102 research outputs found

    De Novo Transcriptome of Safflower and the Identification of Putative Genes for Oleosin and the Biosynthesis of Flavonoids

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    Safflower (Carthamus tinctorius L.) is one of the most extensively used oil crops in the world. However, little is known about how its compounds are synthesized at the genetic level. In this study, Solexa-based deep sequencing on seed, leaf and petal of safflower produced a de novo transcriptome consisting of 153,769 unigenes. We annotated 82,916 of the unigenes with gene annotation and assigned functional terms and specific pathways to a subset of them. Metabolic pathway analysis revealed that 23 unigenes were predicted to be responsible for the biosynthesis of flavonoids and 8 were characterized as seed-specific oleosins. In addition, a large number of differentially expressed unigenes, for example, those annotated as participating in anthocyanin and chalcone synthesis, were predicted to be involved in flavonoid biosynthesis pathways. In conclusion, the de novo transcriptome investigation of the unique transcripts provided candidate gene resources for studying oleosin-coding genes and for investigating genes related to flavonoid biosynthesis and metabolism in safflower

    Towards the genetic architecture of seed lipid biosynthesis and accumulation in Arabidopsis thaliana

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    We report the quantitative genetic analysis of seed oil quality and quantity in six Arabidopsis thaliana recombinant inbred populations, in which the parent accessions were from diverse geographical origins, and were selected on the basis of variation for seed oil content and lipid composition. Although most of the biochemical steps involved in lipid biosynthesis are known and the key genes have been identified, the regulation of the processes that results in the final oil composition and total amount is not understood. By using physically anchored markers it was possible to compare results across populations. A total of 219 quantitative trait loci (QTLs) were identified, of which 81 were significant at P<0.001. Some of these colocalise with QTLs identified previously, but many novel QTLs were also identified. The results highlight the importance of studying traits in multiple populations, which will lead to a better understanding of the contribution that natural variation makes to the genetic architecture of a phenotype
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