Metabolic modeling to identify engineering targets for Komagataella phaffii: The effect of biomass composition on gene target identification

Biotechnology and Bioengineering

Volumn 114, Issue 11, 2017, Pages 2605-2615

  Cankorur Cetinkaya, Ayca ^a   Dikicioglu, Duygu ^a   Oliver, Stephen G ^a  

^a UNIVERSITY OF CAMBRIDGE   (United Kingdom)

Author keywords

biomass composition; genome scale metabolic model; Komagataella phaffii; metabolic target identification; Pichia pastoris; recombinant protein production

Indexed keywords

ASSOCIATION REACTIONS; BIOMASS; GENES; METABOLISM; MICROORGANISMS; RECOMBINANT PROTEINS; YEAST;

BIOMASS COMPOSITIONS; GENOME SCALE METABOLIC MODEL; KOMAGATAELLA PHAFFII; PICHIA PASTORIS; RECOMBINANT PROTEIN PRODUCTIONS; TARGET IDENTIFICATION;

ECOLOGY;

FUNGAL DNA; RECOMBINANT PROTEIN;

ALCOHOL METABOLISM; ARTICLE; BIOCHEMICAL COMPOSITION; BIOMASS PRODUCTION; COMPUTER MODEL; FATTY ACID METABOLISM; FUNGAL BIOMASS; FUNGAL GENE; FUNGAL METABOLISM; FUNGAL STRAIN; FUNGUS GROWTH; FUNGUS MUTANT; FUNGUS MUTATION; GENE DELETION; GENE IDENTIFICATION; GENE MUTATION; GENE OVEREXPRESSION; GENE TARGETING; GENETIC ASSOCIATION; KOMAGATAELLA PASTORIS; METABOLIC ENGINEERING; NONHUMAN; OXIDATION REDUCTION REACTION; PROTEIN LOCALIZATION; STEROID METABOLISM; ASCOMYCETES; BIOLOGICAL MODEL; CELL PROLIFERATION; COMPUTER SIMULATION; CYTOLOGY; METABOLIC FLUX ANALYSIS; METABOLOME; PHYSIOLOGY; PROCEDURES;

ASCOMYCOTA; CELL PROLIFERATION; COMPUTER SIMULATION; METABOLIC ENGINEERING; METABOLIC FLUX ANALYSIS; METABOLOME; MODELS, BIOLOGICAL;

EID: 85030120074     PISSN: 00063592     EISSN: 10970290     Source Type: Journal    
DOI: 10.1002/bit.26380     Document Type: Article

References (47)

