Interleukin 17, a nitric oxide-producing cytokine with a peroxynitrite-independent inhibitory effect on proteoglycan synthesis.
Bibliographic record
Abstract
OBJECTIVE: To compare the potency of 2 cytokines, interleukin 17 (IL-17) and IL-1beta, on rat cartilage proteoglycan synthesis with special attention to nitric oxide (NO) and peroxynitrite formation. METHODS: Chondrocytes in alginate beads were stimulated with human recombinant (rh) IL-17 (0.03 to 300.0 ng/ml) and/or rhIL-1beta (0.25 to 25.0 ng/ml) in the presence or not of L-NMMA or CuDips. Alternatively, rats were injected with either IL-17 (10.0 micro g) or IL-1beta (1.0 micro g) into each knee joint. NO concentrations were determined by a spectrofluorimetric assay, proteoglycan synthesis by 35SO4-2 incorporation, peroxynitrite generation by immunostaining for 3-nitrotyrosine, and IL-1beta mRNA expression by reverse transcription-polymerase chain reaction. RESULTS: IL-17 inhibited proteoglycan synthesis and increased NO production, both in vitro and in vivo, without inducing expression of IL-1beta mRNA in cartilage. Additive effects were observed when IL-17 was combined with low concentrations of IL-1. Surprisingly, a similar NO synthesis between IL-1 and IL-17 led to a less suppressive effect of IL-17 on cartilage anabolism than with IL-1. Both in vitro and in vivo, peroxynitrite formation was extensive with IL-1beta, but negligible or nonexistent with IL-17. L-NMMA and CuDips completely corrected the suppressive effect of IL-1beta on proteoglycan synthesis, unlike with IL-17. CONCLUSION: These data showed that NO is weakly involved in the IL-17 mediated inhibition of proteoglycan synthesis in rat. NO overload may not be predictive of any inhibitory effect on cartilage anabolism, but instead superoxide is a key regulator of NO contribution to chondrocyte dysfunction. Since IL-17 is a NO-producing cytokine with additive effects when combined with IL-1, it may play a pivotal role in cartilage destruction during rheumatoid arthritis, for which infiltrating cells produce high levels of superoxide and proinflammatory cytokines.
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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 | 0.000 |
| 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".