Proteomic Response of Rat Pituitary Under Chronic Mild Stress Reveals Insights Into Vulnerability and Resistance to Anxiety or Depression

The Identification of Potential Anti-Depression/Anxiety Drug Targets by Stress-Induced Rat Brain Regional Proteome and Network Analyses

Depression and anxiety disorders are prevalent stress-related neuropsychiatric disorders and involve multiple molecular changes and dysfunctions across various brain regions. However, the specific and shared pathophysiological mechanisms occurring in these regions remain unclear. Previous research used a rat model of chronic mild stress (CMS) to segregate and identify depression-susceptible, anxiety-susceptible, and insusceptible groups; then the proteomes of six distinct brain regions (the hippocampus, prefrontal cortex, hypothalamus, pituitary, olfactory bulb, and striatum) were separately and quantitatively analyzed. To gain a comprehensive and systematic understanding of the molecular abnormalities, this study aimed to investigate and compare differential proteomics data from the six regions. Differentially expressed proteins (DEPs) were identified in between specific regions and across all regions and subjected to a series of bioinformatics analyses. Regional comparisons showed that stress-induced proteomic changes and corresponding gene ontology and pathway enrichments were largely distinct, attributable to differences in cell populations, protein compositions, and brain functions of these areas. Additionally, a notable degree of overlap in the significantly enriched terms was identified, potentially suggesting strong connections in the enrichment across different regions. Furthermore, intra-regional and inter-regional protein-protein interaction networks and drug-target-DEP networks were constructed. Integrated analysis of the three association networks in the six regions, along with the DisGeNET database, identified ten DEPs as potential targets for anti-depression/anxiety drugs. Collectively, these findings revealed commonalities and differences across different brain regions at the protein level induced by CMS, and identified several novel protein targets for the development of new therapeutics for depression and anxiety.

Neuroproteomics: Unveiling the Molecular Insights of Psychiatric Disorders with a Focus on Anxiety Disorder and Depression

Anxiety and depression are two of the most common mental disorders worldwide, with a lifetime prevalence of up to 30%. These disorders are complex and have a variety of overlapping factors, including genetic, environmental, and behavioral factors. Current pharmacological treatments for anxiety and depression are not perfect. Many patients do not respond to treatment, and those who do often experience side effects. Animal models are crucial for understanding the complex pathophysiology of both disorders. These models have been used to identify potential targets for new treatments, and they have also been used to study the effects of environmental factors on these disorders. Recent proteomic methods and technologies are providing new insights into the molecular mechanisms of anxiety disorder and depression. These methods have been used to identify proteins that are altered in these disorders, and they have also been used to study the effects of pharmacological treatments on protein expression. Together, behavioral and proteomic research will help elucidate the factors involved in anxiety disorder and depression. This knowledge will improve preventive strategies and lead to the development of novel treatments.

Exploring the pathogenesis of depression and potential antidepressants through the integration of reverse network pharmacology, molecular docking, and molecular dynamics

Depression is characterized by a significant and persistent decline in mood and is currently a major threat to physical and mental health. Traditional Chinese medicine can effectively treat depression with few adverse effects. Therefore, this study aimed to examine the use of reverse network pharmacology and computer simulations to identify effective ingredients and herbs for treating depression. Differentially expressed genes associated with depression were obtained from the Gene Expression Omnibus database, after which enrichment analyses were performed. A protein-protein interaction network was constructed using the STRING database to screen core targets. The Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform database was used to screen ingredients related to these core targets, and the core ingredients were screened by constructing the "Targets-Ingredients-Herbs" network. Drug evaluation analysis was performed using the SwissADME and ADMETlab platforms, according to Lipinski Rule of 5. The binding between the targets and ingredients was simulated using molecular docking software. The binding stability was determined using molecular dynamics analysis. The "Ingredients-Herbs" network was constructed, and we annotated it for its characteristics and meridians. Finally, the selected herbs were classified to determine the formulation for treating depression in traditional Chinese medicine. The pathogenesis of depression was associated with changes in SPP1, Plasminogen activator inhibitor 1, CCNB1 protein, CCL3, and other genes. Computer simulations have verified the use of quercetin, luteolin, apigenin, and other ingredients as drugs for treating depression. Most of the top 10 herbs containing these ingredients were attributed to the liver meridian, and their taste was symplectic. Perilla Frutescen, Cyperi Rhizoma, and Linderae Radix, the main components of "Tianxiang Zhengqi Powder," can treat depression owing to Qi stagnation. Epimedium and Citicola, the main traditional Chinese herbs in "Wenshen Yiqi Decoction," have a positive effect on depression of the Yang asthenia type. Fructus Ligustri Lucidi and Ecliptae Herba are from the classic prescription "Erzhi Pills" and can treat depression of the Yin deficiency type. This study identified the key targets and effective medicinal herbs for treating depression. It provides herbal blend references for treating different types of depression according to the theory of traditional Chinese medicine.

