CYFIP2 is highly abundant in CD4<sup>+</sup> cells from multiple sclerosis patients and is involved in T cell adhesion
Bibliographic record
Abstract
DNA microarray profiling of CD4(+) and CD8(+) cells from non-treated relapsing and remitting multiple sclerosis (MS) patients determined that the cytoplasmic binding partner of fragile X protein (CYFIP2, also called PIR121) was increased significantly compared to healthy controls. Western analysis confirmed that CYFIP2 protein was increased approximately fourfold in CD4(+) cells from MS compared to inflammatory bowel disorder (IBD) patients or healthy controls. Because CYFIP2 acts as part of a tetrameric complex that regulates WAVE1 activation we hypothesized that high levels of CYFIP2 facilitate T cell adhesion, which is elevated in MS patients. Several findings indicated that increased levels of CYFIP2 facilitated adhesion. First, adenoviral-mediated overexpression of CYFIP2 in Jurkat cells increased fibronectin-mediated adhesion. Secondly, CYFIP2 knock-down experiments using antisense oligodeoxynucleotides reduced fibronectin-mediated binding in Jurkat and CD4(+) cells. Thirdly, inhibition of Rac-1, a physical partner with CYFIP2 and regulator of WAVE1 activity, reduced fibronectin-mediated adhesion in Jurkat and CD4(+) cells. Finally, inhibition of Rac-1 or reduction of CYFIP2 protein decreased fibronectin-mediated adhesion in CD4(+) cells from MS patients to levels similar to controls. These studies suggest that overabundance of CYFIP2 protein facilitates increased adhesion properties of T cells from MS 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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".