Nexilin is a dynamic component of Listeria monocytogenes and enteropathogenic Escherichia coli actin-rich structures
Bibliographic record
Abstract
The bacterial pathogens Listeria monocytogenes and enteropathogenic Escherichia coli (EPEC) generate motile actin-rich structures (comet tails and pedestals) as part of their infectious processes. Nexilin, an actin-associated protein and a component of focal adhesions, has been suggested to be involved in actin-based motility. To determine whether nexilin is commandeered during L. monocytogenes and EPEC infections, we infected cultured cells and found that nexilin is crucial for L. monocytogenes invasion as levels of internalized bacteria were significantly decreased in nexilin-targeted siRNA-treated cells. In addition, nexilin is a component of the machinery that drives the formation of L. monocytogenes comet tails and EPEC pedestals. Nexilin colocalizes with stationary bacteria and accumulates at the distal portion of comet tails and pedestals of motile bacteria. We also show that nexilin is crucial for efficient comet tail formation as cells pre-treated with nexilin siRNA generate malformed comet tails, whereas nexilin is dispensable during EPEC pedestal generation. These findings demonstrate that nexilin is required for efficient infection with invasive and adherent bacteria and is key to the actin-rich structures these microbes generate.
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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) | 0.001 | 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".