ArticlePlanta2012
dHPLC efficiency for semi-automated cDNA-AFLP analyses and fragment collection in the apple scab-resistance gene model.
Article in Planta, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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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.
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Who cites it
3 citing papers in PubMed.
- Phenotyping, genetics, and "-omics" approaches to unravel and introgress enhanced resistance against apple scab (Horticulture research · 2024Article
- Article
- Gene expression profiling by cDNA-AFLP reveals potential candidate genes for partial resistance of 'Président Roulin' against Venturia inaequalis.BMC genomics · 2014Article
Corrections and comments
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Authors and funding
10 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
cDNA-AFLP analysis for transcript profiling has been successfully applied to study many plant biological systems, particularly plant-microbe interactions. However, the separation of cDNA-AFLP fragments by gel electrophoresis is reported to be labor-intensive with only limited potential for automation, and the collection of differential bands for gene identification is even more cumbersome. In this work, we present the use of dHPLC (denaturing high performance liquid chromatography) and automated DNA fragment collection using the WAVE(®) System to analyze and recover cDNA-AFLP fragments. The method is successfully applied to the Malus-Venturia inaequalis interaction, making it possible to collect 66 different transcript-derived fragments for apple genes putatively involved in the defense response activated by the HcrVf2 resistance gene. The results, validated by real time quantitative RT-PCR, were consistent with the plant-pathogen interaction under investigation and this further supports the suitability of dHPLC for cDNA-AFLP transcript profiling. Features and advantages of this new approach are discussed, evincing that it is an almost fully automatable and cost-effective solution for processing large numbers of samples and collecting differential genes involved in other biological processes and non-model plants.
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Registered trials
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