Genome-wide microarray analysis of gene expression profiling in major depression and antidepressant therapy

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.

Monoamine Oxidase: A Potential Link in Papez Circuit to Generalized Anxiety Disorders

Anxiety is a common mental illness that affects a large number of people around the world, and its treatment is often based on the use of pharmacological substances such as benzodiazepines, serotonin, and 5-hydroxytyrosine (MAO) neurotransmitters. MAO neurotransmitters levels are deciding factors in the biological effects. This review summarizes the current understanding of the MAO system and its role in the modulation of anxiety-related brain circuits and behavior. The MAO-A polymorphisms have been implicated in the susceptibility to generalized anxiety disorder (GAD) in several investigations. The 5-HT system is involved in a wide range of physiological and behavioral processes, involving anxiety, aggressiveness, stress reactions, and other elements of emotional intensity. Among these, 5-HT, NA, and DA are the traditional 5-HT neurons that govern a range of biological activities, including sleep, alertness, eating, thermoregulation, pains, emotion, and memory, as anticipated considering their broad projection distribution in distinct brain locations. The DNMTs (DNA methyltransferase) protein family, which increasingly leads a prominent role in epigenetics, is connected with lower transcriptional activity and activates DNA methylation. In this paper, we provide an overview of the current state of the art in the elucidation of the brain's complex functions in the regulation of anxiety.

Bioinformatics analyses of immune-related genes and immune infiltration associated with lung ischemia-reperfusion injury

BACKGROUND

Ischemia-reperfusion injury (IRI) is a significant complication that can occur following lung transplantation and is known to contribute to poor prognosis. Our research aimed to investigate the potential molecular targets and mechanisms involved in lung IRI (LIRI), in order to improve our understanding of this condition.

METHOD

We downloaded gene expression datasets (GSE127003 and GSE18995) linked to LIRI from the GEO database. Using WGCNA, we identified LIRI-related modules. Functional enrichment analyses were performed on the modules showing significant correlation with LIRI. Core immune-related genes (IRGs) were identified and validated using the GSE18995 dataset. A rat LIRI model was established to validate the expression changes of core IRGs. The LIRI groups were subjected to 60 min of warm ischemia followed by 120 min of reperfusion. Additionally, the xCell algorithm was used to characterize the immune landscape and analyze the relationships between hub IRGs and infiltrating immune cells.

RESULTS

A total of 483 genes from the turquoise module were identified through WGCNA, with a predominant enrichment in immune- and inflammation-related pathways. Three IRGs (PTGS2, CCL2, and RELB) were found to be up-regulated after reperfusion in both GSE127003 and GSE18995 datasets, and this was further confirmed using the rat LIRI model. The xCell analysis revealed that immune score, CD8+ naive T cells, eosinophils, neutrophils, NK cells, and Tregs were upregulated after reperfusion. PTGS2, CCL2, and RELB showed positive correlations with CD8+ naive T cells, monocytes, neutrophils, and Tregs.

CONCLUSION

PTGS2, CCL2, and RELB were found to be potential biomarkers for LIRI. Immune and microenvironment scores were higher after reperfusion compared to before reperfusion. PTGS2, CCL2, and RELB appear to play a crucial role in the development of LIRI and may contribute to it by increasing the number of immune cells. Our findings offer new perspectives on potential treatment targets and the pathogenesis of LIRI.

Integrating genetics and transcriptomics to study major depressive disorder: a conceptual framework, bioinformatic approaches, and recent findings

Major depressive disorder (MDD) is a complex and heterogeneous psychiatric syndrome with genetic and environmental influences. In addition to neuroanatomical and circuit-level disturbances, dysregulation of the brain transcriptome is a key phenotypic signature of MDD. Postmortem brain gene expression data are uniquely valuable resources for identifying this signature and key genomic drivers in human depression; however, the scarcity of brain tissue limits our capacity to observe the dynamic transcriptional landscape of MDD. It is therefore crucial to explore and integrate depression and stress transcriptomic data from numerous, complementary perspectives to construct a richer understanding of the pathophysiology of depression. In this review, we discuss multiple approaches for exploring the brain transcriptome reflecting dynamic stages of MDD: predisposition, onset, and illness. We next highlight bioinformatic approaches for hypothesis-free, genome-wide analyses of genomic and transcriptomic data and their integration. Last, we summarize the findings of recent genetic and transcriptomic studies within this conceptual framework.

Ingestion of taxifolin-rich foods affects brain activity, mental fatigue, and the whole blood transcriptome in healthy young adults: a randomized, double-blind, placebo-controlled, crossover study

The antioxidant properties of polyphenols, which are found in most plants, have been shown to be useful for maintaining health, including enhancing brain function and alleviating stress. We aimed to investigate the effect of a single intake of taxifolin-containing foods on cognitive task performance and whole blood gene expression in healthy young adults. This study was a randomized, placebo-controlled, double-blind, crossover trial in which healthy young adults were administered a single dose of either a placebo or food containing taxifolin. Cognitive tests (serial 3s, serial 7s, and rapid visual information processing) to examine brain activity and visual analog scale questionnaires to analyze mental fatigue were applied. The set of tests was repeated four times. The findings showed that taxifolin intake improved calculation abilities and reduced mental fatigue. An analysis of whole blood gene expression before and after the test revealed that the expression of foreign substance removal-related genes increased following the ingestion of taxifolin and that most differentially expressed genes were enriched in granulocytes. Taxifolin intake was shown to affect the brain activity of healthy young adults and demonstrated an antifatigue effect, thereby reducing subjective fatigue. A single intake of taxifolin may enhance the removal of foreign substances by strengthening the innate immune system and suppressing the occurrence of injury.

