Suk Weon Kim1, Hye Jin Kim2, Jong Hyun Kim3, Yong Kook Kwon1, Myung Suk Ahn3, Young Pyo Jang2, Jang R Liu3. 1. Biological Resource Center, Korea Research Institute of Bioscience and Biotechnology (KRIBB), 125 Gwahak-ro, Yuseong-gu, Daejeon, 305-806, Korea. 2. Division of Pharmacognosy, Kyung Hee University, 1 Hoegi dong Dongdaemun-Gu, Seoul, 130-701, Korea. 3. Plant Systems Engineering Research Center, Korea Research Institute of Bioscience and Biotechnology (KRIBB), 125 Gwahak-ro, Yuseong-gu, Daejeon, 305-806, Korea.
Abstract
BACKGROUND: Efficient high throughput screening systems of useful mutants are prerequisite for study of plant functional genomics and lots of application fields. Advance in such screening tools, thanks to the development of analytic instruments. Direct analysis in real-time (DART)-mass spectrometry (MS) by ionization of complex materials at atmospheric pressure is a rapid, simple, high-resolution analytical technique. Here we describe a rapid, simple method for the genetic discrimination of intact Arabidopsis thaliana mutant seeds using metabolic profiling by DART-MS. RESULTS: To determine whether this DART-MS combined by multivariate analysis can perform genetic discrimination based on global metabolic profiling, intact Arabidopsis thaliana mutant seeds were subjected to DART-MS without any sample preparation. Partial least squares-discriminant analysis (PLS-DA) of DART-MS spectral data from intact seeds classified 14 different lines of seeds into two distinct groups: Columbia (Col-0) and Landsberg erecta (Ler) ecotype backgrounds. A hierarchical dendrogram based on partial least squares-discriminant analysis (PLS-DA) subdivided the Col-0 ecotype into two groups: mutant lines harboring defects in the phenylpropanoid biosynthetic pathway and mutants without these defects. These results indicated that metabolic profiling with DART-MS could discriminate intact Arabidopsis seeds at least ecotype level and metabolic pathway level within same ecotype. CONCLUSION: The described DART-MS combined by multivariate analysis allows for rapid screening and metabolic characterization of lots of Arabidopsis mutant seeds without complex metabolic preparation steps. Moreover, potential novel metabolic markers can be detected and used to clarify the genetic relationship between Arabidopsis cultivars. Furthermore this technique can be applied to predict the novel gene function of metabolic mutants regardless of morphological phenotypes.
BACKGROUND: Efficient high throughput screening systems of useful mutants are prerequisite for study of plant functional genomics and lots of application fields. Advance in such screening tools, thanks to the development of analytic instruments. Direct analysis in real-time (DART)-mass spectrometry (MS) by ionization of complex materials at atmospheric pressure is a rapid, simple, high-resolution analytical technique. Here we describe a rapid, simple method for the genetic discrimination of intact Arabidopsis thaliana mutant seeds using metabolic profiling by DART-MS. RESULTS: To determine whether this DART-MS combined by multivariate analysis can perform genetic discrimination based on global metabolic profiling, intact Arabidopsis thaliana mutant seeds were subjected to DART-MS without any sample preparation. Partial least squares-discriminant analysis (PLS-DA) of DART-MS spectral data from intact seeds classified 14 different lines of seeds into two distinct groups: Columbia (Col-0) and Landsberg erecta (Ler) ecotype backgrounds. A hierarchical dendrogram based on partial least squares-discriminant analysis (PLS-DA) subdivided the Col-0 ecotype into two groups: mutant lines harboring defects in the phenylpropanoid biosynthetic pathway and mutants without these defects. These results indicated that metabolic profiling with DART-MS could discriminate intact Arabidopsis seeds at least ecotype level and metabolic pathway level within same ecotype. CONCLUSION: The described DART-MS combined by multivariate analysis allows for rapid screening and metabolic characterization of lots of Arabidopsis mutant seeds without complex metabolic preparation steps. Moreover, potential novel metabolic markers can be detected and used to clarify the genetic relationship between Arabidopsis cultivars. Furthermore this technique can be applied to predict the novel gene function of metabolic mutants regardless of morphological phenotypes.
Authors: Suchitra Banerjee; K P Madhusudanan; Sunil K Chattopadhyay; Laiq Ur Rahman; Suman P S Khanuja Journal: Biomed Chromatogr Date: 2008-08 Impact factor: 1.902
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Authors: José M Alonso; Anna N Stepanova; Thomas J Leisse; Christopher J Kim; Huaming Chen; Paul Shinn; Denise K Stevenson; Justin Zimmerman; Pascual Barajas; Rosa Cheuk; Carmelita Gadrinab; Collen Heller; Albert Jeske; Eric Koesema; Cristina C Meyers; Holly Parker; Lance Prednis; Yasser Ansari; Nathan Choy; Hashim Deen; Michael Geralt; Nisha Hazari; Emily Hom; Meagan Karnes; Celene Mulholland; Ral Ndubaku; Ian Schmidt; Plinio Guzman; Laura Aguilar-Henonin; Markus Schmid; Detlef Weigel; David E Carter; Trudy Marchand; Eddy Risseeuw; Debra Brogden; Albana Zeko; William L Crosby; Charles C Berry; Joseph R Ecker Journal: Science Date: 2003-08-01 Impact factor: 47.728