PhD student, King Mongkut’s University of Technology Thonburi (KMUTT)
To unwind the complexity of carbon assimilation metabolic network in cassava
The climate change is already having a significant impact around the world. One of the most impacts of the climate change is food crop production for mankind. To ensure the food security, cassava is considerably a potential crop because it can accumulate the high starch content in its storage roots. Nevertheless, the demand of cassava products is much higher than the current production. Hence, the relevant research is mostly focused on crop productivity improvement. Besides a traditional breeding and the genetic modification, the main research is focused on the photosynthesis capability and the carbon assimilation in crop plants. For cassava, the previous physiological studies showed that the photosynthesis capability varies in narrow range among cultivars, yet having distinct phenotype. It suggests that the complexity of carbon assimilation inside plant cell which seems to determine the crop productivity as well as starch production of cassava. Thus, my research aims to unwind the complexity of carbon assimilation metabolic network in cassava through network reconstruction. The metabolic network was reconstructed based on the cassava genome combining with the physiological and biochemical properties of a cell. Moreover, this network contained the subcellular compartments, currency metabolites essential for mass balance, and a well-connected network by curating the metabolic gap reactions. This network helps us to gain insights the carbon assimilation process in root crop. Additionally, it will be used as an initial framework of the starchy root crop to extend the complex metabolic processes and integrate the omics data.
Abstract: Publication date: November 2016 Source:Field Crops Research, Volume 198 Author(s): Michael M. Chipeta, Paul Shanahan, Rob Melis, Julia Sibiya, Ibrahim R.M. Benesi One of the attempts by farmers in counteracting the devastating effects of cassava brown streak disease (CBSD) on yield and quality of cassava is early harvesting. However, most varieties grown by farmers are often late bulking which increases the disease severity while on the other hand early harvesting results in significant yield losses. Farmers, therefore, need early storage root bulking cassava varieties in order to reduce the time to harvest leading to a faster rate of return to investment, while at the same time avoiding devastating effects of CBSD on yield and quality of cassava. The study was, therefore, conducted to identify high-yielding and early storage root bulking cassava genotypes as well as traits associated with early storage root bulking and estimate yield loss if any due to early harvesting. The overall aim was to generate information that would guide future improvement programmes for high-yielding and early-bulking cassava varieties in Malawi and other countries facing similar challenges. Trials were implemented using a square lattice design with three replications at two locations for two growing seasons with three harvest intervals (6, 9 and 12 months after planting, MAP). High yields were obtained of up to 9.5t/ha at 6 and 17.8t/ha at 9 MAP. Furthermore, the study revealed that yields obtained at 9 MAP were higher than those obtained at 12 MAP for some genotypes which suggests that such genotypes would be considered as early storage root bulking. Simple correlation analysis identified harvest index, storage root number, storage root diameter and storage root length as the selection criteria to achieve high fresh storage root yield (t/ha) and dry mass yield (t/ha). Path coefficient analysis allocated harvest index and shoot mass as the major selection criteria in improving fresh storage yield and dry mass yield. The study suggests that both source and sink capacities were important for determining early yield. Therefore, these two traits are the key determinants of early storage root bulking and should be used when selecting early-bulking cultivars and indirectly selecting for storage root number, storage root diameter and storage root length.
Pub.: 17 Sep '16, Pinned: 06 Oct '17
Abstract: The aim of this study was to estimate the genetic parameters and predict the genotypic values of root quality traits in cassava (Manihot esculenta Crantz) using restricted maximum likelihood (REML) and best linear unbiased prediction (BLUP). A total of 471 cassava accessions were evaluated over two years of cultivation. The evaluated traits included amylose content (AML), root dry matter (DMC), cyanogenic compounds (CyC), and starch yield (StYi). Estimates of the individual broad-sense heritability of AML were low (hg(2) = 0.07 ± 0.02), medium for StYi and DMC, and high for CyC. The heritability of AML was substantially improved based on mean of accessions (hm(2) = 0.28), indicating that some strategies such as increasing the number of repetitions can be used to increase the selective efficiency. In general, the observed genotypic values were very close to the predicted average of the improved population, most likely due to the high accuracy (>0.90), especially for DMC, CyC, and StYi. Gains via selection of the 30 best genotypes for each trait were 4.8 and 3.2% for an increase and decrease for AML, respectively, an increase of 10.75 and 74.62% for DMC for StYi, respectively, and a decrease of 89.60% for CyC in relation to the overall mean of the genotypic values. Genotypic correlations between the quality traits of the cassava roots collected were generally favorable, although they were low in magnitude. The REML/BLUP method was adequate for estimating genetic parameters and predicting the genotypic values, making it useful for cassava breeding.
