Multilevel Differential Control of Hormone Gene Expression Programs by hnRNP L and LL in Pituitary Cells
Bibliographic record
Abstract
The pituitary-derived somatolactotrophe GH3 cells secrete both growth hormone (GH) and prolactin (PRL). We have found that the hnRNP L and L-like (LL) paralogs differentially regulate alternative splicing of genes in these cells. Here, we show that hnRNP L is essential for PRL only, but LL is essential for both PRL and GH production. Transcriptome-wide RNA sequencing (RNA-Seq) analysis indicates that they differentially control groups of hormone or hormone-related genes involved in hormone production/regulation at total transcript and alternative exon levels. Interestingly, hnRNP L also specifically binds and prevents the aberrant usage of a nonconserved CA-rich intron piece of Prl pre-mRNA transcripts, and many others involved in endocrine functions, to prevent mostly cryptic last exons and mRNA truncation. Essential for the full hnRNP L effect on specific exons is a proline-rich region that emerged during evolution in vertebrate hnRNP L only but not LL. Together, our data demonstrate that the hnRNP L and its paralog, LL, differentially control hormone gene expression programs at multiple levels, and hnRNP L in particular is critical for protecting the transcriptome from aberrant usage of intronic sequences. The multilevel differential control by hnRNPs likely tailors the transcriptome to help refine and safeguard the different gene expression programs for different hormones.
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 | 0.000 | 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".