Transcriptomic Studies of Antidepressant Action in Rodent Models of Depression: A First Meta-Analysis

Antidepressants (ADs) are, for now, the best everyday treatment we have for moderate to severe major depressive episodes (MDEs). ADs are among the most prescribed drugs in the Western Hemisphere; however, the trial-and-error prescription strategy and side-effects leave a lot to be desired. More than 60% of patients suffering from major depression fail to respond to the first AD they are prescribed. For those who respond, full response is only observed after several weeks of treatment. In addition, there are no biomarkers that could help with therapeutic decisions; meanwhile, this is already true in cancer and other fields of medicine. For years, many investigators have been working to decipher the underlying mechanisms of AD response. Here, we provide the first systematic review of animal models. We thoroughly searched all the studies involving rodents, profiling transcriptomic alterations consecutive to AD treatment in naïve animals or in animals subjected to stress-induced models of depression. We have been confronted by an important heterogeneity regarding the drugs and the experimental settings. Thus, we perform a meta-analysis of the AD signature of fluoxetine (FLX) in the hippocampus, the most studied target. Among genes and pathways consistently modulated across species, we identify both old players of AD action and novel transcriptional biomarker candidates that warrant further investigation. We discuss the most prominent transcripts (immediate early genes and activity-dependent synaptic plasticity pathways). We also stress the need for systematic studies of AD action in animal models that span across sex, peripheral and central tissues, and pharmacological classes.

Machine Learning and Deep Learning for the Pharmacogenomics of Antidepressant Treatments

A growing body of evidence now proposes that machine learning and deep learning techniques can serve as a vital foundation for the pharmacogenomics of antidepressant treatments in patients with major depressive disorder (MDD). In this review, we focus on the latest developments for pharmacogenomics research using machine learning and deep learning approaches together with neuroimaging and multi-omics data. First, we review relevant pharmacogenomics studies that leverage numerous machine learning and deep learning techniques to determine treatment prediction and potential biomarkers for antidepressant treatments in MDD. In addition, we depict some neuroimaging pharmacogenomics studies that utilize various machine learning approaches to predict antidepressant treatment outcomes in MDD based on the integration of research on pharmacogenomics and neuroimaging. Moreover, we summarize the limitations in regard to the past pharmacogenomics studies of antidepressant treatments in MDD. Finally, we outline a discussion of challenges and directions for future research. In light of latest advancements in neuroimaging and multi-omics, various genomic variants and biomarkers associated with antidepressant treatments in MDD are being identified in pharmacogenomics research by employing machine learning and deep learning algorithms.

miR-4454 Promotes Hepatic Carcinoma Progression by Targeting Vps4A and Rab27A

Hepatocellular carcinoma (HCC) has high morbidity and mortality. MicroRNAs (miRNAs), which could be regulated by cancer-derived exosomes, play critical regulatory roles in the initiation and development of cancer. However, the expressions, effects, and mechanisms of abundant miRNAs regulated by HCC cancer-derived exosomes in HCC remain largely unclear. Exosomes of HepG2 cells under heat shock, TGF-β1, doxorubicin, acid and hypoxia/reoxygenation (H/R) conditions, and exosomes were successfully identified by transmission electron microscopy and Western blot analysis. The identified exosomes were then applied to evaluate the miRNA expression profiles by RNA sequencing. Mechanically, we discovered that doxorubicin was upregulated, TGF-β1 downregulated the expressions of Vps4A, Rab27A, Alix, and Hrs in HepG2 cells and exosomes, and Vps4A and Rab27A, as target genes for miR-4454, could also be downregulated by miR-4454. Functionally, we revealed that miR-4454 inhibitor and miR-4454 inhibitor-mediated exosomes could markedly suppress proliferation, migration, invasion, and vascularization and accelerate cycle arrest, apoptosis, and ROS of HepG2 cells. This study provided many potential HCC cancer-derived exosome-mediated miRNAs in HCC under 5 different stimulus conditions. Meanwhile, we certified that miR-4454 in exosomes could provide a novel and effective mechanism for HCC function.

Identification of Hub Genes in Different Stages of Colorectal Cancer through an Integrated Bioinformatics Approach

Colorectal cancer (CRC) is the third most common cancer that contributes to cancer-related morbidity. However, the differential expression of genes in different phases of CRC is largely unknown. Moreover, very little is known about the role of stress-survival pathways in CRC. We sought to discover the hub genes and identify their roles in several key pathways, including oxidative stress and apoptosis in the different stages of CRC. To identify the hub genes that may be involved in the different stages of CRC, gene expression datasets were obtained from the gene expression omnibus (GEO) database. The differentially expressed genes (DEGs) common among the different datasets for each group were obtained using the robust rank aggregation method. Then, gene enrichment analysis was carried out with Gene Ontology and Kyoto Encyclopedia of Genes and Genomes databases. Finally, the protein-protein interaction networks were constructed using the Cytoscape software. We identified 40 hub genes and performed enrichment analysis for each group. We also used the Oncomine database to identify the DEGs related to stress-survival and apoptosis pathways involved in different stages of CRC. In conclusion, the hub genes were found to be enriched in several key pathways, including the cell cycle and p53 signaling pathway. Some of the hub genes were also reported in the stress-survival and apoptosis pathways. The hub DEGs revealed from our study may be used as biomarkers and may explain CRC development and progression mechanisms.

Transcriptomic signaling pathways involved in a naturalistic model of inflammation-related depression and its remission

This study aimed at identifying molecular biomarkers of inflammation-related depression in order to improve diagnosis and treatment. For this, we performed whole-genome expression profiling from peripheral blood in a naturalistic model of inflammation-associated major depressive disorder (MDD) represented by comorbid depression in obese patients. We took advantage of the marked reduction of depressive symptoms and inflammation following bariatric surgery to test the robustness of the identified biomarkers. Depression was assessed during a clinical interview using Mini-International Neuropsychiatric Interview and the 10-item, clinician-administered, Montgomery-Asberg Depression Rating Scale. From a cohort of 100 massively obese patients, we selected 33 of them for transcriptomic analysis. Twenty-four of them were again analyzed 4-12 months after bariatric surgery. We conducted differential gene expression analyses before and after surgery in unmedicated MDD and non-depressed obese subjects. We found that TP53 (Tumor Protein 53), GR (Glucocorticoid Receptor), and NFκB (Nuclear Factor kappa B) pathways were the most discriminating pathways associated with inflammation-related MDD. These signaling pathways were processed in composite z-scores of gene expression that were used as biomarkers in regression analyses. Results showed that these transcriptomic biomarkers highly predicted depressive symptom intensity at baseline and their remission after bariatric surgery. While inflammation was present in all patients, GR signaling over-activation was found only in depressed ones where it may further increase inflammatory and apoptosis pathways. In conclusion, using an original model of inflammation-related depression and its remission without antidepressants, we provide molecular predictors of inflammation-related MDD and new insights in the molecular pathways involved.

