High-throughput sequencing of the synaptome in major depressive disorder

Translational bioinformatics and data science for biomarker discovery in mental health: an analytical review

Translational bioinformatics and data science play a crucial role in biomarker discovery as it enables translational research and helps to bridge the gap between the bench research and the bedside clinical applications. Thanks to newer and faster molecular profiling technologies and reducing costs, there are many opportunities for researchers to explore the molecular and physiological mechanisms of diseases. Biomarker discovery enables researchers to better characterize patients, enables early detection and intervention/prevention and predicts treatment responses. Due to increasing prevalence and rising treatment costs, mental health (MH) disorders have become an important venue for biomarker discovery with the goal of improved patient diagnostics, treatment and care. Exploration of underlying biological mechanisms is the key to the understanding of pathogenesis and pathophysiology of MH disorders. In an effort to better understand the underlying mechanisms of MH disorders, we reviewed the major accomplishments in the MH space from a bioinformatics and data science perspective, summarized existing knowledge derived from molecular and cellular data and described challenges and areas of opportunities in this space.

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.

Hypericum perforatum L. Regulates Glutathione Redox Stress and Normalizes Ggt1/Anpep Signaling to Alleviate OVX-Induced Kidney Dysfunction

Menopause and associated renal complications are linked to systemic redox stress, and the causal factors remain unclear. As the role of Hypericum perforatum L. (HPL) in menopause-induced kidney disease therapy is still ambiguous, we aim to explore the effects of HPL on systemic redox stress under ovariectomy (OVX)-induced kidney dysfunction conditions. Here, using combined proteomic and metabolomic approaches, we constructed a multi-scaled “HPL-disease-gene-metabolite” network to generate a therapeutic “big picture” that indicated an important link between glutathione redox stress and kidney impairment. HPL exhibited the potential to maintain cellular redox homeostasis by inhibiting gamma-glutamyltransferase 1 (Ggt1) overexpression, along with promoting the efflux of accumulated toxic amino acids and their metabolites. Moreover, HPL restored alanyl-aminopeptidase (Anpep) expression and metabolite shifts, promoting antioxidative metabolite processing, and recovery. These findings provide a comprehensive description of OVX-induced glutathione redox stress at multiple levels and support HPL therapy as an effective modulator in renal tissues to locally influence the glutathione metabolism pathway and subsequent redox homeostasis.

Targeted sequencing of the BDNF gene in young Chinese Han people with major depressive disorder

BACKGROUND

Adolescence and young adulthood are considered the peak age for the emergence of many psychiatric disorders, in particular major depressive disorder (MDD). Previous research has shown substantial heritability for MDD. In addition, the brain-derived neurotrophic factor (BDNF) gene is known to be associated with MDD. However, there has been no study conducting targeted sequencing of the BDNF gene in young MDD patients so far.

METHOD

To examine whether the BDNF gene is associated with the occurrence of MDD in young patients, we used targeted sequencing to detect the BDNF gene variants in 259 young Chinese Han people (105 MDD patients and 154 healthy subjects).

RESULTS

The BDNF variant rs4030470 was associated with MDD in young Chinese Han people (uncorrected p = 0.046), but this was no longer significant after applying FDR correction (p = 0.552, after FDR correction). We did not find any significant differences in genotype or haplotype frequencies between the case and control groups, and furthermore discovered no rare mutation variants any of the 259 subjects.

CONCLUSION

Our results do not support an association of the BDNF gene variants with MDD in young people in the Chinese Han population.

A study combining whole-exome sequencing and structural neuroimaging analysis for major depressive disorder

BACKGROUND

Genetic variations associated with major depressive disorder (MDD) may affect the structural aspects of neural networks mediated by the molecular pathways involved in neuronal survival and synaptic plasticity. However, few studies have applied a novel approach such as whole-exome sequencing (WES) analysis to investigate the genetic contribution to the neurostructural changes in MDD.

METHODS

In the first part of the study, we investigated rare variants of selected genes from previous WES studies using a WES analysis in 184 patients with MDD and 82 healthy controls. In the second part of the study, we explored the association between the common genetic variants from the WES analysis and cortical thickness in 91 patients with MDD and 75 healthy controls. The gray-matter thickness of each cortical region was measured using FreeSurfer.

