FOXP3 <sup>+</sup> regulatory T cells use heparanase to access IL-2 bound to ECM in inflamed tissues
Bibliographic record
Abstract
Abstract FOXP3 + regulatory T cells (Treg) depend on exogenous IL-2 for their survival and function, but circulating levels of IL-2 are low, making it unclear how Treg access this critical resource in vivo . Here, we show that Treg use heparanase (HPSE) to access IL-2 sequestered by heparan sulfate (HS) within the extracellular matrix (ECM) of inflamed central nervous system tissue. HPSE expression distinguishes human and murine Treg from conventional T cells and is regulated by the availability of IL-2. HPSE -/- Treg have impaired stability and function in vivo , including the experimental autoimmune encephalomyelitis (EAE) mouse model of multiple sclerosis. Conversely, endowing Treg with HPSE enhances their ability to access HS-sequestered IL-2 and their tolerogenic function in vivo . Together, these data identify novel roles for HPSE and the ECM in immune tolerance, providing new avenues for improving Treg-based therapy of autoimmunity. One-Sentence Summary Regulatory T cells use heparanase to strip IL-2 bound to extracellular matrix within inflamed tissues, thereby supporting their homeostasis and function.
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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 | 0.000 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.013 | 0.005 |
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".