Figures and data in Quantitative insights into the cyanobacterial cell economy

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Tables
Additional files

6 figures, 3 tables and 2 additional files

Figures

Figure 1

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Experimental setup and evaluation of *Synechocystis* sp. PCC 6803 (substrain GT-L) phenotype stability.

Panel A: Photobioreactor setup. Cultures were cultivated in a flat-panel photobioreactor vessel (400 mL) in a turbidostat regime according to Zavřel et al. (2015b). Dilution of actively growing culture was based on measurements of optical density at 680 nm (OD₆₈₀). Inflow air and CO₂ were mixed in a gas mixing unit, the sparging gas flow rate was controlled by a gas analyzing unit. Sparging gas was moistened in a humidifier and, after bubbling through the photobioreactor vessel, separated from the waste culture via a liquid trap. CO₂ concentration in the output gas was measured by an infrared sensor according to Červený et al., 2009. All other parameters were set as described in Nedbal et al. (2008) and Červený et al., 2009. Panel B: Representative measurement of the OD₆₈₀ signal (black lines) within a turbidostat cultivation under increasing red light intensity (supplemented with low intensity of blue light). Calculation of specific growth rates (blue circles) is detailed in Materials and methods. Calculation of uptake and refilling rates of selected nutrients (including Na, (N, S, Ca, Mg, P and Fe) during the turbidostat cultivation is detailed in Figure 1—source data 1 (the elemental composition of *Synechocystis* cells is based on data available in the literature). Panel C: Calculation of growth rates from the OD₆₈₀ signal and from top loading balances that monitored depletion rate of a spare cultivation medium (source data are available in Figure 1—source data 2). Panel D: Comparison of specific growth rates using an identical experimental setup throughout four successive years 2013–2017 (source data are available in Figure 1—source data 3). Panel E: Rates of gross photosynthesis and dark respiration, measured as O₂ evolution and consumption rates directly within the photobioreactor vessel throughout 5 min of light and dark periods in 2016–2017 (this study) and in 2015–2017 (Zavřel et al., 2017). The dashed line represents a P-I curve fit of data from this study according to Platt et al. (1980). Source data are available in Figure 1—source data 4. Figure 1C: n = 6–11, Figure 1D: n = 3–11, Figure 1E: n = 4–6. Error bars (Figure 1C–1E) represent standard deviations.

https://doi.org/10.7554/eLife.42508.002

Figure 1—source data 1 Uptake and refilling rates of selected nutrients during the quasi-continuous cultivation.: https://doi.org/10.7554/eLife.42508.003
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Figure 1—source data 2 Source data for Figure 1C.: https://doi.org/10.7554/eLife.42508.004
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Figure 1—source data 3 Source data for Figure 1D.: https://doi.org/10.7554/eLife.42508.005
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Figure 1—source data 4 Source data for Figure 1E.: https://doi.org/10.7554/eLife.42508.006
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Figure 2 with 1 supplement

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Variations in morphology and composition of *Synechocystis* cells with changing growth rate.

Under increasing light intensity and changing growth rate, the following parameters were estimated: cellular volume (A) and dry weight (B), gross photosynthesis (**C, D**) and dark respiration (**E, F**), and content of glycogen (**G, H**), proteins, DNA (**I, J**), phycobiliproteins (**K, L**), chlorophyll a and carotenoids (**M, N**). The data are plotted relative to cellular dry weight (**C, E, G, I, K, M**) as well as per cell (**D, F, H, J, L, N**). DNA content was normalized to its initial value after standardization per dry weight and per cell, the measurement was only semi-quantitative. All values represent averages from 3 – 11 independent biological replicates, error bars represent standard deviations. If error bars are not visible (panel A), the standard deviation was too small for visualization. Within each figure, data points are displayed in three different color shades to reflect (from bright to dark) light-limited, light-saturated and light-inhibited growth. Data plotted as a function of light intensity are available in Figure 2—figure supplement 1. Comparison with data available in the literature is summarized in Figure 2—source data 2.

https://doi.org/10.7554/eLife.42508.007

Figure 2—source data 1 Source data for Figure 2.: https://doi.org/10.7554/eLife.42508.009
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Figure 2—source data 2 Comparison of the values measured in this study with data reported in the literature.: https://doi.org/10.7554/eLife.42508.010
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Figure 2—figure supplement 1

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Allocation of key cellular resources as a function of light intensity.
https://doi.org/10.7554/eLife.42508.008

Figure 3 with 1 supplement

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*Synechocystis* proteome allocation as a function of growth rate.

