The importance of TCF4 gene in the etiology of recurrent depressive disorders

Mendelian randomization and single-cell expression analyses identify the causal relationship between depression and chronic rhinosinusitis

Background

The causative relationship between chronic rhinosinusitis (CRS) and depression remains unclear. Herein we employed Mendelian randomization (MR) coupled with single-cell analysis to investigate the causality between CRS and depression.

Methods

Data pertaining to CRS and depression were mined from the genome-wide association study database, and a single-cell dataset was sourced from the literature. To explore causality, we conducted bidirectional MR analysis using MR-Egger, weighted median, inverse variance weighted (IVW), simple mode, and weighted mode, with IVW representing the most important method. Further, sensitivity analysis was performed to evaluate the robustness of MR analysis results. Candidate genes were analyzed via single-cell combined MR analysis.

Results

Forward MR analysis indicated depression as a risk factor for CRS when depression was the exposure factor and CRS was the outcome (OR = 1.425, P < 0.001). Reverse MR analysis revealed the same positive relationship between CRS and depression when CRS was the exposure factor and depression was the outcome (OR = 1.012, P = 0.038). Sensitivity analysis validated the robustness of bidirectional MR analysis results. Ten cell types (endothelial, ciliated, basal, myeloid, mast, apical, plasma, glandular, fibroblast, and T cells) were identified in the single-cell dataset. The network of receptor-ligand pairs showed that in normal samples, cell-cell interactions were present among various cell types, such as epithelial, mast, myeloid, and endothelial cells. In contrast, CRS samples featured only one specific receptor-ligand pair, confined to myeloid cells. TCF4 and MEF2C emerged as potentially crucial for CRS-associated depression development.

Conclusions

Our findings suggest a bidirectional causal relationship between CRS and depression, offering a new perspective on the association between CRS and depression.

TCF4 and RBFOX1 as peripheral biomarkers for the differential diagnosis and treatment of major depressive disorder

BACKGROUND

Recent genome-wide association studies on major depressive disorder (MDD) have indicated the involvement of LRFN5 and OLFM4; however, the expression levels and roles of these molecules in MDD remain unclear. The present study aimed to determine the serum levels of TCF4 and RBFOX1 in patients with MDD and to investigate whether these molecules could be used as biomarkers for MDD diagnosis.

METHODS

The study included 99 drug-naïve MDD patients, 90 drug-treated MDD patients, and 81 healthy controls (HCs). Serum TCF4 and RBFOX1 levels were measured by ELISA. Pearson's correlation analysis was conducted to determine the association between TCF4/RBFOX1 and clinical variables. Linear support vector machine classifier was used to evaluate the diagnostic capabilities of TCF4 and RBFOX1.

RESULTS

Serum TCF4 and RBFOX1 levels were substantially higher in MDD patients than in HCs and significantly lower in drug-treated MDD patients than in drug-naïve MDD patients. Moreover, serum TCF4 and RBFOX1 levels were associated with the Hamilton Depression Scale score, duration of illness, serum lipids levels, and hepatic function. Thus, both these molecules showed potential as biomarkers for MDD. TCF4 and RBFOX1 combination exhibited a higher diagnostic performance, with the mean area under the curve values of 0.9861 and 0.9936 in the training and testing sets, respectively.

LIMITATIONS

Small sample size and investigation of only the peripheral nervous system.

CONCLUSIONS

TCF4 and RBFOX1 may be involved in the pathogenesis of MDD, and their combination may serve as a diagnostic biomarker panel for MDD.

