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Record W3203265117 · doi:10.1101/2021.09.29.462476

KRAB Zinc Finger protein Znf684 interacts with Nxf1 to regulate mRNA export

2021· preprint· en· W3203265117 on OpenAlexaff
Alexandra Nitoiu, Syed Nabeel‐Shah, Shaghayegh Farhangmehr, Shuye Pu, Ulrich Braunschweig, Benjamin J. Blencowe, Jack Greenblatt

Bibliographic record

VenuebioRxiv (Cold Spring Harbor Laboratory) · 2021
Typepreprint
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicRNA Research and Splicing
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsZinc fingerMessenger RNAImmunoprecipitationRNA-binding proteinMolecular biologyRNACytoplasmBiologyCell biologyGeneTranscription factorGenetics

Abstract

fetched live from OpenAlex

Abstract Cys2His2 (C2H2) type zinc finger (ZnF) proteins constitute a large class of proteins that are generally considered to be DNA-binding transcription factors. Using affinity purification followed by mass spectrometry, as well as reciprocal co-immunoprecipitation experiments, we determined that the C2H2-ZnF protein Znf684 interacts physically with several proteins involved in mRNA export, including Nxf1 and Alyref. We utilized individual nucleotide resolution cross-linking immunoprecipitation followed by high throughput sequencing (iCLIP-seq) experiments to show that Znf684 binds directly to specific mRNAs in vivo and has an RNA-binding profile similar to those of Nxf1 and Alyref, suggesting a role in mRNA export regulation. Immunofluorescence microscopy (IF) experiments revealed that Znf684 localizes to both the nucleus and cytoplasm. Using cellular fractionation experiments, we demonstrate that overexpression of Znf684 negatively impacts the export of SMAD3 and other target mRNAs. Taken together, our results suggest that Znf684 regulates the export of a subset of transcripts through physical interactions with Nxf1 and specific target mRNAs.

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 score0.007

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 head0.011
GPT teacher head0.232
Teacher spread0.222 · 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

Citations1
Published2021
Admission routes1
Has abstractyes

Explore more

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