Type I interferon limits central nervous system autoimmunity by modulating the microRNA-21–FOXO1 axis in pathogenic T helper 17 cells
Bibliographic record
Abstract
IFN-β, a type I interferon, has been used as a first-line therapy for patients with multiple sclerosis (MS) for more than 30 years; however, the cellular and molecular basis of its therapeutic efficacy remains unclear. Here, we first used experimental autoimmune encephalomyelitis (EAE), a mouse model for MS, to show that the therapeutic effects of IFN-β were associated with a down-regulation of microRNA-21 (miR-21) and pathogenic T H 17 (pT H 17) cells. In vitro experiments demonstrated that genetic knockout of miR-21 directly inhibited pathogenic T H 17 cell differentiation. Further mechanistic investigations revealed that miR-21 promoted pathogenic T H 17 differentiation by inhibiting the transcription factor Forkhead box protein O1 ( Foxo1 ). Accordingly, miR-21 loss abrogated pathogenic T H 17 differentiation and conferred resistance to EAE. Treatment of T cell monocultures with IFN-β showed that IFN-β did not directly limit miR-21 expression. Instead, IFN-β treatment inhibited the secretion of miR-21–inducing cytokines from myeloid cells, reduced miR-21 induction within cocultured T cells, and inhibited pathogenic T H 17 development. In patient samples, immunophenotypic and targeted transcriptomic analyses revealed that compared with IFN-β treatment responders, nonresponders expressed elevated miR-21–inducing cytokines within myeloid cells, alongside increased miR-21 and pathogenic T H 17 cytokines within CD4 + T cells. Direct miR-21 inhibition reduced pathogenic T H 17 differentiation in nonresponder CD4 + T cells. These results suggest that type I IFN signaling limits central nervous system autoimmunity by inhibiting miR-21–mediated pathogenic T H 17 development. miR-21 inhibition may be of potential therapeutic value specifically for the IFN-β nonresponder cohort.
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 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 | 0.000 |
| 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 | 0.001 |
| 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".