Saowalak Kalapanulak

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

BackgroundCassava 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.ResultsIn 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.ConclusionsTo 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.

Genome-wide analysis reveals phytohormone action during cassava storage root initiation.

Development of storage roots is a process associated with a phase change from cell division and elongation to radial growth and accumulation of massive amounts of reserve substances such as starch. Knowledge of the regulation of cassava storage root formation has accumulated over time; however, gene regulation during the initiation and early stage of storage root development is still poorly understood. In this study, transcription profiling of fibrous, intermediate and storage roots at eight weeks old were investigated using a 60-mer-oligo microarray. Transcription and gene expression were found to be the key regulating processes during the transition stage from fibrous to intermediate roots, while homeostasis and signal transduction influenced regulation from intermediate roots to storage roots. Clustering analysis of significant genes and transcription factors (TF) indicated that a number of phytohormone-related TF were differentially expressed; therefore, phytohormone-related genes were assembled into a network of correlative nodes. We propose a model showing the relationship between KNOX1 and phytohormones during storage root initiation. Exogeneous treatment of phytohormones N6-benzylaminopurine and 1-Naphthaleneacetic acid were used to induce the storage root initiation stage and to investigate expression patterns of the genes involved in storage root initiation. The results support the hypothesis that phytohormones are acting in concert to regulate the onset of cassava storage root development. Moreover, MeAGL20 is a factor that might play an important role at the onset of storage root initiation when the root tip becomes swollen.

Transcriptomic Data Integration Inferring the Dominance of Starch Biosynthesis in Carbon Utilization of Developing Cassava Roots

Abstract Carbon metabolism, which is an important process underlying the plant development, has been extensively studied in model plant Arabidopsis, however the understanding in this process for cassava root crop is very little. To enhance our understanding into the process, we studied carbon partitioning during cassava root development at the transcriptional level via transciptomic data integration into the metabolic pathways. The transcriptome data of three different developmental stages of cassava roots—fibrous root (FR), developing storage root (DR), and mature storage root (MR) from Yang et al. [1] —was integrated into the key carbon metabolism pathways reconstructed following the protocol of Rongsirikul et al. [2] . According to the 43 differentially expressed genes (56 proteins IDs) mapped into the pathways, we found that the genes involved in starch biosynthesis are more up-regulated, in contrast to the expression of genes in the cell wall biosynthesis and respiration. The results may imply the significance of starch biosynthesis among the carbon utilization processes in the developing cassava roots. In other words, the carbon source from α-D-glucose-1-phosphate (G1P) might be mostly used for starch biosynthesis rather than cell wall biosynthesis and respiration pathways during cassava root development.

Reconstruction of Starch Biosynthesis Pathway in Cassava Using Comparative Genomic Approach

Cassava is one of the most attractive crops nowadays because it can produce and accumulate large amount of starch in its roots. Cassava starch is widely used as food, feed and raw materials for biochemical industries. Due to the increasing demand of cassava starch, the starch biosynthesis pathway is thus of interest for metabolic engineering, aiming at strain improvement. However, the uncertainties in the metabolic pathway of starch biosynthesis in cassava retard the rate of achievement. Availability of recently released cassava genome motivates us to reconstruct the starch biosynthesis pathway in cassava using comparative genomic approach. Here, nucleotide sequences of the template plants (i.e. Arabidopsis and potato) were compared with the sequence of cassava collected from three sources: Phytozome (genomic sequence), Cassava full-length cDNA and Cassava genome (ESTs) databases. The metabolic pathway of approximately 34 enzymes was constructed, including pathway from sucrose metabolism to amylose and amylopectin synthesis. The resulting pathway is a good initial point toward the complete pathway reconstruction.

Prediction of cassava protein interactome based on interolog method

Cassava is a starchy root crop whose role in food security becomes more significant nowadays. Together with the industrial uses for versatile purposes, demand for cassava starch is continuously growing. However, in-depth study to uncover the mystery of cellular regulation, especially the interaction between proteins, is lacking. To reduce the knowledge gap in protein-protein interaction (PPI), genome-scale PPI network of cassava was constructed using interolog-based method (MePPI-In, available at http://bml.sbi.kmutt.ac.th/ppi). The network was constructed from the information of seven template plants. The MePPI-In included 90,173 interactions from 7,209 proteins. At least, 39 percent of the total predictions were found with supports from gene/protein expression data, while further co-expression analysis yielded 16 highly promising PPIs. In addition, domain-domain interaction information was employed to increase reliability of the network and guide the search for more groups of promising PPIs. Moreover, the topology and functional content of MePPI-In was similar to the networks of Arabidopsis and rice. The potential contribution of MePPI-In for various applications, such as protein-complex formation and prediction of protein function, was discussed and exemplified. The insights provided by our MePPI-In would hopefully enable us to pursue precise trait improvement in cassava.

Genome-Based Screening for Drug Targets Identification: Application to Typhoid Fever

Salmonella enterica serovar Typhi CT18 (S. Typhi) is the causative agent of typhoid fever in human beings. Currently, most of the drugs used to treat this sickness have adverse side-effects. Moreover, drug-resistant strains are emerging as a serious threat for the disease. Therefore, the most effective drug targets are urgently demanded for the development of new faster-acting antibacterial agents. In this paper, a published method for drug targets identification in Mycobacterium tuberculosis metabolismby Kalapanulak was applied to typhoid fever. The whole genome of S. Typhi was investigated and 282 genes were proposed as new drug targets. Interestingly, 34 drug–affected and essential genes from the three current antibiotics are all found in our proposed drug targets.

Networking Omic Data to Envisage Systems Biological Regulation

To understand how biological processes work, it is necessary to explore the systematic regulation governing the behaviour of the processes. Not only driving the normal behavior of organisms, the systematic regulation evidently underlies the temporal responses to surrounding environments (dynamics) and long-term phenotypic adaptation (evolution). The systematic regulation is, in effect, formulated from the regulatory components which collaboratively work together as a network. In the drive to decipher such a code of lives, a spectrum of technologies has continuously been developed in the post-genomic era. With current advances, high-throughput sequencing technologies are tremendously powerful for facilitating genomics and systems biology studies in the attempt to understand system regulation inside the cells. The ability to explore relevant regulatory components which infer transcriptional and signaling regulation, driving core cellular processes, is thus enhanced. This chapter reviews high-throughput sequencing technologies, including second and third generation sequencing technologies, which support the investigation of genomics and transcriptomics data. Utilization of this high-throughput data to form the virtual network of systems regulation is explained, particularly transcriptional regulatory networks. Analysis of the resulting regulatory networks could lead to an understanding of cellular systems regulation at the mechanistic and dynamics levels. The great contribution of the biological networking approach to envisage systems regulation is finally demonstrated by a broad range of examples.