Integrative Analysis of Whole-genome Expression Profiling and Regulatory Network Identifies Novel Biomarkers for Insulin Resistance in Leptin Receptor-deficient Mice

BACKGROUND

Molecular characterization of insulin resistance, a growing health issue worldwide, will help to develop novel strategies and accurate biomarkers for disease diagnosis and treatment.

OBJECTIVE

Integrative analysis of gene expression profiling and gene regulatory network was exploited to identify potential biomarkers early in the development of insulin resistance.

METHODS

RNA was isolated from livers of animals at three weeks of age, and whole-genome expression profiling was performed and analyzed with Agilent mouse 4×44K microarrays. Differentially expressed genes were subsequently validated by qRT-PCR. Functional characterizations of genes and their interactions were performed by Gene Ontology (GO) analysis and gene regulatory network (GRN) analysis.

RESULTS

A total of 197 genes were found to be differentially expressed by fold change ≥2 and P < 0.05 in BKS-db +/+ mice relative to sex and age-matched controls. Functional analysis suggested that these differentially expressed genes were enriched in the regulation of phosphorylation and generation of precursor metabolites which are closely associated with insulin resistance. Then a gene regulatory network associated with insulin resistance (IRGRN) was constructed by integration of these differentially expressed genes and known human protein-protein interaction network. The principal component analysis demonstrated that 67 genes in IRGRN could clearly distinguish insulin resistance from the non-disease state. Some of these candidate genes were further experimentally validated by qRT-PCR, highlighting the predictive role as biomarkers in insulin resistance.

CONCLUSION

Our study provides new insight into the pathogenesis and treatment of insulin resistance and also reveals potential novel molecular targets and diagnostic biomarkers for insulin resistance.

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.

Identification Hub Genes in Colorectal Cancer by Integrating Weighted Gene Co-Expression Network Analysis and Clinical Validation in vivo and vitro

Colorectal cancer (CRC) is the third leading cause of death in the world. However, the key roles of most molecules in CRC remain unclear. This study aimed to identify key modules and hub genes associated with the progression of CRC. The data of the patients with CRC were obtained from the Gene Expression Omnibus (GEO) database and assessed by weighted gene co-expression network analysis (WGCNA), Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses performed in R. by WGCNA, several hub genes that regulate the mechanism of tumorigenesis in CRC were identified, which were associated with clinical traits. Next, we screened hub genes related to the progression of CRC authenticated by The Cancer Genome Atlas (TCGA) and Oncomine databases. Three hub genes (HCLS1, EVI2B, and CD48) were identified, and survival analysis was further performed. Moreover, the results of qPCR and immunohistochemistry staining revealed that HCLS1, EVI2B, and CD48 are tumor suppressor genes. Further, the functional study verified that over-expression of HCLS1, EVI2B, and CD48 can reduce the proliferation, migration, and invasion ability of CRC cells and significantly suppress CRC tumor growth in vivo. In summary, we identified three hub genes that were associated with the progression of CRC that can be applied in treatment.

Weighted gene co-expression network analysis identifies specific modules and hub genes related to subsyndromal symptomatic depression

Objectives: The identification of the potential molecule targets for subsyndromal symptomatic depression (SSD) is critical for improving the effective clinical treatment on the mental illness. In the current study, we mined the genome-wide expression profiling and investigated the novel biological pathways associated with SSD.Methods: Expression of differentially expressed genes (DEGs) were analysed with microarrays of blood tissue cohort of eight SSD patients and eight healthy subjects. The gene co-expression is calculated by WGCNA, an R package software. The function of the genes was annotated by gene ontology and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis.Results: We identified 11 modules from the 9,427 DEGs. Three co-expression modules (blue, cyan and red) showed striking correlation with the phenotypic trait between SSD and healthy controls. Gene ontology and KEGG pathway analysis demonstrated that the function of these three modules was enriched with the pathway of inflammatory response and type II diabetes mellitus. Finally, three hub genes, NT5DC1, SGSM2 and MYCBP, were identified from the blue module as significant genes.Conclusions: This first blood gene expression study in SSD observed distinct patterns between cases and controls which may provide novel insight into understanding the molecular mechanisms of SSD.

Precision Psychiatry Applications with Pharmacogenomics: Artificial Intelligence and Machine Learning Approaches

A growing body of evidence now suggests that precision psychiatry, an interdisciplinary field of psychiatry, precision medicine, and pharmacogenomics, serves as an indispensable foundation of medical practices by offering the accurate medication with the accurate dose at the accurate time to patients with psychiatric disorders. In light of the latest advancements in artificial intelligence and machine learning techniques, numerous biomarkers and genetic loci associated with psychiatric diseases and relevant treatments are being discovered in precision psychiatry research by employing neuroimaging and multi-omics. In this review, we focus on the latest developments for precision psychiatry research using artificial intelligence and machine learning approaches, such as deep learning and neural network algorithms, together with multi-omics and neuroimaging data. Firstly, we review precision psychiatry and pharmacogenomics studies that leverage various artificial intelligence and machine learning techniques to assess treatment prediction, prognosis prediction, diagnosis prediction, and the detection of potential biomarkers. In addition, we describe potential biomarkers and genetic loci that have been discovered to be associated with psychiatric diseases and relevant treatments. Moreover, we outline the limitations in regard to the previous precision psychiatry and pharmacogenomics studies. Finally, we present a discussion of directions and challenges for future research.

