Activated immune-inflammatory pathways are associated with long-standing depressive symptoms: Evidence from gene-set enrichment analyses in the Young Finns Study

Identification and functional characterisation of DNA methylation differences between East- and West-originating Finns

Eastern and Western Finns show a striking difference in coronary heart disease-related mortality; genetics is a known contributor for this discrepancy. Here, we discuss the potential role of DNA methylation in mediating the discrepancy in cardiometabolic disease-risk phenotypes between the sub-populations. We used data from the Young Finns Study (n = 969) to compare the genome-wide DNA methylation levels of East- and West-originating Finns. We identified 21 differentially methylated loci (FDR < 0.05; Δβ >2.5%) and 7 regions (smoothed FDR < 0.05; CpGs ≥ 5). Methylation at all loci and regions associates with genetic variants (p < 5 × 10-8). Independently of genetics, methylation at 11 loci and 4 regions associates with transcript expression, including genes encoding zinc finger proteins. Similarly, methylation at 5 loci and 4 regions associates with cardiometabolic disease-risk phenotypes including triglycerides, glucose, cholesterol, as well as insulin treatment. This analysis was also performed in LURIC (n = 2371), a German cardiovascular patient cohort, and results replicated for the association of methylation at cg26740318 and DMR_11p15 with diabetes-related phenotypes and methylation at DMR_22q13 with triglyceride levels. Our results indicate that DNA methylation differences between East and West Finns may have a functional role in mediating the cardiometabolic disease discrepancy between the sub-populations.

Exploring machine learning strategies for predicting cardiovascular disease risk factors from multi-omic data

BACKGROUND

Machine learning (ML) classifiers are increasingly used for predicting cardiovascular disease (CVD) and related risk factors using omics data, although these outcomes often exhibit categorical nature and class imbalances. However, little is known about which ML classifier, omics data, or upstream dimension reduction strategy has the strongest influence on prediction quality in such settings. Our study aimed to illustrate and compare different machine learning strategies to predict CVD risk factors under different scenarios.

METHODS

We compared the use of six ML classifiers in predicting CVD risk factors using blood-derived metabolomics, epigenetics and transcriptomics data. Upstream omic dimension reduction was performed using either unsupervised or semi-supervised autoencoders, whose downstream ML classifier performance we compared. CVD risk factors included systolic and diastolic blood pressure measurements and ultrasound-based biomarkers of left ventricular diastolic dysfunction (LVDD; E/e' ratio, E/A ratio, LAVI) collected from 1,249 Finnish participants, of which 80% were used for model fitting. We predicted individuals with low, high or average levels of CVD risk factors, the latter class being the most common. We constructed multi-omic predictions using a meta-learner that weighted single-omic predictions. Model performance comparisons were based on the F1 score. Finally, we investigated whether learned omic representations from pre-trained semi-supervised autoencoders could improve outcome prediction in an external cohort using transfer learning.

RESULTS

Depending on the ML classifier or omic used, the quality of single-omic predictions varied. Multi-omics predictions outperformed single-omics predictions in most cases, particularly in the prediction of individuals with high or low CVD risk factor levels. Semi-supervised autoencoders improved downstream predictions compared to the use of unsupervised autoencoders. In addition, median gains in Area Under the Curve by transfer learning compared to modelling from scratch ranged from 0.09 to 0.14 and 0.07 to 0.11 units for transcriptomic and metabolomic data, respectively.

CONCLUSIONS

By illustrating the use of different machine learning strategies in different scenarios, our study provides a platform for researchers to evaluate how the choice of omics, ML classifiers, and dimension reduction can influence the quality of CVD risk factor predictions.

n-3 polyunsaturated fatty acids improve depression-like behavior by inhibiting hippocampal neuroinflammation in mice via reducing TLR4 expression

INTRODUCTION

n-3 polyunsaturated fatty acids (PUFAs) are believed to be implicated in the pathogenesis of many inflammation-related diseases, including depression.

