Dysregulation of leukocyte gene expression in women with medication-refractory depression versus healthy non-depressed controls

Activation of transient receptor potential vanilloid 4 impairs long-term depression in nucleus accumbens and induces depressive-like behavior

Long-term depression (LTD), a form of synaptic plasticity, is impaired in the nucleus accumbens (NAc) in depression. While TRPV4 activation regulates synaptic transmission in the hippocampus, its effects in the NAc remain unclear. Here, we examined the effects of TRPV4 activation on LTD induction in the NAc and depressive-like behavior. Mice that were administered the TRPV4 agonist GSK1016790A into the NAc (GSK-mice) showed depressive-like behavior and impaired LTD induction in NAc slices. Additionally, the mRNA and protein levels of dopamine D2 receptor (D2R) and A-type gamma-aminobutyric acid receptor (GABAAR) were markedly decreased in the NAc of GSK-mice. Meanwhile, administering a D2R (quinpirole) or GABAAR (muscimol) agonist reversed LTD impairment in the NAc. The protein levels of phosphorylated protein kinase C (p-PKC) increased markedly and that of phosphorylated protein kinase B (p-Akt) decreased in the NAc of GSK mice. Administration of a PKC antagonist (GF109203X) or phosphatidylinositol 3-kinase (PI3K) agonist (740 Y-P) significantly increased GABAAR protein levels and restored LTD induction in the NAc of GSK-mice. Administration of quinpirole increased p-Akt and GABAAR protein levels in the NAc of GSK-mice. Finally, administration of quinpirole, muscimol, GF109203X or 740 Y-P improved the depressive-like behavior in GSK-mice. This study suggests that activation of TRPV4 impairs LTD induction in the NAc and induces depressive-like behavior, which is likely mediated by down-regulating D2R to inhibit PI3K-Akt pathway, and activating PKC to decrease the expression of GABAAR.

Causal relationship between rosacea and psychiatric disorders: a bidirectional two-sample Mendelian randomization study

This study investigated the causal association between rosacea and psychiatric disorders, including major depression disorder (MDD), anxiety disorder (AD), and panic disorder (PD). A bidirectional two-sample Mendelian randomization (MR) analysis was performed. The publicly released genome-wide association summary data (GWAS) of rosacea and psychiatric disorders was used in the MR analysis. The random-effects inverse variance weighted method (IVW) was applied as the primary method, and several other estimators were used as complementary methods. MR-Egger, MR-PRESSO, Cochran's Q, and leave-one-out were performed to evaluate sensitivity and robustness. The forward MR results suggested that genetically determined rosacea did not have a causal association with the three psychiatric disorders. In the reverse analysis, the MR analyses consistently showed non-significant causal effects of these psychiatric disorders on rosacea. These findings were robust across different analytical methods. Sensitivity analyses confirmed the consistency of the results and provided no evidence of horizontal pleiotropy or outliers. A validation analysis using different datasets for the exposures and the outcomes, supporting the initial MR analysis results. Overall, the study found no evidence of a bidirectional causal relationship between genetically predicted rosacea and three psychiatric disorders despite observational studies reporting an association between rosacea and psychiatric disorders. The causal effects between these diseases need further exploration.

MicroRNA Expression Profile Is Altered by Short-Term and Chronic Lithium Treatment in a Rat Model of Depression

Depression is a common disease that affects 3.8% of the global population. Despite various antidepressant treatments, one-third of patients do not respond to antidepressants, therefore augmentation with mood stabilizers such as lithium may be required in this group. One of the suggested pathomechanisms of depression is the dysregulation of the hypothalamic-pituitary-adrenal (HPA) axis and recent reports showed that microRNAs (miRNA) can impact its activity by epigenetic regulation. We aimed to explore the miRNA expression profile in the depression model and its changes upon short-term and chronic lithium treatment in the rat brain (pituitary, hypothalamus, and hippocampus). We used a chronic mild stress rat model of depression and short- and long-term lithium treatment. The behavior was assessed by an open-field test. The miRNA expression profile in the pituitary was estimated by sequencing and validated in the hypothalamus and hippocampus with qPCR. We found several miRNAs in the pituitary that were significantly altered between CMS-exposed and control rats as well as after short- and long-term lithium treatment. MicroRNAs chosen for validation in the hypothalamus and hippocampus (rno-miR-146a-5p, rno-miR-127-3p) showed no significant changes in expression. We performed in silico analysis and estimated potential pathways involved in lithium action for miRNAs differentially expressed in the pituitary at different time points. Specific microRNA subsets showed altered expression in the pituitary in depression model upon short- and long-term lithium treatment. We identified that biological pathways of target genes for these altered miRNAs differ, with the Foxo pathway potentially involved in disease development.

