Yeast systems biotechnology for production of value added biochemicals

YEAST SYSTEMS BIOTECHNOLOGY FOR PRODUCTION OF VALUE-ADDED BIOCHEMICALS CHUNG KAI SHENG, BEVAN (B. Eng. (Hons.), NUS) A THESIS SUBMITTED FOR THE DEGREE OF DOCTOR OF PHILOSOPHY NUS Graduate School for Integrative Sciences and Engineering NATIONAL UNIVERSITY OF SINGAPORE 2012 Declaration I hereby declare that this thesis is my original work and it has been written by me in its entirety. I have duly acknowledged all the sources of information which have been used in the thesis. This thesis has also not been submitted for any degree in any university previously. Chung Kai Sheng, Bevan 12 November 2012 i Acknowledgements It gives me great pleasure to express my heartfelt thanks to people who have, in one way or another, contributed to the successful completion of this thesis. First and foremost, I want to thank my Lord, Jesus Christ, whose super-abounding grace has supplied me with all that I need to accomplish my tasks in life. I am grateful to my supervisor Asst. Prof. Lee Dong-Yup who has played an instrumental role in imparting invaluable research skills. Interactions with the Thesis Advisory Committee members, Prof. Karimi, I.A. and Asst. Prof. Matthew Chang, have also helped to hone my analytical skills. I wish to acknowledge the scientists in the Korea Research Institute of Bioscience & Biotechnology (KRIBB), especially Dr. Ahn Jung Oh, Dr. Choi EuiSung and Dr. Lee Hong-Weon, for their valuable advice and for being such hospitable hosts during my research stint in Korea. The colleagues in the Biotechnology Process Engineering Center (BPEC) of KRIBB have also been very accommodating and helpful. I am also thankful for the company of colleagues and fellow Ph.D. students from the Bioinformatics group of Bioprocessing Technology Institute (BTI), A*STAR, and the Department of Chemical and Biomolecular Engineering, NUS, who have contributed to my growth as a researcher through intellectually stimulating discussions and the sharing of useful insights. Finally, I want to thank my loved ones: my parents, Mr. Chung Eng Huat and Ms. Lum Siew Yoke, for their care and support, and Ms. Pan Yihui Summer for her love and encouragement during the course of my Ph.D. ii Table of Contents Summary . vi  List of Tables viii  List of Figures .x  List of Symbols . xiii  Chapter 1. Introduction .1  1.1. Background of yeasts 1  1.2. The Pichia pastoris expression system .2  1.3. Scope of thesis .3  1.4. Organization of thesis 4  Chapter 2. Overview of systems biotechnology .7  2.1. The advent of systems biology 7  2.2. Application of systems biology to biotechnology .9  2.3. In silico modeling of biological systems .10  2.4. Constraints-based flux analysis .13  2.4.1. The basic constraints‐based flux analysis framework  . 14  2.4.2. Exploring metabolic capabilities using constraints‐based flux analysis   18  2.4.3. Strain improvement using constraints‐based flux analysis  . 19  2.5. Genome-scale metabolic model (GSMM) 20  2.5.1. GSMM reconstruction  . 20  2.5.2. GSMM validation  . 22  Chapter 3. Pichia pastoris genome-scale metabolic model reconstruction .24  3.1. Methylotrophic yeast Pichia pastoris .24  3.2. Reconstruction of P. pastoris genome-scale metabolic model .25  3.3. Manual curation and gap-filling 27  3.4. GSMM biomass composition 29  3.4.1. Overall cellular composition   30  3.4.2. Amino acid composition   30  3.4.3. Carbohydrates composition   31  3.4.4. DNA composition  . 32  3.4.5. RNA composition  . 32  3.4.6. Lipid composition   33  iii 3.4.7. Growth associated ATP requirement   34  3.4.8. Other essential biomass components  . 35  3.4.9. Biomass synthesis reaction  . 36  3.5. Uniqueness of P. pastoris metabolism 37  3.6. P. pastoris chemostat culture 41  3.7. GSMM validation 42  3.7.1. Non‐growth associated ATP maintenance requirement  . 42  3.7.2. Validation with chemostat experimental data   43  3.7.3. Validation with omics data   45  3.7.4. Quality of the iPP668 model   49  3.8. GSMM reconstruction in systems biotechnology .50  Chapter 4. Flux-sum analysis 51  4.1. Reaction-centric versus metabolite-centric perspectives 51  4.2. Flux-sum analysis 51  4.3. Flux-sum perturbation .53  4.3.1. Linearization of flux‐sum  . 