RESULTS

We identified recurrent non-silent variants in 24 MDD-related genes including FASN, MYH13, UNC13D, LILRA1, CACNA1B, TRIO, HOMER3, and BCAR3, and observed eleven recurrently altered copy number alternations where a gain on 15q11.2 and losses on 7q34 and 15q11.1-q11.2 in MDD genomes. We also found that rs11592462 in CDH23, a calcium-dependent cell-adhesion molecule encoding gene, was significantly associated with thinning in the right anterior cingulate cortex.

LIMITATION

The small sample size may lead our findings to be underpowered regarding rare variants.

CONCLUSION

The present study identified that non-synonymous rare variants were significantly associated with risk of MDD and found that genetic contributions to the development of MDD may be mediated by alterations in cortical thickness of emotion-processing neural circuits.

Genetic Advance in Depressive Disorder

Major depressive disorder (MDD) and bipolar disorder (BPD) are both chronic, severe mood disorder with high misdiagnosis rate, leading to substantial health and economic burdens to patients around the world. There is a high misdiagnosis rate of bipolar depression (BD) just based on symptomology in depressed patients whose previous manic or mixed episodes have not been well recognized. Therefore, it is important for psychiatrists to identify these two major psychiatric disorders. Recently, with the accumulation of clinical sample sizes and the advances of methodology and technology, certain progress in the genetics of major depression and bipolar disorder has been made. This article reviews the candidate genes for MDD and BD, genetic variation loci, chromosome structural variation, new technologies, and new methods.

Affected Sib-Pair Analyses Identify Signaling Networks Associated With Social Behavioral Deficits in Autism

Autism spectrum disorders (ASDs) are characterized by deficits in three core behavioral domains: reciprocal social interactions, communication, and restricted interests and/or repetitive behaviors. Several hundreds of risk genes for autism have been identified, however, it remains a challenge to associate these genes with specific core behavioral deficits. In multiplex autism families, affected sibs often show significant differences in severity of individual core phenotypes. We hypothesize that a higher mutation burden contributes to a larger difference in the severity of specific core phenotypes between affected sibs. We tested this hypothesis on social behavioral deficits in autism. We sequenced synaptome genes (n = 1,886) in affected male sib-pairs (n = 274) in families from the Autism Genetics Research Exchange (AGRE) and identified rare (MAF ≤ 1%) and predicted functional variants. We selected affected sib-pairs with a large (≥10; n = 92 pairs) or a small (≤4; n = 108 pairs) difference in total cumulative Autism Diagnostic Interview-Revised (ADI-R) social scores (SOCT_CS). We compared burdens of unshared variants present only in sibs with severe social deficits and found a higher burden in SOCT_CS≥10 compared to SOCT_CS ≤ 4 (SOCT_CS≥10: 705.1 ± 16.2; SOCT_CS ≤ 4, 668.3 ± 9.0; p = 0.025). Unshared SOCT_CS≥10 genes only in sibs with severe social deficits are significantly enriched in the SFARI gene set. Network analyses of these genes using InWeb_IM, molecular signatures database (MSigDB), and GeNetMeta identified enrichment for phosphoinositide 3-kinase (PI3K)-AKT-mammalian target of rapamycin (mTOR) (Enrichment Score [eScore] p value = 3.36E−07; n = 8 genes) and Nerve growth factor (NGF) (eScore p value = 8.94E−07; n = 9 genes) networks. These studies support a key role for these signaling networks in social behavioral deficits and present a novel approach to associate risk genes and signaling networks with core behavioral domains in autism.

Comparative genetic architectures of schizophrenia in East Asian and European populations

Schizophrenia is a debilitating psychiatric disorder with approximately 1% lifetime risk globally. Large-scale schizophrenia genetic studies have reported primarily on European ancestry samples, potentially missing important biological insights. Here, we report the largest study to date of East Asian participants (22,778 schizophrenia cases and 35,362 controls), identifying 21 genome-wide significant associations in 19 genetic loci. Common genetic variants that confer risk for schizophrenia have highly similar effects between East Asian and European ancestries (r_g = 0.98 ± 0.03), indicating that the genetic basis of schizophrenia and its biology are broadly shared across populations. A fixed-effect meta-analysis including individuals from East Asian and European ancestries identified 208 significant associations in 176 genetic loci (53 novel). Trans-ancestry fine-mapping reduced the sets of candidate causal variants in 44 loci. Polygenic risk scores had reduced performance when transferred across ancestries, highlighting the importance of including sufficient samples of major ancestral groups to ensure their generalizability across populations.