Ferroptosis-related genes as diagnostic markers for major depressive disorder and their correlations with immune infiltration

Background

Major depression disorder (MDD) is a devastating neuropsychiatric disease, and one of the leading causes of suicide. Ferroptosis, an iron-dependent form of regulated cell death, plays a pivotal role in numerous diseases. The study aimed to construct and validate a gene signature for diagnosing MDD based on ferroptosis-related genes (FRGs) and further explore the biological functions of these genes in MDD.

Methods

The datasets were downloaded from the Gene Expression Omnibus (GEO) database and FRGs were obtained from the FerrDb database and other literatures. Least absolute shrinkage and selection operator (LASSO) regression and stepwise logistic regression were performed to develop a gene signature. Receiver operating characteristic (ROC) curves were utilized to assess the diagnostic power of the signature. Gene ontology (GO) enrichment analysis was used to explore the biological roles of these diagnostic genes, and single sample gene set enrichment analysis (ssGSEA) algorithm was used to evaluate immune infiltration in MDD. Animal model of depression was constructed to validate the expression of the key genes.

Results

Eleven differentially expressed FRGs were identified in MDD patients compared with healthy controls. A signature of three FRGs (ALOX15B, RPLP0, and HP) was constructed for diagnosis of MDD. Afterwards, ROC analysis confirmed the signature's discriminative capacity (AUC = 0.783, 95% CI = 0.719-0.848). GO enrichment analysis revealed that the differentially expressed genes (DEGs) related to these three FRGs were mainly involved in immune response. Furthermore, spearman correlation analysis demonstrated that these three FRGs were associated with infiltrating immune cells. ALOX15B and HP were significantly upregulated and RPLP0 was significantly downregulated in peripheral blood of the lipopolysaccharide (LPS)-induced depressive model.

Conclusion

Our results suggest that the novel FRG signature had a good diagnostic performance for MDD, and these three FRGs correlated with immune infiltration in MDD.

The association between prenatal famine, DNA methylation and mental disorders: a systematic review and meta-analysis

BACKGROUND

Undernutrition in pregnant women is an unfavorable environmental condition that can affect the intrauterine development via epigenetic mechanisms and thus have long-lasting detrimental consequences for the mental health of the offspring later in life. One epigenetic mechanism that has been associated with mental disorders and undernutrition is alterations in DNA methylation. The effect of prenatal undernutrition on the mental health of adult offspring can be analyzed through quasi-experimental studies such as famine studies. The present systematic review and meta-analysis aims to analyze the association between prenatal famine exposure, DNA methylation, and mental disorders in adult offspring. We further investigate whether altered DNA methylation as a result of prenatal famine exposure is prospectively linked to mental disorders.

METHODS

We conducted a systematic search of the databases PubMed and PsycINFO to identify relevant records up to September 2022 on offspring whose mothers experienced famine directly before and/or during pregnancy, examining the impact of prenatal famine exposure on the offspring's DNA methylation and/or mental disorders or symptoms.

RESULTS

The systematic review showed that adults who were prenatally exposed to famine had an increased risk of schizophrenia and depression. Several studies reported an association between prenatal famine exposure and hyper- or hypomethylation of specific genes. The largest number of studies reported differences in DNA methylation of the IGF2 gene. Altered DNA methylation of the DUSP22 gene mediated the association between prenatal famine exposure and schizophrenia in adult offspring. Meta-analysis confirmed the increased risk of schizophrenia following prenatal famine exposure. For DNA methylation, meta-analysis was not suitable due to different microarrays/data processing approaches and/or unavailable data.

CONCLUSION

Prenatal famine exposure is associated with an increased risk of mental disorders and DNA methylation changes. The findings suggest that changes in DNA methylation of genes involved in neuronal, neuroendocrine, and immune processes may be a mechanism that promotes the development of mental disorders such as schizophrenia and depression in adult offspring. Such findings are crucial given that undernutrition has risen worldwide, increasing the risk of famine and thus also of negative effects on mental health.