METHODS

The mouse model of depression was established through chronic unpredictable mild stress (CUMS), the mice were intervened with n-3 PUFAs, and then the expression of toll-like receptor 4 (TLR4) was stimulated with lipopolysaccharides (LPS). Tail suspension test (TST), forced swimming test (FST) and sucrose preference test were performed to monitor the depression behavior of mice. Microglia activation was detected by Iba1 immunofluorescence, and neuronal injury was detected by Nissl staining. Concentrations of tumor necrosis factor (TNF)-α, Interleukin (IL)-6 and IL-1β in the hippocampus were assessed via enzyme linked immunosorbent assay (ELISA). Quantitative real time polymerase chain reaction was used to detect IL-6, IL-1β and TNF-α messenger RNA levels. Western blot was utilized for detection of TLR4 protein expression.

RESULTS

CUMS significantly reduced the sucrose preference in mice, while increased the immobility time in FST and TST. Moreover, CUMS significantly aggravated microglia activation and neuronal damage in mice and increased the levels of IL-6, IL-1β and TNF-α in hippocampal tissues, however, intervention with n-3 PUFAs could improve the above effects. Further, the increased TLR4 induced by LPS partially reversed the inhibition of n-3 PUFAs on depression-like behaviors, microglial activation and inflammatory injury of hippocampal neurons.

CONCLUSION

n-3 PUFAs may ameliorate depression-like behaviors via reducing hippocampal neuroinflammation in CUMS-induced mice by regulating TLR4 expression, suggesting that n-3 PUFAs may be an effective antidepressant, which provides evidence for future treatment of depression.

Multi-Omics Integration in a Twin Cohort and Predictive Modeling of Blood Pressure Values

Abnormal blood pressure is strongly associated with risk of high-prevalence diseases, making the study of blood pressure a major public health challenge. Although biological mechanisms underlying hypertension at the single omic level have been discovered, multi-omics integrative analyses using continuous variations in blood pressure values remain limited. We used a multi-omics regression-based method, called sparse multi-block partial least square, for integrative, explanatory, and predictive interests in study of systolic and diastolic blood pressure values. Various datasets were obtained from the Finnish Twin Cohort for up to 444 twins. Blocks of omics-including transcriptomic, methylation, metabolomic-data as well as polygenic risk scores and clinical data were integrated into the modeling and supported by cross-validation. The predictive contribution of each omics block when predicting blood pressure values was investigated using external participants from the Young Finns Study. In addition to revealing interesting inter-omics associations, we found that each block of omics heterogeneously improved the predictions of blood pressure values once the multi-omics data were integrated. The modeling revealed a plurality of clinical, transcriptomic, and metabolomic factors consistent with the literature and that play a leading role in explaining unit variations in blood pressure. These findings demonstrate (1) the robustness of our integrative method to harness results obtained by single omics discriminant analyses, and (2) the added value of predictive and exploratory gains of a multi-omics approach in studies of complex phenotypes such as blood pressure.

Extracellular vesicle associated and soluble immune marker profiles of psychoneurological symptom clusters in men with prostate cancer: an exploratory study

Psychoneurological symptom clusters are co-occurring and interrelated physiological symptoms that may include cancer-related fatigue, pain, depressive symptoms, cognitive disturbances, and sleep disturbances. These symptoms are hypothesized to share a common systemic proinflammatory etiology. Thus, an investigation of systemic immune biomarkers is an important approach to test this hypothesis. Here, we investigated the associations between extracellular vesicle (EV)-associated and soluble cytokines with immune markers and symptom clusters in men with non-metastatic prostate cancer. This observational study included 40 men with non-metastatic prostate cancer at the start (T1) of external beam radiation therapy (EBRT) and 3 months post treatment (T2), as well as 20 men with non-metastatic prostate cancer on active surveillance (AS) seen at one time point. Collected questionnaires assessed patient-reported fatigue, sleep disturbances, depressive symptoms, and cognitive fatigue. In total, 45 soluble and EV-associated biomarkers in plasma were determined by multiplex assays. Principal component analysis (PCA) was used to identify psychoneurological symptom clusters for each study group and their time points. Bivariate correlation analysis was run for each identified PCA cluster with the concentrations of EV-associated and soluble cytokines and immune markers. Both EV-associated and soluble forms of RANTES significantly correlated with the symptom cluster for EBRT at T1, whereas, at T2, soluble IFNα2, IL-9, and IL-17 correlated with the corresponding symptom cluster. For the AS group, soluble survivin correlated with psychoneurological symptoms. Linking specific inflammatory cytokines with psychoneurological symptom clusters in men receiving prostate cancer treatment can enhance understanding of the underlying mechanisms of this phenomenon and aid in developing targeted interventions.