Identifying disease-critical cell types and cellular processes by integrating single-cell RNA-sequencing and human genetics

Genome-wide association studies provide a powerful means of identifying loci and genes contributing to disease, but in many cases, the related cell types/states through which genes confer disease risk remain unknown. Deciphering such relationships is important for identifying pathogenic processes and developing therapeutics. In the present study, we introduce sc-linker, a framework for integrating single-cell RNA-sequencing, epigenomic SNP-to-gene maps and genome-wide association study summary statistics to infer the underlying cell types and processes by which genetic variants influence disease. The inferred disease enrichments recapitulated known biology and highlighted notable cell-disease relationships, including γ-aminobutyric acid-ergic neurons in major depressive disorder, a disease-dependent M-cell program in ulcerative colitis and a disease-specific complement cascade process in multiple sclerosis. In autoimmune disease, both healthy and disease-dependent immune cell-type programs were associated, whereas only disease-dependent epithelial cell programs were prominent, suggesting a role in disease response rather than initiation. Our framework provides a powerful approach for identifying the cell types and cellular processes by which genetic variants influence disease.

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.

What genes are differentially expressed in individuals with schizophrenia? A systematic review

Schizophrenia is a severe, complex mental disorder characterized by a combination of positive symptoms, negative symptoms, and impaired cognitive function. Schizophrenia is highly heritable (~80%) with multifactorial etiology and complex polygenic genetic architecture. Despite the large number of genetic variants associated with schizophrenia, few causal variants have been established. Gaining insight into the mechanistic influences of these genetic variants may facilitate our ability to apply these findings to prevention and treatment. Though there have been more than 300 studies of gene expression in schizophrenia over the past 15 years, none of the studies have yielded consistent evidence for specific genes that contribute to schizophrenia risk. The aim of this work is to conduct a systematic review and synthesis of case-control studies of genome-wide gene expression in schizophrenia. Comprehensive literature searches were completed in PubMed, EmBase, and Web of Science, and after a systematic review of the studies, data were extracted from those that met the following inclusion criteria: human case-control studies comparing the genome-wide transcriptome of individuals diagnosed with schizophrenia to healthy controls published between January 1, 2000 and June 30, 2020 in the English language. Genes differentially expressed in cases were extracted from these studies, and overlapping genes were compared to previous research findings from the genome-wide association, structural variation, and tissue-expression studies. The transcriptome-wide analysis identified different genes than those previously reported in genome-wide association, exome sequencing, and structural variation studies of schizophrenia. Only one gene, GBP2, was replicated in five studies. Previous work has shown that this gene may play a role in immune function in the etiology of schizophrenia, which in turn could have implications for risk profiling, prevention, and treatment. This review highlights the methodological inconsistencies that impede valid meta-analyses and synthesis across studies. Standardization of the use of covariates, gene nomenclature, and methods for reporting results could enhance our understanding of the potential mechanisms through which genes exert their influence on the etiology of schizophrenia. Although these results are promising, collaborative efforts with harmonization of methodology will facilitate the identification of the role of genes underlying schizophrenia.

Biological, Psychological, and Social Determinants of Depression: A Review of Recent Literature

Depression is one of the leading causes of disability, and, if left unmanaged, it can increase the risk for suicide. The evidence base on the determinants of depression is fragmented, which makes the interpretation of the results across studies difficult. The objective of this study is to conduct a thorough synthesis of the literature assessing the biological, psychological, and social determinants of depression in order to piece together the puzzle of the key factors that are related to this condition. Titles and abstracts published between 2017 and 2020 were identified in PubMed, as well as Medline, Scopus, and PsycInfo. Key words relating to biological, social, and psychological determinants as well as depression were applied to the databases, and the screening and data charting of the documents took place. We included 470 documents in this literature review. The findings showed that there are a plethora of risk and protective factors (relating to biological, psychological, and social determinants) that are related to depression; these determinants are interlinked and influence depression outcomes through a web of causation. In this paper, we describe and present the vast, fragmented, and complex literature related to this topic. This review may be used to guide practice, public health efforts, policy, and research related to mental health and, specifically, depression.

