NLRP3 neuroinflammatory factors may be involved in atopic dermatitis mental disorders: an animal study

Yuan, Huimin; Sun, Yan; Zhang, Shujing; Feng, Jing; Tian, Zijiao; Liu, Jingang; Wang, Hang; Gao, Yu-Shan; Tang, Yang; Zheng, Fengjie

doi:10.3389/fphar.2022.966279

Cited by 5 publications

(3 citation statements)

References 43 publications

(49 reference statements)

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“…As a result of AD, IL-5 synthesis by human Th cells is dependent on MAP kinase activity, which promotes an intense inflammatory state in AD (Mori et al, 1999). In patients with AD, neuroinflammatory response related to AD mental disorders closely (Yuan et al, 2022). Regulation of leukocyte migration was closely related to adhesion molecules on endothelial cells, and which reflected the extent of systemic inflammation, such as AD (Yamashita et al, 1997).…”

Section: Discussionmentioning

confidence: 99%

Study on the Mechanism of Anti-atopic Dermatitis by Herba Siegesbeckiae Based on System Pharmacology

Chen,

Wang,

Zhang

et al. 2024

Pharmacognosy Magazine

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Background: Atopic dermatitis (AD) is a common but complex chronic inflammatory skin condition characterised by intense pruritus, severely impacting patients’ quality of life. At present, there is no specific drug for AD. Herba Siegesbeckiae (HS) had a therapeutic effect on AD, but its mechanism has not been completely elucidated. Purpose: We aimed to understand the mechanism of HS in the treatment of AD in this study. Materials and methods: To investigate the mechanism of HS for AD, including active components, key targets and pathway analyses, systemic pharmacology was used. Molecular docking was conducted to validate the interactions between the key components and their targets. Afterwards, an AD-like animal model was established, skin pathology was observed and immunohistochemical staining was used to confirm the key targets. Results: Six vital active components (daucosterol, d-mannitol, hythiemoside B, 15,16-di- O-acetyldarutoside, 19-acetoxy-15-hydroperoxy-12-oxo-13,14E-dehydro-10,11,14,15-tetrahydr-ogeranylnerol and 17- O-acetyldarutoside) highly correlated with potential targets and the key targets (TNF-α, VEGFA, TLR4, STAT3, TLR2, STAT1, MMP9, IL-6, TRPV1 and PPARG) highly correlated with disease processes were identified, pathway enrichment analysis revealed that the toll-like receptor, PI3K-Akt and HIF-1 signalling pathways were significantly enriched with key targets, suggesting their involvement in the anti-Alzheimer’s disease mechanism of HS. As a result of animal experiments, AD-like mice from the model group showed epidermal thickening and inflammatory cell infiltration; TNF-α, VEGFA, TLR4, STAT3, TLR2, STAT1, MMP9, IL-6, TRPV1 levels are significantly up-regulated and PPARG levels were significantly down-regulated; those of which could be reversed by 3.0 g/kg. Conclusion: HS might exert its anti-AD effects by up-regulating PPARG and down-regulating TNF-α, VEGFA, TLR4, STAT3, TLR2, STAT1, MMP9, IL-6 and TRPV1. This study laid a foundation for understanding HS’s bioactive components and mechanism against AD.

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Section: Discussionmentioning

confidence: 99%

Study on the Mechanism of Anti-atopic Dermatitis by Herba Siegesbeckiae Based on System Pharmacology

Chen,

Wang,

Zhang

et al. 2024

Pharmacognosy Magazine

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“…Hence, a hypothetical “skin-brain axis” has been proposed in which dermal inflammation would trigger mental disorders. Supporting this conjecture, experimental induction of dermatitis in mice increased anxiety- and depressive-like behaviors, along with elevated serum corticosterone levels [ 81 ]. More convincingly, in clinical trials, anti-IL17A [ 82 ] and anti-IL4R [ 83 ] antibodies which target inflammatory signaling in the skin, reduced psoriatic lesions and atopic dermatitis, respectively, as they simultaneously mitigated anxiety and depression.…”

