Sequence Determinants for Nuclear Retention and Cytoplasmic Export of mRNAs and lncRNAs
Bibliographic record
Abstract
Eukaryotes are divided into two major compartments: the nucleus where RNA is synthesized and processed, and the cytoplasm, where mRNA is translated into proteins. Although many different RNAs are made, only a subset is allowed access to the cytoplasm, primarily RNAs involved in protein synthesis (mRNA, tRNA and rRNA). In contrast, nuclear retained transcripts are mostly long non-coding RNAs (lncRNAs) whose role in cell physiology has been a source of much investigation in the past few years. In addition, it is likely that many non-functional RNAs, which arise by spurious transcription and misprocessing of functional RNAs, are also retained in the nucleus and degraded. In this review, the main sequence features that dictate whether any particular mRNA or lncRNA is a substrate for retention in the nucleus, or export to the cytoplasm, are discussed. Although nuclear export is promoted by RNA-splicing due to the fact that the spliceosome can help recruit export factors to the mature RNA, nuclear export does not require splicing. Indeed, most stable unspliced transcripts are well exported and associate with these same export factors in a splicing-independent manner. In contrast, nuclear retention is promoted by specialized cis-elements found in certain RNAs. This new understanding of the determinants of nuclear retention and cytoplasmic export provides a deeper understanding of how information flow is regulated in eukaryotic cells. Ultimately these processes promote the evolution of complexity in eukaryotes by shaping the genomic content through constructive neutral evolution.
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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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".