Association Between FoxO1, A2M, and TGF-β1, Environmental Factors, and Major Depressive Disorder

Depression in maintenance hemodialysis patients: What do we need to know?

Chronic kidney disease (CKD) is now recognized as a major public health problem in the world. The global prevalence of CKD is estimated at 13.4% (11.7-15.1%), with an estimated 490.2 to 7.083 million patients with End stage renal disease requiring renal replacement therapy. Hemodialysis is the main treatment for End stage renal disease patients because of its high safety and efficiency. The survival time of these patients was significantly prolonged, but many psychological problems followed. Depression is a type of mood disorder caused by a variety of causes, often manifested as disproportionate depression and loss of interest, sometimes accompanied by anxiety, agitation, even hallucinations, delusions and other psychotic symptoms. Depression has become the most common mental disorder in maintenance hemodialysis (MHD) patients according to the meta-analysis. In recent years, depression has seriously affected the quality of life and prognosis of MHD patients from dietary, sleep, treatment adherence, energy and other dimensions. This article reviews the epidemiology, etiology, diagnosis and treatment of depression in MHD patients.

A Combined Effect of Polygenic Scores and Environmental Factors on Individual Differences in Depression Level

The risk of depression could be evaluated through its multifactorial nature using the polygenic score (PGS) approach. Assuming a "clinical continuum" hypothesis of mental diseases, a preliminary assessment of individuals with elevated risk for developing depression in a non-clinical group is of high relevance. In turn, epidemiological studies suggest including social/lifestyle factors together with PGS to address the "missing heritability" problem. We designed regression models, which included PGS using 27 SNPs and social/lifestyle factors to explain individual differences in depression levels in high-education students from the Volga-Ural region (VUR) of Eurasia. Since issues related to population stratification in PGS scores may lead to imprecise variant effect estimates, we aimed to examine a sensitivity of PGS calculated on summary statistics of depression and neuroticism GWAS from Western Europeans to assess individual proneness to depression levels in the examined sample of Eastern Europeans. A depression score was assessed using the revised version of the Beck Depression Inventory (BDI) in 1065 young adults (age 18-25 years, 79% women, Eastern European ancestry). The models based on weighted PGS demonstrated higher sensitivity to evaluate depression level in the full dataset, explaining up to 2.4% of the variance (p = 3.42 × 10^-7); the addition of social parameters enhanced the strength of the model (adjusted r² = 15%, p < 2.2 × 10^-16). A higher effect was observed in models based on weighted PGS in the women group, explaining up to 3.9% (p = 6.03 × 10^-9) of variance in depression level assuming a combined SNPs effect and 17% (p < 2.2 × 10^-16)-with the addition of social factors in the model. We failed to estimate BDI-measured depression based on summary statistics from Western Europeans GWAS of clinical depression. Although regression models based on PGS from neuroticism (depression-related trait) GWAS in Europeans were associated with a depression level in our sample (adjusted r² = 0.43%, p = 0.019-for unweighted model), the effect was mainly attributed to the inclusion of social/lifestyle factors as predictors in these models (adjusted r² = 15%, p < 2.2 × 10^-16-for unweighted model). In conclusion, constructed PGS models contribute to a proportion of interindividual variability in BDI-measured depression in high-education students, especially women, from the VUR of Eurasia. External factors, including the specificity of rearing in childhood, used as predictors, improve the predictive ability of these models. Implementation of ethnicity-specific effect estimates in such modeling is important for individual risk assessment.

Thrombospondin1 mimics rapidly relieve depression via Shank3 dependent uncoupling between dopamine D1 and D2 receptors

Deficits in astrocyte function contribute to major depressive disorder (MDD) and suicide, but the therapeutic effect of directly reactivating astrocytes for depression remains unclear. Here, specific gains and losses of astrocytic cell functions in the medial prefrontal cortex (mPFC) bidirectionally regulate depression-like symptoms. Remarkably, recombinant human Thrombospondin-1 (rhTSP1), an astrocyte-secreted protein, exerted rapidly antidepressant-like actions through tyrosine hydroxylase (Th)/dopamine (DA)/dopamine D2 receptors (D2Rs) pathways, but not dopamine D1 receptors (D1Rs), which was dependent on SH3 and multiple ankyrin repeat domains 3 (Shank3) in the mPFC. TSP1 in the mPFC might have potential as a target for treating clinical depression.

A review and meta-analysis of gene expression profiles in suicide

Suicide claims over 800,000 deaths worldwide, making it a serious public health problem. The etiopathophysiology of suicide remains unclear and is highly complex, and postmortem gene expression studies can offer insights into the molecular biological mechanism underlying suicide. In the current study, we conducted a meta-analysis of postmortem brain gene expression in relation to suicide. We identified five gene expression datasets for postmortem orbitofrontal, prefrontal, or dorsolateral prefrontal cortical brain regions from the Gene Expression Omnibus repository. After quality control, the total sample size was 380 (141 suicide deaths and 239 deaths from other causes). We performed the analyses using two meta-analytic approaches. We further performed pathway and cell-set enrichment analyses. We found reduced expression of the KCNJ2 (Potassium Inwardly Rectifying Channel Subfamily J Member 2), A2M (Alpha-2-Macroglobulin), AGT (Angiotensinogen), PMP2 (Peripheral Myelin Protein 2), and VEZF1 (Vascular Endothelial Zinc Finger 1) genes (FDR p<0.05). Our findings support the involvement of astrocytes, stress response, immune system, and microglia in suicide. These findings will require further validation in additional large datasets.

