Myeloid cell transmigration across the CNS vasculature triggers IL-1β–driven neuroinflammation during autoimmune encephalomyelitis in mice
Bibliographic record
Abstract
Growing evidence supports a role for IL-1 in multiple sclerosis and experimental autoimmune encephalomyelitis (EAE), but how it impacts neuroinflammation is poorly understood. We show that susceptibility to EAE requires activation of IL-1R1 on radiation-resistant cells via IL-1β secreted by bone marrow-derived cells. Neutrophils and monocyte-derived macrophages (MDMs) are the main source of IL-1β and produce this cytokine as a result of their transmigration across the inflamed blood-spinal cord barrier. IL-1R1 expression in the spinal cord is found in endothelial cells (ECs) of the pial venous plexus. Accordingly, leukocyte infiltration at EAE onset is restricted to IL-1R1(+) subpial and subarachnoid vessels. In response to IL-1β, primary cultures of central nervous system ECs produce GM-CSF, G-CSF, IL-6, Cxcl1, and Cxcl2. Initiation of EAE or subdural injection of IL-1β induces a similar cytokine/chemokine signature in spinal cord vessels. Furthermore, the transfer of Gr1(+) cells on the spinal cord is sufficient to induce illness in EAE-resistant IL-1β knockout (KO) mice. Notably, transfer of Gr1(+) cells isolated from C57BL/6 mice induce massive recruitment of recipient myeloid cells compared with cells from IL-1β KO donors, and this recruitment translates into more severe paralysis. These findings suggest that an IL-1β-dependent paracrine loop between infiltrated neutrophils/MDMs and ECs drives neuroinflammation.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".