Dataset Information

Improved evidence-based genome-scale metabolic models for maize leaf, embryo, and endosperm.

ABSTRACT: There is a growing demand for genome-scale metabolic reconstructions for plants, fueled by the need to understand the metabolic basis of crop yield and by progress in genome and transcriptome sequencing. Methods are also required to enable the interpretation of plant transcriptome data to study how cellular metabolic activity varies under different growth conditions or even within different organs, tissues, and developmental stages. Such methods depend extensively on the accuracy with which genes have been mapped to the biochemical reactions in the plant metabolic pathways. Errors in these mappings lead to metabolic reconstructions with an inflated number of reactions and possible generation of unreliable metabolic phenotype predictions. Here we introduce a new evidence-based genome-scale metabolic reconstruction of maize, with significant improvements in the quality of the gene-reaction associations included within our model. We also present a new approach for applying our model to predict active metabolic genes based on transcriptome data. This method includes a minimal set of reactions associated with low expression genes to enable activity of a maximum number of reactions associated with high expression genes. We apply this method to construct an organ-specific model for the maize leaf, and tissue specific models for maize embryo and endosperm cells. We validate our models using fluxomics data for the endosperm and embryo, demonstrating an improved capacity of our models to fit the available fluxomics data. All models are publicly available via the DOE Systems Biology Knowledgebase and PlantSEED, and our new method is generally applicable for analysis transcript profiles from any plant, paving the way for further in silico studies with a wide variety of plant genomes.

SUBMITTER: Seaver SM

PROVIDER: S-EPMC4354304 | biostudies-literature | 2015

REPOSITORIES: biostudies-literature

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Improved evidence-based genome-scale metabolic models for maize leaf, embryo, and endosperm.

Seaver Samuel M D SM Bradbury Louis M T LM Frelin Océane O Zarecki Raphy R Ruppin Eytan E Hanson Andrew D AD Henry Christopher S CS

Frontiers in plant science 20150310

There is a growing demand for genome-scale metabolic reconstructions for plants, fueled by the need to understand the metabolic basis of crop yield and by progress in genome and transcriptome sequencing. Methods are also required to enable the interpretation of plant transcriptome data to study how cellular metabolic activity varies under different growth conditions or even within different organs, tissues, and developmental stages. Such methods depend extensively on the accuracy with which gene ...[more]

PMID: 25806041

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Project description:Epigenetic modifications play important roles in plant and animal development. DNA methylation impacts the transposable element (TE) silencing, gene imprinting and expression regulation.Through a genome-wide analysis, DNA methylation peaks were characterized and mapped in maize embryo and endosperm genome, respectively. Distinct methylation level was observed across maize embryo and endosperm. The maize embryo genome contained more DNA methylation than endosperm. Totally, 985,478 CG islands (CGIs) were identified and most of them were unmethylated. More CGI shores were methylated than CGIs in maize suggested that DNA methylation level was not positively correlated with CpG density. The promoter sequence and transcriptional termination region (TTR) were more methylated than the gene body (intron and exon) region based on peak number and methylated depth. Result showed that 99% TEs were methylated in maize embryo, but a large portion of them (34.8%) were not methylated in endosperm. Maize embryo and endosperm exhibit distinct pattern/level of methylation. The most differentially methylated region between embryo and endosperm are CGI shores. Our results indicated that DNA methylation is associated with both gene silencing and gene activation in maize. Many genes involved in embryogenesis and seed development were found differentially methylated in embryo and endosperm. We found 41.5% imprinting genes were similarly methylated and 58.5% imprinting genes were differentially methylated between embryo and endosperm. Methylation level was associated with allelic silencing of only a small number of imprinting genes. The expression of maize DEMETER-like (DME-like) gene and MBD101 gene (MBD4 homolog) were higher in endosperm than in embryo. These two genes may be associated with distinct methylation levels across maize embryo and endosperm.Through MeDIP-seq we systematically analyzed the methylomes of maize embryo and endosperm and results indicated that the global methylation status of embryo was more than that of the endosperm. Differences could be observed at the total number of methylation peaks, DMRs and specific methylated genes which were tightly associated with development of embryo and endosperm. Our results also revealed that many DNA methylation regions didn't affect transcription of the corresponding genes.

Project description:BACKGROUND:Since ancient times the filamentous fungus Aspergillus oryzae has been used in the fermentation industry for the production of fermented sauces and the production of industrial enzymes. Recently, the genome sequence of A. oryzae with 12,074 annotated genes was released but the number of hypothetical proteins accounted for more than 50% of the annotated genes. Considering the industrial importance of this fungus, it is therefore valuable to improve the annotation and further integrate genomic information with biochemical and physiological information available for this microorganism and other related fungi. Here we proposed the gene prediction by construction of an A. oryzae Expressed Sequence Tag (EST) library, sequencing and assembly. We enhanced the function assignment by our developed annotation strategy. The resulting better annotation was used to reconstruct the metabolic network leading to a genome scale metabolic model of A. oryzae. RESULTS:Our assembled EST sequences we identified 1,046 newly predicted genes in the A. oryzae genome. Furthermore, it was possible to assign putative protein functions to 398 of the newly predicted genes. Noteworthy, our annotation strategy resulted in assignment of new putative functions to 1,469 hypothetical proteins already present in the A. oryzae genome database. Using the substantially improved annotated genome we reconstructed the metabolic network of A. oryzae. This network contains 729 enzymes, 1,314 enzyme-encoding genes, 1,073 metabolites and 1,846 (1,053 unique) biochemical reactions. The metabolic reactions are compartmentalized into the cytosol, the mitochondria, the peroxisome and the extracellular space. Transport steps between the compartments and the extracellular space represent 281 reactions, of which 161 are unique. The metabolic model was validated and shown to correctly describe the phenotypic behavior of A. oryzae grown on different carbon sources. CONCLUSION:A much enhanced annotation of the A. oryzae genome was performed and a genome-scale metabolic model of A. oryzae was reconstructed. The model accurately predicted the growth and biomass yield on different carbon sources. The model serves as an important resource for gaining further insight into our understanding of A. oryzae physiology.

Dataset Information

Improved evidence-based genome-scale metabolic models for maize leaf, embryo, and endosperm.

Publications

Improved evidence-based genome-scale metabolic models for maize leaf, embryo, and endosperm.

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