DDIT4L regulates mitochondrial and innate immune activities in early life
Bibliographic record
Abstract
Pattern recognition receptor responses are profoundly attenuated before the third trimester of gestation in the relatively low-oxygen human fetal environment. However, the mechanisms regulating these responses are uncharacterized. Herein, genome-wide transcription and functional metabolic experiments in primary neonatal monocytes linked the negative mTOR regulator DDIT4L to metabolic stress, cellular bioenergetics, and innate immune activity. Using genetically engineered monocytic U937 cells, we confirmed that DDIT4L overexpression altered mitochondrial dynamics, suppressing their activity, and blunted LPS-induced cytokine responses. We also showed that monocyte mitochondrial function is more restrictive in earlier gestation, resembling the phenotype of DDIT4L-overexpressing U937 cells. Gene expression analyses in neonatal granulocytes and lung macrophages in preterm infants confirmed upregulation of the DDIT4L gene in the early postnatal period and also suggested a potential protective role against inflammation-associated chronic neonatal lung disease. Taken together, these data show that DDIT4L regulates mitochondrial activity and provide what we believe to be the first direct evidence for its potential role supressing innate immune activity in myeloid cells during development.
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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 | 0.000 |
| 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".