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

Sex shapes phenotype-linked metabolic signatures of stress exposure in the mouse hypothalamus and pituitary

In chronic stress-induced anxiodepression, sex differences in the dysfunction of the hypothalamic-pituitary-adrenal (HPA) axis are well-documented, yet the underlying molecular mechanisms remain largely unexplored. This study investigated sex-specific metabolic signatures associated with stress exposure in the hypothalamus and pituitary, given the potential significance of brain metabolism in sex-related mechanisms underlying anxiodepression. Utilizing a chronic restraint stress (CRS) model, we conducted a comparative analysis of the metabolic profiles in female and male mice to identify distinct phenotypic expressions related to sex differences. Our findings revealed that metabolite alterations in the pituitary were more pronounced than those in the hypothalamus, indicating significant sex-based variations. These differences facilitated phenotypic differentiation and underscored the relevance of sex-specific metabolic changes and their functional associations to behavioral phenotypes. Moreover, diverging and converging pathways were identified to elucidate the molecular and physiological bases of stress susceptibility in both sexes. Key metabolic and immune-related pathways in the hypothalamus and pituitary, such as histidine, tryptophan, lipid, glycerophospholipid, amino acid, and carbohydrate metabolism, showed specific associations with sex and phenotype. Additionally, correlation analysis uncovered several differential metabolites that were significantly linked to mouse behaviors, with marked sex differences. Collectively, our results demonstrate a pronounced sexual dimorphism at the metabolic level in the hypothalamus and pituitary in response to chronic stress. This study provides a valuable molecular resource for further exploration of the interplay between sex and behavioral phenotypes within the dysregulation of the HPA axis that contributes to stress susceptibility and immune response, emphasizing the critical role of sex-specific metabolic mechanisms in anxiodepressive disorder.

Serum Proteomics Analysis of Patients with Ascending Aortic Dilation

Ascending aortic dilation (AAD) is a complex and life-threatening condition, representing a significant risk factor for acute aortic syndromes. Due to its asymptomatic nature, early diagnosis is frequently missed. Serum diagnostic biomarkers play a crucial role in disease diagnosis, and proteomics offers a valuable approach for identifying such biomarkers in blood samples. In this study, we collected serum samples from patients with AAD, thoracic ascending aortic dissection (TAAD), and healthy controls, using label-free proteomics to identify serum proteins. Differentially abundant proteins (DAPs) were identified between AAD, TAAD, and control groups. Functional analysis of DAPs was performed using the GO and KEGG databases. Compared to controls, 40 DAPs were found in AAD and 52 in dissection. Further analysis showed that the DAPs in AAD are involved in biological processes such as antibacterial humoral response, nucleosome assembly, and inflammatory response, while the DAPs in TAAD are involved in protein localization to CENP-A containing chromatin and negative regulation of megakaryocyte differentiation, etc. The protein expression profiles of TAAD and AAD were directly compared, leading to the identification of 37 DAPs. GO and KEGG analyses were also performed on these proteins. A significant overlap in protein expression was observed between AAD and dissection. Additionally, NUP188 was significantly down-regulated in AAD, and receiver operating characteristic (ROC) curve analysis suggests it may serve as a diagnostic biomarker for AAD.

Serum KNG and FVIII may serve as potential biomarkers for depression

BACKGROUND

The global burden of major depressive disorder (MDD) is rising, with current diagnostic methods hindered by significant subjectivity and low inter-rater reliability. Several studies have implied underlying link between coagulation-related proteins, such as kininogen (KNG) and coagulation factor VIII (FVIII), and depressive symptoms, offering new insights into the exploration of depression biomarkers. This study aims to elucidate the roles of KNG and FVIII in depression, potentially providing a foundational basis for biomarker research in this field.

METHODS

A three-part experiment was conducted: (1) we measured serum levels of KNG and FVIII in the chronic unpredictable mild stress (CUMS) model; (2) KNG adeno-associated-virus overexpression (KNG-AAV-OE) model was constructed to further investigate the roles of KNG and FVIII. Meanwhile, quantity PCR, western blotting and immunofluorescence staining detected the KNG-FVIII pathway. (3) Peripheral blood samples were gathered from healthy control (HC, N = 21), as well as first-episode drug-naive patients with MDD (FEDN-MDD, N = 21), to further confirm the association between KNG, FVIII and depression.

