Document Detail


A global approach to analysis and interpretation of metabolic data for plant natural product discovery.
MedLine Citation:
PMID:  23447050     Owner:  NLM     Status:  MEDLINE    
Abstract/OtherAbstract:
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 publicly 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 datasets with transcriptomic data to create hypotheses concerning specialized metabolisms that generate 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.
Authors:
Manhoi Hur; Alexis Ann Campbell; Marcia Almeida-de-Macedo; Ling Li; Nick Ransom; Adarsh Jose; Matt Crispin; Basil J Nikolau; Eve Syrkin Wurtele
Publication Detail:
Type:  Journal Article; Research Support, N.I.H., Extramural; Research Support, Non-U.S. Gov't; Research Support, U.S. Gov't, Non-P.H.S.; Review    
Journal Detail:
Title:  Natural product reports     Volume:  30     ISSN:  1460-4752     ISO Abbreviation:  Nat Prod Rep     Publication Date:  2013 Apr 
Date Detail:
Created Date:  2013-03-13     Completed Date:  2013-05-09     Revised Date:  2014-04-01    
Medline Journal Info:
Nlm Unique ID:  8502408     Medline TA:  Nat Prod Rep     Country:  England    
Other Details:
Languages:  eng     Pagination:  565-83     Citation Subset:  IM    
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MeSH Terms
Descriptor/Qualifier:
Arabidopsis / genetics,  metabolism
Biological Products*
Databases, Factual
Drug Discovery
Metabolomics*
Plants, Medicinal / chemistry*,  genetics
Grant Support
ID/Acronym/Agency:
GM092521/GM/NIGMS NIH HHS; RC2 GM092521/GM/NIGMS NIH HHS
Chemical
Reg. No./Substance:
0/Biological Products
Comments/Corrections

From MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine


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