Identifying disease-critical cell types and cellular processes across the human body by integration of single-cell profiles and human genetics

Genome-wide association studies (GWAS) provide a powerful means to identify loci and genes contributing to disease, but in many cases the related cell types/states through which genes confer disease risk remain unknown. Deciphering such relationships is important for identifying pathogenic processes and developing therapeutics. Here, we introduce sc-linker, a framework for integrating single-cell RNA-seq (scRNA-seq), epigenomic maps and GWAS summary statistics to infer the underlying cell types and processes by which genetic variants influence disease. We analyzed 1.6 million scRNA-seq profiles from 209 individuals spanning 11 tissue types and 6 disease conditions, and constructed gene programs capturing cell types, disease progression, and cellular processes both within and across cell types. We evaluated these gene programs for disease enrichment by transforming them to SNP annotations with tissue-specific epigenomic maps and computing enrichment scores across 60 diseases and complex traits (average N= 297K). Cell type, disease progression, and cellular process programs captured distinct heritability signals even within the same cell type, as we show in multiple complex diseases that affect the brain (Alzheimer’s disease, multiple sclerosis), colon (ulcerative colitis) and lung (asthma, idiopathic pulmonary fibrosis, severe COVID-19). The inferred disease enrichments recapitulated known biology and highlighted novel cell-disease relationships, including GABAergic neurons in major depressive disorder (MDD), a disease progression M cell program in ulcerative colitis, and a disease-specific complement cascade process in multiple sclerosis. In autoimmune disease, both healthy and disease progression immune cell type programs were associated, whereas for epithelial cells, disease progression programs were most prominent, perhaps suggesting a role in disease progression over initiation. Our framework provides a powerful approach for identifying the cell types and cellular processes by which genetic variants influence disease.

Integration of the Cortical Haemodynamic Response Measured by Functional Near-Infrared Spectroscopy and Amino Acid Analysis to Aid in the Diagnosis of Major Depressive Disorder

Background: Major depressive disorder (MDD) is a debilitating condition with a high disease burden and medical comorbidities. There are currently few to no validated biomarkers to guide the diagnosis and treatment of MDD. In the present study, we evaluated the differences between MDD patients and healthy controls (HCs) in terms of cortical haemodynamic responses during a verbal fluency test (VFT) using functional near-infrared spectroscopy (fNIRS) and serum amino acid profiles, and ascertained if these parameters were correlated with clinical characteristics. Methods: Twenty-five (25) patients with MDD and 25 age-, gender-, and ethnicity-matched HCs were recruited for the study. Real-time monitoring of the haemodynamic response during completion of a VFT was quantified using a 52-channel NIRS system. Serum samples were analysed and quantified by liquid chromatography-mass spectrometry for amino acid profiling. Receiver-operating characteristic (ROC) curves were used to classify potential candidate biomarkers. Results: The MDD patients had lower prefrontal and temporal activation during completion of the VFT than HCs. The MDD patients had lower mean concentrations of oxy-Hb in the left orbitofrontal cortex (OFC), and lower serum histidine levels. When the oxy-haemoglobin response was combined with the histidine concentration, the sensitivity and specificity of results improved significantly from 66.7% to 73.3% and from 65.0% to 90.0% respectively, as compared to results based only on the NIRS response. Conclusions: These findings demonstrate the use of combination biomarkers to aid in the diagnosis of MDD. This technique could be a useful approach to detect MDD with greater precision, but additional studies are required to validate the methodology.

Gene expression studies in Depression development and treatment: an overview of the underlying molecular mechanisms and biological processes to identify biomarkers

A combination of different risk factors, such as genetic, environmental and psychological factors, together with immune system, stress response, brain neuroplasticity and the regulation of neurotransmitters, is thought to lead to the development of major depressive disorder (MDD). A growing number of studies have tried to investigate the underlying mechanisms of MDD by analysing the expression levels of genes involved in such biological processes. These studies have shown that MDD is not just a brain disorder, but also a body disorder, and this is mainly due to the interplay between the periphery and the Central Nervous System (CNS). To this purpose, most of the studies conducted so far have mainly dedicated to the analysis of the gene expression levels using postmortem brain tissue as well as peripheral blood samples of MDD patients. In this paper, we reviewed the current literature on candidate gene expression alterations and the few existing transcriptomics studies in MDD focusing on inflammation, neuroplasticity, neurotransmitters and stress-related genes. Moreover, we focused our attention on studies, which have investigated mRNA levels as biomarkers to predict therapy outcomes. This is important as many patients do not respond to antidepressant medication or could experience adverse side effects, leading to the interruption of treatment. Unfortunately, the right choice of antidepressant for each individual still remains largely a matter of taking an educated guess.

