Dysfunction of thioredoxin triggers inflammation through activation of autophagy in chicken cardiomyocytes

Data-driven analysis that integrates bioinformatics and machine learning uncovers PANoptosis-related diagnostic genes in early pediatric septic shock

Objectives

Sepsis is one of the leading causes of death for children worldwide. Additionally, refractory septic shock is one of the most significant groups that contributes to a high death rate. The interaction of pyroptosis, apoptosis, and necroptosis results in a unique inflammatory cell death mechanism known as PANoptosis. An increasing amount of evidence suggests that PANoptosis can be brought on by several stimuli, including cytokine storms, malignancy, and bacterial or viral infections. The goal of this study is to improve the diagnostic significance of the PANoptosis-related gene signature in early pediatric septic shock.

Design and methods

We examined children with septic shock from the GSE66099 discovery cohort and looked at differentially expressed genes (DEGs). To filter the important modules, weighted gene co-expression network analysis (WCGNA) was employed. In the end, random forest analysis and the least absolute shrinkage and selection operator (LASSO) were used to determine the PANoptosis diagnostic signature genes. To determine the PANoptosis signature genes, we also found four validation cohorts: GSE26378, GSE26440, GSE8121, and GSE13904. The area under the curve (AUC) of the receiver operating characteristic curves (ROCs), along with sensitivity, specificity, positive predictive value, and negative predictive value, were used to assess the diagnostic efficacy of these signature genes.

Results

From GSE66099, 1142 DEGs in total were tested. Following the WGCNA clustering of the data into 16 modules, the MEgrey module showed a significant correlation with pediatric septic shock (p < 0.0001). Following the use of LASSO and random forest algorithms to identify the PANoptosis-related signature genes, which include ANXA3, S100A9, TXN, CLEC5A, and TMEM263. These signature genes' receiver operating characteristic curves (ROCs) were confirmed in the external dataset from GSE26378, GSE26440, GSE8121, and GSE13904, and were 0.994 (95 % CI 0.987-0.999), 0.987 (95 % CI 0.974-0.997), 0.957 (95 % CI 0.927-0.981), 0.974 (95 % CI 0.954-0.988), 0.897 (95 % CI 0.846-0.941), respectively.

Conclusion

In summary, the discovery of PANoptosis genes, ANXA3, S100A9, TXN, CLEC5A, and TMEM263 proved to be quite helpful in the early detection of pediatric septic shock patients. These early results, which need to be further confirmed in basic and clinical research, are extremely important for understanding immune cell infiltration in the pathophysiology of pediatric septic shock.

Exosomal proteomics and cytokine analysis distinguish silicosis cases from controls

Occupational silica exposure caused a serious disease burden of silicosis. There is currently a lack of sensitive and effective biomarkers for silicosis, and the pathogenesis of silicosis is unclear. Exosomes were significant in the pathogenesis of silicosis, and our study was carried out from exosomal proteomics and cytokine analysis. Firstly, the plasma levels of cytokines were detected using a Luminex multiplex assay, and the results indicated that the plasma levels of TNF-α, IL-6, CCL2, CXCL10, and PDGF-AB were significantly higher in silicosis patients than in silica-exposed workers and controls (p < 0.05). After correlation analysis, the plasma levels of cytokines were positively correlated with exosomal protein concentration. Secondly, data-independent acquisition (DIA) was performed on plasma-derived exosomes in the screening population, which identified 88, 151, 293, and 53 differentially expressed proteins (DEPs) in exposure/control, silicosis/control, silicosis/exposure, and silicosis stage Ⅲ/silicosis stage Ⅰ groups respectively. After parallel reaction monitoring (PRM) in an independent verification population, the results indicated that the changing trend of 15 DEPs was coincident in screening and verification results. The result of correlation analysis indicated that the plasma level of TNF-α was negatively correlated with the expression of exosomal DSP, KRT78, SERPINB12, and CALML5. The AUC of combined determination of TNF-α and CALML5 reached 0.900, with a sensitivity of 0.714 and a specificity of 0.933. Overall, our study revealed the exosomal proteomic profiling of silicosis patients, silica-exposed workers, and controls, indicating that exosomes were significant in the pathogenesis of silicosis. It also revealed that the combined of the plasma levels of cytokines and the expression of exosomal DEPs could increase determination efficiency. This study provided directions for the development of silicosis biomarkers and a scientific basis for the pathogenesis research of silicosis in the future.

Exploring potential biomarkers and therapeutic targets of long COVID-associated inflammatory cardiomyopathy

Background

The negative impact of long COVID on social life and human health is increasingly prominent, and the elevated risk of cardiovascular disease in patients recovering from COVID-19 has also been fully confirmed. However, the pathogenesis of long COVID-related inflammatory cardiomyopathy is still unclear. Here, we explore potential biomarkers and therapeutic targets of long COVID-associated inflammatory cardiomyopathy.

Methods

Datasets that met the study requirements were identified in Gene Expression Omnibus (GEO), and differentially expressed genes (DEGs) were obtained by the algorithm. Then, functional enrichment analysis was performed to explore the basic molecular mechanisms and biological processes associated with DEGs. A protein-protein interaction (PPI) network was constructed and analyzed to identify hub genes among the common DEGs. Finally, a third dataset was introduced for validation.