Longitudinal Plasma Proteomics Analysis Reveals Novel Candidate Biomarkers in Acute COVID-19

The host response to COVID-19 pathophysiology over the first few days of infection remains largely unclear, especially the mechanisms in the blood compartment. We report on a longitudinal proteomic analysis of acute-phase COVID-19 patients, for which we used blood plasma, multiple reaction monitoring with internal standards, and data-independent acquisition. We measured samples on admission for 49 patients, of which 21 had additional samples on days 2, 4, 7, and 14 after admission. We also measured 30 externally obtained samples from healthy individuals for comparison at baseline. The 31 proteins differentiated in abundance between acute COVID-19 patients and healthy controls belonged to acute inflammatory response, complement activation, regulation of inflammatory response, and regulation of protein activation cascade. The longitudinal analysis showed distinct profiles revealing increased levels of multiple lipid-associated functions, a rapid decrease followed by recovery for complement activation, humoral immune response, and acute inflammatory response-related proteins, and level fluctuation in the regulation of smooth muscle cell proliferation, secretory mechanisms, and platelet degranulation. Three proteins were differentiated between survivors and nonsurvivors. Finally, increased levels of fructose-bisphosphate aldolase B were determined in patients with exposure to angiotensin receptor blockers versus decreased levels in those exposed to angiotensin-converting enzyme inhibitors. Data are available via ProteomeXchange PXD029437.

Transcriptional biomarkers of response to pharmacological treatments in severe mental disorders: A systematic review

Variation in the expression level and activity of genes involved in drug disposition and action in tissues of pharmacological importance have been increasingly investigated in patients treated with psychotropic drugs. Findings are promising, but reliable predictive biomarkers of response have yet to be identified. Here we conducted a PRISMA-compliant systematic search of PubMed, Scopus and PsycInfo up to 12 September 2020 for studies investigating RNA expression levels in cells or biofluids from patients with major depressive disorder, schizophrenia or bipolar disorder characterized for response to psychotropic drugs (antidepressants, antipsychotics or mood stabilizers) or adverse effects. Among 5497 retrieved studies, 123 (63 on antidepressants, 33 on antipsychotics and 27 on mood stabilizers) met inclusion criteria. Studies were either focused on mRNAs (n = 96), microRNAs (n = 19) or long non-coding RNAs (n = 1), with only a minority investigating both mRNAs and microRNAs levels (n = 7). The most replicated results include genes playing a role in inflammation (antidepressants), neurotransmission (antidepressants and antipsychotics) or mitochondrial function (mood stabilizers). Compared to those investigating response to antidepressants, studies focused on antipsychotics or mood stabilizers more often showed lower sample size and lacked replication. Strengths and limitations of available studies are presented and discussed in light of the specific designs, methodology and clinical characterization of included patients for transcriptomic compared to DNA-based studies. Finally, future directions of transcriptomics of psychopharmacological interventions in psychiatric disorders are discussed.

Biomarkers of Post-COVID Depression

The COVID-19 pandemic is spreading around the world and 187 million people have already been affected. One of its after-effects is post-COVID depression, which, according to the latest data, affects up to 40% of people who have had SARS-CoV-2 infection. A very important issue for the mental health of the general population is to look for the causes of this complication and its biomarkers. This will help in faster diagnosis and effective treatment of the affected patients. In our work, we focused on the search for major depressive disorder (MDD) biomarkers, which are also present in COVID-19 patients and may influence the development of post-COVID depression. For this purpose, we searched PubMed, Scopus and Google Scholar scientific literature databases using keywords such as 'COVID-19', 'SARS-CoV-2', 'depression', 'post-COVID', 'biomarkers' and others. Among the biomarkers found, the most important that were frequently described are increased levels of interleukin 6 (IL-6), soluble interleukin 6 receptor (sIL-6R), interleukin 1 β (IL-1β), tumor necrosis factor α (TNF-α), interferon gamma (IFN-γ), interleukin 10 (IL-10), interleukin 2 (IL-2), soluble interleukin 2 receptor (sIL-2R), C-reactive protein (CRP), Monocyte Chemoattractant Protein-1 (MCP-1), serum amyloid a (SAA1) and metabolites of the kynurenine pathway, as well as decreased brain derived neurotrophic factor (BDNF) and tryptophan (TRP). The biomarkers identified by us indicate the etiopathogenesis of post-COVID depression analogous to the leading inflammatory hypothesis of MDD.

