Alternative Processing: Neuronal Nitric Oxide Synthase
Bibliographic record
Abstract
Abstract The human neuronal nitric oxide synthase (nNOS) gene is expressed from multiple promoters and is subject to alternate messenger ribonucleic acid (mRNA) processing. These complex molecular mechanisms contribute to regulating gene expression and function in multiple tissues and cell types. In the case of nNOS, these distinct promoters give rise to mRNA transcripts with untranslated leader sequences that have differing lengths, nucleotide composition and three‐dimensional structure. These distinct leader sequences have robust effects on translational efficiency. In disease settings, these have functional relevance. For example, in settings of low oxygen content, a unique promoter becomes transcriptionally active. In contrast to basally expressed transcripts, this hypoxia‐induced mRNA is very efficiently translated. To accommodate diverse biological roles, multiple mechanisms have evolved to produce a diverse range of nNOS mRNA transcripts from a single genetic locus. Key Concepts Mammalian genes can have more than one promoter. Having more than one promoter implies that the gene has more than one version of exon 1. Initiation of transcription in different regions of genomic DNA imparts different nucleotide sequences to the leader regions of messenger ribonucleic acid transcripts, also known as the 5′‐untranslated region (5′‐UTR). Different leader sequences may, or may not, affect protein structure depending on whether the initiator AUG codon for translation of the protein lies in exon 1 or downstream. If multiple versions of exon 1 all have their own initiator AUG codon, then the encoded protein will exist in different forms, each with a uniqueN‐terminal peptide sequence. Different 5′‐UTRs of messenger ribonucleic acid transcripts can have functional effects on expression of the encoded protein. The 5′‐UTRs of messenger ribonucleic acid transcripts can affect translational efficiency. The 5′‐UTRs of messenger ribonucleic acid transcripts can affect subcellular messenger ribonucleic acid transcript localisation. The 5′‐UTRs of messenger ribonucleic acid transcripts can affect messenger ribonucleic acid transcript stability. Factors that regulate chromatin and transcription complexes can impact the splicing process.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".