Defects in B Cell Differentiation and Antibody Production due to Biallelic TANK Mutation
Bibliographic record
Abstract
Exposure to bacterial respiratory pathogens is commonplace, but severe recurrent disease requiring hospitalization may suggest an underlying IEI. We studied a pair of siblings with decade-long histories of severe recurrent lower respiratory tract infections and positive cultures for Streptococcus pnuemoniae. These patients had normal total B cell counts but reduced generation of B cell memory and low antibody levels. CyTOF analysis showed all other immune cell subsets were present at normal frequencies. No mutations in known IEI-causal genes explained these patients’ phenotypes. By whole-exome sequencing, we identified a novel mutation in TANK, which segregated in an autosomal recessive manner. This mutation caused a frameshift and early truncation of the TANK protein, and complete TANK deficiency in the patients’ cells. TANK is an adaptor protein with poorly characterized roles in both canonical and noncanonical NF-kB signaling. Using an in vitro B cell differentiation assay, we know that TANK-deficient patients’ B cells seem “blocked” at the IgD-CD27- double-negative stage and proliferate poorly. Using scRNA-seq of patient and healthy control samples, we show an accumulation of intermediate B cells with a unique gene, including high SOX5 expression. Mechanistically, TANK suppresses the canonical NF-kB pathway and serves as a critical determinant of B cell proliferation and differentiation, antibody secretion, and protection from respiratory pathogens.
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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 | 0.001 | 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) | 0.003 | 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".