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Panax notoginseng (Burk) F.H. Chen. (Sanqi or Tianqi in Chinese) is one of the most important and valuable traditional Chinese medicinal herbs widely used for promoting blood circulation, removing blood stasis, relieving swelling, alleviating pain, and nourishing body. The P. notoginseng production can not meet the increasing market demand because of the limited natural resources, difficulties in plant cultivation, and so far no commercial applications of cell or tissue cultivation. Metabolic engineering is a promising solution, while its successful application depends on insights into biosynthetic pathways of the main active compounds and related genes. Due to lack of extensive genomic data, commonly used gene expression methods such as SAGE, microarray are difficult to apply in P. notoginseng. In contrast, cDNA-AFLP, notably preferable for no needing sequence information prior experiment, is particularly suitable for comprehensive gene expression analysis and gene discovery in medicinal plants which lack sequence information, and in turn will facilitate metabolic engineering. MeJA treatment increases the levels of triterpene saponins, which are considered to be the main bioactive components in P. notoginseng. In order to identify MeJA-modulated genes and those involved in the biosynthesis of interseted triterpene saponins, we established an optimal cDNA-AFLP procedure in adventitious roots of P. notoginseng. Several critical affecting factors, including total RNA isolation, double-stranded cDNA synthesis, restriction enzyme combination quantities and reaction time, adaptor quantities, PCR template dilution levels and primer combination concentrations, were studied. Additionally, an amplified fragments visualization platform was developed, using automated capillary electrophoresis based ABI xl 3730 sequencer combined with data processing software GeneMapper v 4.0. Transcript profiling analysis in MeJA treated adventitious roots of P. notoginseng was carried out by cDNA-AFLP in conjunction with PCA and PLS-DA to obtain the temporal and spatial gene expression change patterns and seek differentially TDFs. Remarkably, this approach might be widely applicable to other important nonmodel plants.
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