<em>In Vitro</em> Assay of Bacterial Adhesion onto Mammalian Epithelial Cells
Bibliographic record
Abstract
To cause infections, bacteria must colonize their host. Bacterial pathogens express various molecules or structures able to promote attachment to host cells(1). These adhesins rely on interactions with host cell surface receptors or soluble proteins acting as a bridge between bacteria and host. Adhesion is a critical first step prior to invasion and/or secretion of toxins, thus it is a key event to be studied in bacterial pathogenesis. Furthermore, adhered bacteria often induce exquisitely fine-tuned cellular responses, the studies of which have given birth to the field of 'cellular microbiology'(2). Robust assays for bacterial adhesion on host cells and their invasion therefore play key roles in bacterial pathogenesis studies and have long been used in many pioneer laboratories(3,4). These assays are now practiced by most laboratories working on bacterial pathogenesis. Here, we describe a standard adherence assay illustrating the contribution of a specific adhesin. We use the Escherichia coli strain 2787(5), a human pathogenic strain expressing the autotransporter Adhesin Involved in Diffuse Adherence (AIDA). As a control, we use a mutant strain lacking the aidA gene, 2787ΔaidA (F. Berthiaume and M. Mourez, unpublished), and a commercial laboratory strain of E. coli, C600 (New England Biolabs). The bacteria are left to adhere to the cells from the commonly used HEp-2 human epithelial cell line. This assay has been less extensively described before(6).
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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) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.009 |
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".