Use of Metabolomics and Transcriptomics to Gain Insights into the Regulation and Biosynthesis of Medicinal Compounds: Hypericum as a Model

Crispin, Matthew C.; Wurtele, Eve Syrkin

doi:10.1007/978-3-642-29974-2_17

Cited by 7 publications

(9 citation statements)

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“…Hypericum encompasses over 450 species, few of which have been chemically profiled (Crispin and Wurtele 2013), and is the largest genus within Clusiaceae, fossils of which date to the Late Cretaceous (90 million years ago) (Gustafsson et al 2002). H. perforatum (St. John’s Wort) is commonly used by humans medicinally (Ernst 2003, Birt et al 2009).…”

Section: Introductionmentioning

confidence: 99%

Identification and biosynthesis of acylphloroglucinols in Hypericum gentianoides

Crispin

Hur

Park

et al. 2013

Physiologia Plantarum

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Species of the genus Hypericum contain a rich array of unusual polyketides, however, only a small proportion of the over 450 Hypericum species, other than the popular medicinal supplement St. John’s Wort (H. perforatum), have even been chemically characterized. H. gentianoides, a small annual used medicinally by Cherokee Americans, contains bioactive acylphloroglucinols. Here, we identify acylphloroglucinol constituents of H. gentianoides and determine a potential pathway to their synthesis. Liquid chromatography/electrospray ionization-mass spectrometry (LC/ESI-MS) and HPLC-UV indicate that the level of accumulation and profile of acylphloroglucinols in H. gentianoides vary little seasonally when grown in a greenhouse, but do vary with development and are highly dependent on the accession, highlighting the importance of the selection of plant material for study. We identify the chemical structures of the nine prevalent polyketides, based on LC/ESI-MS and hybrid quadrupole orthogonal time-of-flight mass (Q-TOF) spectrometry; these metabolites include one monomeric phlorisobutyrophenone (PIB) derivative and eight dimeric acylphloroglucinols. Q-TOF spectrometry was used to identify eight additional PIB derivatives that were not detected by LC/ESI-MS. These data lead us to propose that diacylphloroglucinols are synthesized via modification of PIB to yield diverse phloroglucinol and filicinic acids moieties, followed by dimerization of a phloroglucinol and a filicinic acid monomer to yield the observed complement of diacylphloroglucinols. The metabolomics data from H. gentianoides are accessible in PMR (http://www.metnetdb.org/pmr), a public metabolomics database with analysis software for plants and microbial organisms.

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Section: Introductionmentioning

confidence: 99%

Identification and biosynthesis of acylphloroglucinols in Hypericum gentianoides

Crispin

Hur

Park

et al. 2013

Physiologia Plantarum

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“…The list is ordered by most significant pFDR values; thus, of the 30,000 transcripts detected by RNAseq analysis in H. perforatum , hyperforin accumulation four Hyp-1 like transcripts (red arrows) are among the 10 most tightly correlated. The experimental data used in these analysis are from the Medicinal Plants Genomics Consortium; biological materials and metabolomics analysis 32, 33 were from Matt Crispin, Iowa State University. Clicking on Sequence ID row for UDP-glucuronosyl transferase (green arrow) brings the user to detailed information for that sequence (lower panel).…”

Section: Figmentioning

confidence: 99%

“…29 AtMetExpress, (http://prime.psc.riken.jp/lcms/AtMetExpress/) 30 contains data and comprehensive metadata from carefully defined organs and developmental stages of Arabidopsis (for which microarray data is available from AtGeneExpress ), 31 as well as from 20 ecotypes of this species. A third example, The Medicinal Plant Metabolomics Resource ( MPMR, http://www.medicinalplantmetabolimics.org/), 32, 33 presents metabolomics data for 12 species. Its companion database, Medicinal Plant Genomics Resource (http://medicinalplantgenomics.msu.edu/) contains transcriptomics data from the same biological samples.…”