Gene expression profiling in whole blood stimulated ex vivo with lipopolysaccharide as a tool to predict post-stroke depressive symptoms: Proof-of-concept study

Prediction of post-stroke depressive symptoms (DSs) is challenging in patients without a history of depression. Gene expression profiling in blood cells may facilitate the search for biomarkers. The use of an ex vivo stimulus to the blood helps to reveal differences in gene profiles by reducing variation in gene expression. We conducted a proof-of-concept study to determine the usefulness of gene expression profiling in lipopolysaccharide (LPS)-stimulated blood for predicting post-stroke DS. Out of 262 enrolled patients with ischemic stroke, we included 96 patients without a pre-stroke history of depression and not taking any anti-depressive medication before or during the first 3 months after stroke. We assessed DS at 3 months after stroke using the Patient Health Questionnaire-9. We used RNA sequencing to determine the gene expression profile in LPS-stimulated blood samples taken on day 3 after stroke. We constructed a risk prediction model using a principal component analysis combined with logistic regression. We diagnosed post-stroke DS in 17.7% of patients. Expression of 510 genes differed between patients with and without DS. A model containing 6 genes (PKM, PRRC2C, NUP188, CHMP3, H2AC8, NOP10) displayed very good discriminatory properties (area under the curve: 0.95) with the sensitivity of 0.94 and specificity of 0.85. Our results suggest the potential utility of gene expression profiling in whole blood stimulated with LPS for predicting post-stroke DS. This method could be useful for searching biomarkers of post-stroke depression.

Transcriptome-wide gene-gene interaction associations elucidate pathways and functional enrichment of complex traits

It remains unknown to what extent gene-gene interactions contribute to complex traits. Here, we introduce a new approach using predicted gene expression to perform exhaustive transcriptome-wide interaction studies (TWISs) for multiple traits across all pairs of genes expressed in several tissue types. Using imputed transcriptomes, we simultaneously reduce the computational challenge and improve interpretability and statistical power. We discover (in the UK Biobank) and replicate (in independent cohorts) several interaction associations, and find several hub genes with numerous interactions. We also demonstrate that TWIS can identify novel associated genes because genes with many or strong interactions have smaller single-locus model effect sizes. Finally, we develop a method to test gene set enrichment of TWIS associations (E-TWIS), finding numerous pathways and networks enriched in interaction associations. Epistasis is may be widespread, and our procedure represents a tractable framework for beginning to explore gene interactions and identify novel genomic targets.

Glucocorticoid action in the anterior pituitary gland: Insights from corticotroph physiology

The anterior pituitary is exposed to ultradian, circadian and stress-induced rhythms of circulating glucocorticoid hormones. Glucocorticoids feedback at the level of the pituitary corticotroph to control their own production through multiple mechanisms. This review highlights key insights from analysis of the dynamics of rapid and early glucocorticoid feedback that reveal both non-genomic and genomic mechanisms mediated by glucocorticoid receptors. Importantly, a common target is control of electrical excitability and calcium signalling although non-genomic effects may also involve control of hormone secretion distal to calcium signalling. Understanding the mechanisms and functional consequences of pulsatile glucocorticoid signalling in the anterior pituitary promises to elucidate the role of glucocorticoids in health and disease, as well as identifying potential diagnostic and therapeutic targets.