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Rußmayer H, Buchetics M, Mattanovich M, Neubauer S, Steiger M, Graf AB, Koellensperger G, Hann S, Sauer M, Gasser B, Mattanovich D. Customizing amino acid metabolism of Pichia pastoris for recombinant protein production. Biotechnol J 2023; 18:e2300033. [PMID: 37668396 DOI: 10.1002/biot.202300033] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2023] [Revised: 07/31/2023] [Accepted: 08/31/2023] [Indexed: 09/06/2023]
Abstract
Amino acids are the building blocks of proteins. In this respect, a reciprocal effect of recombinant protein production on amino acid biosynthesis as well as the impact of the availability of free amino acids on protein production can be anticipated. In this study, the impact of engineering the amino acid metabolism on the production of recombinant proteins was investigated in the yeast Pichia pastoris (syn Komagataella phaffii). Based on comprehensive systems-level analyses of the metabolomes and transcriptomes of different P. pastoris strains secreting antibody fragments, cell engineering targets were selected. Our working hypothesis that increasing intracellular amino acid levels could help unburden cellular metabolism and improve recombinant protein production was examined by constitutive overexpression of genes related to amino acid metabolism. In addition to 12 genes involved in specific amino acid biosynthetic pathways, the transcription factor GCN4 responsible for regulation of amino acid biosynthetic genes was overexpressed. The production of the used model protein, a secreted carboxylesterase (CES) from Sphingopyxis macrogoltabida, was increased by overexpression of pathway genes for alanine and for aromatic amino acids, and most pronounced, when overexpressing the regulator GCN4. The analysis of intracellular amino acid levels of selected clones indicated a direct linkage of improved recombinant protein production to the increased availability of intracellular amino acids. Finally, fed batch cultures showed that overexpression of GCN4 increased CES titers 2.6-fold, while the positive effect of other amino acid synthesis genes could not be transferred from screening to bioreactor cultures.
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Affiliation(s)
- Hannes Rußmayer
- Department of Biotechnology, Institute of Microbiology and Microbial Biotechnology, University of Natural Resources and Life Sciences, Vienna, Austria
- Austrian Centre of Industrial Biotechnology, Vienna, Austria
| | - Markus Buchetics
- Department of Biotechnology, Institute of Microbiology and Microbial Biotechnology, University of Natural Resources and Life Sciences, Vienna, Austria
- Austrian Centre of Industrial Biotechnology, Vienna, Austria
| | - Matthias Mattanovich
- Department of Biotechnology, Institute of Microbiology and Microbial Biotechnology, University of Natural Resources and Life Sciences, Vienna, Austria
- Novo Nordisk Foundation Centre for Biosustainability, Technical University of Denmark, Lyngby, Denmark
- Novo Nordisk Foundation Centre for Basic Metabolic Research, Copenhagen University, Copenhagen, Denmark
| | - Stefan Neubauer
- Austrian Centre of Industrial Biotechnology, Vienna, Austria
- Department of Chemistry, Institute of Analytical Chemistry, University of Natural Resources and Life Sciences, Vienna, Austria
| | - Matthias Steiger
- Department of Biotechnology, Institute of Microbiology and Microbial Biotechnology, University of Natural Resources and Life Sciences, Vienna, Austria
- Austrian Centre of Industrial Biotechnology, Vienna, Austria
| | - Alexandra B Graf
- Austrian Centre of Industrial Biotechnology, Vienna, Austria
- School of Bioengineering, University of Applied Sciences FH Campus Vienna, Vienna, Austria
| | - Gunda Koellensperger
- Austrian Centre of Industrial Biotechnology, Vienna, Austria
- Department of Chemistry, Institute of Analytical Chemistry, University of Natural Resources and Life Sciences, Vienna, Austria
| | - Stephan Hann
- Austrian Centre of Industrial Biotechnology, Vienna, Austria
- Department of Chemistry, Institute of Analytical Chemistry, University of Natural Resources and Life Sciences, Vienna, Austria
| | - Michael Sauer
- Department of Biotechnology, Institute of Microbiology and Microbial Biotechnology, University of Natural Resources and Life Sciences, Vienna, Austria
- Austrian Centre of Industrial Biotechnology, Vienna, Austria
| | - Brigitte Gasser
- Department of Biotechnology, Institute of Microbiology and Microbial Biotechnology, University of Natural Resources and Life Sciences, Vienna, Austria
- Austrian Centre of Industrial Biotechnology, Vienna, Austria
| | - Diethard Mattanovich
- Department of Biotechnology, Institute of Microbiology and Microbial Biotechnology, University of Natural Resources and Life Sciences, Vienna, Austria
- Austrian Centre of Industrial Biotechnology, Vienna, Austria
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2
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Danko D, Bezdan D, Afshin EE, Ahsanuddin S, Bhattacharya C, Butler DJ, Chng KR, Donnellan D, Hecht J, Jackson K, Kuchin K, Karasikov M, Lyons A, Mak L, Meleshko D, Mustafa H, Mutai B, Neches RY, Ng A, Nikolayeva O, Nikolayeva T, Png E, Ryon KA, Sanchez JL, Shaaban H, Sierra MA, Thomas D, Young B, Abudayyeh OO, Alicea J, Bhattacharyya M, Blekhman R, Castro-Nallar E, Cañas AM, Chatziefthimiou AD, Crawford RW, De Filippis F, Deng Y, Desnues C, Dias-Neto E, Dybwad M, Elhaik E, Ercolini D, Frolova A, Gankin D, Gootenberg JS, Graf AB, Green DC, Hajirasouliha I, Hastings JJA, Hernandez M, Iraola G, Jang S, Kahles A, Kelly FJ, Knights K, Kyrpides NC, Łabaj PP, Lee PKH, Leung MHY, Ljungdahl PO, Mason-Buck G, McGrath K, Meydan C, Mongodin EF, Moraes MO, Nagarajan N, Nieto-Caballero M, Noushmehr H, Oliveira M, Ossowski S, Osuolale OO, Özcan O, Paez-Espino D, Rascovan N, Richard H, Rätsch G, Schriml LM, Semmler T, Sezerman OU, Shi L, Shi T, Siam R, Song LH, Suzuki H, Court DS, Tighe SW, Tong X, Udekwu KI, Ugalde JA, Valentine B, Vassilev DI, Vayndorf EM, Velavan TP, Wu J, Zambrano MM, Zhu J, Zhu S, Mason CE. A global metagenomic map of urban microbiomes and antimicrobial resistance. Cell 2021; 184:3376-3393.e17. [PMID: 34043940 PMCID: PMC8238498 DOI: 10.1016/j.cell.2021.05.002] [Citation(s) in RCA: 129] [Impact Index Per Article: 43.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2020] [Revised: 03/05/2021] [Accepted: 04/29/2021] [Indexed: 01/14/2023]
Abstract
We present a global atlas of 4,728 metagenomic samples from mass-transit systems in 60 cities over 3 years, representing the first systematic, worldwide catalog of the urban microbial ecosystem. This atlas provides an annotated, geospatial profile of microbial strains, functional characteristics, antimicrobial resistance (AMR) markers, and genetic elements, including 10,928 viruses, 1,302 bacteria, 2 archaea, and 838,532 CRISPR arrays not found in reference databases. We identified 4,246 known species of urban microorganisms and a consistent set of 31 species found in 97% of samples that were distinct from human commensal organisms. Profiles of AMR genes varied widely in type and density across cities. Cities showed distinct microbial taxonomic signatures that were driven by climate and geographic differences. These results constitute a high-resolution global metagenomic atlas that enables discovery of organisms and genes, highlights potential public health and forensic applications, and provides a culture-independent view of AMR burden in cities.
