Dual perspective proteomics infectome profiling discovers <i>Salmonella</i> type III secretion system effector functions in macrophages
Bibliographic record
Abstract
Abstract Intracellular bacterial pathogens have evolved sophisticated infection strategies, including the release and secretion of virulence factors to interfere with host cell functions and to perturb immune responses. For Salmonella enterica serovar Typhimurium ( S . Typhimurium), the type III secretion systems encoded on Salmonella pathogenicity islands (SPI) 1 and 2 mediates invasion of the bacterium into innate immune cells and regulates bacterial replication and survival within the hostile environment of the host, respectively. Here, we explore the temporal and strain-specific dual perspective response of both the host and pathogen during cellular infection via quantitative proteomics. We report time- and pathogenicity island-specific expression and secretion of infection-associated proteins (i.e., SL1344_1263, SL1344_3112, SL1344_1563, and YnhG) and regulated immune response proteins in macrophage, including Cd86, Cd40, Casp4, C3, IL-1α, and Cd69). Through intracellular macrophage and in vivo murine models of infection, we reveal a role in virulence for three of the bacterial proteins (SL1344_1263, SL1344_1563, and YnhG), defining their importance as novel T3SS effectors. We characterize the temporal intra- and extracellular production of the effectors and identify their interaction networks in host cells, representing inhibitory and stimulatory pathways mounted by invading bacterial pathogens. Author Summary The relationship between a host and pathogen is intricate, and regulation of the host immune response correlates with the progressive timing of infection and tailored responses to the pathogen. Relying on detection and quantification of protein-level changes using mass spectrometry-based proteomics, we explore the production of known and novel effectors by Salmonella during intracellular survival within macrophage. Our results portray a role for these effectors in bacterial virulence using an in vivo murine model of infection, and we define a dynamic network of interaction between the effectors and host proteins. These interactions reveal opportunity for drug design to disrupt modulation of the host by the invading bacterium as a new strategy for combatting infection. Our approach is dynamic and universal, with the power to alter therapeutic discovery against infectious diseases.
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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) | 0.001 | 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".