Section: Hallmark 1: Integrity Of Barriersmentioning

confidence: 98%

The missing hallmark of health: psychosocial adaptation

López-Otín,

Kroemer

2024

CST

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The eight biological hallmarks of health that we initially postulated (Cell. 2021 Jan 7;184(1):33-63) include features of spatial compartmentalization (integrity of barriers, containment of local perturbations), maintenance of homeostasis over time (recycling & turnover, integration of circuitries, rhythmic oscillations) and an array of adequate responses to stress (homeostatic resilience, hormetic regulation, repair & regeneration). These hallmarks affect all eight somatic strata of the human body (molecules, organelles, cells, supracellular units, organs, organ systems, systemic circuitries and meta-organism). Here we postulate that mental and socioeconomic factors must be added to this 8×8 matrix as an additional hallmark of health (“psychosocial adaptation”) and as an additional stratum (“psychosocial interactions”), hence building a 9×9 matrix. Potentially, perturbation of each of the somatic hallmarks and strata affects psychosocial factors and vice versa. Finally, we discuss the (patho)physiological bases of these interactions and their implications for mental health improvement.

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“…Studies showed that the expression of inflammasome NLR Family Pyrin Domain Containing 3 (NLRP3) and absent in melanoma-2 (AIM2) was increased in AD and was related to epidermal inflammation [ 11 , 12 ]. Moreover, NLRP3, caspase-1, and IL-1B, involved in the pyroptosis canonical pathway, might be related to mental disorders of AD [ 13 ]. These findings indicated that PRGs may act as a pivotal part of AD and identifying more comprehensive potential PRBMs can provide a novel perspective on the diagnosis and treatment of AD.…”

Section: Introductionmentioning

confidence: 99%

Construction and verification of atopic dermatitis diagnostic model based on pyroptosis related biological markers using machine learning methods

et al. 2023

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Objective The aim of this study was to construct a model used for the accurate diagnosis of Atopic dermatitis (AD) using pyroptosis related biological markers (PRBMs) through the methods of machine learning. Method The pyroptosis related genes (PRGs) were acquired from molecular signatures database (MSigDB). The chip data of GSE120721, GSE6012, GSE32924, and GSE153007 were downloaded from gene expression omnibus (GEO) database. The data of GSE120721 and GSE6012 were combined as the training group, while the others were served as the testing groups. Subsequently, the expression of PRGs was extracted from the training group and differentially expressed analysis was conducted. CIBERSORT algorithm calculated the immune cells infiltration and differentially expressed analysis was conducted. Consistent cluster analysis divided AD patients into different modules according to the expression levels of PRGs. Then, weighted correlation network analysis (WGCNA) screened the key module. For the key module, we used Random forest (RF), support vector machines (SVM), Extreme Gradient Boosting (XGB), and generalized linear model (GLM) to construct diagnostic models. For the five PRBMs with the highest model importance, we built a nomogram. Finally, the results of the model were validated using GSE32924, and GSE153007 datasets. Results Nine PRGs were significant differences in normal humans and AD patients. Immune cells infiltration showed that the activated CD4+ memory T cells and Dendritic cells (DCs) were significantly higher in AD patients than normal humans, while the activated natural killer (NK) cells and the resting mast cells were significantly lower in AD patients than normal humans. Consistent cluster analysis divided the expressing matrix into 2 modules. Subsequently, WGCNA analysis showed that the turquoise module had a significant difference and high correlation coefficient. Then, the machine model was constructed and the results showed that the XGB model was the optimal model. The nomogram was constructed by using HDAC1, GPALPP1, LGALS3, SLC29A1, and RWDD3 five PRBMs. Finally, the datasets GSE32924 and GSE153007 verified the reliability of this result. Conclusions The XGB model based on five PRBMs can be used for the accurate diagnosis of AD patients.

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NLRP3 neuroinflammatory factors may be involved in atopic dermatitis mental disorders: an animal study

Cited by 5 publications

References 43 publications

Study on the Mechanism of Anti-atopic Dermatitis by Herba Siegesbeckiae Based on System Pharmacology

Study on the Mechanism of Anti-atopic Dermatitis by Herba Siegesbeckiae Based on System Pharmacology

The missing hallmark of health: psychosocial adaptation

Construction and verification of atopic dermatitis diagnostic model based on pyroptosis related biological markers using machine learning methods

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