Literature DB >> 11800579

Functional genomics: high-throughput mRNA, protein, and metabolite analyses.

David J Oliver1, Basil Nikolau, Eve Syrkin Wurtele.   

Abstract

A tremendous amount of DNA sequence information is now available to scientists and engineers. These DNA sequences provide the foundation for studying how the genome of an organism is functioning and they are particularly useful for metabolic engineers interested in manipulating plants for the production of chemicals and enzymes. Functional genomics relies on high-throughput techniques for measuring the mRNA (the transcriptome), protein (the proteome), and metabolite (the metabolome) components of plants as well as their organs and tissues. Microarray technologies, recent advances in protein mass spectrometry, and high-throughput metabolite analyses are beginning to provide detailed information on the total mRNA, protein, and metabolite components of plants. This knowledge will allow scientists to monitor changes in proteins and metabolites in plants. Ultimately, it may allow them to discover new metabolic pathways and to model metabolic and regulatory networks in plants.

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Year:  2002        PMID: 11800579     DOI: 10.1006/mben.2001.0212

Source DB:  PubMed          Journal:  Metab Eng        ISSN: 1096-7176            Impact factor:   9.783


  14 in total

1.  A new regulatory role for the chloroplast ATP synthase.

Authors:  Stephen K Herbert
Journal:  Proc Natl Acad Sci U S A       Date:  2002-09-23       Impact factor: 11.205

2.  Metabolomics and machine learning: explanatory analysis of complex metabolome data using genetic programming to produce simple, robust rules.

Authors:  Douglas B Kell
Journal:  Mol Biol Rep       Date:  2002       Impact factor: 2.316

Review 3.  A global approach to analysis and interpretation of metabolic data for plant natural product discovery.

Authors:  Manhoi Hur; Alexis Ann Campbell; Marcia Almeida-de-Macedo; Ling Li; Nick Ransom; Adarsh Jose; Matt Crispin; Basil J Nikolau; Eve Syrkin Wurtele
Journal:  Nat Prod Rep       Date:  2013-04       Impact factor: 13.423

4.  In vivo NMR metabolic profiling of Fabrea salina reveals sequential defense mechanisms against ultraviolet radiation.

Authors:  Roberto Marangoni; Debora Paris; Dominique Melck; Lorenzo Fulgentini; Giuliano Colombetti; Andrea Motta
Journal:  Biophys J       Date:  2011-01-05       Impact factor: 4.033

5.  Understanding UV-driven metabolism in the hypersaline ciliate Fabrea salina.

Authors:  Roberto Marangoni; Debora Paris; Dominique Melck; Lorenzo Fulgentini; Giuliano Colombetti; Andrea Motta
Journal:  Eur Biophys J       Date:  2011-11-18       Impact factor: 1.733

6.  Evaluation of metabolic alteration in transgenic rice overexpressing dihydroflavonol-4-reductase.

Authors:  Hideyuki Takahashi; Mitsunori Hayashi; Fumiyuki Goto; Shigeru Sato; Tomoyoshi Soga; Takaaki Nishioka; Masaru Tomita; Maki Kawai-Yamada; Hirofumi Uchimiya
Journal:  Ann Bot       Date:  2006-07-18       Impact factor: 4.357

7.  Metabolic profiling of Arabidopsis thaliana epidermal cells.

Authors:  Berit Ebert; Daniela Zöller; Alexander Erban; Ines Fehrle; Jürgen Hartmann; Annette Niehl; Joachim Kopka; Joachim Fisahn
Journal:  J Exp Bot       Date:  2010-02-11       Impact factor: 6.992

8.  Metabolomic and transcriptomic analysis of the rice response to the bacterial blight pathogen Xanthomonas oryzae pv. oryzae.

Authors:  Theodore R Sana; Steve Fischer; Gert Wohlgemuth; Anjali Katrekar; Ki-Hong Jung; Pam C Ronald; Oliver Fiehn
Journal:  Metabolomics       Date:  2010-05-27       Impact factor: 4.290

9.  Responses of seed germination and shoot metabolic profiles of maize (Zea mays L.) to Y2O3 nanoparticle stress.

Authors:  Chenchen Gong; Linghao Wang; Xiaolu Li; Hongsen Wang; Yuxin Jiang; Wenxing Wang
Journal:  RSC Adv       Date:  2019-09-03       Impact factor: 4.036

10.  MetNet: Software to Build and Model the Biogenetic Lattice of Arabidopsis.

Authors:  Eve Syrkin Wurtele; Jie Li; Lixia Diao; Hailong Zhang; Carol M Foster; Beth Fatland; Julie Dickerson; Andrew Brown; Zach Cox; Dianne Cook; Eun-Kyung Lee; Heike Hofmann
Journal:  Comp Funct Genomics       Date:  2003
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