Genomic and phenotypic analysis of SspH1 identifies a new <i>Salmonella</i> effector, SspH3
Bibliographic record
Abstract
Salmonella is a major foodborne pathogen and is responsible for a range of diseases. Not all Salmonella contributes to severe health outcomes as there is a large degree of genetic heterogeneity among the 2,600 serovars within the genus. This variability across Salmonella serovars is linked to numerous genetic elements that dictate virulence. While several genetic elements encode virulence factors with well-documented contributions to pathogenesis, many genetic elements implicated in Salmonella virulence remain uncharacterized. Many pathogens encode a family of E3 ubiquitin ligases that are delivered into the cells that they infect using a Type 3 Secretion System (T3SS). These effectors, known as NEL-domain E3s, were first characterized in Salmonella. Most Salmonella encodes the NEL-effectors sspH2 and slrP, whereas only a subset of Salmonella encodes sspH1. SspH1 has been shown to ubiquitinate the mammalian protein kinase PKN1, which has been reported to negatively regulate the pro-survival program Akt. We discovered that SspH1 mediates the degradation of PKN1 during infection of a macrophage cell line but that this degradation does not impact Akt signaling. Genomic analysis of a large collection of Salmonella genomes identified a putative new gene, sspH3, with homology to sspH1. SspH3 is a novel NEL-domain effector.
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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 | 0.001 |
| 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 | 0.001 |
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".