Identification of Diagnostic Markers for Major Depressive Disorder Using Machine Learning Methods

Background

Major depressive disorder (MDD) is a global health challenge that impacts the quality of patients’ lives severely. The disorder can manifest in many forms with different combinations of symptoms, which makes its clinical diagnosis difficult. Robust biomarkers are greatly needed to improve diagnosis and to understand the etiology of the disease. The main purpose of this study was to create a predictive model for MDD diagnosis based on peripheral blood transcriptomes.

Materials and Methods

We collected nine RNA expression datasets for MDD patients and healthy samples from the Gene Expression Omnibus database. After a series of quality control and heterogeneity tests, 302 samples from six studies were deemed suitable for the study. R package “MetaOmics” was applied for systematic meta-analysis of genome-wide expression data. Receiver operating characteristic (ROC) curve analysis was used to evaluate the diagnostic effectiveness of individual genes. To obtain a better diagnostic model, we also adopted the support vector machine (SVM), random forest (RF), k-nearest neighbors (kNN), and naive Bayesian (NB) tools for modeling, with the RF method being used for feature selection.

Results

Our analysis revealed six differentially expressed genes (AKR1C3, ARG1, KLRB1, MAFG, TPST1, and WWC3) with a false discovery rate (FDR) < 0.05 between MDD patients and control subjects. We then evaluated the diagnostic ability of these genes individually. With single gene prediction, we achieved a corresponding area under the curve (AUC) value of 0.63 ± 0.04, 0.67 ± 0.07, 0.70 ± 0.11, 0.64 ± 0.08, 0.68 ± 0.07, and 0.62 ± 0.09, respectively, for these genes. Next, we constructed the classifiers of SVM, RF, kNN, and NB with an AUC of 0.84 ± 0.09, 0.81 ± 0.10, 0.73 ± 0.11, and 0.83 ± 0.09, respectively, in validation datasets, suggesting that the SVM classifier might be superior for constructing an MDD diagnostic model. The final SVM classifier including 70 feature genes was capable of distinguishing MDD samples from healthy controls and yielded an AUC of 0.78 in an independent dataset.

Conclusion

This study provides new insights into potential biomarkers through meta-analysis of GEO data. Constructing different machine learning models based on these biomarkers could be a valuable approach for diagnosing MDD in clinical practice.

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

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

Using classification and regression tree modeling to investigate appetite hormones and proinflammatory cytokines as biomarkers to differentiate bipolar I depression from major depressive disorder

BACKGROUND

Altered immunity and metabolic profiles have been compared between bipolar depression (BD) and major depressive disorder (MDD). This study aimed at developing a composite predictor of appetite hormones and proinflammatory cytokines to differentiate BD from MDD.

METHODS

This cross-sectional study enrolled patients with BD and those with MDD aged 20 to 59 years and displaying depressive episodes. Clinical characteristics (age, sex, body mass index, and depression severity), cytokines (C-reactive protein, interleukin [IL]-2, IL-6, tumor necrosis factor [TNF]-α, P-selectin, and monocyte chemoattractant protein), and appetite hormones (leptin, adiponectin, ghrelin, and insulin) were assessed as potential predictors using a classification and regression tree (CRT) model for differentiating BD from MDD.

RESULTS

The predicted probability of a composite predictor of ghrelin and TNF-α was significantly greater (for BD: area under curve = 0.877; for MDD: area under curve = 0.914) than that of any one marker (all P > .05) to distinguish BD from MDD. The most powerful predictors for diagnosing BD were high ghrelin and TNF-α levels, whereas those for MDD were low ghrelin and TNF-α levels.

CONCLUSION

A composite predictor of ghrelin and TNF-α driven by CRT could assist in the differential diagnosis of BD from MDD with high specificity. Further clinical studies are warranted to validate our results and to explore underlying mechanisms.

Identification of key genes involved in post-traumatic stress disorder: Evidence from bioinformatics analysis

BACKGROUND

Post-traumatic stress disorder (PTSD) is a serious stress-related disorder.

AIM

To identify the key genes and pathways to uncover the potential mechanisms of PTSD using bioinformatics methods.

METHODS

Gene expression profiles were obtained from the Gene Expression Omnibus database. The differentially expressed genes (DEGs) were identified by using GEO2R. Gene functional annotation and pathway enrichment were then conducted. The gene-pathway network was constructed with Cytoscape software. Quantitative real-time polymerase chain reaction (qRT-PCR) analysis was applied for validation, and text mining by Coremine Medical was used to confirm the connections among genes and pathways.

