Advancing Drug Development from Medicinal Plants using Transcriptomics and Metabo

描述（由申请人提供）：药用植物产生丰富的药物化合物，例如紫杉醇、长春新碱和吗啡。不幸的是，导致这些化合物的专门的次级代谢途径仍然知之甚少，并且阐明和操纵这些分类学上限制的代谢途径的进展也相应缓慢。药用植物“组学”级资源开发有限，加剧了这种情况，这意味着作为一个整体，药用物种的研究并未像模式植物和农作物物种的研究那样从基因组学革命中获得同等程度的受益。该提案描述了结合使用最先进的测序技术、代谢组学能力和生物信息学来开发不受限制的公共资源，以解决我们在物种特异性植物代谢知识库中日益扩大的差距，并加速对 20 种广泛使用的药用植物物种中参与天然产物生物合成的基因的识别和功能分析。该资源将为研究界提供对每种植物转录组和相关代谢组的 DNA 序列和表达谱的用户友好访问，我们预计这将对药物开发产生转化影响。为了实现这一目标，我们将利用下一代测序方法来确定每个药用植物物种编码的近乎完整的 mRNA 集。将使用 Illumina 的 RNA-Seq 方法对每个物种多达 20 种化学不同的组织/处理进行转录组分析，并与通过 LC-TOF 和 GC-MS 为这些相同样品生成的代谢物图谱进行关联。所有序列和基因表达数据将存入 NCBI 并与代谢物分析数据一起在由研究联盟开发的定制网站medicalplantgenomics.msu.edu 上提供。因此，美国国立卫生研究院的这一重大机会拨款将为药用植物基因序列、表达谱和代谢物提供可搜索和可下载的数据库，研究界可以访问和利用这些数据库，以促进发现负责关键药物生物合成的途径和基因。基因组和转录组的高通量测序彻底改变并加速了整个生命科学研究的步伐和进展，该提案将首次将这些进步大规模地扩展到药用植物领域。 公共健康相关性：该提案描述了结合使用最先进的 DNA 测序技术、代谢组学能力和生物信息学来开发不受限制的公共资源，以推进我们对物种特异性植物代谢的知识库，并加速对 20 种广泛使用的药用植物物种中参与天然产物生物合成的基因的识别和功能分析。该资源将为研究界提供对每种植物转录组和相关代谢组的 DNA 序列和表达谱的用户友好访问，我们预计这将对药物开发产生转化影响。

DESCRIPTION (provided by applicant): Medicinal plants produce a wealth of pharmaceutical compounds such as taxol, vincristine, and morphine. Unfortunately, the specialized secondary metabolic pathways leading to such compounds remain poorly understood and progress in elucidating and manipulating these taxonomically restricted metabolic pathways has been correspondingly slow. This has been exacerbated by the limited development of "omics"-level resources for medicinal plants, which has meant that as a group, research in medicinal species have not benefited to the same extent from the genomics revolution, as have research in model plants and agronomic crop species. This proposal describes the combined use of state-of-the-art sequencing technologies, metabolomics capabilities, and bioinformatics to develop an unrestricted, public resource to address this growing gap in our knowledge base of species-specific plant metabolism and accelerate the identification and functional analysis of genes involved in natural product biosynthesis in 20 widely used medicinal plant species. This resource will provide the research community with user-friendly access to the DNA sequences and expression profiles of each plant's transcriptome and associated metabolome, which we anticipate will have a translational effect on drug development. To achieve this goal, we will utilize next generation sequencing approaches to determine the near-complete set of mRNAs encoded by each medicinal plant species. Transcriptome profiling of up to 20 chemically diverse tissues/treatments per species using the RNA-Seq method from Illumina will be performed and correlated with metabolite profiles generated through LC-TOF and GC-MS for these same samples. All sequence and gene expression data will be deposited into NCBI and made available, along with metabolite profiling data at medicinalplantgenomics.msu.edu, a custom website developed by the research consortium. Thus, this NIH Grand Opportunities Grant will provide searchable and downloadable databases for medicinal plant gene sequences, expression profiles and metabolites that can be accessed and utilized by the research community to facilitate discovery of the pathways and genes responsible for biosynthesis of key pharmaceuticals. High throughput sequencing of genomes and transcriptomes has revolutionized and accelerated the pace and progress of research across the life sciences and this proposal will for the first time extend these advances into the medicinal plant arena on a broad scale. PUBLIC HEALTH RELEVANCE: This proposal describes the combined use of state-of-the-art DNA sequencing technologies, metabolomics capabilities, and bioinformatics to develop an unrestricted, public resource to advance our knowledge base of species-specific plant metabolism and accelerate the identification and functional analysis of genes involved in natural product biosynthesis in 20 widely used medicinal plant species. This resource will provide the research community with user-friendly access to the DNA sequences and expression profiles of each plant's transcriptome and associated metabolome, which we anticipate will have a translational effect on drug development.