Enriching Human Interactome with Functional Mutations to Detect High-Impact Network Modules Underlying Complex Diseases

Rapid progress in high-throughput -omics technologies moves us one step closer to the datacalypse in life sciences. In spite of the already generated volumes of data, our knowledge of the molecular mechanisms underlying complex genetic diseases remains limited. Increasing evidence shows that biological networks are essential, albeit not sufficient, for the better understanding of these mechanisms. The identification of disease-specific functional modules in the human interactome can provide a more focused insight into the mechanistic nature of the disease. However, carving a disease network module from the whole interactome is a difficult task. In this paper, we propose a computational framework, Discovering most IMpacted SUbnetworks in interactoMe (DIMSUM), which enables the integration of genome-wide association studies (GWAS) and functional effects of mutations into the protein-protein interaction (PPI) network to improve disease module detection. Specifically, our approach incorporates and propagates the functional impact of non-synonymous single nucleotide polymorphisms (nsSNPs) on PPIs to implicate the genes that are most likely influenced by the disruptive mutations, and to identify the module with the greatest functional impact. Comparison against state-of-the-art seed-based module detection methods shows that our approach could yield modules that are biologically more relevant and have stronger association with the studied disease. We expect for our method to become a part of the common toolbox for the disease module analysis, facilitating the discovery of new disease markers.

Rare variants in SLC6A4 cause susceptibility to major depressive disorder with suicidal ideation in Han Chinese adolescents and young adults

BACKGROUND

Suicidal ideation (SI) is the most serious symptom of major depressive disorder (MDD) and considered an extreme state. The serotonin transporter gene (SLC6A4) plays a significant role in MDD and suicide pathophysiology. Previous studies have revealed an association between common variants of SLC6A4 with the risk of MDD and suicide. However, very few studies have so far focused on the degree to which rare variants of SLC6A4 are responsible for the depression observed in adolescent and young adult suicide patients. The aim of this study was to examine the impact of common and rare variants of SLC6A4 on the risk of Han Chinese adolescents and young adults suffering MDD with SI.

METHODS

Targeted sequencing of the SLC6A4 gene was conducted using FastTarget technology in Han Chinese adolescents and young adults, of which 74 were MDD patients with SI and 150 were healthy controls. Gene-based association analyses of rare variants were performed using enrichment analysis and a cumulative allele test. An allele association study was performed against common variants.

RESULTS

After sequencing and bioinformatics analysis, a total of 15 single nucleotide variants (SNVs) were detected in the targeted regions from all participants, including 9 common and 6 rare variants. Among these, 5 rare variants were identified within the study group. Enrichment analysis of rare variants demonstrated a statistical difference (p = 0.042) between the study and control groups. Using cumulative allele analysis, alternative alleles in the SLC6A4 gene exhibited an association with MDD patients with SI (cumulative allele: OR = 10.18, 95% CI = 1.18-87.32, p = 0.017). No significant association was found between the 9 common SLC6A4 variants and MDD patients with SI.

CONCLUSIONS

Our results suggest that rare variants of SLC6A4 may contribute to a genetic risk of adolescents and young adults suffering MDD with SI.

Differential gene expression analysis in blood of first episode psychosis patients

BACKGROUND

Psychosis is a condition influenced by an interaction of environmental and genetic factors. Gene expression studies can capture these interactions; however, studies are usually performed in patients who are in remission. This study uses blood of first episode psychosis patients, in order to characterise deregulated pathways associated with psychosis symptom dimensions.

