Evidence map›Paper›PMID 37509720›Full record

ArticleBiomedicines2023

When Size

John C W Hildyard, Richard J Piercy

Open access · goldAbstract read
In one paragraph

Article in Biomedicines, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing papers in PubMed
1.4field-weighted citation impact, top 19% of its field
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

7 citing papers in PubMed, 9 citations in OpenAlex.

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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

2 authors at 1 institution in 1 country.

John C W HildyardComparative Neuromuscular Disease Laboratory, Department of Clinical Science and Services, Royal Veterinary College, London NW1 0TU, UK.ORCID 0000-0003-2283-2118
Richard J PiercyComparative Neuromuscular Disease Laboratory, Department of Clinical Science and Services, Royal Veterinary College, London NW1 0TU, UK.ORCID 0000-0002-4344-6438
Royal Veterinary College · GB

Funding

Wellcome TrustWellcome Trust 101550
6 · The paper itself

Abstract

At 2.3 megabases in length, the dystrophin gene is enormous: transcription of a single mRNA requires approximately 16 h. Principally expressed in skeletal muscle, the dystrophin protein product protects the muscle sarcolemma against contraction-induced injury, and dystrophin deficiency results in the fatal muscle-wasting disease, Duchenne muscular dystrophy. This gene is thus of key clinical interest, and therapeutic strategies aimed at eliciting dystrophin restoration require quantitative analysis of its expression. Approaches for quantifying dystrophin at the protein level are well-established, however study at the mRNA level warrants closer scrutiny: measured expression values differ in a sequence-dependent fashion, with significant consequences for data interpretation. In this manuscript, we discuss these nuances of expression and present evidence to support a transcriptional model whereby the long transcription time is coupled to a short mature mRNA half-life, with dystrophin transcripts being predominantly nascent as a consequence. We explore the effects of such a model on cellular transcriptional dynamics and then discuss key implications for the study of dystrophin gene expression, focusing on both conventional (qPCR) and next-gen (RNAseq) approaches.

Indexed as

DMDdystrophingene expressionmRNARNAseqtranscription

Identifiers

PMID37509720
PMCPMC10377302
OpenAlexW4385377522

What Socratic holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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 Socratic graph.