Methylation status of nc886 epiallele reflects periconceptional conditions and is associated with glucose metabolism through nc886 RNAs

BACKGROUND

Non-coding RNA 886 (nc886) is coded from a maternally inherited metastable epiallele. We set out to investigate the determinants and dynamics of the methylation pattern at the nc886 epiallele and how this methylation status associates with nc886 RNA expression. Furthermore, we investigated the associations between the nc886 methylation status or the levels of nc886 RNAs and metabolic traits in the YFS and KORA cohorts. The association between nc886 epiallele methylation and RNA expression was also validated in induced pluripotent stem cell (iPSC) lines.

RESULTS

We confirm that the methylation status of the nc886 epiallele is mostly binomial, with individuals displaying either a non- or hemi-methylated status, but we also describe intermediately and close to fully methylated individuals. We show that an individual's methylation status is associated with the mother's age and socioeconomic status, but not with the individual's own genetics. Once established, the methylation status of the nc886 epiallele remains stable for at least 25 years. This methylation status is strongly associated with the levels of nc886 non-coding RNAs in serum, blood, and iPSC lines. In addition, nc886 methylation status associates with glucose and insulin levels during adolescence but not with the indicators of glucose metabolism or the incidence of type 2 diabetes in adulthood. However, the nc886-3p RNA levels also associate with glucose metabolism in adulthood.

CONCLUSIONS

These results indicate that nc886 metastable epiallele methylation is tuned by the periconceptional conditions and it associates with glucose metabolism through the expression of the ncRNAs coded in the epiallele region.

Methylation status of nc886 epiallele reflects periconceptional conditions and is associated with glucose metabolism through nc886 RNAs

BACKGROUND

Non-coding RNA 886 (nc886) is coded from a maternally inherited metastable epiallele. We set out to investigate the determinants and dynamics of the methylation pattern at the nc886 epiallele and how this methylation status associates with nc886 RNA expression. Furthermore, we investigated the associations between the nc886 methylation status or the levels of nc886 RNAs and metabolic traits in the YFS and KORA cohorts. The association between nc886 epiallele methylation and RNA expression was also validated in induced pluripotent stem cell (iPSC) lines.

RESULTS

We confirm that the methylation status of the nc886 epiallele is mostly binomial, with individuals displaying either a non- or hemi-methylated status, but we also describe intermediately and close to fully methylated individuals. We show that an individual's methylation status is associated with the mother's age and socioeconomic status, but not with the individual's own genetics. Once established, the methylation status of the nc886 epiallele remains stable for at least 25 years. This methylation status is strongly associated with the levels of nc886 non-coding RNAs in serum, blood, and iPSC lines. In addition, nc886 methylation status associates with glucose and insulin levels during adolescence but not with the indicators of glucose metabolism or the incidence of type 2 diabetes in adulthood. However, the nc886-3p RNA levels also associate with glucose metabolism in adulthood.

CONCLUSIONS

These results indicate that nc886 metastable epiallele methylation is tuned by the periconceptional conditions and it associates with glucose metabolism through the expression of the ncRNAs coded in the epiallele region.

Brain-immune interactions in neuropsychiatric disorders: Lessons from transcriptome studies for molecular targeting

Understanding the pathophysiological mechanisms of neuropsychiatric disorders has been a challenging quest for neurobiologists. Recent years have witnessed enormous technological advances in the field of neuroimmunology, blurring boundaries between the central nervous system and the periphery. Consequently, the discipline has expanded to cover interactions between the nervous and immune systems in health and diseases. The complex interplay between the peripheral and central immune pathways in neuropsychiatric disorders has recently been documented in various studies, but the genetic determinants remain elusive. Recent transcriptome studies have identified dysregulated genes involved in peripheral immune cell activation, blood-brain barrier integrity, glial cell activation, and synaptic plasticity in major depressive disorder, bipolar disorder, autism spectrum disorder, and schizophrenia. Herein, the key transcriptomic techniques applied in investigating differentially expressed genes and pathways responsible for altered brain-immune interactions in neuropsychiatric disorders are discussed. The application of transcriptomics that can aid in identifying molecular targets in various neuropsychiatric disorders is highlighted.

