Transcriptional re-programming of insulin B-chain epitope-specific T-follicular helper cells into anti-diabetogenic T-regulatory type-1 cells
Bibliographic record
Abstract
Systemic delivery of nanoparticles (NPs) coated with mono-specific autoimmune disease-relevant peptide-major histocompatibility complex class II (pMHCII) molecules can resolve organ inflammation in various disease models in a disease-specific manner without impairing normal immunity. These compounds invariably trigger the formation and systemic expansion of cognate pMHCII-specific T-regulatory type 1 (TR1) cells. By focusing on type 1 diabetes (T1D)-relevant pMHCII-NP types that display an epitope from the insulin B-chain bound to the same MHCII molecule (IAg7) on three different registers, we show that pMHCII-NP-induced TR1 cells invariably co-exist with cognate T-Follicular Helper (TFH)-like cells of quasi-identical clonotypic composition and are oligoclonal, yet transcriptionally homogeneous. Furthermore, these three different TR1 specificities have similar diabetes reversal properties in vivo despite being uniquely reactive against the peptide MHCII-binding register displayed on the NPs. Thus, pMHCII-NP treatment using nanomedicines displaying different epitope specificities results in the simultaneous differentiation of multiple antigen-specific TFH-like cell clones into TR1-like cells that inherit the fine antigenic specificity of their precursors while acquiring a defined transcriptional immunoregulatory program.
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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 | 0.000 |
| 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".