Analysis of Differentially Expressed Genes in the Dentate Gyrus and Anterior Cingulate Cortex in a Mouse Model of Depression

Major depressive disorder (MDD) is a prevalent, chronic, and relapse-prone psychiatric disease. However, the intermediate molecules resulting from stress and neurological impairment in different brain regions are still unclear. To clarify the pathological changes in the dentate gyrus (DG) and anterior cingulate cortex (ACC) regions of the MDD brain, which are the most closely related to the disease, we investigated the published microarray profile dataset GSE84183 to identify unpredictable chronic mild stress- (UCMS-) induced differentially expressed genes (DEGs) in the DG and ACC regions. Based on the DEG data, functional annotation, protein-protein interaction, and transcription factor (TF) analyses were performed. In this study, 1071 DEGs (679 upregulated and 392 downregulated) and 410 DEGs (222 upregulated and 188 downregulated) were identified in DG and ACC, respectively. The pathways and GO terms enriched by the DEGs in the DG, such as cell adhesion, proteolysis, ion transport, transmembrane transport, chemical synaptic transmission, immune system processes, response to lipopolysaccharide, and nervous system development, may reveal the molecular mechanism of MDD. However, the DEGs in the ACC involved metabolic processes, proteolysis, visual learning, DNA methylation, innate immune responses, cell migration, and circadian rhythm. Sixteen hub genes in the DG (Fn1, Col1a1, Anxa1, Penk, Ptgs2, Cdh1, Timp1, Vim, Rpl30, Rps21, Dntt, Ptk2b, Jun, Avp, Slit1, and Sema5a) were identified. Eight hub genes in the ACC (Prkcg, Grin1, Syngap1, Rrp9, Grwd1, Pik3r1, Hnrnpc, and Prpf40a) were identified. In addition, eleven TFs (Chd2, Zmiz1, Myb, Etv4, Rela, Tcf4, Tcf12, Chd1, Mef2a, Ubtf, and Mxi1) were predicted to regulate more than two of these hub genes. The expression levels of ten randomly selected hub genes that were specifically differentially expressed in the MDD-like animal model were verified in the corresponding regions in the human brain. These hub genes and TFs may be regarded as potential targets for future MDD treatment strategies, thus aiding in the development of new therapeutic approaches to MDD.

Modulation of cognition and neuronal plasticity in gain- and loss-of-function mouse models of the schizophrenia risk gene Tcf4

The transcription factor TCF4 was confirmed in several large genome-wide association studies as one of the most significant schizophrenia (SZ) susceptibility genes. Transgenic mice moderately overexpressing Tcf4 in forebrain (Tcf4tg) display deficits in fear memory and sensorimotor gating. As second hit, we exposed Tcf4tg animals to isolation rearing (IR), chronic social defeat (SD), enriched environment (EE), or handling control (HC) conditions and examined mice with heterozygous deletion of the exon 4 (Tcf4Ex4δ^+/-) to unravel gene-dosage effects. We applied multivariate statistics for behavioral profiling and demonstrate that IR and SD cause strong cognitive deficits of Tcf4tg mice, whereas EE masked the genetic vulnerability. We observed enhanced long-term depression in Tcf4tg mice and enhanced long-term potentiation in Tcf4Ex4δ^+/- mice indicating specific gene-dosage effects. Tcf4tg mice showed higher density of immature spines during development as assessed by STED nanoscopy and proteomic analyses of synaptosomes revealed concurrently increased levels of proteins involved in synaptic function and metabolic pathways. We conclude that environmental stress and Tcf4 misexpression precipitate cognitive deficits in 2-hit mouse models of relevance for schizophrenia.

