Agammaglobulinemia due to p.E555K <i>TCF3 </i>Dominant-Negative Variant
Bibliographic record
Abstract
Transcription factor 3 (TCF3) dominant negative (DN) variant causes autosomal dominant (AD) E47 transcription factor deficiency. AD E47 transcription factor deficiency is an inborn error of immunity characterized by B-cell deficiency and agammaglobulinemia. Only the p.E555K variant has been reported as the causative variant in this disease. In this study, we describe the first Asian patient with wide phenotypes due to the p.E555K variant and highlight the unique features that contribute to the exclusivity of this variant. The patient was a 25-year-old male presenting with B-cell deficiency, agammaglobulinemia, and mild facial dysmorphic features. We confirmed the diagnosis of AD E47 transcription factor deficiency by the identification of a heterozygous missense variant c.1663G >A; p.E555K in the TCF3 gene. Alanine scanning of the E47 basic region revealed the structural Utsumi T, et al.3 significance of position 555. In addition, a comprehensive mutational analysis centered on position 555 found that only the amino acid substitution for lysine has a potent DN effect. Structurally the change from glutamic acid to lysine at position 555 not only abolished hydrogen bonds related to the protein-DNA interaction but also inverted the charge in the E47 protein surface. Our study reveals a partial aspect of the exclusivity of the E555K in the TCF3 DN variant.
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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) | 0.001 | 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 | 0.001 | 0.000 |
| 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".