The hedgehog pathway suppresses neuropathogenesis in CD4 T cell-driven inflammation
Bibliographic record
Abstract
The concerted actions of the CNS and the immune system are essential to coordinating the outcome of neuroinflammatory responses. Yet, the precise mechanisms involved in this crosstalk and their contribution to the pathophysiology of neuroinflammatory diseases largely elude us. Here, we show that the CNS-endogenous hedgehog pathway, a signal triggered as part of the host response during the inflammatory phase of multiple sclerosis and experimental autoimmune encephalomyelitis, attenuates the pathogenicity of human and mouse effector CD4 T cells by regulating their production of inflammatory cytokines. Using a murine genetic model, in which the hedgehog signalling is compromised in CD4 T cells, we show that the hedgehog pathway acts on CD4 T cells to suppress the pathogenic hallmarks of autoimmune neuroinflammation, including demyelination and axonal damage, and thus mitigates the development of experimental autoimmune encephalomyelitis. Impairment of hedgehog signalling in CD4 T cells exacerbates brain-brainstem-cerebellum inflammation and leads to the development of atypical disease. Moreover, we present evidence that hedgehog signalling regulates the pathogenic profile of CD4 T cells by limiting their production of the inflammatory cytokines granulocyte-macrophage colony-stimulating factor and interferon-γ and by antagonizing their inflammatory program at the transcriptome level. Likewise, hedgehog signalling attenuates the inflammatory phenotype of human CD4 memory T cells. From a therapeutic point of view, our study underlines the potential of harnessing the hedgehog pathway to counteract ongoing excessive CNS inflammation, as systemic administration of a hedgehog agonist after disease onset effectively halts disease progression and significantly reduces neuroinflammation and the underlying neuropathology. We thus unveil a previously unrecognized role for the hedgehog pathway in regulating pathogenic inflammation within the CNS and propose to exploit its ability to modulate this neuroimmune network as a strategy to limit the progression of ongoing neuroinflammation.
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 | 0.000 | 0.001 |
| 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".