Recognition of lipid A variants by the TLR4-MD-2 receptor complex
Bibliographic record
Abstract
Lipopolysaccharide (LPS) is a component of the outer membrane of almost all Gram-negative bacteria and consists of lipid A, core sugars, and O-antigen. LPS is recognized by Toll-like receptor 4 (TLR4) and MD-2 on host innate immune cells and can signal to activate the transcription factor NFκB, leading to the production of pro-inflammatory cytokines that initiate and shape the adaptive immune response. Most of what is known about how LPS is recognized by the TLR4-MD-2 receptor complex on animal cells has been studied using Escherichia coli lipid A, which is a strong agonist of TLR4 signaling. Recent work from several groups, including our own, has shown that several important pathogenic bacteria can modify their LPS or lipid A molecules in ways that significantly alter TLR4 signaling to NFκB. Thus, it has been hypothesized that expression of lipid A variants is one mechanism by which pathogens modulate or evade the host immune response. Additionally, several key differences in the amino acid sequences of human and mouse TLR4-MD-2 receptors have been shown to alter the ability to recognize these variations in lipid A, suggesting a host-specific effect on the immune response to these pathogens. In this review, we provide an overview of lipid A variants from several human pathogens, how the basic structure of lipid A is recognized by mouse and human TLR4-MD-2 receptor complexes, as well as how alteration of this pattern affects its recognition by TLR4 and impacts the downstream immune response.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".