RESULTS

We identified 973 DEGs including 358 upregulated genes and 615 downregulated genes in PTSD. A group of centrality hub genes and significantly enriched pathways (MAPK, Ras, and ErbB signaling pathways) were identified by using gene functional assignment and enrichment analyses. Six genes (KRAS, EGFR, NFKB1, FGF12, PRKCA, and RAF1) were selected to validate using qRT-PCR. The results of text mining further confirmed the correlation among hub genes and the enriched pathways. It indicated that these altered genes displayed functional roles in PTSD via these pathways, which might serve as key signatures in the pathogenesis of PTSD.

CONCLUSION

The current study identified a panel of candidate genes and important pathways, which might help us deepen our understanding of the underlying mechanism of PTSD at the molecular level. However, further studies are warranted to discover the critical regulatory mechanism of these genes via relevant pathways in PTSD.

Peripheral Markers of Depression

Major Depressive Disorder (MDD) is a leading cause of disability worldwide, creating a high medical and socioeconomic burden. There is a growing interest in the biological underpinnings of depression, which are reflected by altered levels of biological markers. Among others, enhanced inflammation has been reported in MDD, as reflected by increased concentrations of inflammatory markers-C-reactive protein, interleukin-6, tumor necrosis factor-α and soluble interleukin-2 receptor. Oxidative and nitrosative stress also plays a role in the pathophysiology of MDD. Notably, increased levels of lipid peroxidation markers are characteristic of MDD. Dysregulation of the stress axis, along with increased cortisol levels, have also been reported in MDD. Alterations in growth factors, with a significant decrease in brain-derived neurotrophic factor and an increase in fibroblast growth factor-2 and insulin-like growth factor-1 concentrations have also been found in MDD. Finally, kynurenine metabolites, increased glutamate and decreased total cholesterol also hold promise as reliable biomarkers for MDD. Research in the field of MDD biomarkers is hindered by insufficient understanding of MDD etiopathogenesis, substantial heterogeneity of the disorder, common co-morbidities and low specificity of biomarkers. The construction of biomarker panels and their evaluation with use of new technologies may have the potential to overcome the above mentioned obstacles.

Altered serum interleukin-7 and interleukin-10 are associated with drug-free major depressive disorder

BACKGROUND

Abnormal expression of inflammatory cytokines in major depressive disorder (MDD) suggests the activation of an inflammatory process. The pattern of alterations in cytokine levels is still ambiguous. The present study aimed to evaluate interleukin-7 (IL-7) and interleukin-10 (IL-10) for their involvement in the pathophysiology of MDD and determine their relationships with depression risk.

METHODS

The study included 166 medication-free subjects: 84 MDD patients and 82 sex- and age-matched healthy controls (HCs). A qualified psychiatrist diagnosed patients and evaluated controls based on the Diagnostic and Statistical Manual of Mental Disorders, 5th edition (DSM-5). Hamilton depression rating scale (Ham-D) was used to measure the severity of depression in MDD patients. Serum IL-7 and IL-10 levels were measured using enzyme-linked immunosorbent assay (ELISA) kits.

RESULTS

Compared with HCs, the serum levels of IL-7 were significantly decreased, whereas that of IL-10 increased in MDD patients. Moreover, the severity of depression is correlated with the altered levels of IL-7 and IL-10 in MDD patients. We found a negative correlation between IL-7 and Hamilton depression rating (Ham-D) scores (r = -0.580, p < 0.05), whereas there was a positive correlation between IL-10 and Ham-D scores (r = 0.555, p < 0.05).

CONCLUSIONS

The altered levels of serum IL-7 and IL-10 in MDD patients may represent a homeostatic mechanism that enhances the inflammatory process during depression. The alterations of these cytokine levels in MDD and their association with the severity of depression support them as promising, but there may still be controversial factors for understanding the pathophysiology of depression.

Association of purinergic receptor P2RX7 gene polymorphisms with depression symptoms

INTRODUCTION

The activation of the ATP-gated P2RX7 (purinergic receptor P2X, ligand-gated ion channel, 7) produces microglial activation, a process which has been demonstrated in depression, bipolar disorder, and schizophrenia. Emerging data over the last years highlighted the importance of P2X7 cation channel as a potential drug target for these central nervous system disorders. The Gln460Arg (rs2230912) polymorphism of the P2RX7 gene has been widely studied in mood disorders, however the results are still controversial. Therefore, we aimed to investigate the C-terminal region of this gene in major depressive and bipolar disorders (MDD and BD) by studying possibly functional, non-synonymous polymorphisms within a 7 kb long region around the Gln460Arg, including Ala348Thr (rs1718119), Thr357Ser (rs2230911), and Glu496Ala (rs3751143) variants. Since Gln460Arg is located at the 3' end of the P2RX7 gene, we included additional, potentially functional single nucleotide polymorphisms (SNPs) from the 3' untranslated region (UTR), which can be in linkage with Gln460Arg. Based on in silico search, we chose two SNPs in putative microRNA target sites which are located in consecutive positions: rs1653625 and rs1718106.

