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Record W2754341339 · doi:10.1261/rna.063099.117

Precursor microRNA-122 inhibits synthesis of <i>Insig1</i> isoform mRNA by modulating polyadenylation site usage

2017· article· en· W2754341339 on OpenAlexfundno aff
Kara L. Norman, Tzu‐Chun Chen, Gusti M. Zeiner, Peter Sarnow

Bibliographic record

VenueRNA · 2017
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicRNA Research and Splicing
Canadian institutionsnot available
FundersNational Institute of Allergy and Infectious DiseasesNational Institutes of HealthAlberta Heritage Foundation for Medical ResearchFondation pour la Recherche Médicale
KeywordsBiologyPolyadenylationGene isoformmicroRNAMessenger RNACell biologyMolecular biologyGeneticsGene

Abstract

fetched live from OpenAlex

The insulin-induced gene 1 protein ( Insig1 ) inhibits the cholesterol biosynthesis pathway by retaining transcription factor SREBP in the endoplasmic reticulum, and by causing the degradation of HMGCR, the rate-limiting enzyme in cholesterol biosynthesis. Liver-specific microRNA miR-122, on the other hand, enhances cholesterol biosynthesis by an unknown mechanism. We have found that Insig1 mRNAs are generated by alternative cleavage and polyadenylation, resulting in specific isoform mRNA species. During high cholesterol abundance, the short 1.4-kb Insig1 mRNA was found to be preferentially translated to yield Insig1 protein. Precursor molecules of miR-122 down-regulated the translation of the 1.4-kb Insig1 isoform mRNA by interfering with the usage of the promoter-proximal cleavage–polyadenylation site that gives rise to the 1.4-kb Insig1 mRNA. These findings argue that precursor miR-122 molecules modulate polyadenylation site usage in Insig1 mRNAs, resulting in down-regulation of Insig1 protein abundance. Thus, precursor microRNAs may have hitherto undetected novel functions in nuclear gene expression.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.017
Threshold uncertainty score0.480

Codex and Gemma teacher scores by category

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)0.0000.000

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.

Opus teacher head0.011
GPT teacher head0.264
Teacher spread0.252 · 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 teacher head, 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

Citations6
Published2017
Admission routes1
Has abstractyes

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