Programming Bordetella pertussis lipid A to promote adjuvanticity
Bibliographic record
Abstract
BACKGROUND: Bordetella pertussis is the causative agent of whooping cough or pertussis. Although both acellular (aP) and whole-cell pertussis (wP) vaccines protect against disease, the wP vaccine, which is highly reactogenic, is better at preventing colonization and transmission. Reactogenicity is mainly attributed to the lipid A moiety of B. pertussis lipooligosaccharide (LOS). Within LOS, lipid A acts as a hydrophobic anchor, engaging with TLR4-MD2 on host immune cells to initiate both MyD88-dependent and TRIF-dependent pathways, thereby influencing adaptive immune responses. Lipid A variants, such as monophosphoryl lipid A (MPLA) can also act as adjuvants. Adjuvants may overcome the shortcomings of aP vaccines. RESULTS: This work used lipid A modifying enzymes from other bacteria to produce an MPLA-like adjuvant strain in B. pertussis. We created B. pertussis strains with distinct lipid A modifications, which were validated using MALDI-TOF. We engineered a hexa-acylated monophosphorylated lipid A that markedly decreased human TLR4 activation and activated the TRIF pathway. The modified lipooligosaccharide (LOS) promoted IRF3 phosphorylation and type I interferon production, similar to MPLA responses. We generated three other variants with increased adjuvanticity properties and reduced endotoxicity. Pyrogenicity studies using the Monocyte Activation Test (MAT) revealed that these four lipid A variants significantly decreased the IL-6, a marker for fever, response in peripheral blood mononuclear cells (PBMCs). CONCLUSION: These findings pave the way for developing wP vaccines that are possibly less reactogenic and designing adaptable adjuvants for current vaccine formulations, advancing more effective immunization strategies against pertussis.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.002 |
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; both teacher heads agree on what is shown here.
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".