Putting primary metabolism into perspective to obtain better fruits

Abstract

Background: One of the key goals of fruit biology is to understand the factors that influence fruit growth and quality, ultimately with a view to manipulating them for improvement of fruit traits.

Scope: Primary metabolism, which is not only essential for growth but is also a major component of fruit quality, is an obvious target for improvement. However, metabolism is a moving target that undergoes marked changes throughout fruit growth and ripening.

Conclusions: Agricultural practice and breeding have successfully improved fruit metabolic traits, but both face the complexity of the interplay between development, metabolism and the environment. Thus, more fundamental knowledge is needed to identify further strategies for the manipulation of fruit metabolism. Nearly two decades of post-genomics approaches involving transcriptomics, proteomics and/or metabolomics have generated a lot of information about the behaviour of fruit metabolic networks. Today, the emergence of modelling tools is providing the opportunity to turn this information into a mechanistic understanding of fruits, and ultimately to design better fruits. Since high-quality data are a key requirement in modelling, a range of must-have parameters and variables is proposed.

Fig. 1.

Simplified representation of fruit primary metabolism. Major primary pathways and compounds involved in fruit growth and quality are represented. Sucrose and amino acids represent the major imported pools. Sucrose is first metabolized via sugar metabolism, which produces starch and precursors of cell wall components, and the major antioxidant ascorbate. A large fraction of sucrose and/or hexoses is stored in the vacuole. Hexose phosphates, which are intermediates of sucrose metabolism, are degraded via glycolysis to release energy and yield precursors of the TCA cycle and fatty acid metabolism. Alternatively, hexose phosphates are metabolized via the pentose phosphate pathway, which yields precursors of nucleotides and specialized metabolites. The TCA cycle coupled to respiration releases energy and provides precursors for amino acid synthesis. Imported amino acids, of which glutamine, glutamate, aspartate and asparagine are often the dominant forms, provide nitrogen and carbon skeletons for the synthesis of further amino acids. The major compounds that mainly influence fruit quality are in bold. Abbreviations: P, phosphate; UDP, uridine diphosphate; acetyl-CoA, acetyl-coenzyme A.

Fig. 2.

Hormonal, enzymatic and metabolic changes occurring in tomato fruit pericarp during development and ripening. Hormone levels are expressed in arbitrary units, metabolite levels in µmol g^–1 fresh weight and protein content in mg g^–1 fresh weight. Enzyme capacities expressed in units mg^–1 protein have been normalized, grouped into four clusters and averaged. Cluster 1:fructokinase, glucokinase, pyruvate kinase, aconitase, NAD-isocitrate dehydrogenase, fumarase, NAD-glutamate dehydrogenase and aspartate aminotransferase. Cluster 2:phosphoglucose isomerase, phosphoglucomutase, ADP-glucose pyrophosphorylase, ATP-phosphofructokinase, PPi-phosphofructokinase, plastidial fructose bisphosphatase, triose phosphate isomerase, NAD-glyceraldehyde-3-phosphate dehydrogenase, phosphoglycerate kinase, enolase, phosphoenolpyruvate carboxylase, NAD-malate dehydrogenase, NAD-malic enzyme and NADP-malic enzyme. Cluster 3:sucrose synthase, UDP-glucose pyrophosphorylase, cytosolic fructose bisphosphatase, NADP-glyceraldehyde-3-phosphate dehydrogenase, NADP-glutamate dehydrogenase and alanine aminotransferase. Cluster 4:acid invertase, neutral invertase, sucrose phosphate synthase, aldolase, glucose-6-phosphate dehydrogenase, citrate synthase, NADP-isocitrate dehydrogenase and succinyl-coenzyme A ligase. Adapted from Zhang et al. (2009) and McAtee et al. (2013) for changes in hormone levels and from Biais et al. (2014) for changes in enzyme activities and metabolite concentrations.

Fig. 3.

Schematic representation of a data integration pipeline during construction and refinement of an enzyme-based kinetic model. Chemical information gives a structural framework describing the model topology. It is composed of chemical compounds connected through reactions and thermodynamic constants. The structural model is further enriched by enzyme data, which constrain the kinetic model. Enzyme rate laws are connected through a set of ordinary differential equations parameterized by numerous kinetic constants, e.g. enzyme capacity (V_max), Michaelis–Menten constants (K_M), as well as activation and inhibition constants. The enzyme-based model is further realistically constrained by metabolomics, first by giving access to co-factor concentrations and output fluxes, and secondly by enabling model parameterization via least-square fit of experimentally determined metabolites. Ultimately, independent data sets, when available, can be used to cross-validate the refined model. An additional layer of information is provided by cytological data. Thus, knowing the volume fraction of subcellular compartments and implementing the model with carriers and enzyme isoforms located in different compartments allows the model to calculate local concentrations and fluxes.

Fig. 4.

Fruit model comparison and integration. The comparison of common variables enables cross-validation. The arrows indicate examples of potential benefits that will be obtained by comparing or coupling kinetic, stoichiometric and/or process-based models describing fruit growth and metabolism. Thus, enzyme-based kinetic models can be used to obtain data for osmolyte concentrations (i.e. sugars and organic acids), which can then be used to improve simulations of the osmotic potential and its link with cell wall properties in process-based models. Thereafter, both kinetic and stoichiometric models can be used to improve flux simulations for, for example, carbon influx or respiration within process-based models. In turn, process-based models can be used to constrain input and output variables, e.g. carbon influx and fluxes towards biomass components. Such constraining also makes it possible to study the effects of environmental variables such as temperature, light and watering on metabolic networks. Model integration necessitates the archiving of data in common or translatable formats. Common variables are summarized in the box between the models.