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Affiliation(s)
- David Danko
- Weill Cornell Medicine, New York, NY, USA; The Bin Talal Bin Abdulaziz Alsaud Institute for Computational Biomedicine, New York, NY, USA
| | - Daniela Bezdan
- Weill Cornell Medicine, New York, NY, USA; The Bin Talal Bin Abdulaziz Alsaud Institute for Computational Biomedicine, New York, NY, USA; Institute of Medical Genetics and Applied Genomics, University of Tübingen, Tübingen, Germany; NGS Competence Center Tübingen (NCCT), University of Tübingen, Tübingen, Germany
| | - Evan E Afshin
- Weill Cornell Medicine, New York, NY, USA; The Bin Talal Bin Abdulaziz Alsaud Institute for Computational Biomedicine, New York, NY, USA
| | | | - Chandrima Bhattacharya
- Weill Cornell Medicine, New York, NY, USA; The Bin Talal Bin Abdulaziz Alsaud Institute for Computational Biomedicine, New York, NY, USA
| | - Daniel J Butler
- Weill Cornell Medicine, New York, NY, USA; The Bin Talal Bin Abdulaziz Alsaud Institute for Computational Biomedicine, New York, NY, USA
| | - Kern Rei Chng
- Genome Institute of Singapore, A(∗)STAR, Singapore, Singapore
| | - Daisy Donnellan
- Weill Cornell Medicine, New York, NY, USA; The Bin Talal Bin Abdulaziz Alsaud Institute for Computational Biomedicine, New York, NY, USA
| | - Jochen Hecht
- Centre for Genomic Regulation (CRG), The Barcelona Institute of Science and Technology, Barcelona, Spain
| | - Katelyn Jackson
- Weill Cornell Medicine, New York, NY, USA; The Bin Talal Bin Abdulaziz Alsaud Institute for Computational Biomedicine, New York, NY, USA
| | - Katerina Kuchin
- Weill Cornell Medicine, New York, NY, USA; The Bin Talal Bin Abdulaziz Alsaud Institute for Computational Biomedicine, New York, NY, USA
| | - Mikhail Karasikov
- ETH Zurich, Department of Computer Science, Biomedical Informatics Group, Zurich, Switzerland; University Hospital Zurich, Biomedical Informatics Research, Zurich, Switzerland; Swiss Institute of Bioinformatics, Lausanne, Switzerland
| | - Abigail Lyons
- Weill Cornell Medicine, New York, NY, USA; The Bin Talal Bin Abdulaziz Alsaud Institute for Computational Biomedicine, New York, NY, USA
| | - Lauren Mak
- Weill Cornell Medicine, New York, NY, USA; The Bin Talal Bin Abdulaziz Alsaud Institute for Computational Biomedicine, New York, NY, USA
| | - Dmitry Meleshko
- Weill Cornell Medicine, New York, NY, USA; The Bin Talal Bin Abdulaziz Alsaud Institute for Computational Biomedicine, New York, NY, USA
| | - Harun Mustafa
- ETH Zurich, Department of Computer Science, Biomedical Informatics Group, Zurich, Switzerland; University Hospital Zurich, Biomedical Informatics Research, Zurich, Switzerland; Swiss Institute of Bioinformatics, Lausanne, Switzerland
| | - Beth Mutai
- Centre for Genomic Regulation (CRG), The Barcelona Institute of Science and Technology, Barcelona, Spain; Kenya Medical Research Institute - Kisumu, Kisumu, Kenya
| | - Russell Y Neches
- Department of Energy, Joint Genome Institute, Lawrence Berkeley National Laboratory, Berkeley, CA, USA
| | - Amanda Ng
- Genome Institute of Singapore, A(∗)STAR, Singapore, Singapore
| | | | | | - Eileen Png
- Genome Institute of Singapore, A(∗)STAR, Singapore, Singapore
| | - Krista A Ryon
- Weill Cornell Medicine, New York, NY, USA; The Bin Talal Bin Abdulaziz Alsaud Institute for Computational Biomedicine, New York, NY, USA
| | - Jorge L Sanchez
- Weill Cornell Medicine, New York, NY, USA; The Bin Talal Bin Abdulaziz Alsaud Institute for Computational Biomedicine, New York, NY, USA
| | - Heba Shaaban
- Weill Cornell Medicine, New York, NY, USA; The Bin Talal Bin Abdulaziz Alsaud Institute for Computational Biomedicine, New York, NY, USA
| | - Maria A Sierra
- Weill Cornell Medicine, New York, NY, USA; The Bin Talal Bin Abdulaziz Alsaud Institute for Computational Biomedicine, New York, NY, USA
| | - Dominique Thomas
- Weill Cornell Medicine, New York, NY, USA; The Bin Talal Bin Abdulaziz Alsaud Institute for Computational Biomedicine, New York, NY, USA
| | - Ben Young
- Weill Cornell Medicine, New York, NY, USA; The Bin Talal Bin Abdulaziz Alsaud Institute for Computational Biomedicine, New York, NY, USA
| | - Omar O Abudayyeh
- Massachusetts Institute of Technology, McGovern Institute for Brain Research, Cambridge, MA, USA
| | - Josue Alicea
- Weill Cornell Medicine, New York, NY, USA; The Bin Talal Bin Abdulaziz Alsaud Institute for Computational Biomedicine, New York, NY, USA
| | - Malay Bhattacharyya
- Machine Intelligence Unit, Indian Statistical Institute, Kolkata, India; Centre for Artificial Intelligence and Machine Learning, Indian Statistical Institute, Kolkata, India
| | | | - Eduardo Castro-Nallar
- Universidad Andres Bello, Center for Bioinformatics and Integrative Biology, Facultad de Ciencias de la Vida, Santiago, Chile
| | - Ana M Cañas
- Weill Cornell Medicine, New York, NY, USA; The Bin Talal Bin Abdulaziz Alsaud Institute for Computational Biomedicine, New York, NY, USA
| | - Aspassia D Chatziefthimiou
- Weill Cornell Medicine, New York, NY, USA; The Bin Talal Bin Abdulaziz Alsaud Institute for Computational Biomedicine, New York, NY, USA
| | | | - Francesca De Filippis
- Department of Agricultural Sciences, Division of Microbiology, University of Naples Federico II, Naples, Italy; Task Force on Microbiome Studies, University of Naples Federico II, Naples, Italy
| | - Youping Deng
- University of Hawaii John A. Burns School of Medicine, Honolulu, HI, USA
| | - Christelle Desnues
- Aix-Marseille Université, Mediterranean Institute of Oceanology, Université de Toulon, CNRS, IRD, UM 110, Marseille, France
| | - Emmanuel Dias-Neto
- Medical Genomics group, A.C.Camargo Cancer Center, São Paulo - SP, Brazil
| | - Marius Dybwad
- Norwegian Defence Research Establishment FFI, Kjeller, Norway
| | - Eran Elhaik
- Department of Biology, Lund University, Lund, Sweden
| | - Danilo Ercolini
- Department of Agricultural Sciences, Division of Microbiology, University of Naples Federico II, Naples, Italy; Task Force on Microbiome Studies, University of Naples Federico II, Naples, Italy
| | - Alina Frolova
- Institute of Molecular Biology and Genetics of National Academy of Sciences of Ukraine, Kyiv, Ukraine; Kyiv Academic University, Kyiv, Ukraine
| | - Dennis Gankin
- Massachusetts Institute of Technology, McGovern Institute for Brain Research, Cambridge, MA, USA
| | - Jonathan S Gootenberg
- Massachusetts Institute of Technology, McGovern Institute for Brain Research, Cambridge, MA, USA
| | | | - David C Green
- Department of Analytical, Environmental and Forensic Sciences, King's College London, London, UK
| | - Iman Hajirasouliha
- Weill Cornell Medicine, New York, NY, USA; The Bin Talal Bin Abdulaziz Alsaud Institute for Computational Biomedicine, New York, NY, USA
| | - Jaden J A Hastings
- Weill Cornell Medicine, New York, NY, USA; The Bin Talal Bin Abdulaziz Alsaud Institute for Computational Biomedicine, New York, NY, USA
| | | | - Gregorio Iraola
- Microbial Genomics Laboratory, Institut Pasteur de Montevideo, Montevideo, Uruguay; Center for Integrative Biology, Universidad Mayor, Santiago de Chile, Santiago, Chile; Wellcome Sanger Institute, Hinxton, UK
| | | | - Andre Kahles
- ETH Zurich, Department of Computer Science, Biomedical Informatics Group, Zurich, Switzerland; Kyiv Academic University, Kyiv, Ukraine; C+, Research Center in Technologies for Society, School of Engineering, Universidad del Desarrollo, Santiago, Chile
| | - Frank J Kelly
- Department of Analytical, Environmental and Forensic Sciences, King's College London, London, UK
| | - Kaymisha Knights
- Weill Cornell Medicine, New York, NY, USA; The Bin Talal Bin Abdulaziz Alsaud Institute for Computational Biomedicine, New York, NY, USA
| | - Nikos C Kyrpides
- Department of Energy, Joint Genome Institute, Lawrence Berkeley National Laboratory, Berkeley, CA, USA
| | - Paweł P Łabaj
- State Key Laboratory of Genetic Engineering (SKLGE) and MOE Key Laboratory of Contemporary Anthropology, School of Life Sciences, Human Phenome Institute, Fudan University, Shanghai, China; Małopolska Centre of Biotechnology, Jagiellonian University, Kraków, Poland; Boku University Viennna, Vienna, Austria
| | - Patrick K H Lee
- School of Energy and Environment, City University of Hong Kong, Hong Kong SAR, China
| | - Marcus H Y Leung
- School of Energy and Environment, City University of Hong Kong, Hong Kong SAR, China
| | - Per O Ljungdahl
- Department of Molecular Biosciences, The Wenner-Gren Institute, Stockholm University, Stockholm, Sweden
| | - Gabriella Mason-Buck
- Department of Analytical, Environmental and Forensic Sciences, King's College London, London, UK
| | - Ken McGrath
- Microba, 388 Queen St, Brisbane City, QLD 4000, Australia
| | - Cem Meydan
- Weill Cornell Medicine, New York, NY, USA; The Bin Talal Bin Abdulaziz Alsaud Institute for Computational Biomedicine, New York, NY, USA
| | - Emmanuel F Mongodin