1. Aguilar-Uscanga, B., & Francois, J. M. (2003). A study of the yeast cell wall composition and structure in response to growth conditions and mode of cultivation. Letters in Applied Microbiology, 37, 268–274.
2. Ashburner, M., Ball, C. A., Blake, J. A., Botstein, D., Butler, H., Cherry, J. M., … Sherlock, G. (2000). Gene Ontology: tool for the unification of biology. Nature Genetics, 25, 25–29.
3. Aung, H. W., Henry, S. A., & Walker, L. P. (2013). Revising the representation of fatty acid, glycerolipid, and glycerophospholipid metabolism in the consensus model of yeast metabolism. Industrial Biotechnology, 9, 215–228.
4. Boyle, E. I., Weng, S., Gollub, J., Jin, H., Botstein, D., Cherry, J. M., & Sherlock, G. (2004). GO::TermFinder-open source software for accessing Gene Ontology information and finding significantly enriched Gene Ontology terms associated with a list of genes. Bioinformatics, 20, 3710–3715.
5. Carnicer, M., Baumann, K., Töplitz, I., Sánchez-Ferrando, F., Mattanovich, D., Ferrer, P., & Albiol, J. (2009). Macromolecular and elemental composition analysis and extracellular metabolite balances of Pichia pastoris growing at different oxygen levels. Microbial Cell Factories, 8, 65.
6. Caspeta, L., Shoaie, S., Agren, R., Nookaew, I., & Nielsen, J. (2012). Genome-scale metabolic reconstructions of Pichia stipitis and Pichia pastoris and in-silico evaluation of their potentials. BMC Systems Biology, 6, 24.
7. Caspi, R., Altman, T., Billington, R., Dreher, K., Foerster, H., Fulcher, C. A., … Karp, P. D. (2014). The MetaCyc database of metabolic pathways and enzymes and the BioCyc collection of Pathway/Genome Databases. Nucleic Acids Research, 42, D459–D471.
8. Chelliah, V., Juty, N., Ajmera, I., Ali, R., Dumousseau, M., Glont, M., … Laibe, C. (2015). BioModels: Ten-year anniversary. Nucleic Acids Research, 43, D542–D548.
9. Choi, H. S., Lee, S. Y., Kim, T. Y., & Woo, H. M. (2010). In silico identification of gene amplification targets for improvement of lycopene production. Applied and Environmental Microbiology, 76, 3097–3105.
10. Chung, B. K., Selvarasu, S., Andrea, C., Ryu, J., Lee, H., Ahn, J., … Lee, D.-Y. (2010). Genome-scale metabolic reconstruction and in silico analysis of methylotrophic yeast Pichia pastoris for strain improvement. Microbial Cell Factories, 9, 50.
11. Demain, A. L., & Vaishnav, P. (2009). Production of recombinant proteins by microbes and higher organisms. Biotechnology Advances, 27, 297–306.
12. Dikicioglu, D., Kırdar, B., & Oliver, S. G. (2015). Biomass composition: The “elephant in the room” of metabolic modelling. Metabolomics, 11, 1690–1701.
13. Durot, M., Bourguignon, P.-Y., & Schachter, V. (2009). Genome-scale models of bacterial metabolism: Reconstruction and applications. FEMS Microbiology Reviews, 33, 164–190.
14. Finney, A., & Hucka, M. (2003). Systems biology markup language: Level 2 and beyond. Biochemical Society Transactions, 31, 1472–1473.
15. Förster, J., Halbfeld, C., Zimmermann, M., & Blank, L. M. (2014). A blueprint of the amino acid biosynthesis network of hemiascomycetes. FEMS Yeast Research, 14.
16. Hastings, J., de Matos, P., Dekker, A., Ennis, M., Harsha, B., Kale, N., … Steinbeck, C. (2013). The ChEBI reference database and ontology for biologically relevant chemistry: Enhancements for 2013. Nucleic Acids Research, 41, D456–D463.
17. Herrgård, M. J., Swainston, N., Dobson, P., Dunn, W. B., Arga, K. Y., Arvas, M., … Kell, D. B. (2008). A consensus yeast metabolic network reconstruction obtained from a community approach to systems biology. Nature Biotechnology, 26, 1155–1160.
18. Hiltunen, J. K., Schonauer, M. S., Autio, K. J., Mittelmeier, T. M., Kastaniotis, A. J., & Dieckmann, C. L. (2009). Mitochondrial fatty acid synthesis type II: More than just fatty acids. The Journal of Biological Chemistry, 284, 9011–9015.
19. Hucka, M., Finney, A., Sauro, H. M., Bolouri, H., Doyle, J. C., Kitano, H., … Wang, J. (2003). The systems biology markup language (SBML): A medium for representation and exchange of biochemical network models. Bioinformatics, 19, 524–531.
20. Idiris, A., Tohda, H., Kumagai, H., & Takegawa, K. (2010). Engineering of protein secretion in yeast: Strategies and impact on protein production. Applied Microbiology and Biotechnology, 86, 403–417.
21. Irani, Z. A., Kerkhoven, E. J., Shojaosadati, S. A., & Nielsen, J. (2016). Genome-scale metabolic model of Pichia pastoris with native and humanized glycosylation of recombinant proteins. Biotechnology and Bioengineering, 113, 961–969.
22. Irani, Z. A., Maghsoudi, A., Shojaosadati, S. A., & Motamedian, E. (2015). Development and in silico analysis of a new nitrogen-limited feeding strategy for fed-batch cultures of Pichia pastoris based on a simple pH-control system. Biochemical Engineering Journal, 98, 1–9.
