Transcript abundance on its own cannot be used to infer fluxes in central metabolism

Schwender, Jörg; König, Christina; Klapperstück, Matthias; Heinzel, Nicolas; Muñz, E.; Hebbelmann, Inga; Hay, Jordan O.; Denolf, Peter; Bodt, Stefanie De; Redestig, Henning; Caestecker, Evelyne; Jakob, Peter M.; Borisjuk, Ljudmilla; Rolletschek, Hardy

doi:10.3389/fpls.2014.00668

Cited by 56 publications

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“…Transcript abundance on its own might not be enough for inferring the metabolic activity in central plant metabolism due to various factors such as post-transcription machinery and different half lives (Schwender et al, 2014). Proteomic analysis bridges the gap between our understanding of genome sequence and cellular behavior since it detects a complete set of proteins that is expressed.…”

Section: Introductionmentioning

confidence: 99%

Time-Series Analyses of Transcriptomes and Proteomes Reveal Molecular Networks Underlying Oil Accumulation in Canola

et al. 2017

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Understanding the regulation of lipid metabolism is vital for genetic engineering of canola (Brassica napus L.) to increase oil yield or modify oil composition. We conducted time-series analyses of transcriptomes and proteomes to uncover the molecular networks associated with oil accumulation and dynamic changes in these networks in canola. The expression levels of genes and proteins were measured at 2, 4, 6, and 8 weeks after pollination (WAP). Our results show that the biosynthesis of fatty acids is a dominant cellular process from 2 to 6 WAP, while the degradation mainly happens after 6 WAP. We found that genes in almost every node of fatty acid synthesis pathway were significantly up-regulated during oil accumulation. Moreover, significant expression changes of two genes, acetyl-CoA carboxylase and acyl-ACP desaturase, were detected on both transcriptomic and proteomic levels. We confirmed the temporal expression patterns revealed by the transcriptomic analyses using quantitative real-time PCR experiments. The gene set association analysis show that the biosynthesis of fatty acids and unsaturated fatty acids are the most significant biological processes from 2-4 WAP and 4-6 WAP, respectively, which is consistent with the results of time-series analyses. These results not only provide insight into the mechanisms underlying lipid metabolism, but also reveal novel candidate genes that are worth further investigation for their values in the genetic engineering of canola.

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Section: Introductionmentioning

confidence: 99%

Time-Series Analyses of Transcriptomes and Proteomes Reveal Molecular Networks Underlying Oil Accumulation in Canola

et al. 2017

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“…However, the coordination of several regulatory levels between gene transcription and reaction rates may not result in a direct correspondence between gene expression and fluxes, and this can affect the usefulness of these methods (Machado and Herrgård, 2014). The correspondence between changes in flux and changes in transcript levels in plant tissues was recently investigated (Schwender et al, 2014).…”

Section: Building and Analyzing Context-specific Metabolic Modelsmentioning

confidence: 99%

Inference and prediction of metabolic network fluxes

Nikoloski

Perez-Storey

2015

Plant Physiol.

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In this Update, we cover the basic principles of the estimation and prediction of the rates of the many interconnected biochemical reactions that constitute plant metabolic networks. This includes metabolic flux analysis approaches that utilize the rates or patterns of redistribution of stable isotopes of carbon and other atoms to estimate fluxes, as well as constraints-based optimization approaches such as flux balance analysis. Some of the major insights that have been gained from analysis of fluxes in plants are discussed, including the functioning of metabolic pathways in a network context, the robustness of the metabolic phenotype, the importance of cell maintenance costs, and the mechanisms that enable energy and redox balancing at steady state. We also discuss methodologies to exploit 'omic data sets for the construction of tissue-specific metabolic network models and to constrain the range of permissible fluxes in such models. Finally, we consider the future directions and challenges faced by the field of metabolic network flux phenotyping.The metabolic systems of plants utilize a continual energy stream (i.e. light in the case of photosynthetic tissues and chemical energy in the case of heterotrophic tissues) to drive a complex network of hundreds of chemical reactions away from equilibrium. Ultimately, this leads to the catalyzed biosynthesis of the ordered polymeric biomolecules that make up the biomass of the cells in each tissue and underpins the growth and development of the plant (Smith and Stitt, 2007). To operate on a time scale relevant for life, the system is dependent upon the acceleration of its chemistry by enzymes. Additionally, because of the highly compartmented nature of plant cells, transporter proteins are required to permit the movement of metabolites (i.e. the substrates and products of biochemical reactions) between subcellular compartments. Transporter proteins are also required to bring substrates into cells and to allow the excretion of waste products and other metabolites such as defense compounds. The sum total of expressed genes encoding enzymes and metabolite transporters determines the metabolic capabilities of a cell. In a growing tissue, the net output of this metabolism is anabolic (i.e. leading to the biosynthesis of biomass constituents such as starch, fructans, cell wall, lipid, and protein), but catabolic metabolism is also required. Most importantly, catabolism generates universal energy currencies such as ATP and NAD(P)H, whose turnover is used to provide the energetic driving force for anabolism. Catabolism is also important to allow the turnover of cellular components for regulatory and repair purposes (Linster et al., 2013;Ishihara et al., 2015). The turnover and resynthesis of cellular components, along with the maintenance of electrochemical potentials across membranes, are important facets of metabolism that need to be considered alongside the biosynthesis of macromolecules for growth (Stitt, 2013;Sweetlove et al., 2013).Metabolism, then, is the entirety of c...