METHODS

Peripheral blood from 149 healthy controls and 131 first episode psychosis patients was profiled using Illumina HT-12 microarrays. A case/control differential expression analysis was performed, followed by correlation of gene expression with positive and negative syndrome scale (PANSS) scores. Enrichment analyses were performed on the associated gene lists. We test for pathway differences between first episode psychosis patients who qualify for a Schizophrenia diagnosis against those who do not.

RESULTS

A total of 978 genes were differentially expressed and enriched for pathways associated to immune function and the mitochondria. Using PANSS scores we found that positive symptom severity was correlated with immune function, while negative symptoms correlated with mitochondrial pathways.

CONCLUSIONS

Our results identified gene expression changes correlated with symptom severity and showed that key pathways are modulated by positive and negative symptom dimensions.

Rare copy number variations affecting the synaptic gene DMXL2 in neurodevelopmental disorders

Background

Ultra-rare genetic variants, including non-recurrent copy number variations (CNVs) affecting important dosage-sensitive genes, are important contributors to the etiology of neurodevelopmental disorders (NDDs). Pairing family-based whole-genome sequencing (WGS) with detailed phenotype data can enable novel gene associations in NDDs.

Methods

We performed WGS of six members from a three-generation family, where three individuals each had a spectrum of features suggestive of a NDD. CNVs and sequence-level variants were identified and further investigated in disease and control databases.

Results

We identified a novel 252-kb deletion at 15q21 that overlaps the synaptic gene DMXL2 and the gene GLDN. The microdeletion segregated in NDD-affected individuals. Additional rare inherited and de novo sequence-level variants were found that may also be involved, including a missense change in GRIK5. Multiple CNVs and loss-of-function sequence variants affecting DMXL2 were discovered in additional unrelated individuals with a range of NDDs.

Conclusions

Disruption of DMXL2 may predispose to NDDs including autism spectrum disorder. The robust interpretation of private variants requires a multifaceted approach that incorporates multigenerational pedigrees and genome-wide and population-scale data.

Transcriptome Changes in Relation to Manic Episode

Bipolar disorder (BD) is highly heritable and well known for its recurrent manic and depressive episodes. The present study focused on manic episode in BD patients and aimed to investigate state-specific transcriptome alterations between acute episode and remission, including messenger RNAs (mRNAs), long noncoding RNAs (lncRNAs), and micro-RNAs (miRNAs), using microarray and RNA sequencing (RNA-Seq) platforms. BD patients were enrolled with clinical information, and peripheral blood samples collected at both acute and remission status spanning for at least 2 months were confirmed by follow-ups. Symptom severity was assessed by Young Mania Rating Scale. We enrolled six BD patients as the discovery samples and used the Affymetrix Human Transcriptome Array 2.0 to capture transcriptome data at the two time points. For replication, expression data from Gene Expression Omnibus that consisted of 11 BD patients were downloaded, and we performed a mega-analysis for microarray data of 17 patients. Moreover, we conducted RNA sequencing (RNA-Seq) in additional samples of 7 BD patients. To identify intraindividual differentially expressed genes (DEGs), we analyzed data using a linear model controlling for symptom severity. We found that noncoding genes were of majority among the top DEGs in microarray data. The expression fold change of coding genes among DEGs showed moderate to high correlations (∼0.5) across platforms. A number of lncRNAs and two miRNAs (MIR181B1 and MIR103A1) exhibited high levels of gene expression in the manic state. For coding genes, we reported that the taste function-related genes, including TAS2R5 and TAS2R3, may be mania state-specific markers. Additionally, four genes showed a nominal p-value of less than 0.05 in all our microarray data, mega-analysis, and RNA-Seq analysis. They were upregulated in the manic state and consisted of MS4A14, PYHIN1, UTRN, and DMXL2, and their gene expression patterns were further validated by quantitative real-time polymerase chain reaction (PCR) (qRT-PCR). We also performed weight gene coexpression network analysis to identify gene modules for manic episode. Genes in the mania-related modules were different from the susceptible loci of BD obtained from genome-wide association studies, and biological pathways in relation to these modules were mainly related to immune function, especially cytokine-cytokine receptor interaction. Results of the present study elucidated potential molecular targets and genomic networks that are involved in manic episode. Future studies are needed to further validate these biomarkers for their roles in the etiology of bipolar illness.

