Understanding carbon utilization routes between high and low starch-producing cultivars of cassava through Flux Balance Analysis.

Analysis of metabolic flux was used for system level assessment of carbon partitioning in Kasetsart 50 (KU50) and Hanatee (HN) cassava cultivars to understand the metabolic routes for their distinct phenotypes. First, the constraint-based metabolic model of cassava storage roots, rMeCBM, was developed based on the carbon assimilation pathway of cassava. Following the subcellular compartmentalization and curation to ensure full network connectivity and reflect the complexity of eukaryotic cells, cultivar specific data on sucrose uptake and biomass synthesis were input, and rMeCBM model was used to simulate storage root growth in KU50 and HN. Results showed that rMeCBM-KU50 and rMeCBM-HN models well imitated the storage root growth. The flux-sum analysis revealed that both cultivars utilized different metabolic precursors to produce energy in plastid. More carbon flux was invested in the syntheses of carbohydrates and amino acids in KU50 than in HN. Also, KU50 utilized less flux for respiration and less energy to synthesize one gram of dry storage root. These results may disclose metabolic potential of KU50 underlying its higher storage root and starch yield over HN. Moreover, sensitivity analysis indicated the robustness of rMeCBM model. The knowledge gained might be useful for identifying engineering targets for cassava yield improvement.

Unlocking conserved and diverged metabolic characteristics in cassava carbon assimilation via comparative genomics approach.

Globally, cassava is an important source of starch, which is synthesized through carbon assimilation in cellular metabolism whereby harvested atmospheric carbon is assimilated into macromolecules. Although the carbon assimilation pathway is highly conserved across species, metabolic phenotypes could differ in composition, type, and quantity. To unravel the metabolic complexity and advantage of cassava over other starch crops, in terms of starch production, we investigated the carbon assimilation mechanisms in cassava through genome-based pathway reconstruction and comparative network analysis. First, MeRecon - the carbon assimilation pathway of cassava was reconstructed based upon six plant templates: Arabidopsis, rice, maize, castor bean, potato, and turnip. MeRecon, available at http://bml.sbi.kmutt.ac.th/MeRecon, comprises 259 reactions (199 EC numbers), 1,052 proteins (870 genes) and 259 metabolites in eight sub-metabolisms. Analysis of MeRecon and the carbon assimilation pathways of the plant templates revealed the overall topology is highly conserved, but variations at sub metabolism level were found in relation to complexity underlying each biochemical reaction, such as numbers of responsible enzymatic proteins and their evolved functions, which likely explain the distinct metabolic phenotype. Thus, this study provides insights into the network characteristics and mechanisms that regulate the synthesis of metabolic phenotypes of cassava.

Starch biosynthesis in cassava: a genome-based pathway reconstruction and its exploitation in data integration.

BACKGROUND: Cassava is a well-known starchy root crop utilized for food, feed and biofuel production. However, the comprehension underlying the process of starch production in cassava is not yet available. RESULTS: In this work, we exploited the recently released genome information and utilized the post-genomic approaches to reconstruct the metabolic pathway of starch biosynthesis in cassava using multiple plant templates. The quality of pathway reconstruction was assured by the employed parsimonious reconstruction framework and the collective validation steps. Our reconstructed pathway is presented in the form of an informative map, which describes all important information of the pathway, and an interactive map, which facilitates the integration of omics data into the metabolic pathway. Additionally, to demonstrate the advantage of the reconstructed pathways beyond just the schematic presentation, the pathway could be used for incorporating the gene expression data obtained from various developmental stages of cassava roots. Our results exhibited the distinct activities of the starch biosynthesis pathway in different stages of root development at the transcriptional level whereby the activity of the pathway is higher toward the development of mature storage roots. CONCLUSIONS: To expand its applications, the interactive map of the reconstructed starch biosynthesis pathway is available for download at the SBI group's website (http://sbi.pdti.kmutt.ac.th/?page_id=33). This work is considered a big step in the quantitative modeling pipeline aiming to investigate the dynamic regulation of starch biosynthesis in cassava roots.