Abiotic Stress in Plants and Metabolic Responses - Abiotic Stress: Plant Responses and Applications in Agriculture - page 37

in the levels of OPP pathway intermediates (6-phosphogluconate, ribose 5-phosphate, ribulose
5-phosphate). Incremental increases in the biosynthesis of GSH and intermediates ( O -acetyl-
L-serine, cysteine, and γ-glutamyl-L-cysteine) are also observed in the menadione-treated rice
cell cultures [106].
9. Perspectives
Metabolome analysis has become an invaluable tool in the study of plant metabolic changes
that occur in response to abiotic stresses. Despite progress achieved, metabolomics is a
developing methodology with room for improvement. From a technical perspective, further
developments are required to improve sensitivity for identification of previously uncharac‐
terized molecules and for quantification of cellular metabolites and their fluxes at much higher
resolution. This will allow the identification of novel metabolites and pathways and will allow
linkage to responses to specific stresses, and, therefore, increase our level of knowledge of the
elegant regulation and precise adjustments of plant metabolic networks in response to stress.
Another challenging task is the integration of metabolic data with data from experiments
profiling the transcriptome, proteome, and genetic variations obtained from the same tissue,
cell type, or plant species in response to a determined environmental condition. Integrated
information can be used to map the loci underlying various metabolites and to link these loci
to crop phenotypes, to understand the mechanisms underlying the inheritance of important
traits, and to understand biochemical pathways and global relationships among metabolic
systems. Elucidation of the regulatory networks involved in the activation/repression of key
genes related to metabolic phenotypes in response to determined abiotic stress is becoming
possible. Transcription factors (TFs) are central player in the signal transduction network,
connecting the processes of stress signal sensing and expression of stress-responsive genes.
Thus engineered TFs have emerged as powerful tools to manipulate complex metabolic
pathways in plants and generate more robust metabolic phenotypes [107, 108].
Metabolic networks are highly dynamic, and changes with time are influenced by stress
severity, plant developmental stage, and cellular compartmentalization. Since metabolic
profiling only reveals the steady-state level of metabolites, detailed kinetics and flux analyses
will support a better understanding of metabolic fluctuations in response to stress [109].
Genome-scale models (GSM) are in silico metabolic flux models derived from genome anno‐
tation that contain stoichiometry of all known metabolic reactions of an organism of interest.
Construction of detailed GSMs applied to plant metabolism will provide information about
distribution of metabolic fluxes at a specific genotype, a determined developmental stage, or
a particular environmental condition. This detailed knowledge of the metabolic and physio‐
logical status of the cell can be used to design rational metabolic engineering strategies and to
predict required genetic modifications to obtain a desired metabolic phenotype such as
optimized biomass production, increased accumulation of a valuable metabolite, accumula‐
tion of a metabolite of response towards abiotic stress, or modification of metabolic flux
through a specific pathway of significance [110]. Recently advances have been made in this