Integrated network analysis and effective tools in plant systems biology

Fukushima, Atsushi; Kanaya, Shigehiko; Nishida, Kozo

doi:10.3389/fpls.2014.00598

Cited by 47 publications

(35 citation statements)

References 109 publications

(126 reference statements)

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“…Such gene diversity derived from genomic architecture may reflect different source-to-sink balance between the both species, causing different photosynthetic rate and the carbon partitioning and allocation [for example, see (Osorio et al, 2014)]. This analysis implies that the pathway-based approach (see reviews by Ramanan et al, 2012; Fukushima et al, 2014) is useful for a better understanding of biological functions, gene interactions, and specific processes in Physalis species.…”

Section: Resultsmentioning

confidence: 99%

Comparative Characterization of the Leaf Tissue of Physalis alkekengi and Physalis peruviana Using RNA-seq and Metabolite Profiling

et al. 2016

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The genus Physalis in the Solanaceae family contains several species of benefit to humans. Examples include P. alkekengi (Chinese-lantern plant, hôzuki in Japanese) used for medicinal and for decorative purposes, and P. peruviana, also known as Cape gooseberry, which bears an edible, vitamin-rich fruit. Members of the Physalis genus are a valuable resource for phytochemicals needed for the development of medicines and functional foods. To fully utilize the potential of these phytochemicals we need to understand their biosynthesis, and for this we need genomic data, especially comprehensive transcriptome datasets for gene discovery. We report the de novo assembly of the transcriptome from leaves of P. alkekengi and P. peruviana using Illumina RNA-seq technologies. We identified 75,221 unigenes in P. alkekengi and 54,513 in P. peruviana. All unigenes were annotated with gene ontology (GO), Enzyme Commission (EC) numbers, and pathway information from the Kyoto Encyclopedia of Genes and Genomes (KEGG). We classified unigenes encoding enzyme candidates putatively involved in the secondary metabolism and identified more than one unigenes for each step in terpenoid backbone- and steroid biosynthesis in P. alkekengi and P. peruviana. To measure the variability of the withanolides including physalins and provide insights into their chemical diversity in Physalis, we also analyzed the metabolite content in leaves of P. alkekengi and P. peruviana at five different developmental stages by liquid chromatography-mass spectrometry. We discuss that comprehensive transcriptome approaches within a family can yield a clue for gene discovery in Physalis and provide insights into their complex chemical diversity. The transcriptome information we submit here will serve as an important public resource for further studies of the specialized metabolism of Physalis species.

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Section: Resultsmentioning

confidence: 99%

Comparative Characterization of the Leaf Tissue of Physalis alkekengi and Physalis peruviana Using RNA-seq and Metabolite Profiling

et al. 2016

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“…A relatively large number of GSMNMs are available for the model plant Arabidopsis thaliana (Table 1) [40*,41,42]. The first reconstruction was targeted at and validated by heterotrophic plant cells grown in suspension [43].…”

Section: Reconstructions and Applications Of Model Organism Metabolicmentioning

confidence: 99%

Metabolic network modeling with model organisms

Yılmaz

Walhout

2017

Current Opinion in Chemical Biology

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Flux balance analysis (FBA) with genome-scale metabolic network models (GSMNM) allows systems level predictions of metabolism in a variety of organisms. Different types of predictions with different accuracy levels can be made depending on the applied experimental constraints ranging from measurement of exchange fluxes to the integration of gene expression data. Metabolic network modeling with model organisms has pioneered method development in this field. In addition, model organism GSMNMs are useful for basic understanding of metabolism, and in the case of animal models, for the study of metabolic human diseases. Here, we discuss GSMNMs of most highly used model organisms with the emphasis on recent reconstructions.

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“…Whole-genome sequences are available for more than 100 plant species (including microalgae; Tohge et al, 2014); this massive acceleration afforded by nextgeneration technologies cannot currently be matched by metabolomics, especially if high-quality speciesoptimized approaches are adopted (Fukushima et al, 2014). The KNApSAcK database, which is one of the largest curated compendia of phytochemicals, contains over 700 compounds for early sequenced plants like Arabidopsis and rice (Oryza sativa) but no entries for recently sequenced species such as goatgrass (Aegilops tauschii) and wild tobacco (Nicotiana tomentosiformis).…”

Section: Integrating Metabolite and Genome Datamentioning

confidence: 99%

“…In this section, we will describe insight gained from combining metabolomic data with genome sequences in three different case studies: (1) a simple comparison of a reference genome with metabolomics data; (2) a comparison of natural allelic and metabolic variance; and (3) integrating genome sequence data into quantitative genetics approaches. The first of these has been covered in considerable detail recently (Fukushima et al, 2014;Tohge et al, 2014), so we will only briefly describe it here. The starting point is to perform genome-wide ortholog searches using functionally annotated genes; best practice is to use cross-species cluster-based BLAST searches such as those housed in the PLAZA database (Proost et al, 2009) or, in the case of photosynthetic microbes, pico-PLAZA (Vandepoele et al, 2013).…”

Section: Integrating Metabolite and Genome Datamentioning

confidence: 99%

Integrative Approaches to Enhance Understanding of Plant Metabolic Pathway Structure and Regulation

2015

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Huge insight into molecular mechanisms and biological network coordination have been achieved following the application of various profiling technologies. Our knowledge of how the different molecular entities of the cell interact with one another suggests that, nevertheless, integration of data from different techniques could drive a more comprehensive understanding of the data emanating from different techniques. Here, we provide an overview of how such data integration is being used to aid the understanding of metabolic pathway structure and regulation. We choose to focus on the pairwise integration of large-scale metabolite data with that of the transcriptomic, proteomics, whole-genome sequence, growth-and yield-associated phenotypes, and archival functional genomic data sets. In doing so, we attempt to provide an update on approaches that integrate data obtained at different levels to reach a better understanding of either single gene function or metabolic pathway structure and regulation within the context of a broader biological process.

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Integrated network analysis and effective tools in plant systems biology

Cited by 47 publications

References 109 publications

Comparative Characterization of the Leaf Tissue of Physalis alkekengi and Physalis peruviana Using RNA-seq and Metabolite Profiling

Comparative Characterization of the Leaf Tissue of Physalis alkekengi and Physalis peruviana Using RNA-seq and Metabolite Profiling

Metabolic network modeling with model organisms

Integrative Approaches to Enhance Understanding of Plant Metabolic Pathway Structure and Regulation

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