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Record W4394114104 · doi:10.6084/m9.figshare.15035377

SRSF5 regulates alternative splicing of <i>DMTF1</i> pre-mRNA through modulating SF1 binding

2021· dataset· en· W4394114104 on OpenAlexaff
Jialiang Li, Guangyue Li, Yige Qi, Yao Lü, Hao Wang, Ke Shi, Dangdang Li, Jinming Shi, Daniel B. Stovall, Guangchao Sui

Bibliographic record

VenueFigshare · 2021
Typedataset
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicRNA Research and Splicing
Canadian institutionsUniversity of OttawaUniversity of AlbertaUniversity of Saskatchewan
Fundersnot available
KeywordsRNA splicingAlternative splicingMessenger RNACell biologyPrecursor mRNAChemistryComputational biologyBiologyBiochemistryGeneRNA

Abstract

fetched live from OpenAlex

Among the three DMTF1 splicing isoforms, DMTF1α acts as a tumour suppressor through promoting p14ARF expression, while DMTF1β exhibits an oncogenic role likely through antagonizing DMTF1α. However, the molecular mechanism underlying alternative splicing of DMTF1 pre-mRNA has not been delineated. In the current study, we discovered SRSF5 as a regulatory protein binding to a region located between DMTF1β and α acceptor splice sites to promote DMTF1β and γ splicing. We demonstrated that SRSF5 expression positively correlated with DMTF1β/α ratio in breast cancer samples, and ectopically expressed SRSF5 promoted the splicing of DMTF1β and γ, but not DMTF1α, when testing endogenous DMTF1 pre-mRNA and a reporter construct. Upon SRSF5 knockdown, we observed significantly decreased DMTF1β and γ ratios of endogenous transcripts. An RNA sequence just upstream of the α acceptor site contains two adjacent SRSF5 binding elements, one of which overlaps with an SF1 binding site. Our mechanistic studies revealed that SRSF5 binding to both elements in this region could consign SF1 to its distal-binding sites close to the β and γ acceptor sites, favouring splicing of their isoforms. Overall, our study revealed SRSF5 as a key regulator to promote DMTF1β and γ splicing, and consequently reduce DMTF1α splicing. ARF: alternative reading frame, that is, p14ARF, or CDKN2A (cyclin-dependent kinase inhibitor 2A); β-gal: β-galactosidase; CLIP-seq: crosslinking and immunoprecipitation-sequencing; DMTF1: the cyclin D binding myb-like transcription factor 1; ESS/ESE: exonic splicing silencer/enhancer; Ex: exon; FBS: fetal bovine serum; Gluc: Gaussia luciferase; hnRNPs: heterogeneous nuclear ribonucleoproteins; In: intron; ISS/ISE: intronic splicing silencer/enhancer; PBS: phosphate-buffered saline; PCR: polymerase chain reaction; PSI: percent-splice-in; qPCR: quantitative real-time PCR; RIP: RNA immunoprecipitation; RNAseq: RNA sequencing; RT: reverse transcription; SF1: splicing factor 1; SR: serine/arginine-rich proteins; SRSF5: serine and arginine-rich splicing factor 5; TCGA: the cancer genome atlas; UCSC: University of California, Santa Cruz. WT: Wild type

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: Not applicable · Consensus signal: none
GenreCandidate signal: Dataset · Consensus signal: none
Teacher disagreement score0.002
Threshold uncertainty score0.005

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)0.0010.000

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 head0.033
GPT teacher head0.313
Teacher spread0.280 · 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 designNot applicable
Domainnot available
GenreDataset

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

Citations0
Published2021
Admission routes1
Has abstractyes

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