Evidence map›Paper›PMID 35735543›Full record

ArticleBiosensors2022

Detection of DNA Methyltransferase Activity via Fluorescence Resonance Energy Transfer and Exonuclease-Mediated Target Recycling.

Tingting Hu, Changbei Ma, Ying Yan, Junxiang Chen

Open access · goldAbstract read
In one paragraph

Article in Biosensors, 2022. 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
0.3field-weighted citation impact, top 45% 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

0 citing papers in PubMed, 5 citations in OpenAlex.

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

4 authors at 1 institution in 1 country.

Tingting HuSchool of Life Sciences, Central South University, Changsha 410013, China.
Changbei MaSchool of Life Sciences, Central South University, Changsha 410013, China.
Ying YanSchool of Life Sciences, Central South University, Changsha 410013, China.
Junxiang ChenDepartment of Urology, The Second Xiangya Hospital of Central South University, Changsha 410011, China.
Central South University · CN

Funding

the Research Innovation Program for Graduates of Central South University 2018zzts384, 2019zzts453
6 · The paper itself

Abstract

In this study, a sensitive method for detecting DNA methyltransferase (MTase) activity was developed by combining the effective fluorescence resonance energy transfer (FRET) of cationic conjugated polymers and exonuclease (Exo) III-mediated signal amplification. DNA adenine MTase targets the GATC sequence within a substrate and converts the adenine in this sequence into N6-methyladenine. In the method developed in this study, the methylated substrate is cleaved using Dpn I, whereby a single-stranded oligodeoxynucleotide (oligo) is released. Afterward, the oligo is hybridized to the 3' protruding end of the F-DNA probe to form a double-stranded DNA, which is then digested by Exo III. Subsequently, due to weak electrostatic interactions, only a weak FRET signal is observed. The introduction of the Exo-III-mediated target-recycling reaction improved the sensitivity for detecting MTase. This detection method was found to be sensitive for MTase detection, with the lowest detection limit of 0.045 U/mL, and was also suitable for MTase-inhibitor screening, whereby such inhibitors can be identified for disease treatment.

Indexed as

Biosensing TechniquesFluorescence Resonance Energy TransferAdenineDNADNA MethylationLimit of DetectionMethyltransferasesAdenineDNAMethyltransferasescationic conjugated polymersDNA methyltransferaseexonuclease III-assisted signal amplificationfluorescence resonance energy transfer

Identifiers

PMID35735543
PMCPMC9221148
OpenAlexW4281733013

What OpenQuestion holds

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LicenceCC BY
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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.