Network analysis: tackling complex data to study plant metabolism

Toubiana, David; Fernie, Alisdair R.; Nikoloski, Zoran; Fait, Aaron

doi:10.1016/j.tibtech.2012.10.011

Cited by 89 publications

(82 citation statements)

References 77 publications

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“…Therefore, the thresholds are not hard coded and depend on the data considered in the network extraction (Toubiana et al, 2013). Positive and negative thresholds were determined separately due to the asymmetric distribution of values for the correlation coefficient.…”

Section: Network Generation and Visualizationmentioning

confidence: 99%

“…This was further confirmed by the significant correlation of 20.376 (p value = 4.391e 205 ) between the Jaccard distance (see Methods) in the first network neighborhoods of the corresponding nodes in HO1 and HO4. However, the Eigenvalue centrality as well as the centrality based on distribution of paths (betweenness and closeness, neglecting the weights due to the presence of negative correlations; Toubiana et al, 2013) do not capture this property, as manifested in the smaller and less significant correlations of 20.241, 20.102, and 20.016. The largest change in degree between the two networks is observed for glycoalkaloids (maximum of 31 and median of 12) in comparison to flavonoids (maximum of 37 and median of 10), hydroxycinnamate derivatives (maximum of 30 and median of 9), and nitrogen-containing metabolites (maximum 33 and median of 8).…”

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Identification and Mode of Inheritance of Quantitative Trait Loci for Secondary Metabolite Abundance in Tomato

et al. 2015

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A large-scale metabolic quantitative trait loci (mQTL) analysis was performed on the well-characterized Solanum pennellii introgression lines to investigate the genomic regions associated with secondary metabolism in tomato fruit pericarp. In total, 679 mQTLs were detected across the 76 introgression lines. Heritability analyses revealed that mQTLs of secondary metabolism were less affected by environment than mQTLs of primary metabolism. Network analysis allowed us to assess the interconnectivity of primary and secondary metabolism as well as to compare and contrast their respective associations with morphological traits. Additionally, we applied a recently established real-time quantitative PCR platform to gain insight into transcriptional control mechanisms of a subset of the mQTLs, including those for hydroxycinnamates, acyl-sugar, naringenin chalcone, and a range of glycoalkaloids. Intriguingly, many of these compounds displayed a dominant-negative mode of inheritance, which is contrary to the conventional wisdom that secondary metabolite contents decreased on domestication. We additionally performed an exemplary evaluation of two candidate genes for glycolalkaloid mQTLs via the use of virus-induced gene silencing. The combined data of this study were compared with previous results on primary metabolism obtained from the same material and to other studies of natural variance of secondary metabolism.

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Section: Network Generation and Visualizationmentioning

confidence: 99%

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Identification and Mode of Inheritance of Quantitative Trait Loci for Secondary Metabolite Abundance in Tomato

et al. 2015

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“…Metabolomics is an important tool in genomics-assisted selection in crops (Fernie and Keurentjes, 2011). Metabolomic analysis contributes significantly to the understanding of the relation between genotype and metabolic outputs by tackling key network components (Toubiana et al, 2013). The candidate genes for specific metabolites which have potential targets for quality improvement and uncovering the mechanisms of complex agronomic traits have been discovered (Gong et al, 2013).…”

Section: Introductionmentioning

confidence: 99%

METABOLOMICS UNRAVEL DIFFERENCES BETWEEN CAMEROON Dura AND DELI Dura OIL PALM (Elaeis guineensis Jacq.) GENETIC BACKGROUNDS AGAINST BASAL STEM ROT

Nurazah¹,

Idris²,

Kushairi³

et al. 2017

JOPR

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Metabolomics is emerging as a powerful tool for screening of metabolites in both phenotyping and diagnostic analyses in plants. Metabolomics coupled with multivariate statistical analysis has provided a fast and unbiased comparative investigation of the metabolite composition in oil palm root. Basal stem rot (BSR) caused by the white rot fungus, Ganoderma boninense is a common (MPOB, 2016). However, the palm is susceptible to various pests and diseases, the most serious in South-east Asia being basal stem rot (BSR) by the white rot fungus, Ganoderma boninense (Cooper et al., 2011;Paterson et al., 2009; Idris et al., 2004a). Integrated control using chemical and biological means has been tried against the disease (Sundram, 2013;Sundram et al., 2008;Susanto et al., 2005; Idris et al., 2004b) but with at most limited success. Resistance to BSR is only partial (Idris et al., 2004a) and the disease management is complicated by the presence of different species of Ganoderma with various degrees of aggressiveness with the dominant species varying in different localities (Wong et al., 2012).The disease triangle -the concept of a disease caused by the trinity of a pathogen attacking a DOI: https://doi.org/10.21894/jopr.2017DOI: https://doi.org/10.21894/jopr. .2902 227