Panel A: The workflow. Samples were harvested and analyzed by mass spectrometry (the proteomics dataset is available in Figure 3—source data 1). A Kruskal-Wallis test was used to distinguish between growth-dependent and growth-independent proteins. 779 growth-dependent and 577 growth-independent proteins were identified. Panel B: Clustering analysis. Based on k-means clustering analysis (Figure 3—figure supplement 1), the 779 growth-dependent proteins were separated into seven clusters. Gray dashed lines represent protein abundances as medians of 5 biological replicates, normalized by the respective means. Blue dashed lines represent centroids of the respective clusters. Panel C: Proteins were annotated using the GO classes, the matrix represents the annotation mapped to GO slim categories. Proteins can be associated to several GO slim categories. The highest ranking annotation per cluster is highlighted in dark blue.

https://doi.org/10.7554/eLife.42508.011

Figure 3—source data 1 Proteomics dataset.: https://doi.org/10.7554/eLife.42508.013
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Figure 3—source data 2 List of growth-dependent proteins.: https://doi.org/10.7554/eLife.42508.014
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Figure 3—source data 3 List of growth-independent proteins.: https://doi.org/10.7554/eLife.42508.015
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Figure 3—figure supplement 1

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Elbow method for the identification of an appropriate number of clusters (grey dashed line at seven clusters).
https://doi.org/10.7554/eLife.42508.012

Figure 4 with 1 supplement

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Proteomaps of proteome reallocation in *Synechocystis* under light-limited (27.5 μmol(photons) m^-2s^-1), light-saturated (440 μmol(photons) m^-2s^-1) and photoinhibited growth (1100 μmol(photons) m^-2s^-1).
https://doi.org/10.7554/eLife.42508.018

Figure 4—figure supplement 1

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Proteomaps of levels 2, 3 and 4.
https://doi.org/10.7554/eLife.42508.019

Figure 5

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A model of phototrophic growth and reproduction of experimental growth curves.

Panel A: A coarse-grained model of phototrophic growth, adopted from Faizi et al. (2018). The model describes optimal proteome allocation under conditions of (i) light-limited, (ii) light-saturated and (iii) light-inhibited growth. Coarse-grained cellular processes include passive ( $v_{d}$ ) and active import ( $v_{t}$ ) of external inorganic carbon $c_{i}^{x}$ , conversion of inorganic carbon $c_{i}$ into amino acids $a a$ ( $v_{m}$ ), light harvesting and provision of cellular energy by photosynthesis ( $v_{1}$ and $v_{2}$ ), as well as maintenance and photodamage ( $m_{v}$ and $v_{i}$ ). Amino acids are translated into coarse-grained protein fractions for transport ( $T$ ), metabolism ( $M$ ), ribosomes ( $R$ ), photosynthetic electron transport ( $P$ ), as well as a growth-independent proteome fraction $Q$ . Translation is limited by the amount of available ribosomes $R$ . Panel B: The model reproduces the measured growth curve (Figure 1C–D) as a function of light intensity. Shown are the specific growth rate μ, as well as the main proteome fractions predicted by the model: ribosome ( $R$ ) fraction, photosynthetic electron transport ( $P$ ) fraction, and metabolism ( $M$ ) fraction, as a function of light intensity.

https://doi.org/10.7554/eLife.42508.020

Figure 6 with 2 supplements

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Changes in protein abundance as a function of specific growth rate compared to the predictions obtained from a computational model of proteome allocation.

Panel A: Schematic representation of ribosome, photosynthetic units and metabolic enzyme classes considered in the proteome allocation model. Panel B: Relative proteomics data ( LFQ, label-free quantification intensities, left axes, mean fold change ± SD) of protein classes in comparison with the model predictions (grey lines, right axes). Panel C: Relative protein abundances obtained by immunoblotting analysis for selected proteins (left axes, median fold change ± SD) in comparison with coarse-grained model predictions (grey lines, right axes). Experimental values represent averages from 5 independent experiments, the error bars represent standard deviations. Panels B-C: The experimental data points are displayed in three different color shading to reflect (from bright to dark) light-limited, light-saturated and light-inhibited growth. The full dataset of the immunoblotting analysis is provided in Figure 6—source data 1 and Figure 6—figure supplement 1. The list of proteins considered for ribosome, photosynthetic unit and metabolic enzyme classes is listed in Figure 6—source data 2. The influence of constant ribosomal, photosynthetic unit and metabolic enzyme classes on cellular growth rate is simulated in Figure 6—figure supplement 2.