METHODS

P2RX7 SNPs from the C-terminal region were selected based on previous functional assays, 3' UTR variants were chosen using PolymiRTS and Patrocles databases. The genotyping of the non-synonymous SNPs was carried out by pre-designed TaqMan^® kits, while the 3' UTR variants were analyzed by PCR-RFLP method. Case-control analyses were carried out between 315 inpatients with acute major depressive episode (195 MDD, 120 BD) and 406 healthy control subjects. The two subscales of the Hospital Anxiety and Depression Scale (HADS) self-report questionnaire were used for quantitative analyses, including an additional, "at-risk" population of 218 patients with diabetes mellitus. The in vitro reporter gene assays were carried out on HEK and SK-N-FI cells transiently transfected with pMIR vector constructs containing the P2RX7 3' UTR downstream of the luciferase gene.

RESULTS

Haplotype analysis indicated a relatively high linkage between the analyzed P2RX7 SNPs. Our case-control study did not yield any association between P2RX7 gene variants and depression. However, dimensional analyses showed significant associations of the HADS depression severity scores with Gln460Arg (rs2230912) and Ala348Thr (rs1718119) in the depressed and diabetic patient groups. In the in vitro experiments, the P2RX7 3' UTR constructs with the lowest predicted binding efficiency to their miRNAs showed the highest expression of the gene. The combination of the depression-associated P2RX7 C-terminal and 3' UTR SNPs contributed to the highest depression severity score in the haplotype analysis.

CONCLUSION

Based on our findings, we propose that a P2RX7 haplotype combination of the Gln460Arg and neighboring SNPs contribute to the observed genetic association with depressive symptoms.

Transient Receptor Potential Channels as Blood Biomarkers for Pain Characteristics in Patients with Chronic Pain

Transient receptor potential (TRP) channels play key roles for the transition from acute to chronic postoperative pain after surgery. To evaluate TRP channels in the peripheral blood cells as blood biomarkers for chronic pain, we collected blood samples for genome-wide assays of the mRNA expression and assessed pain intensity and the number of pain symptoms in 13 patients with chronic pain. There was a significant association between increases in the number of pain symptoms and increases in the TRP vanilloid 1 (TRPV1) expression. Decreases in the TRP ankyrin 1 (TRPA1) expression, however, tended to increase the number of pain symptoms. There was likely an inverse relationship for TRPV1 and TRPA1 expressions with regard to the number of pain symptoms in chronic pain patients.

Consensus paper of the WFSBP Task Force on Genetics: Genetics, epigenetics and gene expression markers of major depressive disorder and antidepressant response

Major depressive disorder (MDD) is a heritable disease with a heavy personal and socio-economic burden. Antidepressants of different classes are prescribed to treat MDD, but reliable and reproducible markers of efficacy are not available for clinical use. Further complicating treatment, the diagnosis of MDD is not guided by objective criteria, resulting in the risk of under- or overtreatment. A number of markers of MDD and antidepressant response have been investigated at the genetic, epigenetic, gene expression and protein levels. Polymorphisms in genes involved in antidepressant metabolism (cytochrome P450 isoenzymes), antidepressant transport (ABCB1), glucocorticoid signalling (FKBP5) and serotonin neurotransmission (SLC6A4 and HTR2A) were among those included in the first pharmacogenetic assays that have been tested for clinical applicability. The results of these investigations were encouraging when examining patient-outcome improvement. Furthermore, a nine-serum biomarker panel (including BDNF, cortisol and soluble TNF-α receptor type II) showed good sensitivity and specificity in differentiating between MDD and healthy controls. These first diagnostic and response-predictive tests for MDD provided a source of optimism for future clinical applications. However, such findings should be considered very carefully because their benefit/cost ratio and clinical indications were not clearly demonstrated. Future tests may include combinations of different types of biomarkers and be specific for MDD subtypes or pathological dimensions.