BAGSE: a Bayesian hierarchical model approach for gene set enrichment analysis

MOTIVATION

Gene set enrichment analysis has been shown to be effective in identifying relevant biological pathways underlying complex diseases. Existing approaches lack the ability to quantify the enrichment levels accurately, hence preventing the enrichment information to be further utilized in both upstream and downstream analyses. A modernized and rigorous approach for gene set enrichment analysis that emphasizes both hypothesis testing and enrichment estimation is much needed.

RESULTS

We propose a novel computational method, Bayesian Analysis of Gene Set Enrichment (BAGSE), for gene set enrichment analysis. BAGSE is built on a Bayesian hierarchical model and fully accounts for the uncertainty embedded in the association evidence of individual genes. We adopt an empirical Bayes inference framework to fit the proposed hierarchical model by implementing an efficient EM algorithm. Through simulation studies, we illustrate that BAGSE yields accurate enrichment quantification while achieving similar power as the state-of-the-art methods. Further simulation studies show that BAGSE can effectively utilize the enrichment information to improve the power in gene discovery. Finally, we demonstrate the application of BAGSE in analyzing real data from a differential expression experiment and a transcriptome-wide association study. Our results indicate that the proposed statistical framework is effective in aiding the discovery of potentially causal pathways and gene networks.

AVAILABILITY AND IMPLEMENTATION

BAGSE is implemented using the C++ programing language and is freely available from https://github.com/xqwen/bagse/. Simulated and real data used in this paper are also available at the Github repository for reproducibility purposes.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

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.

The Bidirectional Relationship of Depression and Inflammation: Double Trouble

Depression represents the number one cause of disability worldwide and is often fatal. Inflammatory processes have been implicated in the pathophysiology of depression. It is now well established that dysregulation of both the innate and adaptive immune systems occur in depressed patients and hinder favorable prognosis, including antidepressant responses. In this review, we describe how the immune system regulates mood and the potential causes of the dysregulated inflammatory responses in depressed patients. However, the proportion of never-treated major depressive disorder (MDD) patients who exhibit inflammation remains to be clarified, as the heterogeneity in inflammation findings may stem in part from examining MDD patients with varied interventions. Inflammation is likely a critical disease modifier, promoting susceptibility to depression. Controlling inflammation might provide an overall therapeutic benefit, regardless of whether it is secondary to early life trauma, a more acute stress response, microbiome alterations, a genetic diathesis, or a combination of these and other factors.

Involvement of inflammatory gene expression pathways in depressed patients with hyperphagia

The pathophysiology of major depressive disorder (MDD) is highly heterogeneous. Previous evidence at the DNA level as well as on the serum protein level suggests that the role of inflammation in MDD pathology is stronger in patients with hyperphagia during an active episode. Which inflammatory pathways differ in MDD patients with hyperphagia inflammatory pathways in terms of gene expression is unknown. We analyzed whole-blood gene expression profiles of 881 current MDD cases and 331 controls from the Netherlands Study of Depression and Anxiety (NESDA). The MDD patients were stratified according to patients with hyperphagia (characterized by increased appetite and/or weight, N = 246) or hypophagia (characterized by decreased appetite and/or weight, N = 342). Using results of differential gene expression analysis between controls and the MDD subgroups, enrichment of curated inflammatory pathways was estimated. The majority of the pathways were significantly (FDR < 0.1) enriched in the expression profiles of MDD cases with hyperphagia, including top pathways related to factors responsible for the onset of inflammatory response ('caspase', 'GATA3', 'NFAT', and 'inflammasomes' pathways). Only two pathways ('adaptive immune system' and 'IL-8- and CXCR2-mediated signaling') were enriched in the MDD with hypophagia subgroup, these were also enriched in the total current MDD group and the group with hyperphagia. This confirms the importance of inflammation in MDD pathology of patients with hyperphagia, and suggests that distinguishing more uniform MDD phenotypes can help in finding their pathophysiological basis.

