Integrated multilaboratory systems biology reveals differences in protein metabolism between two reference yeast strains

ARTICLE Received 30 Jun 2010 | Accepted 29 Nov 2010 | Published 21 Dec 2010 DOI: 10.1038/ncomms1150 Integrated multilaboratory systems biology reveals differences in protein metabolism between two reference yeast strains André B. Canelas1,*, Nicola Harrison2,*, Alessandro Fazio3,*, Jie Zhang4,*, Juha-Pekka Pitkänen5, Joost van den Brink1, Barbara M. Bakker6, Lara Bogner7, Jildau Bouwman6, Juan I. Castrillo2, Ayca Cankorur8, Pramote Chumnanpuen4, Pascale Daran-Lapujade1, Duygu Dikicioglu8, Karen van Eunen6, Jennifer C. Ewald9, Joseph J. Heijnen1, Betul Kirdar8, Ismo Mattila5, Femke I. C. Mensonides6, Anja Niebel7, Merja Penttilä5, Jack T. Pronk1, Matthias Reuss7, Laura Salusjärvi5, Uwe Sauer9, David Sherman10, Martin Siemann-Herzberg7, Hans Westerhoff6, Johannes de Winde1, Dina Petranovic4, Stephen G. Oliver2, Christopher T. Workman3, Nicola Zamboni9 & Jens Nielsen4 The field of systems biology is often held back by difficulties in obtaining comprehensive, highquality, quantitative data sets. In this paper, we undertook an interlaboratory effort to generate such a data set for a very large number of cellular components in the yeast Saccharomyces cerevisiae, a widely used model organism that is also used in the production of fuels, chemicals, food ingredients and pharmaceuticals. With the current focus on biofuels and sustainability, there is much interest in harnessing this species as a general cell factory. In this study, we characterized two yeast strains, under two standard growth conditions. We ensured the high quality of the experimental data by evaluating a wide range of sampling and analytical techniques. Here we show significant differences in the maximum specific growth rate and biomass yield between the two strains. On the basis of the integrated analysis of the highthroughput data, we hypothesize that differences in phenotype are due to differences in protein metabolism. Department of Biotechnology, Kluyver Centre for Genomics of Industrial Fermentation, Delft University of Technology, Julianalaan 67, Delft 2628 BC, The Netherlands. 2 Cambridge Systems Biology Centre & Department of Biochemistry, University of Cambridge, Sanger Building, 80 Tennis Court Road, Cambridge CB2 1GA, UK. 3 Department of Systems Biology, Center for Biological Sequence Analysis, Technical University of Denmark, Lyngby DK-2800 Kgs, Denmark. 4 Department of Chemical and Biological Engineering, Chalmers University of Technology, Goteborg SE-41296, Sweden. 5 VTT Technical Research Centre of Finland, PO Box 1000, Espoo FI-02044 VTT, Finland. 6 Department of Molecular Cell Physiology, Vrije Universiteit Amsterdam, De Boelelaan 1085, Amsterdam 1081 HV, The Netherlands. 7 Institut für Bioverfahrenstechnik, University of Stuttgart, Allmandring 31, Stuttgart D-70569, Germany. 8 Department of Chemical Engineering, Bogazici University, Bebek, Istanbul 34342, Turkey. 9 Institute for Molecular Systems Biology, ETH Zürich, Zürich CH-8093, Switzerland. 10 Laboratoire Bordelais de Recherche en Informatique, UMR CNRS 5800, France. *These authors contributed equally to the work. Correspondence and requests for materials should be addressed to J.N. (email: ). 1 nature communications | 1:145 | DOI: 10.1038/ncomms1150 | www.nature.com/naturecommunications © 2010 Macmillan Publishers Limited. All rights reserved.  ARTICLE nature communications | DOI: 10.1038/ncomms1150 T here are many definitions and interpretations of systems biology, but most involve mathematical modelling, high-throughput (or omics) analysis, mapping of interactions between cellular components and quantification of dynamic responses in living cells1–5. In most cases, the objective of systems biology is to obtain a quantitative description of the biological system under study, and this quantitative description is ideally in the form of a mathematical model that can be used to simulate the operations of the biological system. Even though some mathematical modelling concepts rely only on limited data sets (for example, flux balance analysis), most systems biology efforts will require large sets of high-quality experimental data that enable, for example, to discriminate between different model structures. Generation of such data is therefore the core of many studies that use the systems biology approach. However, the infrastructure and expertise needed to generate the large number of different data required for advanced systems biology studies (for example, transcriptomics, proteomics, metabolomics) is normally beyond the capabilities of a single laboratory. Therefore, there is a trend towards multilaboratory collaboration projects and the establishment of curated databases that contain high-quality data sets6. To ensure proper documentation of experiments, some effort has also been directed at establishing protocol formats, such as MIAME (Minimum Information About a Microarray Experiment) for DNA array experiments7, MIAPE (Minimum Information About a Proteomics Experiment) and PRIDE (PRoteomics IDEntification) for proteome analysis8,9, protocols for microbial metabolome analysis10, and even protocols for documentation of mathematical models such as MIRIAM11 (Minimum Information Requested In the Annotation of biochemical Models). Even though these protocol formats aim to ensure proper documentation of the actual experiments, there is still a need for consolidation of applied experimental conditions and procedures, in order to allow the generation of increasingly large, coherent data sets for the same organism or strain that will eventually represent a rich resource for advanced mathematical modelling and contribute to our understanding of the living cell. In this study, The Yeast Systems Biology Network (YSBN) undertook a major effort to consolidate and compare experimental conditions, procedures and protocols applied for the experimental part of yeast systems biology in 10 different European laboratories, and at the same time performed a comparative analysis of different quantitative analytical methods. This has resulted in the establishment of a well-documented experimental ‘systems biology pipeline’ that is illustrated in Figure 1. The ‘pipeline’ allows for the comparison of different yeast strains or the comparison of a single yeast strain grown under different conditions. In this study, we evaluated the ‘pipeline’ by comparing two different yeast strains grown under two different conditions in biorectors, namely, a traditional batch culture (nutrient excess) and a glucose-limited chemostat culture (specific growth rate controlled by the rate of supply of the limiting nutrient, glucose). The resulting data set will constitute a valuable reference for further studies using these two strains and hence advance the field of yeast systems biology. Furthermore, we were able to illustrate how comprehensive information on multiple omics levels (for example, Transcriptome Affymetrix Agilent qPCR TRAC YSBN2 20 15 10 5 3 Batches (...truncated)