Identification of prohibitin as a target of IVIg on human B cells
Bibliographic record
Abstract
IVIg are therapeutic preparations of human IgG prepared from the plasma of thousands of donors and are currently used to treat more than one hundred autoimmune or inflammatory diseases, although their mechanism of action is not precisely known in any of these diseases. Previous work from our laboratory showed that IVIg could directly influence the fate of human B cells by inducing their differentiation (Blood, 2003; 101:3065). The goal of the present study was to identify the membrane target of IVIg on human B cells. We used two different methods to isolate the B cell membrane proteins recognized by IVIg (classical immunoprecipitation following incubation with IVIg and cell solubilization, and affinity chromatography of soluble membrane proteins on IVIg‐Sepharose). The recovered proteins were identified by LC‐MS. Surprisingly, most of them were not membrane receptors but intracellular proteins such as actin, prohibitin 1 and 2, heat shock cognate 71KDa protein and RPL13. Since prohibitin is known to be associated with the BCR, at least in IgM‐expressing B cells (EMBO J, 1994; 13:3782), and to regulate events such as ERK phosphorylation and progression in the cell cycle, we have examined more closely the association between IVIg, BCR and prohibitin. Preliminary results suggest that IVIg interact with prohibitin in a BCR‐independent way. The role of prohibitin in the effects of IVIg on human B cells is currently under study.
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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) | 0.001 | 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".