Integrated Bioinformatics and Validation Reveal IL1B and Its Related Molecules as Potential Biomarkers in Chronic Spontaneous Urticaria

Plasma proteins and inflammatory dermatoses: proteome-wide Mendelian randomization and colocalization analyses

Current genome-wide association studies (GWAS) of plasma proteomes provide additional possibilities for finding new drug targets for inflammatory dermatoses. We performed proteome-wide Mendelian randomization (MR) and colocalization analyses to identify novel potential drug targets for inflammatory dermatoses. We performed MR and colocalization analysis using genetic variation as instrumental variables to determine the causal relationship between circulating plasma proteins and inflammatory dermatoses. 5 plasma proteins were found to be causally associated with dermatitis eczematosa, SLE, urticaria and psoriasis using cis-pQTLs as instrumental variables, but not found in AD and LP. 19 candidate genes with high colocalization evidence were identified. These potential drug targets still require more research and rigorous validation in future trials.

Identification and analysis of chemokine-related and NETosis-related genes in acute pancreatitis to develop a predictive model

Background: Chemokines and NETosis are significant contributors to the inflammatory response, yet there still needs to be a more comprehensive understanding regarding the specific molecular characteristics and interactions of NETosis and chemokines in the context of acute pancreatitis (AP) and severe AP (SAP). Methods: To address this gap, the mRNA expression profile dataset GSE194331 was utilized for analysis, comprising 87 AP samples (77 non-SAP and 10 SAP) and 32 healthy control samples. Enrichment analyses were conducted for differentially expressed chemokine-related genes (DECRGs) and NETosis-related genes (DENRGs). Three machine-learning algorithms were used for the identification of signature genes, which were subsequently utilized in the development and validation of nomogram diagnostic models for the prediction of AP and SAP. Furthermore, single-gene Gene Set Enrichment Analysis (GSEA) and Gene Set Variation Analysis (GSVA) were performed. Lastly, an interaction network for the identified signature genes was constructed. Results: We identified 12 DECRGs and 7 DENRGs, and enrichment analyses indicated they were primarily enriched in cytokine-cytokine receptor interaction, chemokine signaling pathway, TNF signaling pathway, and T cell receptor signaling pathway. Moreover, these machine learning algorithms finally recognized three signature genes (S100A8, AIF1, and IL18). Utilizing the identified signature genes, we developed nomogram models with high predictive accuracy for AP and differentiation of SAP from non-SAP, as demonstrated by area under the curve (AUC) values of 0.968 (95% CI 0.937-0.990) and 0.862 (95% CI 0.742-0.955), respectively, in receiver operating characteristic (ROC) curve analysis. Subsequent single-gene GESA and GSVA indicated a significant positive correlation between these signature genes and the proteasome complex. At the same time, a negative association was observed with the Th1 and Th2 cell differentiation signaling pathways. Conclusion: We have identified three genes (S100A8, AIF1, and IL18) related to chemokines and NETosis, and have developed accurate diagnostic models that might provide a novel method for diagnosing AP and differentiating between severe and non-severe cases.

Identification of key genes and molecular mechanisms of chronic urticaria based on bioinformatics

Chronic urticaria (CU) is characterized by persistent skin hives, redness, and itching, enhanced by immune dysregulation and inflammation. Our main objective is identifying key genes and molecular mechanisms of chronic urticaria based on bioinformatics. We used the Gene Expression Omnibus (GEO) database and retrieved two GEO datasets, GSE57178 and GSE72540. The raw data were extracted, pre-processed, and analyzed using the GEO2R tool to identify the differentially expressed genes (DEGs). The samples were divided into two groups: healthy samples and CU samples. We defined cut-off values of log2 fold change ≥1 and p < .05. Analyses were performed in the Kyoto Encyclopaedia of Genes and Genomes (KEGG), the Database for Annotation, Visualization and Integrated Discovery (DAVID), Metascape, Search Tool for the Retrieval of Interacting Genes/Proteins (STRING) and CIBERSOFT databases. We obtained 1613 differentially expressed genes. There were 114 overlapping genes in both datasets, out of which 102 genes were up-regulated while 12 were down-regulated. The biological processes included activation of myeloid leukocytes, response to inflammations, and response to organic substances. Moreover, the KEGG pathways of CU were enriched in the Nuclear Factor-Kappa B (NF-kB) signaling pathway, Tumor Necrosis Factor (TNF) signaling pathway, and Janus kinase/signal transducers and activators of transcription (JAK-STAT) signaling pathway. We identified 27 hub genes that were implicated in the pathogenesis of CU, such as interleukin-6 (IL-6), Prostaglandin-endoperoxide synthase 2 (PTGS2), and intercellular adhesion molecule-1 (ICAM1). The complex interplay between immune responses, inflammatory pathways, cytokine networks, and specific genes enhances CU. Understanding these mechanisms paves the way for potential interventions to mitigate symptoms and improve the quality of life of CU patients.

