Evidence map›Paper›PMID 28570512›Full record

ArticleJournal of visualized experiments : JoVE2017

Atomic Force Microscopy Investigations of DNA Lesion Recognition in Nucleotide Excision Repair.

Jonas Gross, Nicolas Wirth, Ingrid Tessmer

Abstract readVideo-Audio Media
In one paragraph

Article in Journal of visualized experiments : JoVE, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Review
  2. Review
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

3 authors.

Jonas GrossRudolf Virchow Center for Experimental Biomedicine, University of Würzburg.
Nicolas WirthRudolf Virchow Center for Experimental Biomedicine, University of Würzburg.
Ingrid TessmerRudolf Virchow Center for Experimental Biomedicine, University of Würzburg; Ingrid.Tessmer@virchow.uni-wuerzburg.de.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

AFM imaging is a powerful technique for the study of protein-DNA interactions. This single molecule method allows the simultaneous resolution of different molecules and molecular assemblies in a heterogeneous sample. In the particular context of DNA interacting protein systems, different protein complex forms and their corresponding binding positions on target sites containing DNA fragments can thus be distinguished. Here, an application of AFM to the study of DNA lesion recognition in the prokaryotic and eukaryotic nucleotide excision DNA repair (NER) systems is presented. The procedures of DNA and protein sample preparations are described and experimental as well as analytical details of the experiments are provided. The data allow important conclusions on the strategies by which target site verification may be achieved by the NER proteins. Interestingly, they indicate different approaches of lesion recognition and identification for the eukaryotic NER system, depending on the type of lesion. Furthermore, distinct structural properties of the two different helicases involved in prokaryotic and eukaryotic NER result in and explain the different strategies observed for these two systems. Importantly, these experimental and analytical approaches can be applied not only to the study of DNA repair but also very similarly to other DNA interacting protein systems such as those involved in replication or transcription processes.

Indexed as

DNA DamageDNA RepairDNAHumansMicroscopy, Atomic ForceNucleic Acid ConformationProtein BindingXeroderma Pigmentosum Group D ProteinDNAERCC2 protein, humanXeroderma Pigmentosum Group D Protein

Identifiers

PMID28570512
PMCPMC5608143

What OpenQuestion holds

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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.