Gene-Environment Interactions and Role of Epigenetics in Anxiety Disorders

Several environmental risk factors such as early adverse childhood experiences, stress, and stressful life events are associated with anxiety disorders. Current approaches such as epigenetics and gene-environment interactions were used to identify candidate biomarkers for anxiety disorders to assess determinants of disease. In this chapter, in relation to gene-environment interactions, a variety of association studies regarding anxiety disorders were surveyed. We then showed supporting results from recent association studies such as human studies and animal models in terms of the epigenetic contribution to disease susceptibility to anxiety disorders. At last, future directions and limitations are highlighted. With the advances in multi-omics technologies, innovative ideas regarding disease prevention and drug responsiveness in anxiety disorders require further research in epigenetics and gene-environment interactions.

Changed cortical risk gene expression in major depression and shared changes in cortical gene expression between major depression and bipolar disorders

BACKGROUND

Mood disorders likely occur in someone with a genetic predisposition who encounters a deleterious environmental factor leading to dysregulated physiological processes due to genetic mutations and epigenetic mechanisms altering gene expression. To gain data to support this hypothesis, we measured levels of gene expression in three cortical regions known to be affected by the pathophysiologies of major depression and bipolar disorders.

METHODS

Levels of RNA were measured using the Affymetrix™ Human Exon 1.0 ST Array in Brodmann's areas 9, 10 and 33 (left hemisphere) from individuals with major depression, bipolar disorder and age- and sex-matched controls with changed expression taken as a fold change in RNA ⩾1.2 at p < 0.01. Data were analysed using JMP^® genomics 6.0 and the probable biological consequences of changes in gene expression determined using Core and Pathway Designer Analyses in Ingenuity Pathway Analysis.

RESULTS

There were altered levels of RNA in Brodmann's area 9 (major depression = 424; bipolar disorder = 331), Brodmann's area 10 (major depression = 52; bipolar disorder = 24) and Brodmann's area 33 (major depression = 59 genes; bipolar disorder = 38 genes) in mood disorders. No gene was differentially expressed in all three regions in either disorder. There was a high correlation between fold changes in levels of RNA from 112 genes in Brodmann's area 9 from major depression and bipolar disorder (r² = 0.91, p < 0.001). Levels of RNA for four risk genes for major depression were lower in Brodmann's area 9 in that disorder.

CONCLUSION

Our data argue that there are complex regional-specific changes in cortical gene expression in major depression and bipolar disorder that includes the expression of some risk genes for major depression in those with that disorder. It could be hypothesised that the common changes in gene expression in major depression and bipolar disorder are involved in the genesis of symptoms common to both disorders.

DNA methylation associated with postpartum depressive symptoms overlaps findings from a genome-wide association meta-analysis of depression

BACKGROUND

Perinatal depressive symptoms have been linked to adverse maternal and infant health outcomes. The etiology associated with perinatal depressive psychopathology is poorly understood, but accumulating evidence suggests that understanding inter-individual differences in DNA methylation (DNAm) patterning may provide insight regarding the genomic regions salient to the risk liability of perinatal depressive psychopathology.

RESULTS

Genome-wide DNAm was measured in maternal peripheral blood using the Infinium MethylationEPIC microarray. Ninety-two participants (46% African-American) had DNAm samples that passed all quality control metrics, and all participants were within 7 months of delivery. Linear models were constructed to identify differentially methylated sites and regions, and permutation testing was utilized to assess significance. Differentially methylated regions (DMRs) were defined as genomic regions of consistent DNAm change with at least two probes within 1 kb of each other. Maternal age, current smoking status, estimated cell-type proportions, ancestry-relevant principal components, days since delivery, and chip position served as covariates to adjust for technical and biological factors. Current postpartum depressive symptoms were measured using the Edinburgh Postnatal Depression Scale. Ninety-eight DMRs were significant (false discovery rate < 5%) and overlapped 92 genes. Three of the regions overlap loci from the latest Psychiatric Genomics Consortium meta-analysis of depression.

CONCLUSIONS

Many of the genes identified in this analysis corroborate previous allelic, transcriptomic, and DNAm association results related to depressive phenotypes. Future work should integrate data from multi-omic platforms to understand the functional relevance of these DMRs and refine DNAm association results by limiting phenotypic heterogeneity and clarifying if DNAm differences relate to the timing of onset, severity, duration of perinatal mental health outcomes of the current pregnancy or to previous history of depressive psychopathology.

Epigenetics and Depression: An Update

OBJECTIVE

Depression is associated with various environmental risk factors such as stress, childhood maltreatment experiences, and stressful life events. Current approaches to assess the pathophysiology of depression, such as epigenetics and gene-environment (GxE) interactions, have been widely leveraged to determine plausible markers, genes, and variants for the risk of developing depression.

METHODS

We focus on the most recent developments for genomic research in epigenetics and GxE interactions.

RESULTS

In this review, we first survey a variety of association studies regarding depression with consideration of GxE interactions. We then illustrate evidence of epigenetic mechanisms such as DNA methylation, microRNAs, and histone modifications to influence depression in terms of animal models and human studies. Finally, we highlight their limitations and future directions.

CONCLUSION

In light of emerging technologies in artificial intelligence and machine learning, future research in epigenetics and GxE interactions promises to achieve novel innovations that may lead to disease prevention and future potential therapeutic treatments for depression.

Umbilical cord blood-based gene signatures related to prenatal major depressive disorder

BACKGROUND

Prenatal exposure to depression has been considered as a risk factor for adverse childhood, while it is accompanied by unknown molecular mechanisms. The aim of this study was to identify differentially expressed genes (DEGs) and associated biological processes between cord blood samples from neonates born to mothers who exposed to major depressive disorder (MDD) and healthy mothers.

