Ubiquitous dysregulation of the Wnt pathway in immune thrombocytopenia genetic susceptibility
Bibliographic record
Abstract
The pathogenesis of immune thrombocytopenia (ITP) is complex and incompletely understood. Multiple cell types have been implicated, and their respective contribution is unclear. A recent genome-wide association study identified single-nucleotide polymorphisms (SNPs) associated with pediatric ITP within or near 5 genes in the canonical Wnt signaling pathway. To investigate whether this pathway was dysregulated in ITP and identify which cell types were involved, we leveraged extensive functional genomics and single-cell RNA sequencing (scRNA-seq) data. By linking the identified SNPs to likely regulated genes, we showed an enrichment in the Wnt pathway and identified 2 additional genes in this pathway involved in ITP. The SNPs affected regulation of the Wnt pathway genes in multiple cell types. Indeed, scRNA-seq showed some of these genes were expressed in lymphocytes, whereas others were expressed in platelets or megakaryocytes. By comparing the cell-specific expression of these genes between individuals with ITP and healthy controls, we demonstrated that several genes in the Wnt pathway were differentially expressed between these 2 groups. Some genes were upregulated, whereas other were downregulated in ITP. In sum, the Wnt signaling pathway is broadly dysregulated in ITP, with a complex pattern that varies across genes and cell types.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".