To re-examine the intersection of microglial activation and neuroinflammation in neurodegenerative diseases from the perspective of pyroptosis

Li, Yuan; Li, Ying-Jie; Zhu, Zhao-Qiong

doi:10.3389/fnagi.2023.1284214

Cited by 4 publications

(1 citation statement)

References 248 publications

(221 reference statements)

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“…Dysbiosis has been increasingly implicated in the pathogenesis of various neurological disorders through several mechanisms, the most prominent being the neuroinflammation. In dysbiosis, there is release of pro-inflammatory mediators such as cytokines (e.g., interleukin-1β, tumor necrosis factor-α) and chemokines from the immune cells which can then migrate to the CNS via the bloodstream or lymphatic system and exacerbate neuroinflammation affecting brain development and behavior [77,[81][82][83].…”

Section: Gut-brain Axismentioning

confidence: 99%

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

Tizabi,

Bennani,

El Kouhen

et al. 2024

Preprint

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

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Section: Gut-brain Axismentioning

confidence: 99%

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

Tizabi,

Bennani,

El Kouhen

et al. 2024

Preprint

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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

Kim,

Choi,

Han

et al. 2025

Journal of Ethnopharmacology

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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

Bai,

Gao,

Yan

et al. 2024

Front. Immunol.

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BackgroundSepsis is a life-threatening organ dysfunction condition produced by dysregulation of the host response to infection. It is now characterized by a high clinical morbidity and mortality rate, endangering patients’ lives and health. The purpose of this study was to determine the value of Long chain non-coding RNA (LncRNA) RP3_508I15.21, RP11_295G20.2, and LDLRAD4_AS1 in the diagnosis of adult sepsis patients and to develop a Nomogram prediction model.MethodsWe screened adult sepsis microarray datasets GSE57065 and GSE95233 from the GEO database and performed differentially expressed genes (DEGs), weighted gene co-expression network analysis (WGCNA), and machine learning methods to find the genes by random forest (Random Forest), least absolute shrinkage and selection operator (LASSO), and support vector machine (SVM), respectively, with GSE95233 as the training set and GSE57065 as the validation set. Differentially expressed genes (DEGs), weighted gene co-expression network analysis (WGCNA), boxplot statistical analysis, and ROC analysis by Random Forest, Least Absolute Shrinkage and Selection Operator (LASSO), and Support Vector Machine (SVM) machine learning methods were used to identify characteristic genes and build the Nomogram Prediction model.ResultsGSE95233 yielded a total of 1069 genes, 102 of which were sepsis-related and 22 of which were non-sepsis controls. GSE57065 yielded a total of 899 genes, with 467 up-regulated and 432 down-regulated, including 82 sepsis-related genes and 25 non-sepsis control genes. WGCNA analysis excluded outlier samples, leaving 2,029 genes for relationship analysis between sepsis- and non-sepsis patient-associated LncRNA network representation modules, as well as Wein plots of differential genes versus genes in key modules of weighted co-expression network analysis to analyze gene intersections. Machine Learning found the sepsis-related characteristic LncRNAs RP3-508I15.21, RP11-295G20.2, LDLRAD4-AS1, and CTD-2542L18.1. The datasets GSE95233 and GSE57065 were analyzed using Boxplot against the screened genes listed above, respectively. The p-value between the sepsis and non-sepsis groups was less than 0.05, indicating that anomalies were statistically significant. CTD-2542L18.1 in dataset GSE57065 had an AUC value of 0.638, which was less than 0.7 and did not indicate diagnostic significance, but RP3-508I15.21, RP11-295G20.2, and LDLRAD4-AS1 had AUC values more than 0.7 after ROC analysis. All four sepsis-associated LncRNA ROC analyses in dataset GSE95233 exhibited AUC values more than 0.7, indicating diagnostic significance.ConclusionLncRNAs RP3_508I15.21, RP11_295G20.2, and LDLRAD4_AS1 have some utility in the diagnosis and treatment of adult sepsis patients, as well as some reference importance in guiding the diagnosis and treatment of clinical sepsis.

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To re-examine the intersection of microglial activation and neuroinflammation in neurodegenerative diseases from the perspective of pyroptosis

Cited by 4 publications

References 248 publications

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

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

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

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

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