Intracellular Voyeurism: Examining the Modulation of Host Cell Activities by <i>Salmonella enterica Serovar Typhimurium</i>
Bibliographic record
Abstract
Salmonella spp. can infect host cells by gaining entry through phagocytosis or by inducing host cell membrane ruffling that facilitates bacterial uptake. With its wide host range, Salmonella enterica serovar Typhimurium has proven to be an important model organism for studying intracellular bacterial pathogenesis. Upon entry into host cells, serovar Typhimurium typically resides within a membrane-bound compartment termed the Salmonella-containing vacuole (SCV). From the SCV, serovar Typhimurium can inject several effector proteins that subvert many normal host cell systems, including endocytic trafficking, cytoskeletal rearrangements, lipid signaling and distribution, and innate and adaptive host defenses. The study of these intracellular events has been made possible through the use of various imaging techniques, ranging from classic methods of transmission electron microscopy to advanced livecell fluorescence confocal microscopy. In addition, DNA microarrays have now been used to provide a "snapshot" of global gene expression in serovar Typhimurium residing within the infected host cell. This review describes key aspects of Salmonella-induced subversion of host cell activities, providing examples of imaging that have been used to elucidate these events. Serovar Typhimurium engages specific host cell machinery from initial contact with the host cell to replication within the SCV. This continuous interaction with the host cell has likely contributed to the extensive arsenal that serovar Typhimurium now possesses, including two type III secretion systems, a range of ammunition in the form of TTSS effectors, and a complex genetic regulatory network that coordinates the expression of hundreds of virulence factors.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".