High serum levels of tenascin-C are associated with suicide attempts in depressed patients

Comparative analysis of the nucleus accumbens transcriptional features in multiple depressive animal models

Chronic stress is deemed a significant clinical contributor to depression. The use of animal models of chronic stress can fully reveal the complex pathological mechanisms and their changing trends in the pathogenesis of depression, which is crucial for both disease prevention and therapy. It is also unknown how various forms of stress differ in their impact on animal physiology and behavior. The nucleus accumbens (NAc), an essential brain area for the pathophysiology of depression, and its underlying neural mechanisms remain unclear. Here, we systematically compared transcriptional signatures in the NAc of four chronic stress models in rats: chronic unpredictable mild stress (CUMS), chronic social defeat stress (CSDS), learned helplessness (LH), chronic restraint stress (CRS). The majority of differentially expressed genes (DEGs) were unique to a single depression model, while the rank-rank hypergeometric overlap analysis showed that the CSDS and CRS models had the greatest overlap, and the CRS and CUMS models had the least. Then, we performed pathway analysis of the differential genes and found that the neuroactive ligand-receptor interaction pathway was significantly enriched not only in the LH, CRS and CSDS stress models, but also significantly enriched in stress genes that were also altered in at least two stress models. Finally, we found three hub genes (Dcx, Tnc and Wdfy4) by constructing co-expression networks for stress genes. In summary, our research has the potential to offer fresh insights into the molecular mechanisms underlying depression induced by different types of stress, highlighting both their similarities and differences. It may provide valuable clues for understanding the pathogenesis of depression.

Biology of Tenascin C and its Role in Physiology and Pathology

Tenascin-C (TNC) is a multimodular extracellular matrix (ECM) protein hexameric with several molecular forms (180-250 kDa) produced by alternative splicing at the pre-mRNA level and protein modifications. The molecular phylogeny indicates that the amino acid sequence of TNC is a well-conserved protein among vertebrates. TNC has binding partners, including fibronectin, collagen, fibrillin-2, periostin, proteoglycans, and pathogens. Various transcription factors and intracellular regulators tightly regulate TNC expression. TNC plays an essential role in cell proliferation and migration. Unlike embryonic tissues, TNC protein is distributed over a few tissues in adults. However, higher TNC expression is observed in inflammation, wound healing, cancer, and other pathological conditions. It is widely expressed in a variety of human malignancies and is recognized as a pivotal factor in cancer progression and metastasis. Moreover, TNC increases both pro-and anti-inflammatory signaling pathways. It has been identified as an essential factor in tissue injuries such as damaged skeletal muscle, heart disease, and kidney fibrosis. This multimodular hexameric glycoprotein modulates both innate and adaptive immune responses regulating the expression of numerous cytokines. Moreover, TNC is an important regulatory molecule that affects the onset and progression of neuronal disorders through many signaling pathways. We provide a comprehensive overview of the structural and expression properties of TNC and its potential functions in physiological and pathological conditions.

Potential Inflammatory Biomarkers for Major Depressive Disorder Related to Suicidal Behaviors: A Systematic Review

Major depressive disorder (MDD) is a highly prevalent psychiatric condition affecting an estimated 280 million individuals globally. Despite the occurrence of suicidal behaviors across various psychiatric conditions, MDD is distinctly associated with the highest risk of suicide attempts and death within this population. In this study, we focused on MDD to identify potential inflammatory biomarkers associated with suicidal risk, given the relationship between depressive states and suicidal ideation. Articles published before June 2023 were searched in PubMed, Embase, Web of Science, and the Cochrane Library to identify all relevant studies reporting blood inflammatory biomarkers in patients with MDD with suicide-related behaviors. Of 571 articles, 24 were included in this study. Overall, 43 significant biomarkers associated with MDD and suicide-related behaviors were identified. Our study provided compelling evidence of significant alterations in peripheral inflammatory factors in MDD patients with suicide-related behaviors, demonstrating the potential roles of interleukin (IL)-1β, IL-6, C-reactive protein, C-C motif chemokine ligand 2, and tumor necrosis factor-α as biomarkers. These findings underscore the intricate relationship between the inflammatory processes of these biomarkers and their interactions in MDD with suicidal risk.

Associations Between Tenascin-C and Testosterone Deficiency in Men with Major Depressive Disorder: A Cross-Sectional Retrospective Study

Background

Elevated levels of tenascin-C are linked to increased risk and severity of major depressive disorder (MDD), while testosterone shows a protective effect. The present study explored associations between serum levels of tenascin-C and testosterone in Chinese men with MDD.

Methods

Testosterone and tenascin-C levels were measured in sera of 412 men with MDD and 237 age- and sex-matched controls. Serum levels of thyroid hormone, lipids, and high-sensitivity C-reactive protein (hs-CRP) were also quantified. Potential associations were examined using covariance, subgroup analysis, and multivariate linear regression analyses.

Results

Significantly higher concentrations of tenascin-C were detected in sera of subjects with MDD than in controls. Among subjects with MDD, testosterone concentrations inversely correlated with tenascin-C levels. This relationship was observed when patients were stratified by age at onset; duration or severity of depression; or concentration of thyroid hormones, low- or high-density lipoprotein, or hs-CRP. The negative association remained even when the statistical model was adjusted for age, smoking status, alcohol use, and body mass index. Linear regression with bootstrap resampling confirmed that high tenascin-C levels inversely correlated with testosterone levels.

Conclusion

In men with MDD, high tenascin-C concentrations correlate with testosterone deficiency. The combination of elevated tenascin-C and testosterone deficiency may be associated with MDD progression.

Potential major depressive disorder biomarkers in pediatric population - a pilot study

Mental disorders affect 10-20 % of the young population in the world. Major depressive disorder (MDD) is a common mental disease with a multifactorial and not clearly explained pathophysiology. Many cases remain undetected and untreated, which influences patients' physical and mental health and their quality of life also in adulthood. The aim of our pilot study was to assess the prediction value of selected potential biomarkers, including blood cell counts, blood cell ratios, and parameters like peroxiredoxin 1 (PRDX1), tenascin C (TNC) and type IV collagen (COL4) between depressive pediatric patients and healthy peers and to evaluate a short effect of antidepressant treatment. In this study, 27 young depressive patients and 26 non-depressed age-matched controls were included. Blood analyses and immunological assays using commercial kits were performed. Platelet count was the only blood parameter for which the case/control status was statistically significant (p=0.01) in a regression model controlling for the age and gender differences. The results from ELISA analyses showed that the case/control status is a significant predictor of the parameters PRDX1 (p=0.05) and COL4 (p=0.009) in respective regression model considering the age and gender differences between MDD patients and controls. A major finding of this study is that values of platelet count, monocyte to lymphocyte ratio, white blood cell, and monocyte counts were assessed by the Random Forest machine learning algorithm as relevant predictors for discrimination between MDD patients and healthy controls with a power of prediction AUC=0.749.