Disruption of Trim9 function abrogates macrophage motility in vivo
Bibliographic record
Abstract
Abstract The vertebrate immune response comprises multiple molecular and cellular components that interface to provide defense against pathogens. Because of the dynamic complexity of the immune system and its interdependent innate and adaptive functionality, an understanding of the whole-organism response to pathogen exposure remains unresolved. Zebrafish larvae provide a unique model for overcoming this obstacle, because larvae are protected against pathogens while lacking a functional adaptive immune system during the first few weeks of life. Zebrafish larvae were exposed to immune agonists for various lengths of time, and a microarray transcriptome analysis was executed. This strategy identified known immune response genes, as well as genes with unknown immune function, including the E3 ubiquitin ligase tripartite motif-9 (Trim9). Although trim9 expression was originally described as “brain specific,” its expression has been reported in stimulated human Mϕs. In this study, we found elevated levels of trim9 transcripts in vivo in zebrafish Mϕs after immune stimulation. Trim9 has been implicated in axonal migration, and we therefore investigated the impact of Trim9 disruption on Mϕ motility and found that Mϕ chemotaxis and cellular architecture are subsequently impaired in vivo. These results demonstrate that Trim9 mediates cellular movement and migration in Mϕs as well as neurons.
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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".