GSDMD suppresses keratinocyte differentiation by inhibiting FLG expression and attenuating KCTD6-mediated HDAC1 degradation in atopic dermatitis

Background

Recent studies have shown that activated pyroptosis in atopic dermatitis (AD) switches inflammatory processes and causes abnormal cornification and epidermal barrier dysfunction. Little research has focused on the interaction mechanism between pyroptosis-related genes and human keratinocyte differentiation.

Methods

The AD dataset from the Gene Expression Omnibus (GEO) was used to identify differently expressed pyroptosis-related genes (DEPRGs). Hub genes were identified and an enrichment analysis was performed to select epithelial development-related genes. Lesions of AD patients were detected via immunohistochemistry (IHC) to verify the hub gene. Human keratinocytes cell lines, gasdermin D (GSDMD) overexpression, Caspase1 siRNA, Histone Deacetylase1 (HDAC1) siRNA, and HDAC1 overexpression vectors were used for gain-and-loss-of-function experiments. Regulation of cornification protein was determined by qPCR, western blot (WB), immunofluorescence (IF), dual-luciferase reporter assay, co-immunoprecipitation (Co-IP), and chromatin immunoprecipitation (ChIP).

Results

A total of 27 DEPRGs were identified between either atopic dermatitis non-lesional skin (ANL) and healthy control (HC) or atopic dermatitis lesional skin (AL) and HC. The enrichment analysis showed that these DEPRGs were primarily enriched in the inflammatory response and keratinocytes differentiation. Of the 10 hub genes identified via the protein-protein interaction network, only GSDMD was statistically and negatively associated with the expression of epithelial tight junction core genes. Furthermore, GSDMD was upregulated in AD lesions and inhibited human keratinocyte differentiation by reducing filaggrin (FLG) expression. Mechanistically, GSDMD activated by Caspase1 reduced FLG expression via HDAC1. HDAC1 decreased FLG expression by reducing histone acetylation at the FLG promoter. In addition, GSDMD blocked the interaction of Potassium Channel Tetramerization Domain Containing 6 (KCTD6) and HDAC1 to prohibit HDAC1 degradation.

Conclusion

This study revealed that GSDMD was upregulated in AD lesions and that GSDMD regulated keratinocytes via epigenetic modification, which might provide potential therapeutic targets for AD.

Identifying target ion channel-related genes to construct a diagnosis model for insulinoma

Background: Insulinoma is the most common functional pancreatic neuroendocrine tumor (PNET) with abnormal insulin hypersecretion. The etiopathogenesis of insulinoma remains indefinable. Based on multiple bioinformatics methods and machine learning algorithms, this study proposed exploring the molecular mechanism from ion channel-related genes to establish a genetic diagnosis model for insulinoma. Methods: The mRNA expression profile dataset of GSE73338 was applied to the analysis, which contains 17 insulinoma samples, 63 nonfunctional PNET (NFPNET) samples, and four normal islet samples. Differently expressed ion channel-related genes (DEICRGs) enrichment analyses were performed. We utilized the protein-protein interaction (PPI) analysis and machine learning of LASSO and support vector machine-recursive feature elimination (SVM-RFE) to identify the target genes. Based on these target genes, a nomogram diagnostic model was constructed and verified by a receiver operating characteristic (ROC) curve. Moreover, immune infiltration analysis, single-gene gene set enrichment analysis (GSEA), and gene set variation analysis (GSVA) were executed. Finally, a drug-gene interaction network was constructed. Results: We identified 29 DEICRGs, and enrichment analyses indicated they were primarily enriched in ion transport, cellular ion homeostasis, pancreatic secretion, and lysosome. Moreover, the PPI network and machine learning recognized three target genes (MCOLN1, ATP6V0E1, and ATP4A). Based on these target genes, we constructed an efficiently predictable diagnosis model for identifying insulinomas with a nomogram and validated it with the ROC curve (AUC = 0.801, 95% CI 0.674-0.898). Then, single-gene GSEA analysis revealed that these target genes had a significantly positive correlation with insulin secretion and lysosome. In contrast, the TGF-beta signaling pathway was negatively associated with them. Furthermore, statistically significant discrepancies in immune infiltration were revealed. Conclusion: We identified three ion channel-related genes and constructed an efficiently predictable diagnosis model to offer a novel approach for diagnosing insulinoma.

