Genome-Wide Transcriptional Analyses of Islet-Specific CD4+ T Cells Identify <i>Idd9</i> Genes Controlling Diabetogenic T Cell Function
Bibliographic record
Abstract
Type 1 diabetes (T1D) is a polygenic disease with multiple insulin-dependent diabetes (Idd) loci predisposing humans and NOD mice to disease. NOD.B10 Idd9 congenic mice, in which the NOD Idd9 chromosomal region is replaced by the Idd9 from T1D-resistant C57BL/10 mice, are significantly protected from T1D development. However, the genes and pathways conferring T1D development or protection by Idd9 remain to be fully elucidated. We have developed novel NOD.B10-Idd9 (line 905) congenic mice that predominantly harbor islet-reactive CD4(+) T cells expressing the BDC2.5 TCR (BDC-Idd9.905 mice). To establish functional links between the Idd9 genotype and its phenotype, we used microarray analyses to investigate the gene expression profiles of ex vivo and Ag-activated CD4(+) T cells from these mice and BDC2.5 (BDC) NOD controls. Among the differentially expressed genes, those located within the Idd9 region were greatly enriched in islet-specific CD4(+) T cells. Bioinformatics analyses of differentially expressed genes between BDC-Idd9.905 and BDC CD4(+) T cells identified Eno1, Rbbp4, and Mtor, all of which are encoded by Idd9 and part of gene networks involved in cellular growth and development. As predicted, proliferation and Th1/Th17 responses of islet-specific CD4(+) T cells from BDC-Idd9.905 mice following Ag stimulation in vitro were reduced compared with BDC mice. Furthermore, proliferative responses to endogenous autoantigen and diabetogenic function were impaired in BDC-Idd9.905 CD4(+) T cells. These findings suggest that differential expression of the identified Idd9 genes contributed to Idd9-dependent T1D susceptibility by controlling the diabetogenic function of islet-specific CD4(+) T cells.
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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 | 0.001 |
| 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) | 0.001 | 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".