- University of Maryland School of Medicine, Institute for Genome Sciences, Baltimore, MD, USA
| | | | | | | | - Houtan Noushmehr
- University of São Paulo, Ribeirão Preto Medical School, Ribeirão Preto - SP, Brazil
| | - Manuela Oliveira
- Instituto de Patologia e Imunologia Molecular da Universidade do Porto, Porto, Portugal
| | - Stephan Ossowski
- Centre for Genomic Regulation (CRG), The Barcelona Institute of Science and Technology, Barcelona, Spain; Institute of Medical Genetics and Applied Genomics, University of Tübingen, Tübingen, Germany; NGS Competence Center Tübingen (NCCT), University of Tübingen, Tübingen, Germany
| | - Olayinka O Osuolale
- Applied Environmental Metagenomics and Infectious Diseases Research (AEMIDR), Department of Biological Sciences, Elizade University, Ilara-Mokin, Nigeria
| | - Orhan Özcan
- Acibadem Mehmet Ali Aydınlar University, Istanbul, Turkey
| | - David Paez-Espino
- Department of Energy, Joint Genome Institute, Lawrence Berkeley National Laboratory, Berkeley, CA, USA
| | - Nicolás Rascovan
- Microbial Paleogenomics Unit, Institut Pasteur, CNRS UMR2000, Paris 75015, France
| | - Hugues Richard
- Sorbonne University, Faculty of Science, Institute of Biology Paris-Seine, Laboratory of Computational and Quantitative Biology, Paris, France; Robert Koch Institute, Berlin, Germany
| | - Gunnar Rätsch
- ETH Zurich, Department of Computer Science, Biomedical Informatics Group, Zurich, Switzerland; University Hospital Zurich, Biomedical Informatics Research, Zurich, Switzerland; Swiss Institute of Bioinformatics, Lausanne, Switzerland
| | - Lynn M Schriml
- University of Maryland School of Medicine, Institute for Genome Sciences, Baltimore, MD, USA
| | | | | | - Leming Shi
- Center for Pharmacogenomics, School of Life Sciences and Shanghai Cancer Center, Fudan University, Shanghai, China; State Key Laboratory of Genetic Engineering (SKLGE) and MOE Key Laboratory of Contemporary Anthropology, School of Life Sciences, Human Phenome Institute, Fudan University, Shanghai, China
| | - Tieliu Shi
- The Center for Bioinformatics and Computational Biology, Shanghai Key Laboratory of Regulatory Biology, the Institute of Biomedical Sciences and School of Life Sciences, East China Normal University, Shanghai, China
| | - Rania Siam
- University of Medicine and Health Sciences, St. Kitts, West Indies and American University in Cairo, Cairo, Egypt
| | - Le Huu Song
- 108 Military Central Hospital, Hanoi, Vietnam; Vietnamese-German Center for Medical Research (VG-CARE), Hanoi, Vietnam
| | | | - Denise Syndercombe Court
- Department of Analytical, Environmental and Forensic Sciences, King's College London, London, UK
| | | | - Xinzhao Tong
- School of Energy and Environment, City University of Hong Kong, Hong Kong SAR, China
| | - Klas I Udekwu
- Department of Molecular Biosciences, The Wenner-Gren Institute, Stockholm University, Stockholm, Sweden; SciLife EVP, Department of Aquatic Sciences Assessment, Swedish University of Agricultural Sciences, Uppsala, Sweden
| | - Juan A Ugalde
- Millennium Initiative for Collaborative Research on Bacterial Resistance, Santiago, Chile; C+, Research Center in Technologies for Society, School of Engineering, Universidad del Desarrollo, Santiago, Chile
| | - Brandon Valentine
- Weill Cornell Medicine, New York, NY, USA; The Bin Talal Bin Abdulaziz Alsaud Institute for Computational Biomedicine, New York, NY, USA
| | - Dimitar I Vassilev
- Faculty of Mathematics and Informatics, Sofia University "St. Kliment Ohridski," Sofia, Bulgaria
| | - Elena M Vayndorf
- Institute of Arctic Biology, University of Alaska, Fairbanks, Fairbanks, AK, USA
| | - Thirumalaisamy P Velavan
- Institute of Tropical Medicine, Univeristätsklinikum Tübingen, Tübingen, Germany; Faculty of Medicine, Duy Tan University, Da Nang, Vietnam
| | - Jun Wu
- The Center for Bioinformatics and Computational Biology, Shanghai Key Laboratory of Regulatory Biology, the Institute of Biomedical Sciences and School of Life Sciences, East China Normal University, Shanghai, China
| | | | - Jifeng Zhu
- Weill Cornell Medicine, New York, NY, USA; The Bin Talal Bin Abdulaziz Alsaud Institute for Computational Biomedicine, New York, NY, USA
| | - Sibo Zhu
- State Key Laboratory of Genetic Engineering (SKLGE) and MOE Key Laboratory of Contemporary Anthropology, School of Life Sciences, Human Phenome Institute, Fudan University, Shanghai, China; Department of Epidemiology, School of Public Health, Fudan University, Shanghai, China
| | - Christopher E Mason
- Weill Cornell Medicine, New York, NY, USA; The Bin Talal Bin Abdulaziz Alsaud Institute for Computational Biomedicine, New York, NY, USA; The WorldQuant Initiative for Quantitative Prediction, Weill Cornell Medicine, New York, NY, USA.
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3
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Abstract
BACKGROUND Simulated metagenomic reads are widely used to benchmark software and workflows for metagenome interpretation. The results of metagenomic benchmarks depend on the assumptions about their underlying ecosystems. Conclusions from benchmark studies are therefore limited to the ecosystems they mimic. Ideally, simulations are therefore based on genomes, which resemble particular metagenomic communities realistically. RESULTS We developed Tamock to facilitate the realistic simulation of metagenomic reads according to a metagenomic community, based on real sequence data. Benchmarks samples can be created from all genomes and taxonomic domains present in NCBI RefSeq. Tamock automatically determines taxonomic profiles from shotgun sequence data, selects reference genomes accordingly and uses them to simulate metagenomic reads. We present an example use case for Tamock by assessing assembly and binning method performance for selected microbiomes. CONCLUSIONS Tamock facilitates automated simulation of habitat-specific benchmark metagenomic data based on real sequence data and is implemented as a user-friendly command-line application, providing extensive additional information along with the simulated benchmark data. Resulting benchmarks enable an assessment of computational methods, workflows, and parameters specifically for a metagenomic habitat or ecosystem of a metagenomic study. AVAILABILITY Source code, documentation and install instructions are freely available at GitHub ( https://github.com/gerners/tamock ).
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Affiliation(s)
- Samuel M Gerner
- Division of Computational System Biology, Department of Microbiology and Ecosystem Science, University of Vienna, Vienna, Austria
- Department Bioengineering, University of Applied Sciences FH Campus Wien, Vienna, Austria
| | - Alexandra B Graf
- Department Bioengineering, University of Applied Sciences FH Campus Wien, Vienna, Austria
| | - Thomas Rattei
- Division of Computational System Biology, Department of Microbiology and Ecosystem Science, University of Vienna, Vienna, Austria.
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Valli M, Grillitsch K, Grünwald-Gruber C, Tatto NE, Hrobath B, Klug L, Ivashov V, Hauzmayer S, Koller M, Tir N, Leisch F, Gasser B, Graf AB, Altmann F, Daum G, Mattanovich D. A subcellular proteome atlas of the yeast Komagataella phaffii. FEMS Yeast Res 2021; 20:5700286. [PMID: 31922548 PMCID: PMC6981350 DOI: 10.1093/femsyr/foaa001] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/29/2019] [Accepted: 01/09/2020] [Indexed: 12/11/2022] Open
Abstract
The compartmentalization of metabolic and regulatory pathways is a common pattern of living organisms. Eukaryotic cells are subdivided into several organelles enclosed by lipid membranes. Organelle proteomes define their functions. Yeasts, as simple eukaryotic single cell organisms, are valuable models for higher eukaryotes and frequently used for biotechnological applications. While the subcellular distribution of proteins is well studied in Saccharomyces cerevisiae, this is not the case for other yeasts like Komagataella phaffii (syn. Pichia pastoris). Different to most well-studied yeasts, K. phaffii can grow on methanol, which provides specific features for production of heterologous proteins and as a model for peroxisome biology. We isolated microsomes, very early Golgi, early Golgi, plasma membrane, vacuole, cytosol, peroxisomes and mitochondria of K. phaffii from glucose- and methanol-grown cultures, quantified their proteomes by liquid chromatography-electrospray ionization-mass spectrometry of either unlabeled or tandem mass tag-labeled samples. Classification of the proteins by their relative enrichment, allowed the separation of enriched proteins from potential contaminants in all cellular compartments except the peroxisomes. We discuss differences to S. cerevisiae, outline organelle specific findings and the major metabolic pathways and provide an interactive map of the subcellular localization of proteins in K. phaffii.
