Inhibition of IgE and IgE/anti-IgE mediated responses in mast cells by Omalizumab
Bibliographic record
Abstract
IgE binding via the high affinity FcεRI receptor modulates FcεRI expression and cytokine production in mast cells. Antigen crosslinking of bound IgE further activates mast cells, inducing degranulation and inflammatory mediator release. Omalizumab (Xolair; Genentech Inc) is a recombinant human monoclonal anti-IgE antibody that prevents IgE binding to FcεRI. We investigated the effects of omalizumab on IgE-mediated responses in human mast cells. LAD2 and CD34 -derived human mast cell degranulation was determined by measuring the release of the granular enzyme, β-hexosaminidase. Toll-like receptor (TLR) expression was measured by quantitative (qPCR) and western blot analysis. IgE binding and FcεRI expression was determined by flow cytometry. Omalizumab (10 ug/mL) inhibited IgE binding to LAD2 cells by 78% (P=0.007) compared to untreated control. Omalizumab (10 ug/mL) further blocked IgE-dependent upregulation of FcεRI expression by 90% (P=0.03). In addition, omalizumab removed FcεRI-bound IgE in a time-dependent manner; an effect that was detected as early as 24 hrs (57% removal; P<0.001) after addition of omalizumab resulting in a concomitant decrease in IgE-dependent FcεRI expression (30%; P<0.001). Omalizumab attenuated degranulation induced by anti-IgE crosslinking of bound IgE in a dose dependent manner, with 66% inhibition (P<0.0001) at 25 ug/ml. Furthermore, 100 ug/ml omaluzimab prevented cysteinyl leukotriene production and FcεRI–dependent modulation of TLR expression. Omalizumab inhibits IgE and IgE/anti-IgE dependent degranulation and receptor expression by human mast cells. Furthermore, omalizumab is able to remove pre-bound IgE from sensitized mast cells thereby reducing their response to FcεRI-dependent signals. This data suggests that omalizumab is an effective inhibitor of both sensitized and unsensitized human mast cells.
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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) | 0.001 | 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 | 0.001 |
| 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".