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New Connections across Pathways and Cellular Processes: Industrialized Mutant Screening Reveals Novel Associations between Diverse Phenotypes in Arabidopsis

作者
Yan Lu,Linda Savage,Imad Ajjawi,Kathleen M. Imre,David W. Yoder,Christoph Benning,Dean DellaPenna,John B. Ohlrogge,Katherine W. Osteryoung,Andreas P.M. Weber,Curtis G. Wilkerson,Robert L. Last
出处
期刊:Plant Physiology [Oxford University Press]
卷期号:146 (4): 1482-1500 被引量:86
标识
DOI:10.1104/pp.107.115220
摘要

In traditional mutant screening approaches, genetic variants are tested for one or a small number of phenotypes. Once bona fide variants are identified, they are typically subjected to a limited number of secondary phenotypic screens. Although this approach is excellent at finding genes involved in specific biological processes, the lack of wide and systematic interrogation of phenotype limits the ability to detect broader syndromes and connections between genes and phenotypes. It could also prevent detection of the primary phenotype of a mutant. As part of a systems biology approach to understand plastid function, large numbers of Arabidopsis thaliana homozygous T-DNA lines are being screened with parallel morphological, physiological, and chemical phenotypic assays (www.plastid.msu.edu). To refine our approaches and validate the use of this high-throughput screening approach for understanding gene function and functional networks, approximately 100 wild-type plants and 13 known mutants representing a variety of phenotypes were analyzed by a broad range of assays including metabolite profiling, morphological analysis, and chlorophyll fluorescence kinetics. Data analysis using a variety of statistical approaches showed that such industrial approaches can reliably identify plant mutant phenotypes. More significantly, the study uncovered previously unreported phenotypes for these well-characterized mutants and unexpected associations between different physiological processes, demonstrating that this approach has strong advantages over traditional mutant screening approaches. Analysis of wild-type plants revealed hundreds of statistically robust phenotypic correlations, including metabolites that are not known to share direct biosynthetic origins, raising the possibility that these metabolic pathways have closer relationships than is commonly suspected.

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