Determinants of Human B Cell Migration Across Brain Endothelial Cells
Bibliographic record
Abstract
Circulating B cells enter the CNS as part of normal immune surveillance and in pathologic states, including the common and disabling illness multiple sclerosis. However, little is known about the molecular mechanisms that mediate human B cell interaction with the specialized brain endothelial cells comprising the blood-brain barrier (BBB). We studied the molecular mechanisms that regulate the migration of normal human B cells purified ex vivo, across human adult brain-derived endothelial cells (HBECs). We found that B cells migrated across HBECs more efficiently than T cells from the same individuals. B cell migration was significantly inhibited by blocking Abs to the adhesion molecules ICAM-1 and VLA-4, but not VCAM-1, similar to the results previously reported for T cells. Blockade of the chemokines monocyte chemoattractant protein-1 and IL-8, but not RANTES or IFN-gamma-inducible protein-10, significantly inhibited B cell migration, and these results were correlated with the chemokine receptor expression of B cells measured by flow cytometry and by RNase protection assay. Tissue inhibitor of metalloproteinase-1, a natural inhibitor of matrix metalloproteinases, significantly decreased B cell migration across the HBECs. A comprehensive RT-PCR comparative analysis of all known matrix metalloproteinases and tissue inhibitors of metalloproteinases in human B and T cells revealed distinct profiles of expression of these molecules in the different cell subsets. Our results provide insights into the molecular mechanisms that underlie human B cell migration across the BBB. Furthermore, they identify potential common, and unique, therapeutic targets for limiting CNS B cell infiltration and predict how therapies currently developed to target T cell migration, such as anti-VLA-4 Abs, may impact on B cell trafficking.
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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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".