Evidence for a Role of TGF-β-Activated Kinase 1 and MAP3K7 Binding Protein 3 in Peanut-Specific T-Cell Responses
Bibliographic record
Abstract
Peanut allergy is considered to be the most common cause for food-induced anaphylaxis. Currently, no approved treatment is available. Avoidance is the only measure to prevent anaphylactic reactions to peanuts. T-helper cells are of special importance for the sensitization process and the maintenance of allergic inflammation. Identifying markers of allergen-specific T-cell responses may help to develop novel treatment approaches. Therefore, we aimed to define new T-cell target genes in Ara h 2-specific T cells and to investigate the possibility of using them as biomarkers of peanut allergy in peripheral blood mononuclear cells (PBMCs). We performed whole mRNA array analysis (whole human genome oligo microarray) of in vitro expanded Ara h 2-specific T cells (CFSElowCD3+CD4+) from 5 peanut-allergic (PA) and 5 non-peanut-sensitized individuals. Expression of selected genes as a result of a two-step bioinformatic approach was confirmed in a second cohort by quantitative PCR. TGF-β- activated kinase 1 and MAP3K7 binding protein 3 (TAB3), calcium/calmodulin-dependent protein kinase type IV (CAMK4) and HemK methyltransferase family member 1 (HEMK1) were significantly upregulated in Ara h 2-specific T cells of PA patients. In addition, the expression of these genes was also assessed in unstimulated PBMCs from a cohort (n = 43) of PA, atopic non-PA, and nonatopic controls. Interestingly, in unstimulated PBMCs, TAB3 expression was significantly downregulated in PA patients compared to atopic non-PA individuals. Thus, TAB3 may play a significant role at the level of T-cell activation and may also be a candidate biomarker for PA.
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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".