Determining the Bordetella LPS structural features that influence TLR4 downstream signaling
Bibliographic record
Abstract
Upon recognizing bacterial lipopolysaccharide (LPS), human TLR4 initiates two distinct signaling pathways: the MyD88 pathway from the cell surface or the TRIF pathway following endocytosis. While the first is associated with strong pro-inflammatory responses, the latter is linked to dendritic cell maturation and T cell priming. Changes in LPS structure can influence the activation of either or both pathways. This study investigates the influence of specific structural features of Bordetella LPS on these pathways: the O antigen, the number of acyl chains in lipid A and the glucosamine modification of the phosphates of the lipid A diglucosamine backbone. Systematically engineered Bordetella LPS differing in one or more of these features were studied by quantifying NFκB and IRF3 activation—indicators of MyD88 and TRIF pathway activation, respectively. The findings reveal that the glucosamine modification of lipid A plays a dominant role in TLR4-mediated signaling, overriding the influence of the O antigen and lipid A acylation. The absence of glucosamine modification significantly reduced the activation of both MyD88 and TRIF pathways, underscoring its importance in promoting TLR4 dimerization. Furthermore, under-acylation of LPS (with 4 or 5 acyl chains) partially reduced NFκB activation, while completely abrogating TRIF pathway activation. In contrast, hexa-and hepta-acylated LPS equally and robustly activated both pathways. Lastly, the Bordetella O antigen selectively biased signaling towards the TRIF pathway without affecting the MyD88 pathway. This study provides valuable insights into how specific LPS structural modifications can be leveraged to tailor TLR4-mediated signaling.
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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".