Molecular Mechanisms Involved in Lymphocyte Recruitment in Inflamed Brain Microvessels: Critical Roles for P-Selectin Glycoprotein Ligand-1 and Heterotrimeric Gi-Linked Receptors
Bibliographic record
Abstract
Lymphocyte recruitment into the brain is a critical event in the pathogenesis of multiple sclerosis and experimental autoimmune encephalomyelitis. We developed a novel intravital microscopy model to directly analyze through the skull the interactions between lymphocytes and the endothelium in cerebral venules of mice. No adhesive interactions were observed between lymphocytes and the nonactivated endothelium in the cerebral microcirculation. When brain venules were activated by pretreating mice with TNF-alpha or LPS, proteolipid protein 139-151 autoreactive T lymphocytes rolled and arrested; notably, only a few peripheral lymph node cells rolled and firmly adhered. Abs anti-P-selectin glycoprotein ligand-1 and anti-E- and P-selectin blocked tethering and rolling of autoreactive lymphocytes, suggesting that P-selectin glycoprotein ligand-1/endothelial selectins are critical in the recruitment of lymphocytes in inflamed brain venules. E- and P-selectin were expressed on cerebral vessels upon in vivo activation and had a patchy distribution during the preclinical phase of active and passive experimental autoimmune encephalomyelitis. LFA-1/ICAM-1 and alpha(4) integrins/VCAM-1 supported rolling, but were not relevant to rolling velocity. Firm arrest was mainly mediated by LFA-1 and ICAM-1. Pretreatment of autoreactive lymphocytes with pertussis toxin blocked integrin-dependent arrest, implicating a requirement for G(i) protein-dependent signaling in vessels from nonlymphoid districts. In conclusion, our data unveils the molecular mechanisms controlling the recruitment of autoreactive lymphocytes in inflamed cerebral vessels and suggest new insights into the pathogenesis of autoimmune inflammatory diseases 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) | 0.000 | 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".