Global and Gene-Specific Translational Regulation in Rat Lung Development
Bibliographic record
Abstract
During the peripartum period, the lung must respond to dramatic changes in circulating hormones, nutritional factors, and physiologic signals during its transition to becoming the organ of gas exchange. Protein synthesis consumes a significant proportion of metabolic resources and is inhibited by many environmental stresses. We hypothesized that translational control mechanisms play a role in the perinatal lung. Immunoblots of late-gestation (Fetal Day [FD] 17-22) rat lung extracts revealed gradual decreases in phosphorylated forms of the mammalian target of rapamycin effectors, eukaryotic initiation factor (eIF) 4E-binding protein, p70 S6 kinase, and ribosomal protein S6, followed by sharp increases on Postnatal Day 1 (P1). Immunohistochemistry showed phospho-S6 staining was most prominent in epithelial cells of the large and small airways. m(7)GTP-sepharose pulldown experiments showed a decrease in association of translation initiation factor, eIF4E, with its inhibitor, eIF4E-binding protein, and a concomitant increase in eIF4E association with eIF4G immediately after birth, and polysome profiles confirmed a decrease in abundance of large polysomes between FD19 and FD22, which was reversed on P1. Microarray analysis of polysomal versus total RNA from FD19, FD22, and P1 lungs was used to identify specific genes, the association of which with large polysomes changed either pre- or postnatally. RT-PCR and Northern blotting were used to confirm translational changes in selected candidate genes, including a prenatal increase in IL-18 and a postnatal decrease in regulatory subunit 2 of protein phosphatase 1. Translational regulation of IL-18 and protein phosphatase 1 regulatory (inhibitor) subunit 2 is gene-specific, as these changes contrast with the corresponding global changes in polysome abundance.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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".