Outer Membrane Vesicles Hijack TIM-1 for Cellular Uptake
Bibliographic record
Abstract
Abstract Outer membrane vesicles (OMVs) are nanoscale proteoliposomes shed by Gram-negative bacteria that mediate host-pathogen interactions and hold promise as platforms for vaccines and targeted drug delivery. Despite their biological and translational significance, the cellular mechanisms governing OMV entry into host cells remain poorly understood. Here, we demonstrate that E. coli OMVs are internalized by epithelial cells via clathrin-mediated, receptor-dependent endocytosis. Using a high-throughput screen of over 1,500 human single-pass transmembrane proteins, we identify T-cell immunoglobulin and mucin-domain 1 (TIM-1) as a strong OMV-binding receptor. Functional validation revealed that TIM-1 overexpression markedly increased OMV uptake, whereas TIM-1 knockout and antibody-mediated blockade significantly impaired internalization across multiple cell lines. Mechanistic studies demonstrate that TIM-1 binds to lipopolysaccharide (LPS) on the OMV surface via its known phosphatidylserine-binding domain. Uptake of OMVs by TIM-1 triggers proinflammatory cytokine production which can be reduced by preventing this interaction. Additionally, OMVs from multiple bacterial species hijack TIM-1 for entry, making it an intriguing antivirulence strategy. Our findings establish TIM-1 as a critical host receptor mediating OMV uptake and provide a novel approach to modulate vesicle-driven pathogenesis and enhance OMV-based therapies.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".