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Freilich S.,Newe Yaar Research Center | Lev S.,Newe Yaar Research Center | Gonda I.,Newe Yaar Research Center | Reuveni E.,Newe Yaar Research Center | And 16 more authors.
BMC Plant Biology | Year: 2015

Background: Melon (Cucumis melo) fruits exhibit phenotypic diversity in several key quality determinants such as taste, color and aroma. Sucrose, carotenoids and volatiles are recognized as the key compounds shaping the above corresponding traits yet the full network of biochemical events underlying their synthesis have not been comprehensively described. To delineate the cellular processes shaping fruit quality phenotypes, a population of recombinant inbred lines (RIL) was used as a source of phenotypic and genotypic variations. In parallel, ripe fruits were analyzed for both the quantified level of 77 metabolic traits directly associated with fruit quality and for RNA-seq based expression profiles generated for 27,000 unigenes. First, we explored inter-metabolite association patterns; then, we described metabolites versus gene association patterns; finally, we used the correlation-based associations for predicting uncharacterized synthesis pathways. Results: Based on metabolite versus metabolite and metabolite versus gene association patterns, we divided metabolites into two key groups: a group including ethylene and aroma determining volatiles whose accumulation patterns are correlated with the expression of genes involved in the glycolysis and TCA cycle pathways; and a group including sucrose and color determining carotenoids whose accumulation levels are correlated with the expression of genes associated with plastid formation. Conclusions: The study integrates multiple processes into a genome scale perspective of cellular activity. This lays a foundation for deciphering the role of gene markers associated with the determination of fruit quality traits. © Freilich et al.; licensee BioMed Central. Source


Topfer N.,Max Planck Institute of Molecular Plant Physiology | Topfer N.,Weizmann Institute of Science | Kleessen S.,Max Planck Institute of Molecular Plant Physiology | Kleessen S.,Targenomix GmbH | Nikoloski Z.,Max Planck Institute of Molecular Plant Physiology
Frontiers in Plant Science | Year: 2015

Metabolite levels together with their corresponding metabolic fluxes are integrative outcomes of biochemical transformations and regulatory processes and they can be used to characterize the response of biological systems to genetic and/or environmental changes. However, while changes in transcript or to some extent protein levels can usually be traced back to one or several responsible genes, changes in fluxes and particularly changes in metabolite levels do not follow such rationale and are often the outcome of complex interactions of several components. The increasing quality and coverage of metabolomics technologies have fostered the development of computational approaches for integrating metabolic read-outs with large-scale models to predict the physiological state of a system. Constraint-based approaches, relying on the stoichiometry of the considered reactions, provide a modeling framework amenable to analyses of large-scale systems and to the integration of high-throughput data. Here we review the existing approaches that integrate metabolomics data in variants of constrained-based approaches to refine model reconstructions, to constrain flux predictions in metabolic models, and to relate network structural properties to metabolite levels. Finally, we discuss the challenges and perspectives in the developments of constraint-based modeling approaches driven by metabolomics data. © 2015 Töpfer, Kleessen and Nikoloski. Source


Endler A.,Max Planck Institute of Molecular Plant Physiology | Endler A.,Targenomix GmbH | Kesten C.,Max Planck Institute of Molecular Plant Physiology | Kesten C.,University of Melbourne | And 7 more authors.
Cell | Year: 2015

Abiotic stress, such as salinity, drought, and cold, causes detrimental yield losses for all major plant crop species. Understanding mechanisms that improve plants' ability to produce biomass, which largely is constituted by the plant cell wall, is therefore of upmost importance for agricultural activities. Cellulose is a principal component of the cell wall and is synthesized by microtubule-guided cellulose synthase enzymes at the plasma membrane. Here, we identified two components of the cellulose synthase complex, which we call companion of cellulose synthase (CC) proteins. The cytoplasmic tails of these membrane proteins bind to microtubules and promote microtubule dynamics. This activity supports microtubule organization, cellulose synthase localization at the plasma membrane, and renders seedlings less sensitive to stress. Our findings offer a mechanistic model for how two molecular components, the CC proteins, sustain microtubule organization and cellulose synthase localization and thus aid plant biomass production during salt stress. Video Abstract © 2015 Elsevier Inc. Source


Rajasundaram D.,University of Potsdam | Rajasundaram D.,Max Planck Institute of Molecular Plant Physiology | Selbig J.,University of Potsdam | Selbig J.,Max Planck Institute of Molecular Plant Physiology | And 4 more authors.
Annals of Botany | Year: 2014

Background and Aims A key challenge in biology is to systematically investigate and integrate the different levels of information available at the global and single-cell level. Recent studies have elucidated spatiotemporal expression patterns of root cell types in Arabidopsis thaliana, and genome-wide quantification of polysome-associated mRNA levels, i.e. the translatome, has also been obtained for corresponding cell types. Translational control has been increasingly recognized as an important regulatory step in protein synthesis. The aim of this study was to investigate coupled transcription and translation by use of publicly available root datasets. Methods Using cell-type-specific datasets of the root transcriptome and translatome of arabidopsis, a systematic assessment was made of the degree of co-ordination and divergence between these two levels of cellular organization. The computational analysis considered correlation and variation of expression across cell types at both system levels, and also provided insights into the degree of co-regulatory relationships that are preserved between the two processes. Key Results The overall correlation of expression and translation levels of genes resemble an almost bimodal distribution (mean/median value of 0·08/0·12), with a second, less strongly pronounced 'mode' for negative Pearson's correlation coefficient values. The analysis conducted also confirms that previously identified key transcriptional activators of secondary cell wall development display highly conserved patterns of transcription and translation across the investigated cell types. Moreover, the biological processes that display conserved and divergent patterns based on the cell-type-specific expression and translation levels were identified. Conclusions In agreement with previous studies in animal cells, a large degree of uncoupling was found between the transcriptome and translatome. However, components and processes were also identified that are under co-ordinated transcriptional and translational control in plant root cells. © 2014 The Author. Source


Kleessen S.,Targenomix GmbH | Irgang S.,Max Planck Institute of Molecular Plant Physiology | Klie S.,Targenomix GmbH | Giavalisco P.,Max Planck Institute of Molecular Plant Physiology | Nikoloski Z.,Max Planck Institute of Molecular Plant Physiology
Plant Journal | Year: 2015

Flux phenotypes predicted by constraint-based methods can be refined by the inclusion of heterogeneous data. While recent advances facilitate the integration of transcriptomics and proteomics data, purely stoichiometry-based approaches for the prediction of flux phenotypes by considering metabolomics data are lacking. Here we propose a constraint-based method, termed TREM-Flux, for integrating time-resolved metabolomics and transcriptomics data. We demonstrate the applicability of TREM-Flux in the dissection of the metabolic response of Chlamydomonas reinhardtii to rapamycin treatment by integrating the expression levels of 982 genes and the content of 45 metabolites obtained from two growth conditions. The findings pinpoint cysteine and methionine metabolism to be most affected by the rapamycin treatment. Our study shows that the integration of time-resolved unlabeled metabolomics data in addition to transcriptomics data can specify the metabolic pathways involved in the system's response to a studied treatment. Significance StatementIntegration of transcriptomics and metabolomics time-series data in large-scale models of biochemical reactions is used to derive representative flux phenotypes. Comparative analysis of the flux phenotypes from Chlamydomonas pinpoints the most affected metabolic functions in response to the rapamycin treatment under different growth conditions. © 2015 The Authors The Plant Journal © 2015 John Wiley & Sons Ltd. Source

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