Cholesterol-Enriched Membrane Microdomains Are Required for Inducing Host Cell Cytoskeleton Rearrangements in Response to Attaching-Effacing <i>Escherichia coli</i>
Bibliographic record
Abstract
The diarrheal pathogens enterohemorrhagic Escherichia coli (EHEC) O157:H7 strain CL56 and enteropathogenic Escherichia coli (EPEC) O127:H6 strain E2348/69 adhere intimately to epithelial cells through attaching-effacing lesions, which are characterized by rearrangements of the host cytoskeleton, intimate adherence, and destruction of microvilli. These cytoskeletal responses require activation of host signal transduction pathways. Lipid rafts are signaling microdomains enriched in sphingolipid and cholesterol in the plasma membrane. The effect of perturbing plasma membrane cholesterol on bacterial intimate adherence was assessed. Infection of both HEp-2 cells and primary skin fibroblasts with strains CL56 and E2348/69 caused characteristic rearrangements of the cytoskeleton at sites of bacterial adhesion. CL56- and E2348/69-induced cytoskeletal rearrangements were inhibited following cholesterol depletion. Addition of exogenous cholesterol to depleted HEp-2 cells restored cholesterol levels and rescued bacterially induced alpha-actinin mobilization. Quantitative bacterial adherence assays showed that EPEC adherence to HEp-2 cells was dramatically reduced following cholesterol depletion, whereas the adherence of EHEC remained high. Cytoskeletal rearrangements on skin fibroblasts obtained from children with Niemann-Pick type C disease were markedly reduced. These findings indicate that host membrane cholesterol contained in lipid rafts is necessary for the cytoskeletal rearrangements following infection with attaching-effacing Escherichia coli. Differences in initial adherence indicate divergent roles for host membrane cholesterol in the pathogenesis of EHEC and EPEC infections.
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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".