Fatty liver is associated with blood pathways of inflammatory response, immune system activation and prothrombotic state in Young Finns Study

Fatty liver (FL) disease is the most common type of chronic liver disease. We hypothesized that liver’s response to the process where large droplets of triglyceride fat accumulate in liver cells is reflected also in gene pathway expression in blood. Peripheral blood genome wide gene expression analysis and ultrasonic imaging of liver were performed for 1,650 participants (316 individuals with FL and 1,334 controls) of the Young Finns Study. Gene set enrichment analysis (GSEA) was performed for the expression data. Fourteen gene sets were upregulated (false discovery rate, FDR < 0.05) in subjects with FL. These pathways related to extracellular matrix (ECM) turnover, immune response regulation, prothrombotic state and neural tissues. After adjustment for known risk factors and biomarkers of FL, we found i) integrin A4B1 signaling, ii) leukocyte transendothelial migration, iii) CD40/CD40L and iv) netrin-1 signaling pathways to be upregulated in individuals with FL (nominal p < 0.05). From these all but not ii) remained significantly upregulated when analyzing only subjects without history of heavy alcohol use. In conclusion, FL was associated with blood gene sets of ECM turnover, inflammatory response, immune system activation and prothrombotic state. These may form a systemic link between FL and the development of cardiovascular diseases.

The NCAM1 gene set is linked to depressive symptoms and their brain structural correlates in healthy individuals

Depressive symptoms exist on a continuum, the far end of which is found in depressive disorders. Utilizing the continuous spectrum of depressive symptoms may therefore contribute to the understanding of the biological underpinnings of depression. Gene set enrichment analysis (GSEA) is an important tool for the identification of gene groups linked to complex traits, and was applied in the present study on genome-wide association study (GWAS) data of depression scores and their brain-level structural correlates in healthy young individuals. On symptom level (i.e. depression scores), robust enrichment was identified for two gene sets: NCAM1 Interactions and Collagen Formation. Depression scores were also associated with decreased fractional anisotropy (FA) - a brain white matter property - within the forceps minor and the left superior temporal longitudinal fasciculus. Within each of these tracts, mean FA value of depression score-associated voxels was used as a phenotype in a subsequent GSEA. The NCAM1 Interactions gene set was significantly enriched in these tracts. By linking the NCAM1 Interactions gene set to depression scores and their structural brain correlates in healthy participants, the current study contributes to the understanding of the molecular underpinnings of depressive symptomatology.

Genetic Contributions of Inflammation to Depression

This paper describes the effects of immune genes genetic variants and mRNA expression on depression's risk, severity, and response to antidepressant treatment, through a systematic review on all papers published between 2000 and 2016. Our results, based largely on case-control studies, suggest that common genetic variants and gene-expression pathways are involved in both immune activation and depression. The most replicated and relevant genetic variants include polymorphisms in the genes for interleukin (IL)-1β, IL-6, IL-10, monocyte chemoattractant protein-1, tumor necrosis factor-alpha, C-reactive protein, and phospholipase A2. Moreover, increased blood cytokines mRNA expression (especially of IL-1β) identifies patients that are less likely to respond to conventional antidepressants. However, even for the most replicated findings there are inconsistent results, not only between studies, but also between the immune effects of the genetic variants and the resulting effects on depression. We find evidence that these discrepant findings may be explained, at least in part, by the heterogeneity of the depression immunophenotype, by environmental influences and gene × environment interactions, and by the complex interfacing of genetic variants with gene expression. Indeed, some of the most robust findings have been obtained in patients developing depression in the context of treatment with interferon-alpha, a widely used model to mimic depression in the context of inflammation. Further 'omics' approaches, through GWAS and transcriptomics, will finally shed light on the interaction between immune genes, their expression, and the influence of the environment, in the pathogenesis of depression.

Therapeutic Implications of Brain-Immune Interactions: Treatment in Translation

A wealth of data has been amassed that details a complex, yet accessible, series of pathways by which the immune system, notably inflammation, can influence the brain and behavior. These data have opened the window to a diverse array of novel targets whose potential efficacy is tied to specific neurotransmitters and neurocircuits as well as specific behaviors. What is clear is that the impact of inflammation on the brain cuts across psychiatric disorders and engages dopaminergic and glutamatergic pathways that regulate motivation and motor activity as well as the sensitivity to threat. Given the ability to identify patient populations with increased inflammation, the precision of interventions can be further tuned, in conjunction with the ability to establish target engagement in the brain through the use of multiple neuroimaging strategies. After a brief overview of the mechanisms by which inflammation affects the brain and behavior, this review examines the extant literature on the efficacy of anti-inflammatory treatments, while forging guidelines for future intelligent clinical trial design. An examination of the most promising therapeutic strategies is also provided, along with some of the most exciting clinical trials that are currently being planned or underway.

