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Quantitative Insights Into the Cyanobacterial Cell Economy bioRxiv preprint doi: https://doi.org/10.1101/446179; this version posted October 17, 2018. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY 4.0 International license. Quantitative insights into the cyanobacterial cell economy Tomáš Zavřel,1†∗ Marjan Faizi,2y Cristina Loureiro,3 Gereon Poschmann,4 Kai Stühler,4;5 Maria Sinetova,6 Anna Zorina,6 Ralf Steuer,2∗ Jan Červený1 1Laboratory of Adaptive Biotechnologies, Global Change Research Institute CAS, Brno, Czech Republic 2Humboldt-Universität zu Berlin, Institut für Biologie, Fachinstitut für Theoretische Biologie, Berlin, Germany 3Department of Applied Physics, Polytechnic University of Valencia, Valencia, Spain 4Molecular Proteomics Laboratory, BMFZ, Heinrich-Heine-Universität Düsseldorf, Düsseldorf, Germany 5Institute for Molecular Medicine, University Hospital Düsseldorf, Düsseldorf, Germany 6Timiryazev Institute of Plant Physiology, Russian Academy of Sciences, Moscow, Russian Federation ∗Correspondence: [email protected], [email protected] yThese authors contributed equally to this work ABSTRACT growth rates, high productivity and amenability to genetic manipulations, cyanobacteria are considered as promising Phototrophic microorganisms are promising resources host organisms for synthesis of renewable bioproducts from for green biotechnology. Compared to heterotrophic atmospheric CO2 (Al-Haj et al., 2016; Zavřel et al., 2016), microorganisms, however, the cellular economy of and serve as important model organisms to understand and phototrophic growth is still insufficiently understood. improve photosynthetic productivity. We provide a quantitative analysis of light-limited, Understanding the cellular limits of photosynthetic pro- light-saturated, and light-inhibited growth of the ductivity in cyanobacteria, however, requires quantitative cyanobacterium Synechocystis sp. PCC 6803 within data about cellular physiology and growth: accurate ac- a reproducible cultivation setup. We report key counting is central to understand the organization, growth physiological parameters, including growth rate, cell and proliferation of cells (Vázquez-Laslop and Mankin, size, and photosynthetic activity over a wide range of 2014). While quantitative insight into the cellular econ- light intensities. Intracellular proteins were quantified omy of phototrophic microorganisms is still scarce, the to monitor proteome allocation as a function of cellular economy of heterotrophic growth has been stud- growth rate. Among other physiological adaptations, ied extensively—starting with the seminal works of Monod, we identify an upregulation of the translational Neidhardt, and others (Neidhardt et al., 1990; Neidhardt, machinery and downregulation of light harvesting 1999; Jun et al., 2018) to more recent quantitative stud- components with increasing light intensity and growth ies of microbial resource allocation (Molenaar et al., 2009; rate. The resulting growth laws are discussed in the Klumpp et al., 2009; Scott et al., 2010; Scott and Hwa, context of a coarse-grained model of phototrophic 2011; Bosdriesz et al., 2015; Maitra and Dill, 2015; Weiße growth and available data obtained by a comprehen- et al., 2015). In response to changing environments, het- sive literature search. Our insights into quantitative erotrophic microorganisms are known to differentially allo- aspects of cyanobacterial adaptations to different cate their protein resources: with increasing growth rate, growth rates have implications to understand and heterotrophic microorganisms typically exhibit upregulation optimize photosynthetic productivity. of ribosomes and other proteins related to translation and protein synthesis (Scott et al., 2010; Molenaar et al., 2009; INTRODUCTION Peebo et al., 2015), exhibit complex changes in transcrip- tion profiles, e.g. (Klumpp et al., 2009; Matsumoto et al., Cyanobacteria are key primary producers in many, if not 2013), as well as increased cell size (Kafri et al., 2016). most, ecosystems and are an integral part of the global The molecular limits of heterotrophic growth have been de- biogeochemical carbon and nitrogen cycle. Due to their fast scribed thoroughly (Kafri et al., 2016; Erickson et al., 2017; 1 bioRxiv preprint doi: https://doi.org/10.1101/446179; this version posted October 17, 2018. