Beyond TLR4 and Its Alternative Lipopolysaccharide (LPS) Sensing Pathways in Zebrafish
Bibliographic record
Abstract
Due to their evolutionary divergence from mammals, zebrafish (Zf, Danio rerio), which are frequently employed in biomedical research, provide a distinctive viewpoint on innate immune systems. The Toll-like receptor 4/myeloid differentiation factor 2/cluster of differentiation 14 (TLR4/MD-2/CD14) complex in mammals detects lipopolysaccharide (LPS), a crucial component of Gram-negative bacteria, and it causes potent inflammatory reactions through a Toll/interleukin-1 receptor domain-containing adapter-inducing interferon-β (TRIF)-dependent and myeloid differentiation primary response 88 (MyD88)-dependent pathways. However, key components of this system, such as a responsive TLR4 axis and a functional CD14 ortholog, are absent in Zf. The Zf species nevertheless reacts to LPS, which leads to research into other recognition systems. This review looks at a number of TLR4-independent processes in Zf, such as scavenger receptors (SRs) including scavenger receptor class B type 1 (SR-BI) and cluster of differentiation 36 (CD36), nucleotide-binding oligomerization domain-containing protein 1 (NOD1)-dependent cytosolic sensing, peptidoglycan recognition proteins (PGRPs), Complement Component 3 (C3), and caspase-1-like protein 2 (Caspy2)-mediated inflammasome activation. An alternative and flexible immune system that makes up for the lack of canonical TLR4 signaling is revealed by these mechanisms. Additionally, the discovery of lymphocyte antigen 96 (ly96), an ortholog of MD-2 found in Zf, suggests evolutionary similarity; however, as it is only functional in artificial systems, it demonstrates minimal overlap with mammalian MD-2 activity. Knowing these pathways provides important information for studying inflammation, infection, and immunological modulation in vertebrates using Zf as a model. It also clarifies the evolutionary flexibility of innate immune recognition.
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 | 0.001 | 0.000 |
| 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 | 0.000 |
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".