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Record W2969670076 · doi:10.1101/740498

TPR is required for the nuclear export of mRNAs and lncRNAs from intronless and intron-poor genes

2019· preprint· en· W2969670076 on OpenAlexafffund
Eliza S. Lee, Eric J. Wolf, Harrison W. Smith, Andrew Emili, Alexander F. Palazzo

Bibliographic record

VenuebioRxiv (Cold Spring Harbor Laboratory) · 2019
Typepreprint
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicRNA Research and Splicing
Canadian institutionsUniversity of Toronto
FundersCanadian Institutes of Health Research
KeywordsIntronNuclear export signalRNA splicingBiologyGeneMessenger RNACell biologyNuclear proteinNuclear geneCell nucleusNucleusMolecular biologyGeneticsRNATranscription factorGenome

Abstract

fetched live from OpenAlex

Abstract While splicing has been shown to enhance nuclear export, it has remained unclear whether mRNAs generated from intronless genes use specific machinery to promote their export. Here we investigate the role of the major nuclear pore basket protein, TPR, in regulating mRNA and lncRNA nuclear export in human cells. By sequencing mRNA from the nucleus and cytosol of control and TPR-depleted cells, we provide evidence that TPR is required for the nuclear export of mRNAs and lncRNAs that are generated from intronless and intron-poor genes, and we validate this with reporter constructs. Moreover, in TPR-depleted cells reporter mRNAs generated from intronless genes accumulate in nuclear speckles and are bound to Nxf1. These observations suggest that TPR acts downstream of Nxf1 recruitment, and may allow mRNAs to leave nuclear speckles and properly dock with the nuclear pore. In summary, our study provides one of the first examples of a factor that is required for the nuclear export of intronless and intron-poor mRNAs and lncRNAs.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.001

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.

Opus teacher head0.014
GPT teacher head0.237
Teacher spread0.223 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations7
Published2019
Admission routes2
Has abstractyes

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