Transcriptional re-programming of liver-resident iNKT cells into T-regulatory type-1-like liver iNKT cells involves extensive gene de-methylation
Bibliographic record
Abstract
Unlike conventional CD4+ T cells, which are phenotypically and functionally plastic, invariant NKT (iNKT) cells generally exist in a terminally differentiated state. Naïve CD4+ T cells can acquire alternative epigenetic states in response to different cues, but it remains unclear whether peripheral iNKT cells are epigenetically stable or malleable. Repetitive encounters of liver-resident iNKT cells (LiNKTs) with alpha-galactosylceramide (αGalCer)/CD1d-coated nanoparticles (NPs) can trigger their differentiation into a LiNKT cell subset expressing a T regulatory type 1 (TR1)-like (LiNKTR1) transcriptional signature. Here we dissect the epigenetic underpinnings of the LiNKT-LiNKTR1 conversion as compared to those underlying the peptide-major histocompatibility complex (pMHC)-NP-induced T-follicular helper (TFH)-to-TR1 transdifferentiation process. We show that gene upregulation during the LINKT-to-LiNKTR1 cell conversion is associated with demethylation of gene bodies, inter-genic regions, promoters and distal gene regulatory elements, in the absence of major changes in chromatin exposure or deposition of expression-promoting histone marks. In contrast, the naïve CD4+ T cell-to-TFH differentiation process involves extensive remodeling of the chromatin and the acquisition of a broad repertoire of epigenetic modifications that are then largely inherited by TFH cell-derived TR1 cell progeny. These observations indicate that LiNKT cells are epigenetically malleable and particularly susceptible to gene de-methylation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".