Loss of T Cell Progenitor Checkpoint Control Underlies Leukemia Initiation in <i>Rag1</i>-Deficient Nonobese Diabetic Mice
Bibliographic record
Abstract
NOD mice exhibit major defects in the earliest stages of T cell development in the thymus. Genome-wide genetic and transcriptome analyses were used to investigate the origins and consequences of an early T cell developmental checkpoint breakthrough in Rag1-deficient NOD mice. Quantitative trait locus analysis mapped the presence of checkpoint breakthrough cells to several known NOD diabetes susceptibility regions, particularly insulin-dependent diabetes susceptibility genes (Idd)9/11 on chromosome 4, suggesting common genetic origins for T cell defects affecting this trait and autoimmunity. Genome-wide RNA deep-sequencing of NOD and B6 Rag1-deficient thymocytes revealed the effects of genetic background prior to breakthrough, as well as the cellular consequences of the breakthrough. Transcriptome comparison between the two strains showed enrichment in differentially expressed signal transduction genes, prominently tyrosine kinase and actin-binding genes, in accord with their divergent sensitivities to activating signals. Emerging NOD breakthrough cells aberrantly expressed both stem cell-associated proto-oncogenes, such as Lmo2, Hhex, Lyl1, and Kit, which are normally repressed at the commitment checkpoint, and post-β-selection checkpoint genes, including Cd2 and Cd5. Coexpression of genes characteristic of multipotent progenitors and more mature T cells persists in the expanding population of thymocytes and in the thymic leukemias that emerge with age in these mice. These results show that Rag1-deficient NOD thymocytes have T cell defects that can collapse regulatory boundaries at two early T cell checkpoints, which may predispose them to both leukemia and autoimmunity.
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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 | 0.001 |
| 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".