The <i>Salmonella</i> Effector SspH2 Facilitates Spatially Selective Ubiquitination of NOD1 to Enhance Inflammatory Signaling
Bibliographic record
Abstract
serovar Typhimurium delivers effector proteins into host cells. One effector is SspH2, a member of the so-called novel E3 ubiquitin ligase family, that interacts with and enhances, NOD1 pro-inflammatory signaling, though the underlying mechanisms are unclear. Here, we report that SspH2 interacts with multiple members of the NLRC family to enhance pro-inflammatory signaling by targeted ubiquitination. We show that SspH2 modulates host innate immunity by interacting with both NOD1 and NOD2 in mammalian epithelial cell culture via the NF-κB pathway. Moreover, purified SspH2 and NOD1 directly interact, where NOD1 potentiates SspH2 E3 ubiquitin ligase activity. Mass spectrometry and mutational analyses identified four key lysine residues in NOD1 that are required for its enhanced activation by SspH2, but not its basal activity. These critical lysine residues are positioned in the same region of NOD1 and define a surface on the receptor that appears to be targeted by SspH2. Overall, this work provides evidence for post-translational modification of NOD1 by ubiquitin and uncovers a unique mechanism of spatially selective ubiquitination to enhance the activation of an archetypal NLR.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".