53  4.3.2. Flux‐sum maximization   54  4.3.3. Attenuation and intensification of flux‐sum  . 55  4.4. Case study: Metabolite flux-sums of E. coli .56  4.4.1. Basal metabolite flux‐sums  . 57  4.4.2. Flux‐sum maxima  . 59  4.4.3. Flux‐sum attenuation analysis  . 61  4.4.4. Flux‐sum intensification analysis  . 64  4.4.5. Flux‐sum based metabolite classification  . 67  4.5. Flux-sum analysis for enhancing succinate production 68  4.5.1. Flux‐sum attenuation target for improved succinate production   70  4.5.2. Flux‐sum intensification targets for improved succinate production  . 74  4.5.3. Flux‐sum perturbation for metabolic engineering   75  Chapter 5. P. pastoris GSMM analysis 76  5.1. P. pastoris GSMM for recombinant protein production .76  5.2. Protein synthesis in P. pastoris GSMM 77  5.3. Carbon source analysis for recombinant protein production 80  iv 5.4. P. pastoris for whole-cell biotransformation 84  Chapter 6. Codon optimization methodology .87  6.1. Designing synthetic genes for heterologous protein expression .87  6.2. Codon usage diversity .88  6.3. Individual codon usage optimization (ICO) 91  6.3.1. Preliminaries   91  6.3.2. Definition of fitness  . 92  6.3.3. ICO mathematical formulation   94  6.3.4. Solving the ICO problem   95  6.4. Codon context optimization (CCO) 97  6.4.1. CCO mathematical formulation  . 98  6.4.2. Solving the CCO problem  . 101  6.5. Multi-objective codon optimization (MOCO) 104  6.5.1. MOCO mathematical formulation  . 104  6.5.2. Solving the MOCO problem  . 106  Chapter 7. Comparison of ICO and CCO .109  7.1. Codon optimization in P. pastoris .109  7.2. ICU and CC preference of P. pastoris 110  7.2.1. Pearson’s chi‐squared test for biasness in ICU and CC distributions   112  7.2.2. Principal component analysis of ICU and CC distributions  . 115  7.2.3. Alternative methods of evaluating ICU and CC preference   116  7.3. Cross-validation of codon optimization approaches .117  7.4. In vivo protein expression of optimized sequences .120  7.5. Efficacy of CCO 123  7.6. Potential applications of CCO .124  7.7. Rare codons and protein folding .125  Chapter 8. Conclusion .126  8.1. Summary of contributions .126  8.2. Future perspectives 127  Bibliography .130  v Summary The earliest industrial exploitation of yeast micro-organisms dates back thousands of years ago when the fermentation capability of Saccharomyces cerevisiae was harnessed for baking bread and producing alcoholic beverages. With advancements in cellular engineering technology, genetically engineered yeasts have become important microbial cell factories for producing a wide range of biochemicals in the biotechnological industry. Among them, the methylotrophic yeast Pichia pastoris has been recognized as a popular host organism for expressing protein molecules due to factors such as (1) its ability to achieve high cell density under respiratory growth, (2) its capability of performing eukaryotic post-translational modifications, (3) simplicity of applying genetic manipulation techniques to the organism and (4) low levels of endogeneous protein secretion leading to easier heterologous protein product purification procedures. While many experimental studies on recombinant protein expression in P. pastoris have been performed, a rational framework for engineering the methylotrophic yeast still eludes researchers. Towards this end, this thesis aims to develop analysis tools that can characterize the cellular physiology of P. pastoris to facilitate the rational design of strain improvement strategies for enhancing the microbe’s performance. A genome-scale metabolic model was reconstructed to characterize the metabolic capabilities of P. pastoris. The analysis of cellular metabolism using the constraints-based flux analysis approach enables the rational identification of metabolic engineering targets for strain improvement. A novel computational framework, known as “flux-sum analysis”, was developed to analyze the metabolite turnover rates during cell growth and recombinant protein production. The flux-sum vi analysis was able to identify essential metabolites in P. pastoris, and further elucidated the organism’s potential as a whole-cell biocatalyst for reducing ketone