Qi Zhu | PubMed Health

PubMed2026

Plasma Proteomics Identifies Thousand-and-One-Amino Acid Kinase 3 as a Potential Biomarker of Rheumatoid Arthritis Activity and a Novel Therapeutic Target.

OBJECTIVE: Bone destruction associated with active rheumatoid arthritis (RA) remains a major therapeutic challenge, with a lack of reliable molecular markers reflecting bone injury. This study aims to identify novel biomarkers linked to bone destruction in active RA through proteomic analysis, providing new strategies for precise monitoring and targeted therapy. METHODS: Data-independent acquisition mass spectrometry was used for proteomic quantification and bioinformatic analysis on plasma samples from 160 patients with RA and 40 healthy controls. Key proteins associated with bone destruction were screened by integrating Sharp scores with synovial single-cell RNA sequencing data and subsequently validated in two independent cohorts (N1 = 50 and N2 = 10) using enzyme-linked immunosorbent assay and multiplex immunohistochemistry. Functional studies were conducted using fibroblast-like synoviocytes (FLSs) in vitro and a collagen-induced arthritis (CIA) mouse model in vivo. RESULTS: A total of 4,998 plasma proteins were identified, with 506 showing significant differential expression between active and remitted RA. Thousand-and-one-amino acid kinase 3 (TAOK3) levels were positively associated with Sharp scores and markedly elevated in patients with active RA. Combining TAOK3 with C-reactive protein improved diagnostic accuracy for active RA (area under the curve = 0.915). High TAOK3 expression was also associated with increased relapse frequency. Functional studies showed that TAOK3 knockdown suppressed the tumor-like phenotype of FLSs and down-regulated matrix metalloproteinase 1/2/3 and cathepsin K, whereas TAOK3 overexpression promoted pannus cell-mediated bone erosion, mitigated by TAOK3-targeted inhibitor. In vivo, its inhibition showed therapeutic effects in CIA mice. CONCLUSION: TAOK3 serves as a potential biomarker for bone destruction in active RA and as a therapeutic target for precision monitoring and intervention.

Arthritis, Rheumatoid↗

PubMed2025

MWENA: a novel sample re-weighting-based algorithm for disease classification and data interpretation using extracellular vesicles omics data.

BACKGROUND AND OBJECTIVE: Extracellular vesicles (EVs), considered as a form of liquid biopsy, have gained significant attention in recent years due to their stability and the preservation of disease markers. Research studies underscore the clinical significance of molecules found in EVs, highlighting their role as communicative mediators between cells. However, analyzing this data is challenging due to noisy measurements, having far more variables than samples, and some groups (e.g., disease subtypes or experimental conditions) having much less data than others. We therefore develop an algorithm to address aforementioned challenges for the classification of imbalanced EVs omics data. METHODS AND RESULTS: We propose the EV Meta-Weight Elastic Net Algorithm (MWENA), which utilizes logistic regression with elastic net regularization for the classification and identification of EV signatures, effectively addressing the challenges posed by high-dimensional small sample sizes. To mitigate issues related to class imbalance and high noise levels, MWENA incorporates an automatic sample re-weighting function, which uses a meta-net to adaptively learn generalizable patterns directly from the data itself. We validate the MWENA algorithm on both simulated data and EVs omics data, covering six classification tasks that involve four different types of diseases (pancreatic ductal adenocarcinoma, interstitial lung diseases, colorectal cancer, and ovarian cancer) and three clinical scenarios (disease diagnosis, disease-stage screening, and disease-subtype classification). Compared to other machine learning methods, MWENA demonstrates superiority in identifying small class samples and achieves the highest scores in both sensitivity and G-means. Biological analysis is also performed to further explore the significance of selected signatures as biological markers and their roles in disease mechanisms. CONCLUSIONS: We anticipate that our proposed approach will take a modest step in harnessing EV omics data to discover biomarkers, aiding researchers in gaining a comprehensive understanding of biological processes.

Extracellular Vesicles↗