COVAIN: a toolbox for uni- and multivariate statistics, time-series and correlation network analysis and inverse estimation of the differential Jacobian from metabolomics covariance data

Sun, Xiaoliang; Weckwerth, Wolfram

doi:10.1007/s11306-012-0399-3

Cited by 142 publications

(205 citation statements)

References 52 publications

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“…Compounds that could not be annotated by manual searches were only considered in multivariate statistics, which were performed with three biological replicates (Supplemental Table S3) using COVAIN (Sun and Weckwerth, 2012), a MATLAB tool box including a graphical user interface (MATLAB version 2010b; Math Works).…”

Section: Discussionmentioning

confidence: 99%

“…After addition of antimycin A the inhibited tube growth is mainly determined by principal component 1. Additionally, we performed a biclustering analysis using COVAIN software (Sun and Weckwerth, 2012). In Supplemental Figure S8B, three major sample clusters can be observed: (1) 0 to 75 min, (2) 90 min, and (3) a cluster including the inhibited tube growth time points (90i-240i min) as well as the time points of tube growth 120 and 240 min.…”

Section: Principal Component Analysismentioning

confidence: 99%

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Dynamic Adaption of Metabolic Pathways during Germination and Growth of Lily Pollen Tubes after Inhibition of the Electron Transport Chain

et al. 2013

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Investigation of the metabolome and the transcriptome of pollen of lily (Lilium longiflorum) gave a comprehensive overview of metabolic pathways active during pollen germination and tube growth. More than 100 different metabolites were determined simultaneously by gas chromatography coupled to mass spectrometry, and expressed genes of selected metabolic pathways were identified by next-generation sequencing of lily pollen transcripts. The time-dependent changes in metabolite abundances, as well as the changes after inhibition of the mitochondrial electron transport chain, revealed a fast and dynamic adaption of the metabolic pathways in the range of minutes. The metabolic state prior to pollen germination differed clearly from the metabolic state during pollen tube growth, as indicated by principal component analysis of all detected metabolites and by detailed observation of individual metabolites. For instance, the amount of sucrose increased during the first 60 minutes of pollen culture but decreased during tube growth, while glucose and fructose showed the opposite behavior. Glycolysis, tricarbonic acid cycle, glyoxylate cycle, starch, and fatty acid degradation were activated, providing energy during pollen germination and tube growth. Inhibition of the mitochondrial electron transport chain by antimycin A resulted in an immediate production of ethanol and a fast rearrangement of metabolic pathways, which correlated with changes in the amounts of the majority of identified metabolites, e.g. a rapid increase in g-aminobutyric acid indicated the activation of a g-aminobutyric acid shunt in the tricarbonic acid cycle, while ethanol fermentation compensated the reduced ATP production after inhibition of the oxidative phosphorylation.

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Section: Discussionmentioning

confidence: 99%

Section: Principal Component Analysismentioning

confidence: 99%

Dynamic Adaption of Metabolic Pathways during Germination and Growth of Lily Pollen Tubes after Inhibition of the Electron Transport Chain

et al. 2013

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“…Granger causality of proteins, metabolites, FW, Fv/Fm, starch, and lipids was tested with the statistical toolbox COVAIN (52). Parameter settings for COVAIN were Granger lag time 1 and Granger significance p value 0.01 according to (53).…”

Section: Gc-ms: Polarmentioning

confidence: 99%

“…Additionally, we performed Granger causality analysis to reveal time-shift relationships between metabolites and proteins during the cold adaptation process using a statistical toolbox COVAIN (52,53). Granger causality analysis looks for time shift in the correlation patterns (57).…”