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Affiliation(s)
- Minoska Valli
- Austrian Centre of Industrial Biotechnology (ACIB), Muthgasse 11, 1190 Vienna, Austria.,Department of Biotechnology, BOKU-University of Natural Resources and Life Sciences, Muthgasse 18, 1190 Vienna, Austria
| | - Karlheinz Grillitsch
- Austrian Centre of Industrial Biotechnology (ACIB), Muthgasse 11, 1190 Vienna, Austria
| | - Clemens Grünwald-Gruber
- Austrian Centre of Industrial Biotechnology (ACIB), Muthgasse 11, 1190 Vienna, Austria.,Department of Chemistry, University of Natural Resources and Life Sciences, Muthgasse 18, 1190 Vienna, Austria
| | - Nadine E Tatto
- Austrian Centre of Industrial Biotechnology (ACIB), Muthgasse 11, 1190 Vienna, Austria.,Department of Biotechnology, BOKU-University of Natural Resources and Life Sciences, Muthgasse 18, 1190 Vienna, Austria
| | - Bernhard Hrobath
- Institute of Statistics, University of Natural Resources and Life Sciences, Peter-Jordan-Straße 82, 1190 Vienna, Austria
| | - Lisa Klug
- Austrian Centre of Industrial Biotechnology (ACIB), Muthgasse 11, 1190 Vienna, Austria.,Institute of Biochemistry, Graz University of Technology, Petersgasse 12/II, 8010, Graz, Austria
| | - Vasyl Ivashov
- Institute of Biochemistry, Graz University of Technology, Petersgasse 12/II, 8010, Graz, Austria
| | - Sandra Hauzmayer
- School of Bioengineering, University of Applied Sciences FH-Campus Vienna, Muthgasse 11, 1190 Vienna, Austria
| | - Martina Koller
- School of Bioengineering, University of Applied Sciences FH-Campus Vienna, Muthgasse 11, 1190 Vienna, Austria
| | - Nora Tir
- Department of Biotechnology, BOKU-University of Natural Resources and Life Sciences, Muthgasse 18, 1190 Vienna, Austria
| | - Friedrich Leisch
- Austrian Centre of Industrial Biotechnology (ACIB), Muthgasse 11, 1190 Vienna, Austria.,Institute of Statistics, University of Natural Resources and Life Sciences, Peter-Jordan-Straße 82, 1190 Vienna, Austria
| | - Brigitte Gasser
- Austrian Centre of Industrial Biotechnology (ACIB), Muthgasse 11, 1190 Vienna, Austria.,Department of Biotechnology, BOKU-University of Natural Resources and Life Sciences, Muthgasse 18, 1190 Vienna, Austria
| | - Alexandra B Graf
- Austrian Centre of Industrial Biotechnology (ACIB), Muthgasse 11, 1190 Vienna, Austria.,School of Bioengineering, University of Applied Sciences FH-Campus Vienna, Muthgasse 11, 1190 Vienna, Austria
| | - Friedrich Altmann
- Austrian Centre of Industrial Biotechnology (ACIB), Muthgasse 11, 1190 Vienna, Austria.,Department of Chemistry, University of Natural Resources and Life Sciences, Muthgasse 18, 1190 Vienna, Austria
| | - Günther Daum
- Austrian Centre of Industrial Biotechnology (ACIB), Muthgasse 11, 1190 Vienna, Austria.,Institute of Biochemistry, Graz University of Technology, Petersgasse 12/II, 8010, Graz, Austria
| | - Diethard Mattanovich
- Austrian Centre of Industrial Biotechnology (ACIB), Muthgasse 11, 1190 Vienna, Austria.,Department of Biotechnology, BOKU-University of Natural Resources and Life Sciences, Muthgasse 18, 1190 Vienna, Austria
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5
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De S, Rebnegger C, Moser J, Tatto N, Graf AB, Mattanovich D, Gasser B. Pseudohyphal differentiation in Komagataella phaffii: investigating the FLO gene family. FEMS Yeast Res 2020; 20:5884885. [PMID: 32766781 PMCID: PMC7419694 DOI: 10.1093/femsyr/foaa044] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2020] [Accepted: 08/05/2020] [Indexed: 12/12/2022] Open
Abstract
Many yeasts differentiate into multicellular phenotypes in adverse environmental conditions. Here, we investigate pseudohyphal growth in Komagataella phaffii and the involvement of the flocculin (FLO) gene family in its regulation. The K. phaffii FLO family consists of 13 members, and the conditions inducing pseudohyphal growth are different from Saccharomyces cerevisiae. So far, this phenotype was only observed when K. phaffii was cultivated at slow growth rates in glucose-limited chemostats, but not upon nitrogen starvation or the presence of fusel alcohols. Transcriptional analysis identified that FLO11, FLO400 and FLO5-1 are involved in the phenotype, all being controlled by the transcriptional regulator Flo8. The three genes exhibit a complex mechanism of expression and repression during transition from yeast to pseudohyphal form. Unlike in S. cerevisiae, deletion of FLO11 does not completely prevent the phenotype. In contrast, deletion of FLO400 or FLO5-1 prevents pseudohyphae formation, and hampers FLO11 expression. FAIRE-Seq data shows that the expression and repression of FLO400 and FLO5-1 are correlated to open or closed chromatin regions upstream of these genes, respectively. Our findings indicate that K. phaffii Flo400 and/or Flo5-1 act as upstream signals that lead to the induction of FLO11 upon glucose limitation in chemostats at slow growth and chromatin modulation is involved in the regulation of their expression.
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Affiliation(s)
- Sonakshi De
- Austrian Centre of Industrial Biotechnology, Muthgasse 11, 1190 Vienna, Austria.,Department of Biotechnology, BOKU University of Natural Resources and Life Sciences, Muthgasse 18, 1190 Vienna, Austria
| | - Corinna Rebnegger
- Department of Biotechnology, BOKU University of Natural Resources and Life Sciences, Muthgasse 18, 1190 Vienna, Austria.,CD-Laboratory for Growth-decoupled Protein Production in Yeast, BOKU University of Natural Resources and Life Sciences, Muthgasse 18, 1190 Vienna, Austria
| | - Josef Moser
- Austrian Centre of Industrial Biotechnology, Muthgasse 11, 1190 Vienna, Austria.,School of Bioengineering, University of Applied Sciences-FH Campus Wien, Muthgasse 11, 1190 Vienna, Austria
| | - Nadine Tatto
- Austrian Centre of Industrial Biotechnology, Muthgasse 11, 1190 Vienna, Austria.,Department of Biotechnology, BOKU University of Natural Resources and Life Sciences, Muthgasse 18, 1190 Vienna, Austria
| | - Alexandra B Graf
- Austrian Centre of Industrial Biotechnology, Muthgasse 11, 1190 Vienna, Austria.,School of Bioengineering, University of Applied Sciences-FH Campus Wien, Muthgasse 11, 1190 Vienna, Austria
| | - Diethard Mattanovich
- Austrian Centre of Industrial Biotechnology, Muthgasse 11, 1190 Vienna, Austria.,Department of Biotechnology, BOKU University of Natural Resources and Life Sciences, Muthgasse 18, 1190 Vienna, Austria
| | - Brigitte Gasser
- Austrian Centre of Industrial Biotechnology, Muthgasse 11, 1190 Vienna, Austria.,Department of Biotechnology, BOKU University of Natural Resources and Life Sciences, Muthgasse 18, 1190 Vienna, Austria.,CD-Laboratory for Growth-decoupled Protein Production in Yeast, BOKU University of Natural Resources and Life Sciences, Muthgasse 18, 1190 Vienna, Austria
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6
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Abstract
BACKGROUND Microbial communities play a crucial role in our environment and may influence human health tremendously. Despite being the place where human interaction is most abundant we still know little about the urban microbiome. This is highlighted by the large amount of unclassified DNA reads found in urban metagenome samples. The only in silico approach that allows us to find unknown species, is the assembly and classification of draft genomes from a metagenomic dataset. In this study we (1) investigate the applicability of an assembly and binning approach for urban metagenome datasets, and (2) develop a new method for the generation of in silico gold standards to better understand the specific challenges of such datasets and provide a guide in the selection of available software. RESULTS We applied combinations of three assembly (Megahit, SPAdes and MetaSPAdes) and three binning tools (MaxBin, MetaBAT and CONCOCT) to whole genome shotgun datasets from the CAMDA 2017 Challenge. Complex in silico gold standards with a simulated bacterial fraction were generated for representative samples of each surface type and city. Using these gold standards, we found the combination of SPAdes and MetaBAT to be optimal for urban metagenome datasets by providing the best trade-off between the number of high-quality genome draft bins (MIMAG standards) retrieved, the least amount of misassemblies and contamination. The assembled draft genomes included known species like Propionibacterium acnes but also novel species according to respective ANI values. CONCLUSIONS In our work, we showed that, even for datasets with high diversity and low sequencing depth from urban environments, assembly and binning-based methods can provide high-quality genome drafts. Of vital importance to retrieve high-quality genome drafts is sequence depth but even more so a high proportion of the bacterial sequence fraction too achieve high coverage for bacterial genomes. In contrast to read-based methods relying on database knowledge, genome-centric methods as applied in this study can provide valuable information about unknown species and strains as well as functional contributions of single community members within a sample. Furthermore, we present a method for the generation of sample-specific highly complex in silico gold standards. REVIEWERS This article was reviewed by Craig Herbold, Serghei Mangul and Yana Bromberg.
