Bacterial Pathogenesis: Assessment of Intracellular Positioning of Pathogen‐Containing Vacuoles During Infection
Bibliographic record
Abstract
Intracellular bacterial pathogens implement a diverse array of strategies to target host cells and establish infection. For vacuolar pathogens, the process of pathogen-containing vacuole movement within host cells, termed intracellular trafficking, is central to both pathogen survival and infection progression. Typically a process mediated by secreted virulence factors that manipulate the host cytoskeletal machinery, internalized pathogen-containing vacuoles traffic to the site of replication to establish a unique replicative niche, and if applicable, traffic back toward the host cell periphery for cell-to-cell spread. As such, the intracellular positioning of pathogen-containing vacuoles represents a fundamental measure of infection progression. Here, we describe a fluorescence microscopy-based method to quantitatively assess bacterial intracellular positioning, using Salmonella enterica serovar Typhimurium infection of epithelial cells as a model. This experimental approach can be modified to study infection in diverse host cell types, and with a broad array of pathogens. The system can also be adapted to examine the kinetics of infection, identify secreted virulence factors that mediate host trafficking, investigate host factors that are targeted by the pathogen for trafficking, and assess functional domains within a virulence factor responsible for mediating the phenotype. Collectively, these tools can provide fundamental insight into the pathogenesis of a diverse array of intracellular bacterial pathogens, and new host factors that are hijacked to mediate infection. © 2024 The Authors. Current Protocols published by Wiley Periodicals LLC. Basic Protocol 1: Culture and preparation of host cells Alternate Protocol: Culture and preparation of host cells to assess host factor contribution to bacterial positioning Basic Protocol 2: Infection of epithelial cells with S. Typhimurium Basic Protocol 3: Fluorescence staining for analysis of bacterial positioning Basic Protocol 4: Fluorescence microscopy analysis of bacterial positioning.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.004 |
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".