Dataset Information

Sorokina2011_StarchMetabolism_Ostreococcus

ABSTRACT: This model is from the article: Microarray data can predict diurnal changes of starch content in the picoalga Ostreococcus. Sorokina O, Corellou F, Dauvillée D, Sorokin A, Goryanin I, Ball S, Bouget FY, Millar AJ BMC Systems Biology 2011, 5:36 21352558 , Abstract: BACKGROUND: The storage of photosynthetic carbohydrate products such as starch is subject to complex regulation, effected at both transcriptional and post-translational levels. The relevant genes in plants show pronounced daily regulation. Their temporal RNA expression profiles, however, do not predict the dynamics of metabolite levels, due to the divergence of enzyme activity from the RNA profiles.Unicellular phytoplankton retains the complexity of plant carbohydrate metabolism, and recent transcriptomic profiling suggests a major input of transcriptional regulation. RESULTS: We used a quasi-steady-state, constraint-based modelling approach to infer the dynamics of starch content during the 12 h light/12 h dark cycle in the model alga Ostreococcus tauri. Measured RNA expression datasets from microarray analysis were integrated with a detailed stoichiometric reconstruction of starch metabolism in O. tauri in order to predict the optimal flux distribution and the dynamics of the starch content in the light/dark cycle. The predicted starch profile was validated by experimental data over the 24 h cycle. The main genetic regulatory targets within the pathway were predicted by in silico analysis. CONCLUSIONS: A single-reaction description of starch production is not able to account for the observed variability of diurnal activity profiles of starch-related enzymes. We developed a detailed reaction model of starch metabolism, which, to our knowledge, is the first attempt to describe this polysaccharide polymerization while preserving the mass balance relationships. Our model and method demonstrate the utility of a quasi-steady-state approach for inferring dynamic metabolic information in O. tauri directly from time-series gene expression data. To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information. In summary, you are entitled to use this encoded model in absolutely any manner you deem suitable, verbatim, or with modification, alone or embedded it in a larger context, redistribute it, commercially or not, in a restricted way or not. To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Novère N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

SUBMITTER: Oksana Sorokina

PROVIDER: MODEL1204240000 | BioModels | 2005-01-01

REPOSITORIES: BioModels

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Microarray data can predict diurnal changes of starch content in the picoalga Ostreococcus.

Sorokina Oksana O Corellou Florence F Dauvillée David D Sorokin Anatoly A Goryanin Igor I Ball Steven S Bouget François-Yves FY Millar Andrew J AJ

BMC systems biology 20110226

<h4>Background</h4>The storage of photosynthetic carbohydrate products such as starch is subject to complex regulation, effected at both transcriptional and post-translational levels. The relevant genes in plants show pronounced daily regulation. Their temporal RNA expression profiles, however, do not predict the dynamics of metabolite levels, due to the divergence of enzyme activity from the RNA profiles.Unicellular phytoplankton retains the complexity of plant carbohydrate metabolism, and rece ...[more]

PMID: 21352558

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Project description:Plant diurnal oscillation is a 24-hour period based variation. The correlation between diurnal genes and biological pathways was widely revealed by microarray analysis in different species. Rice (Oryza sativa) is the major food staple for about half of the world's population. The rice flag leaf is essential in providing photosynthates to the grain filling. However, there is still no comprehensive view about the diurnal transcriptome for rice leaves. In this study, we applied rice microarray to monitor the rhythmically expressed genes in rice seedling and flag leaves. We developed a new computational analysis approach and identified 6,266 (10.96%) diurnal probe sets in seedling leaves, 13,773 (24.08%) diurnal probe sets in flag leaves. About 65% of overall transcription factors were identified as flag leaf preferred. In seedling leaves, the peak of phase distribution was from 2:00am to 4:00am, whereas in flag leaves, the peak was from 8:00pm to 2:00am. The diurnal phase distribution analysis of gene ontology (GO) and cis-element enrichment indicated that, some important processes were waken by the light, such as photosynthesis and abiotic stimulus, while some genes related to the nuclear and ribosome involved processes were active mostly during the switch time of light to dark. The starch and sucrose metabolism pathway genes also showed diurnal phase. We conducted comparison analysis between Arabidopsis and rice leaf transcriptome throughout the diurnal cycle. In summary, our analysis approach is feasible for relatively unbiased identification of diurnal transcripts, efficiently detecting some special periodic patterns with non-sinusoidal periodic patterns. Compared to the rice flag leaves, the gene transcription levels of seedling leaves were relatively limited to the diurnal rhythm. Our comprehensive microarray analysis of seedling and flag leaves of rice provided an overview of the rice diurnal transcriptome and indicated some diurnal regulated biological processes and key functional pathways in rice.