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY 4.0 International license. Scott et al., 2014; Metzl-Raz et al., 2017; Klumpp et al., analysis of changes in individual protein fractions revealed 2013). phototrophic "growth laws": abundances of proteins asso- In contrast, only few studies so far have addressed the ciated with light harvesting decreased with increasing light limits of cyanobacterial growth from an experimental per- intensity and growth rate, whereas abundances of proteins spective (Bernstein et al., 2016; Yu et al., 2015; Abernathy associated with translation and biosynthesis increased with et al., 2017; Ungerer et al., 2018). Of particular inter- increasing light intensity and growth rate—in good agree- est were the adaptations that enable fast photoautotrophic ment with recent computational models of cyanobacterial growth (Bernstein et al., 2016; Yu et al., 2015; Abernathy resource allocation (Burnap, 2015; Rügen et al., 2015; et al., 2017; Ungerer et al., 2018). The cyanobacterium with Mueller et al., 2017; Reimers et al., 2017; Faizi et al., 2018). the highest known photoautotrophic growth rate, growing with a doubling time of up to TD ∼ 1:9h, is the strain RESULTS Synechococcus elongatus UTEX 2973. Compared to its closest relative, Synechococcus elongatus PCC 7942, the Establishing a controlled and reproducible cultivation strain shows several physiological adaptation, such as higher setup PSI and cytochrome b6f content per cell (Ungerer et al., The Synechocystis substrain GT-L (Zavřel et al., 2015b) 2018), lower metabolite pool in central metabolism, less was cultivated in flat panel photobioreactors (Figure 1A) glycogen accumulation, and higher electron charge (Aber- using at least 5 independent reactors in a quasi-continuous nathy et al., 2017). Recently, high light was reported to (turbidostat) regime, as shown in Figure 1B, with red light promote the transcription of genes associated with central intensities of 27:5 − 1100 µmol(photons) m−2s−1, supple- metabolic pathways, repression of phycobilisome genes, and mented with a blue light intensity of 27:5 µmol(photons) accelerated glycogen accumulation rates (Tan et al., 2018) m−2s−1. The addition of blue light avoids possible growth While these studies point to strain-specific differences limitations in the absence of short wavelength pho- and are important for characterizing non-model microbial tons (Golden, 1995). Steady-state specific growth rates in metabolism (Abernathy et al., 2017), the general principles turbidostat mode were calculated from monitoring the op- of resource allocation in photoautotrophic metabolism and tical density measured at a wavelength of 680 nm (OD680) the laws of phototrophic growth are still poorly understood. as well as from the rate of depletion of spare cultivation Therefore, the aim of this study is to provide a consistent medium (as measured by top loading balances). Both meth- quantitative dataset of cyanobacterial physiology and pro- ods resulted in similar average values (Figure 1C). Estima- tein abundance for a range of different light intensities and tion of the specific growth rate based on the medium deple- growth rates—and put the data in the context of published tion, however, exhibited higher variance. For further anal- values obtained by a comprehensive literature search as well ysis, therefore, only values obtained from the OD680 signal as in the context of a recent model of photosynthetic re- are reported. source allocation (Faizi et al., 2018). To this end, we chose The measured specific growth rates increased from µ = the widely used model strain Synechocystis sp. PCC 6803 0:025 ± 0:002 h−1 to µ = 0:104 ± 0:009 h−1 (correspond- (Synechocystis hereafter). Since Synechocystis exhibits sig- ing to doubling times of TD ≈ 27:7h − 6:9h) with increas- nificant variations with respect to genotype (Ikeuchi and ing light intensities up to 660 µmol(photons) m−2s−1 of Tabata, 2001) and phenotype (Morris et al., 2016; Zavřel red light. For higher light intensities the cultures exhibited et al., 2017), we chose the substrain GT-L, a strain that photoinhibition—a reduction of the specific growth rate in- has a documented stable phenotype for at least four years duced by high light intensities. Under the highest intensity preceding this study. All data are obtained under highly of 1100 µmol(photons) m−2s−1, the specific growth rate de- reproducible and controlled experimental conditions, using creased to µ = 0:093 ± 0:011 h−1, corresponding to a dou- flat-panel photobioreactors (Nedbal et al., 2008) within an bling time of TD = 7:5 h (Figure 1C-D). The growth curve identical setup as in previous studies (Zavřel et al., 2015b). is consistent with previous measurements of cyanobacterial The data obtained in this work provide a resource growth (Zavřel et al., 2015b; Cordara et al., 2018) and for quantitative insight into the allocation of cellular can be subdivided into three
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