Edwardsiella induces microtubule-severing in host epithelial cells
Bibliographic record
Abstract
Edwardsiella bacteria cause economic losses to a variety of commercially important fish globally. Human infections are rare and result in a gastroenteritis-like illness. Because these bacteria are evolutionarily related to other Enterobacteriaceae and the host cytoskeleton is a common target of enterics, we hypothesized that Edwardsiella may cause similar phenotypes. Here we use HeLa and Caco-2 infection models to show that microtubules are severed during the late infections. This microtubule alteration phenotype was not dependant on the type III or type VI secretion system (T3SS and T6SS) of the bacteria as ΔT3SS and ΔT6SS mutants of E. piscicida EIB202 and E. tarda ATCC15947 that lacks both also caused microtubule disassembly. Immunolocalization experiments showed the host katanin catalytic subunits A1 and A like 1 proteins at regions of microtubule severing, suggesting their involvement in the microtubule disassembly events. To identify bacterial components involved in this phenotype, we screened a 2,758 transposon library of E. piscicida EIB202 and found that 4 single mutations in the atpFHAGDC operon disrupted microtubule disassembly in HeLa cells. We then constructed three atp deletion mutants; they all could not disassemble host microtubules. This work provides the first clear evidence of host cytoskeletal alterations during Edwardsiella 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) | 0.002 | 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".