Project description:This proposal is aimed at providing transcriptome data to underpin a long-term joint research programme of Steve Smith and Alison Smith. We are jointly studying starch synthesis and breakdown in Arabidopsis leaves, and individually studying other enzymes of carbohydrate metabolism (eg. sucrose synthases, invertases, sugar transporters). Collectively these enzymes are encoded by up to 100 known genes, but there are many others of relevance to our studies. For several years we have employed a defined set of growth conditions for this work (resulting in numerous publications). We have extensive data for changes in the amounts of starch, malto-oligosaccharides and sugars throughout the diurnal cycle in these plants, and we intend to quantitate numerous key enzymes. We now wish to profile changes in transcripts in these plants, so that this information can be correlated with changes in the amounts of key enzymes and metabolites. Analysis of the data sets will help us to relate the synthesis of certain enzymes to particular metabolic functions, and also to help identify genes encoding novel proteins involved in carbohydrate metabolism. Current research is aimed at discovering such novel proteins using proteomics approaches (BBSRC Grant: _Discovery and Functional Analysis of the Starch Proteome_). While the growth conditions (12h photoperiod, 150 umol/m2/s, 20C) and ecotype (Col-0) are specifically tailored to our experiments, they will be of value to the wider community, and could form the basis for collaborative studies with other groups. The experiment has involved sampling leaves at eleven different time points as follows: 0, 1, 2, 4, 8, 12, 13, 14, 16, 20, and 24 h (where time 0 is the onset of dark and 12 h is the onset of light). The 24 h time point is a repeat of 0 h. This sequence provides relatively more samples immediately after the light-dark transitions, when changes in metabolism are most pronounced. Plants were grown in a controlled environment chamber under defined conditions, and selected for harvest according to a random number generator. Three fully expanded leaves were harvested from each of 20 plants for each sample. Leaves were frozen at -80 C and a portion saved for metabolite and enzyme assays while RNA was isolated from the remainder. The RNA has been purified and is ready to send to Nottingham. Experiment Overall Design: Number of plants pooled:20 Experiment Overall Design: Biological replicates: 2

Project description:This proposal is aimed at providing transcriptome data to underpin a long-term joint research programme of Steve Smith and Alison Smith. We are jointly studying starch synthesis and breakdown in Arabidopsis leaves, and individually studying other enzymes of carbohydrate metabolism (eg. sucrose synthases, invertases, sugar transporters). Collectively these enzymes are encoded by up to 100 known genes, but there are many others of relevance to our studies. For several years we have employed a defined set of growth conditions for this work (resulting in numerous publications). We have extensive data for changes in the amounts of starch, malto-oligosaccharides and sugars throughout the diurnal cycle in these plants, and we intend to quantitate numerous key enzymes. We now wish to profile changes in transcripts in these plants, so that this information can be correlated with changes in the amounts of key enzymes and metabolites. Analysis of the data sets will help us to relate the synthesis of certain enzymes to particular metabolic functions, and also to help identify genes encoding novel proteins involved in carbohydrate metabolism. Current research is aimed at discovering such novel proteins using proteomics approaches (BBSRC Grant: _Discovery and Functional Analysis of the Starch Proteome_). While the growth conditions (12h photoperiod, 150 umol/m2/s, 20C) and ecotype (Col-0) are specifically tailored to our experiments, they will be of value to the wider community, and could form the basis for collaborative studies with other groups. The experiment has involved sampling leaves at eleven different time points as follows: 0, 1, 2, 4, 8, 12, 13, 14, 16, 20, and 24 h (where time 0 is the onset of dark and 12 h is the onset of light). The 24 h time point is a repeat of 0 h. This sequence provides relatively more samples immediately after the light-dark transitions, when changes in metabolism are most pronounced. Plants were grown in a controlled environment chamber under defined conditions, and selected for harvest according to a random number generator. Three fully expanded leaves were harvested from each of 20 plants for each sample. Leaves were frozen at -80 C and a portion saved for metabolite and enzyme assays while RNA was isolated from the remainder. <br> This experiment was reloaded in February 2011, replacing the existing raw data files which had identifier problems with the original CEL files from ftp://arabidopsis.info/pub/NASCArrays/Data/By_Experiment_ID/Exp60/ and adding experimental factors.

Dataset Information

Sorokina2011_StarchMetabolism_Ostreococcus

Publications

Microarray data can predict diurnal changes of starch content in the picoalga Ostreococcus.

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