Results

Ultimately, 3,098 upregulated DEGs and 1965 downregulated DEGs were extracted from the inflammatory cardiomyopathy dataset. A total of 89 upregulated DEGs and 217 downregulated DEGs were extracted from the dataset of convalescent COVID patients. Enrichment analysis and construction of the PPI network confirmed VEGFA, FOXO1, CXCR4, and SMAD4 as upregulated hub genes and KRAS and TXN as downregulated hub genes. The separate dataset of patients with COVID-19 infection used for verification led to speculation that long COVID-associated inflammatory cardiomyopathy is mainly attributable to the immune-mediated response and inflammation rather than to direct infection of cells by the virus.

Conclusion

Screening of potential biomarkers and therapeutic targets sheds new light on the pathogenesis of long COVID-associated inflammatory cardiomyopathy as well as potential therapeutic approaches. Further clinical studies are needed to explore these possibilities in light of the increasingly severe negative impacts of long COVID.

Decreased thioredoxin reductase 3 expression promotes nickel-induced damage to cardiac tissue via activating oxidative stress-induced apoptosis and inflammation

Thioredoxin reductase 3 (Txnrd3) plays a crucial role in antioxidant and anti-cancer activities, and sperm maturation. The damage of heavy metals, including Nickel (Ni), is the most prominent harm in social development, and hampering Txnrd3 might exacerbate Ni-induced cardiac damage. In this study, a total of 160 8-week-old C57BL/N male mice with 25-30 g weight of Txnrd3+/+ wild-type and Txnrd3-/- homozygote-type were randomly divided into eight groups. The mice in the control and Ni groups were gavaged with distilled water and a freshly prepared 10 mg/kg NiCl₂ solution. Melatonin (Mel) groups were administered at a concentration of 2 mg/kg for 21 days at the mice's 0.1 ml/10 g body weight. Ni exposure up-regulated the messenger RNA (mRNA) levels of mitochondrial apoptosis (caspase-3, caspase-9, cytochrome c, p53, and BAX), autophagy (LC3, ATG 1, ATG 7, and Beclin-1), and inflammation (TNF-α, COX 2, IL-1β, IL-2, IL-6, and IL-7)-related markers, but down-regulated the mRNA levels of BCL-2, p62 and mTOR (p < .05). Ni exposure decreased the expression of BCL-2 and p62 protein but increased the expression levels of caspase-3, caspase-9, cytochrome c, p53, BAX, ATG 7, Beclin-1, TNF-α, COX 2, IL-1β and IL-2 protein (p < .05). Ni increased the contents of glutathione disulfide (GSSG) and malondialdehyde (MDA) and decreased the activities of catalase (CAT) and total superoxide dismutase (T-SOD) (p < .05). Decreased Txnrd3 expression significantly exacerbated changes compared to the Ni exposure (p < .05). Mel significantly attenuated these changes, but the effect decreased when Txnrd3 was inhibited (p < .05). In conclusion, decreased Txnrd3 expression promoted Ni-induced mitochondrial apoptosis and inflammation via oxidative stress and aggravated heart damage in mice. Decreased Txnrd3 expression significantly reduced the protective effect of Mel to Ni exposure.

Schisandrin B Induced ROS-Mediated Autophagy and Th1/Th2 Imbalance via Selenoproteins in Hepa1-6 Cells

Schisandrin B (Sch B) is well-known for its antitumor effect; however, its underlying mechanism remains confusing. Our study aimed to investigate the role of selenoproteins in Sch B-induced autophagy and Th1/Th2 imbalance in Hepa1-6 cells. Hepa1-6 cells were chosen to explore the antitumor mechanism and were treated with 0, 25, 50, and 100 μM of Sch B for 24 h, respectively. We detected the inhibition rate of proliferation, transmission electron microscopy (TEM), monodansylcadaverine (MDC) staining, reactive oxygen species (ROS) level and oxidative stress-related indicators, autophagy-related genes, related Th1/Th2 cytokines, and selenoprotein mRNA expression. Moreover, the heat map, principal component analysis (PCA), and correlation analysis were used for further bioinformatics analysis. The results revealed that Sch B exhibited well-inhibited effects on Hepa1-6 cells. Subsequently, under Sch B treatment, typical autophagy characteristics were increasingly apparent, and the level of punctate MDC staining enhanced and regulated the autophagy-related genes. Overall, Sch B induced autophagy in Hepa1-6 cells. In addition, Sch B-promoted ROS accumulation eventually triggered autophagy initiation. Results of Th1 and Th2 cytokine mRNA expression indicated that Th1/Th2 immune imbalance was observed by Sch B treatment in Hepa1-6 cells. Intriguingly, Sch B downregulated the majority of selenoprotein expression. Also, the heat map results observed significant variation of autophagy-related genes, related Th1/Th2 cytokines, and selenoprotein expression in response to Sch B treatment. PCA outcome suggested the key role of Txnrd1, Txnrd3, Selp, GPX2, Dio3, and Selr with its potential interactions in ROS-mediated autophagy and Th1/Th2 imbalance of Hepa1-6 cells. In conclusion, Sch B induced ROS-mediated autophagy and Th1/Th2 imbalance in Hepa1-6 cells. More importantly, the majority of selenoproteins were intimately involved in the process of autophagy and Th1/Th2 imbalance, Txnrd3, Selp, GPX2, Dio3, and Selr had considerable impacts on the process.