Involvement of CCL2 and CH25H Genes and TNF signaling pathways in mast cell activation and pathogenesis of chronic spontaneous urticaria

Chronic spontaneous urticaria (CSU), a mast cell-driven disease, substantially affects the quality of life. While genetics affect CSU susceptibility and severity, the specific genetic factors associated with mast cell activation in CSU remain elusive. We aimed to identify key genetic factors and investigate their roles in CSU pathogenesis. Two gene expression datasets from the Gene Expression Omnibus were merged and validated using principal component analysis and boxplots. The merged dataset was subjected to limma and weighted gene co-expression network analyses. Genes whose expression correlated highly with CSU were identified and analyzed using Gene Set Enrichment Analysis (GSEA), Gene Ontology (GO), and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses. As GSEA, GO, and KEGG analyses highlighted the importance of chemokine (C-C motif) ligand 2 (CCL2) and cholesterol 25-hydroxylase (CH25H) gene and tumor necrosis factor (TNF) signaling pathways in CSU; the three corresponding genes were knocked down in human mast cell line-1 (HMC-1), followed by incubation with thrombin to mimic CSU pathogenesis. CCL2, CH25H, and TNF knockdown reduced excitability and cytokine production in HMC-1. Our findings suggest that genes involved in the CCL2, CH25H, and TNF pathways play crucial roles in CSU pathogenesis, providing insights into potential therapeutic targets for CSU treatment.

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

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.

miRNAs' Cross-Involvement in Skin Allergies: A New Horizon for the Pathogenesis, Diagnosis and Therapy of Atopic Dermatitis, Allergic Contact Dermatitis and Chronic Spontaneous Urticaria

Skin inflammation is a common underlying feature of atopic dermatitis, allergic contact dermatitis and chronic spontaneous urticaria. The pathogenetic mechanisms have not been fully elucidated. The purpose of this study was to examine whether miRNA, by regulating inflammatory mechanisms through the modulation of innate and adaptive immune responses, could play a major role in the pathogenesis of these skin conditions. We conducted a narrative review using the Pubmed and Embase scientific databases and search engines to find the most relevant miRNAs related to the pathophysiology, severity and prognosis of skin conditions. The studies show that miRNAs are involved in the pathogenesis and regulation of atopic dermatitis and can reveal an atopic predisposition or indicate disease severity. In chronic spontaneous urticaria, different miRNAs which are over-expressed during urticaria exacerbations not only play a role in the possible response to therapy or remission, but also serve as a marker of chronic autoimmune urticaria and indicate associations with other autoimmune diseases. In allergic contact dermatitis, miRNAs are upregulated in inflammatory lesions and expressed during the sensitization phase of allergic response. Several miRNAs have been identified as potential biomarkers of these chronic skin conditions, but they are also possible therapeutic targets.

The role of short-chain fatty acids in inflammatory skin diseases

Short-chain fatty acids (SCFAs) are metabolites of gut microbes that can modulate the host inflammatory response, and contribute to health and homeostasis. Since the introduction of the gut-skin axis concept, the link between SCFAs and inflammatory skin diseases has attracted considerable attention. In this review, we have summarized the literature on the role of SCFAs in skin inflammation, and the correlation between SCFAs and inflammatory skin diseases, especially atopic dermatitis, urticaria, and psoriasis. Studies show that SCFAs are signaling factors in the gut-skin axis and can alleviate skin inflammation. The information presented in this review provides new insights into the molecular mechanisms driving gut-skin axis regulation, along with possible pathways that can be targeted for the treatment and prevention of inflammatory skin diseases.

The Emerging Roles of Pyroptosis, Necroptosis, and Ferroptosis in Non-Malignant Dermatoses: A Review

Unlike apoptosis, pyroptosis, necroptosis, and ferroptosis are recently identified modes of programmed cell death (PCD) with unique molecular pathways. Increasing evidence has indicated that these PCD modes play a crucial role in the pathogenesis of various non-malignant dermatoses (a group of cutaneous disorders), including infective dermatoses, immune-related dermatoses, allergic dermatoses, benign proliferative dermatoses, etc. Moreover, their molecular mechanisms have been suggested as potential therapeutic targets for the prevention and treatment of these dermatoses. In this article, we aim to review and summarize the molecular mechanisms of pyroptosis, necroptosis, and ferroptosis and their roles in the pathogenesis of some non-malignant dermatoses.

The Role of Crosstalk of Immune Cells in Pathogenesis of Chronic Spontaneous Urticaria

Chronic spontaneous urticaria (CSU) is defined as recurrent episodes of spontaneous wheal development and/or angioedema for more than six weeks and at least twice a week. The core link in the pathogenesis of CSU is the activation of mast cells, T cells, eosinophils, and other immune cells infiltrating around the small venules of the lesion. Increased vascular permeability, vasodilatation, and recruitment of inflammatory cells directly depend on mast cell mediators’ release. Complex regulatory systems tightly influence the critical roles of mast cells in the local microenvironment. The bias toward Th2 inflammation and autoantibodies derived from B cells, histamine expressed by basophils, and initiation of the extrinsic coagulation pathway by eosinophils or monocytes exerts powerful modulatory influences on mast cells. Cell-to-cell interactions between mast cells and eosinophils/T cells also are regulators of their function and may involve CSU’s pathomechanism. This review summarizes up-to-date knowledge regarding the crosstalk between mast cells and other immune cells, providing the impetus to develop new research concepts and treatment strategies for CSU.