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Affiliation(s)
- Samuel M. Gerner
- Department Bioengineering, University of Applied Sciences FH Campus Wien, Vienna, Austria
- Division of Computational System Biology, Department of Microbiology and Ecosystem Science, University of Vienna, Vienna, Austria
| | - Thomas Rattei
- Division of Computational System Biology, Department of Microbiology and Ecosystem Science, University of Vienna, Vienna, Austria
| | - Alexandra B. Graf
- Department Bioengineering, University of Applied Sciences FH Campus Wien, Vienna, Austria
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7
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Burgard J, Valli M, Graf AB, Gasser B, Mattanovich D. Biomarkers allow detection of nutrient limitations and respective supplementation for elimination in Pichia pastoris fed-batch cultures. Microb Cell Fact 2017; 16:117. [PMID: 28693509 PMCID: PMC5504661 DOI: 10.1186/s12934-017-0730-9] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/07/2017] [Accepted: 06/28/2017] [Indexed: 11/04/2022] Open
Abstract
BACKGROUND Industrial processes for recombinant protein production challenge production hosts, such as the yeast Pichia pastoris, on multiple levels. During a common P. pastoris fed-batch process, cells experience strong adaptations to different metabolic states or suffer from environmental stresses due to high cell density cultivation. Additionally, recombinant protein production and nutrient limitations are challenging in these processes. RESULTS Pichia pastoris producing porcine carboxypeptidase B (CpB) was cultivated in glucose or methanol-limited fed-batch mode, and the cellular response was analyzed using microarrays. Thereby, strong transcriptional regulations in transport-, regulatory- and metabolic processes connected to sulfur, phosphorus and nitrogen metabolism became obvious. The induction of these genes was observed in both glucose- and methanol- limited fed batch cultivations, but were stronger in the latter condition. As the transcriptional pattern was indicative for nutrient limitations, we performed fed-batch cultivations where we added the respective nutrients and compared them to non-supplemented cultures regarding cell growth, productivity and expression levels of selected biomarker genes. In the non-supplemented reference cultures we observed a strong increase in transcript levels of up to 89-fold for phosphorus limitation marker genes in the late fed-batch phase. Transcript levels of sulfur limitation marker genes were up to 35-fold increased. By addition of (NH4)2SO4 or (NH4)2HPO4, respectively, we were able to suppress the transcriptional response of the marker genes to levels initially observed at the start of the fed batch. Additionally, supplementation had also a positive impact on biomass generation and recombinant protein production. Supplementation with (NH4)2SO4 led to 5% increase in biomass and 52% higher CpB activity in the supernatant, compared to the non-supplemented reference cultivations. In (NH4)2HPO4 supplemented cultures 9% higher biomass concentrations and 60% more CpB activity were reached. CONCLUSIONS Transcriptional analysis of P. pastoris fed-batch cultivations led to the identification of nutrient limitations in the later phases, and respective biomarker genes for indication of limitations. Supplementation of the cultivation media with those nutrients eliminated the limitations on the transcriptional level, and was also shown to enhance productivity of a recombinant protein. The biomarker genes are versatily applicable to media and process optimization approaches, where tailor-made solutions are envisioned.
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Affiliation(s)
- Jonas Burgard
- Austrian Centre of Industrial Biotechnology, Vienna, Austria
- Department of Biotechnology, BOKU-University of Natural Resources and Life Sciences, Muthgasse 18, 1190 Vienna, Austria
| | - Minoska Valli
- Austrian Centre of Industrial Biotechnology, Vienna, Austria
- Department of Biotechnology, BOKU-University of Natural Resources and Life Sciences, Muthgasse 18, 1190 Vienna, Austria
| | - Alexandra B. Graf
- Department of Biotechnology, BOKU-University of Natural Resources and Life Sciences, Muthgasse 18, 1190 Vienna, Austria
- School of Bioengineering, University of Applied Sciences FH Campus Vienna, Vienna, Austria
| | - Brigitte Gasser
- Austrian Centre of Industrial Biotechnology, Vienna, Austria
- Department of Biotechnology, BOKU-University of Natural Resources and Life Sciences, Muthgasse 18, 1190 Vienna, Austria
| | - Diethard Mattanovich
- Austrian Centre of Industrial Biotechnology, Vienna, Austria
- Department of Biotechnology, BOKU-University of Natural Resources and Life Sciences, Muthgasse 18, 1190 Vienna, Austria
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8
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Moser JW, Prielhofer R, Gerner SM, Graf AB, Wilson IBH, Mattanovich D, Dragosits M. Implications of evolutionary engineering for growth and recombinant protein production in methanol-based growth media in the yeast Pichia pastoris. Microb Cell Fact 2017; 16:49. [PMID: 28302114 PMCID: PMC5356285 DOI: 10.1186/s12934-017-0661-5] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2017] [Accepted: 03/08/2017] [Indexed: 12/24/2022] Open
Abstract
BACKGROUND Pichia pastoris is a widely used eukaryotic expression host for recombinant protein production. Adaptive laboratory evolution (ALE) has been applied in a wide range of studies in order to improve strains for biotechnological purposes. In this context, the impact of long-term carbon source adaptation in P. pastoris has not been addressed so far. Thus, we performed a pilot experiment in order to analyze the applicability and potential benefits of ALE towards improved growth and recombinant protein production in P. pastoris. RESULTS Adaptation towards growth on methanol was performed in replicate cultures in rich and minimal growth medium for 250 generations. Increased growth rates on these growth media were observed at the population and single clone level. Evolved populations showed various degrees of growth advantages and trade-offs in non-evolutionary growth conditions. Genome resequencing revealed a wide variety of potential genetic targets associated with improved growth performance on methanol-based growth media. Alcohol oxidase represented a mutational hotspot since four out of seven evolved P. pastoris clones harbored mutations in this gene, resulting in decreased Aox activity, despite increased growth rates. Selected clones displayed strain-dependent variations for AOX-promoter based recombinant protein expression yield. One particularly interesting clone showed increased product titers ranging from a 2.5-fold increase in shake flask batch culture to a 1.8-fold increase during fed batch cultivation. CONCLUSIONS Our data indicate a complex correlation of carbon source, growth context and recombinant protein production. While similar experiments have already shown their potential in other biotechnological areas where microbes were evolutionary engineered for improved stress resistance and growth, the current dataset encourages the analysis of the potential of ALE for improved protein production in P. pastoris on a broader scale.
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Affiliation(s)
- Josef W Moser
- Department of Chemistry, University of Natural Resources and Life Sciences, Muthgasse 11, 1190, Vienna, Austria.,Austrian Centre of Industrial Biotechnology (ACIB), 1190, Vienna, Austria
| | - Roland Prielhofer
- Austrian Centre of Industrial Biotechnology (ACIB), 1190, Vienna, Austria.,Department of Biotechnology, University of Natural Resources and Life Sciences, Vienna, Austria
| | - Samuel M Gerner
- University of Applied Sciences FH-Campus Wien, Bioengineering, Vienna, Austria
| | - Alexandra B Graf
- Austrian Centre of Industrial Biotechnology (ACIB), 1190, Vienna, Austria.,University of Applied Sciences FH-Campus Wien, Bioengineering, Vienna, Austria
| | - Iain B H Wilson
- Department of Chemistry, University of Natural Resources and Life Sciences, Muthgasse 11, 1190, Vienna, Austria
| | - Diethard Mattanovich
- Austrian Centre of Industrial Biotechnology (ACIB), 1190, Vienna, Austria.,Department of Biotechnology, University of Natural Resources and Life Sciences, Vienna, Austria
| | - Martin Dragosits
- Department of Chemistry, University of Natural Resources and Life Sciences, Muthgasse 11, 1190, Vienna, Austria.
