Spectrin is a crucial component of EPEC pedestals
Bibliographic record
Abstract
Enteropathogenic E.coli (EPEC) is one of the main pathogens contributing to roughly 2 million diarrhea related child deaths worldwide each year. Upon ingestion of contaminated food or water, EPEC colonize the intestinal tracts of their hosts, attach to the surface of enterocytes and generate characteristic attaching and effacing (A/E) lesions. These lesions involve the collapse of host microvilli and the generation of pedestal‐like structures atop infected cells. Pedestal formation and disease progression is induced by bacterial proteins (effectors) that are translocated into the host cell cytoplasm by a syringe‐like type‐three secretion system (T3SS). The three major components of the host cell cytoskeleton; actin, microtubules and intermediate filaments, are all altered during EPEC pathogenesis. We have identified another major host cytoskeletal network that is involved in EPEC disease, the spectrin cytoskeleton. Here we demonstrate that spectrin is recruited to the base of EPEC pedestals and through the use of EPEC effector mutants, we show that this localization is mediated by the translocated intimin receptor (Tir). Spectrin knockdowns using siRNA have revealed that spectrin is necessary for pedestal formation to occur. Our findings provide the first evidence of spectrin's involvement in EPEC pathogenesis and identify it as a crucial protein involved in pedestal formation and disease progression. Grant Funding Source: CIHR, NSERC
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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".