FMNL1 promotes T cell extravasation and trafficking to sites of inflammation
Bibliographic record
Abstract
Abstract To execute their effector function, activated T cells traffic through the blood stream to sites of inflammation. To enter tissues, T cells extravasate through the vascular endothelial cell wall by a process known as transendothelial migration (TEM). While many of the adhesion molecules and chemokine signaling pathways required for TEM have been previously characterized, little is known about how downstream cytoskeletal effectors mediate the mechanical forces and shape changes needed for T cell TEM. Formin-like 1 (FMNL1) is a terminal cytoskeletal effector directly involved 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 FMNL1 in activated T cell extravasation and trafficking to sites of inflammation. While activated FMNL1 deficient T cells had normal migration in response to chemokine across 5μm pore transwell membranes, they displayed an impaired ability to migrate through more restrictive 3μm pores. In vitro time-lapse microscopy data suggest that FMNL1 deficient T cells have a reduced ability to complete TEM across brain endothelium. FMNL1 deficient T cells were also impaired in their ability to traffic to the inflamed central nervous system in vivo. Finally, adoptively transferred autoreactive FMNL1 deficient T cells were impaired in their ability to induce Experimental Autoimmune Encephalitis. Together these data suggest that FMNL1 is required for the efficient migration of activated T cells though restrictive endothelial barriers and may be a promising therapeutic target for regulating T cell trafficking in autoimmunity.
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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 | 0.001 |
| 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".