A community resource for high-throughput quantitative RT-PCR analysis of transcription factor gene expression in Medicago truncatula
Medicago truncatula is a model legume species that is currently the focus of an international genome sequencing effort. Although several different oligonucleotide and cDNA arrays have been produced for genome-wide transcript analysis of this species, intrinsic limitations in the sensitivity of hybridization-based technologies mean that transcripts of genes expressed at low-levels cannot be measured accurately with these tools.
Amongst such genes are many encoding transcription factors (TFs), which are arguably the most important class of regulatory proteins. Quantitative reverse transcription-polymerase chain reaction (qRT-PCR) is the most sensitive method currently available for transcript quantification, and one that can be scaled up to analyze transcripts of thousands of genes in parallel.
Thus, qRT-PCR is an ideal method to tackle the problem of TF transcript quantification in Medicago and other plants.
Results: We established a bioinformatics pipeline to identify putative TF genes in Medicago truncatula and to design gene-specific oligonucleotide primers for qRT-PCR analysis of TF transcripts. We validated the efficacy and gene-specificity of over 1000 TF primer pairs and utilized these to identify sets of organ-enhanced TF genes that may play important roles in organ development or differentiation in this species.
This community resource will be developed further as more genome sequence becomes available, with the ultimate goal of producing validated, gene-specific primers for all Medicago TF genes.
Conclusion: High-throughput qRT-PCR using a 384-well plate format enables rapid, flexible, and sensitive quantification of all predicted Medicago transcription factor mRNAs. This resource has been utilized recently by several groups in Europe, Australia, and the USA, and we expect that it will become the 'gold-standard' for TF transcript profiling in Medicago truncatula.
Author: Klementina Kakar, Maren Wandrey, Tomasz Czechowski, Tanja Gaertner, Wolf-Rudiger Scheible, Mark Stitt, Ivone Torres-Jerez, Yongli Xiao, Julia Redman, Hank Wu, Foo Cheung, Christopher Town and Michael Udvardi
Credits/Source: Plant Methods 2008,
Published on: 2008-07-08
Copyright by the authors listed above - made available via BioMedCentral (Open Access). Please
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