TLR4 Contributes to Disease-Inducing Mechanisms Resulting in Central Nervous System Autoimmune Disease
Bibliographic record
Abstract
Environmental factors strongly influence the development of autoimmune diseases, including multiple sclerosis. Despite this clear association, the mechanisms through which environment mediates its effects on disease are poorly understood. Pertussis toxin (PTX) functions as a surrogate for environmental factors to induce animal models of autoimmunity, such as experimental autoimmune encephalomyelitis. Although very little is known about the molecular mechanisms behind its function in disease development, PTX has been hypothesized to facilitate immune cell entry to the CNS by increasing permeability across the blood-brain barrier. Using intravital microscopy of the murine cerebromicrovasculature, we demonstrate that PTX alone induces the recruitment of leukocytes and of active T cells to the CNS. P-selectin expression was induced by PTX, and leukocyte/endothelial interactions could be blocked with a P-selectin-blocking Ab. P-selectin blockade also prevented PTX-induced increase in permeability across the blood-brain barrier. Therefore, permeability is a secondary result of recruitment, rather than the primary mechanism by which PTX induces disease. Most importantly, we show that PTX induces intracellular signals through TLR4, a receptor intimately associated with innate immune mechanisms. We demonstrate that PTX-induced leukocyte recruitment is dependent on TLR4 and give evidence that the disease-inducing mechanisms initiated by PTX are also at least partly dependent on TLR4. We propose that this innate immune pathway is a novel mechanism through which environment can initiate autoimmune disease of the CNS.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".