Knocking E. coli off of their pedestals: Understanding the strategies microbes exploit to generate morphological structures during their disease processes
Bibliographic record
Abstract
When the extracellular attaching and effacing pathogens [enteropathogenic E. coli, enterohaemorrhagic E. coli and Citrobacter rodentium] exploit their host cells, morphologically distinct actin‐rich structures are generated beneath the attached bacteria. These structures, called pedestals, protrude from the cell surface, enable the bacteria to “surf” atop infected cells and are hallmarks of the infections. Contained within these structures are numerous actin‐associated components that would be predicted to be at dynamic actin‐rich structures including the Arp2/3 complex, cortactin, profilin and cofilin. Recently my lab has discovered that unexpected proteins are also present within pedestals. These include clathrin‐mediated endocytic proteins and protein components of the spectrin cytoskeleton. These proteins are crucial for pedestal generation as their alteration blocks pedestal formation and often also inhibits attachment of the bacteria to their target cells, thus halting the infections. By using E. coli pedestals as a model system to also study general cell motility we have shown novel roles for a variety of cellular proteins. Taken together our examination of E. coli pedestals has already provided potential targets for pharmaceutical intervention as well as novel insights into general cell biological processes.
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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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".