Immunolocalization of Hsc70 at Bacterially‐Generated Actin‐Rich Structures
Bibliographic record
Abstract
The actin cytoskeleton is commonly hijacked by bacterial pathogens. Listeria commandeers this filament system during their internalization, and for actin‐rich comet tail formation that enable the microbes to move within the infected cells as well as transfer from cell to cell. Enteropathogenic Escherichia coli (EPEC), an extracellular microbe, controls this cytoskeletal system when they generate E. coli pedestals as part of their firm docking to the plasma membrane of their host's cells and as they “surf” atop the infected cells. Finally, Salmonella exploit the host actin cytoskeleton to enter eukaryotic cells by forcing the generation of actin‐based membrane ruffling. We recently identified Hsc70 in a proteomics screen of EPEC pedestal components. This protein is normally involved with chaperoning functions within the cell. Despite there being a lack of evidence for an association of Hsc70 and actin, due to our proteomics identification of Hsc70 at EPEC pedestals we hypothesized that Hsc70 could be a common target of bacterial pathogens that control the actin cytoskeleton during their disease processes. To test this hypothesis we immunolocalized Hsc70 during Listeria , EPEC and Salmonella infections and found it concentrated at all actin structures generated by the microbes, demonstrating that this protein is a universal component for bacterial actin‐based structures. Support or Funding Information Funding provided through NSERC. (Grant No. 355316 to J. A. G.) This abstract is from the Experimental Biology 2018 Meeting. There is no full text article associated with this abstract published in The FASEB Journal .
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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 | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".