Salmonella Effectors SseK1 and SseK3 Target Death Domain Proteins in the TNF and TRAIL Signaling Pathways*
Bibliographic record
Abstract
Strains of Salmonella utilize two distinct type three secretion systems to deliver effector proteins directly into host cells. The Salmonella effectors SseK1 and SseK3 are arginine glycosyltransferases that modify mammalian death domain containing proteins with N -acetyl glucosamine (GlcNAc) when overexpressed ectopically or as recombinant protein fusions. Here, we combined Arg-GlcNAc glycopeptide immunoprecipitation and mass spectrometry to identify host proteins GlcNAcylated by endogenous levels of SseK1 and SseK3 during Salmonella infection. We observed that SseK1 modified the mammalian signaling protein TRADD, but not FADD as previously reported. Overexpression of SseK1 greatly broadened substrate specificity, whereas ectopic co-expression of SseK1 and TRADD increased the range of modified arginine residues within the death domain of TRADD. In contrast, endogenous levels of SseK3 resulted in modification of the death domains of receptors of the mammalian TNF superfamily, TNFR1 and TRAILR, at residues Arg 376 and Arg 293 respectively. Structural studies on SseK3 showed that the enzyme displays a classic GT-A glycosyltransferase fold and binds UDP-GlcNAc in a narrow and deep cleft with the GlcNAc facing the surface. Together our data suggest that salmonellae carrying sseK1 and sseK3 employ the glycosyltransferase effectors to antagonise different components of death receptor signaling.
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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 | 0.001 |
| 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".