Multiple Salmonella-pathogenicity island 2 effectors are required to facilitate bacterial establishment of its intracellular niche and virulence
Bibliographic record
Abstract
The pathogenesis of Salmonella Typhimurium depends on the bacterium's ability to survive and replicate within host cells. The formation and maintenance of a unique membrane-bound compartment, termed the Salmonella-containing vacuole (SCV), is essential for S. Typhimurium pathogenesis. SCV-bound S. Typhimurium induces formation of filamentous tubules that radiate outwards from the SCV, termed Salmonella-induced filaments (SIFs). SIF formation is concomitant with the onset of replication within host epithelial cells. SIF biogenesis, formation and maintenance of the SCV, and the intracellular positioning of the SCV within the host cell requires translocation of bacterial proteins (effectors) into the host cell. Effectors secreted by the type III secretion system encoded on Salmonella pathogenicity island 2 (T3SS2) function to interfere with host cellular processes and promote both intracellular survival and replication of S. Typhimurium. Seven T3SS2-secreted effectors, SifA, SopD2, PipB2, SteA, SseJ, SseF, and SseG have previously been implicated to play complementary, redundant, and/or antagonistic roles with respect to SIF biogenesis, intracellular positioning of the SCV, and SCV membrane dynamics modulation during infection. We undertook a systematic study to delineate the contribution of each effector to these processes by (i) deleting all seven of these effectors in a single S. Typhimurium strain; and (ii) deleting combinations of multiple effectors based on putative effector function. Using this deletion mutant library, we show that each of SIF biogenesis, intracellular SCV localization, intramacrophage replication, colonization, and virulence depends on the activities of multiple effectors. Together, our data demonstrates the complex interplay between these seven effectors and highlights the necessity to study T3SS2-secreted effectors as groups, rather than studies of individual effectors.
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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 | 0.000 |
| 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 | 0.000 | 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".