substrates into valuable chiral alcohols which are important precursors for producing fine chemicals and active pharmaceutical ingredients. Apart from the analysis of cellular metabolism, this thesis also examines potential issues in heterologous protein synthesis during the translation of mRNA to protein. The typically low expression of heterologous proteins has been largely attributed to discrepancies in codon usage patterns between the host’s native genes and the foreign gene. Therefore, the design of synthetic genes to enhance codon usage patterns was studied in detail. Computational procedures for optimizing individual codon usage (ICU) and codon pair usage, also known as codon context (CC), were developed. Surprisingly, the comparison of results from different codon optimization approaches revealed that CC is a relatively more important design parameter than the commonly considered ICU. Hence, the incorporation of CC optimization into existing synthetic gene design tools, which were mainly based on ICU optimization, is expected to produce sequences with improved protein expression capabilities. The in silico tools developed in this thesis are capable of incorporating highthroughput genomic, transcriptomic and metabolomic data for the analysis and optimization of P. pastoris from a systems perspective. With the increasing amount of biological data being generated with time, the presented systems biotechnology framework will become an important tool for harnessing these large-scale data to systematically study and engineer living organisms for industrial applications. vii List of Tables Table 2.1. Composition of M9 minimal medium. . 23  Table 3.1. Composition of major cellular components 30  Table 3.2. Calculation of amino acid composition. . 31  Table 3.3. Carbohydrate composition. . 32  Table 3.4. DNA composition. 32  Table 3.5. RNA composition. 33  Table 3.6. Fatty acid composition. . 33  Table 3.7. Phospholipid composition . 34  Table 3.8. Sterol composition. . 34  Table 3.9. Growth associated ATP requirement. . 35  Table 3.10. Trace components. 36  Table 3.11. Comparison of two yeast GSMMs. Data for S. cerevisiae obtained from iMM904 GSMM (Mo et al, 2009). 38  Table 3.12. Functional classification of metabolic reactions. 39  Table 3.13. Chemostat experimental data. . 42  Table 3.14. Prediction of metabolite utilization. Metabolites involved in reactions with nonzero fluxes are marked with a tick while the rest are marked with a cross. . 47  Table 5.1. Amino acid requirements for EPO synthesis. . 79  Table 6.1. Synonymous codon(s) of amino acids. . 89  Table 7.1. Pearson’s chi-squared tests. Singular amino acids (pairs) and those with expected counts less than are not amenable to the chi-squared test and classified as “unevaluated”. Abbreviations: DH, codon (pair) distribution of highexpression genes; DA, codon (pair) distribution of all genes; U, uniform distribution. 114  Table 7.2. Summary of fitness values and similarity measures. The p M values are computed through pairwise comparison of the different types of sequences. . 119  viii Table 7.3. Tournament matrix. The number in each cell indicates the number of wins (losses) per 100 tournaments by the optimization approach indicated in the leftmost (topmost) column (row). 120  ix Bibliography _____________________________________________________________________ Chatterjee A, Li Y, Zhang Y, Grove TL, Lee M, Krebs C, Booker SJ, Begley TP, Ealick SE (2008) Reconstitution of ThiC in thiamine pyrimidine biosynthesis expands the radical SAM superfamily. Nat Chem Biol 4: 758-765 Chiba Y, Akeboshi H (2009) Glycan engineering and production of 'humanized' glycoprotein in yeast cells. Biol Pharm Bull 32: 786-795 Chung BK, Lee DY (2009) Flux-sum analysis: a metabolite-centric approach for understanding the metabolic network. 