Systemic neuro-dysregulation in depression: Evidence from genome-wide association

Depression is the world's leading cause of disability. Greater understanding of the neurobiological basis of depression is necessary for developing novel treatments with improved efficacy and acceptance. Recently, major advances have been made in the search for genetic variants associated with depression which may help to elucidate etiological mechanisms. The present review has two major objectives. First, we offer a brief review of two major biological systems with strong evidence for involvement in depression pathology: neurotransmitter systems and the stress response. Secondly, we provide a synthesis of the functions of the 269 genes implicated by the most recent genome-wide meta-analysis, supporting the importance of these systems in depression and providing insights into other possible mechanisms involving neurodevelopment, neurogenesis, and neurodegeneration. Our goal is to undertake a broad, preliminary stock-taking of the most recent hypothesis-free findings and examine the weight of the evidence supporting these existing theories and highlighting novel directions. This qualitative review and accompanying gene function table provides a valuable resource and guide for basic and translational researchers, with suggestions for future mechanistic research, leveraging genetics to prioritize studies on the neurobiological processes involved in depression etiology and treatment.

Genome-scale metabolic modelling predicts biomarkers and therapeutic targets for neuropsychiatric disorders

Distinguishing neuropsychiatric disorders is challenging due to the overlap in symptoms and genetic risk factors. People suffering from these disorders face personal and professional challenges. Understanding the dysregulation of brain metabolism under disease condition can aid in effective diagnosis and in developing treatment strategies based on the metabolism. In this study, we reconstructed the metabolic network of three major neuropsychiatric disorders, schizophrenia (SCZ), bipolar disorder (BD) and major depressive disorder (MDD) using transcriptomic data and constrained based modelling approach. We integrated brain transcriptomic data from six independent studies with a recent comprehensive genome-scale metabolic model Recon3D. The analysis of the reconstructed network revealed the flux-level alterations in the peroxisome-mitochondria-golgi axis in neuropsychiatric disorders. We also extracted reporter metabolites and pathways that distinguish these three neuropsychiatric disorders. We found differences with respect to fatty acid oxidation, aromatic and branched chain amino acid metabolism, bile acid synthesis, glycosaminoglycans synthesis and modifications, and phospholipid metabolism. Further, we predicted network perturbations that transform the disease metabolic state to a healthy metabolic state for each disorder. These analyses provide local and global views of the metabolic changes in SCZ, BD and MDD, which may have clinical implications.

Immune dysregulation in depression: Evidence from genome-wide association

A strong body of evidence supports a role for immune dysregulation across many psychiatric disorders including depression, the leading cause of global disability. Recent progress in the search for genetic variants associated with depression provides the opportunity to strengthen our current understanding of etiological factors contributing to depression and generate novel hypotheses. Here, we provide an overview of the literature demonstrating a role for immune dysregulation in depression, followed by a detailed discussion of the immune-related genes identified by the most recent genome-wide meta-analysis of depression. These genes represent strong evidence-based targets for future basic and translational research which aims to understand the role of the immune system in depression pathology and identify novel points for therapeutic intervention.

Genetic variants associated with psychotic symptoms across psychiatric disorders

BACKGROUND

Recent evidence suggests that psychiatric symptoms share a common genetic liability across diagnostic categories. The present study investigated the effects of variants within previously identified relevant genes on specific symptom clusters, independently from the diagnosis.

METHODS

1550 subjects affected by Schizophrenia (SCZ), Major Depressive Disorder or Bipolar Disorder were included. Symptoms were assessed using the Positive and Negative Syndrome Scale (PANSS) and the Hamilton Depression Rating Scale (HDRS). Principal component analysis and a further clinical refinement were used to define symptom clusters. Clusters scores were tested for association with 46 genetic variants within nine genes previously linked to one or more major psychiatric disorders by large genome wide association studies (ANK3, CACNA1C, CACNB2, FKBP5, FZD3, GRM7, ITIH3, SYNE1, TCF4). Exploratory analyses were performed in each disorder separately to further elucidate the SNPs effects.