Section: Introductionmentioning

confidence: 99%

“…Its companion database, Medicinal Plant Genomics Resource (http://medicinalplantgenomics.msu.edu/) contains transcriptomics data from the same biological samples. To date, these resources have supported the identification of metabolic intermediates, reactions and genes from medicinal species including: identification of a gene encoding a cytochrome P450 which catalyzes a step in the synthesis of the alkaloid 19-O-acetylhorhammericine in Catharanthus roseus ; 34 identification of unusual phloroacylglucinols in Hypericum gentianoides ; 33 characterization of the evolutionary origin of different accessions of Prunellla vulgaris ; 32 insight into the stereospecificity of quinoline alkaloid synthesis in Camptotheca acuminata ; 32 cloning of three enzymes of cardenolide synthase, C4 sterol methyloxidase, and progesterone 5b-reductase from Digitalis purpurea ; 35 and identification of genes encoding valerena-1,10-diene synthase in Valeriana officinalis, 32 and their role in the synthesis of sesquiterpenes in that species that have biological activities in mammals. 36 …”

Section: Introductionmentioning

confidence: 99%

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A global approach to analysis and interpretation of metabolic data for plant natural product discovery

et al. 2013

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Discovering molecular components and their functionality is key to the development of hypotheses concerning the organization and regulation of metabolic networks. The iterative experimental testing of such hypotheses is the trajectory that can ultimately enable accurate computational modelling and prediction of metabolic outcomes. This information can be particularly important for understanding the biology of natural products, whose metabolism itself is often only poorly defined. Here, we describe factors that must be in place to optimize the use of metabolomics in predictive biology. A key to achieving this vision is a collection of accurate time-resolved and spatially defined metabolite abundance data and associated metadata. One formidable challenge associated with metabolite profiling is the complexity and analytical limits associated with comprehensively determining the metabolome of an organism. Further, for metabolomics data to be efficiently used by the research community, it must be curated in publically available metabolomics databases. Such databases require clear, consistent formats, easy access to data and metadata, data download, and accessible computational tools to integrate genome system-scale datasets. Although transcriptomics and proteomics integrate the linear predictive power of the genome, the metabolome represents the nonlinear, final biochemical products of the genome, which results from the intricate system(s) that regulate genome expression. For example, the relationship of metabolomics data to the metabolic network is confounded by redundant connections between metabolites and gene-products. However, connections among metabolites are predictable through the rules of chemistry. Therefore, enhancing the ability to integrate the metabolome with anchor-points in the transcriptome and proteome will enhance the predictive power of genomics data. We detail a public database repository for metabolomics, tools and approaches for statistical analysis of metabolomics data, and methods for integrating these dataset with transcriptomic data to create hypotheses concerning specialized metabolism that generates the diversity in natural product chemistry. We discuss the importance of close collaborations among biologists, chemists, computer scientists and statisticians throughout the development of such integrated metabolism-centric databases and software.

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“…Los numerosos estudios sobre especies medicinales aún son pocos en comparación con todo lo que falta por conocer acerca de los procesos de síntesis de metabolitos secundarios, tanto en términos de las rutas metabólicas y enzimas involucradas como de los factores internos y externos que afectan su acumulación (Crispin & Wurtele, 2013). En consecuencia, recientemente se ha adicionado la secuenciación y análisis de transcriptomas, ya sea por mapeo contra un genoma o bien mediante el ensamblaje de novo, como una estrategia innovadora para generar información sobre la expresión de genes de ruta como de regulación (Sitakanta et al, 2006).…”

Section: Introductionunclassified

Identificación de secuencias génicas relacionadas con la ruta metabólica de síntesis de glicósidos en Stevia rebaudiana

Jiménez-Quesada¹,

Álvarez-Figueroa²,

Garro-Monge³

2017

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Use of Metabolomics and Transcriptomics to Gain Insights into the Regulation and Biosynthesis of Medicinal Compounds: Hypericum as a Model

Cited by 7 publications

References 68 publications

Identification and biosynthesis of acylphloroglucinols in Hypericum gentianoides

Identification and biosynthesis of acylphloroglucinols in Hypericum gentianoides

A global approach to analysis and interpretation of metabolic data for plant natural product discovery

Identificación de secuencias génicas relacionadas con la ruta metabólica de síntesis de glicósidos en Stevia rebaudiana

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