Quantitative Mass Spectrometry Catalogues Salmonella Pathogenicity Island-2 Effectors and Identifies Their Cognate Host Binding Partners
Bibliographic record
Abstract
Gram-negative bacterial pathogens have developed specialized secretion systems to transfer bacterial proteins directly into host cells. These bacterial effectors are central to virulence and reprogram host cell processes to favor bacterial survival, colonization, and proliferation. Knowing the complete set of effectors encoded by a particular pathogen is the key to understanding bacterial disease. In addition, the identification of the molecular assemblies that these effectors engage once inside the host cell is critical to determining the mechanism of action of each effector. In this work we used stable isotope labeling of amino acids in cell culture (SILAC), a powerful quantitative proteomics technique, to identify the proteins secreted by the Salmonella pathogenicity island-2 type three secretion system (SPI-2 T3SS) and to characterize the host interaction partners of SPI-2 effectors. We confirmed many of the known SPI-2 effectors and were able to identify several novel substrate candidates of this secretion system. We verified previously published host protein-effector binding pairs and obtained 11 novel interactions, three of which were investigated further and confirmed by reciprocal co-immunoprecipitation. The host cell interaction partners identified here suggest that Salmonella SPI-2 effectors target, in a concerted fashion, cellular processes such as cell attachment and cell cycle control that are underappreciated in the context of infection. The technology outlined in this study is specific and sensitive and serves as a robust tool for the identification of effectors and their host targets that is readily amenable to the study of other bacterial pathogens.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".