Blood hsa-miR-122-5p and hsa-miR-885-5p levels associate with fatty liver and related lipoprotein metabolism-The Young Finns Study

MicroRNAs are involved in disease development and may be utilized as biomarkers. We investigated the association of blood miRNA levels and a) fatty liver (FL), b) lipoprotein and lipid pathways involved in liver lipid accumulation and c) levels of predicted mRNA targets in general population based cohort. Blood microRNA profiling (TaqMan OpenArray), genome-wide gene expression arrays and nuclear magnetic resonance metabolomics were performed for Young Finns Study participants aged 34–49 years (n = 871). Liver fat status was assessed ultrasonographically. Levels of hsa-miR-122-5p and -885-5p were up-regulated in individuals with FL (fold change (FC) = 1.55, p = 1.36 * 10⁻¹⁴ and FC = 1.25, p = 4.86 * 10⁻⁴, respectively). In regression model adjusted with age, sex and BMI, hsa-miR-122-5p and -885-5p predicted FL (OR = 2.07, p = 1.29 * 10⁻⁸ and OR = 1.41, p = 0.002, respectively). Together hsa-miR-122-5p and -885-5p slightly improved the detection of FL beyond established risk factors. These miRNAs may be associated with FL formation through the regulation of lipoprotein metabolism as hsa-miR-122-5p levels associated with small VLDL, IDL, and large LDL lipoprotein subclass components, while hsa-miR-885-5p levels associated inversely with XL HDL cholesterol levels. Hsa-miR-885-5p levels correlated inversely with oxysterol-binding protein 2 (OSBPL2) expression (r = −0.143, p = 1.00 * 10⁻⁴) and suppressing the expression of this lipid receptor and sterol transporter could link hsa-miR-885-5p with HDL cholesterol levels.

Loneliness, Depression, and Inflammation: Evidence from the Multi-Ethnic Study of Atherosclerosis

Objective

Both objective and subjective aspects of social isolation have been associated with alterations in immune markers relevant to multiple chronic diseases among older adults. However, these associations may be confounded by health status, and it is unclear whether these social factors are associated with immune functioning among relatively healthy adults. The goal of this study was to examine the associations between perceived loneliness and circulating levels of inflammatory markers among a diverse sample of adults.

Methods

Data come from a subset of the Multi-Ethnic Study of Atherosclerosis (n = 441). Loneliness was measured by three items derived from the UCLA Loneliness Scale. The association between loneliness and C-reactive protein (CRP) and fibrinogen was assessed using multivariable linear regression analyses. Models were adjusted for demographic and health characteristics.

Results

Approximately 50% of participants reported that they hardly ever felt lonely and 17.2% felt highly lonely. Individuals who were unmarried/unpartnered or with higher depressive symptoms were more likely to report being highly lonely. There was no relationship between perceived loneliness and ln(CRP) (β = -0.051, p = 0.239) adjusting for demographic and health characteristics. Loneliness was inversely associated with ln(fibrinogen) (β = -0.091, p = 0.040), although the absolute magnitude of this relationship was small.

Conclusion

These results indicate that loneliness is not positively associated with fibrinogen or CRP among relatively healthy middle-aged adults.

Systems Genomics Support for Immune and Inflammation Hypothesis of Depression

BACKGROUND

Immune system plays an important role in brain development and function. With the discovery of increased circulating inflammatory cytokine levels in depression over two decades ago, evidence implicating immune system alterations in the disease has increasingly accumulated.

OBJECTIVE

To assess the underlying etiology and pathophysiology, a brief overview of the hypothesis free genomic, transcriptomic and proteomic studies in depression is presented here in order to specifically examine if the immune and inflammation hypothesis of depression is supported.

RESULTS

It is observed that genes identified in genome-wide association studies, and genes showing differential expression in transcriptomic studies in human depression do separately overrepresent processes related to both development as well as functioning of the immune system, and inflammatory response. These processes are also enriched in differentially expressed genes reported in animal models of antidepressant treatment. It is further noted that some of the genes identified in genome sequencing and proteomic analyses in human depression, and transcriptomic studies in chronic social defeat stress, an established animal model of depression, relate to immune and inflammatory pathways.

CONCLUSION

In conclusion, integrative genomics evidence supports the immune and inflammation hypothesis of depression.