https://doi.org/10.7554/eLife.42508.021

Figure 6—source data 1 Results of the immunoblotting analysis.: https://doi.org/10.7554/eLife.42508.024
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Figure 6—source data 2 List of proteins considered for ribosome, photosynthetic unit and metabolic enzyme classes.: https://doi.org/10.7554/eLife.42508.025
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Figure 6—figure supplement 1

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Immunoblots and a list of antibodies used for the immunoblotting analysis.
https://doi.org/10.7554/eLife.42508.022

Figure 6—figure supplement 2

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Model simulations for investigating the influence of constant enzyme fractions on the cellular growth rate.
https://doi.org/10.7554/eLife.42508.023

Tables

Table 1

Gene Ontology (GO) slim categories (Klopfenstein et al., 2018) with the amount of associated growth-dependent and independent proteins.

A complete list of the GO slim categories is provided in Table 1—source data 1. Here, only categories that exhibit a significant difference (Fisher's exact test, $p - v a l u e < 0.05$ ) between growth-dependent and independent groups are listed. Shown is the number of annotations per category.

https://doi.org/10.7554/eLife.42508.016

Gene ontology categories	Growth dependent	Growth independent
Translation	40	13
Transport	36	14
Photosynthesis	36	8
Catabolic process	32	4
Protein folding	14	3
Cell division	12	0
Cell wall organization or biogenesis	10	1
Cell cycle	9	0

Table 1—source data 1 List of all 40 GO slim categories with the respective amounts of growth-dependent and growth-independent proteins (and their cluster associations).: https://doi.org/10.7554/eLife.42508.017
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Table 2

Quantification of selected protein complexes in Synechocystis cells.

Protein abundances were estimated as molecules per cell, as inferred from mass spectrometry, immunoblotting and spectrophotometric analysis. The stoichiometries of protein complexes were based on Uniprot (www.uniprot.org, (UniProt Consortium, 2018)) and RCSB (www.rcsb.org, (Berman, 2000)) databases. Protein abundances are not precise estimates but indicate ranges. The range in the second column reflects the minimal and maximal protein amounts estimated across all light intensities studied in this work. Estimation of protein abundances is detailed in Table 2—Source data 1, a list of all proteins is provided in Table 2—Source data 2. The experimental conditions of (Moal and Lagoutte, 2012) are comparable to the conditions used in this study with the exception of high light used here and distinct Synechocystis substrains (Figure 2—source data 2).

