Follicular helper T cell signature in type 1 diabetes
Bibliographic record
Abstract
The strong genetic association between particular HLA alleles and type 1 diabetes (T1D) indicates a key role for CD4+ T cells in disease; however, the differentiation state of the responsible T cells is unclear. T cell differentiation originally was considered a dichotomy between Th1 and Th2 cells, with Th1 cells deemed culpable for autoimmune islet destruction. Now, multiple additional T cell differentiation fates are recognized with distinct roles. Here, we used a transgenic mouse model of diabetes to probe the gene expression profile of islet-specific T cells by microarray and identified a clear follicular helper T (Tfh) cell differentiation signature. Introduction of T cells with a Tfh cell phenotype from diabetic animals efficiently transferred diabetes to recipient animals. Furthermore, memory T cells from patients with T1D expressed elevated levels of Tfh cell markers, including CXCR5, ICOS, PDCD1, BCL6, and IL21. Defects in the IL-2 pathway are associated with T1D, and IL-2 inhibits Tfh cell differentiation in mice. Consistent with these previous observations, we found that IL-2 inhibited human Tfh cell differentiation and identified a relationship between IL-2 sensitivity in T cells from patients with T1D and acquisition of a Tfh cell phenotype. Together, these findings identify a Tfh cell signature in autoimmune diabetes and suggest that this population could be used as a biomarker and potentially targeted for T1D interventions.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".