A Novel Karyopherin-β Homolog Is Developmentally and Hormonally Regulated in Fetal Lung
Bibliographic record
Abstract
To investigate molecular mechanisms of lung organogenesis, we used representational difference analysis to search for glucocorticoid-inducible genes in developing lung in a fetal rat model. Messenger RNA prepared from fetal and adult rat lung was used to prepare "representative amplicons." Adult-lung complementary DNA (cDNA) amplicons were used as "driver" in successive rounds of subtractive hybridization/amplification to isolate target fetal lung-specific cDNAs. A single clone, which was conserved and had near-perfect homology to eight human/rodent expressed sequence tags, was used as template for 5' and 3' rapid amplification of cDNA ends and SPICE (system for polymerase chain reaction amplification of cDNA ends) reactions to obtain the 3.6-kb cDNA, LGL2 (Genbank, AF 110195) encoding a deduced polypeptide (lgl2) of 963 amino acids. Northern analysis confirmed that LGL2 is differentially expressed in fetal lung (maximal during the pseudoglandular stage, gestational Days 14 to 16), induced by glucocorticoid, and enriched in epithelium relative to the mesenchyme. LGL2 was also detected in human fetal lung at gestational Week 16 as well as in human and rat fetal brain, heart, intestine, and kidney. We mapped LGL2 to chromosome 1p33-34.2. Comparison with sequences in the genome database identified lgl2 as a member of the karyopherin-beta family of nuclear import proteins, with greatest homology to transportin SR. Maximal expression of LGL2 in the pseudoglandular stage of development is coordinate with that of key transcription factors that regulate prominent signal transduction pathways in fetal lung organogenesis. We propose a role for lgl2 in nuclear import of transcription factors that regulate signal transduction during fetal lung 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".