https://doi.org/10.7554/eLife.42508.026

Protein complex	Molecules per cell	Method	Stoichiometry	Reference
Elongation factor	179000–274000	Proteomics	TufA	This study
Phosphoglycerate kinase	45000–73000	Proteomics	Pgk	This study
Ribosome small subunit	36000–66000	Proteomics	Rps1A,1B,B,C,D,E,F,G,H,I,J,K,L,M,N,O,P,Q,R,S,T,U	This study
Phycobilisome (phycocyanin)	12000–23000	Proteomics	((CpcA,B) $_{18}$ ,C1,C2,D,G) $_{6}$	This study
	26000–66000	Spectrophotometry		This study
Photosystem I	31000–63000	Proteomics	(PsaA,B,C,D,E,F,I,J,K,L,M,X) $_{3}$	This study
	96000	Spetroscopy		Keren et al., 2004
	540000	Spetroscopy		Moal and Lagoutte, 2012
Ribosome large subunit	33000–54000	Proteomics	RplA,B,C,D,E,F,I,J,K,L,M,N,O,P,Q,R,S,T,U,V,W,X,Y, RpmA,B,C,E,F,G,H,I,J	This study
Transketolase	31000–50000	Proteomics	TktA $_{2}$	This study
PII signal transducing protein	36000–46000	Proteomics	GlnB $_{3}$	This study
Photosystem II	23000–46000	Proteomics	(PsbA1,A2,B,C,D,E,F,H,I,J,K,L,M,N,O,T,U,V,X,Y,Z, Ycf12) $_{2}$	This study
	17000–29000	Immunoblotting		This study
	100000	Spetroscopy		Moal and Lagoutte, 2012
RuBisCO	26000–43000	Proteomics	(RbcL, RbcS) $_{8}$	This study
	39000–63000	Immunoblotting		This study
Ferredoxin-NADP reductase (FNR)	33000–42000	Proteomics	PetH	This study
	140000	Immunoblotting		Moal and Lagoutte, 2012
D-fructose 1,6-bisphosphatase class 2	29000–36000	Proteomics	Slr20944	This study
Phycobilisome (allophycocyanin)	19000–38000	Proteomics	(ApcA,B) $_{34}$ ,C $_{6}$ ,D $_{2}$ ,E $_{6}$ ,F $_{2}$	This study
	9000–19000	Spectrophotometry		This study
G3P dehydrogenase	21000–32000	Proteomics	Gap2 $_{4}$	This study
Plastocyanin	15000–29000	Proteomics	PetE	This study
Superoxide dismutase [Fe]	14000–25000	Proteomics	SodB $_{2}$	This study
Orange carotenoid protein	15000–24000	Proteomics	Slr1963 $_{2}$	This study
RNA polymerase	8000–15000	Proteomics	RpoA $_{2}$ ,B,C1,C2,D,E,F	This study
Cytochrome b6/f	8000–15000	Proteomics	(PetA,B,C2,D,G,L,M,N) $_{2}$	This study
Chaperonine GroEL	7000–13000	Proteomics	GroL1 $_{14}$	This study
Ribosome recycling factor	6000–7000	Proteomics	Frr	This study
Phosphoglycerate dehydrogenase	3000–5000	Proteomics	SerA $_{4}$	This study
Pyruvate dehydrogenase	3000–4000	Proteomics	(PdhA, PdhB) $_{2}$	This study
Glutamine synthetase	2000–4000	Proteomics	GlnA $_{12}$	This study
Isocitrate dehydrogenase	2000–3000	Proteomics	Icd $_{2}$	This study
Glycogen synthase	2000–3000	Proteomics	GlgA1	This study
DNA polymerase III	1000–2000	Proteomics	DnaN $_{2}$	This study
Pyruvate kinase	1000–2000	Proteomics	Pyk2 $_{4}$	This study
Acetyl-coenzyme A carboxylase	1000	Proteomics	AccB, AccC, AccA $_{2}$ ,ACCD $_{2}$	This study
Carbonic anhydrase	400–700	Proteomics	IcfA $_{6}$	This study
Acetyl-coenzyme A reductase	300–600	Proteomics	PhaB $_{4}$	This study
Circadian clock proteins KaiA/KaiB/KaiC	200–500	Proteomics	KaiA $_{2}$ /KaiB $_{4}$ /KaiC $_{6}$	This study

Table 2—source data 1 Calculations of selected protein complex copies in Synechocystis cells.: https://doi.org/10.7554/eLife.42508.027
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Table 2—source data 2 List of all proteins quantified by proteomics measurements in Synechocystis cells.: https://doi.org/10.7554/eLife.42508.028
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Key resources table

Reagent type (species) or resource	Designation	Source or reference	Identifiers	Additional information
Antibody	Rabbit Anti-PsbA	Agrisera	Cat. #: AS05 084RRID:AB_2172617	WB (1:10000)
Antibody	Rabbit Anti-PsaC	Agrisera	Cat. #: AS10 939 RRID:AB_10752085	WB (1:1000)
Antibody	Rabbit Anti-RbcL	Agrisera	Cat. #: AS03 037 RRID:AB_2175406	WB (1:5000)
Antibody	Rabbit Anti-S1	Agrisera	Cat. #: AS08 309 RRID:AB_1271140	WB (1:2000)
Antibody	Rabbit Anti-L1	Agrisera	Cat. #: AS11 1738 RRID:AB_10754471	WB (1:1000)
Peptide, recombinant protein	Recombinant PsbA from Synechocystissp. PCC 6803	Agrisera	Cat. #: AS01 016S	41.5 kDa
Peptide, recombinant protein	Recombinant PsaC from Synechocystissp. PCC 6803	Agrisera	Cat. #: AS04 042S	11.5 kDa
Peptide, recombinant protein	Puri1ed spinach RbcL	Agrisera	Cat. #: AS01 017S	52.7 kDa