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“…In Arabidopsis (Arabidopsis thaliana), about 17,500 distinct mRNAs (approximately 60% of the full gene complement) are transcribed in the seed during its development, with about 1,300 of these being seed specific (Belmonte et al, 2013). While this set of genes no doubt provides a full picture of the stage-and tissue-specific framework of gene activity, many examples have been described in plants where the pairs of transcript versus protein or protein versus flux are rather discordant (Junker et al, 2007;Hajduch et al, 2010;Fernie and Stitt, 2012;Schwender et al, 2014). Thus, monitoring of gene expression is not sufficient to define metabolic activities in vivo.…”

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confidence: 99%

Quantitative Multilevel Analysis of Central Metabolism in Developing Oilseeds of Oilseed Rape during in Vitro Culture

et al. 2015

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Seeds provide the basis for many food, feed, and fuel products. Continued increases in seed yield, composition, and quality require an improved understanding of how the developing seed converts carbon and nitrogen supplies into storage. Current knowledge of this process is often based on the premise that transcriptional regulation directly translates via enzyme concentration into flux. In an attempt to highlight metabolic control, we explore genotypic differences in carbon partitioning for in vitro cultured developing embryos of oilseed rape (Brassica napus). We determined biomass composition as well as 79 net fluxes, the levels of 77 metabolites, and 26 enzyme activities with specific focus on central metabolism in nine selected germplasm accessions. Overall, we observed a tradeoff between the biomass component fractions of lipid and starch. With increasing lipid content over the spectrum of genotypes, plastidic fatty acid synthesis and glycolytic flux increased concomitantly, while glycolytic intermediates decreased. The lipid/starch tradeoff was not reflected at the proteome level, pointing to the significance of (posttranslational) metabolic control. Enzyme activity/flux and metabolite/flux correlations suggest that plastidic pyruvate kinase exerts flux control and that the lipid/starch tradeoff is most likely mediated by allosteric feedback regulation of phosphofructokinase and ADP-glucose pyrophosphorylase. Quantitative data were also used to calculate in vivo mass action ratios, reaction equilibria, and metabolite turnover times. Compounds like cyclic 39,59-AMP and sucrose-6-phosphate were identified to potentially be involved in so far unknown mechanisms of metabolic control. This study provides a rich source of quantitative data for those studying central metabolism.Seeds develop by absorbing nutrients from their mother plant and using these to synthesize a combination of starch, protein, and lipid. The size and number of seeds that finally develop determine the crop's yield, while their composition determines the end-use quality of the crop. The conversion of nutrients into storage products involves a complex network of metabolic reactions, many of which are subject to transcriptional, translational, and posttranslational regulation. Attempting to engineer seed composition clearly requires a firm understanding of these regulatory networks.The seed's central metabolism differs markedly from those of both a photosynthesizing leaf and a root. In most species, the immature seed is green for a period during its development, so during this phase it is regarded as being photoheterotrophic. A further level of complexity arises as a result of spatial heterogeneity within the seed (Rolletschek et al., 2011; Borisjuk et al.,

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Transcript abundance on its own cannot be used to infer fluxes in central metabolism

Cited by 56 publications

References 53 publications

Time-Series Analyses of Transcriptomes and Proteomes Reveal Molecular Networks Underlying Oil Accumulation in Canola

Time-Series Analyses of Transcriptomes and Proteomes Reveal Molecular Networks Underlying Oil Accumulation in Canola

Inference and prediction of metabolic network fluxes

Quantitative Multilevel Analysis of Central Metabolism in Developing Oilseeds of Oilseed Rape during in Vitro Culture

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