Targeting phosphatase-dependent proteoglycan switch for rheumatoid arthritis therapy
Bibliographic record
Abstract
Despite the availability of several therapies for rheumatoid arthritis (RA) that target the immune system, a large number of RA patients fail to achieve remission. Joint-lining cells, called fibroblast-like synoviocytes (FLS), become activated during RA and mediate joint inflammation and destruction of cartilage and bone. We identify RPTPσ, a transmembrane tyrosine phosphatase, as a therapeutic target for FLS-directed therapy. RPTPσ is reciprocally regulated by interactions with chondroitin sulfate or heparan sulfate containing extracellular proteoglycans in a mechanism called the proteoglycan switch. We show that the proteoglycan switch regulates FLS function. Incubation of FLS with a proteoglycan-binding RPTPσ decoy protein inhibited cell invasiveness and attachment to cartilage by disrupting a constitutive interaction between RPTPσ and the heparan sulfate proteoglycan syndecan-4. RPTPσ mediated the effect of proteoglycans on FLS signaling by regulating the phosphorylation and cytoskeletal localization of ezrin. Furthermore, administration of the RPTPσ decoy protein ameliorated in vivo human FLS invasiveness and arthritis severity in the K/BxN serum transfer model of RA. Our data demonstrate that FLS are regulated by an RPTPσ-dependent proteoglycan switch in vivo, which can be targeted for RA therapy. We envision that therapies targeting the proteoglycan switch or its intracellular pathway in FLS could be effective as a monotherapy or in combination with currently available immune-targeted agents to improve control of disease activity in RA patients.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".