Effects of intranasal administration of the peptide antagonist of type I vaniloid receptor (TRPV1) in the rodent central nervous system

Intranasal administration of the polypeptide APHC3, an antagonist of the TRPV1 receptor, had acute anxiolytic and antidepressant effects, as well as an ability to modify the microglial response to proinflammatory stress and cytokine profile of the hippocampus. However, the acute antidepressant effect of the polypeptide was not related to the attenuation of neuroiflammation and probably had a different mechanism. The use of intranasal administration of the APHC3 peptide as a therapeutic approach aimed at decreasing depression symptoms needs additional studies in order to find the mechanism of action of this polypeptide in the central nervous system (CNS).

Replication of genome-wide association study (GWAS) susceptibility loci in a Latino bipolar disorder cohort

OBJECTIVES

Recent genome-wide association studies (GWASs) have identified numerous putative genetic polymorphisms associated with bipolar disorder (BD) and/or schizophrenia (SC). We hypothesized that a portion of these polymorphisms would also be associated with BD in the Latino American population. To identify such regions, we tested previously identified genetic variants associated with BD and/or SC and ancestral haploblocks containing these single nucleotide polymorphisms (SNPs) in a sample of Latino subjects with BD.

METHODS

A total of 2254 Latino individuals were genotyped for 91 SNPs identified in previous BD and/or SC GWASs, along with selected SNPs in strong linkage disequilibrium with these markers. Family-based single marker and haplotype association testing was performed using the PBAT software package. Empirical P-values were derived from 10 000 permutations.

RESULTS

Associations of eight a priori GWAS SNPs with BD were replicated with nominal (P≤.05) levels of significance. These included SNPs within nuclear factor I A (NFIA), serologically defined colon cancer antigen 8 (SDCCAG8), lysosomal associated membrane protein 3 (LAMP3), nuclear factor kappa B subunit 1 (NFKB1), major histocompatibility complex, class I, B (HLA-B) and 5'-nucleotidase, cytosolic II (NT5C2) and SNPs within intragenic regions microRNA 6828 (MIR6828)-solute carrier family 7 member 14 (SLC7A14) and sonic hedgehog (SHH)-long intergenic non-protein coding RNA 1006 (LINC01006). Of the 76 ancestral haploblocks that were tested for associations with BD, our top associated haploblock was located in LAMP3; however, the association did not meet statistical thresholds of significance following Bonferroni correction.

CONCLUSIONS

These results indicate that some of the gene variants found to be associated with BD or SC in other populations are also associated with BD risk in Latinos. Variants in six genes and two intragenic regions were associated with BD in our Latino sample and provide additional evidence for overlap in genetic risk between SC and BD.

GLUCOCORTICOID RECEPTOR-RELATED GENES: GENOTYPE AND BRAIN GENE EXPRESSION RELATIONSHIPS TO SUICIDE AND MAJOR DEPRESSIVE DISORDER

INTRODUCTION

We tested the relationship between genotype, gene expression and suicidal behavior and major depressive disorder (MDD) in live subjects and postmortem samples for three genes, associated with the hypothalamic-pituitary-adrenal axis, suicidal behavior, and MDD; FK506-binding protein 5 (FKBP5), Spindle and kinetochore-associated protein 2 (SKA2), and Glucocorticoid Receptor (NR3C1).

MATERIALS AND METHODS

Single-nucleotide polymorphisms (SNPs) and haplotypes were tested for association with suicidal behavior and MDD in a live (N = 277) and a postmortem sample (N = 209). RNA-seq was used to examine gene and isoform-level brain expression postmortem (Brodmann Area 9; N = 59). Expression quantitative trait loci (eQTL) relationships were examined using a public database (UK Brain Expression Consortium).

RESULTS

We identified a haplotype within the FKBP5 gene, present in 47% of the live subjects, which was associated with increased risk of suicide attempt (OR = 1.58, t = 6.03, P = .014). Six SNPs on this gene, three SNPs on SKA2, and one near NR3C1 showed before-adjustment association with attempted suicide, and two SNPs of SKA2 with suicide death, but none stayed significant after adjustment for multiple testing. Only the SKA2 SNPs were related to expression in the prefrontal cortex (pFCTX). One NR3C1 transcript had lower expression in suicide relative to nonsuicide sudden death cases (b = -0.48, SE = 0.12, t = -4.02, adjusted P = .004).

CONCLUSION

We have identified an association of FKBP5 haplotype with risk of suicide attempt and found an association between suicide and altered NR3C1 gene expression in the pFCTX. Our findings further implicate hypothalamic pituitary axis dysfunction in suicidal behavior.