METHODS

The microarray data GSE114852 were downloaded to analyze the mRNA expression profiles of umbilical cord blood with 31 samples exposed to prenatal MDD and 62 samples with healthy mothers. Kyoto Encyclopedia of Genes and Genomes pathway and Gene ontology enrichment analyses were conducted to identify associated biochemical pathways and functional categories of the DEGs. The protein-protein interaction network was constructed and the top 10 hub genes in the network were predicted.

RESULTS

The results showed several immunity related processes, such as "phagosome", "Epstein-Barr virus infection", "proteasome", "positive regulation of I-kappaB kinase/NF-kappaB signaling", "interferon-gamma-mediated signaling pathway", and "tumor necrosis factor" presented significant differences between two groups. Most of the hub genes (for example PSMD2, PSMD6, PSMB8, PSMB9) were also associated with immune pathways.

CONCLUSION

This bioinformatic analysis demonstrated immune-mediated mechanisms might play a fatal role in abnormalities in fetal gene expression profiles caused by prenatal MDD.

Further Support for the Involvement of Genetic Variants Related to the Serotonergic Pathway in the Antidepressant Response in Children and Adolescents After a 12-Month Follow-Up: Impact of the HTR2A rs7997012 Polymorphism

Objective: Fluoxetine is an effective and well-tolerated pharmacological treatment for children and adolescents with major depressive disorder (MDD). However, a high percentage of patients do not respond. There is a substantial genetic contribution to this variable clinical outcome. Based on previous genetic results of our group and given the lack of pharmacogenetics studies of antidepressant response with a long follow-up period, we evaluated the influence of single nucleotide polymorphisms (SNPs) in genes related to the serotonergic pathway on remission and recovery in children and adolescents diagnosed with MDD after 12 months of initiating fluoxetine treatment. Methods: The assessment was performed in 46 patients. All of them were visited at least once a month during the 12-month follow-up. Psychiatrists interviewed patients and their parents to explore clinical improvement. A total of 75 genotyped SNPs in 10 candidate genes were included in the genetic association analysis with remission and recovery. Bonferroni correction for multiple testing was applied to avoid false positive results. Results: The HTR2A rs7997012 SNP was significantly associated after Bonferroni correction with clinical improvement. Particularly, the homozygotes for the major allele (GG) showed the highest percentage of remitters and the highest score reductions on the Clinical Global Impressions-Severity (CGI-S) scale. Moreover, although the results were on the border of statistical significance, the GG homozygotes also tended to experience fewer readmissions during the follow-up period Conclusions: These results provide more evidence of the involvement of genetic variants related to the serotonergic pathway in the antidepressant response. Studies with larger cohorts are needed to integrate all relevant variants into clinical predictors of antidepressant response.

Distinct Expression Pattern of Epigenetic Machinery Genes in Blood Leucocytes and Brain Cortex of Depressive Patients

In major depressive disorder (MDD), altered gene expression in brain cortex and blood leucocytes may be due to aberrant expression of epigenetic machinery coding genes. Here, we explore the expression of these genes both at the central and peripheral levels. Using real-time quantitative PCR technique, we first measured expression levels of genes encoding DNA and histone modifying enzymes in the dorsolateral prefrontal cortex (DLPFC) and cingulate cortex (CC) of MDD patients (n = 24) and healthy controls (n = 12). For each brain structure, transcripts levels were compared between subject groups. In an exploratory analysis, we then compared the candidate gene expressions between a subgroup of MDD patients with psychotic characteristics (n = 13) and the group of healthy subjects (n = 12). Finally, we compared transcript levels of the candidate genes in blood leucocytes between separate samples of MDD patients (n = 17) and healthy controls (n = 16). In brain and blood leucocytes of MDD patients, we identified an overexpression of genes encoding enzymes which transfer repressive transcriptional marks: HDAC4-5-6-8 and DNMT3B in the DLPFC, HDAC2 in the CC and blood leucocytes. In the DLPFC of patients with psychotic characteristics, two genes (KAT2A and UBE2A) were additionally overexpressed suggesting a shift to a more transcriptionally permissive conformation of chromatin. Aberrant activation of epigenetic repressive systems may be involved in MDD pathogenesis both in brain tissue and blood leucocytes.

DNA Methylation as a Biomarker of Treatment Response Variability in Serious Mental Illnesses: A Systematic Review Focused on Bipolar Disorder, Schizophrenia, and Major Depressive Disorder

So far, genetic studies of treatment response in schizophrenia, bipolar disorder, and major depression have returned results with limited clinical utility. A gene × environment interplay has been proposed as a factor influencing not only pathophysiology but also the treatment response. Therefore, epigenetics has emerged as a major field of research to study the treatment of these three disorders. Among the epigenetic marks that can modify gene expression, DNA methylation is the best studied. We performed a systematic search (PubMed) following Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA guidelines for preclinical and clinical studies focused on genome-wide and gene-specific DNA methylation in the context of schizophrenia, bipolar disorders, and major depressive disorder. Out of the 112 studies initially identified, we selected 31 studies among them, with an emphasis on responses to the gold standard treatments in each disorder. Modulations of DNA methylation levels at specific CpG sites have been documented for all classes of treatments (antipsychotics, mood stabilizers, and antidepressants). The heterogeneity of the models and methodologies used complicate the interpretation of results. Although few studies in each disorder have assessed the potential of DNA methylation as biomarkers of treatment response, data support this hypothesis for antipsychotics, mood stabilizers and antidepressants.

