Evidence map›Paper›PMID 33152073›Full record

ArticleNucleic acids research2020

High resolution biosensor to test the capping level and integrity of mRNAs.

Ignacio Moya-Ramírez, Clement Bouton, Cleo Kontoravdi, Karen Polizzi

Open access · goldAbstract read
In one paragraph

Article in Nucleic acids research, 2020. 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
0.6field-weighted citation impact, top 34% 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, 14 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

4 authors at 1 institution in 1 country.

Ignacio Moya-RamírezDepartment of Chemical Engineering, Imperial College London, London SW7 2AZ, UK.
Clement BoutonDepartment of Infectious Disease, Imperial College London, London W2 1NY, UK.
Cleo KontoravdiDepartment of Chemical Engineering, Imperial College London, London SW7 2AZ, UK.
Karen PolizziDepartment of Chemical Engineering, Imperial College London, London SW7 2AZ, UK.
Imperial College London · GB

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

5' Cap structures are ubiquitous on eukaryotic mRNAs, essential for post-transcriptional processing, translation initiation and stability. Here we describe a biosensor designed to detect the presence of cap structures on mRNAs that is also sensitive to mRNA degradation, so uncapped or degraded mRNAs can be detected in a single step. The biosensor is based on a chimeric protein that combines the recognition and transduction roles in a single molecule. The main feature of this sensor is its simplicity, enabling semi-quantitative analyses of capping levels with minimal instrumentation. The biosensor was demonstrated to detect the capping level on several in vitro transcribed mRNAs. Its sensitivity and dynamic range remained constant with RNAs ranging in size from 250 nt to approximately 2700 nt and the biosensor was able to detect variations in the capping level in increments of at least 20%, with a limit of detection of 2.4 pmol. Remarkably, it also can be applied to more complex analytes, such mRNA vaccines and mRNAs transcribed in vivo. This biosensor is an innovative example of a technology able to detect analytically challenging structures such as mRNA caps. It could find application in a variety of scenarios, from quality analysis of mRNA-based products such as vaccines to optimization of in vitro capping reactions.

Indexed as

Biosensing TechniquesTranscription, GeneticProtein BiosynthesisRNA CapsRNA, MessengerRNA StabilityRNA CapsRNA, Messenger

Identifiers

PMID33152073
PMCPMC7736790
OpenAlexW3096211358

What OpenQuestion 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 OpenQuestion graph.