Section: System Level Changes In Chlamydomonas Reinhardtii Following mentioning

confidence: 99%

Systemic Cold Stress Adaptation of Chlamydomonas reinhardtii

Valledor

Furuhashi

Hanak

et al. 2013

Molecular & Cellular Proteomics

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Chlamydomonas reinhardtii is one of the most important model organisms nowadays phylogenetically situated between higher plants and animals (Merchant et al. 2007). Stress adaptation of this unicellular model algae is in the focus because of its relevance to biomass and biofuel production. Here, we have studied cold stress adaptation of C. reinhardtii hitherto not described for this algae whereas intensively studied in higher plants. Toward this goal, high throughput mass spectrometry was employed to integrate proteome, metabolome, physiological and cell-morphological changes during a time-course from 0 to 120 h. These data were complemented with RT-qPCR for target genes involved in central metabolism, signaling, and lipid biosynthesis. Using this approach dynamics in central metabolism were linked to cold-stress dependent sugar and autophagy pathways as well as novel genes in C. reinhardtii such as CKIN1, CKIN2 and a hitherto functionally not annotated protein named CKIN3. Cold stress affected extensively the physiology and the organization of the cell. Gluconeogenesis and starch biosynthesis pathways are activated leading to a pronounced starch and sugar accumulation. Quantitative lipid profiles indicate a sharp decrease in the lipophilic fraction and an increase in polyunsaturated fatty acids suggesting this as a mechanism of maintaining membrane fluidity. The proteome is completely remodeled during cold stress: specific candidates of the ribosome and the spliceosome indicate altered biosynthesis and degradation of proteins important for adaptation to low temperatures. Specific proteasome degradation may be mediated by the observed cold-specific changes in the ubiquitinylation system. Sparse partial least squares regression analysis was applied for protein correlation network analysis using proteins as predictors and Fv/Fm, FW, total lipids, and starch as responses. We applied also Granger causality analysis and revealed correlations between proteins and metabolites otherwise not detectable. Twenty percent of the proteins responsive to cold are uncharacterized proteins. This presents a considerable resource for new discoveries in cold stress biology in alga and plants. Molecular &

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“…A prominent example of how mathematical modeling can promote the understanding of plant-environment interaction is given by the modeling approach of photosynthesis which was developed decades ago [3,4] and is central to current model approaches on global net primary production (NPP) on earth [5,6]. Focusing the question how mathematical modeling can be applied to functionally integrate highthroughput experiments on the metabolome of plant cells, we recently published a strategy of inversely calculating the Jacobian matrix of a biochemical network from experimental high-throughput data [7,8]. When a system of ordinary differential equations (ODEs) is applied to describe a biochemcial system of interest, the Jacobian matrix represents the first-order partial derivative of functions f i of metabolite concentrations M i with respect to metabolite concentrations (Equation (1)):…”

Section: Introductionmentioning

confidence: 99%

Eigenvalues of Jacobian Matrices Report on Steps of Metabolic Reprogramming in a Complex Plant-Environment Interaction

Nägele

Weckwerth²

2013

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Mathematical modeling of biochemical systems aims at improving the knowledge about complex regulatory networks. The experimental high-throughput measurement of levels of biochemical components, like metabolites and proteins, has become an integral part for characterization of biological systems. Yet, strategies of mathematical modeling to functionally integrate resulting data sets is still challenging. In plant biology, regulatory strategies that determine the metabolic output of metabolism as a response to changes in environmental conditions are hardly traceable by intuition. Mathematical modeling has been shown to be a promising approach to address such problems of plant-environment interaction promoting the comprehensive understanding of plant biochemistry and physiology. In this context, we recently published an inversely calculated solution for first-order partial derivatives, i.e. the Jacobian matrix, from experimental high-throughput data of a plant biochemical model system. Here, we present a biomathematical strategy, comprising 1) the inverse calculation of a biochemical Jacobian; 2) the characterization of the associated eigenvalues and 3) the interpretation of the results with respect to biochemical regulation. Deriving the real parts of eigenvalues provides information about the stability of solutions of inverse calculations. We found that shifts of the eigenvalue real part distributions occur together with metabolic shifts induced by short-term and long-term exposure to low temperature. This indicates the suitability of mathematical Jacobian characterization for recognizing perturbations in the metabolic homeostasis of plant metabolism. Together with our previously published results on inverse Jacobian calculation this represents a comprehensive strategy of mathematical modeling for the analysis of complex biochemical systems and plant-environment interactions from the molecular to the ecosystems level.

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COVAIN: a toolbox for uni- and multivariate statistics, time-series and correlation network analysis and inverse estimation of the differential Jacobian from metabolomics covariance data

Cited by 142 publications

References 52 publications

Dynamic Adaption of Metabolic Pathways during Germination and Growth of Lily Pollen Tubes after Inhibition of the Electron Transport Chain

Dynamic Adaption of Metabolic Pathways during Germination and Growth of Lily Pollen Tubes after Inhibition of the Electron Transport Chain

Systemic Cold Stress Adaptation of Chlamydomonas reinhardtii

Eigenvalues of Jacobian Matrices Report on Steps of Metabolic Reprogramming in a Complex Plant-Environment Interaction

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