Differentially expressed genes related to major depressive disorder and antidepressant response: genome-wide gene expression analysis

Treatment response to antidepressants is limited and varies among patients with major depressive disorder (MDD). To discover genes and mechanisms related to the pathophysiology of MDD and antidepressant treatment response, we performed gene expression analyses using peripheral blood specimens from 38 MDD patients and 14 healthy individuals at baseline and at 6 weeks after the initiation of either selective serotonin reuptake inhibitor (SSRI) or mirtazapine treatment. The results were compared with results from public microarray data. Seven differentially expressed genes (DEGs) between MDD patients and controls were identified in our study and in the public microarray data: CD58, CXCL8, EGF, TARP, TNFSF4, ZNF583, and ZNF587. CXCL8 was among the top 10 downregulated genes in both studies. Eight genes related to SSRI responsiveness, including BTNL8, showed alterations in gene expression in MDD. The expression of the FCRL6 gene differed between SSRI responders and nonresponders and changed after SSRI treatment compared to baseline. In evaluating the response to mirtazapine, 21 DEGs were identified when comparing MDD patients and controls and responders and nonresponders. These findings suggest that the pathophysiology of MDD and treatment response to antidepressants are associated with a number of processes, including DNA damage and apoptosis, that can be induced by immune activation and inflammation.

Differences in the expression of several genes before and after different antidepressant treatments were found in patients with major depressive disorder (MDD), and may help identify patients most likely to benefit from specific drugs. Researchers in South Korea led by Doh Kwan Kim and Soo-Youn Lee at Samsung Medical Center, Seoul, examined gene expression across the 28,869 genes in 38 patients with MDD and 14 healthy individuals. They also validated their findings using existing databases of gene expression in patients with MDD and healthy controls. The research suggests that genes involved in the immune response and inflammation are significantly alternated in MDD and are predictable in which patients respond well to antidepressants. These findings may help develop new approaches to antidepressant therapies, and assist tailoring of treatment to the specific needs of different patients.

A Deep Learning Approach for Predicting Antidepressant Response in Major Depression Using Clinical and Genetic Biomarkers

In the wake of recent advances in scientific research, personalized medicine using deep learning techniques represents a new paradigm. In this work, our goal was to establish deep learning models which distinguish responders from non-responders, and also to predict possible antidepressant treatment outcomes in major depressive disorder (MDD). To uncover relationships between the responsiveness of antidepressant treatment and biomarkers, we developed a deep learning prediction approach resulting from the analysis of genetic and clinical factors such as single nucleotide polymorphisms (SNPs), age, sex, baseline Hamilton Rating Scale for Depression score, depressive episodes, marital status, and suicide attempt status of MDD patients. The cohort consisted of 455 patients who were treated with selective serotonin reuptake inhibitors (treatment-response rate = 61.0%; remission rate = 33.0%). By using the SNP dataset that was original to a genome-wide association study, we selected 10 SNPs (including ABCA13 rs4917029, BNIP3 rs9419139, CACNA1E rs704329, EXOC4 rs6978272, GRIN2B rs7954376, LHFPL3 rs4352778, NELL1 rs2139423, NUAK1 rs2956406, PREX1 rs4810894, and SLIT3 rs139863958) which were associated with antidepressant treatment response. Furthermore, we pinpointed 10 SNPs (including ARNTL rs11022778, CAMK1D rs2724812, GABRB3 rs12904459, GRM8 rs35864549, NAALADL2 rs9878985, NCALD rs483986, PLA2G4A rs12046378, PROK2 rs73103153, RBFOX1 rs17134927, and ZNF536 rs77554113) in relation to remission. Then, we employed multilayer feedforward neural networks (MFNNs) containing 1-3 hidden layers and compared MFNN models with logistic regression models. Our analysis results revealed that the MFNN model with 2 hidden layers (area under the receiver operating characteristic curve (AUC) = 0.8228 ± 0.0571; sensitivity = 0.7546 ± 0.0619; specificity = 0.6922 ± 0.0765) performed maximally among predictive models to infer the complex relationship between antidepressant treatment response and biomarkers. In addition, the MFNN model with 3 hidden layers (AUC = 0.8060 ± 0.0722; sensitivity = 0.7732 ± 0.0583; specificity = 0.6623 ± 0.0853) achieved best among predictive models to predict remission. Our study indicates that the deep MFNN framework may provide a suitable method to establish a tool for distinguishing treatment responders from non-responders prior to antidepressant therapy.

Replicable and Coupled Changes in Innate and Adaptive Immune Gene Expression in Two Case-Control Studies of Blood Microarrays in Major Depressive Disorder

BACKGROUND

Peripheral inflammation is often associated with major depressive disorder (MDD), and immunological biomarkers of depression remain a focus of investigation.

METHODS

We used microarray data on whole blood from two independent case-control studies of MDD: the GlaxoSmithKline-High-Throughput Disease-specific target Identification Program [GSK-HiTDiP] study (113 patients and 57 healthy control subjects) and the Janssen-Brain Resource Company study (94 patients and 100 control subjects). Genome-wide differential gene expression analysis (18,863 probes) resulted in a p value for each gene in each study. A Bayesian method identified the largest p-value threshold (q = .025) associated with twice the number of genes differentially expressed in both studies compared with the number of coincidental case-control differences expected by chance.

RESULTS

A total of 165 genes were differentially expressed in both studies with concordant direction of fold change. The 90 genes overexpressed (or UP genes) in MDD were significantly enriched for immune response to infection, were concentrated in a module of the gene coexpression network associated with innate immunity, and included clusters of genes with correlated expression in monocytes, monocyte-derived dendritic cells, and neutrophils. In contrast, the 75 genes underexpressed (or DOWN genes) in MDD were associated with the adaptive immune response and included clusters of genes with correlated expression in T cells, natural killer cells, and erythroblasts. Consistently, the MDD patients with overexpression of UP genes also had underexpression of DOWN genes (correlation > .70 in both studies).

CONCLUSIONS

MDD was replicably associated with proinflammatory activation of the peripheral innate immune system, coupled with relative inactivation of the adaptive immune system, indicating the potential of transcriptional biomarkers for immunological stratification of patients with depression.

A comprehensive regional analysis of genome-wide expression profiles for major depressive disorder

BACKGROUND

Major depressive disorder (MDD) is a global health challenge. In recent years, a large number of genome-wide expression studies (GWES) have been carried out to identify the transcriptomic profiles for MDD. The objective of this work was to carry out a comprehensive meta-analysis of available GWES for MDD.