RESULTS

Firstly, serum KNG and FVIII levels were significantly elevated in the CUMS model. Then, the rats exhibited pronounced depressive-like behaviors in the KNG-AAV-OE model, with corresponding increases in serum KNG and FVIII. Lastly, clinical data showed increased KNG and FVIII levels in FEDN-MDD compared to HC. Furthermore, KNG and FVIII levels exhibited a strong positive correlation with the scores of the 24-item Hamilton Depression Scale and the 14-item Hamilton Anxiety Scale.

CONCLUSION

To sum up, this study highlights critical roles of serum KNG and FVIII in depression and the KNG-AAV-OE may lead the augment of FVIII in serum. Consequently, our research may offer new evidence and foundation for depression biomarkers research in the future.

Cardio-metabolic and cytoskeletal proteomic signatures differentiate stress hypersensitivity in dystrophin-deficient mdx mice

Extreme heterogeneity exists in the hypersensitive stress response exhibited by the dystrophin-deficient mdx mouse model of Duchenne muscular dystrophy. Because stress hypersensitivity can impact dystrophic phenotypes, this research aimed to understand the peripheral pathways driving this inter-individual variability. Male and female mdx mice were phenotypically stratified into "stress-resistant" or "stress-sensitive" groups based on their response to two laboratory stressors. Quantitative proteomics of striated muscle revealed that stress-resistant females were most dissimilar from all other groups, with over 250 proteins differentially regulated with stress hypersensitivity. Males showed less proteomic variation with stress hypersensitivity; however, these changes were associated with pathway enrichment. In the heart, stress-sensitive males had significant enrichment of pathways related to mitochondrial ATP synthesis, suggesting that increased cardio-metabolic capacity is associated with stress hypersensitivity in male mdx mice. In both sexes, stress hypersensitivity was associated with greater expression of beta-actin-like protein 2, indicative of altered cytoskeletal organisation. Despite identifying proteomic signatures associated with stress hypersensitivity, these did not correlate with differences in the serum metabolome acutely after a stressor. These data suggest that the heterogeneity in stress hypersensitivity in mdx mice is partially driven by cytoskeletal organisation, but that sex-specific cardio-metabolic reprogramming may also underpin this phenotype. SIGNIFICANCE: Duchenne muscular dystrophy (DMD) is a fatal muscle wasting disease which is associated with a premature loss of ambulation and neurocognitive dysfunction. The hypersensitive stress response in DMD is a heterogeneous phenotype which is poorly understood. This study provided the first investigation of the peripheral mechanisms regulating the hypersensitive stress response by undertaking multi-omics analysis of phenotypically stratified mdx mice. Variations in behaviour and the striated muscle proteomic profiles suggest that cardio-metabolic remodelling and cytoskeletal organisation may contribute to this phenotype. This research offers significant insights into understanding how peripheral dystrophin deficiency relates to the cognitive abnormalities seen in patients with DMD.

Identification of hub genes, diagnostic model, and immune infiltration in preeclampsia by integrated bioinformatics analysis and machine learning

PURPOSE

This study aimed to identify novel biomarkers for preeclampsia (PE) diagnosis by integrating Weighted Gene Co-expression Network Analysis (WGCNA) with machine learning techniques.

PATIENTS AND METHODS

We obtained the PE dataset GSE25906 from the gene expression omnibus (GEO) database. Analysis of differentially expressed genes (DEGs) and module genes with Limma and Weighted Gene Co-expression Network analysis (WGCNA). Candidate hub genes for PE were identified using machine learning. Subsequently, we used western-blotting (WB) and real-time fluorescence quantitative (qPCR) to verify the expression of F13A1 and SCCPDH in preeclampsia patients. Finally, we estimated the extent of immune cell infiltration in PE samples by employing the CIBERSORT algorithms.

RESULTS

Our findings revealed that F13A1 and SCCPDH were the hub genes of PE. The nomogram and two candidate hub genes had high diagnostic values (AUC: 0.90 and 0.88, respectively). The expression levels of F13A1 and SCCPDH were verified by WB and qPCR. CIBERSORT analysis confirmed that the PE group had a significantly larger proportion of plasma cells and activated dendritic cells and a lower portion of resting memory CD4 + T cells.

CONCLUSION

The study proposes F13A1 and SCCPDH as potential biomarkers for diagnosing PE and points to an improvement in early detection. Integration of WGCNA with machine learning could enhance biomarker discovery in complex conditions like PE and offer a path toward more precise and reliable diagnostic tools.

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.