FTY720 Attenuates LPS-Induced Inflammatory Bone Loss by Inhibiting Osteoclastogenesis via the NF-κB and HDAC4/ATF Pathways

Chen, Chongwei; Zong, Sujing; Wang, Zhenyu; Yang, Ruijia; Guo, Yanjing; Wang, Yunfei; Chen, Xinping; Li, Yue; Wang, Shaowei

doi:10.1155/2023/8571649

Cited by 3 publications

(1 citation statement)

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“…The activation of inflammatory cells is associated with various molecular structures, including cell membrane receptors for central nervous system signaling molecules, cell membrane channels, intracellular signal transduction pathways, and activation of intracellular transcription factors ( 8 ). This activation further leads to the release of proinflammatory factors, such as interleukin-1β (IL-1β), interleukin-6 (IL-6), and tumor necrosis factor-α (TNF-α), thereby causing neuronal damage and loss ( 9 , 10 ). Specifically, the intimate connection between inflammation and ASD can be attributed to the upregulated expression of proinflammatory factors, which closely relates to hippocampal and brain parenchymal damage in newborns.…”

Section: Introductionmentioning

confidence: 99%

Unraveling the immunogenetic landscape of autism spectrum disorder: a comprehensive bioinformatics approach

Ma,

Liu,

Zhao

et al. 2024

Front. Immunol.

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BackgroundAutism spectrum disorder (ASD) is a disease characterized by social disorder. Recently, the population affected by ASD has gradually increased around the world. There are great difficulties in diagnosis and treatment at present.MethodsThe ASD datasets were obtained from the Gene Expression Omnibus database and the immune-relevant genes were downloaded from a previously published compilation. Subsequently, we used WGCNA to screen the modules related to the ASD and immune. We also choose the best combination and screen out the core genes from Consensus Machine Learning Driven Signatures (CMLS). Subsequently, we evaluated the genetic correlation between immune cells and ASD used GNOVA. And pleiotropic regions identified by PLACO and CPASSOC between ASD and immune cells. FUMA was used to identify pleiotropic regions, and expression trait loci (EQTL) analysis was used to determine their expression in different tissues and cells. Finally, we use qPCR to detect the gene expression level of the core gene.ResultsWe found a close relationship between neutrophils and ASD, and subsequently, CMLS identified a total of 47 potential candidate genes. Secondly, GNOVA showed a significant genetic correlation between neutrophils and ASD, and PLACO and CPASSOC identified a total of 14 pleiotropic regions. We annotated the 14 regions mentioned above and identified a total of 6 potential candidate genes. Through EQTL, we found that the CFLAR gene has a specific expression pattern in neutrophils, suggesting that it may serve as a potential biomarker for ASD and is closely related to its pathogenesis.ConclusionsIn conclusion, our study yields unprecedented insights into the molecular and genetic heterogeneity of ASD through a comprehensive bioinformatics analysis. These valuable findings hold significant implications for tailoring personalized ASD therapies.

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Section: Introductionmentioning

confidence: 99%