23. Jordà, J., de Jesus, S. S., Peltier, S., Ferrer, P., & Albiol, J. (2014). Metabolic flux analysis of recombinant Pichia pastoris growing on different glycerol/methanol mixtures by iterative fitting of NMR-derived (13)C-labelling data from proteinogenic amino acids. New Biotechnology, 31, 120–132.
24. Kanehisa, M., & Goto, S. (2000). KEGG: Kyoto encyclopedia of genes and genomes. Nucleic Acids Research, 28, 27–30.
25. Kanehisa, M., Sato, Y., Kawashima, M., Furumichi, M., & Tanabe, M. (2016). KEGG as a reference resource for gene and protein annotation. Nucleic Acids Research, 44, D457–D462.
26. Kim, D.-U., Hayles, J., Kim, D., Wood, V., Park, H.-O., Won, M., … Hoe, K.-L. (2010). Analysis of a genome-wide set of gene deletions in the fission yeast Schizosaccharomyces pombe. Nature Biotechnology, 28, 617–623. Available online at: http://www.nature.com/doifinder/10.1038/nbt.1628
27. Kim, I.-K., Roldão, A., Siewers, V., & Nielsen, J. (2012). A systems-level approach for metabolic engineering of yeast cell factories. FEMS Yeast Research, 12, 228–248.
28. Laibe, C., & Le Novère, N. (2007). MIRIAM Resources: Tools to generate and resolve robust cross-references in systems biology. BMC Systems Biology, 1, 58.
29. Nash, R., Weng, S., Hitz, B., Balakrishnan, R., Christie, K. R., Costanzo, M. C., … Cherry, J. M. (2007). Expanded protein information at SGD: New pages and proteome browser. Nucleic Acids Research, 35, D468–D471.
30. Nielsen, J., & Keasling, J. D. (2016). Engineering cellular metabolism. Cell, 164, 1185–1197.
31. Nocon, J., Steiger, M. G., Pfeffer, M., Sohn, S. B., Kim, T. Y., Maurer, M., … Mattanovich, D. (2014). Model based engineering of Pichia pastoris central metabolism enhances recombinant protein production. Metabolic Engineering, 24, 129–138.
32. Ostergaard, S., Olsson, L., & Nielsen, J. (2000). Metabolic engineering of Saccharomyces cerevisiae. Microbiology and Molecular Biology Reviews, 64, 34–50.
33. Prielhofer, R., Maurer, M., Klein, J., Wenger, J., Kiziak, C., Gasser, B., & Mattanovich, D. (2013). Induction without methanol: Novel regulated promoters enable high-level expression in Pichia pastoris. Microbial Cell Factories, 12, 5.
34. Puxbaum, V., Mattanovich, D., & Gasser, B. (2015). Quo vadis? The challenges of recombinant protein folding and secretion in Pichia pastoris. Applied Microbiology and Biotechnology, 99, 2925–2938.
35. Ravikrishnan, A., & Raman, K. (2015). Critical assessment of genome-scale metabolic networks: The need for a unified standard. Briefings in Bioinformatics, 16, 1057–1068.
36. Rußmayer, H., Buchetics, M., Gruber, C., Valli, M., Grillitsch, K., Modarres, G., … Gasser, B. (2015). Systems-level organization of yeast methylotrophic lifestyle. BMC Biology, 13, 80.
37. Schellenberger, J., Que, R., Fleming, R. M. T., Thiele, I., Orth, J. D., Feist, A. M., … Palsson, B. (2011). Quantitative prediction of cellular metabolism with constraint-based models: The COBRA Toolbox v2.0. Nature Protocols, 6, 1290–1307.
38. Shen, W., Xue, Y., Liu, Y., Kong, C., Wang, X., Huang, M., … Zhou, M. (2016). A novel methanol-free Pichia pastoris system for recombinant protein expression. Microbial Cell Factories, 15, 178.
39. Sohn, S. B., Graf, A. B., Kim, T. Y., Gasser, B., Maurer, M., Ferrer, P., … Lee, S. Y. (2010). Genome-scale metabolic model of methylotrophic yeast Pichia pastoris and its use for in silico analysis of heterologous protein production. Biotechnology Journal, 5, 705–715.
40. Sonnhammer, E. L. L., & Östlund, G. (2015). InParanoid 8: Orthology analysis between 273 proteomes, mostly eukaryotic. Nucleic Acids Research, 43, D234–D239.
41. Stanford, N. J., Millard, P., & Swainston, N. (2015). RobOKoD: Microbial strain design for (over)production of target compounds. Frontiers in Cell and Developmental Biology, 3, 17.
42. Stark, C., Breitkreutz, B.-J., Reguly, T., Boucher, L., Breitkreutz, A., & Tyers, M. (2006). BioGRID: A general repository for interaction datasets. Nucleic Acids Research, 34, D535–D539.
43. Stephanopoulos, G. N., Aristidou, A. A., & Nielsen, J. (1998). Metabolic engineering: Principles and methodologies. San Diego, California: Academic Press.
44. Sterck, L., Billiau, K., Abeel, T., Rouzé, P., & Van de Peer, Y. (2012). ORCAE: Online resource for community annotation of eukaryotes. Nature Methods, 9, 1041–1041.
45. Szappanos, B., Kovács, K., Szamecz, B., Honti, F., Costanzo, M., Baryshnikova, A., … Papp, B. (2011). An integrated approach to characterize genetic interaction networks in yeast metabolism. Nature Genetics, 43, 656–662.
46. Tomàs-Gamisans, M., Ferrer, P., & Albiol, J. (2016). Integration and validation of the genome-scale metabolic models of Pichia pastoris: A comprehensive update of protein glycosylation pathways, lipid and energy metabolism. PLoS ONE, 11, e0148031.
47. UniProt Consortium. (2008). The universal protein resource (UniProt). Nucleic Acids Research, 36, D190–D195. Available online at: http://www.ncbi.nlm.nih.gov/pubmed/18045787