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9
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Valli M, Tatto NE, Peymann A, Gruber C, Landes N, Ekker H, Thallinger GG, Mattanovich D, Gasser B, Graf AB. Curation of the genome annotation ofPichia pastoris(Komagataella phaffii) CBS7435 from gene level to protein function. FEMS Yeast Res 2016; 16:fow051. [DOI: 10.1093/femsyr/fow051] [Citation(s) in RCA: 53] [Impact Index Per Article: 6.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 06/13/2016] [Indexed: 11/14/2022] Open
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10
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Bellasio M, Peymann A, Steiger MG, Valli M, Sipiczki M, Sauer M, Graf AB, Marx H, Mattanovich D. Complete genome sequence and transcriptome regulation of the pentose utilizing yeastSugiyamaella lignohabitans. FEMS Yeast Res 2016; 16:fow037. [DOI: 10.1093/femsyr/fow037] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 04/07/2016] [Indexed: 01/17/2023] Open
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11
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Rußmayer H, Buchetics M, Gruber C, Valli M, Grillitsch K, Modarres G, Guerrasio R, Klavins K, Neubauer S, Drexler H, Steiger M, Troyer C, Al Chalabi A, Krebiehl G, Sonntag D, Zellnig G, Daum G, Graf AB, Altmann F, Koellensperger G, Hann S, Sauer M, Mattanovich D, Gasser B. Systems-level organization of yeast methylotrophic lifestyle. BMC Biol 2015; 13:80. [PMID: 26400155 PMCID: PMC4580311 DOI: 10.1186/s12915-015-0186-5] [Citation(s) in RCA: 90] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/02/2015] [Accepted: 09/03/2015] [Indexed: 12/20/2022] Open
Abstract
Background Some yeasts have evolved a methylotrophic lifestyle enabling them to utilize the single carbon compound methanol as a carbon and energy source. Among them, Pichia pastoris (syn. Komagataella sp.) is frequently used for the production of heterologous proteins and also serves as a model organism for organelle research. Our current knowledge of methylotrophic lifestyle mainly derives from sophisticated biochemical studies which identified many key methanol utilization enzymes such as alcohol oxidase and dihydroxyacetone synthase and their localization to the peroxisomes. C1 assimilation is supposed to involve the pentose phosphate pathway, but details of these reactions are not known to date. Results In this work we analyzed the regulation patterns of 5,354 genes, 575 proteins, 141 metabolites, and fluxes through 39 reactions of P. pastoris comparing growth on glucose and on a methanol/glycerol mixed medium, respectively. Contrary to previous assumptions, we found that the entire methanol assimilation pathway is localized to peroxisomes rather than employing part of the cytosolic pentose phosphate pathway for xylulose-5-phosphate regeneration. For this purpose, P. pastoris (and presumably also other methylotrophic yeasts) have evolved a duplicated methanol inducible enzyme set targeted to peroxisomes. This compartmentalized cyclic C1 assimilation process termed xylose-monophosphate cycle resembles the principle of the Calvin cycle and uses sedoheptulose-1,7-bisphosphate as intermediate. The strong induction of alcohol oxidase, dihydroxyacetone synthase, formaldehyde and formate dehydrogenase, and catalase leads to high demand of their cofactors riboflavin, thiamine, nicotinamide, and heme, respectively, which is reflected in strong up-regulation of the respective synthesis pathways on methanol. Methanol-grown cells have a higher protein but lower free amino acid content, which can be attributed to the high drain towards methanol metabolic enzymes and their cofactors. In context with up-regulation of many amino acid biosynthesis genes or proteins, this visualizes an increased flux towards amino acid and protein synthesis which is reflected also in increased levels of transcripts and/or proteins related to ribosome biogenesis and translation. Conclusions Taken together, our work illustrates how concerted interpretation of multiple levels of systems biology data can contribute to elucidation of yet unknown cellular pathways and revolutionize our understanding of cellular biology. Electronic supplementary material The online version of this article (doi:10.1186/s12915-015-0186-5) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Hannes Rußmayer
- Department of Biotechnology, BOKU - University of Natural Resources and Life Sciences Vienna, Muthgasse 18, 1190, Vienna, Austria.,Austrian Centre of Industrial Biotechnology, A-1190, Vienna, Austria
| | - Markus Buchetics
- Department of Biotechnology, BOKU - University of Natural Resources and Life Sciences Vienna, Muthgasse 18, 1190, Vienna, Austria.,Austrian Centre of Industrial Biotechnology, A-1190, Vienna, Austria
| | - Clemens Gruber
- Austrian Centre of Industrial Biotechnology, A-1190, Vienna, Austria.,Department of Chemistry, BOKU - University of Natural Resources and Life Sciences Vienna, A-1190 Vienna, Austria
| | - Minoska Valli
- Department of Biotechnology, BOKU - University of Natural Resources and Life Sciences Vienna, Muthgasse 18, 1190, Vienna, Austria.,Austrian Centre of Industrial Biotechnology, A-1190, Vienna, Austria
| | - Karlheinz Grillitsch
- Institute of Biochemistry, Graz University of Technology, A-8010 Graz, Austria.,Austrian Centre of Industrial Biotechnology, A-8010 Graz, Austria
| | - Gerda Modarres
- Austrian Centre of Industrial Biotechnology, A-1190, Vienna, Austria.,School of Bioengineering, University of Applied Sciences FH Campus, A-1190 Vienna, Austria
| | - Raffaele Guerrasio
- Austrian Centre of Industrial Biotechnology, A-1190, Vienna, Austria.,Department of Chemistry, BOKU - University of Natural Resources and Life Sciences Vienna, A-1190 Vienna, Austria.,Present addresses: Sandoz GmbH, A-6250 Kundl, Austria
| | - Kristaps Klavins
- Austrian Centre of Industrial Biotechnology, A-1190, Vienna, Austria.,Department of Chemistry, BOKU - University of Natural Resources and Life Sciences Vienna, A-1190 Vienna, Austria.,Present addresses: BIOCRATES Life Sciences AG, A-6020 Innsbruck, Austria
| | - Stefan Neubauer
- Austrian Centre of Industrial Biotechnology, A-1190, Vienna, Austria.,Department of Chemistry, BOKU - University of Natural Resources and Life Sciences Vienna, A-1190 Vienna, Austria.,University of Tübingen, D-72076 Tübingen, Germany
| | - Hedda Drexler
- Austrian Centre of Industrial Biotechnology, A-1190, Vienna, Austria.,Department of Chemistry, BOKU - University of Natural Resources and Life Sciences Vienna, A-1190 Vienna, Austria
| | - Matthias Steiger
- Department of Biotechnology, BOKU - University of Natural Resources and Life Sciences Vienna, Muthgasse 18, 1190, Vienna, Austria.,Austrian Centre of Industrial Biotechnology, A-1190, Vienna, Austria
| | - Christina Troyer
- Austrian Centre of Industrial Biotechnology, A-1190, Vienna, Austria.,Department of Chemistry, BOKU - University of Natural Resources and Life Sciences Vienna, A-1190 Vienna, Austria
| | | | | | | | - Günther Zellnig
- Institute of Plant Sciences, NAWI Graz, University of Graz, A-8010 Graz, Austria
| | - Günther Daum
- Institute of Biochemistry, Graz University of Technology, A-8010 Graz, Austria.,Austrian Centre of Industrial Biotechnology, A-8010 Graz, Austria
| | - Alexandra B Graf
- Austrian Centre of Industrial Biotechnology, A-1190, Vienna, Austria.,Austrian Centre of Industrial Biotechnology, A-8010 Graz, Austria
| | - Friedrich Altmann
- Austrian Centre of Industrial Biotechnology, A-1190, Vienna, Austria.,Department of Chemistry, BOKU - University of Natural Resources and Life Sciences Vienna, A-1190 Vienna, Austria
| | | | - Stephan Hann
- Austrian Centre of Industrial Biotechnology, A-1190, Vienna, Austria.,Department of Chemistry, BOKU - University of Natural Resources and Life Sciences Vienna, A-1190 Vienna, Austria
| | - Michael Sauer
- Department of Biotechnology, BOKU - University of Natural Resources and Life Sciences Vienna, Muthgasse 18, 1190, Vienna, Austria.,Austrian Centre of Industrial Biotechnology, A-1190, Vienna, Austria
| | - Diethard Mattanovich
- Department of Biotechnology, BOKU - University of Natural Resources and Life Sciences Vienna, Muthgasse 18, 1190, Vienna, Austria. .,Austrian Centre of Industrial Biotechnology, A-1190, Vienna, Austria.
| | - Brigitte Gasser
- Department of Biotechnology, BOKU - University of Natural Resources and Life Sciences Vienna, Muthgasse 18, 1190, Vienna, Austria.,Austrian Centre of Industrial Biotechnology, A-1190, Vienna, Austria
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12
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Prielhofer R, Cartwright SP, Graf AB, Valli M, Bill RM, Mattanovich D, Gasser B. Pichia pastoris regulates its gene-specific response to different carbon sources at the transcriptional, rather than the translational, level. BMC Genomics 2015; 16:167. [PMID: 25887254 PMCID: PMC4408588 DOI: 10.1186/s12864-015-1393-8] [Citation(s) in RCA: 69] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2014] [Accepted: 02/24/2015] [Indexed: 11/20/2022] Open
Abstract
Background The methylotrophic, Crabtree-negative yeast Pichia pastoris is widely used as a heterologous protein production host. Strong inducible promoters derived from methanol utilization genes or constitutive glycolytic promoters are typically used to drive gene expression. Notably, genes involved in methanol utilization are not only repressed by the presence of glucose, but also by glycerol. This unusual regulatory behavior prompted us to study the regulation of carbon substrate utilization in different bioprocess conditions on a genome wide scale. Results We performed microarray analysis on the total mRNA population as well as mRNA that had been fractionated according to ribosome occupancy. Translationally quiescent mRNAs were defined as being associated with single ribosomes (monosomes) and highly-translated mRNAs with multiple ribosomes (polysomes). We found that despite their lower growth rates, global translation was most active in methanol-grown P. pastoris cells, followed by excess glycerol- or glucose-grown cells. Transcript-specific translational responses were found to be minimal, while extensive transcriptional regulation was observed for cells grown on different carbon sources. Due to their respiratory metabolism, cells grown in excess glucose or glycerol had very similar expression profiles. Genes subject to glucose repression were mainly involved in the metabolism of alternative carbon sources including the control of glycerol uptake and metabolism. Peroxisomal and methanol utilization genes were confirmed to be subject to carbon substrate repression in excess glucose or glycerol, but were found to be strongly de-repressed in limiting glucose-conditions (as are often applied in fed batch cultivations) in addition to induction by methanol. Conclusions P. pastoris cells grown in excess glycerol or glucose have similar transcript profiles in contrast to S. cerevisiae cells, in which the transcriptional response to these carbon sources is very different. The main response to different growth conditions in P. pastoris is transcriptional; translational regulation was not transcript-specific. The high proportion of mRNAs associated with polysomes in methanol-grown cells is a major finding of this study; it reveals that high productivity during methanol induction is directly linked to the growth condition and not only to promoter strength. Electronic supplementary material The online version of this article (doi:10.1186/s12864-015-1393-8) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Roland Prielhofer
- Department of Biotechnology, BOKU University of Natural Resources and Life Sciences Vienna, Muthgasse 18, 1190, Vienna, Austria. .,Austrian Centre of Industrial Biotechnology (ACIB), Muthgasse 11, 1190, Vienna, Austria.
