Efficacy of Antigonococcal CMP-Nonulosonate Therapeutics Require Cathelicidins
Bibliographic record
Abstract
Novel therapies to counteract multidrug-resistant gonorrhea are urgently needed. A unique gonococcal immune evasion strategy involves capping of lipooligosaccharide (LOS) with sialic acid by gonococcal sialyltransferase (Lst), utilizing host-derived CMP-sialic acid (CMP-Neu5Ac in humans). LOS sialylation renders gonococci resistant to complement and cationic peptides, and down-regulates the inflammatory response by engaging siglecs. CMP-sialic acid analogs (CMP-nonulosonates [CMP-NulOs]) such as CMP-Leg5,7Ac2 and CMP-Kdn are also utilized by Lst. Incorporation of these NulO analogs into LOS maintains gonococci susceptible to complement. Intravaginal administration of CMP-Kdn or CMP-Leg5,7Ac2 attenuates gonococcal colonization of mouse vaginas. Here, we identify a key mechanism of action for the efficacy of CMP-NulOs. Surprisingly, CMP-NulOs remained effective in complement C1q-/- and C3-/- mice. LOS Neu5Ac, but not Leg5,7Ac2 or Kdn, conferred resistance to the cathelicidins LL-37 (human) and mouse cathelicidin-related antimicrobial peptide in vitro. CMP-NulOs were ineffective in Camp-/- mice, revealing that cathelicidins largely mediate the efficacy of therapeutic CMP-NulOs.
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 | 0.001 |
| 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".