Gene Expression Factor Analysis to Differentiate Pathways Linked to Fibromyalgia, Chronic Fatigue Syndrome, and Depression in a Diverse Patient Sample

OBJECTIVE

To determine if independent candidate genes can be grouped into meaningful biologic factors, and whether these factors are associated with the diagnosis of chronic fatigue syndrome (CFS) and fibromyalgia syndrome (FMS), while controlling for comorbid depression, sex, and age.

METHODS

We included leukocyte messenger RNA gene expression from a total of 261 individuals, including healthy controls (n = 61), patients with FMS only (n = 15), with CFS only (n = 33), with comorbid CFS and FMS (n = 79), and with medication-resistant (n = 42) or medication-responsive (n = 31) depression. We used exploratory factor analysis (EFA) on 34 candidate genes to determine factor scores and regression analysis to examine whether these factors were associated with specific diagnoses.

RESULTS

EFA resulted in 4 independent factors with minimal overlap of genes between factors, explaining 51% of the variance. We labeled these factors by function as 1) purinergic and cellular modulators, 2) neuronal growth and immune function, 3) nociception and stress mediators, and 4) energy and mitochondrial function. Regression analysis predicting these biologic factors using FMS, CFS, depression severity, age, and sex revealed that greater expression in factors 1 and 3 was positively associated with CFS and negatively associated with depression severity (Quick Inventory for Depression Symptomatology score), but not associated with FMS.

CONCLUSION

Expression of candidate genes can be grouped into meaningful clusters, and CFS and depression are associated with the same 2 clusters, but in opposite directions, when controlling for comorbid FMS. Given high comorbid disease and interrelationships between biomarkers, EFA may help determine patient subgroups in this population based on gene expression.

Purinergic system dysfunction in mood disorders: a key target for developing improved therapeutics

Uric acid and purines (such as adenosine) regulate mood, sleep, activity, appetite, cognition, memory, convulsive threshold, social interaction, drive, and impulsivity. A link between purinergic dysfunction and mood disorders was first proposed a century ago. Interestingly, a recent nationwide population-based study showed elevated risk of gout in subjects with bipolar disorder (BD), and a recent meta-analysis and systematic review of placebo-controlled trials of adjuvant purinergic modulators confirmed their benefits in bipolar mania. Uric acid may modulate energy and activity levels, with higher levels associated with higher energy and BD spectrum. Several recent genetic studies suggest that the purinergic system - particularly the modulation of P1 and P2 receptor subtypes - plays a role in mood disorders, lending credence to this model. Nucleotide concentrations can be measured using brain spectroscopy, and ligands for in vivo positron emission tomography (PET) imaging of adenosine (P1) receptors have been developed, thus allowing potential target engagement studies. This review discusses the key role of the purinergic system in the pathophysiology of mood disorders. Focusing on this promising therapeutic target may lead to the development of therapies with antidepressant, mood stabilization, and cognitive effects.

Leukocyte Gene Expression in Patients with Medication Refractory Depression before and after Treatment with ECT or Isoflurane Anesthesia: A Pilot Study

Objective. To evaluate leukocyte gene expression for 9 selected genes (mRNAs) as biological markers in patients with medication refractory depression before and after treatment with ECT or isoflurane anesthesia (ISO). Methods. In a substudy of a nonrandomized open-label trial comparing effects of ECT to ISO therapy, blood samples were obtained before and after treatment from 22 patients with refractory depression, and leukocyte mRNA was assessed by quantitative PCR. Patients' mRNAs were also compared to 17 healthy controls. Results. Relative to controls, patients before treatment showed significantly higher IL10 and DBI and lower ADRA2A and ASIC3 mRNA (P < 0.025). Both ECT and ISO induced significant decreases after treatment in 4 genes: IL10, NR3C1, DRD4, and Sult1A1. After treatment, patients' DBI, ASIC3, and ADRA2A mRNA remained dysregulated. Conclusion. Significant differences from controls and/or significant changes after ECT or ISO treatment were observed for 7 of the 9 mRNAs studied. Decreased expression of 4 genes after effective treatment with either ECT or ISO suggests possible overlap of underlying mechanisms. Three genes showing dysregulation before and after treatment may be trait-like biomarkers of medication refractory depression. Gene expression for these patients has the potential to facilitate diagnosis, clarify pathophysiology, and identify potential biomarkers for treatment effects.