The <i>Tug1</i> Locus is Essential for Male Fertility
Bibliographic record
Abstract
ABSTRACT Background Several long noncoding RNAs (lncRNAs) have been shown to function as central components of molecular machines that play fundamental roles in biology. While the number of annotated lncRNAs in mammalian genomes has greatly expanded, their functions remain largely uncharacterized. This is compounded by the fact that identifying lncRNA loci that have robust and reproducible phenotypes when mutated has been a challenge. Results We previously generated a cohort of 20 lncRNA loci knockout mice. Here, we extend our initial study and provide a more detailed analysis of the highly conserved lncRNA locus, Taurine Upregulated Gene 1 ( Tug1 ). We report that Tug1 knockout male mice are sterile with complete penetrance due to a low sperm count and abnormal sperm morphology. Having identified a lncRNA loci with a robust phenotype, we wanted to determine which, if any, potential elements contained in the Tug1 genomic region (DNA, RNA, protein, or the act of transcription) have activity. Using engineered mouse models and cell-based assays, we provide evidence that the Tug1 locus harbors three distinct regulatory activities – two noncoding and one coding: (i) a cis DNA repressor that regulates many neighboring genes, (ii) a lncRNA that can regulate genes by a trans -based function, and finally (iii) Tug1 encodes an evolutionary conserved peptide that when overexpressed impacts mitochondrial membrane potential. Conclusions Our results reveal an essential role for the Tug1 locus in male fertility and uncover three distinct regulatory activities in the Tug1 locus, thus highlighting the complexity present at lncRNA loci.
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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) | 0.003 | 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".