Genetic Markers in Psychiatry

Psychiatric disorders such as addiction (substance use and addictive disorders), depression, eating disorders, schizophrenia, and post-traumatic stress disorder (PTSD) are severe, complex, multifactorial mental disorders that carry a high social impact, enormous public health costs, and various comorbidities as well as premature morbidity. Their neurobiological foundation is still not clear. Therefore, it is difficult to uncover new set of genes and possible genetic markers of these disorders since the understanding of the molecular imbalance leading to these disorders is not complete. The integrative approach is needed which will combine genomics and epigenomics; evaluate epigenetic influence on genes and their influence on neuropeptides, neurotransmitters, and hormones; examine gene × gene and gene × environment interplay; and identify abnormalities contributing to development of these disorders. Therefore, novel genetic approaches based on systems biology focused on improvement of the identification of the biological underpinnings might offer genetic markers of addiction, depression, eating disorders, schizophrenia, and PTSD. These markers might be used for early prediction, detection of the risk to develop these disorders, novel subtypes of the diseases and tailored, personalized approach to therapy.

Genetic variants in major depressive disorder: From pathophysiology to therapy

In spite of promising preclinical results there is a decreasing number of new registered medications in major depression. The main reason behind this fact is the lack of confirmation in clinical studies for the assumed, and in animals confirmed, therapeutic results. This suggests low predictive value of animal studies for central nervous system disorders. One solution for identifying new possible targets is the application of genetics and genomics, which may pinpoint new targets based on the effect of genetic variants in humans. The present review summarizes such research focusing on depression and its therapy. The inconsistency between most genetic studies in depression suggests, first of all, a significant role of environmental stress. Furthermore, effect of individual genes and polymorphisms is weak, therefore gene x gene interactions or complete biochemical pathways should be analyzed. Even genes encoding target proteins of currently used antidepressants remain non-significant in genome-wide case control investigations suggesting no main effect in depression, but rather an interaction with stress. The few significant genes in GWASs are related to neurogenesis, neuronal synapse, cell contact and DNA transcription and as being nonspecific for depression are difficult to harvest pharmacologically. Most candidate genes in replicable gene x environment interactions, on the other hand, are connected to the regulation of stress and the HPA axis and thus could serve as drug targets for depression subgroups characterized by stress-sensitivity and anxiety while other risk polymorphisms such as those related to prominent cognitive symptoms in depression may help to identify additional subgroups and their distinct treatment. Until these new targets find their way into therapy, the optimization of current medications can be approached by pharmacogenomics, where metabolizing enzyme polymorphisms remain prominent determinants of therapeutic success.

Cell Death Pathways: a Novel Therapeutic Approach for Neuroscientists

In the first part, the following mechanisms involved in different forms of cell death are considered, with a view to identifying potential therapeutic targets: tumour necrosis factor receptors (TNFRs) and their engagement by tumour necrosis factor-alpha (TNF-α); poly [ADP-ribose] polymerase (PARP)-1 cleavage; the apoptosis signalling kinase (ASK)-c-Jun N-terminal kinase (JNK) axis; lysosomal permeability; activation of programmed necrotic cell death; oxidative stress, caspase-3 inhibition and parthanatos; activation of inflammasomes by reactive oxygen species and the development of pyroptosis; oxidative stress, calcium dyshomeostasis and iron in the development of lysosomal-mediated necrosis and lysosomal membrane permeability; and oxidative stress, lipid peroxidation, iron dyshomeostasis and ferroptosis. In the second part, there is a consideration of the role of lethal and sub-lethal activation of these pathways in the pathogenesis and pathophysiology of neurodegenerative and neuroprogressive disorders, with particular reference to the TNF-α-TNFR signalling axis; dysregulation of ASK-1-JNK signalling; prolonged or chronic PARP-1 activation; the role of pyroptosis and chronic inflammasome activation; and the roles of lysosomal permeabilisation, necroptosis and ferroptosis. Finally, it is suggested that, in addition to targeting oxidative stress and inflammatory processes generally, neuropsychiatric disorders may respond to therapeutic targeting of TNF-α, PARP-1, the Nod-like receptor NLRP3 inflammasome and the necrosomal molecular switch receptor-interacting protein kinase-3, since their widespread activation can drive and/or exacerbate peripheral inflammation and neuroinflammation even in the absence of cell death. To this end, the use is proposed of a combination of the tetracycline derivative minocycline and N-acetylcysteine as adjunctive treatment for a range of neuropsychiatric disorders.

