Design of a Transcription-based Secretion Activity Reporter (TSAR) for the Type III Secretion Apparatus of Shigella flexneri and Uses Thereof
Bibliographic record
Abstract
Many gram-negative bacterial pathogens, including Shigella flexneri, are able to translocate bacterial proteins, dubbed effectors, across the host cell plasma membrane into the host cell cytosol using a syringe-like structure, the type three secretion apparatus (T3SA). While some bacteria use their T3SA to modulate their phagosomal environment (Salmonella spp.), establish pedestal structure to form microcolonies on the plasma membrane (Enteropathogenic Escherichi coli) or lyse their entry vacuole (Shigella spp.), they all have in common a tightly regulated activity of their T3SA. However, the tracking of the activity of the T3SA in infected cells and tissue has been difficult to perform. Using the property of MxiE-dependent promoters that are upregulated when the T3SA is active, we have recently designed a transcription-based secretion activity reporter (TSAR) that allows the following of the activity of the S. flexneri T3SA in real-time in tissue culture cells and in vivo using fast maturing GFP intrinsic fluorescence. Herein we describe the design of the TSAR and its application to fixed and live samples for microscopy and flow cytometry in a colonic epithelial cell model using TC7 tissue culture cells.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".