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Pichia pastoris has immense importance an experimental organism. Koichi Ogata first described the ability of certain yeast species to utilize methanol as a sole source of carbon and energy almost thirty years ago. The methylotrophs attracted attention as potential sources of single-cell protein (SCP) to be marketed primarily as high-protein animal feed. During the 1970s, Phillips Petroleum Company developed media and protocols for growing Pichia pastoris on methanol in continuous culture at high cell densities. There was a steep increase in the cost of methane due to the oil crisis in the 1970. And on the other hand the price of soybeans, the major alternative source of animal feed, fell. Thus the SCP production from methanol was not favourable economically.The Salk Institute Biotechnology/Industrial Associates, Inc. (SIBIA) was contracted by Phillips Petroleum to develop P. pastoris as an organism for heterologous protein expression. Researchers at SIBIA isolated the gene and promoter for alcohol oxidase, and generated vectors, strains, and corresponding protocols for the molecular genetic manipulation of P. pastoris. The combination of the fermentation methods developed for the SCP process and the alcohol oxidase promoter's strong, regulated expression resulted in surprisingly high expression levels of foreign protein. Phillips Petroleum sold its P. pastoris expression system patent position to Research Corporation Technologies in 1993, which is current patent holder. Phillips Petroleum also licensed Invitrogen Corporation to sell components of the system(Cereghino and Cregg 2000; Cregg et al., 2000).
Pichia pastoris is an advantageous host for the production of recombinant proteins
.The alcohol oxidase (AOX 1) promoter is suitable for controlled expression of foreign gene but alternative promoters like GAP (glyceraldehyde 3 phosphate dehydrogenase) promoter (Menendez et al., 2004),AOX 2(alcohol oxidase),FLD
1(Formaldehyde dehydrogenase)andILC 1(isocitrate lyase)can also be used(Patrick et al., 2005). The methylotrophic yeast Pichia pastoris is well known for efficient secretion of heterologous proteins, and has come into focus for metabolic
engineering applicationsalso(Mattanovich et al., 2009).
Figure1.1 Pichia pastoris
The phylogenetic similarity of Pichia pastoris to Saccharomyces cerevisiae, ease of genetic manipulation eg. gene targeting, high frequency DNA transformation, ability to perform higher eukaryotic protein modifications like glycosylation, disulphide bond formation, proteolytic processing(Patrick et al., 2005) and the ability to culture at high cell densities also make Pichia pastoris one of the choicest organism. It is a superior host than Saccharomyces cerevisiae because of negative crabtree effect and low hyper mannosylation of proteins (Chung etal., 2010).The N-glycosylation pathways have been engineered for the production of heterologous proteins with human like N-glycan structures(Hamilton et al.,2007). Pichia pastoris can also secrete heterologous protein like Human serum albumin (Kobayash et al., 2000), glycoengineered monoclonal antibodies of high purity (Potgieter et al., 2008). The fermentation can be readily scaled up to meet greater demands, and parameters influencing protein productivity and activity, such as pH, aeration and carbon source feed rate, can be controlled (Patrick et al., 2005).
Metabolic reconstruction is the process of creating a metabolic network model which corresponds to the genome of the organism under study (Pitkanen et al.).Metabolism is the interaction between metabolites, enzymes and regulatory factors. With the advent of modern genome-sequencing capabilities, the reconstruction of metabolic pathwaysis rapidly increasing. The in silico genome scale metabolic reconstruction requires the genome sequence of the organism. The
exploitation of this information for understanding the organization principle of
genetic and metabolic networks is one of the important research areas in the post- genome era (Ma et al., 2003).
A metabolic reconstruction is an attempt to develop a detailed overview of an organismââ‚¬â„¢s metabolism from an analysis of genomic sequence (Osterman and Overbeek 2003). The strain specific metabolic network reconstruction is important for structural and functional studies. Since these networks are very large and complex methods are necessary for their rational representation and quantitative analysis. Together, the reactions that have been demonstrated to potentially occur in the cell form the metabolic network of the organism (Durot et al., 2008).
After genome sequencing the genome is annotated to identify all the open reading frames (ORFââ‚¬â„¢s) coding for the enzymes. Metabolic networks can be generated for organism for which little direct biochemical information is available in the published literature (Feist et al., 2009). For the purpose of reconstruction, metabolism of new organism can be inferred from the pathway in related organism (Durot et al., 2008). The building of metabolic network requires identification of specific chemical reactions catalysed by each enzyme and the reaction stoichiometry (figure 1.2). Functional annotation of enzymes is translated into chemical equations. The Enzyme Commission (EC) numbers classification enables us to identify enzyme catalysed reactions. This relationship between reactions and E.C. numbers is provided by metabolic databases which contain comprehensive information on biochemical reactions and thus provide reference knowledge required to build the metabolic networks. The metabolic reconstruction of a new organism is also facilitated by the pathways in related organism. Metabolic databases group reactions into pathways that indicate known relationships between reactions that occur across several organisms. Metabolic network reconstruction utilizes the information of molecular pathway for reconstruction of complete functional unit. Metabolic networks represent the enzymes in their metabolic context. In silico genome-scale cell models are promising tools for accelerating the design of cells with improved and desired properties (Bro et al., 2006).For metabolic network enzyme, reaction stoichiometry, cofactor specificity, directionality or reversibility andcellular location all need to be included in the network and this information is available from online databases such as KEGG (Ogata et al., 1998), MetaCyc (Caspi et al., 2012), Saccharomyces Genome Database(Cherry et al.,2004), Yeast Metabolome Data Base(Jewison et al.,2012),Expasy ENZYME(Gasteiger et al., 2003). These databases act as repository for well characterized pathways and reconstruction. The KEGG PATHWAY is a widely known database which contains information on various kinds of pathways including pathway image files. The KEGG PATHWAY includes several linked databases which haveinformation about biomolecules and reactions in metabolic pathways (You et al., 2006).The KEGG pathway diagram represents a
network of interacting molecules (Bono et al., 1998).