Additional files

Supplementary file 1 Summary of the proteome allocatioin model.: https://doi.org/10.7554/eLife.42508.029
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Transparent reporting form: https://doi.org/10.7554/eLife.42508.030
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Tomáš Zavřel
Marjan Faizi
Cristina Loureiro
Gereon Poschmann
Kai Stühler
Maria Sinetova
Anna Zorina
Ralf Steuer
Jan Červený

(2019)

Quantitative insights into the cyanobacterial cell economy

eLife 8:e42508.

https://doi.org/10.7554/eLife.42508

Figures

Experimental setup and evaluation of Synechocystis sp. PCC 6803 (substrain GT-L) phenotype stability.

Figure 1—source data 1

Figure 1—source data 2

Figure 1—source data 3

Figure 1—source data 4

Variations in morphology and composition of Synechocystis cells with changing growth rate.

Figure 2—source data 1

Figure 2—source data 2

Allocation of key cellular resources as a function of light intensity.

Synechocystis proteome allocation as a function of growth rate.

Figure 3—source data 1

Figure 3—source data 2

Figure 3—source data 3

Elbow method for the identification of an appropriate number of clusters (grey dashed line at seven clusters).

Proteomaps of proteome reallocation in Synechocystis under light-limited (27.5 μmol(photons) m^-2s^-1), light-saturated (440 μmol(photons) m^-2s^-1) and photoinhibited growth (1100 μmol(photons) m^-2s^-1).

Proteomaps of levels 2, 3 and 4.

A model of phototrophic growth and reproduction of experimental growth curves.

Changes in protein abundance as a function of specific growth rate compared to the predictions obtained from a computational model of proteome allocation.

Figure 6—source data 1

Figure 6—source data 2

Immunoblots and a list of antibodies used for the immunoblotting analysis.

Model simulations for investigating the influence of constant enzyme fractions on the cellular growth rate.

Tables

Gene Ontology (GO) slim categories (Klopfenstein et al., 2018) with the amount of associated growth-dependent and independent proteins.

Table 1—source data 1

Quantification of selected protein complexes in Synechocystis cells.

Table 2—source data 1

Table 2—source data 2

Additional files

Supplementary file 1

Transparent reporting form

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Experimental setup and evaluation of Synechocystis sp. PCC 6803 (substrain GT-L) phenotype stability.

Figure 1—source data 1

Figure 1—source data 2

Figure 1—source data 3

Figure 1—source data 4

Variations in morphology and composition of Synechocystis cells with changing growth rate.

Figure 2—source data 1

Figure 2—source data 2

Allocation of key cellular resources as a function of light intensity.

Synechocystis proteome allocation as a function of growth rate.

Figure 3—source data 1

Figure 3—source data 2

Figure 3—source data 3

Elbow method for the identification of an appropriate number of clusters (grey dashed line at seven clusters).

Proteomaps of proteome reallocation in Synechocystis under light-limited (27.5 μmol(photons) m-2s-1), light-saturated (440 μmol(photons) m-2s-1) and photoinhibited growth (1100 μmol(photons) m-2s-1).

Proteomaps of levels 2, 3 and 4.

A model of phototrophic growth and reproduction of experimental growth curves.

Changes in protein abundance as a function of specific growth rate compared to the predictions obtained from a computational model of proteome allocation.

Figure 6—source data 1

Figure 6—source data 2

Immunoblots and a list of antibodies used for the immunoblotting analysis.

Model simulations for investigating the influence of constant enzyme fractions on the cellular growth rate.

Gene Ontology (GO) slim categories (Klopfenstein et al., 2018) with the amount of associated growth-dependent and independent proteins.

Table 1—source data 1

Quantification of selected protein complexes in Synechocystis cells.

Table 2—source data 1

Table 2—source data 2

Supplementary file 1

Transparent reporting form

Downloads (link to download the article as PDF)

Open citations (links to open the citations from this article in various online reference manager services)

Cite this article (links to download the citations from this article in formats compatible with various reference manager tools)

Proteomaps of proteome reallocation in Synechocystis under light-limited (27.5 μmol(photons) m^-2s^-1), light-saturated (440 μmol(photons) m^-2s^-1) and photoinhibited growth (1100 μmol(photons) m^-2s^-1).