MetaCrop 2.0: managing and exploring information about crop plant metabolism

Schreiber, Falk; Colmsee, Christian; Czauderna, Tobias; Grafahrend-Belau, Eva; Hartmann, Anja; Junker, Astrid; Junker, Björn H.; Klapperstück, Matthias; Scholz, Uwe; Weise, Stéphan

doi:10.1093/nar/gkr1004

Cited by 51 publications

(31 citation statements)

References 21 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The second-generation maize leaf model was developed using a combination of gene, protein, and reaction information from the previously developed maize model iRS1563 (Saha et al, 2011), biological databases such as the Kyoto Encyclopedia of Genes and Genomes (Kanehisa et al, 2014), MaizeCyc (Monaco et al, 2013), and MetaCrop (Schreiber et al, 2012), as well as published literature sources. The model contains 5,824 genes and 8,525 reactions, a significant increase from the iRS1563 model, which contained 1,563 genes and 1,985 reactions.…”

Section: Development Of the Second-generation Maize Leaf Modelmentioning

confidence: 99%

“…Our previously developed maize model, iRS1563 (Saha et al, 2011), and biological databases such as MetaCrop (downloaded in December 2012;Schreiber et al, 2012) and MaizeCyc (version 2.0.2; Monaco et al, 2013) provided information pertaining to the genes, proteins, reactions, and metabolites used to reconstruct the second-generation maize leaf genome-scale model. In addition, available proteomic and transcriptomic data, maize-specific biological databases, namely MetaCrop and MaizeCyc, and published literature were used to assign cellular (i.e.…”

Section: Metabolite Extraction and Analysesmentioning

confidence: 99%

“…The normalized spectral abundance factor is a weighted adjSPC based on the number of theoretical tryptic peptides with a relevant length (Ehleringer et al, 1997;Friso et al, 2010). Additional intracellular compartmentalization was carried out based on the MetaCrop database (Schreiber et al, 2012), the MaizeCyc database (Monaco et al, 2013), and primary literature sources (Chang et al, 2012;Zhao et al, 2013). The intracellular compartmentalization was determined based first on the MetaCrop database (Schreiber et al, 2012), literature sources (Friso et al, 2010;Chang et al, 2012), compartmentalization information in the MaizeCyc database, and finally the Plant Proteomics Database (Sun et al, 2009).…”

Section: Model Development and Curationmentioning

confidence: 99%

“…Maize production is increasing at the greatest rate among all cereals, with a worldwide trend of 0.06 tons ha 21 year 21 (Leveau et al, 2011) and a record 877 million tons produced in the 2011-2012 fiscal year (International Grains Council, 2013). With the recent completion of the maize genome in 2009 along with the creation and curation of databases such as MaizeGDB in 2011 (Schaeffer et al, 2011), MaizeCyc in 2013 (Monaco et al, 2013), and MetaCrop 2.0 in 2012 (Schreiber et al, 2012), there is a need for an updated genome-scale metabolic model (GSM; Saha et al, 2011) that will integrate all newly available information from diverse sources. The integration of this information with experimental transcriptomic data, proteomic data, and biomass composition measurements obtained with wildtype plants grown under optimal nitrogen (N + WT) conditions and limited nitrogen (N 2 WT) conditions (Amiour et al, 2012), as well as two Gln synthetase (GS) mutants grown under optimal nitrogen (N), gln1-3 and gln1-4 (Martin et al, 2006), has provided a more accurate assessment of N metabolism within the maize leaf.…”

mentioning

confidence: 99%

“…Both the primary and secondary metabolic pathways of maize are included, by combining information from MetaCrop (Schreiber et al, 2012), MaizeCyc (Monaco et al, 2013), and the earlier iRS1563 (Saha et al, 2011) models. In comparison with the iRS1563 model, this second-generation model spans an additional 4,261 genes and 6,540 reactions.…”

mentioning

confidence: 99%

See 4 more Smart Citations

Assessing the Metabolic Impact of Nitrogen Availability Using a Compartmentalized Maize Leaf Genome-Scale Model

