The cationic amino acid exporter Slc7a7 is vital for and induced in tissue macrophages with sustained efferocytic activity
Bibliographic record
Abstract
ABSTRACT Most tissues harbor a substantial population of resident macrophages. It is not quite known yet how their quite diverse phenotypes are shaped by the functions that they assume in each tissue. In this study, we elucidate a functional link between the Slc7a7 cationic amino acid transporter and tissue macrophages. We had identified a mutant zebrafish devoid of microglia due to a mutation in the slc7a7 gene. We found that in Slc7a7 deficient larvae, macrophages do enter the retina and brain to become microglia, but then die during the developmental wave of neuronal apoptosis, which triggers intense efferocytic work from them. A similar macrophage demise occurs at other tissues and stages whereby macrophages have to engulf many cell corpses, be it due to developmental or experimentally triggered cell death. We found that slc7a7 is by far the main cationic amino acid transporter gene expressed in macrophages of wild type zebrafish larvae, and that its expression is induced in tissue macrophages within 1-2 hrs upon efferocytosis. Our data altogether indicate that a high level of Slc7a7 is vital not only for microglia but also for any steadily efferocytic tissue macrophages, and that slc7a7 gene induction is one of the adaptive responses that allow them to cope with the catabolism of numerous dead cells without compromising their own viability.
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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) | 0.001 | 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".