The metabolic reconstruction gives support to metabolic engineering and also for systems biology study and visualisation of metabolic maps. The metabolic capacities of the cell have to be determined for research in biotechnology. The networks can be used to study the cellular properties of the organism by integrating functional genomic data such as gene expression and proteomics (Notebaart et al., 2006). Metabolic engineering can be defined as directed modification of cellular metabolism and properties through the introduction, deletion and modification of metabolic pathways by using recombinant DNA and other molecular biological tools (Lee et al.,2002).For making the production process economically feasible and efficient, it is important to make significant efforts for improving the P.pastoris strain by identifying key targets for strain improvement through systems biotechnology approach (Chung et al., 2010).
To gain insight into cell synthesis and the metabolic capability through mathematical modelling, a natural first step is to reconstruct the underlying metabolic network, as this is responsible for the synthesis capacity of the cell, and, as well, it allows detailed analysis of the interactions between the individual pathways functioning in the cell (Forster et al., 2002). Quantification of the metabolic network of an organism offers insights into possible ways of developing a mutant strain for better productivity of an extra-cellular metabolite. (Chellapandi et al., 2010).Network representations are also increasingly used to characterize the function of the objects (genes, proteins, metabolite) they interconnect, in comparative study to highlight the differences and similarities existing in the organisation of the intracellular mechanisms of multiple species and evolution of species (Mazurie et al., 2010; Oberhardt et al., 2009). The reconstructed networks form the basis for experimental data analysis andcomputational studies in systems biology (Feist et al., 2009). Metabolic network reconstructions can be used for the analysis of network properties of metabolism, prediction of growth phenotypes (Durot et al., 2008). Metabolic models can be used to elucidate interesting features of the methylotrophic yeast and identify engineering targets for achieving enhanced physiological properties of the strain(Chung et al., 2010).Graph based data mining can be applied to find the meaningful patterns in the biological network which is represented as a graph (You et al., 2006).Metabolic reconstructions can reveal new aspects of metabolism in well-studied organisms (from Escherichia coli to humans), predict the metabolic potential of physiologically uncharacterized organisms, set the stage for network modelling, and support pathway re-engineering and the development of new therapies (Osterman and Overbeek 2003).
In our work we have reconstructed the amino acid metabolic pathways of Pichia pastoris CBS 7435 which is the parental strain of commonly used Pichia pastorisrecombinant protein production hosts (Kuberl et al., 2011).It consists of four chromosomes, chromosome 1(2,891,190basepair),chromosome2(2,399,323
basepair), chromosome 3(2,256,069basepair), chromosome 4(1,820,458basepair)
and the total genome size is of 9.44 Mb. It comprises of 5245 genes and 5033 proteins. The metabolic network of amino acid pathways was reconstructed using MetNetMaker which is a free and open-source tool for the creation of novel metabolic networks in SBML format (Forth et al., 2010)and visualized using Cytoscape which is a software environment for integrated models of biomolecular interaction network (Shannon et al., 2003).We have also tried to present an exhaustive list containing the enzymes along with E.C. number, reactions catalysed by these enzymes, the compartments where each enzyme is located and metabolic maps of amino acid pathways as visualised through Cytoscape.
The reconstruction of amino acid pathways can provide a basis for successful use and availability of amino acid in the recombinant protein production as production of protein is closely related to the cellular machinery of the organism producing the protein and Pichia pastoris has the ability to express high levels of heterologous protein. It may also be used as an alternative host for the commercial production of amino acids, identification of metabolic engineering targets, designing of culture media. The reconstruction of metabolic pathway of Pichia pastoris CBS 7435would also facilitate systems biology based studies and provide an insight into the
metabolism of this specific strain of Pichia pastoris.
Retrieveal of annotated genome from
Pathway Databases: KEGG, MetaCyc
Organism Specific Databases: YMDB, SGD
Survey of literature for collecting information on organism and its phylogenetic relative, metabolic pathways & gap filling.
Compilation of assembled information &
metabolic network reconstruction using
Metabolic pathway visualisation using
Figure 1.2 WorkÃ¯Â¬â€šow along with the tools and databases used in Reconstruction
and Visualisation of Amino Acid Metabolic Pathways Pichia pastoris CBS 7435
1. To study and analyse genome of Pichia pastoris CBS7435.
2. To retrieve the list of enzymes involved in amino acid metabolic pathways.
3. To retrieve enzyme location, coding region and genes of the enzymes involved in amino acid metabolic pathways from Pichia pastoris CBS7435.
4. To determine the reaction and enzyme localisation and reaction directionality of the reactions present in Pichia pastoris CBS7435.
5. To reconstruct the metabolic networks of the pathways involved in amino
acid metabolism of Pichia pastoris CBS7435.
6. To visualise the reconstructed metabolic pathways using network visualisation tool.