METHODS

GWES for MDD with available raw data were searched in NCBI GEO, Array Express and Stanley databases. Raw GWES data were preprocessed and normalized and meta-analytical procedures were carried out with the Network Analyst program. 743 samples from 24 primary studies were included in our meta-analyses for blood (Blo), amygdala (Amy), cerebellum (Cer), anterior cingulate cortex (ACC) and prefrontal cortex (PFC) regions. A functional enrichment analysis was carried out.

RESULTS

We identified 35, 793, 231, 668 and 252 differentially expressed (DE) genes for Blo, Amy, Cer, ACC and PFC regions. A region-dependent significant enrichment for several functional categories, such as gene ontologies, signaling pathways and topographic parameters, was identified. There was convergence with other available genome-wide studies, such as GWAS, DNA methylation analyses and miRNA expression studies.

LIMITATIONS

Raw data were not available for several primary studies that have been published previously.

CONCLUSIONS

This is the largest meta-analysis for GWES in MDD. The examination of convergence of genome-wide evidence and of the functional enrichment analysis provides a global overview of potential neural signaling mechanisms dysregulated in MDD. Our comprehensive analysis of several brain regions identified lists of DE genes for MDD that are interesting candidates for further studies.

Drug induces depression-like phenotypes and alters gene expression profiles in Drosophila

BACKGROUND

Major depressive disorder (MDD) is a severe mental illness that affects more than 350 million people worldwide. However, the molecular mechanisms of depression are currently unclear. Studies suggest that Drosophila and humans have similar depression-like symptoms under pressure. In this research, we choose Drosophila melanogaster as the animal model to explore the molecular mechanisms that trigger depression.

RESULTS

We found that feeding D. melanogaster with the medium containing Levodopa or Chlorpromazine could induce depression-like phenotypes in both behavioral and biochemical biomarkers, including significantly decreased food intake, mating frequency, serotonin (5-HT) concentration, and increased malondialdehyde (MDA) concentration as well as reduced activity of superoxide dismutase (SOD). Moreover, the progeny of Chlorpromazine-treated flies also showed these depression-like features. By RNA-seq technology, we identified 467 genes that were differentially expressed between Chlorpromazine treated (CPZ) and control male flies [fold-change of ≥2 (q-value<5%)]. When comparing CPZ with control flies, 312 genes were upregulated and 155 genes downregulated. Differential expression of genes related to metabolic pathway, Parkinson's disease, Huntington's disease, Alzheimer's disease and lysozyme pathways were observed. Quantitative reverse transcriptase PCR (qRT-PCR) confirmed that 19 genes are differentially expressed in CPZ and control male flies.

CONCLUSIONS

Levodopa, or Chlorpromazine can induce depression-like phenotypes in D. melanogaster regarding changes of appetite and sexual activity, and some key biochemical markers. A total of 467 genes were identified by RNA-seq analysis to have at least a 2-fold-change in expression between CPZ and control flies, including genes involved in metabolism, neurological diseases and lysozyme pathways. Our data provide additional insight into molecular mechanisms underlying depressive disorders in humans and may also contribute to clinical treatment.

Machine learning and systems genomics approaches for multi-omics data

In light of recent advances in biomedical computing, big data science, and precision medicine, there is a mammoth demand for establishing algorithms in machine learning and systems genomics (MLSG), together with multi-omics data, to weigh probable phenotype-genotype relationships. Software frameworks in MLSG are extensively employed to analyze hundreds of thousands of multi-omics data by high-throughput technologies. In this study, we reviewed the MLSG software frameworks and future directions with respect to multi-omics data analysis and integration. Our review was targeted at researching recent approaches and technical solutions for the MLSG software frameworks using multi-omics platforms.

Identifying a gene expression signature of cluster headache in blood

Cluster headache is a relatively rare headache disorder, typically characterized by multiple daily, short-lasting attacks of excruciating, unilateral (peri-)orbital or temporal pain associated with autonomic symptoms and restlessness. To better understand the pathophysiology of cluster headache, we used RNA sequencing to identify differentially expressed genes and pathways in whole blood of patients with episodic (n = 19) or chronic (n = 20) cluster headache in comparison with headache-free controls (n = 20). Gene expression data were analysed by gene and by module of co-expressed genes with particular attention to previously implicated disease pathways including hypocretin dysregulation. Only moderate gene expression differences were identified and no associations were found with previously reported pathogenic mechanisms. At the level of functional gene sets, associations were observed for genes involved in several brain-related mechanisms such as GABA receptor function and voltage-gated channels. In addition, genes and modules of co-expressed genes showed a role for intracellular signalling cascades, mitochondria and inflammation. Although larger study samples may be required to identify the full range of involved pathways, these results indicate a role for mitochondria, intracellular signalling and inflammation in cluster headache.

Genetic Biomarkers on Age-Related Cognitive Decline

With ever-increasing elder populations, age-related cognitive decline, which is characterized as a gradual decline in cognitive capacity in the aging process, has turned out to be a mammoth public health concern. Since genetic information has become increasingly important to explore the biological mechanisms of cognitive decline, the search for genetic biomarkers of cognitive aging has received much attention. There is growing evidence that single-nucleotide polymorphisms (SNPs) within the ADAMTS9, BDNF, CASS4, COMT, CR1, DNMT3A, DTNBP1, REST, SRR, TOMM40, circadian clock, and Alzheimer's diseases-associated genes may contribute to susceptibility to cognitive aging. In this review, we first illustrated evidence of the genetic contribution to disease susceptibility to age-related cognitive decline in recent studies ranging from approaches of candidate genes to genome-wide association studies. We then surveyed a variety of association studies regarding age-related cognitive decline with consideration of gene-gene and gene-environment interactions. Finally, we highlighted their limitations and future directions. In light of advances in precision medicine and multi-omics technologies, future research in genomic medicine promises to lead to innovative ideas that are relevant to disease prevention and novel drugs for cognitive aging.

