Heavy Metals Interactions with Neuroglia and Gut Microbiota: Implications for Huntington’s Disease

Published: June 12, 2024

Huntington’s disease (HD) is a rare but progressive and devastating neurodegenerative characterized by involuntary movements, cognitive decline, executive dysfunction, neuropsychiatric conditions such as anxiety depression. It follows an autosomal dominant inheritance pattern. Thus, child who has parent with the mutated huntingtin (mHTT) gene 50% chance of developing disease. Since HTT protein involved in many critical cellular processes including neurogenesis, brain development, energy metabolism, transcriptional regulation, synaptic activity, vesicle trafficking, cell signaling, autophagy, its aberrant aggregates lead to disruption numerous pathways neurodegeneration. Essential heavy metals are vital at low concentrations, however, higher can exacerbate HD disrupting glial-neuronal communication, and/or causing dysbiosis (disturbance gut microbiota, GM), both which neuroinflammation further Here, we discuss detail interactions iron, manganese copper glial-neuron communication GM indicate how this knowledge may pave way for development new generation disease-modifying therapies HD.

Language: Английский

Ashwagandha (Withania somnifera (L.) Dunal) root extract containing withanolide A alleviates depression-like behavior in mice by enhancing the brain-derived neurotrophic factor pathway under unexpected chronic mild stress

Journal of Ethnopharmacology, Journal Year: 2024, Volume and Issue: unknown, P. 119224 - 119224

Published: Dec. 1, 2024

Language: Английский

The significance of long chain non-coding RNA signature genes in the diagnosis and management of sepsis patients, and the development of a prediction model

Frontiers in Immunology, Journal Year: 2024, Volume and Issue: 15

Published: Dec. 12, 2024

Background Sepsis is a life-threatening organ dysfunction condition produced by dysregulation of the host response to infection. It now characterized high clinical morbidity and mortality rate, endangering patients’ lives health. The purpose this study was determine value Long chain non-coding RNA (LncRNA) RP3_508I15.21, RP11_295G20.2, LDLRAD4_AS1 in diagnosis adult sepsis patients develop Nomogram prediction model. Methods We screened microarray datasets GSE57065 GSE95233 from GEO database performed differentially expressed genes (DEGs), weighted gene co-expression network analysis (WGCNA), machine learning methods find random forest (Random Forest), least absolute shrinkage selection operator (LASSO), support vector (SVM), respectively, with as training set validation set. Differentially boxplot statistical analysis, ROC Random Forest, Least Absolute Shrinkage Selection Operator Support Vector Machine (SVM) were used identify characteristic build Prediction Results yielded total 1069 genes, 102 which sepsis-related 22 non-sepsis controls. 899 467 up-regulated 432 down-regulated, including 82 25 control genes. WGCNA excluded outlier samples, leaving 2,029 for relationship between sepsis- patient-associated LncRNA representation modules, well Wein plots differential versus key modules analyze intersections. Learning found LncRNAs RP3-508I15.21, RP11-295G20.2, LDLRAD4-AS1, CTD-2542L18.1. analyzed using Boxplot against listed above, respectively. p-value groups less than 0.05, indicating that anomalies statistically significant. CTD-2542L18.1 dataset had an AUC 0.638, 0.7 did not indicate diagnostic significance, but LDLRAD4-AS1 values more after analysis. All four sepsis-associated analyses exhibited 0.7, significance. Conclusion have some utility treatment patients, reference importance guiding sepsis.

Language: Английский