Evidence map›Paper›PMID 39677669›Full record

ArticlebioRxiv : the preprint server for biology2024

High-Throughput Quantification of miRNA-3'-Untranslated-Region Regulatory Effects.

Stephen Mastriano, Shaveta Kanoria, William Rennie, Chaochun Liu, Dan Li, Jijun Cheng, Ye Ding, Jun Lu

Abstract readPreprint
In one paragraph

Article in bioRxiv : the preprint server for biology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors.

Stephen MastrianoDepartment of Genetics and Yale Stem Cell Center, Yale University, New Haven, CT 06520, USA.
Shaveta KanoriaDivision of Genetics, Wadsworth Center, New York State Department of Health, Albany, NY 12201, USA.
William RennieDivision of Genetics, Wadsworth Center, New York State Department of Health, Albany, NY 12201, USA.
Chaochun LiuDivision of Genetics, Wadsworth Center, New York State Department of Health, Albany, NY 12201, USA.
Dan LiDepartment of Genetics and Yale Stem Cell Center, Yale University, New Haven, CT 06520, USA.
Jijun ChengDepartment of Genetics and Yale Stem Cell Center, Yale University, New Haven, CT 06520, USA.
Ye DingDivision of Genetics, Wadsworth Center, New York State Department of Health, Albany, NY 12201, USA.
Jun LuDepartment of Genetics and Yale Stem Cell Center, Yale University, New Haven, CT 06520, USA.

Funding

PREDOCTORAL TRAINING PROGRAM IN GENETICST32GM007499 · NIGMS · YALE UNIVERSITY · PI CARLSON, JOHN R, REINKE, VALERIE J · 1985 to 2022
$13.9M
Novel Approaches to Mammalian MicroRNA Target PredictionR01GM099811 · NIGMS · WADSWORTH CENTER · PI DING, YE, LU, JUN · 2012 to 2015
$2.4M
Novel Bioinformatics Tools for Quantitative Prediction of Primary MicroRNA ProcessingR01GM138856 · NIGMS · YALE UNIVERSITY · PI DING, YE, LU, JUN · 2020 to 2023
$1.8M
The role of microRNAs in leukemic initiation and maintenanceR01CA149109 · NCI · YALE UNIVERSITY · PI LU, JUN · 2011 to 2015
$1.7M
NCI NIH HHS R01 CA149109NIGMS NIH HHS R01 GM099811NIGMS NIH HHS R01 GM138856NIGMS NIH HHS T32 GM007499
6 · The paper itself

Abstract

MicroRNAs (miRNAs) regulate gene expression post-transcriptionally, primarily through binding sites in 3' untranslated regions (3' UTRs). While computational and biochemical approaches have been developed to predict miRNA binding sites on target messenger RNAs, reliable and high-throughput assessment of the regulatory effects of miRNAs on full-length 3' UTRs can still be challenging. Utilizing a miniaturized and high-throughput reporter assay, we present a 'pilot miRNA-targeting map', containing 4,994 successfully measured miRNA:3' UTR regulatory outputs by pairwise assays between 461 miRNAs and eleven 3' UTRs. This collection represents a large experimental miRNA:3' UTR dataset to date on a single platform. The methodology can be generally applied to studies of miRNA-mediated regulation of critical genes. We found that seedless sites can lead to substantial downregulation. We utilized this dataset in the development of a quantitative total score for modeling the total regulatory effects by both seed and seedless sites on a full-length 3' UTR. To assess the predictive value of the total score, we analyzed data from mRNA expression and proteomics studies. We found that the score can discriminate the potent miRNA inhibition from the weak inhibition and is thus useful for quantitative prediction of miRNA regulation. The score has been added to the STarMir program of the Sfold package now available via GitHub at https://github.com/Ding-RNA-Lab/Sfold.

Identifiers

PMID39677669
PMCPMC11643113

What OpenQuestion holds

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.