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Genetic background and environmental conditions drive metabolic variation in wild type and transgenic soybean (Glycine max) seeds
Authors:Hagai Cohen  Ofer M Shir  Yang Yu  Wensheng Hou  Shi Sun  Tianfu Han  Rachel Amir
Institution:1. Faculty of Biology, Technion ‐ Israel Institute of Technology, Haifa, Israel;2. Migal Galilee Technology Center, Kiryat Shmona, Israel;3. Tel‐Hai College, Upper Galilee, Israel;4. MOA Key Laboratory of Soybean Biology, Institute of Crop Science, The Chinese Academy of Agricultural Sciences, Beijing, China
Abstract:The metabolic profiles and composition of storage reserves of agricultural crop seeds are strongly regulated by heritable and environmental factors. Yet, very little is known about the genetic and environmental determinants of adaptive metabolic variation amongst wild type as well as transgenic seed populations derived from the same genetic background, grown under natural field conditions. The goal of the current study was to investigate the effects of natural environmental conditions on wild type and transgenic soybean seeds expressing a feedback‐insensitive form of cystathionine γ‐synthase, a methionine main regulatory enzyme. The seeds were grown in four geographically distinct habitats in China and then assayed for primary metabolic profiles using gas chromatography mass spectrometry, morphological traits and storage reserve accumulation. The analyses revealed changes in the levels of primary metabolites which evidently exhibited high correlation to methionine regardless of changes in environmental conditions. The environment, however, constituted a major determinant of metabolic profiles amongst seeds, as much more metabolites were observed to be affected by this variable, particularly along the north‐to‐south latitudinal gradient. The observations suggest that metabolic variation amongst seeds grown under natural field conditions depends upon the complex relationships existing amongst their genetic background and the environmental conditions characterizing their cultivation areas.
Keywords:GC‐MS  metabolite profiling  methionine  statistical learning
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