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Electron. j. biotechnol ; Electron. j. biotechnol;35: 48-56, sept. 2018. tab, graf
Artigo em Inglês | LILACS | ID: biblio-1047771

RESUMO

Background: Tamarix ramosissima is a desert forest tree species that is widely distributed in the drought-stricken areas to sustain the fragile ecosystem. Owing to its wide usage in the desert restoration of Asia, it can be used as an ecophysiological model plant. To obtain reliable and accurate results, a set of reference genes should be screened before gene expression. However, up to date, systematical evaluation of reference genes has not been conducted in T. ramosissima. Results: In this study, we used eigenvalues derived from principal component analysis to identify stable expressed genes from 72,035 unigenes from diurnal transcriptomes under natural field conditions. With combined criteria of read counts above 900 and CV of FPKM below 0.3, a total of 7385 unigenes could be qualified as candidate reference genes in T. ramosissima. By using three statistical algorithm packages, geNorm, NormFinder, and BestKeeper, the stabilities of these novel reference genes were further compared with a panel of traditional reference genes. The expression patterns of three aquaporins (AQPs) suggested that at least UBQ (high expression), EIF4A2 (low expression), and GAPDH (moderate expression) could be qualified as ideal reference genes in both RT-PCR and RNA-seq analysis of T. ramosissima. Conclusions: This work will not only facilitate future studies on gene expression and functional analysis of genetic resources of desert plants but also improve our understanding of the molecular regulation of water transport in this plant, which could provide a new clue to further investigate the drought adaptation mechanism of desert plant species under harsh environments.


Assuntos
Tamaricaceae/genética , Transcriptoma , Padrões de Referência , Adaptação Biológica , Expressão Gênica , Ecossistema , Folhas de Planta/genética , Deserto , Recuperação e Remediação Ambiental , Secas , Reação em Cadeia da Polimerase em Tempo Real , RNA-Seq
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