Plasma Proteomics Identifies Thousand‐and‐One–Amino Acid Kinase 3 as a Potential Biomarker of Rheumatoid Arthritis Activity and a Novel Therapeutic Target
Bibliographic record
Abstract
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 (N 1 = 50 and N 2 = 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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".