Trisomy 8-associated Autoinflammatory Disease (TRIAD) is Characterized by Dysregulated Myeloid Cells
Bibliographic record
Abstract
Abstract Trisomy 8 mosaicism (T8M) has been associated with a Behçet’s-like inflammatory disease, but immunologic features and treatment responses are not well-characterized. Here, we characterize 20 individuals with constitutional T8M and inflammatory disease. Most participants had congenital dysmorphologies and developmental delay. Two developed hematologic malignancies, two had bleeding diathesis due to platelet dense granule deficiency, and most had macrocytosis. The majority had recurrent fever and severe oral ulcerations, while nearly half had genital ulcers or rash. Colchicine, apremilast, and IL-1 and TNFa inhibitors were effective therapies. With ddPCR on sorted cell populations, we found that cells from the myeloid lineage (monocytes, neutrophils, megakaryocytes, erythroid progenitors) had a significantly higher percentage of trisomy 8 mosaicism compared to those from the lymphoid lineage (T and B cells) in both the peripheral blood and bone marrow, suggesting that trisomy 8 is tolerated to a greater degree by myeloid cells. Furthermore, we found that participants with T8M had more classical monocytes and had upregulation of genes associated with activated neutrophils and monocytes in their whole blood compared to healthy controls. With single cell RNAseq, we identifed which cells were trisomy 8 and disomy based on chromosome 8 gene expression and found that trisomy 8 monocytes had distinct transcriptional signatures and alteration of innate immune genes. Our findings suggest that patients with T8M are prone to a distinct autoinflammatory disease which we propose calling trisomy 8 associated autoinflammatory disease (TRIAD) and have complications due to dysregulation of cells arising from the myeloid lineage. This work was supported by the Intramural Research Program of NIAID, NHGRI, and NHLBI, NIH.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".