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Chapter 2 Overview of systems biotechnology _ Figure 2.1 The systems biology framework An integration of information, systems and life sciences provides a holistic approach towards understanding physiological phenomena 2.2 Application of systems biology to biotechnology The scientific approach of systems biology has been widely used for the discovery of novel biomolecular... modulate cellular physiology (Butcher et al, 2004; Kitano, 2002) The translation of this scientific knowledge into state -of- the-art technologies for industrial production of value- added biochemicals is the embodiment of biotechnology A key objective in 9 Chapter 2 Overview of systems biotechnology _ biotechnology is to develop high-yield and low-cost bioprocesses through microorganism... contributions made in this thesis and highlights future perspectives of systems biotechnology research 6 Chapter 2 Overview of systems biotechnology _ Chapter 2 Overview of systems biotechnology 2.1 The advent of systems biology The variety of physiological behaviors observed in a living cell is a result of complex interactions between biomolecules These interactions can... industrial production of value- added biochemicals Among the various biochemicals, protein-based drug molecules produced by biopharmaceutical companies were considered the most lucrative products in the market The sales of protein drugs, such as Enbrel, Remicade and Avastin, accounts for almost 20% of the global biopharmaceutical market with a value of close to US$ 100 billion (Walsh, 2010) Therefore, cellular... Number of occurrence of amino acid j in the host’s selected genes j θ A,1 Number of occurrence of amino acid j in the target coding sequence j θ AA,0 Number of occurrence of amino acid pair j in the host’s selected genes j θ AA,1 Number of occurrence of amino acid pair j in the target coding sequence k θ C,0 Number of occurrence of codon k in the host’s selected genes k θ C,1 Number of occurrence of codon... follows: Chapter 2 provides an overview of developments in yeast systems biotechnology The application of systems biology to biotechnological studies is discussed with particular emphasis on the importance of in silico modeling for cellular metabolism characterization In order to provide a detailed representation of in vivo metabolic behavior, the model has to account for all possible metabolic functions... genes p1 Vector of frequencies defining codon distribution of the target coding sequence k q0 Frequency of occurrence of codon pair k in the host’s selected genes q1k Frequency of occurrence of codon pair k in the target coding sequence q0 Vector of frequencies defining codon pair distribution of the host’s selected genes xiii q1 Vector of frequencies defining codon pair distribution of the target coding... analysis profiles Only the profiles of potential targets capable of achieving at least 10% of maximum theoretical succinate yield are shown 70  Figure 4.11 Mixed acid fermentation pathways 72  Figure 4.12 Effects of pyruvate flux-sum attenuation In glycolysis, pyruvate kinase is the key producer of ATP while glyceraldehyde-3-phosphate dehydrogenase is the key consumer of NAD The production of acetate,... arbitrary large value n Number of amino acids/codons in the target coding sequence n′ Number of amino acids/codons among the host’s selected genes H Oij Observed number of codon i encoding amino acid j in high expression genes k p0 Frequency of occurrence of codon k in the host p1k Frequency of occurrence of codon k in the target coding sequence p0 Vector of frequencies defining codon distribution of the host’s... Stoichiometric coefficient of metabolite i in reaction j t Time (hr) vj Metabolic flux of reaction j (mmol/gDCW-hr) v max j Upper limit for the metabolic flux of reaction j (mmol/gDCW-hr) v min j Lower limit for the metabolic flux of reaction j (mmol/gDCW-hr) 2 Χ1, j Chi-squared statistic for testing codon (pair) distribution bias of amino acid j in high expression genes with respect to the uniform distribution . YEAST SYSTEMS BIOTECHNOLOGY FOR PRODUCTION OF VALUE- ADDED BIOCHEMICALS CHUNG KAI SHENG, BEVAN (B. Eng. (Hons.), NUS) A THESIS SUBMITTED FOR THE DEGREE OF DOCTOR OF PHILOSOPHY. 2 1.3. Scope of thesis 3 1.4. Organization of thesis 4 Chapter 2. Overview of systems biotechnology 7 2.1. The advent of systems biology 7 2.2. Application of systems biology to biotechnology. culture broth. Indeed, yeasts are one of the oldest microorganisms being exploited by humankind for industrial production of fermented products. Earliest records of yeast biotechnology date back