Metabolomics,Unknown,Transcriptomics,Genomics,Proteomics

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Transcriptome analysis during seed development provides insights into altered gene expression in Glycine max

ABSTRACT: The temporal expression profile of Glycine max seeds was carried out to identify genes that are differentially expressed (DE) during seed development. Using the Affymetrix chip, we have for the first time provided a holistic view of the transcriptional landscape during seed development in four different developmental stages in Glycine max. cv. Pusa 16. The analysis of the differential expression patterns and functional category enrichment of DE genes highlighted specific and common significant coordination and enrichment of various biological processes during seed development which have led to the identification of few candidate genes related to inositol metabolism and especially in phytate biosynthesis. In conclusion, we have shown here a logical approach to identify possible candidate genes for fine tuning the metabolic flux for phytate generation, which may be altered by metabolic engineering in developing a low phytate phenotype. Seeds of Glycine max grown at 28/26°C, 16h/8h light/dark were collected at different developmental stages( 0-4mm, 4-8mm, 8-12mm and 12-16mm ) and analyzed. Samples in triplicates from each developmental stage were used for data generation on Affymetrix Chip, each time the earlier developmental stage was considered as control Vs the treatment, corresponding to the later developmental stage. For each biological replicates, RNA was extracted from 20 seeds collected from 5 different plants, grown in the same conditions using the Trizol method. RNA integrity was analyzed using bio analyzer (with RIN value more than 8).

ORGANISM(S): Glycine max

SUBMITTER: Archana Sachdev

PROVIDER: E-GEOD-69821 | biostudies-arrayexpress |

REPOSITORIES: biostudies-arrayexpress

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Project description:Soybean (Glycine max) seeds are an important source of seed storage compounds, including protein, oil, and sugar used for food, feed, chemical, and biofuel production. We assessed detailed temporal transcriptional and metabolic changes in developing soybean embryos to gain a systems biology view of developmental and metabolic changes and to identify potential targets for metabolic engineering. Two major developmental and metabolic transitions were captured enabling identification of potential metabolic engineering targets specific to seed filling and to desiccation. The first transition involved a switch between different types of metabolism in dividing and elongating cells. The second transition involved the onset of maturation and desiccation tolerance during seed filling and a switch from photoheterotrophic to heterotrophic metabolism. Clustering analyses of metabolite and transcript data revealed clusters of functionally related metabolites and transcripts active in these different developmental and metabolic programs. The gene clusters provide a resource to generate predictions about the associations and interactions of unknown regulators with their targets based on “guilt-by-association” relationships. The inferred regulators also represent potential targets for future metabolic engineering of relevant pathways and steps in central carbon and nitrogen metabolism in soybean embryos and drought and desiccation tolerance in plants. SUBMITTER_CITATION: Biology 2013, 2(4), 1311-1337; doi:10.3390/biology2041311 Changes in RNA Splicing in Developing Soybean (Glycine max) Embryos Delasa Aghamirzaie, Mahdi Nabiyouni, Yihui Fang, Curtis Klumas, Lenwood S. Heath, Ruth Grene and Eva Collakova SUBMITTER_CITATION: Metabolites 2013, 3(2), 347-372; doi:10.3390/metabo3020347 Metabolic and Transcriptional Reprogramming in Developing Soybean (Glycine max) Embryos Eva Collakova, Delasa Aghamirzaie, Yihui Fang, Curtis Klumas, Farzaneh Tabataba, Akshay Kakumanu, Elijah Myers, Lenwood S. Heath and Ruth Grene Total mRNA profiles of 10 time course samples of Soybean developing embryos with three replicates per sample were generated by deep sequencing, using Illumina HiSeq 2000

Project description:Soybean (Glycine max (L.) Merr. cv Jack) seed development was separated into nine defined stages (S1 to S9). Testa (seed coats) were removed from developing seeds at stages S2, 4, 6, 8, and 9, and subjected to shotgun proteomic profiling. For each stage "total proteins" were isolated from 150mg dry weight of seed coat using a phenol-based method, then reduced, alkylated, and digested with trypsin. The tryptic peptides were separated using a C18-reversed phase matrix, then analyzed using an LTQ Orbitrap Mass Spectrometer. Spectra were searched against the Phytozome G. max DB using the Sorcerer 2 IDA Sequest-based search algorithm. Identities were verified using Scaffold 3. A total of 306 (S2), 328 (S4), 273 (S6), 193 (S8), and 272 (S9) proteins were identified in three out of three biological replicates, and sorted into 11 functional groups: Primary Metabolism, Secondary Metabolism, Cellular Structure, Stress Responses, Nucleic Acid metabolism, Protein Synthesis, Protein Folding, Protein Targeting, Hormones and Signaling, Seed Storage Proteins, and Proteins of Unknown Function. In selected instances, individual seed coat proteins were quantified by spectral counting. The number of proteins involved in intermediary metabolism, flavonoid biosynthesis, protein folding and degradation are discussed as they relate to seed coat function. A database was constructed using the open reading frames of Phytozome G. max DB (updated August 8, 2009), plus a decoy DB generated by reversing the target sequences. Spectra were searched against the combined target-decoy DB using the Sorcerer 2 IDA Sequest-based search algorithm. Identities were verified using the Scaffold 3 program, and the false discovery rate (FDR) was estimated as (Ndecoy/Ntarget). For 186,364 peptide spectra examined, the FDR was between 5.18 and 5.26%. Samples were analyzed in biological triplicate; proteins were included in the results only if they were identified in all three biological replicates. Relative protein abundance was quantified by spectral counting ; data presented are the mean of three independent determinations. The coefficient of variation values calculated for each developmental stage are < 0.0516.

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Transcriptome analysis during seed development provides insights into altered gene expression in Glycine max

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