| | - Stephanie P Cartwright
- School of Life and Health Sciences, Aston University, Aston Triangle, Birmingham, B4 7ET, UK.
| | - Alexandra B Graf
- Austrian Centre of Industrial Biotechnology (ACIB), Muthgasse 11, 1190, Vienna, Austria. .,School of Bioengineering, University of Applied Sciences FH Campus Wien, Vienna, Austria.
| | - Minoska Valli
- Department of Biotechnology, BOKU University of Natural Resources and Life Sciences Vienna, Muthgasse 18, 1190, Vienna, Austria. .,Austrian Centre of Industrial Biotechnology (ACIB), Muthgasse 11, 1190, Vienna, Austria.
| | - Roslyn M Bill
- School of Life and Health Sciences, Aston University, Aston Triangle, Birmingham, B4 7ET, UK.
| | - Diethard Mattanovich
- Department of Biotechnology, BOKU University of Natural Resources and Life Sciences Vienna, Muthgasse 18, 1190, Vienna, Austria. .,Austrian Centre of Industrial Biotechnology (ACIB), Muthgasse 11, 1190, Vienna, Austria.
| | - Brigitte Gasser
- Department of Biotechnology, BOKU University of Natural Resources and Life Sciences Vienna, Muthgasse 18, 1190, Vienna, Austria. .,Austrian Centre of Industrial Biotechnology (ACIB), Muthgasse 11, 1190, Vienna, Austria.
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13
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Delic M, Graf AB, Koellensperger G, Haberhauer-Troyer C, Hann S, Mattanovich D, Gasser B. Overexpression of the transcription factor Yap1 modifies intracellular redox conditions and enhances recombinant protein secretion. Microb Cell 2014; 1:376-386. [PMID: 28357216 PMCID: PMC5349127 DOI: 10.15698/mic2014.11.173] [Citation(s) in RCA: 25] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
Oxidative folding of secretory proteins in the endoplasmic reticulum (ER) is a
redox active process, which also impacts the redox conditions in the cytosol. As
the transcription factor Yap1 is involved in the transcriptional response to
oxidative stress, we investigate its role upon the production of secretory
proteins, using the yeast Pichia pastoris as model, and report
a novel important role of Yap1 during oxidative protein folding. Yap1 is needed
for the detoxification of reactive oxygen species (ROS) caused by increased
oxidative protein folding. Constitutive co-overexpression of
PpYAP1 leads to increased levels of secreted recombinant
protein, while a lowered Yap1 function leads to accumulation of ROS and strong
flocculation. Transcriptional analysis revealed that more than 150 genes were
affected by overexpression of YAP1, in particular genes coding
for antioxidant enzymes or involved in oxidation-reduction processes. By
monitoring intracellular redox conditions within the cytosol and the ER using
redox-sensitive roGFP1 variants, we could show that overexpression of
YAP1 restores cellular redox conditions of
protein-secreting P. pastoris by reoxidizing the cytosolic
redox state to the levels of the wild type. These alterations are also reflected
by increased levels of oxidized intracellular glutathione (GSSG) in the
YAP1 co-overexpressing strain. Taken together, these data
indicate a strong impact of intracellular redox balance on the secretion of
(recombinant) proteins without affecting protein folding per se. Re-establishing
suitable redox conditions by tuning the antioxidant capacity of the cell reduces
metabolic load and cell stress caused by high oxidative protein folding load,
thereby increasing the secretion capacity.
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Affiliation(s)
- Marizela Delic
- Department of Biotechnology, BOKU University of Natural Resources and Life Sciences Vienna, Vienna, Austria. ; Austrian Centre of Industrial Biotechnology (ACIB), Vienna, Austria
| | - Alexandra B Graf
- Austrian Centre of Industrial Biotechnology (ACIB), Vienna, Austria. ; School of Bioengineering, University of Applied Sciences FH Campus Wien, Vienna, Austria
| | - Gunda Koellensperger
- Department of Biotechnology, BOKU University of Natural Resources and Life Sciences Vienna, Vienna, Austria. ; Department of Chemistry, BOKU University of Natural Resources and Life Sciences Vienna, Vienna, Austria
| | - Christina Haberhauer-Troyer
- Department of Biotechnology, BOKU University of Natural Resources and Life Sciences Vienna, Vienna, Austria. ; Department of Chemistry, BOKU University of Natural Resources and Life Sciences Vienna, Vienna, Austria
| | - Stephan Hann
- Department of Biotechnology, BOKU University of Natural Resources and Life Sciences Vienna, Vienna, Austria. ; Department of Chemistry, BOKU University of Natural Resources and Life Sciences Vienna, Vienna, Austria
| | - Diethard Mattanovich
- Department of Biotechnology, BOKU University of Natural Resources and Life Sciences Vienna, Vienna, Austria. ; Austrian Centre of Industrial Biotechnology (ACIB), Vienna, Austria
| | - Brigitte Gasser
- Department of Biotechnology, BOKU University of Natural Resources and Life Sciences Vienna, Vienna, Austria. ; Austrian Centre of Industrial Biotechnology (ACIB), Vienna, Austria
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14
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Maccani A, Hackl M, Leitner C, Steinfellner W, Graf AB, Tatto NE, Karbiener M, Scheideler M, Grillari J, Mattanovich D, Kunert R, Borth N, Grabherr R, Ernst W. Identification of microRNAs specific for high producer CHO cell lines using steady-state cultivation. Appl Microbiol Biotechnol 2014; 98:7535-48. [PMID: 25052466 PMCID: PMC4139590 DOI: 10.1007/s00253-014-5911-4] [Citation(s) in RCA: 18] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2014] [Revised: 06/23/2014] [Accepted: 06/24/2014] [Indexed: 01/06/2023]
Abstract
MicroRNAs are short non-coding RNAs that play an important role in the regulation of gene expression. Hence, microRNAs are considered as potential targets for engineering of Chinese hamster ovary (CHO) cells to improve recombinant protein production. Here, we analyzed and compared the microRNA expression patterns of high, low, and non-producing recombinant CHO cell lines expressing two structurally different model proteins in order to identify microRNAs that are involved in heterologous protein synthesis and secretion and thus might be promising targets for cell engineering to increase productivity. To generate reproducible and comparable data, the cells were cultivated in a bioreactor under steady-state conditions. Global microRNA expression analysis showed that mature microRNAs were predominantly upregulated in the producing cell lines compared to the non-producer. Several microRNAs were significantly differentially expressed between high and low producers, but none of them commonly for both model proteins. The identification of target messenger RNAs (mRNAs) is essential to understand the biological function of microRNAs. Therefore, we negatively correlated microRNA and global mRNA expression data and combined them with computationally predicted and experimentally validated targets. However, statistical analysis of the identified microRNA-mRNA interactions indicated a considerable false positive rate. Our results and the comparison to published data suggest that the reaction of CHO cells to the heterologous protein expression is strongly product- and/or clone-specific. In addition, this study highlights the urgent need for reliable CHO-specific microRNA target prediction tools and experimentally validated target databases in order to facilitate functional analysis of high-throughput microRNA expression data in CHO cells.
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Affiliation(s)
- Andreas Maccani
- Austrian Centre of Industrial Biotechnology (ACIB GmbH), Muthgasse 11, 1190, Vienna, Austria,
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15
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Rebnegger C, Graf AB, Valli M, Gasser B, Maurer M, Mattanovich D. From slow to fast: effects of growth rate on global gene expression and recombinant protein secretion in Pichia pastoris. N Biotechnol 2014. [DOI: 10.1016/j.nbt.2014.05.1628] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
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Rebnegger C, Graf AB, Valli M, Steiger MG, Gasser B, Maurer M, Mattanovich D. In Pichia pastoris, growth rate regulates protein synthesis and secretion, mating and stress response. Biotechnol J 2014; 9:511-25. [PMID: 24323948 PMCID: PMC4162992 DOI: 10.1002/biot.201300334] [Citation(s) in RCA: 73] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2013] [Revised: 10/21/2013] [Accepted: 12/06/2013] [Indexed: 12/12/2022]
Abstract
Protein production in yeasts is related to the specific growth rate μ. To elucidate on this correlation, we studied the transcriptome of Pichia pastoris at different specific growth rates by cultivating a strain secreting human serum albumin at μ = 0.015 to 0.15 h(-1) in glucose-limited chemostats. Genome-wide regulation revealed that translation-related as well as mitochondrial genes were upregulated with increasing μ, while autophagy and other proteolytic processes, carbon source-responsive genes and other targets of the TOR pathway as well as many transcriptional regulators were downregulated at higher μ. Mating and sporulation genes were most active at intermediate μ of 0.05 and 0.075 h(-1) . At very slow growth (μ = 0.015 h(-1) ) gene regulation differs significantly, affecting many transporters and glucose sensing. Analysis of a subset of genes related to protein folding and secretion reveals that unfolded protein response targets such as translocation, endoplasmic reticulum genes, and cytosolic chaperones are upregulated with increasing growth rate while proteolytic degradation of secretory proteins is downregulated. We conclude that a high μ positively affects specific protein secretion rates by acting on multiple cellular processes.