Molecular mechanisms underlying the antidepressant actions of arketamine: beyond the NMDA receptor

The discovery of robust antidepressant actions exerted by the N-methyl-D-aspartate receptor (NMDAR) antagonist (R,S)-ketamine has been a crucial breakthrough in mood disorder research. (R,S)-ketamine is a racemic mixture of equal amounts of (R)-ketamine (arketamine) and (S)-ketamine (esketamine). In 2019, an esketamine nasal spray from Johnson & Johnson was approved in the United States of America and Europe for treatment-resistant depression. However, an increasing number of preclinical studies show that arketamine has greater potency and longer-lasting antidepressant-like effects than esketamine in rodents, despite the lower binding affinity of arketamine for the NMDAR. In clinical trials, non-ketamine NMDAR-related compounds did not exhibit ketamine-like robust antidepressant actions in patients with depression, despite these compounds showing antidepressant-like effects in rodents. Thus, the rodent data do not necessarily translate to humans due to the complexity of human psychiatric disorders. Collectively, the available studies indicate that it is unlikely that NMDAR plays a major role in the antidepressant action of (R,S)-ketamine and its enantiomers, although the precise molecular mechanisms underlying antidepressant actions of (R,S)-ketamine and its enantiomers remain unclear. In this paper, we review recent findings on the molecular mechanisms underlying the antidepressant actions of (R,S)-ketamine and its potent enantiomer arketamine. Furthermore, we discuss the possible role of the brain-gut-microbiota axis and brain-spleen axis in stress-related psychiatric disorders and in the antidepressant-like action of arketamine. Finally, we discuss the potential of arketamine as a treatment for cognitive impairment in psychiatric disorders, Parkinson's disease, osteoporosis, inflammatory bowel diseases, and stroke.

Analysis of Major Depression Risk Genes Reveals Evolutionary Conservation, Shared Phenotypes, and Extensive Genetic Interactions

Major depressive disorder (MDD) affects around 15% of the population at some stage in their lifetime. It can be gravely disabling and it is associated with increased risk of suicide. Genetics play an important role; however, there are additional environmental contributions to the pathogenesis. A number of possible risk genes that increase liability for developing symptoms of MDD have been identified in genome-wide association studies (GWAS). The goal of this study was to characterize the MDD risk genes with respect to the degree of evolutionary conservation in simpler model organisms such as Caenorhabditis elegans and zebrafish, the phenotypes associated with variation in these genes and the extent of network connectivity. The MDD risk genes showed higher conservation in C. elegans and zebrafish than genome-to-genome comparisons. In addition, there were recurring themes among the phenotypes associated with variation of these risk genes in C. elegans. The phenotype analysis revealed enrichment for essential genes with pleiotropic effects. Moreover, the MDD risk genes participated in more interactions with each other than did randomly-selected genes from similar-sized gene sets. Syntenic blocks of risk genes with common functional activities were also identified. By characterizing evolutionarily-conserved counterparts to the MDD risk genes, we have gained new insights into pathogenetic processes relevant to the emergence of depressive symptoms in man.

Elucidating the Possible Role of FoxO in Depression

Forkhead box-O (FoxO) transcriptional factors perform essential functions in several physiological and biological processes. Recent studies have shown that FoxO is implicated in the pathophysiology of depression. Changes in the upstream mediators of FoxOs including brain-derived neurotrophic factor (BDNF) and protein kinase B have been associated with depressive disorder and the antidepressant agents are known to alter the phosphorylation of FoxOs. Moreover, FoxOs might be regulated by serotonin or noradrenaline signaling and the hypothalamic-pituitary-adrenal (HPA)-axis,both of them are associated with the development of the depressive disorder. FoxO also regulates neural morphology, synaptogenesis, and neurogenesis in the hippocampus, which accounts for the pathogenesis of the depressive disorder. The current article underlined the potential functions of FoxOs in the etiology of depressive disorder and formulate few essential proposals for further investigation. The review also proposes that FoxO and its signal pathway might establish possible therapeutic mediators for the management of depressive disorder.

Machine learning and bioinformatic analysis of brain and blood mRNA profiles in major depressive disorder: A case-control study

This study analyzed gene expression messenger RNA data, from cases with major depressive disorder (MDD) and controls, using supervised machine learning (ML). We built on the methodology of prior studies to obtain more generalizable/reproducible results. First, we obtained a classifier trained on gene expression data from the dorsolateral prefrontal cortex of post-mortem MDD cases (n = 126) and controls (n = 103). An average area-under-the-receiver-operating-characteristics-curve (AUC) from 10-fold cross-validation of 0.72 was noted, compared to an average AUC of 0.55 for a baseline classifier (p = .0048). The classifier achieved an AUC of 0.76 on a previously unused testing-set. We also performed external validation using DLPFC gene expression values from an independent cohort of matched MDD cases (n = 29) and controls (n = 29), obtained from Affymetrix microarray (vs. Illumina microarray for the original cohort) (AUC: 0.62). We highlighted gene sets differentially expressed in MDD that were enriched for genes identified by the ML algorithm. Next, we assessed the ML classification performance in blood-based microarray gene expression data from MDD cases (n = 1,581) and controls (n = 369). We observed a mean AUC of 0.64 on 10-fold cross-validation, which was significantly above baseline (p = .0020). Similar performance was observed on the testing-set (AUC: 0.61). Finally, we analyzed the classification performance in covariates subgroups. We identified an interesting interaction between smoking and recall performance in MDD case prediction (58% accurate predictions in cases who are smokers vs. 43% accurate predictions in cases who are non-smokers). Overall, our results suggest that ML in combination with gene expression data and covariates could further our understanding of the pathophysiology in MDD.