mDia1 promotes T cell extravasation and enables induction of Experimental Autoimmune Encephalitis
Bibliographic record
Abstract
Abstract Self-reactive T cells play a central role in Multiple Sclerosis (MS) and invade the central nervous system (CNS) to drive neurological damage. To enter tissues, T cells extravasate through the vascular endothelial cell wall via a process known as transendothelial migration (TEM). While many of the adhesion molecules and chemokine signaling pathways required for TEM are well characterized, little is known about how downstream cytoskeletal effectors mediate the mechanical forces and shape changes needed for T cell TEM. Mouse Diaphanous Homolog 1 (mDia1) is a terminal cytoskeletal effector involved directly in actin network remodeling. It has been associated with the formation of membrane protrusions that likely function in migration and cell-cell interactions and is highly expressed by activated T cells. The goal of our study was to examine the role of mDia1 in T cell extravasation into the CNS and subsequent induction of neuroinflammation. mDia1 knock-out (KO) mice were partially protected from Experimental Autoimmune Encephalomyelitis (EAE), a mouse model of MS. mDia1 deficient ex-vivo activated auto-reactive T cells displayed similar activation kinetics compared to control T cells. However, when these activated mDia1 deficient T cells were transferred into wild-type mice, they were impaired in their ability to induce EAE. In vitro time-lapse microscopy data suggest that mDia1 deficient T cells have a reduced ability to complete TEM across brain endothelium. This defect may explain the reduced ability of mDia1 deficient T cells to induce EAE. Collectively our data suggest that mDia1 may be a promising therapeutic target for regulating T cell trafficking in MS.
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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 | 0.000 | 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 | 0.001 |
| 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".