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Affiliation(s)
- Corinna Rebnegger
- Department of Biotechnology, University of Natural Resources and Life Sciences, Vienna, Austria
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Delic M, Valli M, Graf AB, Pfeffer M, Mattanovich D, Gasser B. The secretory pathway: exploring yeast diversity. FEMS Microbiol Rev 2013; 37:872-914. [PMID: 23480475 DOI: 10.1111/1574-6976.12020] [Citation(s) in RCA: 140] [Impact Index Per Article: 12.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/18/2012] [Revised: 02/14/2013] [Accepted: 02/17/2013] [Indexed: 12/11/2022] Open
Abstract
Protein secretion is an essential process for living organisms. In eukaryotes, this encompasses numerous steps mediated by several hundred cellular proteins. The core functions of translocation through the endoplasmic reticulum membrane, primary glycosylation, folding and quality control, and vesicle-mediated secretion are similar from yeasts to higher eukaryotes. However, recent research has revealed significant functional differences between yeasts and mammalian cells, and even among diverse yeast species. This review provides a current overview of the canonical protein secretion pathway in the model yeast Saccharomyces cerevisiae, highlighting differences to mammalian cells as well as currently unresolved questions, and provides a genomic comparison of the S. cerevisiae pathway to seven other yeast species where secretion has been investigated due to their attraction as protein production platforms, or for their relevance as pathogens. The analysis of Candida albicans, Candida glabrata, Kluyveromyces lactis, Pichia pastoris, Hansenula polymorpha, Yarrowia lipolytica, and Schizosaccharomyces pombe reveals that many - but not all - secretion steps are more redundant in S. cerevisiae due to duplicated genes, while some processes are even absent in this model yeast. Recent research obviates that even where homologous genes are present, small differences in protein sequence and/or differences in the regulation of gene expression may lead to quite different protein secretion phenotypes.
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Affiliation(s)
- Marizela Delic
- Department of Biotechnology, University of Natural Resources and Life Sciences (BOKU), Vienna, Austria; Austrian Centre of Industrial Biotechnology (ACIB GmbH), Vienna, Austria
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Pfeffer M, Maurer M, Köllensperger G, Hann S, Graf AB, Mattanovich D. Modeling and measuring intracellular fluxes of secreted recombinant protein in Pichia pastoris with a novel 34S labeling procedure. Microb Cell Fact 2011; 10:47. [PMID: 21703020 PMCID: PMC3147017 DOI: 10.1186/1475-2859-10-47] [Citation(s) in RCA: 34] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2011] [Accepted: 06/26/2011] [Indexed: 12/24/2022] Open
Abstract
Background The budding yeast Pichia pastoris is widely used for protein production. To determine the best suitable strategy for strain improvement, especially for high secretion, quantitative data of intracellular fluxes of recombinant protein are very important. Especially the balance between intracellular protein formation, degradation and secretion defines the major bottleneck of the production system. Because these parameters are different for unlimited growth (shake flask) and carbon-limited growth (bioreactor) conditions, they should be determined under "production like" conditions. Thus labeling procedures must be compatible with minimal production media and the usage of bioreactors. The inorganic and non-radioactive 34S labeled sodium sulfate meets both demands. Results We used a novel labeling method with the stable sulfur isotope 34S, administered as sodium sulfate, which is performed during chemostat culivations. The intra- and extracellular sulfur 32 to 34 ratios of purified recombinant protein, the antibody fragment Fab3H6, are measured by HPLC-ICP-MS. The kinetic model described here is necessary to calculate the kinetic parameters from sulfur ratios of consecutive samples as well as for sensitivity analysis. From the total amount of protein produced intracellularly (143.1 μg g-1 h-1 protein per yeast dry mass and time) about 58% are degraded within the cell, 35% are secreted to the exterior and 7% are inherited to the daughter cells. Conclusions A novel 34S labeling procedure that enables in vivo quantification of intracellular fluxes of recombinant protein under "production like" conditions is described. Subsequent sensitivity analysis of the fluxes by using MATLAB, indicate the most promising approaches for strain improvement towards increased secretion.
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Affiliation(s)
- Martin Pfeffer
- University of Natural Resources and Life Sciences, Department of Biotechnology, Muthgasse 18, Vienna, Austria
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Baumann K, Dato L, Graf AB, Frascotti G, Dragosits M, Porro D, Mattanovich D, Ferrer P, Branduardi P. The impact of oxygen on the transcriptome of recombinant S. cerevisiae and P. pastoris - a comparative analysis. BMC Genomics 2011; 12:218. [PMID: 21554735 PMCID: PMC3116504 DOI: 10.1186/1471-2164-12-218] [Citation(s) in RCA: 33] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2010] [Accepted: 05/09/2011] [Indexed: 01/05/2023] Open
Abstract
Background Saccharomyces cerevisiae and Pichia pastoris are two of the most relevant microbial eukaryotic platforms for the production of recombinant proteins. Their known genome sequences enabled several transcriptomic profiling studies under many different environmental conditions, thus mimicking not only perturbations and adaptations which occur in their natural surroundings, but also in industrial processes. Notably, the majority of such transcriptome analyses were performed using non-engineered strains. In this comparative study, the gene expression profiles of S. cerevisiae and P. pastoris, a Crabtree positive and Crabtree negative yeast, respectively, were analyzed for three different oxygenation conditions (normoxic, oxygen-limited and hypoxic) under recombinant protein producing conditions in chemostat cultivations. Results The major differences in the transcriptomes of S. cerevisiae and P. pastoris were observed between hypoxic and normoxic conditions, where the availability of oxygen strongly affected ergosterol biosynthesis, central carbon metabolism and stress responses, particularly the unfolded protein response. Steady state conditions under low oxygen set-points seemed to perturb the transcriptome of S. cerevisiae to a much lesser extent than the one of P. pastoris, reflecting the major tolerance of the baker's yeast towards oxygen limitation, and a higher fermentative capacity. Further important differences were related to Fab production, which was not significantly affected by oxygen availability in S. cerevisiae, while a clear productivity increase had been previously reported for hypoxically grown P. pastoris. Conclusions The effect of three different levels of oxygen availability on the physiology of P. pastoris and S. cerevisiae revealed a very distinct remodelling of the transcriptional program, leading to novel insights into the different adaptive responses of Crabtree negative and positive yeasts to oxygen availability. Moreover, the application of such comparative genomic studies to recombinant hosts grown in different environments might lead to the identification of key factors for efficient protein production.
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Affiliation(s)
- Kristin Baumann
- Department of Chemical Engineering, Autonomous University of Barcelona, Spain
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Baumann K, Carnicer M, Dragosits M, Graf AB, Stadlmann J, Jouhten P, Maaheimo H, Gasser B, Albiol J, Mattanovich D, Ferrer P. A multi-level study of recombinant Pichia pastoris in different oxygen conditions. BMC Syst Biol 2010; 4:141. [PMID: 20969759 PMCID: PMC2987880 DOI: 10.1186/1752-0509-4-141] [Citation(s) in RCA: 123] [Impact Index Per Article: 8.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/05/2010] [Accepted: 10/22/2010] [Indexed: 12/24/2022]
Abstract
Background Yeasts are attractive expression platforms for many recombinant proteins, and there is evidence for an important interrelation between the protein secretion machinery and environmental stresses. While adaptive responses to such stresses are extensively studied in Saccharomyces cerevisiae, little is known about their impact on the physiology of Pichia pastoris. We have recently reported a beneficial effect of hypoxia on recombinant Fab secretion in P. pastoris chemostat cultivations. As a consequence, a systems biology approach was used to comprehensively identify cellular adaptations to low oxygen availability and the additional burden of protein production. Gene expression profiling was combined with proteomic analyses and the 13C isotope labelling based experimental determination of metabolic fluxes in the central carbon metabolism. Results The physiological adaptation of P. pastoris to hypoxia showed distinct traits in relation to the model yeast S. cerevisiae. There was a positive correlation between the transcriptomic, proteomic and metabolic fluxes adaptation of P. pastoris core metabolism to hypoxia, yielding clear evidence of a strong transcriptional regulation component of key pathways such as glycolysis, pentose phosphate pathway and TCA cycle. In addition, the adaptation to reduced oxygen revealed important changes in lipid metabolism, stress responses, as well as protein folding and trafficking. Conclusions This systems level study helped to understand the physiological adaptations of cellular mechanisms to low oxygen availability in a recombinant P. pastoris strain. Remarkably, the integration of data from three different levels allowed for the identification of differences in the regulation of the core metabolism between P. pastoris and S. cerevisiae. Detailed comparative analysis of the transcriptomic data also led to new insights into the gene expression profiles of several cellular processes that are not only susceptible to low oxygen concentrations, but might also contribute to enhanced protein secretion.
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Affiliation(s)
- Kristin Baumann
- Department of Chemical Engineering, Autonomous University of Barcelona, Spain
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Sohn SB, Graf AB, Kim TY, Gasser B, Maurer M, Ferrer P, Mattanovich D, Lee SY. Genome-scale metabolic model of methylotrophic yeastPichia pastorisand its use forin silicoanalysis of heterologous protein production. Biotechnol J 2010; 5:705-15. [DOI: 10.1002/biot.201000078] [Citation(s) in RCA: 100] [Impact Index Per Article: 7.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/04/2023]
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