RESULTS

five PANSS clusters (Negative; Impulsiveness; Cognitive; Psychotic; Depressive) and four HDRS clusters (Core Depressive; Somatic; Psychotic-like; Insomnia) were identified. CACNA1C rs11615998 was associated with HDRS Psychotic cluster in the whole sample. In the SCZ sample, CACNA1C rs11062296 was associated with PANSS Impulsiveness cluster and CACNA1C rs2238062 was associated with PANSS negative cluster.

DISCUSSION

CACNA1C rs11615998 was associated with psychotic symptoms (C-allele carriers have decreased psychotic-risk) independently from the diagnosis, in line with the evidence of a cross disorder effect of many risk variants. This gene was previously associated with SCZ and cross-disorder liability to psychiatric disorders. Our findings confirmed that deep phenotyping is pivotal to clarify the role of genetic variants on symptoms patterns.

Genome-wide meta-analysis of depression identifies 102 independent variants and highlights the importance of the prefrontal brain regions

Major depression is a debilitating psychiatric illness that is typically associated with low mood and anhedonia. Depression has a heritable component that has remained difficult to elucidate with current sample sizes due to the polygenic nature of the disorder. To maximize sample size, we meta-analyzed data on 807,553 individuals (246,363 cases and 561,190 controls) from the three largest genome-wide association studies of depression. We identified 102 independent variants, 269 genes, and 15 genesets associated with depression, including both genes and gene pathways associated with synaptic structure and neurotransmission. An enrichment analysis provided further evidence of the importance of prefrontal brain regions. In an independent replication sample of 1,306,354 individuals (414,055 cases and 892,299 controls), 87 of the 102 associated variants were significant after multiple testing correction. These findings advance our understanding of the complex genetic architecture of depression and provide several future avenues for understanding etiology and developing new treatment approaches.

Profiling and Co-expression Network Analysis of Learned Helplessness Regulated mRNAs and lncRNAs in the Mouse Hippocampus

Although studies provide insights into the neurobiology of stress and depression, the exact molecular mechanisms underlying their pathologies remain largely unknown. Long non-coding RNA (lncRNA) has been implicated in brain functions and behavior. A potential link between lncRNA and psychiatric disorders has been proposed. However, it remains undetermined whether IncRNA regulation, in the brain, contributes to stress or depression pathologies. In this study, we used a valid animal model of depression-like symptoms; namely learned helplessness, RNA-seq, Gene Ontology and co-expression network analyses to profile the expression pattern of lncRNA and mRNA in the hippocampus of mice. We identified 6346 differentially expressed transcripts. Among them, 340 lncRNAs and 3559 protein coding mRNAs were differentially expressed in helpless mice in comparison with control and/or non-helpless mice (inescapable stress resilient mice). Gene Ontology and pathway enrichment analyses indicated that induction of helplessness altered expression of mRNAs enriched in fundamental biological functions implicated in stress/depression neurobiology such as synaptic, metabolic, cell survival and proliferation, developmental and chromatin modification functions. To explore the possible regulatory roles of the altered lncRNAs, we constructed co-expression networks composed of the lncRNAs and mRNAs. Among our differentially expressed lncRNAs, 17% showed significant correlation with genes. Functional co-expression analysis linked the identified lncRNAs to several cellular mechanisms implicated in stress/depression neurobiology. Importantly, 57% of the identified regulatory lncRNAs significantly correlated with 18 different synapse-related functions. Thus, the current study identifies for the first time distinct groups of lncRNAs regulated by induction of learned helplessness in the mouse brain. Our results suggest that lncRNA-directed regulatory mechanisms might contribute to stress-induced pathologies; in particular, to inescapable stress-induced synaptic modifications.