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“…At high concentrations, this inhibition can lead to starvation of a downstream metabolic product, Met, which inhibits plant growth (Bright et al, 1982;Rognes et al, 1983;Heremans and Jacobs, 1995). In addition, several amino acid catabolic products, such as those emanating from branched-chain amino acids (BCAAs) or Lys degradation pathways, can be channeled toward cellular energy production under both standard and stress growth conditions (Angelovici et al, 2009(Angelovici et al, , 2011 Araújo et al, 2010;Peng et al, 2015).Reconstructions of seed metabolic networks in several model species and tissues have offered important insights into these underlying metabolic interactions and regulation Toubiana et al, 2013Toubiana et al, , 2015. For example, a correlation-based network metabolic [ reconstruction of FAA levels revealed that individual FAAs are strongly correlated during seed development compared with leaf or fruit development (Toubiana et al, 2012), which suggests a much tighter interaction of this metabolic network in seeds.…”

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confidence: 99%

“…Reconstructions of seed metabolic networks in several model species and tissues have offered important insights into these underlying metabolic interactions and regulation Toubiana et al, 2013Toubiana et al, , 2015. For example, a correlation-based network metabolic [ reconstruction of FAA levels revealed that individual FAAs are strongly correlated during seed development compared with leaf or fruit development (Toubiana et al, 2012), which suggests a much tighter interaction of this metabolic network in seeds.…”

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Network-Guided GWAS Improves Identification of Genes Affecting Free Amino Acids

et al. 2016

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Amino acids are essential for proper growth and development in plants. Amino acids serve as building blocks for proteins but also are important for responses to stress and the biosynthesis of numerous essential compounds. In seed, the pool of free amino acids (FAAs) also contributes to alternative energy, desiccation, and seed vigor; thus, manipulating FAA levels can significantly impact a seed's nutritional qualities. While genome-wide association studies (GWAS) on branched-chain amino acids have identified some regulatory genes controlling seed FAAs, the genetic regulation of FAA levels, composition, and homeostasis in seeds remains mostly unresolved. Hence, we performed GWAS on 18 FAAs from a 313-ecotype Arabidopsis (Arabidopsis thaliana) association panel. Specifically, GWAS was performed on 98 traits derived from known amino acid metabolic pathways (approach 1) and then on 92 traits generated from an unbiased correlation-based metabolic network analysis (approach 2), and the results were compared. The latter approach facilitated the discovery of additional novel metabolic interactions and singlenucleotide polymorphism-trait associations not identified by the former approach. The most prominent network-guided GWAS signal was for a histidine (His)-related trait in a region containing two genes: a cationic amino acid transporter (CAT4) and a polynucleotide phosphorylase resistant to inhibition with fosmidomycin. A reverse genetics approach confirmed CAT4 to be responsible for the natural variation of His-related traits across the association panel. Given that His is a semiessential amino acid and a potent metal chelator, CAT4 orthologs could be considered as candidate genes for seed quality biofortification in crop plants.Free amino acids (FAAs) play a pivotal role in the central metabolism of plants. FAAs serve as building blocks for protein synthesis and also are precursors for osmolytes, alternative energy, hormones, and key secondary metabolites (Rai, 2002; Araújo et al., 2010;Tzin and Galili, 2010; Angelovici et al., 2011). Studies of both developing and germinating seeds also have implicated FAAs in proper seed development and germination (Angelovici et al., 2010;Galili and Amir, 2013). Still, the genetic control of FAA metabolism in seed remains poorly understood. One reason is that FAA metabolism is tightly intertwined with essential cellular processes in plants, and manipulating FAA levels can have strong deleterious, pleiotropic effects on the entire system (Guyer et al., 1995;Galili, 2011;Ingle, 2011;Pratelli and Pilot, 2014). For example, increasing Lys and Thr inhibits the activity of Asp kinase via a feedback inhibition loop (Clark and Lu, 2015). At high concentrations, this inhibition can lead to starvation of a downstream metabolic product, Met, which inhibits plant growth (Bright et al., 1982;Rognes et al., 1983;Heremans and Jacobs, 1995). In addition, several amino acid catabolic products, such as those emanating from branched-chain amino acids (BCAAs) or Lys degradation pathways, can be chan...

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Network analysis: tackling complex data to study plant metabolism

Cited by 89 publications

References 77 publications

Identification and Mode of Inheritance of Quantitative Trait Loci for Secondary Metabolite Abundance in Tomato

Identification and Mode of Inheritance of Quantitative Trait Loci for Secondary Metabolite Abundance in Tomato

METABOLOMICS UNRAVEL DIFFERENCES BETWEEN CAMEROON Dura AND DELI Dura OIL PALM (Elaeis guineensis Jacq.) GENETIC BACKGROUNDS AGAINST BASAL STEM ROT

Network-Guided GWAS Improves Identification of Genes Affecting Free Amino Acids

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