et al. 2014

View full text Add to dashboard Cite

Maize (Zea mays) is an important C 4 plant due to its widespread use as a cereal and energy crop. A second-generation genomescale metabolic model for the maize leaf was created to capture C 4 carbon fixation and investigate nitrogen (N) assimilation by modeling the interactions between the bundle sheath and mesophyll cells. The model contains gene-protein-reaction relationships, elemental and charge-balanced reactions, and incorporates experimental evidence pertaining to the biomass composition, compartmentalization, and flux constraints. Condition-specific biomass descriptions were introduced that account for amino acids, fatty acids, soluble sugars, proteins, chlorophyll, lignocellulose, and nucleic acids as experimentally measured biomass constituents. Compartmentalization of the model is based on proteomic/transcriptomic data and literature evidence. With the incorporation of information from the MetaCrop and MaizeCyc databases, this updated model spans 5,824 genes, 8,525 reactions, and 9,153 metabolites, an increase of approximately 4 times the size of the earlier iRS1563 model. Transcriptomic and proteomic data have also been used to introduce regulatory constraints in the model to simulate an N-limited condition and mutants deficient in glutamine synthetase, gln1-3 and gln1-4. Model-predicted results achieved 90% accuracy when comparing the wild type grown under an N-complete condition with the wild type grown under an N-deficient condition.

show abstract

Section: Development Of the Second-generation Maize Leaf Modelmentioning

confidence: 99%

Section: Metabolite Extraction and Analysesmentioning

confidence: 99%

Section: Model Development and Curationmentioning

confidence: 99%

mentioning

confidence: 99%

mentioning

confidence: 99%

See 3 more Smart Citations

Assessing the Metabolic Impact of Nitrogen Availability Using a Compartmentalized Maize Leaf Genome-Scale Model

et al. 2014

View full text Add to dashboard Cite

show abstract

Nitrogen Deficiency Induced Alterations in the Root Proteome of a Pair of Potato (Solanum tuberosum L.) Varieties Contrasting for their Response to Low N

et al. 2017

View full text Add to dashboard Cite

Improving crop nitrogen use efficiency is important both from the economic and the environmental viewpoint. Here, the aim is to highlight differences between the proteomic response of the roots of two potato cultivars contrasting in their response to nitrogen (N) deficiency, in an effort to understand which proteins and metabolic pathways contribute to the tolerance of N deprivation. The two cultivars ''Topas'' (tolerant) and ''Lambada'' (sensitive) are grown under both an N sufficient and an N deficient regime, using an in vitro-based cultivation system. Responsive proteins are identified and quantified using label-free quantitative shotgun proteomics. The contrasting cultivars differed with respect to components of the glutamine synthetase/glutamine oxoglutarate aminotransferase pathway, tricarboxylic acid cycle, the glycolysis/gluconeogenesis pathway as well as protein and amino acid synthesis machinery. Additional differences are associated with protein catabolism and defense mechanisms.

show abstract

Metabolomics in Plant Stress Physiology

Ghatak

Chaturvedi

2018

Plant Genetics and Molecular Biology

View full text Add to dashboard Cite

Metabolomics is an essential technology for functional genomics and systems biology. It plays a key role in functional annotation of genes and understanding towards cellular and molecular, biotic and abiotic stress responses. Different analytical techniques are used to extend the coverage of a full metabolome. The commonly used techniques are NMR, CE-MS, LC-MS, and GC-MS. The choice of a suitable technique depends on the speed, sensitivity, and accuracy. This chapter provides insight into plant metabolomic techniques, databases used in the analysis, data mining and processing, compound identification, and limitations in metabolomics. It also describes the workflow of measuring metabolites in plants. Metabolomic studies in plant responses to stress are a key research topic in many laboratories worldwide. We summarize different approaches and provide a generic overview of stress responsive metabolite markers and processes compiled from a broad range of different studies. Graphical Abstract.

show abstract

MetaCrop 2.0: managing and exploring information about crop plant metabolism

Cited by 51 publications

References 21 publications

Assessing the Metabolic Impact of Nitrogen Availability Using a Compartmentalized Maize Leaf Genome-Scale Model

Assessing the Metabolic Impact of Nitrogen Availability Using a Compartmentalized Maize Leaf Genome-Scale Model

Nitrogen Deficiency Induced Alterations in the Root Proteome of a Pair of Potato (Solanum tuberosum L.) Varieties Contrasting for their Response to Low N

Metabolomics in Plant Stress Physiology

Contact Info

Product

Resources

About