Identification and characterization of zebrafish Tlr4 co-receptor Md-2
Bibliographic record
Abstract
ABSTRACT The zebrafish ( Danio rerio ) is a powerful model organism for studies of the innate immune system. One apparent difference between human and zebrafish innate immunity is the cellular machinery for LPS-sensing. In amniotes, the protein complex formed by Toll-like receptor 4 and myeloid differentiation factor 2 (Tlr4/Md-2) recognizes the bacterial molecule lipopolysaccharide (LPS) and triggers an inflammatory response. It is believed that zebrafish have neither Md-2 nor Tlr4: Md-2 has not been identified outside of amniotes, while the zebrafish tlr4 genes appear to be paralogs, not orthologs, of amniote TLR4s . We revisited these conclusions. We identified a zebrafish gene encoding Md-2, ly96 . Using single-cell RNA-Seq, we found that ly96 is transcribed in cells that also transcribe genes diagnostic for innate immune cells, including the zebrafish tlr4 -like genes. Unlike amniote LY96 , zebrafish ly96 expression is restricted to a small number of macrophage-like cells. In a functional assay, zebrafish Md-2 and Tlr4a form a complex that activates NF-κB signaling in response to LPS, but ly96 loss-of-function mutations gave little protection against LPS-toxicity in larval zebrafish. Finally, by analyzing the genomic context of tlr4 genes in eleven jawed vertebrates, we found that tlr4 arose prior to the divergence of teleosts and tetrapods. Thus, an LPS-sensitive Tlr4/Md-2 complex is likely an ancestral feature shared by mammals and zebrafish, rather than a de novo invention on the tetrapod lineage. We hypothesize that zebrafish retain an ancestral, low-sensitivity Tlr4/Md-2 complex that confers LPS-responsiveness to a specific subset of innate immune cells.
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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 | 0.000 |
| 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) | 0.003 | 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; 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".