Lymphocyte infiltration and key differentially expressed genes in the ulcerative colitis

Identification and validation of coagulation and fibrinolytic-related diagnostic biomarkers for ulcerative colitis by bioinformatics analysis

Abnormalities in coagulation and fibrinolytic status have been demonstrated to be relevant to inflammatory bowel disease. Nevertheless, there is no study to methodically examine the role of the coagulation and fibrinolysis-related genes in the diagnosis of ulcerative colitis (UC). UC-related datasets (GSE169568 and GSE94648) were originated from the Gene Expression Omnibus database. The biomarkers related to coagulation and fibrinolysis were identified through combining differentially expressed analysis and machine learning algorithms. Moreover, Gene Set Enrichment Analysis and immune analysis were carried out. A total of 4 biomarkers (MAP2K1, CREBBP, TAF1, and HP) were identified, and biomarkers were markedly enriched in pathways related to immunity, such as T-cell receptor signaling pathway, primary immunodeficiency, chemokine signaling pathway, etc. In total, the infiltrating abundance of 4 immune cells between UC and control was markedly different, namely eosinophils, macrophage M0, resting mast cells, and regulatory T cells. And all biomarkers were significantly relevant to eosinophils. Our findings detected 4 coagulation and fibrinolysis-related biomarkers (MAP2K1, CREBBP, TAF1, and HP) for UC, which contributed to the advancement of UC for further clinical investigation.

Dendritic cells: the yin and yang in disease progression

Dendritic cells (DCs) are antigen presenting cells that link innate and adaptive immunity. DCs have been historically considered as the most effective and potent cell population to capture, process and present antigens to activate naïve T cells and originate favorable immune responses in many diseases, such as cancer. However, in the last decades, it has been observed that DCs not only promote beneficial responses, but also drive the initiation and progression of some pathologies, including inflammatory bowel disease (IBD). In line with those notions, different therapeutic approaches have been tested to enhance or impair the concentration and role of the different DC subsets. The blockade of inhibitory pathways to promote DCs or DC-based vaccines have been successfully assessed in cancer, whereas the targeting of DCs to inhibit their functionality has proved to be favorable in IBD. In this review, we (a) described the general role of DCs, (b) explained the DC subsets and their role in immunogenicity, (c) analyzed the role of DCs in cancer and therapeutic approaches to promote immunogenic DCs and (d) analyzed the role of DCs in IBD and therapeutic approaches to reduced DC-induced inflammation. Therefore, we aimed to highlight the "yin-yang" role of DCs to improve the understand of this type of cells in disease progression.

Identifying the Mechanisms and Molecular Targets of Guchang Zhixie Pills on Ulcerative Colitis: Coupling Network Pharmacology with GEO Database and Experiment Verification

OBJECTIVE

This research investigates the mechanisms and molecular targets of the Guchang Zhixie pill (GCZXP) against ulcerative colitis (UC) in silico and in vivo.

METHODS

The compounds and related targets of GCZXP were collected from the traditional Chinese medicine systems pharmacology database. UC targets were from Gene Expression Omnibus and GeneCards databases. Hub genes were acquired through Cytoscape. Gene ontology and Kyoto Encyclopedia of Genes and Genomes enrichment were performed in the David database. R packages were used to investigate the relationship between immune cells and hub genes and the diagnostic model. AutoDock was used to verify the molecular docking of the core compounds and hub genes, as well as nuclear factor-kappa B (NF-κB) p65 and IκBα. The hub genes and NF-κB pathway were verified via experiment.

RESULTS

In GCZXP, a total of 51 active compounds were discovered. Enrichment analysis was used to study inflammation, chemokine activity, NF-κB signalling pathway, etc. Thirteen key therapeutic targets were involved, of which included three hub genes PTGS2, IL-1β and CXCL8. Immune infiltration revealed that all of the 3 hub genes were positively correlated with M1 macrophages, neutrophils, and activated memory CD4 cells, and negatively correlated with plasma cells. In the training and validation sets, the area under the curve (AUC) of the diagnostic model developed by hub genes reached 0.929 and 0.905, respectively, indicating a good forecasting potential. The rat experiment proved that GCZXP significantly reduced the expressions of IL-1β, CXCL8, COX-2, and NF-κB p65 while increasing IκBα and Bcl-2, alleviated colonic inflammatory injury and promoted ulcer healing.

CONCLUSION

GCZXP reduced the release of cytokines and regulated Bcl-2 in the treatment of UC by inhibiting the NF-κB signalling pathway.

Increased SERPINA3 Level Is Associated with Ulcerative Colitis

Ulcerative colitis (UC) is a recurrent, chronic intestinal disease that is currently incurable. Its pathogenesis remains to be further understood. Therefore, seeking new biomarkers and potential drug targets is urgent for the effective treatment of UC. In this study, the gene expression profile GSE38713 was obtained from the GEO (Gene Expression Omnibus) database. Data normalisation and screening of the differentially expressed genes (DEGs) were conducted using R software, and gene ontology (GO) enrichment was performed using Metascape online tools. The PubMed database was used to screen new genes that have not been reported, and SERPINA3 was selected. The correlation between SERPINA3 and other inflammatory factors was analysed by Spearman correlation analysis. Finally, colitis model mice and an in-vitro model were established to validate the function of the SERPINA3 gene. SERPINA3 gene expression was markedly increased in UC patient samples, colitis models and in-vitro models and showed an association with other inflammatory factors. ROC analysis indicated that SERPINA3 could represent a potential biomarker of active UC. Additionally, silencing SERPINA3 in an in-vitro intestinal epithelial inflammatory model significantly decreased the mRNA level of inflammatory factors. This study provides supportive evidence that SERPINA3 may act as a key biomarker and potential drug target in UC treatment.

Screening of characteristic genes in ulcerative colitis by integrating gene expression profiles

Background

This study aimed to screen the feature modules and characteristic genes related to ulcerative colitis (UC) and construct a support vector machine (SVM) classifier to distinguish UC patients.

Methods

Four datasets that contained UC and control samples were obtained from the Gene Expression Omnibus database. Differentially expressed genes (DEGs) with consistency were screened via the MetaDE method. The weighted gene coexpression network (WGCNA) was used to distinguish significant modules based on the four datasets. The protein–protein interaction network was established based on intersection genes. Enrichment analysis of Gene Ontology (GO) biological processes (BPs) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment were established based on DAVID. An SVM combined with recursive feature elimination was also applied to construct a disease classifier for the disease diagnosis of UC patients. The efficacy of the SVM classifier was evaluated through receiver operating characteristic curves.

Results

Twelve highly preserved modules were obtained using the WGCNA, and 2009 DEGs with significant consistency were selected using the MetaDE method. Sixteen significantly related GO BPs and 12 KEGG pathways were obtained, such as cytokine-cytokine receptor interaction, cell adhesion molecules, and leukocyte transendothelial migration. Subsequently, 41 genes were used to construct an SVM classifier, such as CXCL1, CCR2, IL1B, and IL1A. The area under the curve (AUC) was 0.999 in the training dataset, whereas the AUC was 0.886, 0.790, and 0.819 in the validation set (GSE65114, GSE37283, and GSE36807, respectively).

Conclusions

An SVM classifier based on feature genes might correctly identify healthy people or UC patients.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12876-021-01940-0.