Dysregulation of ribosome-related genes in ankylosing spondylitis: a systems biology approach and experimental method

Exploring hub pyroptosis-related genes, molecular subtypes, and potential drugs in ankylosing spondylitis by comprehensive bioinformatics analysis and molecular docking

BACKGROUND

Ankylosing spondylitis (AS) is a chronic inflammatory autoimmune disease, and the diagnosis and treatment of AS have been limited because its pathogenesis is still unclear. Pyroptosis is a proinflammatory type of cell death that plays an important role in the immune system. However, the relationship between pyroptosis genes and AS has never been elucidated.

METHODS

GSE73754, GSE25101, and GSE221786 datasets were collected from the Gene Expression Omnibus (GEO) database. Differentially expressed pyroptosis-related genes (DE-PRGs) were identified by R software. Machine learning and PPI networks were used to screen key genes to construct a diagnostic model of AS. AS patients were clustered into different pyroptosis subtypes according to DE-PRGs using consensus cluster analysis and validated using principal component analysis (PCA). WGCNA was used for screening hub gene modules between two subtypes. Gene Ontology (GO) terms and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways were used for enrichment analysis to elucidate underlying mechanisms. The ESTIMATE and CIBERSORT algorithms were used to reveal immune signatures. The connectivity map (CMAP) database was used to predict potential drugs for the treatment of AS. Molecular docking was used to calculate the binding affinity between potential drugs and the hub gene.

RESULTS

Sixteen DE-PRGs were detected in AS compared to healthy controls, and some of these genes showed a significant correlation with immune cells such as neutrophils, CD8 + T cells, and resting NK cells. Enrichment analysis showed that DE-PRGs were mainly related to pyroptosis, IL-1β, and TNF signaling pathways. The key genes (TNF, NLRC4, and GZMB) screened by machine learning and the protein-protein interaction (PPI) network were used to establish the diagnostic model of AS. ROC analysis showed that the diagnostic model had good diagnostic properties in GSE73754 (AUC: 0.881), GSE25101 (AUC: 0.797), and GSE221786 (AUC: 0.713). Using 16 DE-PRGs, AS patients were divided into C1 and C2 subtypes, and these two subtypes showed significant differences in immune infiltration. A key gene module was identified from the two subtypes using WGCNA, and enrichment analysis suggested that the module was mainly related to immune function. Three potential drugs, including ascorbic acid, RO 90-7501, and celastrol, were selected based on CMAP analysis. Cytoscape showed GZMB as the highest-scoring hub gene. Finally, molecular docking results showed that GZMB and ascorbic acid formed three hydrogen bonds, including ARG-41, LYS-40, and HIS-57 (affinity: -5.3 kcal/mol). GZMB and RO-90-7501 formed one hydrogen bond, including CYS-136 (affinity: -8.8 kcal/mol). GZMB and celastrol formed three hydrogen bonds, including TYR-94, HIS-57, and LYS-40 (affinity: -9.4 kcal/mol).

CONCLUSIONS

Our research systematically analyzed the relationship between pyroptosis and AS. Pyroptosis may play an essential role in the immune microenvironment of AS. Our findings will contribute to a further understanding of the pathogenesis of AS.

The RPL4P4 Pseudogene Is a Prognostic Biomarker and Is Associated with Immune Infiltration in Glioma

Objective

Research over the past decade has suggested important roles for pseudogenes in gliomas. Our previous study found that the RPL4P4 pseudogene is highly expressed in gliomas. However, its biological function in gliomas remains unclear.

Methods

In this study, we analyzed clinical data on patients with glioma obtained from The Cancer Genome Atlas (TCGA), the Chinese Glioma Genome Atlas (CGGA), the Genotype-Tissue Expression (GTEx), and the GEPIA2 databases. We used the R language for the main analysis. Correlations among RPL4P4 expression, pathological characteristics, clinical outcome, and biological function were evaluated. In addition, the correlations of RPL4P4 expression with immune cell infiltration and glioma progression were analyzed. Finally, wound healing, Transwell, and CCK-8 assays were performed to analyze the function of RPL4P4 in glioma cells.

Result

We found that RPL4P4 is highly expressed in glioma tissues and is associated with poor prognosis, IDH1 wild type, codeletion of 1p19q, and age. Multivariate analysis and the nomogram model showed that high RPL4P4 expression was an independent risk factor for glioma prognosis and had better prognostic prediction power. Moreover, high RPL4P4 expression correlated with immune cell infiltration, which showed a significant positive association with M2-type macrophages. Finally, RPL4P4 knockdown in glioma cell lines caused decreased glioma cell proliferation, invasion, and migration capacity.

Conclusion

Our data suggest that RPL4P4 can function as an independent prognostic predictor of glioma. It also shows that RPL4P4 expression correlates with immune cell infiltration and that targeting RPL4P4 may be a new strategy for the treatment of glioma patients.

Comparative proteomic analysis identifies differentially expressed proteins and reveals potential mechanisms of traumatic heterotopic ossification progression

Background

Traumatic Heterotopic Ossification (tHO) is one of complications of elbow fractures to the detriment of patients' rehabilitation, and the severity of tHO corresponds to the size of ectopic bone. It has yet to be elucidated which proteins and pathways underlying the progression of tHO, and biomarkers to predict the severity of tHO at early stage of the disease also need further investigation.

Methods

In this study, a new rat model with distinct volume of ectopic bone was established first. Then a data-independent acquisition proteomics approach was used to investigate injured site tissues sequentially obtained from these rats (2, 7, 14, and 28 days post-injury). Differentially expressed analysis, functional annotation and co-expression analysis and protein-protein interaction network were performed to explore the pathways and hub proteins in the tHO progression. Clinical samples from a nest case-control study were used to validate the selected proteins for predicting the severity of tHO.

Results

The Achilles Tenotomy (AT) induced significantly larger sizes of ectopic bone compared to Partial Achilles Tenotomy (PAT) in rat models. A total of 3547 quantifiable proteins were screened for differential expression analysis among the AT, PAT and control groups. The hierarchical clustering and expression pattern analysis revealed more apparent difference in the pathways such as oxidative phosphorylation, mitochondrial function, and sirtuin signaling between AT and PAT group at the early stage (2 dpi) of tHO. The co-expression analysis identified five hub proteins, UBA1, EIF3E, RPL17, RPL27, and RPS28. qPCR assay, immunoblot assay and immunohistochemistry assay verified that these proteins had higher expression level in the tissue samples of clinically relevant HO patients and clinically irrelevant HO patients than HO negative patients.

Conclusion

The new established animal model and proteome profile could serve as a solid foundation for the comprehensive investigation of the progression of traumatic heterotopic ossification. And the identified 5 proteins (UBA1, EIF3E, RPL17, RPL27, and RPS28) may serve as potential biomarkers to predict the severity of tHO.

The translational potential of this article

The proteins identified in this study may be the potential biomarkers and therapeutic targets for predicting and treating the tHO at early stage.