Proteomics of intracellular Salmonella enterica reveals roles of Salmonella pathogenicity island 2 in metabolism and antioxidant defense
Bibliographic record
Abstract
Intracellular Salmonella enterica serovar Typhimurium (STM) deploy the Salmonella Pathogenicity Island 2-encoded type III secretion system (SPI2-T3SS) for the massive remodeling of the endosomal system for host cells. This activity results in formation of an extensive interconnected tubular network of Salmonella-induced filaments (SIFs) connected to the Salmonella-containing vacuole (SCV). Such network is absent in cells infected with SPI2-T3SS-deficient mutant strains such as ΔssaV. A tubular network with reduced dimensions is formed if SPI2-T3SS effector protein SseF is absent. Previous single cell live microscopy-based analyses revealed that intracellular proliferation of STM is directly correlated to the ability to transform the host cell endosomal system into a complex tubular network. This network may also abrogate host defense mechanisms such as delivery of antimicrobial effectors to the SCV. To test the role of SIFs in STM patho-metabolism, we performed quantitative comparative proteomics of STM recovered from infected murine macrophages. We infected RAW264.7 cells with STM wild type (WT), ΔsseF or ΔssaV strains, recovered bacteria 12 h after infection and determined proteome compositions. Increased numbers of proteins characteristic for nutritional starvation were detected in STM ΔsseF and ΔssaV compared to WT. In addition, STM ΔssaV, but not ΔsseF showed signatures of increased exposure to stress by antimicrobial defenses, in particular reactive oxygen species, of the host cells. The proteomics analyses presented here support and extend the role of SIFs for the intracellular lifestyle of STM. We conclude that efficient manipulation of the host cell endosomal system by effector proteins of the SPI2-T3SS contributes to nutrition, as well as to resistance against antimicrobial host defense mechanisms.
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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 | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".