Pub.: 02 Sep '14, Pinned: 06 Oct '17
Abstract: Understanding root traits is a necessary research front for selection of favorable genotypes or cultivation practices. Root and tuber crops having most of their economic potential stored below ground are favorable candidates for such studies. The ability to image and quantify subsurface root structure would allow breeders to classify root traits for rapid selection and allow agronomist the ability to derive effective cultivation practices. In spite of the huge role of Cassava (Manihot esculenta Crantz), for food security and industrial uses, little progress has been made in understanding the onset and rate of the root-bulking process and the factors that influence it. The objective of this research was to determine the capability of ground penetrating radar (GPR) to predict root-bulking rates through the detection of total root biomass during its growth cycle. Our research provides the first application of GPR for detecting below ground biomass in cassava.Through an empirical study, linear regressions were derived to model cassava bulking rates. The linear equations derived suggest that GPR is a suitable measure of root biomass (r = .79). The regression analysis developed accounts for 63% of the variability in cassava biomass below ground. When modeling is performed at the variety level, it is evident that the variety models for SM 1219-9 and TMS 60444 outperform the HMC-1 variety model (r(2) = .77, .63 and .51 respectively).Using current modeling methods, it is possible to predict below ground biomass and estimate root bulking rates for selection of early root bulking in cassava. Results of this approach suggested that the general model was over predicting at early growth stages but became more precise in later root development.
Pub.: 11 Aug '17, Pinned: 06 Oct '17
Abstract: Bioinformatics tools have facilitated the reconstruction and analysis of cellular metabolism of various organisms based on information encoded in their genomes. Characterization of cellular metabolism is useful to understand the phenotypic capabilities of these organisms. It has been done quantitatively through the analysis of pathway operations. There are several in silico approaches for analyzing metabolic networks, including structural and stoichiometric analysis, metabolic flux analysis, metabolic control analysis, and several kinetic modeling based analyses. They can serve as a virtual laboratory to give insights into basic principles of cellular functions. This article summarizes the progress and advances in software and algorithm development for metabolic network analysis, along with their applications relevant to cellular physiology, and metabolic engineering with an emphasis on microbial strain optimization. Moreover, it provides a detailed comparative analysis of existing approaches under different categories.
Pub.: 30 Mar '13, Pinned: 26 Sep '17
Abstract: Flux is a key measure of the metabolic phenotype. Recently, complete (genome-scale) metabolic network models have been established for Arabidopsis (Arabidopsis thaliana), and flux distributions have been predicted using constraints-based modeling and optimization algorithms such as linear programming. While these models are useful for investigating possible flux states under different metabolic scenarios, it is not clear how close the predicted flux distributions are to those occurring in vivo. To address this, fluxes were predicted for heterotrophic Arabidopsis cells and compared with fluxes estimated in parallel by (13)C-metabolic flux analysis (MFA). Reactions of the central carbon metabolic network (glycolysis, the oxidative pentose phosphate pathway, and the tricarboxylic acid [TCA] cycle) were independently analyzed by the two approaches. Net fluxes in glycolysis and the TCA cycle were predicted accurately from the genome-scale model, whereas the oxidative pentose phosphate pathway was poorly predicted. MFA showed that increased temperature and hyperosmotic stress, which altered cell growth, also affected the intracellular flux distribution. Under both conditions, the genome-scale model was able to predict both the direction and magnitude of the changes in flux: namely, increased TCA cycle and decreased phosphoenolpyruvate carboxylase flux at high temperature and a general decrease in fluxes under hyperosmotic stress. MFA also revealed a 3-fold reduction in carbon-use efficiency at the higher temperature. It is concluded that constraints-based genome-scale modeling can be used to predict flux changes in central carbon metabolism under stress conditions.
Pub.: 08 Jul '10, Pinned: 26 Sep '17
Abstract: 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.
Pub.: 24 Sep '15, Pinned: 26 Sep '17
Abstract: Understanding metabolic acclimation of plants to challenging environmental conditions is essential for dissecting the role of metabolic pathways in growth and survival. As stresses involve simultaneous physiological alterations across all levels of cellular organization, a comprehensive characterization of the role of metabolic pathways in acclimation necessitates integration of genome-scale models with high-throughput data. Here, we present an integrative optimization-based approach, which, by coupling a plant metabolic network model and transcriptomics data, can predict the metabolic pathways affected in a single, carefully controlled experiment. Moreover, we propose three optimization-based indices that characterize different aspects of metabolic pathway behavior in the context of the entire metabolic network. We demonstrate that the proposed approach and indices facilitate quantitative comparisons and characterization of the plant metabolic response under eight different light and/or temperature conditions. The predictions of the metabolic functions involved in metabolic acclimation of Arabidopsis thaliana to the changing conditions are in line with experimental evidence and result in a hypothesis about the role of homocysteine-to-Cys interconversion and Asn biosynthesis. The approach can also be used to reveal the role of particular metabolic pathways in other scenarios, while taking into consideration the entirety of characterized plant metabolism.
Pub.: 25 Apr '13, Pinned: 26 Sep '17