Immunomodulation of human B cells following treatment with intravenous immunoglobulins involves increased phosphorylation of extracellular signal-regulated kinases 1 and 2
Bibliographic record
Abstract
In the treatment of autoimmune diseases, intravenous Igs (IVIg) are assumed to modulate immune cells through the binding of surface receptors. IVIg act upon definite human B cell populations to modulate Ig repertoire, and such modulation might proceed through intracellular signaling. However, the heterogeneity of human B cell populations complicates investigations of the intracellular pathways involved in IVIg-induced B cell modulation. The aim of this study was to establish a model allowing the screening of IVIg signal transduction in human B cell lines and to attempt transposing observations made in cell lines to normal human B lymphocytes. Nine human B cell lines were treated with IVIg with the goal of selecting the most suitable model for human B lymphocytes. The IgG(+) DB cell line, whose response was similar to that of human B lymphocytes, showed reduced IVIg modulation following addition of PD98059, an inhibitor of extracellular signal-regulated protein kinase 1/2 (ERK1/2). The IVIg-induced ERK1/2 phosphorylation was indeed proportional to the dosage of monomeric IVIg used when tested on DB cells as well as Pfeiffer cells, another IgG(+) cell line. In addition, two other intermediates, Grb2-associated binder 1 (Gab1) and Akt, showed increased phosphorylation in IVIg-treated DB cells. IVIg induction of ERK1/2 phosphorylation was finally observed in peripheral human B lymphocytes, specifically within the IgG(+) B cell population. In conclusion, IVIg immunomodulation of human B cells can thus be linked to intracellular transduction pathways involving the phosphorylation of ERK1/2, which in combination with Gab1 and Akt, may be related to B cell antigen receptor signaling.
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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) | 0.002 | 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".