Auto Arginine-GlcNAcylation Is Crucial for Bacterial Pathogens in Regulating Host Cell Death
Bibliographic record
Abstract
Many Gram-negative bacterial pathogens utilize the type III secretion system (T3SS) to inject virulence factors, named effectors, into host cells. These T3SS effectors manipulate host cellular signaling pathways to facilitate bacterial pathogenesis. Death receptor signaling plays an important role in eukaryotic cell death pathways. NleB from enteropathogenic Escherichia coli (EPEC) and SseK1/3 from Salmonella enterica serovar Typhimurium (S. Typhimurium) are T3SS effectors. They are defined as a family of arginine GlcNAc transferase to modify a conserved arginine residue in the death domain (DD) of the death receptor TNFR and their corresponding adaptors to hijack death receptor signaling. Here we identified that these enzymes, NleB, SseK1, and SseK3 could catalyze auto-GlcNAcylation. Residues, including Arg13/53/159/293 in NleB, Arg30/158/339 in SseK1, and Arg153/184/305/335 in SseK3 were identified as the auto-GlcNAcylation sites by mass spectrometry. Mutation of the auto-modification sites of NleB, SseK1, and SseK3 abolished or attenuated the capability of enzyme activity toward their death domain targets during infection. Loss of this ability led to the increased susceptibility of the cells to TNF- or TRAIL-induced cell death during bacterial infection. Overall, our study reveals that the auto-GlcNAcylation of NleB, SseK1, and SseK3 is crucial for their biological activity during infection.
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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".