Comparative study of GBP recruitment on two cytosol-dwelling pathogens, <i>Francisella novicida</i> and <i>Shigella flexneri</i> highlights differences in GBP repertoire and in GBP1 motif requirements
Bibliographic record
Abstract
Guanylate-Binding Proteins are interferon-inducible GTPases that play a key role in cell autonomous responses against intracellular pathogens. Despite sharing high sequence similarity, subtle differences among GBPs translate into functional divergences that are still largely not understood. A key GBP feature is the formation of supramolecular GBP complexes on the bacterial surface. Such complexes are observed when GBP1 binds lipopolysaccharide (LPS) from Shigella and Salmonella and further recruits GBP2-4. Here, we compared GBP recruitment on two cytosol-dwelling pathogens, Francisella novicida and S. flexneri. Francisella novicida was coated by GBP1 and GBP2 and to a lower extent by GBP4 in human macrophages. Contrary to S. flexneri, F. novicida was not targeted by GBP3, a feature independent of T6SS effectors. Multiple GBP1 features were required to promote targeting to F. novicida while GBP1 targeting to S. flexneri was much more permissive to GBP1 mutagenesis suggesting that GBP1 has multiple domains that cooperate to recognize F. novicida atypical LPS. Altogether our results indicate that the repertoire of GBPs recruited onto specific bacteria is dictated by GBP-specific features and by specific bacterial factors that remain to be identified.
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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".