Evidence map›Paper›PMID 31321687›Full record

ArticleTransgenic research2019

Sequence-specific nucleases as tools for enhancing disease resistance in crops.

Vladimir Nekrasov

Open access · hybridAbstract read
PubMed Publisher
In one paragraph

Article in Transgenic research, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
0.3field-weighted citation impact, top 41% 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

3 citing papers in PubMed, 7 citations in OpenAlex.

  1. Article
  2. Article
  3. Article
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

1 author at 1 institution in 1 country.

Vladimir NekrasovPlant Sciences Department, Rothamsted Research, Harpenden, AL5 2JQ, UK. vladimir.nekrasov@rothamsted.ac.uk.ORCID 0000-0001-9386-1683
Rothamsted Research · GB

Funding

Biotechnology and Biological Sciences Research Council BB/P016855/1
6 · The paper itself

Abstract

Genome editing technologies, such as CRISPR/Cas, have recently become valuable tools for plant reverse genetics as well as crop improvement, including enhancement of disease resistance. Targeting susceptibility (S) genes by genome editing has proven to be a viable strategy for generating resistance to both bacterial and fungal pathogens in various crops. Examples include generating loss-of-function mutations in promoter elements of the SWEET S genes, which are targeted by transcription activator-like effectors secreted by many phytopathogenic Xanthomonas bacteria, as well as in the conserved MLO locus that confers susceptibility to powdery mildew fungal pathogens in many monocots and dicots. In addition to genome editing applications, CRISPR/Cas systems can be used as means of defending plants against viruses via targeting viral genomic DNA or RNA. Genome editing is therefore a highly promising approach that enables engineering disease resistance to various plant pathogens directly in elite cultivar background in a highly precise manner. Unlike conventional crop breeding, genome editing approaches are not relying on lengthy and laborious crosses/back-crosses involving parental and progeny lines and can significantly shorten the breeding timeline. Taking into account the high potential of genome editing technologies for both basic and applied plant science, the recent decision of the European Court of Justice to define transgene-free genetically edited crops as GMOs is, clearly, a backward step for the EU.

Indexed as

CRISPR-Cas SystemsDisease ResistanceGene EditingGenome, PlantLoss of Function MutationPlant DiseasesPlants, Genetically ModifiedXanthomonasCRISPRDiseaseGMOMLOPlantTALE

Identifiers

PMID31321687
OpenAlexW2963808381

What OpenQuestion holds

Textmetadata
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.