Pyroptosis as a candidate therapeutic target for Alzheimer’s disease

Huang, Yanlin; Li, Xiaoyü; Luo, Guifei; Wang, Junli; Li, Ranhui; Zhou, Chuyi; Wan, Teng; Yang, Fenglian

doi:10.3389/fnagi.2022.996646

Cited by 14 publications

(2 citation statements)

References 84 publications

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“…Pyroptosis is an in ammation-related programmed cell death mode mediated by gasdermin protein, dependent on in ammatory caspases, and accompanied by the release of a large number of proin ammatory factors, which is closely related to the occurrence and development of neurodegenerative diseases. related [20]. However, the speci c mechanism of pyroptosis involved in AD is still unclear.…”

Section: Discussionmentioning

confidence: 99%

Identification of pyroptosis-related molecular clusters in alzheimer’s disease based on multiple machine learning models

Fan

Niu

Wang

et al. 2023

Preprint

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Background: Alzheimer's disease (AD) is the most common neurodegenerative disease. Pyroptosis is a new type of programmed cell death, which can lead to the progression of various diseases. The aim of this study was to explore the role of pyroptosis-related genes (PRGs) in Alzheimer's disease and to build the predictive model. Methods: The expression of PRGs in AD was analyzed based on the GSE33000 dataset, and molecular clustering and immune microenvironment analysis were performed on 310 patient samples. The WGCNA algorithm was used to identify the genes that were specifically expressed between different clusters, and then four machine learning models (RF, GLM, SVM and XGB) were used to construct the predictive models for the risk of AD. The prediction capability of the model was verified by nomogram, calibration, decision curve analyses and five external data sets. Results: Multiple PRGs were differentially expressed between AD and normal brain tissue. Based on differentially expressed PRGs, 310 AD patients were divided into two subtypes by consistent clustering. Immune microenvironment analysis showed significant differences in the degree of immune activation among different subtypes. WGCNA algorithm identified the specific genes between AD and normal individuals, Cluster 1 and Cluster 2. The SVM model has the best prediction performance with low residual error and root mean square error, and high area under ROC curve (AUC=0.933). Finally, a prediction model based on five genes (GPR4, STAT3, CASP4, CLIC1 and TNFRSF10B) was constructed and showed satisfactory performance on five externally validated data sets. Nomogram, calibration curve and decision curve analysis proved the prediction performance of the model. Conclusions: This study systematically analyzed the complex relationship between PRGs and AD, and constructed a good prediction model to distinguish AD from normal individuals, which is expected to provide reference for related research.

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Section: Discussionmentioning

confidence: 99%

Identification of pyroptosis-related molecular clusters in alzheimer’s disease based on multiple machine learning models

Fan

Niu

Wang

et al. 2023

Preprint

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“…Under physiological conditions, pyroptosis facilitates the immune system to eliminate pathogens in the host, while excessive pyroptosis aggravates inflammatory reactions leading to disease pathology (Man et al, 2017). Microglia pyroptosis has been proved to closely correlate with stroke, intracerebral haemorrhage, multiple sclerosis, depression and many other neurological disorders such as Alzheimer's disease (Huang et al, 2022; Wu et al, 2022). On the other hand, microglia have conflicting roles, such as neurotoxic and neuroprotective property, in the pathogenesis of neurodegenerative diseases.…”

Section: Introductionmentioning

confidence: 99%

The critical role of P2XR/PGC‐1α signalling pathway in hypoxia‐mediated pyroptosis and M1/M2 phenotypic differentiation of mouse microglia

Yang,

Shao,

Jin

et al. 2024

Eur J of Neuroscience

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Microglia are endogenous immune cells in the brain, and their pyroptosis and phenotype dichotomy are proved to play roles in neurodegenerative diseases. We investigated whether and how hypoxia affected pyroptosis and phenotype polarization in mouse microglia. Primary mouse microglia and BV2 microglia were exposed to hypoxia. Pyroptosis and M1/M2 phenotype were assessed by measuring gasdermin D truncation and M1/M2 surface marker expression. Mechanisms including purinergic ionotropic receptor (P2XR), peroxisome proliferator‐activated receptor coactivator‐1α (PGC‐1α) and NOD‐like receptor protein 3 (NLRP3) inflammasome were investigated. We reported hypoxia (90% N2, 5% O2 and 5% CO2) induced pyroptosis and promoted M1 phenotype polarization in primary mouse microglia and BV2 microglia, and the effect appeared after 6 h exposure. Although hypoxia (90% N2, 5% O2 and 5% CO2, 6 h) had no effect on P2X1R and P2X7R expression, it increased P2X4R expression and decreased PGC‐1α expression. Interestingly, blockade of P2X4R or P2X7R abolished hypoxia‐modulated PGC‐1α expression, pyroptosis and M1 polarization. PGC‐1α overexpression or overactivation alleviated hypoxia‐induced pyroptosis and M1 polarization, while PGC‐1α knockdown or deactivation promoted pyroptosis and M1 polarization under normoxic situation. Further, hypoxia induced NLRP3 expression and activated caspase‐1 and induced the phosphorylation of NF‐κB and reduced the phosphorylation of STAT3/6. NLRP3 inhibitor and caspase‐1 inhibitor abolished hypoxia‐induced pyroptosis, while NF‐κB inhibitor and STAT phosphorylation inducer ameliorated hypoxia‐induced M1 polarization. In addition, NF‐κB activator and STAT3/6 inhibitor caused microglia M1 polarization under normoxic situation. We concluded in cultured mouse microglia, hypoxia may induce pyroptosis via P2XR/PGC‐1α/NLRP3/caspase‐1 pathway and trigger M1 polarization through P2XR/PGC‐1α/NF‐κB/STAT3/6 pathway.

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