Impact of the Listeria monocytogenes Protein InlC on Infection in Mice
Bibliographic record
Abstract
The bacterial pathogen Listeria monocytogenes causes serious food-borne illnesses in pregnant women and the immunocompromised. L. monocytogenes promotes its internalization into host epithelial cells and then uses an F-actin-dependent motility process to spread from infected cells to surrounding healthy cells. In cultured enterocytes, efficient spread of L. monocytogenes requires the secreted bacterial protein InlC. InlC promotes dissemination by physically interacting with and antagonizing the function of the human adaptor protein Tuba. Here we examine the role of InlC and its interaction with host Tuba during infection in mice. The study took advantage of a single-amino-acid substitution (K173A) in InlC that impairs binding to human Tuba but does not affect InlC-mediated inhibition of the NF-κB pathway. Mice were inoculated intravenously with the wild-type L. monocytogenes strain EGD, an isogenic strain deleted for the inlC gene (ΔinlC), or a strain expressing K173A mutant InlC (inlC.K173A). The 50% lethal doses (LD(50)) for the ΔinlC or inlC.K173A mutant strain were approximately 4- or 6-fold greater than that for the wild-type strain, indicating a role for inlC in virulence. Compared to the wild-type strain, the inlC.K173A mutant strain exhibited lower bacterial loads in the liver. Histological analysis of livers indicated that the two inlC mutant strains produced smaller foci of infection than did the wild-type strain. These smaller foci are consistent with a role for InlC in cell-to-cell spread in vivo. Taken together, these results provide evidence that interaction of InlC with host Tuba is important for full virulence.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 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 | 0.000 |
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".