A rare missense variant in RCL1 segregates with depression in extended families

Depression is the most prevalent psychiatric disorder with a complex and elusive etiology that is moderately heritable. Identification of genes would greatly facilitate the elucidation of the biological mechanisms underlying depression, however, its complex etiology has proved to be a major bottleneck in the identification of its genetic risk factors, especially in genome-wide association-like studies. In this study, we exploit the properties of a genetic isolate and its family-based structure to explore whether relatively rare exonic variants influence the burden of depressive symptoms in families. Using a multistep approach involving linkage and haplotype analyses followed by exome sequencing in the Erasmus Rucphen Family (ERF) study, we identified a rare (minor allele frequency (MAF)=1%) missense c.1114C>T mutation (rs115482041) in the RCL1 gene segregating with depression across multiple generations. Rs115482041 showed significant association with depressive symptoms (N=2393, β_T-allele=2.33, P-value=1 × 10^-4) and explained 2.9% of the estimated genetic variance of depressive symptoms (22%) in ERF. Despite being twice as rare (MAF<0.5%), c.1114C>T showed similar effect and significant association with depressive symptoms in samples from the independent population-based Rotterdam study (N=1604, β_T-allele=3.60, P-value=3 × 10^-2). A comparison of RCL1 expression in human and mouse brain revealed a striking co-localization of RCL1 with the layer 1 interlaminar subclass of astrocytes found exclusively in higher-order primates. Our findings identify RCL1 as a novel candidate gene for depression and offer insights into mechanisms through which RCL1 may be relevant for depression.

Genome-wide association analyses identify 44 risk variants and refine the genetic architecture of major depression

Major depressive disorder (MDD) is a common illness accompanied by considerable morbidity, mortality, costs, and heightened risk of suicide. We conducted a genome-wide association meta-analysis based in 135,458 cases and 344,901 controls and identified 44 independent and significant loci. The genetic findings were associated with clinical features of major depression and implicated brain regions exhibiting anatomical differences in cases. Targets of antidepressant medications and genes involved in gene splicing were enriched for smaller association signal. We found important relationships of genetic risk for major depression with educational attainment, body mass, and schizophrenia: lower educational attainment and higher body mass were putatively causal, whereas major depression and schizophrenia reflected a partly shared biological etiology. All humans carry lesser or greater numbers of genetic risk factors for major depression. These findings help refine the basis of major depression and imply that a continuous measure of risk underlies the clinical phenotype.

Nonsynonymous Variation in NKPD1 Increases Depressive Symptoms in European Populations

BACKGROUND

Despite high heritability, little success was achieved in mapping genetic determinants of depression-related traits by means of genome-wide association studies.

METHODS

To identify genes associated with depressive symptomology, we performed a gene-based association analysis of nonsynonymous variation captured using exome-sequencing and exome-chip genotyping in a genetically isolated population from the Netherlands (n = 1999). Finally, we reproduced our significant findings in an independent population-based cohort (n = 1604).

RESULTS

We detected significant association of depressive symptoms with a gene NKPD1 (p = 3.7 × 10^-08). Nonsynonymous variants in the gene explained 0.9% of sex- and age-adjusted variance of depressive symptoms in the discovery study, which is translated into 3.8% of the total estimated heritability (h² = 0.24). Significant association of depressive symptoms with NKPD1 was also observed (n = 1604; p = 1.5 × 10^-03) in the independent replication sample despite little overlap with the discovery cohort in the set of nonsynonymous genetic variants observed in the NKPD1 gene. Meta-analysis of the discovery and replication studies improved the association signal (p = 1.0 × 10^-09).

CONCLUSIONS

Our study suggests that nonsynonymous variation in the gene NKPD1 affects depressive symptoms in the general population. NKPD1 is predicted to be involved in the de novo synthesis of sphingolipids, which have been implicated in the pathogenesis of depression.