During an inflammatory response, zebrafish <i>tnfa</i> and <i>tnfb</i> are expressed by different cell types and have distinct expression kinetics
Bibliographic record
Abstract
Abstract Tumour necrosis factor (TNF, TNFSF2) is a key orchestrator of inflammation. Though mammalian TNF biology is well studied, the pleotropic nature of this cytokine during inflammation or infection in other vertebrates remains elusive. Interestingly, zebrafish possess two homologues of mammalian TNF, Tnfa and Tnfb. Previous studies have predominantly focused on the role of tnfa during infection or inflammation, while tnfb may also have important roles, yet has remained largely unexplored. Here, we generated and characterized two transgenic reporter lines, Tg(-3.2tnfb:eGFP-F) ump21Tg and Tg(-6.2tnfb:mCherry-F) ump22Tg , marking tnfb expression in zebrafish. By combining our tnfb reporters with other available reporter lines, with high-resolution in vivo microscopy, and with public scRNAseq datasets, we define, in a cell type-specific manner, the expression kinetics of tnfb compared to those of tnfa and il1b . We report on constitutive tnfb expression in neuromast mantle cells from 32 hours post-fertilization onwards, and inducible tnfb expression in subpopulations of macrophages and neutrophils after caudal fin fold amputation and E. coli injection. After amputation, tnfb kinetics of expression are cell-dependent, with expression in macrophages detected prior to that in neutrophils. When analysing the kinetics of expression of tnfb, tnfa and il1b after wounding, our data indicate that, at least in macrophages, a sequential expression occurs, with tnfb being expressed first, followed by tnfa and il1b . Combined, these data show that tnfb has distinct characteristics from tnfa , indicative of a differential role of these cytokines. Our new tnfb reporter lines, set the stage for future studies in which both TNF homologs can be investigated placing zebrafish as an especially suited model to dissect the pleiotropic nature of Tnf in non-mammalian vertebrates.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".