Global Proteomic Analyses Reveals Abnormal Immune Regulation in Patients With New Onset Ankylosing Spondylitis

Human peripheral blood mononuclear cells as a valuable source of disease-related biomarkers: Evidence from comparative proteomics studies

PURPOSE

The discovery of specific and sensitive disease-associated biomarkers for early diagnostic purposes of many diseases is still highly challenging due to various complex molecular mechanisms triggered, high variability of disease-related interactions, and an overlap of manifestations among diseases. Human peripheral blood mononuclear cells (PBMCs) contain protein signatures corresponding to essential immunological interplay. Certain diseases stimulate PBMCs and contribute towards modulation of their proteome which can be effectively identified and evaluated via the comparative proteomics approach.

EXPERIMENTAL DESIGN

In this review, we made a detailed survey of the PBMCS-derived protein biomarker candidates for a variety of diseases, published in the last 15 years. Articles were preselected to include only comparative proteomics studies.

RESULTS

PBMC-derived biomarkers were investigated for cancer, glomerular, neurodegenerative/neurodevelopmental, psychiatric, chronic inflammatory, autoimmune, endocrinal, infectious, and other diseases. A detailed review of these studies encompassed the proteomics platforms, proposed candidate biomarkers, their immune cell type specificity, and potential clinical application.

CONCLUSIONS

Overall, PBMCs have shown a solid potential in giving early diagnostic and prognostic biomarkers for many diseases. The future of PBMC biomarker research should reveal its full potential through well-designed comparative studies and extensive testing of the most promising protein biomarkers identified so far.

Tandem mass tag (TMT) quantitative protein analysis-based proteomics and parallel reaction monitoring (PRM) validation revealed that MST4 accelerates osteosarcoma proliferation by increasing MRC2 activity

Osteosarcoma is one of the most common orthopedic malignancies and is characterized by rapid disease progression and a poor prognosis. Currently, research on methods to inhibit osteosarcoma proliferation is still limited. In this study, we found that MST4 levels were significantly increased in osteosarcoma cell lines and tumor tissues compared to normal controls and demonstrated that MST4 is an influential factor in promoting osteosarcoma proliferation both in vivo and in vitro. Proteomic analysis was performed on osteosarcoma cells in the MST4 overexpression and vector expression groups, and 545 significantly differentially expressed proteins were identified and quantified. The candidate differentially expressed protein MRC2 was then identified using parallel reaction monitoring validation. Subsequently, MRC2 expression was silenced with small interfering RNA (siRNA), and we were surprised to find that this alteration affected the cell cycle of MST4-overexpressing osteosarcoma cells, promoted apoptosis and impaired the positive regulation of osteosarcoma growth by MST4. In conclusion, this study identified a novel approach for suppressing osteosarcoma proliferation. Reduction of MRC2 activity inhibits osteosarcoma proliferation in patients with high MST4 expression by altering the cell cycle, which may be valuable for treating osteosarcoma and improving patient prognosis.

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.

Quantitative proteomic screening uncovers candidate diagnostic and monitoring serum biomarkers of ankylosing spondylitis

BACKGROUND

We sought to discover serum biomarkers of ankylosing spondylitis (AS) for diagnosis and monitoring disease activity.

METHODS

We studied biologic-treatment-naïve AS and healthy control (HC) patients' sera. Eighty samples matched by age, gender, and race (1:1:1 ratio) for AS patients with active disease, inactive disease, and HC were analyzed with SOMAscan™, an aptamer-based discovery platform. T-tests tests were performed for high/low-disease activity AS patients versus HCs (diagnosis) and high versus low disease activity (Monitoring) in a 2:1 and 1:1 ratio, respectively, to identify differentially expressed proteins (DEPs). We used the Cytoscape Molecular Complex Detection (MCODE) plugin to find clusters in protein-protein interaction networks and Ingenuity Pathway Analysis (IPA) for upstream regulators. Lasso regression analysis was performed for diagnosis.

RESULTS

Of the 1317 proteins detected in our diagnosis and monitoring analyses, 367 and 167 (317 and 59, FDR-corrected q < .05) DEPs, respectively, were detected. MCODE identified complement, IL-10 signaling, and immune/interleukin signaling as the top 3 diagnosis PPI clusters. Complement, extracellular matrix organization/proteoglycans, and MAPK/RAS signaling were the top 3 monitoring PPI clusters. IPA showed interleukin 23/17 (interleukin 22, interleukin 23A), TNF (TNF receptor-associated factor 3), cGAS-STING (cyclic GMP-AMP synthase, Stimulator of Interferon Gene 1), and Jak/Stat (Signal transducer and activator of transcription 1), signaling in predicted upstream regulators. Lasso regression identified a Diagnostic 13-protein model predictive of AS. This model had a sensitivity of 0.75, specificity of 0.90, a kappa of 0.59, and overall accuracy of 0.80 (95% CI: 0.61-0.92). The AS vs HC ROC curve was 0.79 (95% CI: 0.61-0.96).

CONCLUSION

We identified multiple candidate AS diagnostic and disease activity monitoring serum biomarkers using a comprehensive proteomic screen. Enrichment analysis identified key pathways in AS diagnosis and monitoring. Lasso regression identified a multi-protein panel with modest predictive ability.

HSP90β promotes osteoclastogenesis by dual-activation of cholesterol synthesis and NF-κB signaling

Heat shock protein 90β (Hsp90β, encoded by Hsp90ab1 gene) is the most abundant proteins in the cells and contributes to variety of biological processes including metabolism, cell growth and neural functions. However, genetic evidences showing Hsp90β in vivo functions using tissue specific knockout mice are still lacking. Here, we showed that Hsp90β exerted paralogue-specific role in osteoclastogenesis. Using myeloid-specific Hsp90ab1 knockout mice, we provided the first genetic evidence showing the in vivo function of Hsp90β. Hsp90β binds to Ikkβ and reduces its ubiquitylation and proteasomal degradation, thus leading to activated NF-κB signaling. Meanwhile, Hsp90β increases cholesterol biosynthesis by activating Srebp2. Both pathways promote osteoclastogenic genes expression. Genetic deletion of Hsp90ab1 in osteoclast or pharmacological inhibition of Hsp90β alleviates bone loss in ovariectomy-induced mice. Therefore, Hsp90β is a promising druggable target for the treatment of osteoporosis.