Predisposition to treatment response in major depressive episode: A peripheral blood gene coexpression network analysis

Antidepressant efficacy is insufficient, unpredictable and poorly understood in major depressive episode (MDE). Gene expression studies allow for the identification of significantly dysregulated genes but can limit the exploration of biological pathways. In the present study, we proposed a gene coexpression analysis to investigate biological pathways associated with treatment response predisposition and their regulation by microRNAs (miRNAs) in peripheral blood samples of MDE and healthy control subjects. We used a discovery cohort that included 34 MDE patients that were given 12-week treatment with citalopram and 33 healthy controls. Two replication cohorts with similar design were also analyzed. Expression-based gene network was built to define clusters of highly correlated sets of genes, called modules. Association between each module's first principal component of the expression data and clinical improvement was tested in the three cohorts. We conducted gene ontology analysis and miRNA prediction based on the module gene list. Nine of the 59 modules from the gene coexpression network were associated with clinical improvement. The association was partially replicated in other cohorts. Gene ontology analysis demonstrated that 4 modules were associated with cytokine production, acute inflammatory response or IL-8 functions. Finally, we found 414 miRNAs that may regulate one or several modules associated with clinical improvement. By contrast, only 12 miRNAs were predicted to specifically regulate modules unrelated to clinical improvement. Our gene coexpression analysis underlines the importance of inflammation-related pathways and the involvement of a large miRNA program as biological processes predisposing associated with antidepressant response.

Molecular Mechanism for Stress-Induced Depression Assessed by Sequencing miRNA and mRNA in Medial Prefrontal Cortex

Background

Major depression is a prevalent mood disorder. Chronic stress is presumably main etiology that leads to the neuron and synapse atrophies in the limbic system. However, the intermediate molecules from stresses to neuronal atrophy remain elusive, which we have studied in the medial prefrontal cortices from depression mice.

Methods and Results

The mice were treated by the chronic unpredictable mild stress (CUMS) until they expressed depression-like behaviors confirmed by the tests of sucrose preference, forced swimming and Y-maze. High-throughput sequencings of microRNA and mRNA in the medial prefrontal cortices were performed in CUMS-induced depression mice versus control mice to demonstrate the molecular profiles of major depression. In the medial prefrontal cortices of depression-like mice, the levels of mRNAs that translated the proteins for the GABAergic synapses, dopaminergic synapses, myelination, synaptic vesicle cycle and neuronal growth were downregulated. miRNAs of regulating these mRNAs are upregulated.

Conclusion

The deteriorations of GABAergic and dopaminergic synapses as well as axonal growth are associated with CUMS-induced depression.

Blood-based gene expression signatures of medication-free outpatients with major depressive disorder: integrative genome-wide and candidate gene analyses

Several microarray-based studies have investigated gene expression profiles in major depressive disorder (MDD), yet with highly variable findings. We examined blood-based genome-wide expression signatures of MDD, focusing on molecular pathways and networks underlying differentially expressed genes (DEGs) and behaviours of hypothesis-driven, evidence-based candidate genes for depression. Agilent human whole-genome arrays were used to measure gene expression in 14 medication-free outpatients with MDD who were at least moderately ill and 14 healthy controls matched pairwise for age and sex. After filtering, we compared expression of entire probes between patients and controls and identified DEGs. The DEGs were evaluated by pathway and network analyses. For the candidate gene analysis, we utilized 169 previously prioritized genes and examined their case-control separation efficiency and correlational co-expression network in patients relative to controls. The 317 screened DEGs mapped to a significantly over-represented pathway, the "synaptic transmission" pathway. The protein-protein interaction network was also significantly enriched, in which a number of key molecules for depression were included. The co-expression network of candidate genes was markedly disrupted in patients. This study provided evidence for an altered molecular network along with several key molecules in MDD and confirmed that the candidate genes are worthwhile targets for depression research.

Ernst Rüdin's Unpublished 1922-1925 Study "Inheritance of Manic-Depressive Insanity": Genetic Research Findings Subordinated to Eugenic Ideology

In the early 20th century, there were few therapeutic options for mental illness and asylum numbers were rising. This pessimistic outlook favoured the rise of the eugenics movement. Heredity was assumed to be the principal cause of mental illness. Politicians, scientists and clinicians in North America and Europe called for compulsory sterilisation of the mentally ill. Psychiatric genetic research aimed to prove a Mendelian mode of inheritance as a scientific justification for these measures. Ernst Rüdin's seminal 1916 epidemiological study on inheritance of dementia praecox featured large, systematically ascertained samples and statistical analyses. Rüdin's 1922-1925 study on the inheritance of "manic-depressive insanity" was completed in manuscript form, but never published. It failed to prove a pattern of Mendelian inheritance, counter to the tenets of eugenics of which Rüdin was a prominent proponent. It appears he withheld the study from publication, unable to reconcile this contradiction, thus subordinating his carefully derived scientific findings to his ideological preoccupations. Instead, Rüdin continued to promote prevention of assumed hereditary mental illnesses by prohibition of marriage or sterilisation and was influential in the introduction by the National Socialist regime of the 1933 "Law for the Prevention of Hereditarily Diseased Offspring" (Gesetz zur Verhütung erbkranken Nachwuchses).

The promise of biomarkers in diagnosing major depression in primary care: the present and future

Major depressive disorder (MDD) is the most prevalent psychiatric disorder, but it can be underdiagnosed or misdiagnosed. Most people with depression are seen in primary care settings, where there are limited resources to diagnose and treat the patient. There is a lack of clinically validated objective laboratory-based diagnostic tests to diagnose MDD; however, it is clear that these tests could greatly improve the correct and timely diagnosis. This review aims to give a cross-sectional view of current efforts of DNA methylomic, transcriptomic, and proteomic approaches to identify biomarkers. We outline our view of the biomarker developmental steps from discovery to clinical application. We then propose that better cooperation will lead us closer to the common goal of identifying biological biomarkers for major depression. "The important thing is not to stop questioning. Curiosity has its own reason for existing." Albert Einstein.