorder (MDD) is a heterogeneous disease affecting one out of five individuals and is the leading cause of disability worldwide. Presently, MDD is considered a multifactorial disease with various causes such as genetic susceptibility, stress, and other pathological processes. Multiple studies allowed the formulation of several theories attempting to describe the development of MDD. However, none of these hypotheses are comprehensive because none of them can explain all cases, mechanisms, and symptoms of MDD. Nevertheless, all of these theories share some common pathways, which lead us to believe that these hypotheses depict several pieces of the same big puzzle. Therefore, in this review, we provide a brief description of these theories and their strengths and weaknesses in an attempt to highlight the common mechanisms and relationships of all major theories of depression and combine them together to present the current overall picture. The analysis of all hypotheses suggests that there is interdependence between all the brain structures and various substances involved in the pathogenesis of MDD, which could be not entirely universal, but can affect all of the brain regions, to one degree or another, depending on the triggering factor, which, in turn, could explain the different subtypes of MDD.